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3 pages, 644 KiB

Open AccessEditorial

Forest Resources Assessments: Mensuration, Inventory and Planning

by Iciar Alberdi

Forests 2021, 12(3), 296; https://doi.org/10.3390/f12030296 - 4 Mar 2021

Cited by 1 | Viewed by 2193

Abstract

There is much demand for forest information at the regional, national, and international level, covering aspects as varied as growing stock, carbon pools, and non-wood forest products, as well as information on forest biodiversity, risks, and disturbances, or social indicators [...] Full article

(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)

Research

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35 pages, 8860 KiB

Open AccessArticle

Analyzing the Joint Effect of Forest Management and Wildfires on Living Biomass and Carbon Stocks in Spanish Forests

by Patricia Adame, Isabel Cañellas, Daniel Moreno-Fernández, Tuula Packalen, Laura Hernández and Iciar Alberdi

Forests 2020, 11(11), 1219; https://doi.org/10.3390/f11111219 - 19 Nov 2020

Cited by 7 | Viewed by 2819

Abstract

Research Highlights: This is the first study that has considered forest management and wildfires in the balance of living biomass and carbon stored in Mediterranean forests. Background and Objectives: The Kyoto Protocol and Paris Agreement request countries to estimate and report [...] Read more.

Research Highlights: This is the first study that has considered forest management and wildfires in the balance of living biomass and carbon stored in Mediterranean forests. Background and Objectives: The Kyoto Protocol and Paris Agreement request countries to estimate and report carbon emissions and removals from the forest in a transparent and reliable way. The aim of this study is to forecast the carbon stored in the living biomass of Spanish forests for the period 2000–2050 under two forest management alternatives and three forest wildfires scenarios. Materials and Methods: To produce these estimates, we rely on data from the Spanish National Forest Inventory (SNFI) and we use the European Forestry Dynamics Model (EFDM). SNFI plots were classified according to five static (forest type, known land-use restrictions, ownership, stand structure and bioclimatic region) and two dynamic factors (quadratic mean diameter and total volume). The results were validated using data from the latest SNFI cycle (20-year simulation). Results: The increase in wildfire occurrence will lead to a decrease in biomass/carbon between 2000 and 2050 of up to 22.7% in the medium–low greenhouse gas emissions scenario (B2 scenario) and of up to 32.8% in the medium–high greenhouse gas emissions scenario (A2 scenario). Schoolbook allocation management could buffer up to 3% of wildfire carbon loss. The most stable forest type under both wildfire scenarios are Dehesas. As regards bioregions, the Macaronesian area is the most affected and the Alpine region, the least affected. Our validation test revealed a total volume underestimation of 2.2% in 20 years. Conclusions: Forest wildfire scenarios provide more realistic simulations in Mediterranean forests. The results show the potential benefit of forest management, with slightly better results in schoolbook forest management compared to business-as-usual forest management. The EFDM harmonized approach simulates the capacity of forests to store carbon under different scenarios at national scale in Spain, providing important information for optimal decision-making on forest-related policies. Full article

(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)

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21 pages, 3937 KiB

Open AccessArticle

Harmonized Classification of Forest Types in the Iberian Peninsula Based on National Forest Inventories

by Leónia Nunes, Mauro Moreno, Iciar Alberdi, Juan Gabriel Álvarez-González, Paulo Godinho-Ferreira, Stefano Mazzoleni and Francisco Castro Rego

Forests 2020, 11(11), 1170; https://doi.org/10.3390/f11111170 - 2 Nov 2020

Cited by 11 | Viewed by 3871

Abstract

National Forest Inventories (NFIs) collect and provide a large amount of information regarding the forest volume, carbon stocks, vitality, biodiversity, non-wood forest products and their changes. Forest stands variables data are paramount to understanding their composition, especially on those related with understory characteristics [...] Read more.

National Forest Inventories (NFIs) collect and provide a large amount of information regarding the forest volume, carbon stocks, vitality, biodiversity, non-wood forest products and their changes. Forest stands variables data are paramount to understanding their composition, especially on those related with understory characteristics and the coverage of species according to canopy layers; they are essential to assess biodiversity and to support forest management. At the same time, these inventories allow the development of harmonized forest descriptions beyond the national scale. This study aims to develop a homogeneous characterization of the Iberian Peninsula’s forests, in order to classify and identify the forest types. For this purpose, harmonized data from NFIs of Portugal and Spain were used to assess the composition of species, dominance and the percentage of cover for each species in a vertical space defined by seven canopy layers. Using the “K-means” clustering algorithm, a set of clusters was identified and georeferenced using forest polygons from land use and cover maps of both countries. The interpretation and description of the clusters lead to the establishment of 28 forest types that characterize all of the Iberian Peninsula forests. Each forest area has been described through one of the forest types and their relation with other ecological characteristics of the stands was analyzed. Shrubs formations are generally widely distributed in the forest area of the Iberian Peninsula, however their abundance in terms of cover is lower in comparison with tree species. Around 71% of the forest types are dominated by trees, mainly species from the genera Pinus and Quercus, and 21% are dominated by shrub formations with species of Ulex spp., Cytisus spp., and Cistus spp. The Quercus ilex s.l. L. and Pinus pinaster Aiton are the common species of importance for both NFIs. The results represent a powerful and homogenous multi-use tool describing the Iberian Peninsula’s forestlands with applications on landscape analysis, forest management and conservation. This information can be used for comparisons at larger scales, allowing cross-border analysis in relation to various aspects, such as hazards and wildfires, as well as management and conservation of forest biodiversity. The developed method is adaptable to an updated dataset from more recent NFIs and to other study areas. Full article

