Search Results (10,321)

Desert and semi-desert ecosystems cover a large proportion of global land area, but their tree-ring materials had traditionally been studied less intensively than that of forest ecosystems. In this study, we presented the time series of growth rings from eight typical shrub species of the semi-desert region in the northern foot of Yinshan Mountain, Inner Mongolia, China. The results showed that all those shrub species had recognizably demarcated annual rings of main stems, and tree-ring chronologies could been constructed successfully. The climate-growth analysis indicated that the chronologies was positively correlated with precipitation and PDSI but negatively correlated with temperature variables, indicating that drought stress had primary importance in the control of the relative ring width from year to year for those shrub species. Interestingly, the annual growth rate of those shrub species had no noticeable downward trend in recent decades, indicating that shrub growth had not negatively impacted the recently developed warm–dry climate in the sample sites. Our results provide evidence that growth rings in the main stems of shrub species in the northern foot of Yinshan Mountain should be a reliable proxy of annual fluctuation in the semi-desert environment of China. Full article

Accurate information on crop planting and spatial distribution is critical for understanding and tracking long-term land use changes. The method of using deep learning (DL) to extract crop information has been applied in large-scale datasets and plain areas. However, current crop classification methods face some challenges, such as poor image time continuity, difficult data acquisition, rugged terrain, fragmented plots, and diverse planting conditions in complex scenes. In this study, we propose the Complex Scene Crop Classification U-Net (CSCCU), which aims to improve the mapping accuracy of staple crops in complex scenes by combining multi-spectral bands with spectral features. CSCCU features a dual-branch structure: the main branch concentrates on image feature extraction, while the auxiliary branch focuses on spectral features. In our method, we use the hierarchical feature-level fusion mechanism. Through the hierarchical feature fusion of the shallow feature fusion module (SFF) and the deep feature fusion module (DFF), feature learning is optimized and model performance is improved. We conducted experiments using GaoFen-2 (GF-2) images in Xiuwen County, Guizhou Province, China, and established a dataset consisting of 1000 image patches of size 256, covering seven categories. In our method, the corn and rice accuracies are 89.72% and 88.61%, and the mean intersection over union (mIoU) is 85.61%, which is higher than the compared models (U-Net, SegNet, and DeepLabv3+). Our method provides a novel solution for the classification of staple crops in complex scenes using high-resolution images, which can help to obtain accurate information on staple crops in larger regions in the future. Full article

Tropical evergreen forests represent the richest biodiversity in terrestrial ecosystems, and the fine spatial-temporal resolution mapping of these forests is essential for the study and conservation of this vital natural resource. The current methods for mapping tropical evergreen forests frequently exhibit coarse spatial resolution and lengthy production cycles. This can be attributed to the inherent challenges associated with monitoring diverse surface changes and the persistence of cloudy, rainy conditions in the tropics. We propose a novel approach to automatically map annual 10 m tropical evergreen forest covers from 2017 to 2023 with the Sentinel-2 Dynamic World dataset in the biodiversity-rich and conservation-sensitive Central African Republic (CAR). The Copernicus Global Land Cover Layers (CGLC) and Global Forest Change (GFC) products were used first to track stable evergreen forest samples. Then, initial evergreen forest cover maps were generated by determining the threshold of evergreen forest cover for each of the yearly median forest cover probability maps. From 2017 to 2023, the annual modified 10 m tropical evergreen forest cover maps were finally produced from the initial evergreen forest cover maps and NEFI (Non-Evergreen Forest Index) images with the estimated thresholds. The results produced by the proposed method achieved an overall accuracy of >94.10% and a Cohen’s Kappa of >87.63% across all years (F1-Score > 94.05%), which represents a significant improvement over the performance of previous methods, including the CGLC evergreen forest cover maps and yearly median forest cover probability maps based on Sentinel-2 Dynamic World. Our findings demonstrate that the proposed method provides detailed spatial characteristics of evergreen forests and time-series change in the Central African Republic, with substantial consistency across all years. Full article

