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Forests
Volume 15
Issue 11
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Forests, Volume 15, Issue 11 (November 2024) – 213 articles

Cover Story (view full-size image): The American elm (Ulmus americana L.), once a dominant species in North American floodplain forests, has suffered significant population declines due to Dutch elm disease (DED). Surviving elms may exhibit disease resistance and climate-adaptive traits necessary for restoration. This study evaluated mid-winter shoot cold tolerance of elm across a climatic gradient. We used relative electrolyte leakage methods to assess mid-winter cold tolerance of current-year shoots of DED-resistant and -susceptible American elm genotypes as well as commercially available DED-resistant sources. Genotypes that evolved in colder regions have greater cold tolerance in winter, which may have implications for future restoration efforts. View this paper
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14 pages, 3326 KiB  
Article
The bHLH Transcription Factor PubHLH66 Improves Salt Tolerance in Daqing Poplar (Populus ussuriensis)
by Dandan Li, Jindan Wang, Yuxin Pan, Hui Wang, Xinyao Dang, Shihao Zhao and Yucheng Wang
Forests 2024, 15(11), 2051; https://doi.org/10.3390/f15112051 - 20 Nov 2024
Viewed by 720
Abstract
Elevated salinity negatively impacts plant growth and yield, presenting substantial challenges to agricultural and forestry productivity. The bHLH transcription factor family is vital for plants to cope with various abiotic stresses. However, it remains uncertain whether bHLH transcription factors can regulate salt stress [...] Read more.
Elevated salinity negatively impacts plant growth and yield, presenting substantial challenges to agricultural and forestry productivity. The bHLH transcription factor family is vital for plants to cope with various abiotic stresses. However, it remains uncertain whether bHLH transcription factors can regulate salt stress in Populus ussuriensis. In the following study, a salt-induced bHLH transcription factor PubHLH66 was identified from P. ussuriensis. PubHLH66 has a typical and conserved bHLH domain. Subcellular localization and yeast two-hybrid (Y2H) assays confirmed that it is a nucleus-localized transactivator and the activation region is located at the N-terminus. PubHLH66-OE and PubHLH66-SRDX transgenic P. ussuriensis were obtained through Agrobacterium-mediated leaf disc transformation. Morphological and physiological results demonstrated that PubHLH66-OE enhanced salinity tolerance, as indicated by reduced electrolyte leakage (EL), malondialdehyde (MDA), and H2O2 levels, along with increased proline contents and activities of peroxidase (POD) and superoxide dismutase (SOD). In contrast, PuHLH66-SRDX poplar showed decreased salt tolerance. Quantitative real-time PCR (RT-qPCR) confirmed that PubHLH66 enhanced salt tolerance by regulating the expression of genes such as PuSOD, PuPOD, and PuP5CS, resulting in reduced reactive oxygen species (ROS) accumulation and an improved osmotic potential. Thus, PubHLH66 could be a candidate gene for molecular breeding to enhance salt tolerance in plants. These results laid a foundation for exploring the mechanisms of salt tolerance in P. ussuriensis, facilitating the development of more salt-tolerant trees to combat the increasing issue of soil salinization globally. Full article
(This article belongs to the Special Issue Abiotic and Biotic Stress Responses in Trees Species)
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<p>Sequence analysis and gene expression pattern of <span class="html-italic">PubHLH66</span>. (<b>A</b>) Multiple sequence alignments and analysis of PubHLH66 with homologous bHLH proteins from different plant species. The accession numbers corresponding to these proteins in NCBI ID are OsbHLH66 (XP_015627343), AtbHLH66 (BAD44153), MdbHLH66 (XP_028964276), and CtbHLH66 (XP_006473971). (<b>B</b>) Analysis of the phylogenetic tree constructed using the neighbor-joining (NJ) method, with a bootstrap test performed using 1000 iterations in MEGA. The black dot represents PubHLH66. The scale bar represents 0.1 substitutions per site. (<b>C</b>) Relative expression level of <span class="html-italic">PubHLH66</span> in the root, stem and leaves of plants under 150 mM NaCl stress determined using RT-qPCR. Error bars represent the variability among three biological replicates. The <span class="html-italic">x</span>-axis represents the time points following treatment with 150 mM NaCl.</p> Full article ">Figure 2
<p>Subcellular localization and transactivation activity of PubHLH66. (<b>A</b>) Subcellular localization of PubHLH66. The 35S::GFP (control) and 35S::PubHLH66-GFP translational fusion constructs were transiently introduced into onion epidermal cells, and DAPI was utilized as a marker for the nucleus. Bar = 50 μm. (<b>B</b>) pGBKT7-PubHLH66, pGBKT7-PubHLH66<sup>N241</sup>, pGBKT7-PubHLH66<sup>C214</sup>, and pGBKT7 (negative control) were transformed in the Y2H Gold yeast strain. Yeast transformants were cultured in either SD/-Trp or SD/-Trp/-His/-Ade/X-α-Gal media. LacZ activity was measured in the presence of X-α-Gal with pGBKT7. The gray bars represent BD, and blue bars represent gene segment of PubHLH66.</p> Full article ">Figure 3
<p>Salt tolerance analysis of the <span class="html-italic">PubHLH66</span> transgenic <span class="html-italic">P. ussuriensis</span>. (<b>A</b>) Phenotype of <span class="html-italic">PubHLH66</span> transgenic and WT poplars during salinity treatments. Bars, 10 cm. (<b>B</b>) Survival rates, (<b>C</b>) dry weights, (<b>D</b>) chlorophyll contents, (<b>E</b>) electrolyte leakage (EL), (<b>F</b>) malondialdehyde (MDA) contents, and (<b>G</b>) proline contents of the poplars after growth under normal and NaCl stress conditions for 7 d. (<b>H</b>) The expression pattern of <span class="html-italic">PuP5CS1</span> and <span class="html-italic">PuP5CS2</span>. WT and <span class="html-italic">PubHLH66</span> transgenic <span class="html-italic">P. ussuriensis</span> were subjected to 150 mM NaCl for 24 h. The WT line was used as a control and set to 1. The <span class="html-italic">PuActin</span> gene served as a housekeeping gene. Asterisks (* <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01) indicate significant differences determined using Student’s <span class="html-italic">t</span>-test compared to WT plants.</p> Full article ">Figure 4
<p>Analysis of the antioxidant capacity of <span class="html-italic">PubHLH66</span>. (<b>A</b>,<b>B</b>) DAB and NBT staining. Poplar leaves subjected to NaCl treatment were infiltrated with DAB (<b>A</b>) for hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) detection and with NBT (<b>B</b>) for superoxide (O<sub>2</sub><sup>−</sup>) detection. (<b>C</b>) H<sub>2</sub>O<sub>2</sub> content assay (<b>D</b>,<b>E</b>) measurement of POD and SOD activities in the poplars after growth under normal and NaCl stress conditions for 7 d. Data are presented as the means and SDs of three independent experiments. Asterisks (*) represent <span class="html-italic">p</span> &lt; 0.05 and (**) <span class="html-italic">p</span> &lt; 0.01.</p> Full article ">Figure 5
<p>PubHLH66 regulates the expression of <span class="html-italic">PuPOD</span>s and <span class="html-italic">PuSOD</span>s. (<b>A</b>,<b>B</b>) The expression pattern of <span class="html-italic">PuPOD</span>s, (<b>C</b>,<b>D</b>) the expression pattern of <span class="html-italic">PuSOD</span> genes after exposure to 150 mM NaCl for 24 h. Wild type (WT) plants were utilized as a control and normalized to 1, while the <span class="html-italic">PuActin</span> gene served as the internal control. The asterisk (*) represents <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">
20 pages, 4712 KiB  
Article
CCE-UNet: Forest and Water Body Coverage Detection Method Based on Deep Learning: A Case Study in Australia’s Nattai National Forest
by Bangjun Huang, Xiaomei Yi, Lufeng Mo, Guoying Wang and Peng Wu
Forests 2024, 15(11), 2050; https://doi.org/10.3390/f15112050 - 20 Nov 2024
Viewed by 663
Abstract
Severe forest fires caused by extremely high temperatures have resulted in devastating disasters in the natural forest reserves of New South Wales, Australia. Traditional forest research methods primarily rely on manual field surveys, which have limited generalization capabilities. In order to monitor forest [...] Read more.
Severe forest fires caused by extremely high temperatures have resulted in devastating disasters in the natural forest reserves of New South Wales, Australia. Traditional forest research methods primarily rely on manual field surveys, which have limited generalization capabilities. In order to monitor forest ecosystems more comprehensively and maintain the stability of the regional forest ecosystem, as well as to monitor post-disaster ecological restoration efforts, this study employed high-resolution remote sensing imagery and proposed a semantic segmentation architecture named CCE-UNet. This architecture focuses on the precise identification of forest coverage while simultaneously monitoring the distribution of water resources in the area. This architecture utilizes the Contextual Information Fusion Module (CIFM) and introduces the dual attention mechanism strategy to effectively filter background information and enhance image edge features. Meanwhile, it employs a multi-scale feature fusion algorithm to maximize the retention of image details and depth information, achieving precise segmentation of forests and water bodies. We have also trained seven semantic segmentation models as candidates. Experimental results show that the CCE-UNet architecture achieves the best performance, demonstrating optimal performance in forest and water body segmentation tasks, with the MIoU reaching 91.07% and the MPA reaching 95.15%. This study provides strong technical support for the detection of forest and water body coverage in the region and is conducive to the monitoring and protection of the forest ecosystem. Full article
(This article belongs to the Section Natural Hazards and Risk Management)
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<p>Study area in Nattai National Park, New South Wales, Australia.</p> Full article ">Figure 2
<p>Example map of forest coverage in partially disaster-affected areas.</p> Full article ">Figure 3
<p>Examples of data augmentation.</p> Full article ">Figure 4
<p>Architecture of Contextual Information Fusion (CIFM) Module.</p> Full article ">Figure 5
<p>Architecture of a CBAM module.</p> Full article ">Figure 6
<p>Architecture of an ECA module.</p> Full article ">Figure 7
<p>Architecture of CCE-UNet.</p> Full article ">Figure 8
<p>Comparison of visualization segmentation effects (taking CNN as an example).</p> Full article ">Figure 9
<p>Comparison of visualization segmentation effects (using transformer and their variants as examples).</p> Full article ">
23 pages, 2968 KiB  
Article
Multi-Stakeholder Game Relationships in Promoting the Development of the Non-Timber Forest Product Industry by State-Owned Forest Farms
by Qin Qiao, Zhenyu Lin, Zhongrui Sun, Wenting Zhang, Meijuan Zhang, Yong Sun and Xinting Gao
Forests 2024, 15(11), 2049; https://doi.org/10.3390/f15112049 - 20 Nov 2024
Cited by 1 | Viewed by 674
Abstract
State-owned forest farms are key players in managing forestry resources worldwide, playing a pivotal role in advancing the development of the non-timber forest product industry. This paper constructs a tripartite evolutionary game model involving “government–state-owned forest farms–farmer households” to delve into how state-owned [...] Read more.
State-owned forest farms are key players in managing forestry resources worldwide, playing a pivotal role in advancing the development of the non-timber forest product industry. This paper constructs a tripartite evolutionary game model involving “government–state-owned forest farms–farmer households” to delve into how state-owned forest farms collaborate with governments and farmer households to propel the growth of the non-timber forest product industry. Additionally, it explores the interactive relationships among multiple stakeholders and their asymptotic stability. The findings reveal that (1) under certain conditions, the game model can achieve four stable equilibrium strategies: (0,0,0), (0,1,0), (0,1,1), and (1,1,1). (2) Key factors influencing the tripartite game include the political performance and administrative costs of local governments involved in the industry’s development, assessment performance and reduced management and protection expenses of state-owned forest farms, and sales revenue and planting costs of farmers’ under-forest products. (3) The market development costs shared by state-owned forest farms and government subsidies for under-forest planting should be within a reasonable range. This ensures effective promotion of farmers’ participation in under-forest planting while maintaining the willingness of state-owned forest farms and governments to actively engage. These findings provide concrete guidelines that policymakers can use to spur sustainable growth in the NTFP sector. Full article
(This article belongs to the Section Forest Economics, Policy, and Social Science)
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<p>Research framework for the game among three parties.</p> Full article ">Figure 2
<p>Impact of government behavioral parameters on evolutionary games. (<b>a</b>) Impact of <span class="html-italic">R<sub>g</sub></span>; (<b>b</b>) Impact of <span class="html-italic">C<sub>g</sub></span>; (<b>c</b>) Impact of <span class="html-italic">A</span>.</p> Full article ">Figure 3
<p>Impact of state-owned forest farm behavioral parameters on evolutionary games. (<b>a</b>) Impact of <span class="html-italic">R<sub>v</sub></span>; (<b>b</b>) Impact of <span class="html-italic">U<sub>h</sub></span>; (<b>c</b>) Impact of <span class="html-italic">D<sub>f</sub></span>; (<b>d</b>) Impact of <span class="html-italic">L</span>.</p> Full article ">Figure 4
<p>Impact of farmers’ behavioral parameters on evolutionary games. (<b>a</b>) Impact of <span class="html-italic">R<sub>n</sub></span>; (<b>b</b>) Impact of <span class="html-italic">P<sub>s</sub></span>; (<b>c</b>) Impact of <span class="html-italic">D<sub>c</sub></span>; (<b>d</b>) Impact of <span class="html-italic">C<sub>f</sub></span>.</p> Full article ">Figure 5
<p>Optimization strategies for stakeholder engagement in non-timber forest product industry.</p> Full article ">
23 pages, 8717 KiB  
Article
Net Forest Carbon Loss Induced by Forest Cover Change and Compound Drought and Heat Events in Two Regions of China
by Chenfeng Gu, Tongyu Wang, Wenjuan Shen, Zhiguo Tai, Xiaokun Su, Jiaying He, Tao He, Weishu Gong and Chengquan Huang
Forests 2024, 15(11), 2048; https://doi.org/10.3390/f15112048 - 20 Nov 2024
Cited by 1 | Viewed by 801
Abstract
Compound drought and heat events (CDHEs) and forest cover change influence regional forest carbon dynamics. Changes in regional vegetation biomass and soil carbon storage induced by forest cover change often exhibit considerable uncertainty, and previous research on the impacts of CDHEs on forest [...] Read more.