(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)

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19 pages, 3953 KiB

Open AccessFeature PaperArticle

Using Continuous Forest Inventory Data for Control of Wood Production and Use in Large Areas: A Case Study in Lithuania

by Andrius Kuliešis, Albertas Kasperavičius, Gintaras Kulbokas, Andrius A. Kuliešis, Aidas Pivoriūnas, Marius Aleinikovas, Benas Šilinskas, Mindaugas Škėma and Lina Beniušienė

Forests 2020, 11(10), 1039; https://doi.org/10.3390/f11101039 - 25 Sep 2020

Cited by 2 | Viewed by 2451

Abstract

Background and Objectives: Significant progress in developing European national forest inventory (NFI) systems could ensure accurate evaluations of gross annual increment (GAI) and its components by employing direct measurements. However, the use of NFI data is insufficient for increasing the efficiency of forest [...] Read more.

Background and Objectives: Significant progress in developing European national forest inventory (NFI) systems could ensure accurate evaluations of gross annual increment (GAI) and its components by employing direct measurements. However, the use of NFI data is insufficient for increasing the efficiency of forest management and the use of wood, as well as for meeting sustainable forestry needs. Specification of forest characteristics, such as GAI and its components, identification of the main factors that impact forest growth, accumulation of wood, and natural losses are among the key elements promoting the productivity of forest stands and possibilities of rational use of wood in large forest areas. The aims of this research were (a) to validate the quality of forest statistics provided by a standwise forest inventory (SFI) and (b) to reveal the potential benefits of rational wood use at the country level through the analysis of forest management results, which are based on GAI, including its components derived from the NFI. Materials and Methods: SFI and NFI data from 1998–2017 were collected from 5600 permanent sample plots and used to evaluate the main forest characteristics. Potential wood use was estimated based on the assumption that 50–70% of the total GAI is accumulated for final forest use. Results: Mean growing stock volume (GSV) is underestimated by 7–14% on average in the course of SFI. Therefore, continuous monitoring of the yield changes in forest stands, detection of factors negatively affecting yield and its accumulation, and regulation of these processes by silviculture measures could increase potential forest use in Lithuania. Conclusions: Implementation of sample-based NFI resulted in an improvement of forest characteristics and led to an increase in GSV and GAI. Continuously gathered data on GAI and its components are a prerequisite for efficient forest management and control of the use of wood. Full article

(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)

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Changes of forest stand area distribution by age classes during 2007–2017 in state forest available for wood supply (FAWS). Full article ">Figure 2
Gross annual increment (a) and its components (b) in FAWS according to national forest inventory (NFI) 2007 (1) and NFI 2017 (2) data. Full article ">Figure 3
Comparison of annually dead tree stem volume in Lithuanian forests according to NFI data from 2003–2017. Full article ">Figure 4
Comparison of annually dead (a) and accumulated dead (b) tree stem volume in mature stands of Lithuanian FAWS, 1998–2017. Full article ">Figure 5
The current (2004–2013) and prospective tendencies of area changes of final felling in 2014–2053 in state and private FAWS. Full article ">Figure 6
Dynamics of gross volume increment and its accumulated share in pine stands growing in sites of average productivity according to yield model [<a href="#B29-forests-11-01039" class="html-bibr">29</a>] and NFI 2012 data. Full article ">Figure 7
Prediction of growing stock volume of mature stands in FAWS of state forests for the years 2017 and 2027 using data from the NFI 2007 and NFI 2017, respectively. Full article ">

23 pages, 1940 KiB

Open AccessArticle

The Structure of Northern Siberian Spruce–Scots Pine Forests at Different Stages of Post-Fire Succession

by Natalia I. Stavrova, Vadim V. Gorshkov, Paul N. Katjutin and Irina Ju. Bakkal

Forests 2020, 11(5), 558; https://doi.org/10.3390/f11050558 - 15 May 2020

Cited by 10 | Viewed by 2828

Abstract

The process of post-fire recovery in mixed Siberian spruce–Scots pine forests (Picea obovata Ledeb.-Pinus sylvestris L.), typical for the European North-West, was studied in the Kola peninsula (Russia). We used the spatial–temporal approach to reveal the size structure (diameter at breast [...] Read more.