This study is aimed at a comprehensive assessment of the chemical composition of surface waters in the Turkestan region and their impact on regional landscapes. The primary objective of the research is to systematically evaluate the level of chemical pollution in the region’s water resources and determine its indirect effects on landscape-ecological stability. In August 2024, water samples from eight sampling points (S1–S8) were analyzed for 24 physicochemical parameters, including total hardness (mg*eq/L), pH, dry residue (mg/L), electrical conductivity (µS/cm), total salinity (mg/L), Al, As, B, Ca, Cd, Co, Cr, Ti, Fe, Pb, Cu, Mg, K, Mn, Na, Ni, Zn, SO₄²⁻, and C₆H₅OH. To determine the degree of pollution, variational-statistical analysis, principal component analysis (PCA), as well as the calculation of the OIP, NPI, and HPI indices were performed. For land use and land cover change (LULC) analysis, LULC classification was carried out based on Landsat data from 2000 to 2020, forming the basis for land resource management and planning. The research results showed a deterioration in the ecological condition of water resources and an increasing anthropogenic impact. Specifically, at point S8, the concentration of Al was found to be 56 times higher than the maximum allowable limit, while the concentration of Fe was 42 times higher. High levels of pollution were also recorded at points S1, S4, S5, and S6, where the increase in Al and Na concentrations caused a sharp rise in the OIP value. The main factors influencing water pollution include industrial effluents, agricultural waste, and irrigation drainage waters. The pollution’s negative impact on regional landscapes has led to issues related to the distribution of vegetation, soil fertility, and landscape stability. To improve the current ecological situation and restore natural balance, the phytoremediation method is proposed. The research results will serve as the foundation for developing water resource management strategies for the Turkestan region and making informed decisions aimed at ensuring ecological sustainability. Full article

National Forest Inventories (NFIs) provide valuable land cover (LC) information but often lack spatial continuity and an adequate update frequency. Satellite-based remote sensing offers a viable alternative, employing machine learning to extract thematic data. State-of-the-art methods such as convolutional neural networks rely on fully pixel-level annotated images, which are difficult to obtain. Although reference LC datasets have been widely used to derive annotations, NFIs consist of point-based data, providing only sparse annotations. Weakly supervised and self-supervised learning approaches help address this issue by reducing dependence on fully annotated images and leveraging unlabeled data. However, their potential for large-scale LC mapping needs further investigation. This study explored the use of NFI data with deep learning and weakly supervised and self-supervised methods. Using Sentinel-2 images and the Portuguese NFI, which covers other LC types beyond forest, as sparse labels, we performed weakly supervised semantic segmentation with a convolutional neural network to create an updated and spatially continuous national LC map. Additionally, we investigated the potential of self-supervised learning by pretraining a masked autoencoder on 65,000 Sentinel-2 image chips and then fine-tuning the model with NFI-derived sparse labels. The weakly supervised baseline achieved a validation accuracy of 69.60%, surpassing Random Forest (67.90%). The self-supervised model achieved 71.29%, performing on par with the baseline using half the training data. The results demonstrated that integrating both learning approaches enabled successful countrywide LC mapping with limited training data. Full article

Automatic glacier extraction from remote sensing images is the most important approach for large scale glacier monitoring. Commonly used band calculation indices to enhance glacier information are not effective in identifying shadowed glaciers and debris-covered glaciers. In this study, we used the Kolmogorov–Smirnov test as the theoretical basis and determined the most suitable band calculation indices to distinguish different land cover classes by comparing inter-sample separability and reasonable threshold range ratios of different indices. We then constructed a glacier classification decision tree. This approach resulted in the development of a method to automatically extract glacier areas at given spatial and temporal scales. In comparison with the commonly used indices, this method demonstrates an improvement in Cohen’s kappa coefficient by more than 3.8%. Notably, the accuracy for shadowed glaciers and debris-covered glaciers, which are prone to misclassification, is substantially enhanced by 108.0% and 6.3%, respectively. By testing the method in the Qilian Mountains, the positive prediction value of glacier extraction was calculated to be 91.8%, the true positive rate was 94.0%, and Cohen’s kappa coefficient was 0.924, making it well suited for glacier extraction. This method can be used for monitoring glacier changes in global mountainous regions, and provide support for climate change research, water resource management, and disaster early warning systems. Full article