Compound drought and heat events (CDHEs) and forest cover change influence regional forest carbon dynamics. Changes in regional vegetation biomass and soil carbon storage induced by forest cover change often exhibit considerable uncertainty, and previous research on the impacts of CDHEs on forest carbon dynamics is limited. To accurately quantify the specific effects of forest cover change and CDHEs on forest carbon dynamics in different regions, we employed a combined algorithm of the Carnegie–Ames–Stanford Approach (CASA) and bookkeeping empirical models to examine the impact of regional forest cover changes on forest carbon dynamics during 2000–2022 in Nanjing and Shaoguan, Southern China. Using the Geographical Detector model, we then analyzed the effects of CDHEs on forest carbon dynamics. Next, we used the photosynthesis equation and the optimal response time of forests to drought (heat) events to calculate the changes in forest carbon sequestration caused by CDHEs in both regions during 2000–2022. The results indicated that afforestation and deforestation led to +0.269 TgC and +1.509 TgC of carbon sequestration and 0.491 TgC and 2.802 TgC of carbon emissions in Nanjing and Shaoguan, respectively. The overall effects of CDHEs on the change in forest carbon sequestration were manifested as net carbon loss. In Nanjing, the net carbon loss caused by CDHEs (0.186 TgC) was lower than the loss due to forest cover change (0.222 TgC). In Shaoguan, the net forest carbon loss caused by CDHEs (3.219 TgC) was much more significant than that caused by forest cover change (1.293 TgC). This study demonstrated that forest carbon dynamics are dominated by different factors in different regions, which provides a scientific basis for local governments to formulate targeted forest management policies. Full article
(This article belongs to the Special Issue Image Processing for Forest Characterization)
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<p>Locations of the study area. The background image shows the elevation in two regions based on the digital elevation model (DEM).</p> Full article ">Figure 2
<p>The workflow of this study.</p> Full article ">Figure 3
<p>Area of main land use types in Nanjing and Shaoguan from 2000 to 2022 (unit: ha).</p> Full article ">Figure 4
<p>Comparison between simulated and observed forest carbon density in Shaoguan, 2020.</p> Full article ">Figure 5
<p>Forest cover change in Shaoguan from 2000 to 2010 (<b>a</b>) and 2010 to 2022 (<b>b</b>).</p> Full article ">Figure 6
<p>Forest cover change in Nanjing from 2000 to 2010 (<b>a</b>) and 2010 to 2022 (<b>b</b>).</p> Full article ">Figure 7
<p>Carbon storage change between 2000 and 2022 in Shaoguan (<b>a</b>) and Nanjing (<b>b</b>).</p> Full article ">Figure 8
<p>The carbon budget caused by changes in forest cover types in the two regions from 2000 to 2022.</p> Full article ">Figure 9
<p>Spatiotemporal patterns of drought and heat events in Shaoguan from 2000 to 2022.</p> Full article ">Figure 10
<p>Spatiotemporal patterns of drought and heat events in Nanjing from 2000 to 2022.</p> Full article ">Figure 11
<p>The q value of the impacts of drought and heat events on VNPP in Nanjing and Shaoguan. Note: + indicates that drought (heat) events are positively correlated with VNPP and − indicates that drought (heat) events are negatively correlated with VNPP; * suggests that the 95% significance test is passed.</p> Full article ">Figure 12
<p>Forest cover change and CDHEs caused net carbon loss in the two regions during 2000–2022.</p> Full article ">
22 pages, 6555 KiB  
Article
Mangrove Extraction from Compact Polarimetric Synthetic Aperture Radar Images Based on Optimal Feature Combinations
by Sijing Shu, Ji Yang, Wenlong Jing, Chuanxun Yang and Jianping Wu
Forests 2024, 15(11), 2047; https://doi.org/10.3390/f15112047 - 20 Nov 2024
Viewed by 676
Abstract
As a polarimetric synthetic aperture radar (SAR) mode capable of simultaneously acquiring abundant surface information and conducting large-width observations, compact polarimetric synthetic aperture radar (CP SAR) holds great promise for mangrove dynamics monitoring. Nevertheless, there have been no studies on mangrove identification using [...] Read more.
As a polarimetric synthetic aperture radar (SAR) mode capable of simultaneously acquiring abundant surface information and conducting large-width observations, compact polarimetric synthetic aperture radar (CP SAR) holds great promise for mangrove dynamics monitoring. Nevertheless, there have been no studies on mangrove identification using CP SAR. This study aims to explore the potential of C-band CP SAR for mangrove monitoring applications, with the objective of identifying the most effective CP SAR descriptors for mangrove discrimination. A systematic comparison of 52 well-known CP features is provided, utilizing CP SAR data derived from the reconstruction of C-band Gaofen-3 quad-polarimetric data. Among all the features, Shannon entropy (SE), a random polarimetric constituent (VB), Shannon entropy (SEI), and the Bragg backscattering constituent (VG) exhibited the best performance. By combining these four features, we designed three supervised classifiers—support vector machine (SVM), maximum likelihood (ML), and artificial neural network (ANN)—for comparative analysis experiments. The results demonstrated that the optimal polarimetric feature combination not only reduced the redundancy of polarimetric feature data but also enhanced overall accuracy. The highest accuracy of mangrove extraction reached 98.04%. Among the three classifiers, SVM outperformed the other classifiers in mangrove extraction, while ML achieved the highest overall classification accuracy. Full article
(This article belongs to the Special Issue Forest and Urban Green Space Ecosystem Services and Management)
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<p>Study area and data images. (<b>a</b>) Geographical location of the Leizhou Peninsula; (<b>b</b>) optical satellite image; (<b>c</b>) SAR data image in HH polarimetric mode; (<b>d</b>) SAR data image in VH polarimetric mode; (<b>e</b>) SAR data image in HV polarimetric mode; (<b>f</b>) SAR data image in VV polarimetric mode.</p> Full article ">Figure 2
<p>Optimal polarimetric feature selection flow.</p> Full article ">Figure 3
<p>Euclidean distances between different classes in CP feature images. (<b>a</b>) denotes the Euclidean distance between mangrove and water; (<b>b</b>) denotes the Euclidean distance between mangrove and land; (<b>c</b>) denotes the Euclidean distance between mangrove and seawater; (<b>d</b>) denotes the Euclidean distance between water and land; (<b>e</b>) denotes the Euclidean distance between water and seawater; (<b>f</b>) denotes the Euclidean distance between land and seawater.</p> Full article ">Figure 3 Cont.
<p>Euclidean distances between different classes in CP feature images. (<b>a</b>) denotes the Euclidean distance between mangrove and water; (<b>b</b>) denotes the Euclidean distance between mangrove and land; (<b>c</b>) denotes the Euclidean distance between mangrove and seawater; (<b>d</b>) denotes the Euclidean distance between water and land; (<b>e</b>) denotes the Euclidean distance between water and seawater; (<b>f</b>) denotes the Euclidean distance between land and seawater.</p> Full article ">Figure 4
<p>CP feature image.</p> Full article ">Figure 5
<p>Differences in eigenvalue responses between mangroves and other cover classes in feature images with enhanced combined performance.</p> Full article ">Figure 6
<p>SVM classification results are based on a single polarimetric feature input. Mangroves are shown in red, water in blue, land in yellow, and seawater in blue.</p> Full article ">Figure 7
<p>Mangrove extraction results are based on a single polarimetric feature input.</p> Full article ">Figure 8
<p>Classification results based on optimal polarimetric feature combination input. Mangroves are in red, water in blue, land in yellow, and seawater in blue.</p> Full article ">Figure 9
<p>Mangrove extraction results based on optimal polarimetric feature combination input.</p> Full article ">Figure 10
<p>Comparison of mangrove extraction accuracy, OA, and Kappa coefficient values of the different classifiers and features.</p> Full article ">Figure 11
<p>Euclidean distance and classification accuracy. (<b>a</b>) Euclidean distance and classification accuracy between mangrove and land, where O(M-L) denotes the Euclidean distance between mangrove and land in the feature image, and AM and AL denote the classification accuracy of mangrove and land, respectively. (<b>b</b>) Euclidean distance and classification accuracy between water and seawater, where O(W-S) denotes the Euclidean distance between water and seawater in the feature image, and AW and AS indicate the classification accuracy of water and seawater, respectively.</p> Full article ">
28 pages, 9338 KiB  
Article
Numerical Analysis of Fire Resistance in Cross-Laminated Timber (CLT) Constructions Using CFD: Implications for Structural Integrity and Fire Protection
by Nikola Perković, Davor Skejić and Vlatka Rajčić
Forests 2024, 15(11), 2046; https://doi.org/10.3390/f15112046 - 20 Nov 2024
Viewed by 861
Abstract
Fire represents a serious challenge to the safety and integrity of buildings, especially timber structures exposed to high temperatures and intense heat radiation. The combustibility of timber is one of the main reasons why regulations strictly limit timber as a building material, especially [...] Read more.
Fire represents a serious challenge to the safety and integrity of buildings, especially timber structures exposed to high temperatures and intense heat radiation. The combustibility of timber is one of the main reasons why regulations strictly limit timber as a building material, especially in multi-storey structures. This investigation seeks to assess the fire behaviour of cross-laminated timber (CLT) edifices and examine the ramifications for structural integrity and fire protection. Utilising computational fluid dynamics (CFD) simulations, critical variables including charring rate, heat emission, and smoke generation were analysed across two scenarios: one featuring exposed CLT and another incorporating protected CLT. The outcomes indicated that protective layers markedly diminish charring rates and heat emission, thereby augmenting fire resistance and constraining smoke dissemination. These revelations imply that CFD-based methodologies can proficiently inform fire protection design paradigms for CLT structures, presenting potential cost efficiencies by optimising material utilisation and minimising structural impairment. Full article
(This article belongs to the Special Issue Development and Performance of Wood-Based Products)
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<p>Three-dimensional model—PyroSim.</p> Full article ">Figure 2
<p>Mesh alignments [<a href="#B16-forests-15-02046" class="html-bibr">16</a>].</p> Full article ">Figure 3
<p>Meshes.</p> Full article ">Figure 4
<p>Fire location and selected fire sector: (<b>a</b>) floor plan, (<b>b</b>) 3D view, and (<b>c</b>) 3D view of the fire compartment.</p> Full article ">Figure 4 Cont.
<p>Fire location and selected fire sector: (<b>a</b>) floor plan, (<b>b</b>) 3D view, and (<b>c</b>) 3D view of the fire compartment.</p> Full article ">Figure 5
<p>CLT reaction: (<b>a</b>) HRRPUA and ignition temperature and (<b>b</b>) HRRPUA normalised.</p> Full article ">Figure 6
<p>HRR diagram.</p> Full article ">Figure 7
<p>Arrangement of measuring devices: (<b>a</b>) floor plan and (<b>b</b>) side view.</p> Full article ">Figure 8
<p>Temperature development (variant A)—time frame: every 100 s.</p> Full article ">Figure 9
<p>Temperature development (variant B)—time frame: every 100 s.</p> Full article ">Figure 10
<p>Comparison of HRR in the fire compartment of protected (B) and exposed (A) CLT.</p> Full article ">Figure 11
<p>Average gas temperature 200 cm above the floor level.</p> Full article ">Figure 12
<p>Arrangement of measuring devices at a height of 1.6 m above the floor.</p> Full article ">Figure 13
<p>Temperatures in the fire compartment at a height of 1.6 m above the floor.</p> Full article ">Figure 14
<p>Temperatures of sold bodies—CLT: (<b>a</b>) arrangement of CLT elements; (<b>b</b>) solid-phase temperatures.</p> Full article ">Figure 15
<p>Temperatures of exposed CLT: (<b>a</b>) temperature development of the wall at a depth of 40 mm; (<b>b</b>) wall temperature; (<b>c</b>) temperature development of the ceiling at a depth of 40 mm; (<b>d</b>) ceiling temperature.</p> Full article ">Figure 15 Cont.
<p>Temperatures of exposed CLT: (<b>a</b>) temperature development of the wall at a depth of 40 mm; (<b>b</b>) wall temperature; (<b>c</b>) temperature development of the ceiling at a depth of 40 mm; (<b>d</b>) ceiling temperature.</p> Full article ">Figure 16
<p>Visibility and soot density—variant A: (<b>a</b>) after 100 s, (<b>b</b>) after 400 s, and (<b>c</b>) after 900 s.</p> Full article ">Figure 17
<p>Visibility and density of soot—variant B: (<b>a</b>) after 100 s, (<b>b</b>) after 400 s, and (<b>c</b>) after 900 s.</p> Full article ">Figure 18
<p>Visibility inside the fire compartment: (<b>a</b>) arrangement of devices and (<b>b</b>) visibility inside the fire compartment.</p> Full article ">
16 pages, 4628 KiB  
Article
Enhancing Silvicultural Practices: A Productivity and Quality Comparison of Manual and Semi-Mechanized Planting Methods in KwaZulu-Natal, South Africa
by Mduduzi J. Khoza, Muedanyi M. Ramantswana, Raffaele Spinelli and Natascia Magagnotti
Forests 2024, 15(11), 2045; https://doi.org/10.3390/f15112045 - 19 Nov 2024
Viewed by 776
Abstract
Planting plays a significant role in commercial forestry. Labour-related issues (scarcity, increased wages, absenteeism, ageing and high turnover), inconsistent work quality, increased operational costs, and poor ergonomics prompted the development of innovative planting techniques. This study aimed to assess the productivity (plants/productive machine [...] Read more.
Planting plays a significant role in commercial forestry. Labour-related issues (scarcity, increased wages, absenteeism, ageing and high turnover), inconsistent work quality, increased operational costs, and poor ergonomics prompted the development of innovative planting techniques. This study aimed to assess the productivity (plants/productive machine hour), worker productivity (plants/worker PMH), work quality and tree survival of a manual and a semi-mechanised planting method. Two study sites at Flatcrown and Kwambonambi, consisting of 37 plots, were planted at 1333 stems/ha, alternating across the study sites. Block-level and elemental-level time studies were conducted, followed by work quality assessments. The manual method planted 7.23 ha/shift (Flatcrown) and 5.89 ha/shift (Kwambonambi), whilst the semi-mechanised method planted 4.72 ha/shift (Flatcrown) and 3.19 ha/shift (Kwambonambi). The manual method was 50% to 60% more productive (plants/PMH) than the semi-mechanised method. In terms of plants/worker PMH, higher worker efficiency of 30%–40% was observed at the Flatcrown site, which was characterised by level terrain and low residue presence compared to the Kwambonambi site, which had a gentle (<20%) terrain and medium residue presence. Planting quality was conducted according to planting specifications. The two methods did not significantly differ, although the study suggests that the proportion of good-quality plantings could be somewhat higher for the manual method. There was no significant difference in tree survival across methods and sites after one month. Full article
(This article belongs to the Special Issue Management of the Sustainable Forest Operations and Silviculture)
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<p>Planting methods working at the different research sites. Manual method—Kwambonambi (<b>left</b>); and semi-mechanised method—Flatcrown (<b>right</b>).</p> Full article ">Figure 2
<p>Research sites in KwaZulu-Natal.</p> Full article ">Figure 3
<p>Study design plot layout for the two methods applied in both research sites. The 3 m represents the distance between rows and 2.5 m is the distance between planting positions. The arrows show the planting direction of each method which alternated across each site.</p> Full article ">Figure 4
<p>Manual and semi-mechanised planting method productivity at the different sites based on the Mann–Whitney U test at 95% confidence. Upper case vs. lower case indicates significant differences between methods per site. Differences between methods across sites are indicated by upper case versus upper case (A vs. B) and lower case versus lower case (a vs. b). Note: The graph does not depict productivity differences between methods across sites, only the productivity differences between methods within a site and productivity differences between sites within a method.</p> Full article ">Figure 5
<p>Delay composition of manual and semi-mechanised planting methods.</p> Full article ">Figure 6
<p>Elemental time distribution for various planting operation roles.</p> Full article ">Figure 7
<p>Planting quality assessment for manual and semi-mechanised methods.</p> Full article ">
30 pages, 4747 KiB  
Article
Optimizing Forest Management: Balancing Environmental and Economic Goals Using Game Theory and Multi-Objective Approaches
by Neda Amiri and Soleiman Mohammadi Limaei
Forests 2024, 15(11), 2044; https://doi.org/10.3390/f15112044 - 19 Nov 2024
Viewed by 918
Abstract
Forests are complex ecosystems that require integrated management to balance economic, social, and environmental dimensions. Conflicting objectives among stakeholders make optimal decision-making particularly challenging. This study seeks to balance the economic gains of forest harvesting with the goals of environmental conservation, with a [...] Read more.