The process of post-fire recovery in mixed Siberian spruce–Scots pine forests (Picea obovata Ledeb.-Pinus sylvestris L.), typical for the European North-West, was studied in the Kola peninsula (Russia). We used the spatial–temporal approach to reveal the size structure (diameter at breast height (DBH) distribution) and vital state of Siberian spruce and Scots pine stands, tree regeneration and species structure of the dwarf shrub–herb and lichen–moss layers at different stages of post-fire succession (8–380 years after the fire). It was found that in both forest-forming species, the process of stand stratification results in the allocation of two size groups of trees. In Siberian spruce, these groups persist throughout the succession. In Scots pine, DBH distributions become more homogeneous at the middle of succession (150–200 years after the fire) due to the extinction of small-size individuals. Siberian spruce stands are dominated by moderately and strongly weakened trees at all succession stages. The vitality status of Scots pine stands is higher compared to Siberian spruce up to 150 years after a fire. The dynamics of regeneration activity is similar in both species, with a minimum at the middle of the restoration period. The results indicate that in Siberian spruce–Scots pine forests, the stand structure and regeneration activity differs substantially in the first half of succession (up to 200 years after the fire) and become similar in the late-succession community. The study of lower layers revealed that the cover of moss–lichen and dwarf shrub–herb layers stabilize 150 years after a fire. Changes in species structure in both layers are observed until the late stage of succession. The originality of the structure and dynamics of mixed Siberian spruce–Scots pine forests is revealed based on a comparison with pure Siberian spruce forests in the same region. Full article

(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)

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18 pages, 4888 KiB

Open AccessArticle

Regional Variability of the Romanian Main Tree Species Growth Using National Forest Inventory Increment Cores

by Gheorghe Marin, Vlad C. Strimbu, Ioan V. Abrudan and Bogdan M. Strimbu

Forests 2020, 11(4), 409; https://doi.org/10.3390/f11040409 - 6 Apr 2020

Cited by 7 | Viewed by 2991

Abstract

In many countries, National Forest Inventory (NFI) data is used to assess the variability of forest growth across the country. The identification of areas with similar growths provides the foundation for development of regional models. The objective of the present study is to [...] Read more.

In many countries, National Forest Inventory (NFI) data is used to assess the variability of forest growth across the country. The identification of areas with similar growths provides the foundation for development of regional models. The objective of the present study is to identify areas with similar diameter and basal area growth using increment cores acquired by the NFI for the three main Romanian species: Norway spruce (Picea abies L. Karst), European beech (Fagus sylvatica L.), and Sessile oak (Quercus petraea (Matt.) Liebl.). We used 6536 increment cores with ages less than 100 years, a total of 427,635 rings. The country was divided in 21 non-overlapping ecoregions based on geomorphology, soil, geology and spatial contiguousness. Mixed models and multivariate analyses were used to assess the differences in annual dimeter at breast height and basal area growth among ecoregions. Irrespective of the species, the mixed models analysis revealed significant differences in growth between the ecoregions. However, some ecoregions were similar in terms of growth and could be aggregated. Multivariate analysis reinforced the difference between ecoregions and showed no temporal grouping for spruce and beech. Sessile oak growth was separated not only by ecoregions, but also by time, with some ecoregions being more prone to draught. Our study showed that countries of median size, such as Romania, could exhibit significant spatial differences in forest growth. Therefore, countrywide growth models incorporate too much variability to be considered operationally feasible. Furthermore, it is difficult to justify the current growth and yield models as a legal binding planning tool. Full article

(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
The PSC of the Romanian NFI overlaid on the general location of the forest, in green. The color of the PSC reflects the geomorphology: red for plains, blue for hills, and black for mountains. The spatial distribution of the PSCs does not reflect the actual density [<a href="#B12-forests-11-00409" class="html-bibr">12</a>]. Full article ">Figure 2
Romanian ecoregions delineated according to spatial contiguity, geology, soils, and geomorphology maps of the Romanian Academy [<a href="#B16-forests-11-00409" class="html-bibr">16</a>]. Full article ">Figure 3
DBH increment and basal area increment mean by ecoregion for Norway spruce (a,b), European beech (c,d) and Sessile oak (e,f) over time. Full article ">Figure 4
Mean DBH increment versus mean DBH and mean basal area increment versus mean basal area by ecoregion for Norway spruce (a,b), European beech (c,d) and Sessile oak (e,f). Full article ">Figure 5
Grouping by ecoregions for Norway spruce (a), European beech (b), and Sessile oak (c) with principal components 1 and 2 as axes. Because similar results were obtained for DBHi and BAi, we have represented only the results for DBHi. Full article ">Figure 6
Identification of the number of groups of ecoregions using Ward’s Minimum Variance and Cubic Clustering Criterion for Norway spruce (a), European beech (b) and Sessile oak (c). Because similar results were obtained for DBHi or BAi, we have represented only the results for DBHi. Full article ">Figure 7
Grouping by ecoregions for Norway spruce (a), European beech (b), and Sessile oak (c) with canonical variables as axes. Because the same conclusion is reached for DBHi and BAi only the plots for DBHi are presented. Full article ">

14 pages, 3520 KiB

Open AccessArticle

Comparison of GF2 and SPOT6 Imagery on Canopy Cover Estimating in Northern Subtropics Forest in China

by Jingjing Zhou, Yuanyong Dian, Xiong Wang, Chonghuai Yao, Yongfeng Jian, Yuan Li and Zeming Han

Forests 2020, 11(4), 407; https://doi.org/10.3390/f11040407 - 5 Apr 2020

Cited by 13 | Viewed by 2593

Abstract

Canopy cover is an important vegetation attribute used for many environmental applications such as defining management objectives, thinning and ecological modeling. However, the estimation of canopy cover from high spatial resolution imagery is still a difficult task due to limited spectral information and [...] Read more.