Forests are complex ecosystems that require integrated management to balance economic, social, and environmental dimensions. Conflicting objectives among stakeholders make optimal decision-making particularly challenging. This study seeks to balance the economic gains of forest harvesting with the goals of environmental conservation, with a focus on the Shafarood forest in Northern Iran. We applied multi-objective optimization and game theory to maximize the net present value (NPV) of forest harvesting while enhancing carbon sequestration. The research utilized data on stumpage prices, harvesting costs, tree density, volume per ha, growth rates, interest rates, carbon sequestration, and labour costs. Applying the epsilon-constraint method, we derived Pareto optimal solutions for a bi-objective model, and game theory was applied to negotiate between economic and environmental stakeholders. In the fifth round of bargaining, a Nash equilibrium was achieved between the two players. At this equilibrium point, the economic player achieved NPV from forest harvesting of 9001.884 (IRR 10,000/ha) and amount of carbon sequestration of 159.9383 tons/ha. Meanwhile, the environmental player achieved NPV from forest harvesting of 7861.248 (IRR 10,000/ha), along with a carbon sequestration of 159.9731 tons/ha. Results indicate significant trade-offs but reveal potential gains for both economic and environmental goals. These findings provide a robust framework for sustainable forest management and offer practical tools to support informed decision-making for diverse stakeholders. Full article
(This article belongs to the Special Issue Optimization of Forestry and Forest Supply Chain)
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<p>Study area, from left to right: Guilan province, Shafarood watershed, Bargah Zamin (District, 7).</p> Full article ">Figure 2
<p>Game theory and multi-objective modeling for optimal forest management.</p> Full article ">Figure 3
<p>Range of objective function variations for each player at stock level 1.</p> Full article ">Figure 4
<p>Range of objective function variations for each player at stock level 2.</p> Full article ">Figure 5
<p>Range of objective function variations for each player at stock level 3.</p> Full article ">Figure 6
<p>Range of objective function variations for each player at stock level 4.</p> Full article ">Figure 7
<p>Range of objective function variations for each player at stock level 5.</p> Full article ">Figure 8
<p>Sensitivity of NPV to interest rate changes for optimal stock of 457 (m<sup>3</sup>/ha).</p> Full article ">Figure 9
<p>Sensitivity of NPV to interest rate changes for optimal standing inventory of 457 (m<sup>3</sup>/ha).</p> Full article ">Figure A1
<p>Pareto optimal frontier at stock level 1.</p> Full article ">Figure A2
<p>Pareto optimal frontier at stock level 2.</p> Full article ">Figure A3
<p>Pareto optimal frontier at stock level 3.</p> Full article ">Figure A4
<p>Pareto optimal frontier at stock level 4.</p> Full article ">Figure A5
<p>Pareto optimal frontier at forest stock level 5.</p> Full article ">
15 pages, 3658 KiB  
Article
The Effect of Particles from Rotten Spruce Logs and Recycled Wooden Composites on Changes in the Bio-Resistance of Three-Layer Particleboards Against the Decaying Fungus Coniophora puteana and Mixture of Moulds
by Zuzana Vidholdová, Viktória Satinová and Ladislav Reinprecht
Forests 2024, 15(11), 2043; https://doi.org/10.3390/f15112043 - 19 Nov 2024
Viewed by 718
Abstract
Wood-based particleboards (PBs) are widely used in construction and interior applications, yet their durability, particularly against biological degradation, remains a challenge. Recycling wood and incorporating degraded particles from rotted wood can potentially enhance PB sustainability and align with circular bioeconomy principles. This study [...] Read more.
Wood-based particleboards (PBs) are widely used in construction and interior applications, yet their durability, particularly against biological degradation, remains a challenge. Recycling wood and incorporating degraded particles from rotted wood can potentially enhance PB sustainability and align with circular bioeconomy principles. This study investigates the biological resistance of the three-layer, laboratory-prepared PBs with varied amounts of particles, from sound spruce wood to particles, and from spruce logs attacked by brown- or white rot, respectively, to particles from recycled wooden composites of laminated particleboards (LPBs) or blockboards (BBs), i.e., 100:0, 80:20, 50:50, and 0:100. The bio-resistance of PBs was evaluated against the brown-rot fungus Coniophora puteana, as well as against a mixture of moulds’ “microscopic fungi”, such as Aspergillus versicolor BAM 8, Aspergillus niger BAM 122, Penicillium purpurogenum BAM 24, Stachybotrys chartarum BAM 32, and Rhodotorula mucilaginosa BAM 571. PBs containing particles from brown-rotten wood or from recycled wood composites, particularly LPBs, had a partly enhanced decay resistance, but their mass loss was nevertheless more than 30%. On the other hand, the mould resistance of all variants of PBs, evaluated in the 21st day, was very poor, with the highest mould growth activity (MGA = 4). These findings suggested that some types of rotten and recycled wood particles can improve the biological resistance of PBs; however, their effectiveness is influenced by the type of wood degradation and the source of recycled materials. Further, the results highlight the need for improved biocidal, chemical, or thermal modifications of wood particles to enhance the overall biological durability of PBs for specific uses. Full article
(This article belongs to the Special Issue Composites from Recycled and Modified Woods—Technology, Properties, Application—2nd Edition)
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<p>Display of PB samples 50 mm × 50 mm × 16 mm: (<b>a</b>) PB-BR: Manufactured from brown-rotten particles; (<b>b</b>) PB-WR: manufactured from white-rotten particles; (<b>c</b>) PB-LPB: manufactured from recycled laminated PB particles; and (<b>d</b>) PB-BB: manufactured from recycled blockboard particles. Amount of rotten/recycled particles in PB—w<sub>R</sub> (%): (Ⅰ) Control = 0%, (Ⅱ) 20%, (Ⅲ) 50%, and (Ⅳ) = 100%.</p> Full article ">Figure 2
<p>Display of PB and virulence wood samples in Kolle flasks: (<b>a</b>) before test initiation; (<b>b</b>) after 16 weeks of mycological testing with <span class="html-italic">Coniophora puteana</span>.</p> Full article ">Figure 3
<p>Display of mould growth activity (MGA) on the top PB surface and control Norway spruce wood (<span class="html-italic">P. abies</span> Karst. L.) samples in Petri dishes during testing: (<b>a</b>) start of the mould test; (<b>b</b>) day 7; (<b>c</b>) day 14; (<b>d</b>) day 21. PB with 100% recycled BB particles (PB 100 BB) is shown.</p> Full article ">Figure 4
<p>Mass loss—Δm [%] (<b>a</b>) and moisture content—w<sub>decayed</sub> [%] (<b>b</b>) of PBs containing different amounts of brown-rot particles after being subjected to the brown-rot fungus <span class="html-italic">Coniophora puteana</span>.</p> Full article ">Figure 5
<p>Mass loss—Δm [%] (<b>a</b>) and moisture content—w<sub>decayed</sub> [%] (<b>b</b>) of PBs containing different amount of white-rotten particles after being subjected to the brown-rot fungus <span class="html-italic">Coniophora puteana</span>.</p> Full article ">Figure 6
<p>Mass loss—Δm [%] (<b>a</b>) and moisture content—w<sub>decayed</sub> [%] (<b>b</b>) of PBs containing different amount of recycled particles from laminated PBs after being subjected to the brown-rot fungus <span class="html-italic">Coniophora puteana</span>.</p> Full article ">Figure 7
<p>Mass loss—Δm [%] (<b>a</b>) and moisture content—w<sub>decayed</sub> [%] (<b>b</b>) of PBs containing different amount of recycled particles from blockboards after their attack by the brown-rot fungus <span class="html-italic">Coniophora puteana</span>.</p> Full article ">
13 pages, 4506 KiB  
Article
Identification of Key Soil Quality Indicators for Predicting Mean Annual Increment in Pinus patula Forest Plantations in Tanzania
by Joshua Maguzu, Salim M. Maliondo, Ilstedt Ulrik and Josiah Zephaniah Katani
Forests 2024, 15(11), 2042; https://doi.org/10.3390/f15112042 - 19 Nov 2024
Viewed by 704
Abstract
There is an unexplored knowledge gap regarding the relationship between soil quality and mean annual increment (MAI) in forest plantations in Tanzania. Therefore, this study aimed to identify soil quality indicators and their impact on the mean annual increment (MAI) of Pinus patula [...] Read more.
There is an unexplored knowledge gap regarding the relationship between soil quality and mean annual increment (MAI) in forest plantations in Tanzania. Therefore, this study aimed to identify soil quality indicators and their impact on the mean annual increment (MAI) of Pinus patula at Sao Hill (SHFP) and Shume forest plantations (SFP) in Tanzania. The forests were stratified into four site classes based on management records. Tree growth data were collected from 3 quadrat plots at each site, resulting in 12 plots in each plantation, while soil samples were taken from 0 to 40 cm soil depth. Analysis of variance examined the variation in soil quality indicators between site classes at two P. patula plantation sites. Covariance analysis assessed the differences in MAI and stand variables across various site classes, taking into account the differing ages of some stands, with stand age serving as a covariate. Linear regression models explored the relationship between soil quality indicators and MAI, while partial least squares regression predicted MAI using soil quality indicators. The results showed that, at SHFP, sand, organic carbon (OC), cation exchange capacity, calcium (Ca), magnesium (Mg), and available P varied significantly between site classes, while silt, clay, and available P varied significantly at SFP. At SHFP, sand and clay content were positively correlated with MAI, while at SFP, silt content, available P (Avail P), potassium (K), Ca, and Mg showed significant positive correlations. Soil quality indicators, including physical and chemical properties (porosity, clay percentages, sand content, and OC) and only chemical (K, Mg, Avail P, and soil pH) properties were better predictors of the forest mean annual increment at SHFP and SFP, respectively. This study underscores the importance of monitoring the quality of soils in enhancing MAI and developing soil management strategies for long-term sustainability in forests production. Full article
(This article belongs to the Special Issue Forest Soil Physical, Chemical, and Biological Properties)
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<p>Locations of soil sampling plots at Sao Hill and Shume Forest Plantations in Tanzania.</p> Full article ">Figure 2
<p>Variation of soil physical properties among the site classes. Plotted above the error line, distinct lowercase letters signify significant differences among site classes at Sao Hill and Shume Forest Plantations.</p> Full article ">Figure 3
<p>Variation of soil chemical properties among the site classes. Plotted above the error line, distinct lowercase letters signify significant differences among site classes at Sao Hill and Shume Forest Plantations in Tanzania.</p> Full article ">Figure 4
<p>Distribution difference of mean annual increment and other stand attributes in different site classes. Above the error line, different small letters indicate that there are significant differences among different site classes.</p> Full article ">Figure 5
<p>Illustrates the correlations between soil physical properties and mean annual increment. The fitted linear relationships reflect the potential relationship of MAI-physical properties interactions at Sao Hill and Shume Forest Plantations in Tanzania.</p> Full article ">Figure 6
<p>Illustrates the correlations between soil chemical properties and mean annual increment. The fitted linear relationships reflect the potential relationship of MAI-chemical properties interactions at Sao Hill and Shume Forest Plantations in Tanzania.</p> Full article ">
20 pages, 3099 KiB  
Article
Yield and Survival of 19 Cultivars of Willow (Salix spp.) Biomass Crops over Eight Rotations
by Shane Santucci, Mark Eisenbies and Timothy Volk
Forests 2024, 15(11), 2041; https://doi.org/10.3390/f15112041 - 19 Nov 2024
Viewed by 683
Abstract
This study reveals patterns of yield and survival of short-rotation coppice (SRC) willow cultivars over eight rotations (1993–2019). Cultivars fell into four broad categories: commercial, released, stable, and decline. SV1, the singular cultivar that advanced to commercial deployments, had first-rotation yields of 8.9 [...] Read more.
This study reveals patterns of yield and survival of short-rotation coppice (SRC) willow cultivars over eight rotations (1993–2019). Cultivars fell into four broad categories: commercial, released, stable, and decline. SV1, the singular cultivar that advanced to commercial deployments, had first-rotation yields of 8.9 Mg ha−1 a−1, peaking at 15.2 Mg ha−1 a−1 by the fourth. Mean yields from rotations 2–8 were still 36% above first-rotation yields, confirming the commercial potential for this cultivar over 26 years. The released group (four cultivars) had stable yields over six rotations (approximately 3 to 7 Mg ha−1 a−1), rising to match commercial yields (10 Mg ha−1 a−1) between the sixth and eighth rotation. Most of the cultivars were in the stable group that had relatively consistent yields over time. First-rotation yields in this group were approximately 5 Mg ha−1 a−1, and average yield increased by 23% for rotations 2–8. The two cultivars in the decline group were impacted by disease and browsing that lowered survival and growth. These findings are crucial for understanding willow systems’ potential over their full lifespan as a bioenergy crop, which is a crucial input into yield, economic, and environmental models. Full article
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<p>Arithmetic means of the annualized yield for 19 willow cultivars across eight rotations from 1993 to 2019 at Tully, NY. Error bars indicate the standard error. Letters indicate significant differences at <span class="html-italic">p</span> = 0.05 based on the least square means.</p> Full article ">Figure 2
<p>Annualized yield for 19 willow cultivars across eight rotations organized into four different groups (commercial, released, stable and decline) based on yield patterns. Dark lines for the group cultivars are overlayed over gray lines for the other groups to assist in visual scaling and comparison. Specific cultivars are described in <a href="#app1-forests-15-02041" class="html-app">Appendix A</a>.</p> Full article ">Figure 3
<p>Changes in yield between rotations for each of the 19 cultivars in the trial over rotations 2 through 8. Cultivars are grouped in the left column by the four categories in <a href="#forests-15-02041-f002" class="html-fig">Figure 2</a> (commercial, released, stable, decline). There were significant increases in yield for 10 cultivars in the second rotation. Yield decreased in the third (9 cultivars) and sixth rotations (5 cultivars) across many of the cultivars. Yield increases are dark or light green and yield decreases are orange or red. Significant changes in yield from one rotation to the next are marked as * for 0.10 &lt; <span class="html-italic">p</span> &gt; 0.05, ** for 0.05 &lt; <span class="html-italic">p</span> &gt; 0.01, and *** for <span class="html-italic">p</span> &lt; 0.01.</p> Full article ">Figure 4
<p>Harvest-year percent survival spanning rotations 1 (1996) through 8 (2019) for four yield pattern groupings (<a href="#forests-15-02041-f002" class="html-fig">Figure 2</a>). Dark lines for the group cultivars are overlayed over gray lines for the other groups to assist in visual scaling and comparison. Specific cultivars are described in <a href="#app1-forests-15-02041" class="html-app">Appendix A</a>.</p> Full article ">Figure A1
<p>Schematic of field design and layout with random placement of cultivars in each of three blocks. White squares are block 1, medium gray are block 2, and dark gray are block 3.</p> Full article ">Figure A2
<p>Change in survival between harvest rotations for 19 willow cultivars. Cultivars are color-coded by grouping reported in <a href="#forests-15-02041-f002" class="html-fig">Figure 2</a> (commercial, released, stable, decline).</p> Full article ">
22 pages, 20099 KiB  
Article
Allelochemicals from Moso Bamboo: Identification and Their Effects on Neighbor Species
by Anke Wang, Kaiwen Huang, Yilin Ning and Yufang Bi
Forests 2024, 15(11), 2040; https://doi.org/10.3390/f15112040 - 19 Nov 2024
Viewed by 734
Abstract
Moso bamboo, which is essential to China’s economy, is currently facing significant threats due to declining profits. Inadequate management of moso bamboo can negatively impact the surrounding ecosystems. This study investigated allelopathy in moso bamboo forests by identifying potential allelochemicals and their effects [...] Read more.
Moso bamboo, which is essential to China’s economy, is currently facing significant threats due to declining profits. Inadequate management of moso bamboo can negatively impact the surrounding ecosystems. This study investigated allelopathy in moso bamboo forests by identifying potential allelochemicals and their effects on coexisting plants. Fresh leaves and litter from moso bamboo were collected to examine allelochemicals released through natural processes such as rainwater leaching and litter decomposition. Seven substances with potential allelopathic effects were identified using liquid chromatography–mass spectrometry (LC–MS). Four of these substances—DBP, PHBA, citric acid, and CGA—were selected for a detailed analysis of their effects on the photosynthetic and antioxidant systems of two naturally coexisting plants, Phoebe chekiangensis and Castanopsis sclerophylla. The results indicated that the four chemicals influenced P. chekiangensis and C. sclerophylla through different patterns of interference. DBP, PHBA, and citric acid negatively impacted the transfer of electrons during photosynthesis in both plants but had a lesser effect on the antioxidant system-related indicators in P. chekiangensis. In C. sclerophylla, these four chemicals led to a significant accumulation of reactive oxygen species (ROS) and increased malondialdehyde (MDA) content and catalase (CAT) activity to varying degrees. Furthermore, the relative abundance of fungi and bacteria in the soil was also affected by the DBP treatment. The identification of allelochemicals from moso bamboo, along with the investigation of their mechanisms, provides valuable insights into competitive interactions among plant species, particularly between moso bamboo and other species, along with the expansion of moso bamboo forests. Full article
(This article belongs to the Section Forest Ecophysiology and Biology)
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<p>The relative content of dibutyl phthalate, citric acid, 4-hydroxybenzoic acid, quinic acid, caffeic acid, sinapic acid, and chlorogenic acid in fresh leaves and litter leaves of moso bamboo in P1 and P2. The peak area can be used to estimate the relative content of different substances. Because there was a big gap in the peak area between chlorogenic acid and the other seven chemicals, the Y-axis for chlorogenic acid was separately positioned on the right side.</p> Full article ">Figure 2
<p>Chlorophyll a fluorescence induction curve of <span class="html-italic">P. chekiangensis</span> and <span class="html-italic">C. sclerophylla</span> under treatment with DBP, PHBA, citric acid, and CGA. (<b>a</b>): Chl a fluorescence induction curve of <span class="html-italic">P. chekiangensis</span> under treatment with DBP and PHBA. (<b>b</b>): Chl a fluorescence induction curve of <span class="html-italic">P. chekiangensis</span> under treatment with citric acid and CGA. (<b>c</b>): Chl a fluorescence induction curve of <span class="html-italic">C. sclerophylla</span> under treatment with DBP and PHBA. (<b>d</b>): Chl a fluorescence induction curve of <span class="html-italic">C. sclerophylla</span> under treatment with citric acid and CGA.</p> Full article ">Figure 3
<p>Illustrates a spider plot showcasing selected Chl a fluorescence parameter that characterizes the photosystem II (PSII) of both <span class="html-italic">P. chekiangensis</span> and <span class="html-italic">C. sclerophylla</span> under various treatments, including DBP, PHBA, citric acid, and chlorogenic acid. Each parameter is represented on its individual scale. Significance markers (*) indicate instances where significant differences from the control group (CK) were observed at a <span class="html-italic">p</span>-value of 0.05. The chl a fluorescence parameters of <span class="html-italic">P. chekiangensis</span> under DBP, PHBA, citric acid, and CGA are represented by (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>), respectively. The chl a fluorescence parameters of <span class="html-italic">C. sclerophylla</span> under DBP, PHBA, citric acid, and CGA are represented by (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>), respectively.</p> Full article ">Figure 4
<p>Phylum of bacteria and fungi. Lowercase letters indicate instances where significant differences were observed at a <span class="html-italic">p</span>-value of 0.05.</p> Full article ">Figure 5
<p>Genus of fungi. Lowercase letters indicate instances where significant differences were observed at a <span class="html-italic">p</span>-value of 0.05.</p> Full article ">Figure 6
<p>Genus of bacteria. Lowercase letters indicate instances where significant differences were observed at a <span class="html-italic">p</span>-value of 0.05. Large differences in abundance are labeled by a different Y-axis. <span class="html-italic">Burkholderia</span> spp. recently reclassified into <span class="html-italic">Caballeronia</span> and <span class="html-italic">Paraburkholderia</span> labeled as Burkholderia-C-P here.</p> Full article ">
18 pages, 25665 KiB  
Article
Remote Sensing Identification of Picea schrenkiana var. tianschanica in GF-1 Images Based on a Multiple Mixed Attention U-Net Model
by Jian Zheng, Donghua Chen, Hanchi Zhang, Guohui Zhang, Qihang Zhen, Saisai Liu, Naiming Zhang and Haiping Zhao
Forests 2024, 15(11), 2039; https://doi.org/10.3390/f15112039 - 19 Nov 2024
Viewed by 621
Abstract
Remote sensing technology plays an important role in woodland identification. However, in mountainous areas with complex terrain, accurate extraction of woodland boundary information still faces challenges. To address this problem, this paper proposes a multiple mixed attention U-Net (MMA-U-Net) semantic segmentation model using [...] Read more.
Remote sensing technology plays an important role in woodland identification. However, in mountainous areas with complex terrain, accurate extraction of woodland boundary information still faces challenges. To address this problem, this paper proposes a multiple mixed attention U-Net (MMA-U-Net) semantic segmentation model using 2015 and 2022 GF-1 PMS images as data sources to improve the ability to extract the boundary features of Picea schrenkiana var. tianschanica forest. The U-Net architecture serves as its underlying network, and the feature extraction ability of the Picea schrenkiana var. tianschanica is improved by adding hybrid attention CBAM and replacing the original skip connection with the DCA module to improve the accuracy of the model segmentation. The results show that on the remote sensing dataset with GF-1 PMS images, compared with the original U-Net and other models, the accuracy of the multiple mixed attention U-Net model is increased by 5.42%–19.84%. By statistically analyzing the spatial distribution of Picea schrenkiana var. tianschanica as well as their changes, the area was 3471.38 km2 in 2015 and 3726.10 km2 in 2022. Combining the predicted results with the DEM data, it was found that the Picea schrenkiana var. tianschanica were most distributed at an altitude of 1700–2500 m. The method proposed in this study can accurately identify Picea schrenkiana var. tianschanica and provides a theoretical basis and research direction for forest monitoring. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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<p>Location of the study area.</p> Full article ">Figure 2
<p>Examples of remote sensing datasets produced in this study.</p> Full article ">Figure 3
<p>Diagram of each attention sub-module. As illustrated, the channel sub-module utilizes both max-pooling outputs and average-pooling outputs with a shared network; the spatial sub-module utilizes two similar outputs that are pooled along the channel axis and forward them to a convolution layer.</p> Full article ">Figure 4
<p>Diagram of each attention sub-module. As illustrated, the CCA module first extracts a patch from each encoder stage for layer normalization. Then, the module splices it along the channel dimension, processes the output of the cross-attention using depth separable convolution, and feeds it into the SCA module. The SCA module is given the processed output of the CCA module, processes it along the channel dimension. The layer is normalized and spliced, and the output of SCA is processed using depth separable convolution to obtain the output of the final DCA.</p> Full article ">Figure 5
<p>The framework of the U-Net model.</p> Full article ">Figure 6
<p>(<b>a</b>) The CBAM module was added to the downsampling. (<b>b</b>) The DCA module replaces the original jump connection. (<b>c</b>) The MMA-U-Net modelling framework proposed in this study.</p> Full article ">Figure 7
<p><b>The</b> MMA-U-Net model loss curve for the model.</p> Full article ">Figure 8
<p>Predicted results of the ablation experiments and the identification of <span class="html-italic">Picea schrenkiana</span> var. <span class="html-italic">tianschanica</span> at different elevations; The red boxes show areas where there are clear gaps in the categorisation results.</p> Full article ">Figure 9
<p>Predictions from experiments comparing different attention models and the identification of <span class="html-italic">Picea schrenkiana</span> var. <span class="html-italic">tianschanica</span> at different elevations; The red boxes show areas where there are clear gaps in the categorisation results.</p> Full article ">Figure 10
<p>Predicted distribution of <span class="html-italic">Picea schrenkiana</span> var. <span class="html-italic">tianschanica</span>. (<b>a</b>) <span class="html-italic">Picea schrenkiana</span> var. <span class="html-italic">tianschanica</span> distribution map in 2015. (<b>b</b>) <span class="html-italic">Picea schrenkiana</span> var. <span class="html-italic">tianschanica</span> distribution map in 2022.</p> Full article ">Figure 11
<p><span class="html-italic">Picea schrenkiana</span> var. <span class="html-italic">tianschanica</span> area and changes. (<b>a</b>) Area of <span class="html-italic">Picea schrenkiana</span> var. <span class="html-italic">tianschanica</span> in 2015. (<b>b</b>) Area of <span class="html-italic">Picea schrenkiana</span> var. <span class="html-italic">tianschanica</span> in 2022. (<b>c</b>) Change in <span class="html-italic">Picea schrenkiana</span> var. <span class="html-italic">tianschanica</span> area from 2015 to 2022.</p> Full article ">Figure 12
<p>Comparison of predicted results for spruce forests.</p> Full article ">
14 pages, 2804 KiB  
Article
Thinning Modulates the Soil Organic Carbon Pool, Soil Enzyme Activity, and Stoichiometric Characteristics in Plantations in a Hilly Zone
by Jing Guo, Wenjie Tang, Haochuan Tu, Jingjing Zheng, Yeqiao Wang, Pengfei Yu and Guibin Wang
Forests 2024, 15(11), 2038; https://doi.org/10.3390/f15112038 - 19 Nov 2024
Viewed by 673
Abstract
Thinning, a core forest management measure, is implemented to adjust stand density and affect soil biogeochemical processes by changing biotic and abiotic properties. However, the responses of soil organic carbon (SOC), soil enzyme activity (EEA), and stoichiometry (EES) in plantations in hilly zones [...] Read more.
Thinning, a core forest management measure, is implemented to adjust stand density and affect soil biogeochemical processes by changing biotic and abiotic properties. However, the responses of soil organic carbon (SOC), soil enzyme activity (EEA), and stoichiometry (EES) in plantations in hilly zones to thinning have received little attention. To test the hypothesis that thinning has regulatory effects on the SOC pool, EEA, and EES characteristics, field sampling and indoor analysis were conducted 9 years after thinning. Thinning significantly influenced the soil properties, especially in the topsoil, and significantly greater SOC and mineral-associated organic carbon (MAOC) contents were observed in the high-density treatment. The EEAs in the topsoil tended to increase with increasing density. SOC, MAOC, and C to phosphorus (C:P) had the greatest influence on the soil EEAs and EESs. Microbial metabolic limitations tended to change from nitrogen to phosphorus with increasing density. The soil properties, SOC fractions, available nutrients, and elemental stoichiometry drove microbial metabolic limitations and were significantly positively correlated with β-glucosidase, elemental stoichiometry, and EES. This study deepens our understanding of EEAs, SOC, and nutrient dynamics under thinning practices and elucidates how forest tending measures affect soil biogeochemical processes, thereby providing ideas for developing strategies to mitigate the adverse impacts of human interventions. Full article
(This article belongs to the Section Forest Soil)
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<p>Variations in soil properties at different stand densities and soil depths. (<b>A</b>) pH; (<b>B</b>) TN, soil total nitrogen; (<b>C</b>) TP, total phosphorus; (<b>D</b>) TK, total potassium; (<b>E</b>) AN, ammonium N; (<b>F</b>) NN, nitrate N; (<b>G</b>) AP, available P; and (<b>H</b>) AK, available K. Different lowercase letters indicate significant differences among the three densities at the <span class="html-italic">p</span> &lt; 0.05 level.</p> Full article ">Figure 2
<p>Soil organic carbon (SOC, (<b>A</b>)), particulate organic carbon (POC, (<b>B</b>)), and mineral-associated organic carbon (MAOC, (<b>C</b>)) contents in plantations with different stand densities and soil depths. The calculated proportions of POC and MAOC in the SOC at different densities in the 0–10 cm (<b>D</b>–<b>F</b>) and 10–20 cm (<b>G</b>–<b>I</b>) soil layers. Different lowercase letters indicate significant differences among the three densities at the <span class="html-italic">p</span> &lt; 0.05 level.</p> Full article ">Figure 3
<p>Soil microbial biomass carbon (MBC), nitrogen (MBN), phosphorus (MBP) contents, elemental stoichiometry, and microbial biomass stoichiometry. (<b>A</b>) C:N ratios; (<b>B</b>) C:P ratios; (<b>C</b>) N:P ratios; (<b>D</b>) MBC contents; (<b>E</b>) MBN contents; (<b>F</b>) MBP contents; (<b>G</b>) MBC:MBN ratios; (<b>H</b>) MBC:MBP ratios; (<b>I</b>) MBN:MBP ratios. Different lowercase letters indicate significant differences among the three densities at the <span class="html-italic">p</span> &lt; 0.05 level.</p> Full article ">Figure 4
<p>C-acquisition ((<b>A</b>), BG, β-1,4-glucosidase), N-acquisition ((<b>B</b>), NAG+LAP; NAG, β-1,4-N-acetylglucosaminidase; LAP, leucine aminopeptidase), and P-acquisition ((<b>C</b>), ALP, alkaline phosphatase) activities and the corresponding stoichiometries at different stand densities in the 0–10 cm and 10–20 cm soil layers. (<b>D</b>) BG:(NAG+LAP) ratios; (<b>E</b>) BG:ALP ratios; (<b>F</b>) (NAG+LAP):ALP ratios. Different lowercase letters indicate significant differences among the three densities at the <span class="html-italic">p</span> &lt; 0.05 level.</p> Full article ">Figure 5
<p>The variation in vector length (<b>A</b>) and angle (<b>B</b>). Vector analysis for evaluating microbial nutrient limitation. A vector angle &lt;45° indicates N limitation, and a vector angle &gt;45° indicates P limitation.</p> Full article ">Figure 6
<p>Redundancy analysis of EEA, stoichiometry, vector L, and vector A (red arrows) with soil properties (black arrows).</p> Full article ">Figure 7
<p>Heatmap showing the Pearson’s correlation (r) and Mantel test results for vector L and vector A with respect to the soil properties, EEA, and stoichiometry. The colors indicate the correlations between pairwise comparisons of variables. The arc width corresponds to Mantel’s r statistic for the corresponding distance correlations, and the arc color indicates the significance of Mantel’s p statistic.</p> Full article ">
11 pages, 3124 KiB  
Article
Interspecific Relationship Between Monochamus alternatus Hope and Arhopalus rusticus (L.) in Pinus thunbergii Affected by Pine Wilt Disease
by Yingchao Ji, Chenyu Song, Long Chen, Xue Zheng, Chunyan Jia and Yanxue Liu
Forests 2024, 15(11), 2037; https://doi.org/10.3390/f15112037 - 19 Nov 2024
Viewed by 561
Abstract
Monochamus alternatus Hope and Arhopalus rusticus (L.) are important stem-boring pests that co-occur on weakened Pinus spp. Their larvae damage the xylem and phloem of the trunks and branches. At present, the consequences of the interspecific relationship between two longicorn beetles on the [...] Read more.
Monochamus alternatus Hope and Arhopalus rusticus (L.) are important stem-boring pests that co-occur on weakened Pinus spp. Their larvae damage the xylem and phloem of the trunks and branches. At present, the consequences of the interspecific relationship between two longicorn beetles on the same host of Pinus trees are unclear. The population dynamics and spatial distribution of these two species on Pinus thunbergii trees were investigated to clarify the ecological niches and interspecific relationship of two longicorn beetles on the different degrees of decline in P. thunbergii trees. The results showed temporal niche overlap values from 0.02 ± 0.01 to 0.05 ± 0.02, suggesting a very high degree of temporal ecological niche segregation and no competition in temporal niche resources. There is significant interspecific competition between the two longicorn beetles in spatial distribution, and the spatial niche overlap values are 0.67 ± 0.11 and 0.61 ± 0.09 in the middle and late stages of the decline in P. thunbergii trees, respectively. Full article
(This article belongs to the Section Forest Health)
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<p>The characteristics of the four decline stages of <span class="html-italic">P. thunbergia</span>: (<b>A</b>) early stage of decline, (<b>B</b>) middle stage of decline, (<b>C</b>) later stage of decline, and (<b>D</b>) wilting death stage.</p> Full article ">Figure 2
<p>Two longicorn beetle larvae and the stump of <span class="html-italic">Pinus thunbergii</span>. (<b>A</b>) <span class="html-italic">M. alternatus</span> larvae, (<b>B</b>) <span class="html-italic">A. rusticus</span> larvae, (<b>C</b>) stump in the current year, and (<b>D</b>) stump in the next year.</p> Full article ">Figure 3
<p>Seasonal dynamics of the two species of longicorn beetles in a <span class="html-italic">P. thunbergii</span> forest: (<b>A</b>) population in 2019; (<b>B</b>) population in 2020.</p> Full article ">Figure 4
<p>The within-trunk distribution of <span class="html-italic">Monochamus alternatus</span> and <span class="html-italic">Arhopalus rusticus</span> larvae in their host plant <span class="html-italic">P. thunbergii</span> at various vigor levels: (<b>A</b>) early stage of decline, (<b>B</b>) middle stage of decline, (<b>C</b>) stage of decline, and (<b>D</b>) dead. Data in the figure are mean ± SD of five replications (<span class="html-italic">n</span> = 5). Lowercase letters and capital letters indicate significant differences of distribution proportion at different heights for <span class="html-italic">M. alternatus</span> and <span class="html-italic">A. rusticus</span> larvae, respectively (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">
16 pages, 10577 KiB  
Article
Designing a Multitemporal Analysis of Land Use Changes and Vegetation Indices to Assess the Impacts of Severe Forest Fires Before Applying Control Measures
by Casandra Muñoz-Gómez and Jesús Rodrigo-Comino
Forests 2024, 15(11), 2036; https://doi.org/10.3390/f15112036 - 18 Nov 2024
Viewed by 1166
Abstract
Forest fires represent a significant intersection between nature and society, often leading to the loss of natural resources, soil nutrients, and economic opportunities, as well as causing desertification and the displacement of communities. Therefore, the objective of this work is to analyze the [...] Read more.