Canopy cover is an important vegetation attribute used for many environmental applications such as defining management objectives, thinning and ecological modeling. However, the estimation of canopy cover from high spatial resolution imagery is still a difficult task due to limited spectral information and the heterogeneous pixel values of the same canopy. In this paper, we compared the capacity of two high spatial resolution sensors (SPOT6 and GF2) using three ensemble learning models (Adaptive Boosting (AdaBoost), Gradient Boosting (GDBoost), and random forest (RF)), to estimate canopy cover (CC) in a Chinese northern subtropics forest. Canopy cover across 97 plots was measured across 41 needle forest plots, 24 broadleaf forest plots, and 32 mixed forest plots. Results showed that (1) the textural features performed more importantly than spectral variables according to the number of variables in the top ten predictors in estimating canopy cover (CC) in both SPOT6 and GF2. Moreover, the vegetation indices in spectral variables had a lower relative importance value than the band reflectance variables. (2) GF2 imagery outperformed SPOT6 imagery in estimating CC when using the ensemble learning model in our data. On average across the models, the R² was almost 0.08 higher for GF2 over SPOT6. Likewise, the average RMSE and average MAE were 0.002 and 0.01 lower in GF2 than in SPOT6. (3) The ensemble learning model showed good results in estimating CC, yet the different models performed a little differently in the results. Additionally, the GDBoost model performed the best of all the ensemble learning models with R² = 0.92, root mean square error (RMSE) = 0.001 and mean absolute error (MAE) = 0.022. Full article

(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)

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Location of the research area and the filed plots identified in GF2 imagery. Full article ">Figure 2
Canopy cover per plot, which was determined by the tree location and crown diameter in NS and WE direction (a) tree location (b) tree canopy cover area (c) canopy cover in a plot. Full article ">Figure 3
Relative importance value (RIV) of different AdaBoost, GDBoost and RF models in SPOT and GF2 (a) RIV based on AdaBoost in SPOT6 (b) RIV based on GDBoost in SPOT6 (c) RIV based on RF in SPOT6 (d) RIV based on AdaBoost in GF2 (e) RIV based on GDBoost in GF2 (f) RIV based on RF in GF2. (b, g, r, and nir represent reflectance in the blue, green, red, and near-infrared wavelengths, respectively, NDVI, NDVIg, CIg, EVI, and SAVI represent vegetation indices, MEAN, HOM, CON, DIS, ENT, VAR, ASM, and COR represent texture parameters derived from gray level co-occurrence matrix (GLCM) Full article ">Figure 4
The coefficient of determination (R2) heat map of the search space in the grid search with SPOT6, GF2 images, and different models. (a) R2 heat map with the AdaBoost model in SPOT6 (b) R2 heat map with the GDBoost model in SPOT6 (c) R2 heat map with the RF model in SPOT6 (d) R2 heat map with the AdaBoost model in GF2 (e) R2 heat map with the GDBoost model in GF2 (f) R2 heat map with the RF model in GF2. (ntree represents number of trees, learning_rate represents learning rate and max features represents the size of random subsets of features to consider in constructing trees. Full article ">Figure 5
Observed vs. predicted canopy cover using SPOT6 and GF2 imagery with different regression models. The solid line indicates the 1:1 correlation between observed and predicted values. (a) results of the AdaBoost model in GF2 (b) results of the GDBoost model in GF2 (c) results of the RF model in GF2 (d) results of the AdaBoost model in SPOT6 (e) results of the GDBoost model in SPOT6 (f) results of the RF model in SPOT6. (CC represents canopy cover, AdaBoost represets Adaptive boosting method, GDBoost represents Gradient boosting model, and RF represents random forest. R2 represents coefficient of determination, RMSE means root mean square error.) Full article ">Figure 6
Predicted CC map based on the GDBoost model using GF2 imagery. Full article ">

20 pages, 2730 KiB

Open AccessArticle

Improving the Modeling of the Height–Diameter Relationship of Tree Species with High Growth Variability: Robust Regression Analysis of Ochroma pyramidale (Balsa-Tree)

by Jorge Danilo Zea-Camaño, José R. Soto, Julio Eduardo Arce, Allan Libanio Pelissari, Alexandre Behling, Gabriel Agostini Orso, Marcelino Santiago Guachambala and Rozane de Loyola Eisfeld

Forests 2020, 11(3), 313; https://doi.org/10.3390/f11030313 - 12 Mar 2020

Cited by 9 | Viewed by 3264

Abstract

Ochroma pyramidale (Cav. ex. Lam.) Urb. (balsa-tree) is a commercially important tree species that ranges from Mexico to northern Brazil. Due to its low weight and mechanical endurance, the wood is particularly well-suited for wind turbine blades, sporting equipment, boats and aircrafts; as [...] Read more.

Ochroma pyramidale (Cav. ex. Lam.) Urb. (balsa-tree) is a commercially important tree species that ranges from Mexico to northern Brazil. Due to its low weight and mechanical endurance, the wood is particularly well-suited for wind turbine blades, sporting equipment, boats and aircrafts; as such, it is in high market demand and plays an important role in many regional economies. This tree species is also well-known to exhibit a high degree of variation in growth. Researchers interested in modeling the height–diameter relationship typically resort to using ordinary least squares (OLS) to fit linear models; however, this method is known to suffer from sensitivity to outliers. Given the latter, the application of these models may yield potentially biased tree height estimates. The use of robust regression with iteratively reweighted least squares (IRLS) has been proposed as an alternative to mitigate the influence of outliers. This study aims to improve the modeling of height–diameter relationships of tree species with high growth variation, by using robust regressions with IRLS for data-sets stratified by site-index and age-classes. We implement a split sample approach to assess the model performance using data from Ecuador’s continuous forest inventory (n = 32,279 trees). A sensitivity analysis of six outlier scenarios is also conducted using a subsample of the former (n = 26). Our results indicate that IRLS regression methods can give unbiased height predictions. At face value, the sensitivity analysis indicates that OLS performs better in terms of standard error of estimate. However, we found that OLS suffers from skewed residual distributions (i.e., unreliable estimations); conversely, IRLS seems to be less affected by this source of bias and the fitted parameters indicate lower standard errors. Overall, we recommend using robust regression methods with IRLS to produce consistent height predictions for O. pyramidale and other tree species showing high growth variation. Full article