Forest fires represent a significant intersection between nature and society, often leading to the loss of natural resources, soil nutrients, and economic opportunities, as well as causing desertification and the displacement of communities. Therefore, the objective of this work is to analyze the multitemporal conditions of a sixth-generation forest fire through the use and implementation of tools such as remote sensing, photointerpretation with geographic information systems (GISs), thematic information on land use, and the use of spatial indices such as the Normalized Difference Vegetation Index (NDVI), the Normalized Burned Ratio (NBR), and its difference (dNBR) with satellite images from Sentinel-2. To improve our understanding of the dynamics and changes that occurred due to the devastating forest fire in Los Guájares, Granada, Spain, in September 2022, which affected 5194 hectares and had a perimeter of 150 km, we found that the main land use in the study area was forest, followed by agricultural areas which decreased from 1956 to 2003. We also observed the severity of burning, shown with the dNBR, reflecting moderate–low and moderate–high levels of severity. Health and part of the post-fire recovery process, as indicated by the NDVI, were also observed. This study provides valuable information on the spatial and temporal dimensions of forest fires, which will favor informed decision making and the development of effective prevention strategies. Full article
(This article belongs to the Topic Application of Remote Sensing in Forest Fire)
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<p>Localization of the study area and photographs during the fieldwork campaign.</p> Full article ">Figure 2
<p>Maps of elevation and inclination of the study area.</p> Full article ">Figure 3
<p>Land use maps showing the changes among selected dates.</p> Full article ">Figure 4
<p>Maps considering land use changes between specific intervals of years.</p> Full article ">Figure 5
<p>Satellite images with natural color from March 2022 to September 2022.</p> Full article ">Figure 6
<p>Satellite images with natural color from October 2022 to March 2023.</p> Full article ">Figure 7
<p>Satellite images with natural color from August to October in 2021 and 2023.</p> Full article ">Figure 8
<p>Normalize Difference Vegetation Index (NDVI) from March 2022 to September 2022.</p> Full article ">Figure 9
<p>Normalize Difference Vegetation Index (NDVI) from October 2022 to March 2023.</p> Full article ">Figure 10
<p>Normalized Difference Vegetation Index (NDVI) from August to October in 2021 and 2023.</p> Full article ">Figure 11
<p>Normalized Burn Ratio (NBR) from August and October 2022.</p> Full article ">Figure 12
<p>Difference Normalized Burn Ratio (dNBR) from August and October 2022.</p> Full article ">
26 pages, 4751 KiB  
Article
Long-Term Cumulative Effect of Management Decisions on Forest Structure and Biodiversity in Hemiboreal Forests
by Teele Paluots, Jaan Liira, Mare Leis, Diana Laarmann, Eneli Põldveer, Jerry F. Franklin and Henn Korjus
Forests 2024, 15(11), 2035; https://doi.org/10.3390/f15112035 - 18 Nov 2024
Viewed by 739
Abstract
We evaluated the long-term impacts of various forest management practices on the structure and biodiversity of Estonian hemiboreal forests, a unique ecological transition zone between temperate and boreal forests, found primarily in regions with cold winters and moderately warm summers, such as the [...] Read more.
We evaluated the long-term impacts of various forest management practices on the structure and biodiversity of Estonian hemiboreal forests, a unique ecological transition zone between temperate and boreal forests, found primarily in regions with cold winters and moderately warm summers, such as the northern parts of Europe, Asia, and North America. The study examined 150 plots across stands of different ages (65–177 years), including commercial forests and Natura 2000 habitat 9010* “Western Taiga”. These plots varied in stand origin—multi-aged (trees of varying ages) versus even-aged (uniform tree ages), management history—historical (practices before the 1990s) and recent (post-1990s practices), and conservation status—protected forests (e.g., Natura 2000 areas) and commercial forests focused on timber production. Data on forest structure, including canopy tree diameters, deadwood volumes, and species richness, were collected alongside detailed field surveys of vascular plants and bryophytes. Management histories were assessed using historical maps and records. Statistical analyses, including General Linear Mixed Models (GLMMs), Multi-Response Permutation Procedures (MRPP), and Indicator Species Analysis (ISA), were used to evaluate the effects of origin, management history, and conservation status on forest structure and species composition. Results indicated that multi-aged origin forests had significantly higher canopy tree diameters and deadwood volumes compared to even-aged origin stands, highlighting the benefits of varied-age management for structural diversity. Historically managed forests showed increased tree species richness, but lower deadwood volumes, suggesting a biodiversity–structure trade-off. Recent management, however, negatively impacted both deadwood volume and understory diversity, reflecting short-term forestry consequences. Protected areas exhibited higher deadwood volumes and bryophyte richness compared to commercial forests, indicating a small yet persistent effect of conservation strategies in sustaining forest complexity and biodiversity. Indicator species analysis identified specific vascular plants and bryophytes as markers of long-term management impacts. These findings highlight the ecological significance of integrating historical legacies and conservation priorities into modern management to support forest resilience and biodiversity. Full article
(This article belongs to the Special Issue Forest Diversity, Structure and Functions: Theories, Concepts and Analyses)
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<p>The timescale (state, year) and data (data sources) used for classifying (origin, management, conservation) study areas in Estonia.</p> Full article ">Figure 2
<p>Study outline. Data (2015–2023) from 150 forest plots (blue) across various site types (white) and supplemented by historical data (orange) were used to classify plots into categories of stand origin (grey), historical management (brown), recent management (yellow), and conservation status (green).</p> Full article ">Figure 3
<p>Location of the studied sample plots in Estonia. The map shows the geographic distribution of the 150 sample plots. Legend codes represent various management and conservation factors. O indicates stand historic origin, where 0 (solid fill) represents mixed-aged stands and 1 (pattern fill) represents even-aged stands. H represents historic management, with 0 (circle) for not managed and 1 (triangle) for managed. R stands for recent management, with 0 (green fill) for not managed and 1 (red fill) for managed. C represents conservation status, where 0 (red edge) indicates commercial forest and 1 (green edge) represents protected area.</p> Full article ">Figure 4
<p>NDMS varimax ordination of 150 sample plots based on vascular plants and bryophytes. The first axis explains 78% of the variance (<span class="html-italic">p</span> = 0.004), while the second axis accounts for 10% of the variance (<span class="html-italic">p</span> = 0.004). The ordination was performed using raw logarithm data for species with a frequency &gt;3 per plot (n = 204 species) and 25 environmental variables (see <a href="#app4-forests-15-02035" class="html-app">Appendix C</a> for full list). Only environmental factors significantly related to the ordination axes (<span class="html-italic">p</span> &lt; 0.05) are shown, with a cut-off of R<sup>2</sup> = 0.2 for vector inclusion. Plots are color-coded by site type: blue for <span class="html-italic">Ox-Myrt</span>, red for <span class="html-italic">Ox-Rhod</span>, and green for <span class="html-italic">Oxalis</span>. The pNDMS ordination without the effects of site type and region is available in <a href="#app7-forests-15-02035" class="html-app">Appendix F</a>. The final stress value of the ordination is 14.62225, indicating the goodness of fit.</p> Full article ">Figure 5
<p>Heatmap showing GLMM analysis results (<span class="html-italic">p</span>-values) for each structural trait in different categories. The significant difference between sites was tested using the Type I model for structural traits. More detailed results in <a href="#app4-forests-15-02035" class="html-app">Appendix C</a>.</p> Full article ">Figure 6
<p>General Linear Mixed Model (GLMM) Type I tests to evaluate the effects of interventions (stand origin, historic and recent management, conservation) on the traits (structural features and biodiversity components) of forest stands). Each point indicates the mean effect size with its confidence interval, showing influence across traits. Variance inflation factor (VIF) values for all predictors were below 5, indicating no multicollinearity among the independent variables.</p> Full article ">Figure 7
<p>Multi-response permutation procedure (MRPP) results comparing species composition by different management regimes for stand origin, historic management, recent management, and conservation, with test statistic (T) and agreement (A) values.</p> Full article ">Figure A1
<p>First (40% of variance, <span class="html-italic">p</span> = 0.004) and second (23% of variance, <span class="html-italic">p</span> = 0.004) axes of the pNDMS varimax ordination for 150 sample plots (final stress= 17.49488) using vascular plants and bryophytes logarithm residuals data without site type and region effect with species frequency &gt; 3 on plot (<span class="html-italic">n</span> = 204) and 25 environmental variables (App 3). The plots are classified after site type (blue—<span class="html-italic">Ox-Myrt</span>, red—<span class="html-italic">Ox-Rhod</span>, green—<span class="html-italic">Oxalis</span>).</p> Full article ">
14 pages, 2526 KiB  
Article
Study on the Spatial–Temporal Variation of Groundwater Depth and Its Impact on Vegetation Coverage in Ejina Oasis
by Dongyang Song, Xiaolong Pei, Lei Mao, Jiangyulong Wang, Ye Tian, Xiaoyu An and Hongyan An
Forests 2024, 15(11), 2034; https://doi.org/10.3390/f15112034 - 18 Nov 2024
Viewed by 683
Abstract
Ejina, a representative inland river basin situated in the arid region of northwest China, exhibits a delicate ecological environment and its vegetation coverage is intrinsically linked to regional ecological security. Based on MOD13Q1-NDVI data from 2018 to 2023 and groundwater depth monitoring data [...] Read more.
Ejina, a representative inland river basin situated in the arid region of northwest China, exhibits a delicate ecological environment and its vegetation coverage is intrinsically linked to regional ecological security. Based on MOD13Q1-NDVI data from 2018 to 2023 and groundwater depth monitoring data during the same period, this study analyzed the spatial–temporal variation characteristics of vegetation coverage and its relationship with groundwater depth in Ejina. It is found that the vegetation coverage in Ejina is generally low and mainly distributed along the riverbanks in the form of strips. During the study period, the overall trend of vegetation coverage showed a fluctuating pattern of first increasing and then decreasing, revealing the fragility of the regional ecology. The groundwater depth shows the characteristic of being higher in the east river than the west, and the trend of groundwater depth along the river flow is first increasing and then decreasing. The spatial groundwater depth indicates that the east river is higher than that of the west river, and the groundwater depth along the river flow first increases and then decreases. In terms of inter-annual changes, the groundwater depth experiences a process of first decreasing and then stabilizing. Further analysis indicates that vegetation growth and coverage in Ejina are significantly affected by water conditions, and areas with high Normalized Difference Vegetation Index (NDVI) values are mainly distributed along the riverbanks. In addition, there is a certain degree of correlation between groundwater depth and NDVI. When the depth of groundwater is too deep or too shallow, the positive correlation between NDVI and groundwater depth increases slightly and the negative correlation decreases slightly. The findings of this study are of great significance for understanding and predicting the response of vegetation coverage to groundwater changes in arid areas, and provide a scientific basis for water resources management and ecological protection in Ejina. Full article
(This article belongs to the Special Issue Soil Carbon Storage in Forests: Dynamics and Management)
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<p>Geographical location and distribution of groundwater observation stations in Ejina Oasis.</p> Full article ">Figure 2
<p>(<b>a</b>) Spatial distribution of vegetation cover in 2018, 2020 and 2023 (<b>b</b>) Vegetation cover class area transition diagram from 2018 to 2023 (<b>c</b>) Proportion of area for each vegetation cover class.</p> Full article ">Figure 3
<p>(<b>a</b>) Spatial distribution of groundwater depth in March and August (<b>b</b>) Annual variation of groundwater depth.</p> Full article ">Figure 4
<p>(<b>a</b>) Groundwater depth variation diagram (<b>b</b>) Interannual trend of groundwater depth in different river sections.</p> Full article ">Figure 5
<p>(<b>a</b>) Contour plots of NDVI and groundwater depths (<b>b</b>) Trends of NDVI at different water depths.</p> Full article ">Figure 6
<p>(<b>a</b>) Spatial distribution of the correlation between NDVI and groundwater depth (<b>b</b>) Proportion of NDVI correlation in different groundwater depth intervals.</p> Full article ">Figure 7
<p>Monthly trend of evaporation intensity at different soil depths.</p> Full article ">
15 pages, 5030 KiB  
Article
Infiltration and Hydrophobicity in Burnt Forest Soils on Mediterranean Mountains
by Jorge Mongil-Manso, Verónica Ruiz-Pérez and Aida López-Sánchez
Forests 2024, 15(11), 2033; https://doi.org/10.3390/f15112033 - 18 Nov 2024
Viewed by 868
Abstract
Forest fires are a major global environmental problem, especially for forest ecosystems and specifically in Mediterranean climate zones. These fires can seriously impact hydrologic processes and soil erosion, which can cause water pollution and flooding. The aim of this work is to assess [...] Read more.