(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)

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18 pages, 919 KiB

Open AccessArticle

Carbon and Nitrogen Stocks in Three Types of Larix gmelinii Forests in Daxing’an Mountains, Northeast China

by Ruihan Xiao, Xiuling Man and Beixing Duan

Forests 2020, 11(3), 305; https://doi.org/10.3390/f11030305 - 11 Mar 2020

Cited by 23 | Viewed by 2926

Abstract

Studying carbon and nitrogen stocks in different types of larch forest ecosystems is of great significance for assessing the carbon sink capacity and nitrogen level in larch forests. To evaluate the effects of the differences of forest type on the carbon and nitrogen [...] Read more.

Studying carbon and nitrogen stocks in different types of larch forest ecosystems is of great significance for assessing the carbon sink capacity and nitrogen level in larch forests. To evaluate the effects of the differences of forest type on the carbon and nitrogen stock capacity of the larch forest ecosystem, we selected three typical types of larch forest ecosystems in the northern part of Daxing’an Mountains, which were the Rhododendron simsii-Larix gmelinii forest (RL), Ledum palustre-Larix gmelinii forest (LL) and Sphagnum-Bryum-Ledum palustre-Larix gmelinii forest (SLL), to determine the carbon and nitrogen stocks in the vegetation (trees and understories), litter and soil. Results showed that there were significant differences in carbon and nitrogen stocks among the three types of larch forest ecosystems, showing a sequence of SLL (288.01 Mg·ha⁻¹ and 25.19 Mg·ha⁻¹) > LL (176.52 Mg·ha⁻¹ and 14.85 Mg·ha⁻¹) > RL (153.93 Mg·ha⁻¹ and 10.00 Mg·ha⁻¹) (P < 0.05). The largest proportions of carbon and nitrogen stocks were found in soils, accounting for 83.20%, 72.89% and 64.61% of carbon stocks and 98.61%, 97.58% and 96.00% of nitrogen stocks in the SLL, LL and RL, respectively. Also, it was found that significant differences among the three types of larch forest ecosystems in terms of soil carbon and nitrogen stocks (SLL > LL > RL) (P < 0.05) were the primary reasons for the differences in the ecosystem carbon and nitrogen stocks. More than 79% of soil carbon and 51% of soil nitrogen at a depth of 0–100 cm were stored in the upper 50 cm of the soil pool. In the vegetation layer, due to the similar tree biomass carbon and nitrogen stocks, there were no significant differences in carbon and nitrogen stocks among the three types of larch forest ecosystems. The litter carbon stock in the SLL was significantly higher than that in the LL and RL (P < 0.05), but no significant differences in nitrogen stock were found among them (P > 0.05). These findings suggest that different forest types with the same tree layer and different understory vegetation can greatly affect the carbon and nitrogen stock capacity of the forest ecosystem. This indicates that understory vegetation may have significant effects on the carbon and nitrogen stocks in soil and litter, which highlights the need to consider the effects of understory in future research into the carbon and nitrogen stock capacity of forest ecosystems. Full article

(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)

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25 pages, 3502 KiB

Open AccessArticle

A Tutorial on Model-Assisted Estimation with Application to Forest Inventory

by Kelly S. McConville, Gretchen G. Moisen and Tracey S. Frescino

Forests 2020, 11(2), 244; https://doi.org/10.3390/f11020244 - 22 Feb 2020

Cited by 25 | Viewed by 5529

Abstract

National forest inventories in many countries combine expensive ground plot data with remotely-sensed information to improve precision in estimators of forest parameters. A simple post-stratified estimator is often the tool of choice because it has known statistical properties, is easy to implement, and [...] Read more.

National forest inventories in many countries combine expensive ground plot data with remotely-sensed information to improve precision in estimators of forest parameters. A simple post-stratified estimator is often the tool of choice because it has known statistical properties, is easy to implement, and is intuitive to the many users of inventory data. Because of the increased availability of remotely-sensed data with improved spatial, temporal, and thematic resolutions, there is a need to equip the inventory community with a more diverse array of statistical estimators. Focusing on generalized regression estimators, we step the reader through seven estimators including: Horvitz Thompson, ratio, post-stratification, regression, lasso, ridge, and elastic net. Using forest inventory data from Daggett county in Utah, USA as an example, we illustrate how to construct, as well as compare the relative performance of, these estimators. Augmented by simulations, we also show how the standard variance estimator suffers from greater negative bias than the bootstrap variance estimator, especially as the size of the assisting model grows. Each estimator is made readily accessible through the new R package, mase. We conclude with guidelines in the form of a decision tree on when to use which an estimator in forest inventory applications. Full article

(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)

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16 pages, 4512 KiB

Open AccessEditor’s ChoiceArticle

Comparing Individual Tree Height Information Derived from Field Surveys, LiDAR and UAV-DAP for High-Value Timber Species in Northern Japan

by Kyaw Thu Moe, Toshiaki Owari, Naoyuki Furuya and Takuya Hiroshima

Forests 2020, 11(2), 223; https://doi.org/10.3390/f11020223 - 15 Feb 2020

Cited by 65 | Viewed by 8664

Abstract

High-value timber species such as monarch birch (Betula maximowicziana Regel), castor aralia (Kalopanax septemlobus (Thunb.) Koidz), and Japanese oak (Quercus crispula Blume) play important ecological and economic roles in forest management in the cool temperate mixed forests in northern Japan. [...] Read more.

High-value timber species such as monarch birch (Betula maximowicziana Regel), castor aralia (Kalopanax septemlobus (Thunb.) Koidz), and Japanese oak (Quercus crispula Blume) play important ecological and economic roles in forest management in the cool temperate mixed forests in northern Japan. The accurate measurement of their tree height is necessary for both practical management and scientific reasons such as estimation of biomass and site index. In this study, we investigated the similarity of individual tree heights derived from conventional field survey, digital aerial photographs derived from unmanned aerial vehicle (UAV-DAP) data and light detection and ranging (LiDAR) data. We aimed to assess the applicability of UAV-DAP in obtaining individual tree height information for large-sized high-value broadleaf species. The spatial position, tree height, and diameter at breast height (DBH) were measured in the field for 178 trees of high-value broadleaf species. In addition, we manually derived individual tree height information from UAV-DAP and LiDAR data with the aid of spatial position data and high resolution orthophotographs. Tree heights from three different sources were cross-compared statistically through paired sample t-test, correlation coefficient, and height-diameter model. We found that UAV-DAP derived tree heights were highly correlated with LiDAR tree height and field measured tree height. The performance of individual tree height measurement using traditional field survey is likely to be influenced by individual species. Overall mean height difference between LiDAR and UAV-DAP derived tree height indicates that UAV-DAP could underestimate individual tree height for target high-value timber species. The height-diameter models revealed that tree height derived from LiDAR and UAV-DAP could be better explained by DBH with lower prediction errors than field measured tree height. We confirmed the applicability of UAV-DAP data for obtaining the individual tree height of large-size high-value broadleaf species with comparable accuracy to LiDAR and field survey. The result of this study will be useful for the species-specific forest management of economically high-value timber species. Full article

(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)

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Location of the study area. (a) The University of Tokyo Hokkaido Forest, (b) Sub-compartment 36 B and 59 A, (c) Measured trees at sub-compartment 36B, (d) Measured trees at sub-compartment 59A. Full article ">Figure 2
High-value timber species. (a) Monarch birch (Betula maximowicziana), (b) Castor aralia (Kalopanax septemlobus), and (c) Japanese oak (Quercus crispula). Full article ">Figure 3
Correlation between tree heights derived from field survey, LiDAR, and UAV-DAP data. Red, green, and blue dots represent individual monarch birch, castor aralia, and Japanese oak tree heights, respectively. Full article ">Figure 4
Height differences between measurement methods. (a) Difference between field height and LiDAR height, (b) Difference between field height and UAV-DAP height, and (c) Difference between LiDAR height and UAV-DAP height. Red, green, and blue dots represent individual monarch birch, castor aralia, and Japanese oak tree height respectively. Full article ">Figure 5
Correlation between individual tree DBH and tree height derived from Field, LiDAR, and UAV-DAP. Red, green, and blue dots represent individual monarch birch, castor aralia, and Japanese oak tree respectively. Full article ">Figure 6
Distribution of prediction error across DBH and height. Red, green, and blue dots represent prediction errors for field height, LiDAR height, and UAV-DAP height, respectively. Full article ">Figure 7
Mean prediction errors and height classes (field measured height). Blue, red, and gray lines represent mean prediction errors for field height, LiDAR height, and UAV-DAP height, respectively. Full article ">Figure 8
Height-Diameter curves. Blue, red, and black lines represent field measured height, predicted LiDAR height, and predicted UAV-DAP height, respectively. Full article ">

28 pages, 14559 KiB

Open AccessEditor’s ChoiceArticle

Estimating Coarse Woody Debris Volume Using Image Analysis and Multispectral LiDAR

by Gustavo Lopes Queiroz, Gregory J. McDermid, Julia Linke, Christopher Hopkinson and Jahan Kariyeva

Forests 2020, 11(2), 141; https://doi.org/10.3390/f11020141 - 25 Jan 2020

Cited by 17 | Viewed by 5884

Abstract

Coarse woody debris (CWD, parts of dead trees) is an important factor in forest management, given its roles in promoting local biodiversity and unique microhabitats, as well as providing carbon storage and fire fuel. However, parties interested in monitoring CWD abundance lack accurate [...] Read more.