Forest fires are a major global environmental problem, especially for forest ecosystems and specifically in Mediterranean climate zones. These fires can seriously impact hydrologic processes and soil erosion, which can cause water pollution and flooding. The aim of this work is to assess the effect of forest fire on the hydrologic processes in the soil, depending on soil properties. For this purpose, the infiltration rate has been measured by ring infiltration tester, and the hydrophobicity has been quantified by the “water drop penetration time” method in several soils of burnt and unburnt forest areas in the Mediterranean mountains. The infiltration rates obtained are higher in burnt than in unburnt soils (1130 and 891 mm·h−1, respectively), which contradicts most of the research in Mediterranean climates in southeast Spain with calcareous soils. Burnt soils show no hydrophobicity on the surface, but it is there when the soil is excavated by 1 cm. Additionally, burnt soils reveal a low frequency of hydrophobicity (in less than 30% of the samples) but more severe hydrophobicity (above 300 s); whereas, in unburnt soils, the frequency is higher (50%) but the values of hydrophobicity are lower. The results obtained clearly show the infiltration processes modified by fire, and these results may be useful for land managers, hydrologists, and those responsible for decision-making regarding the forest restoration of burnt land. Full article
(This article belongs to the Section Forest Hydrology)
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<p>Location of the study area in the Tajo River basin and sampling plots (B1, B2, U1, and U2).</p> Full article ">Figure 2
<p>Representative soil profile diagrams for unburnt and burnt sites.</p> Full article ">Figure 3
<p>Sampling plots in burnt area (B1 and B2) and unburnt area (U1 and U2).</p> Full article ">Figure 4
<p>Infiltest infiltrometer used in this study.</p> Full article ">Figure 5
<p>Infiltration curves for the four plots, obtained by Horton’s equation [<a href="#B58-forests-15-02033" class="html-bibr">58</a>].</p> Full article ">Figure 6
<p>Final steady infiltration rates (f<sub>C</sub>) and standard deviation for the four plots.</p> Full article ">Figure 7
<p>Water repellency time (s) depending on the interaction of treatment and soil depth (N = 240 samples). Forest = non-fire-affected forest area; Burnt forest = fire-affected forest area. Excavated soil = soil excavated 1 cm; Original soil = non-excavated soil; Different letters (a, b) denote statistically significant differences, α = 0.05.</p> Full article ">Figure 8
<p>Frequency of the presence of hydrophobicity in burnt compared to unburnt soils. The graph shows the percentage of samples in each hydrophobicity class: hydrophilic if WDPT &lt; 5 s, hydrophobic if WDPT &gt; 5 s.</p> Full article ">Figure A1
<p>Infiltration rate (mm/h) depending on interaction of treatment and soil properties: (<b>i</b>) treatment–density bulk; (<b>ii</b>) treatment–organic matter; (<b>iii</b>) treatment–gravel; and (<b>iv</b>) treatment–sand (N = 12 samples). Forest = non-fire-affected forest area; Burn forest = fire-affected forest area.</p> Full article ">Figure A2
<p>Water repellence time (s) depending on interaction of treatment and soil properties: (<b>i</b>) treatment–density bulk; (<b>ii</b>) treatment–gravel; and (<b>iii</b>) treatment–sand (N = 240 samples). Forest = non-fire-affected forest area; Burn forest = fire-affected forest area.</p> Full article ">Figure A3
<p>Water repellence time (s) depending on interaction of soil depth and soil properties: (<b>i</b>) soil depth–density bulk; (<b>ii</b>) soil depth–organic matter (N = 240 samples). Excavated soil = soil excavated 1 cm; Original soil = non-excavated soil.</p> Full article ">
21 pages, 21195 KiB  
Article
Mapping the Future: Climate-Induced Changes in Aboveground Live-Biomass Carbon Density Across Mexico’s Coniferous Forests
by Carmela Sandoval-García, Jorge Méndez-González, Flores Andrés, Eulalia Edith Villavicencio-Gutiérrez, Fernando Paz-Pellat, Celestino Flores-López, Eladio Heriberto Cornejo-Oviedo, Alejandro Zermeño-González, Librado Sosa-Díaz, Marino García-Guzmán and José Ángel Villarreal-Quintanilla
Forests 2024, 15(11), 2032; https://doi.org/10.3390/f15112032 - 18 Nov 2024
Viewed by 2069
Abstract
Climate variations in temperature and precipitation significantly impact forest productivity. Precipitation influences the physiology and growth of species, while temperature regulates photosynthesis, respiration, and transpiration. This study developed bioclimatic models to assess how climate change will affect the carbon density of aboveground biomass [...] Read more.
Climate variations in temperature and precipitation significantly impact forest productivity. Precipitation influences the physiology and growth of species, while temperature regulates photosynthesis, respiration, and transpiration. This study developed bioclimatic models to assess how climate change will affect the carbon density of aboveground biomass (cdAGB) in Mexico’s coniferous forests for 2050 and 2070. We used cdAGB data from the National Forest and Soils Inventory (INFyS) of Mexico and 19 bioclimatic variables from WorldClim ver. 2.0. The best predictors of cdAGB were obtained using machine learning techniques with the “caret” library in R. The model was trained with 80% of the data and validated with the remaining 20% using Generalized Linear Models (GLMs). Current cdAGB prediction maps were generated using the best predictors. Future cdAGB was calculated with the average of three general circulation models (GCMs) of future climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5), under four Representative Concentration Pathways (RCPs): 2.6, 4.5, 6.0, and 8.5 W/m2. The results indicate cdAGB losses in all climate scenarios, reaching up to 15 Mg C ha−1, and could occur under the RCP 8.5 scenario by 2070 in the central region of the country. Temperature-related variables are more important than precipitation variables. Bioclimatic variables can explain up to 20% of the total variance in cdAGB. The temperature in the study area is expected to increase by 2.66 °C by 2050 and 3.36 °C by 2070, while precipitation is expected to fluctuate by ±10% relative to the current values, which could geographically redistribute the cdAGB of the country’s coniferous forests. These findings underscore the need for forest management to focus not only on biodiversity conservation but also on the carbon storage capacity of these ecosystems. Full article
(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives)
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<p>Study area in the global context (<b>left</b>) and regional context (<b>right</b>).</p> Full article ">Figure 2
<p>Distribution of sites from the National Forest and Soil Inventory (2009–2012): stratum I (<b>a</b>), stratum II (<b>b</b>), and stratum III (<b>c</b>). The size of the circles and the color gradient indicate the values of carbon density in the aboveground live biomass (Mg C ha<sup>−1</sup>).</p> Full article ">Figure 3
<p>Prediction of current carbon density of aboveground live biomass in Mexican conifer forests through bioclimatic models: stratum I (<b>a</b>), stratum II (<b>b</b>), and stratum III (<b>c</b>). Circle size and color gradient indicate values of carbon density of aboveground live biomass (Mg C ha<sup>−1</sup>).</p> Full article ">Figure 4
<p>Changes in carbon density of aboveground live biomass in Mexican conifer forests under the RCP 2.6 to 8.5 scenarios (<b>a</b>–<b>d</b>) for the years 2050 and 2070 (<b>e</b>–<b>h</b>) in stratum I. Colored areas represent 40 km radius buffers around each INFyS site; uncolored areas correspond to other vegetation types different from conifer forests.</p> Full article ">Figure 5
<p>Changes in carbon density of aboveground live biomass in Mexican conifer forests under the RCP 2.6 to 8.5 scenarios (<b>a</b>–<b>d</b>), for the years 2050 and 2070 (<b>e</b>–<b>h</b>) in stratum II. Colored areas represent 40 km radius buffers around each INFyS site; uncolored areas correspond to other vegetation types different from conifer forests.</p> Full article ">Figure 6
<p>Changes in the carbon density of aboveground live biomass in Mexican conifer forests under the RCP 2.6 to 8.5 scenarios (<b>a</b>–<b>d</b>) for the years 2050 and 2070 (<b>e</b>–<b>h</b>) in stratum III. Colored areas represent 40 km radius buffers around each INFyS site; uncolored areas correspond to other vegetation types different from conifer forests.</p> Full article ">Figure 7
<p>Wilcoxon test for comparing medians of the current variable with each climate scenario, in strata I (<b>a</b>,<b>b</b>), II (<b>c</b>,<b>d</b>), and III (<b>e</b>,<b>f</b>). Bio 05: Max Temperature of Warmest Month (°C); Bio 10: Mean Temperature of Warmest Quarter (°C); Bio 12: Annual Precipitation (mm); Bio 13: Precipitation of Wettest Month (mm); Bio 18: Precipitation of Warmest Quarter (mm). Significance levels: ns (Not significant), * (Significant at 0.05%), ** (Significant at 1%), *** (Significant at 0.1%), **** (Significant at 0.01%).</p> Full article ">Figure 8
<p>Estimated uncertainty (standard error) of carbon density of aboveground live biomass in Mexican conifer forests, for strata I (<b>a</b>), II (<b>b</b>), and III (<b>c</b>) under RCP 85 and for the year 2070.</p> Full article ">
17 pages, 3502 KiB  
Article
The Driving Factors of the Tradeoff-Synergistic Relationship Among Forest Ecosystem Service Values in the Yangtze River Delta, China
by Shulin Chen and Jian Wu
Forests 2024, 15(11), 2031; https://doi.org/10.3390/f15112031 - 18 Nov 2024
Viewed by 704
Abstract
The forest ecosystem is one of the planet’s critical ecosystems. Identifying the tradeoff-synergistic relationships among forest ecosystem service values and exploring their driving factors in the Yangtze River Delta are crucial for promoting the optimal overall benefits of regional ecosystem service values and [...] Read more.
The forest ecosystem is one of the planet’s critical ecosystems. Identifying the tradeoff-synergistic relationships among forest ecosystem service values and exploring their driving factors in the Yangtze River Delta are crucial for promoting the optimal overall benefits of regional ecosystem service values and realizing a mutually beneficial scenario that harmonizes regional socio-economic development with ecological and environmental conservation. The forest ecosystem service value in the Yangtze River Delta was evaluated through the improved equivalent factor method. Furthermore, an examination of the tradeoff-synergistic relationship among these ecosystem service values, along with their driving factors, was performed utilizing both the Pearson correlation coefficient method and the Geodetector model. The findings reveal that from 2000 to 2020, the forest ecosystem service values presented a general growth trend in the Yangtze River Delta, with higher values noted in the southern areas and lower values found in the northern regions. The average annual forest ecosystem service value was 279 billion RMB. The tradeoff-synergistic relationship among forest ecosystem service values mainly showed a synergistic relationship, while a significant tradeoff relationship was observed between the values of support and cultural services. The factors influencing the tradeoff-synergistic relationship among forest ecosystem service values included precipitation, normalized difference vegetation index, and temperature. Consequently, local governments should enhance forest coverage, particularly by expanding the regions of evergreen broadleaf, deciduous broadleaf, and coniferous forests. They should also proactively seek ways to realize the value of forest ecosystem services. Full article
(This article belongs to the Special Issue Managing Forests for Multiple Ecosystem Services Under Changing Climate)
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<p>The location of the Yangtze River Delta.</p> Full article ">Figure 2
<p>Flowchart of this study.</p> Full article ">Figure 3
<p>The spatial pattern of the (<b>a</b>) average forest ESV, and (<b>b</b>) growth rate of forest ESV from 2000 to 2020.</p> Full article ">Figure 4
<p>The tradeoff-synergistic relationship between the value of (<b>a</b>) support and cultural services, (<b>b</b>) supply and support services, (<b>c</b>) support and regulation services, (<b>d</b>) supply and cultural services, (<b>e</b>) cultural and regulation services, and (<b>f</b>) supply and regulation services from 2000 to 2020.</p> Full article ">Figure 5
<p>The <span class="html-italic">q</span>-value of the single factors and two-factor interactions influencing the tradeoff-synergistic relationship between the values of (<b>a</b>) supply and cultural services, (<b>b</b>) cultural and regulation services, (<b>c</b>) support and cultural services, (<b>d</b>) supply and support services, (<b>e</b>) support and regulation services, and (<b>f</b>) supply and regulation services from 2000 to 2020. T represents temperature, DEM represents elevation, and P represents precipitation.</p> Full article ">Figure 6
<p>The spatial patterns of (<b>a</b>) forest type and (<b>b</b>) NDVI in forest land.</p> Full article ">
18 pages, 5191 KiB  
Article
A Multi-System Coupling Coordination Assessment to Achieve the Integrated Objectives of Forest Conservation, Marine Governance, and Socioeconomic Development in the Bay Area: A Case Study in the Bay Area of the Fujian River Delta
by Zhixun Huang, Yingjie Li, Xiuzhi Chen, Xiang Yu and Wei Shui
Forests 2024, 15(11), 2030; https://doi.org/10.3390/f15112030 - 18 Nov 2024
Viewed by 673
Abstract
The bay area contains terrestrial forests and coastal mangroves with vital ecosystem functions, which provide essential ecosystem services such as carbon sequestration and biodiversity maintenance. Meanwhile, the bay area usually hosts intensive socioeconomic activities. High-intensity anthropogenic activities in the bay area have threatened [...] Read more.
The bay area contains terrestrial forests and coastal mangroves with vital ecosystem functions, which provide essential ecosystem services such as carbon sequestration and biodiversity maintenance. Meanwhile, the bay area usually hosts intensive socioeconomic activities. High-intensity anthropogenic activities in the bay area have threatened the terrestrial ecosystem and marine environment. Harmonizing the relationship between terrestrial ecosystem conservation, marine environmental governance, and socioeconomic development is crucial for realizing the national “coordinated land and marine development” strategy and promoting sustainability in the bay area. This study constructed a coupling coordination assessment system of the terrestrial ecosystem, marine environmental system, and socioeconomic system. Taking the bay area of the Fujian River Delta as a case study, multiple ecological models were integrated to quantify the coupling coordination degree between these three systems and present its spatial distribution characteristics. Furthermore, the constraint types on the coupling coordination degree were spatially revealed in the bay area. The results suggested that there are significant spatial differences in the coupling coordination degree of the three systems in the bay area of the Fujian River Delta. The areas with a relatively low coupling coordination degree are mainly focused on the central part of the Xiamen Bay area and the southeastern part of the Quanzhou Bay area. Regions with high socioeconomic development tend to present weak terrestrial or marine eco-environmental conditions. The critical constraint factor of the coupling coordination degree in the Zhangzhou Bay area is its backward socioeconomic development level. The backwardness of both the terrestrial ecosystem and marine environmental system exists in most districts of the Xiamen Bay area. In addition, the marine environmental conditions in the Xiamen Bay area are worse than those in the Quanzhou Bay Area and the Zhangzhou Bay area. Full article
(This article belongs to the Special Issue Advances in Forest Cover Change and Its Ecological and Environmental Effects—2nd Edition)
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<p>The location and LULC of the bay area of the Fujian River Delta.</p> Full article ">Figure 2
<p>Systems’ construction of terrestrial ecosystem, marine environmental system, and socioeconomic system for bay area.</p> Full article ">Figure 3
<p>Spatial distribution of the development level of terrestrial ecosystem (<b>A</b>), socioeconomic system (<b>B</b>), and marine environmental system (<b>C</b>) in the bay area of the Fujian River Delta.</p> Full article ">Figure 4
<p>Spatial distribution of coupling coordination degree between terrestrial ecosystem, marine environmental system, and socioeconomic system in the bay area of the Fujian River Delta.</p> Full article ">Figure 5
<p>Overlapping map of the coupling coordination degree and the development level of each system in the bay area (the parallel lines in the figure are the average of the development level of each system. NA: Nan’an, YX: Yunxiao, SM: Siming, ZP: Zhangpu, ZA: Zhao’an, QG: Quangang, TA: Tong’an, JM: Jimei, LH: Longhai, HA: Hui’an, SS: Shishi, HC: Haicang, JJ: Jinjiang, DS: Dongshan, XA: Xiang’an, FZ: Fengze, HL: Huli).</p> Full article ">Figure 6
<p>The map revealing the spatial view of the constraint types on the coupling coordination of the bay area of the Fujian River Delta (TE: terrestrial ecosystem, ME: marine environmental system, SE: socioeconomic system).</p> Full article ">
17 pages, 32903 KiB  
Article
Prediction of Wildfire Occurrence in the Southern Forest Regions of China in the Future Scenario
by Jing Li, Duan Huang, Beiping Long, Yakui Shao, Mengwei Xiao, Linhao Sun, Xusheng Li, Aiai Wang, Xuanchi Chen and Weike Li
Forests 2024, 15(11), 2029; https://doi.org/10.3390/f15112029 - 18 Nov 2024
Cited by 1 | Viewed by 759
Abstract
In the context of global climate warming, climate change is subtly reshaping the patterns of wildfires. Therefore, it is particularly urgent to conduct in-depth research on climate change, wildfires, and their management strategies. This study relies on detailed fire point data from 2001 [...] Read more.
In the context of global climate warming, climate change is subtly reshaping the patterns of wildfires. Therefore, it is particularly urgent to conduct in-depth research on climate change, wildfires, and their management strategies. This study relies on detailed fire point data from 2001 to 2020, skillfully incorporating a spatial autocorrelation analysis to uncover the mysteries of spatial heterogeneity, while comprehensively considering the influences of multiple factors such as climate, terrain, vegetation, and socioeconomic conditions. To simulate fire conditions under future climates, we adopted the BCC-CSM2-MR climate model, presetting temperature and precipitation data for two scenarios: a sustainable low-development path and a high-conventional-development path. The core findings of the study include the following: (i) In terms of spatial heterogeneity exploration, global autocorrelation analysis reveals a striking pattern: within the southern forest region, 63 cities exhibiting a low–low correlation are tightly clustered in provinces such as Hubei, Anhui, and Zhejiang, while 48 cities with a high–high correlation are primarily distributed in Guangxi and Guangdong. Local autocorrelation analysis further refines this observation, indicating that 24 high–high correlated cities are highly concentrated in specific areas, 14 low–low correlated cities are located in Hainan, and there are only 3 sparsely distributed cities with a low–high correlation. (ii) During the model construction and validation process, this study innovatively adopted the LR-RF-SVM ensemble model, which demonstrated exceptional performance indicators: an accuracy of 91.97%, an AUC value of 97.09%, an F1 score of 92.13%, a precision of 90.75%, and a recall rate of 93.55%. These figures, significantly outperforming those of the single models SVM and RF, strongly validate the superiority of the ensemble learning approach. (iii) Regarding predictions under future climate scenarios, the research findings indicate that, compared to the current fire situation in southern forest areas, the spatial distribution of wildfires will exhibit a noticeable expansion trend. High-risk regions will not only encompass multiple cities in Hunan, Hubei, southern Anhui, all of Jiangxi, and Zhejiang but will also extend northward into southern forest areas that were previously considered low-risk, suggesting a gradual northward spread of fire risk. Notably, despite the relatively lower fire risk in some areas of Fujian Province under the SS585 scenario, overall, the probability of wildfires occurring in 2090 is slightly higher than that in 2030, further highlighting the persistent intensification of forest fire risk due to climate change. Full article
(This article belongs to the Section Natural Hazards and Risk Management)
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<p>The map of the southern forest regions (The blue line represents the coastline, and the dashed line represents the Nine-Dashed Line).</p> Full article ">Figure 2
<p>Technical roadmap of this study.</p> Full article ">Figure 3
<p>Schematic diagram of the model used in this study.</p> Full article ">Figure 4
<p>(<b>a</b>) Fire occurrences (2001–2020); (<b>b</b>) forest fire risk mapping using ensemble learning models under current climate conditions.</p> Full article ">Figure 5
<p>(<b>a</b>) The global Moran’s I index for forest fire risk and (<b>b</b>) the aggregation chart of local indicators of spatial association (LISA) for forest fire risk levels.</p> Full article ">Figure 6
<p>Evaluation charts for machine learning and ensemble learning.</p> Full article ">Figure 7
<p>Predictions of forest fire occurrences in the southern forest region of China, utilizing the BCC-CSM2-MR scenarios for the years 2030 to 2090 (the lighter the color, the lower the probability of occurrence; the darker the color, the higher the probability).</p> Full article ">Figure 8
<p>Evaluation of relative changes in forest fire occurrences based on current and future climate scenarios (green represents negative values).</p> Full article ">
14 pages, 2842 KiB  
Article
Integrating Multi-Source Remote Sensing Data for Forest Fire Risk Assessment
by Xinzhu Liu, Change Zheng, Guangyu Wang, Fengjun Zhao, Ye Tian and Hongchen Li
Forests 2024, 15(11), 2028; https://doi.org/10.3390/f15112028 - 18 Nov 2024
Viewed by 1166
Abstract
Forest fires are a frequent and destructive phenomenon in Southwestern China, posing significant threats to ecological systems and human lives and property. In response to the growing need for effective forest fire prevention, this study introduces an innovative method for predicting and assessing [...] Read more.
Forest fires are a frequent and destructive phenomenon in Southwestern China, posing significant threats to ecological systems and human lives and property. In response to the growing need for effective forest fire prevention, this study introduces an innovative method for predicting and assessing forest fire risk. By integrating multi-source data, including optical and microwave remote sensing, meteorological, topographic, and human activity data, the approach enhances the sensitivity of risk models to vegetation water content and other critical factors. The vegetation water content is derived from both Vegetation Optical Depth and optical remote sensing data, allowing for a more accurate assessment of changes in vegetation moisture that influence fire risk. A time series prediction model, incorporating attention mechanisms, is used to assess the probability of fire occurrence. Additionally, the method includes fire spread simulations based on Cellular Automaton and Monte Carlo approaches to evaluate potential burn areas. This combined approach can provide a comprehensive fire risk assessment using the probability of both fire occurrence and potential fire spread. Experimental results show that the integration of microwave data and attention mechanisms improves prediction accuracy by 2.8%. This method offers valuable insights for forest fire management, aiding in targeted prevention strategies and resource allocation. Full article
(This article belongs to the Topic Application of Remote Sensing in Forest Fire)
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<p>(<b>a</b>) Study area showing the historical (2015–2018) fire point extracted from the NASA website and the DEM (Digital Elevation Model) as the background image. (<b>b</b>) Land classification in the study area extracted from MCD12Q1.</p> Full article ">Figure 2
<p>(<b>a</b>) Monthly and (<b>b</b>) yearly fire frequency from 2015 to 2018, calculated from NASA website data in the study area, with trends highlighting high fire frequency from January to May.</p> Full article ">Figure 3
<p>Driving factors in predicting forest fire occurrence: (<b>a</b>) temperature, (<b>b</b>) precipitational, (<b>c</b>) humidity, (<b>d</b>) wind speed, (<b>e</b>) VOD, (<b>f</b>) NDVI, (<b>g</b>) DEM, (<b>h</b>) slope, (<b>i</b>) aspect, (<b>j</b>) railway, and (<b>k</b>) highway.</p> Full article ">Figure 4
<p>Deep learning model framework for predicting forest fire occurrence probability.</p> Full article ">Figure 5
<p>The resulting ROC curve of the proposed mode.</p> Full article ">Figure 6
<p>(<b>a</b>) The forest fire occurrence probability map using the proposed mode. (<b>b</b>) The forest fire potential burn probability using simulation. (<b>c</b>) The forest fire risk in the study area.</p> Full article ">
20 pages, 5686 KiB  
Article
Genome-Wide Identification and Characterization of bHLH Gene Family in Hevea brasiliensis
by Zheng Wang, Yuan Yuan, Fazal Rehman, Xin Wang, Tingkai Wu, Zhi Deng and Han Cheng
Forests 2024, 15(11), 2027; https://doi.org/10.3390/f15112027 - 18 Nov 2024
Viewed by 835
Abstract
The basic helix-loop-helix (bHLH) transcription factors play crucial roles in plant growth, development, and stress responses. However, their identification and insights into the understanding of their role in rubber trees remain largely uncovered. In this study, the bHLH gene family was explored and [...] Read more.
The basic helix-loop-helix (bHLH) transcription factors play crucial roles in plant growth, development, and stress responses. However, their identification and insights into the understanding of their role in rubber trees remain largely uncovered. In this study, the bHLH gene family was explored and characterized in rubber trees using systematic bioinformatics approaches. In total, 180 bHLH genes were identified in the rubber tree genome, distributed unevenly across 18 chromosomes, and phylogenetic analysis classified these genes into 23 distinct subfamilies. Promoter regions revealed a high density of cis-elements responsive to light and hormones. Enrichment analysis indicated involvement in numerous biological processes, including growth, development, hormone responses, abiotic stress resistance, and secondary metabolite biosynthesis. Protein interaction network analysis identified extensive interactions between HbbHLH genes and other functional genes, forming key clusters related to iron homeostasis, plant growth, and stomatal development. Expression profiling of HbbHLH genes have demonstrated varied responses to endogenous and environmental changes. RT-qPCR of eleven HbbHLH genes in different tissues and under ethylene, jasmonic acid, and cold treatments revealed tissue-specific expression patterns and significant responses to these stimuli, highlighting the roles of these genes in hormone and cold stress responses. These findings establish a framework for exploring the molecular functions of bHLH transcription factors in rubber trees. Full article
(This article belongs to the Special Issue Stress Resistance of Rubber Trees: From Genetics to Ecosystem, 2nd Edition)
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<p>Phylogenetic tree of diverse species showing the number of bHLH families. The phylogenetic tree reflects the evolutionary relationships and divergence times of various plant species as determined using the TimeTree database (<a href="http://www.timetree.org" target="_blank">http://www.timetree.org</a>, accessed on 28 March 2024). Different colored nodes represent various classifications, including <span class="html-italic">Chlorophyta</span> (yellow), <span class="html-italic">Spermatophyta</span> (green), <span class="html-italic">Solanaceae</span> (red), <span class="html-italic">Brassicaceae</span> (purple), <span class="html-italic">Euphorbiaceae</span> (blue), and <span class="html-italic">Rosaceae</span> (orange), among others. A linear scale of time in MYA (millions of years ago) and a geological timescale are shown at the bottom of the tree.</p> Full article ">Figure 2
<p>The bHLH domain is highly conserved across all the <span class="html-italic">HbbHLH</span> proteins. The overall height of the stack indicates the sequence conservation at that position. Capital letters indicate amino acids with more than 50% conservation, whereas asterisks indicate amino acids with more than 75% conservation across the 180 <span class="html-italic">HbbHLH</span> domains.</p> Full article ">Figure 3
<p>Phylogenetic analysis of bHLH gene families. The phylogenetic tree was generated using MEGA 7.0 with 1000 bootstrap replicates. Different colors indicate different subgroups. Red triangles represent <span class="html-italic">AtbHLH</span> proteins and blue triangles represent <span class="html-italic">HbbHLH</span> proteins.</p> Full article ">Figure 4
<p>Gene structure, <span class="html-italic">cis</span>-regulatory elements, and chromosomal localization of <span class="html-italic">HbbHLH</span> genes. (<b>a</b>) Gene structure and domain positions of <span class="html-italic">HbbHLH</span> IIId and IIIe subfamilies. (<b>b</b>) Statistics of the three categories of <span class="html-italic">cis</span>-regulatory elements in <span class="html-italic">HbbHLH</span> genes. (<b>c</b>) Chromosomal localization of <span class="html-italic">HbbHLH</span> genes, with tandemly duplicated genes marked in green.</p> Full article ">Figure 5
<p>Collinear analysis of <span class="html-italic">HbbHLH</span> genes. Gray lines in the background indicate all collinear blocks within the rubber tree genome, whereas red lines indicate collinear gene pairs of <span class="html-italic">bHLH</span> genes.</p> Full article ">Figure 6
<p>The Gene Ontology enrichment analysis of <span class="html-italic">HbbHLH</span> genes in rubber trees. Categorized into biological processes (BP), (only the top ten processes are shown), molecular functions (MF), and cellular components (CC).</p> Full article ">Figure 7
<p>Protein interaction network of <span class="html-italic">HbbHLH</span> genes mapped to <span class="html-italic">Arabidopsis</span> genes. The circle size indicates the number of interaction partners, with larger circles representing more extensive interaction networks. The thickness of the connecting lines reflects the combined interaction scores, with thicker lines denoting stronger interactions. Orange circles highlight rubber tree bHLH genes, with black text indicating rubber tree gene ID and white text (in parentheses) showing their corresponding <span class="html-italic">Arabidopsis</span> homologues. Non-orange circles represent non-bHLH proteins labelled with <span class="html-italic">Arabidopsis</span> homologue names. Different colors denote distinct functional classifications.</p> Full article ">Figure 8
<p>Temporal and spatial expression patterns of <span class="html-italic">HbbHLH</span> genes in 16 rubber tree varieties. (<b>a</b>) Heatmap of <span class="html-italic">HbbHLH</span> gene expression patterns in rubber trees. Each row represents an <span class="html-italic">HbbHLH</span> gene and the column names are formatted as variety_tissue_treatment. The variety numbers represent the following rubber tree varieties: 1: BT3410, 2: CATAS7-20-59, 3: CATAS7-33-97, 4: CATAS8-79, 5: CATAS88-13, 6: CATAS93-114, 7: FX3864, 8: GT1, 9: PR107, 10: PR255, 11: REKEN501, 12: RRΙΙ105, 13: RRIM600, 14: RRIM928, 15: TB1, and 16: Wencang11. (<b>b</b>,<b>c</b>) The series of diagrams illustrates the patterns of dynamic changes in <span class="html-italic">HbbHLH</span> DEGs during ethylene treatment and cold exposure, respectively, using Mfuzz.</p> Full article ">Figure 9
<p>Transcriptional analysis of five <span class="html-italic">HbbHLH</span> genes across different tissues in the rubber tree with error bars representing the standard deviation of three technical replicates. Bk, bark; Lf, leaf; Lx, latex; FF, female flower; MF, male flower. Statistical significance was determined using one-way ANOVA and Tukey’s multiple comparison test, with differences denoted by lowercase letters.</p> Full article ">Figure 10
<p>Transcriptional analysis of 11 <span class="html-italic">HbbHLH</span> genes in latex following treatment with ethylene (<b>a</b>) and methyl jasmonate (<b>b</b>). The x-axis labels denote ethylene (ET) and methyl jasmonate (JA). Error bars represent the standard deviation of three technical replicates. Statistical significance was assessed using one-way ANOVA and Tukey’s multiple comparison test, with differences indicated by lowercase letters.</p> Full article ">Figure 11
<p>Transcriptional analysis of 12 <span class="html-italic">HbbHLH</span> genes in leaves at low temperatures (4 °C). Error bars represent the standard deviations of three technical replicates. Statistical significance was assessed using one-way ANOVA and Tukey’s multiple comparison test, with differences indicated by lowercase letters.</p> Full article ">
10 pages, 6014 KiB  
Article
Physiological Indices of Five Hybrid Larch Seedlings Under Low-Temperature Stress
by Yajing Ning, Wenna Zhao, Chengpeng Cui, Xinxin Zhang, Xin Zhao, Yu Liu, Chen Wang, Hanguo Zhang and Shujuan Li
Forests 2024, 15(11), 2026; https://doi.org/10.3390/f15112026 - 18 Nov 2024
Viewed by 712
Abstract
Larch is a cold-temperate tree species native to the northern hemisphere and tolerant to low temperatures. It is one of the most significant timber species in Northeast China. This study examined growth changes in hybrid larch seedlings from five lines to explore the [...] Read more.
Larch is a cold-temperate tree species native to the northern hemisphere and tolerant to low temperatures. It is one of the most significant timber species in Northeast China. This study examined growth changes in hybrid larch seedlings from five lines to explore the physiological responses of these seedlings to low-temperature stress. Using 8-month-old hybrids of larch seedlings, we subjected the plants to cold stress at 4 °C and freezing stress at −20 °C over three periods of 6, 12, and 24 h, and treatment at 25 °C was used as a control. Results showed that significant correlations were found among the growth indicators, with larch line 1306 having the lowest incremental growth indicators, the largest root-to-crown ratio, and better cold tolerance than the other larch lines. The levels of soluble sugars (SSs), soluble proteins (SPs), malondialdehyde (MDA), and relative electrolyte leakage (REC) increased significantly in all lines under low-temperature stress. The activities of superoxide dismutase (SOD) and catalase (CAT) showed variation over time. Significant correlations were found between MDA and REL, SS, SR, Pro, CAT, and SOD in most of the lines; no significant correlation was found between MDA and the other indices in lines 1301 and 1309; and significant correlations were found between most of the physiological indices in line 1306. Full article
(This article belongs to the Section Forest Ecophysiology and Biology)
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<p>Effects of low-temperature stress on Pro content in larch seedlings. Different lower-case letters in the same family indicate significant differences between treatments at different times (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 2
<p>Effect of different temperatures and time of stress on SP of larch seedlings. Different lower-case letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 3
<p>Effect of different temperatures and time of stress on SS of larch seedlings. Different lower-case letters in the same family indicate significant differences between treatments at different times (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>Effect of different temperatures and time of stress on CAT of larch seedlings. Different lower-case letters in the same family indicate significant differences between treatments at different times (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 5
<p>Effect of different temperatures and time of stress on SOD of larch seedlings. Different lower-case letters in the same family indicate significant differences between treatments at different times (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 6
<p>Effect of different temperatures and time of stress on MDA of larch seedlings. Different lower-case letters in the same family indicate significant differences between treatments at different times (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 7
<p>Effect of different temperatures and time of stress on MDA of larch seedlings. Different lower-case letters in the same family indicate significant differences between treatments at different times (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 8
<p>Correlation between different lines and physiological indicators. * <span class="html-italic">p</span> &lt; 0.05 = significantly correlated; ** <span class="html-italic">p</span> &lt; 0.01 = extremely significantly correlated. Subfigures (<b>A</b>–<b>E</b>) are lines 1301, 1305, 1306, 1307, and 1309, respectively.</p> Full article ">
17 pages, 3025 KiB  
Article
A Spectral–Spatial Approach for the Classification of Tree Cover Density in Mediterranean Biomes Using Sentinel-2 Imagery
by Michail Sismanis, Ioannis Z. Gitas, Nikos Georgopoulos, Dimitris Stavrakoudis, Eleni Gkounti and Konstantinos Antoniadis
Forests 2024, 15(11), 2025; https://doi.org/10.3390/f15112025 - 18 Nov 2024
Viewed by 941
Abstract
Tree canopy cover is an important forest inventory parameter and a critical component for the in-depth mapping of forest fuels. This research examines the potential of employing single-date Sentinel-2 multispectral imagery, combined with contextual spatial information, to classify areas based on their tree [...] Read more.