Coarse woody debris (CWD, parts of dead trees) is an important factor in forest management, given its roles in promoting local biodiversity and unique microhabitats, as well as providing carbon storage and fire fuel. However, parties interested in monitoring CWD abundance lack accurate methods to measure CWD accurately and extensively. Here, we demonstrate a novel strategy for mapping CWD volume (m³) across a 4300-hectare study area in the boreal forest of Alberta, Canada using optical imagery and an infra-canopy vegetation-index layer derived from multispectral aerial LiDAR. Our models predicted CWD volume with a coefficient of determination (R2) value of 0.62 compared to field data, and a root-mean square error (RMSE) of 0.224 m³/100 m². Models using multispectral LiDAR data in addition to image-analysis data performed with up to 12% lower RMSE than models using exclusively image-analysis layers. Site managers and researchers requiring reliable and comprehensive maps of CWD volume may benefit from the presented workflow, which aims to streamline the process of CWD measurement. As multispectral LiDAR radiometric calibration routines are developed and standardized, we expect future studies to benefit increasingly more from such products for CWD detection underneath canopy cover. Full article

(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)

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15 pages, 3418 KiB

Open AccessArticle

Image Data Acquisition for Estimating Individual Trees Metrics: Closer Is Better

by Hospice A. Akpo, Gilbert Atindogbé, Maxwell C. Obiakara, Arios B. Adjinanoukon, Madaï Gbedolo, Philippe Lejeune and Noël H. Fonton

Forests 2020, 11(1), 121; https://doi.org/10.3390/f11010121 - 19 Jan 2020

Cited by 7 | Viewed by 3358

Abstract

Background and Objectives: The recent use of Structure-from-Motion with Multi-View Stereo photogrammetry (SfM-MVS) in forestry has underscored its robustness in tree mensuration. This study evaluated the differences in tree metrics resulting from various related SfM-MVS photogrammetric image acquisition scenarios. Materials and Methods: Scaled [...] Read more.

Background and Objectives: The recent use of Structure-from-Motion with Multi-View Stereo photogrammetry (SfM-MVS) in forestry has underscored its robustness in tree mensuration. This study evaluated the differences in tree metrics resulting from various related SfM-MVS photogrammetric image acquisition scenarios. Materials and Methods: Scaled tri-dimensional models of 30 savanna trees belonging to five species were built from photographs acquired in a factorial design with shooting distance (d = 1, 2, 3, 4 and 5 m away from tree) and angular shift (α = 15°, 30°, 45° and 60°; nested in d). Tree stem circumference at 1.3 m and bole volume were estimated using models resulting from each of the 20 scenarios/tree. Mean absolute percent error (MAPE) was computed for both metrics in order to compare the performance of each scenario in relation to reference data collected using a measuring tape. Results: An assessment of the effect of species identity (s), shooting distance and angular shift showed that photographic point cloud density was dependent on α and s, and optimal for 15° and 30°. MAPEs calculated on stem circumferences and volumes significantly differed with d and α, respectively. There was a significant interaction between α and s for both circumference and volume MAPEs, which varied widely (1.6 ± 0.4%–20.8 ± 23.7% and 2.0 ± 0.6%–36.5 ± 48.7% respectively), and were consistently lower for smaller values of d and α. Conclusion: The accuracy of photogrammetric estimation of individual tree attributes depended on image-capture approach. Acquiring images 2 m away and with 30° intervals around trees produced reliable estimates of stem circumference and bole volume. Research Highlights: This study indicates that the accuracy of photogrammetric estimations of individual tree attributes is species-dependent. Camera positions in relation to the subject substantially influence the level of uncertainty in measurements. Full article

(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)

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Map of study area. Full article ">Figure 2
Illustration of image acquisition scenarios. Full article ">Figure 3
Scaled 3-D model of V. paradoxa. Image acquisition was on a circular path, 1 m from the stem, based on 15° angular shifts. Full article ">Figure 4
A stem cross-section reconstructed from a sparse point cloud. Type 1 (C1) and Type 2 (C2) circumferences are convex hulls based on the shortest distance between any two vertices (a) and a straight line between the closest outermost vertices (b). Full article ">Figure 5
Tri-dimensional model quality in relation to tree species identity and angular shift (A), and distance (B). Ano: Anogeissus leiocarpa; Bom: Bombax costatum; Bir: Sclerocarya birrea; Ter: Terminalia laxiflora; Vit: Vitellaria paradoxa. Full article ">Figure 6
Proportion of useful images in relation to angular shift (A) and species (B). Predicted probability of the reconstruction of species-specific 3-D models is also shown in relation to angular shift (C). Ano: Anogeissus leiocarpa; Bom: Bombax costatum; Bir: Sclerocarya birrea; Ter: Terminalia laxiflora; Vit: Vitellaria paradoxa. Means with different letters (a, b, c and d) are statistically different (Waller-Duncan test, p < 0.05). Full article ">Figure 7
MAPE of breast height circumference in relation to distance (A,C) and species (B,D). Ano: Anogeissus leiocarpa; Bom: Bombax costatum; Bir: Sclerocarya birrea; Ter: Terminalia laxiflora; Vit: Vitellaria paradoxa. Histogram bars with different letters (a and b) differ significantly (Waller-Duncan test, p < 0.05). Full article ">Figure 8
MAPE of stem volume in relation to angular shift (A,C) and species (B,D). Ano: Anogeissus leiocarpa; Bom: Bombax costatum; Bir: Sclerocarya birrea; Ter: Terminalia laxiflora; Vit: Vitellaria paradoxa. Histogram bars with different letters (a and b) are significantly different (Waller-Duncan test, p < 0.05). Full article ">

16 pages, 8532 KiB

Open AccessArticle

Mapping the Spatial Distribution of Tea Plantations Using High-Spatiotemporal-Resolution Imagery in Northern Zhejiang, China

by Nan Li, Dong Zhang, Longwei Li and Yinlong Zhang

Forests 2019, 10(10), 856; https://doi.org/10.3390/f10100856 - 1 Oct 2019

Cited by 23 | Viewed by 4577

Abstract

Tea plantations are widely distributed in the southern provinces of China and have expanded rapidly in recent years due to their high economic value. This expansion has caused ecological problems such as soil erosion, and it is therefore urgent to clarify the spatial [...] Read more.