Tree canopy cover is an important forest inventory parameter and a critical component for the in-depth mapping of forest fuels. This research examines the potential of employing single-date Sentinel-2 multispectral imagery, combined with contextual spatial information, to classify areas based on their tree cover density using Random Forest classifiers. Three spatial information extraction methods are investigated for their capacity to acutely detect canopy cover: two based on Gray-Level Co-Occurrence Matrix (GLCM) features and one based on segment statistics. The research was carried out in three different biomes in Greece, in a total study area of 23,644 km2. Three tree cover classes were considered, namely, non-forest (cover < 15%), open forest (cover = 15%–70%), and closed forest (cover ≥ 70%), based on the requirements set for fuel mapping in Europe. Results indicate that the best approach identified delivers F1-scores ranging 70%–75% for all study areas, significantly improving results over the other alternatives. Overall, the synergistic use of spectral and spatial features derived from Sentinel-2 images highlights a promising approach for the generation of tree cover density information layers in Mediterranean regions, enabling the creation of additional information in support of the detailed mapping of forest fuels. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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<p>Map of the three different biomes examined in this study in Greece.</p> Full article ">Figure 2
<p>Flowchart of the spectral–spatial workflow for the classification of tree cover density in Mediterranean ecosystems.</p> Full article ">Figure 3
<p>Tree cover density map generated using the best-performing method for study area A in GGRS87 coordinate reference system.</p> Full article ">Figure 4
<p>Tree cover density map generated using the best-performing method for study area B in GGRS87 coordinate reference system.</p> Full article ">Figure 5
<p>Tree cover density map generated using the best-performing method for study area C in GGRS87 coordinate reference system.</p> Full article ">
28 pages, 31167 KiB  
Article
Optimizing GEDI Canopy Height Estimation and Analyzing Error Impact Factors Under Highly Complex Terrain and High-Density Vegetation Conditions
by Runbo Chen, Xinchuang Wang, Xuejie Liu and Shunzhong Wang
Forests 2024, 15(11), 2024; https://doi.org/10.3390/f15112024 - 17 Nov 2024
Cited by 1 | Viewed by 1300
Abstract
The Global Ecosystem Dynamics Investigation (GEDI) system provides essential data for estimating forest canopy height on a global scale. However, factors such as complex topography and dense canopy can significantly reduce the accuracy of GEDI canopy height estimations. We selected the South Taihang [...] Read more.
The Global Ecosystem Dynamics Investigation (GEDI) system provides essential data for estimating forest canopy height on a global scale. However, factors such as complex topography and dense canopy can significantly reduce the accuracy of GEDI canopy height estimations. We selected the South Taihang region of Henan Province, China, as our study area and proposed an optimization framework to improve GEDI canopy height estimation accuracy. This framework includes correcting geolocation errors in GEDI footprints, screening and analyzing features that affect estimation errors, and combining two regression models with feature selection methods. Our findings reveal a geolocation error of 4 to 6 m in GEDI footprints at the orbital scale, along with an overestimation of GEDI canopy height in the South Taihang region. Relative height (RH), waveform characteristics, topographic features, and canopy cover significantly influenced the estimation error. Some studies have suggested that GEDI canopy height estimates for areas with high canopy cover lead to underestimation, However, our study found that accuracy increased with higher canopy cover in complex terrain and dense vegetation. The model’s performance improved significantly after incorporating the canopy cover parameter into the optimization model. Overall, the R2 of the best-optimized model was improved from 0.06 to 0.61, the RMSE was decreased from 8.73 m to 2.23 m, and the rRMSE decreased from 65% to 17%, resulting in an accuracy improvement of 74.45%. In general, this study reveals the factors affecting the accuracy of GEDI canopy height estimation in areas with complex terrain and dense vegetation cover, on the premise of minimizing GEDI geolocation errors. Employing the proposed optimization framework significantly enhanced the accuracy of GEDI canopy height estimates. This study also highlighted the crucial role of canopy cover in improving the precision of GEDI canopy height estimation, providing an effective approach for forest monitoring in such regions and vegetation conditions. Future studies should further improve the classification of tree species and expand the diversity of sample tree species to test the accuracy of canopy height estimated by GEDI in different forest structures, consider the distortion of optical remote sensing images caused by rugged terrain, and further mine the information in GEDI waveforms so as to enhance the applicability of the optimization framework in more diverse forest environments. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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<p>In the figure, (<b>a</b>) shows the location of Henan Province in China, (<b>b</b>) illustrates the study area’s location within Henan Province, and (<b>c</b>) presents the DEM of the study area, with each individual area number corresponding to the ALS aerial flight areas.</p> Full article ">Figure 2
<p>CHM raster maps based on ALS acquisition: bottom images are true color images of Sentinel-2 in May 2023; black dots are GEDI footprints.</p> Full article ">Figure 3
<p>The distribution of slope and canopy cover within the aerial flight zone after cropping based on ALS slope and canopy cover raster maps. Panel (<b>a</b>) shows the slope distribution following the cropping of the ALS airspace slope raster map using the extent of forested land from the Land Use Survey. Panel (<b>b</b>) shows the slope distribution across ALS airspace. Panels (<b>c</b>,<b>d</b>) show the canopy cover, where the vertical axis represents the number of raster pixels and the horizontal axis indicates the canopy cover (0-1).</p> Full article ">Figure 4
<p>A square fishing net with a length and width of 5 m was used to calculate statistics of the DEM and slope within each grid, and the mean value, range, standard deviation, and mean slope of the DEM were calculated. Due to the huge amount of data, the data of all grids were not counted, but 5000 grids were randomly selected in each ALS region for statistics. The change from blue to red means the density goes from small to large.</p> Full article ">Figure 5
<p>The principle of geolocation error correction is illustrated as follows: (<b>a</b>) displays the displacement mode of the footprint, where the red circle in the center represents the original GEDI location, and the cyan spot indicates the position after displacement. The angular step is set at 30°, while the distance step is 2 m. (<b>b</b>) shows the waveform corresponding to the GEDI location. (<b>c</b>) depicts the simulated waveform from ALS, and (<b>d</b>) presents the aligned GEDI waveform and ALS simulated waveform.</p> Full article ">Figure 6
<p>Overall frame flowchart.</p> Full article ">Figure 7
<p>The effect of geolocation error correction for a single footprint. Toploc and botloc refer to the start and end positions of the signal, respectively. Panels (<b>a</b>,<b>b</b>) display the original and corrected geolocation waveforms of the complex footprint, while panels (<b>c</b>,<b>d</b>) show the original and corrected geolocation waveforms of the simple footprint.</p> Full article ">Figure 8
<p>The statistics of all R averages after displacing footprints to the same location for the same acquisition date. Each polar plot represents the average correction effect of geolocation errors for all footprints corresponding to the same acquisition date. The top label of each polar plot indicates the data acquisition date in the format YYYYDDD.</p> Full article ">Figure 9
<p>R-values between individual features and GEDI canopy height estimation error, All feature parameters in the figure are significantly correlated (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>≤</mo> <mn>0.05</mn> </mrow> </semantics></math>), with a positive correlation in blue and a negative correlation in orange.</p> Full article ">Figure 10
<p>Box plots of error distribution in different intervals of each feature with the absolute value of R above 0.3. The left vertical axis is the error (m) and the right vertical axis is the RMSE (m) of RH96 and CHM96.</p> Full article ">Figure 11
<p>This figure shows the importance of each feature parameter with respect to the residuals: the upper figure shows the top 30 feature parameters in terms of importance, and the lower figure shows the thumbnail of the importance distribution of all feature parameters, where the blue part is the detailed distribution of the importance of the top 30 features in the upper figure.</p> Full article ">Figure 12
<p>Box plots of error distribution in different intervals of each feature with the absolute value of RF importance above 1%. The left vertical axis is error (m) and the right vertical axis is the RMSE (m) of RH_96 and CHM_96.</p> Full article ">Figure 13
<p>In the case of selecting different numbers of features, the model effects of various combinations of regression models and feature extraction methods are presented. The results are organized in the vertical coordinates from top to bottom in the order of <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math> (m), and <math display="inline"><semantics> <mrow> <mi>r</mi> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math> (%). The horizontal coordinates indicate the number of feature parameters used in the model.</p> Full article ">Figure 14
<p>The left and right panels show the data distribution of RH_96 and RHT_96 with CHM96, respectively.</p> Full article ">Figure 15
<p>The upper panel is a localized thumbnail of the remote sensing image, the blue part is the non-shadowed area, the white part is the shadowed area, and the lower two panels are the reflectance distributions of the red, green, and blue bands in the shadowed and non-shadow areas, respectively.</p> Full article ">
21 pages, 5059 KiB  
Article
Developing a Method to Estimate Above-Ground Carbon Stock of Forest Tree Species Pinus densata Using Remote Sensing and Climatic Data
by Kai Luo, Yafei Feng, Yi Liao, Jialong Zhang, Bo Qiu, Kun Yang, Chenkai Teng and Tangyan Yin
Forests 2024, 15(11), 2023; https://doi.org/10.3390/f15112023 - 16 Nov 2024
Cited by 1 | Viewed by 957
Abstract
Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it is crucial to estimate the AGCS accurately. In this research, we added the climatic and topographic factors to the estimation process by a remote sensing approach to explore [...] Read more.
Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it is crucial to estimate the AGCS accurately. In this research, we added the climatic and topographic factors to the estimation process by a remote sensing approach to explore their impact and to achieve more precise estimations. We hope to develop a more accurate estimation method for AGCS based on remote sensing data and climate data. The random forest (RF) method has good robustness and wide applicability. Therefore, we modeled and predicted the AGCS by RF based on sixty field sample plots of Pinus densata pure forests in southwest China and the factors extracted from Landsat 8 OLI images (source I), Sentinel-2A images (source II), and combined Landsat 8 OLI and Sentinel-2A images (source III). We added the topographic and climatic factors to establish the AGCS estimation model and compared the results. The topographic factors contain elevation, slope, and aspect. Climatic factors contain mean annual temperature, annual precipitation, annual potential evapotranspiration, and monthly mean potential evapotranspiration. It was found that the R2 and RMSE of the model based on source III were better than the R2 and RMSE of the models based on source I and source II. Compared to the models based on source I and source II, the model based on source III improved R2 by up to 0.08, reduced RMSE by up to 2.88 t/ha, and improved P by up to 4.29%. Among the models without adding factors, the model based on source III worked the best, with an R2 of 0.87, an RMSE of 10.81 t/ha, an rRMSE of 23.19%, and a P of 79.71%. Among the models that added topographic factors, the model based on source III worked best after adding elevation, with an R2 of 0.89, an RMSE of 10.01 t/ha, an rRMSE of 21.47%, and a P of 82.17%. Among the models that added climatic factors, the model that added the annual precipitation factor had the best modeling result, with an R2 of 0.90, an RMSE of 9.53 t/ha, an rRMSE of 20.59%, and a P of 83.00%. The prediction result exhibited that the AGCS of the Pinus densata forest in 2021 was 9,737,487.52 t. The combination of Landsat 8 OLI and Sentinel-2A could improve the prediction accuracy of the AGCS. The addition of annual precipitation can effectively improve the accuracy of AGCS estimation. Higher resolution of climate data is needed to enhance the modeling in future work. Full article
(This article belongs to the Special Issue Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation)
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<p>Research area.</p> Full article ">Figure 2
<p>Technical route.</p> Full article ">Figure 3
<p>Model fitting effect based on three data sources: (<b>a</b>) Model fitting effect of remote sensing factors of source I, (<b>b</b>) model fitting effect of remote sensing factors of source II, and (<b>c</b>) model fitting effect of remote sensing factors of source III.</p> Full article ">Figure 4
<p>Model fitting effect after adding topographic factors to each data source: (<b>a</b>–<b>c</b>) Model fitting effect after adding elevation, slope, and aspect, respectively, based on source I; (<b>d</b>–<b>f</b>) model fitting effect after adding elevation, slope, and aspect, respectively, based on source II; (<b>g</b>–<b>i</b>) model fitting effect after adding elevation, slope, and aspect, respectively, based on source III.</p> Full article ">Figure 5
<p>The comparison of improved percentage of the modeling effect after adding topographic factors.</p> Full article ">Figure 6
<p>The model fitting effect after adding climatic factors to each data source: (<b>a</b>–<b>d</b>) Model fitting effect of source I adding AP, MAT, APET, and MMPET, respectively; (<b>e</b>–<b>h</b>) model fitting effect of source II adding AP, MAT, APET, and MMPET, respectively; (<b>i</b>–<b>l</b>) model fitting effect of the combination of source III adding AP, MAT, APET, and MMPET, respectively.</p> Full article ">Figure 7
<p>The comparison of improved percentage about modeling effect after adding climatic factors.</p> Full article ">Figure 8
<p>Prediction and mapping results of <span class="html-italic">Pinus densata</span> AGCS in Shangri-La.</p> Full article ">
18 pages, 11180 KiB  
Article
Global Warming Will Drive Spatial Expansion of Prunus mira Koehne in Alpine Areas, Southeast Qinghai–Tibet Plateau
by Jinkai Gu, Qiang He, Qingwan Li, Qinglin Li, Shengjian Xiang, Wanchi Li, Aohang Jin, Shunbin Wang, Feipeng Liu and Guoyong Tang
Forests 2024, 15(11), 2022; https://doi.org/10.3390/f15112022 - 16 Nov 2024
Viewed by 918
Abstract
Global climate change exerts great effects on plant distributions. However, the response of Prunus mira Koehne, one of the most important species for ecological protection in the southeast of the Qinghai–Tibet Plateau, to climate change remains unclear. To explore the ecological factors affecting [...] Read more.
Global climate change exerts great effects on plant distributions. However, the response of Prunus mira Koehne, one of the most important species for ecological protection in the southeast of the Qinghai–Tibet Plateau, to climate change remains unclear. To explore the ecological factors affecting the distribution of P. mira in the context of global climate change, the MaxENT model is used to predict suitable habitats for P. mira. Our study indicated that the distribution of Prunus mira Koehn is primarily influenced by temperature rather than precipitation, and warming can facilitate the growth of P. mira. When the temperature seasonality (bio4) ranges from 134 to 576 and the mean temperature of the coldest quarter (bio11) ranges from −2.6 °C to 2.7 °C, it is most conducive to the growth of P. mira. Among the four climate scenarios, the optimal habitat for P. mira is predominantly concentrated in river valley areas and is expected to expand into higher altitude regions, particularly in the north and southeast. SSP245 and SSP370 climate pathways are conducive to the growth and spatial expansion of P. mira. Our findings highlight the significant impact of temperature not precipitation on the distribution of P. mira, and this insight is crucial for the stability and conservation of this ecologically significant plant species. Full article
(This article belongs to the Section Forest Ecology and Management)
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<p>Distribution records of <span class="html-italic">P. mira</span> in Nyingchi.</p> Full article ">Figure 2
<p>Correlation heat map of environment factors.</p> Full article ">Figure 3
<p>Current suitable distribution of <span class="html-italic">P. mira</span>.</p> Full article ">Figure 4
<p>Regularized training gain with only variable.</p> Full article ">Figure 5
<p>Response curves of the main influencing factors of <span class="html-italic">P. mira</span>.</p> Full article ">Figure 6
<p>Distribution of suitable habits under the 4 climate pathways.</p> Full article ">Figure 7
<p>Change in the spatial pattern under the 4 climate pathways.</p> Full article ">
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