Tea plantations are widely distributed in the southern provinces of China and have expanded rapidly in recent years due to their high economic value. This expansion has caused ecological problems such as soil erosion, and it is therefore urgent to clarify the spatial distribution and area of tea plantations. In this study, we developed a simple method to accurately map tea plantations based on their unique phenological characteristics observed from VENμS high-spatiotemporal-resolution multispectral imagery. The normalized difference vegetation index (NDVI) and red—green ratio index (RGRI) of time series were calculated using 40 VENμS images taken in 2018 to evaluate the phenology of tea plantations. The unique phenological period of tea plantations in northern Zhejiang is from April to May, with obvious deep pruning, which is very different from the phenological period of other vegetation. During this period, the RGRI values of tea plantations were much higher than those of other vegetation such as broadleaf forest and bamboo forest. Therefore, it is possible to identify tea plantations from the vegetation in images acquired during their phenological period. This method was applied to tea plantation mapping in northern Zhejiang. The NDVI value of the winter image was used to extract a vegetation coverage map, and spatial intersection analysis combined with maps of tea plantation phenological information was performed to obtain a tea plantation distribution map. The resulting tea plantation map had a high accuracy, with a 94% producer accuracy and 95.9% user accuracy. The method was also applied to Sentinel-2 images at the regional scale, and the obtained tea plantation distribution map had an accuracy of 88.7%, indicating the good applicability of the method. Full article

(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)

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The location of the study area in the Zhejiang Province of China and its topography. The red rectangle represents the study area covered by VENμS images, and the green polygon represents the extended area covered by Sentinel images. Full article ">Figure 2
Phenological features of tea plantations and other land cover types in the study area: (a–c) are true color composites (band 7 in red, band 4 in green, and band 3 in blue) created from VENμS images taken in February, May, and August, 2018, respectively; A to E are tea plantation, broadleaf forest, bamboo forest, bare soil, and impervious surface, respectively; (d–f) are their spectral characteristics at the corresponding time; (g–i) are photos of tea plantations at the corresponding time. Full article ">Figure 3
Framework for mapping tea plantations using dense VENμS time series data and multitemporal Sentinel-2 data. Full article ">Figure 4
Intra-annual variation in the normalized difference vegetation index (NDVI) and red–green ratio index (RGRI) for typical land cover types in the study area. Full article ">Figure 5
(a) The spatial distribution of forests; (b) the spatial distribution of the phenology of tea plantations; (c) distribution of tea plantations obtained from spatial intersection analysis. Yellow, blue, red, and green represent phenological information obtained for different dates. (a1), (b1) and (c1) are enlarged views of typical area. Full article ">Figure 6
The spatial distribution of tea plantations. (a) The location of the study area for which Sentinel imagery was used; (b–d) are typical areas. Full article ">

Review

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17 pages, 1233 KiB

Open AccessReview

Catering Information Needs from Global to Local Scales—Potential and Challenges with National Forest Inventories

by Annika Kangas, Minna Räty, Kari T. Korhonen, Jari Vauhkonen and Tuula Packalen

Forests 2019, 10(9), 800; https://doi.org/10.3390/f10090800 - 12 Sep 2019

Cited by 12 | Viewed by 3766

Abstract

Forest information is needed at global, national and local scales. This review aimed at providing insights of potential of national forest inventories (NFIs) as well as challenges they have to cater to those needs. Within NFIs, the authors address the methodological challenges introduced [...] Read more.

Forest information is needed at global, national and local scales. This review aimed at providing insights of potential of national forest inventories (NFIs) as well as challenges they have to cater to those needs. Within NFIs, the authors address the methodological challenges introduced by the multitude of scales the forest data are needed, and the challenges in acknowledging the errors due to the measurements and models in addition to sampling errors. Between NFIs, the challenges related to the different harmonization tasks were reviewed. While a design-based approach is often considered more attractive than a model-based approach as it is guaranteed to provide unbiased results, the model-based approach is needed for downscaling the information to smaller scales and acknowledging the measurement and model errors. However, while a model-based inference is possible in small areas, the unknown random effects introduce biased estimators. The NFIs need to cater for the national information requirements and maintain the existing time series, while at the same time providing comparable information across the countries. In upscaling the NFI information to continental and global information needs, representative samples across the area are of utmost importance. Without representative data, the model-based approaches enable provision of forest information with unknown and indeterminable biases. Both design-based and model-based approaches need to be applied to cater to all information needs. This must be accomplished in a comprehensive way In particular, a need to have standardized quality requirements has been identified, acknowledging the possibility for bias and its implications, for all data used in policy making. Full article

(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)

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