In recent years, ecofriendly and renewable energy solutions have gained relevance mainly to lessen the effects of climate change. Governments and companies across the world have commitments to reduce fuel consumption and emissions as part of the 2030 Sustainable Development Goals. Solar energy systems have great importance as a renewable energy source; however, they often have large space requirements to be effective, e.g., large areas covered by solar panels, as well as low efficiency and strong dependance on the weather. On the other hand, origami, the art of folding paper, can be a source of inspiration for new technologies and solutions for modern problems. In this paper, origami-inspired solar panels are presented as a potential solution for naval and mining operations. Prototype panels are manufactured based on the Miura-Ori pattern. Using this pattern, the photovoltaic modules can be folded by just one movement, thus reducing their footprint by up to 90%. The prototype photovoltaic modules are then tested on land and on board a vessel, where their efficiency and resistance can be tested. It is shown that naval and mining operations, where fuel consumption can be extremely high and available space is a major constraint, benefit greatly from this kind of development. Full article

Deforestation caused by gold mining in the Ecuadorian Amazon has increased by 300% in the last decade, leading to severe environmental degradation of water and land resources. Effective remediation and revegetation technologies are still needed to address this issue. This study aimed to foster revegetation on 0.5 hectares of degraded land in Naranjalito, a mining site in the Ecuadorian Amazon, by applying plant-based biocompost and biochar and planting Ochroma pyramidale and Arachis pintoi, two pioneer species. The project’s objective was to evaluate the impact of these treatments on vegetation cover recovery through physicochemical and microbiological improvements in the soil. Four blocks and sixteen experimental plots were established: eight plots received treatments with varying doses of biocompost+biol (BIOC), four plots included plantations without biocompost (Not-BIOC), and four served as control plots (bare land). Over six months, dasometric characteristics of O. pyramidale and the expansion of A. pintoi were recorded. The data were analyzed using multivariate methods. The results revealed significant differences between treatments, with BIOC plots T4 and T1 showing greater improvements in vegetation development compared to Not-BIOC plots T3 and T2, confirming the positive influence of biocompost+biol. The BIOC treatment favored not only the planted species but also the secondary successional plant communities including certain grasses, leguminous plants, and other shrub and tree species, thus accelerating the revegetation process. This study demonstrates that biocompost application is an effective strategy to enhance plant recolonization on land severely degraded by gold mining in the Ecuadorian Amazon. Full article

In recent years, large-scale afforestation in the Yellow River Basin (YRB) has attracted widespread attention due to its significant impact on surface water, playing a crucial role in the ecological sustainability and high-quality development of the basin. In this study, we used a combination of Theil–Sen and Mann–Kendall trend analysis to detect the spatiotemporal dynamic changes of NDVI, surface water storage (SWS), and its components in the YRB from 2001 to 2020, and explored the time lag and instantaneous effects between them using methods such as cross-correlation. The results show that from 2001 to 2020, NDVI and SWS in the YRB increased at rates of 0.41%/year and 1.95 mm/year, respectively, with fluctuations. Spatially, NDVI exhibited a significant upward trend in most areas of the YRB, while regions with significant increases in SWS, canopy surface water (CSW), snow water equivalent (SWE), and soil moisture (SM) were primarily located in the upper reaches. There was a time lag effect of about 2 months between NDVI and SWS in the YRB, and the time lags between SWE, SM, and NDVI were 5 months and 2 months, respectively. Except for CSW, the lag between NDVI and SWE was longer than that between NDVI and SWS or SM across all land cover types. Regarding the instantaneous effect, we found that the effect of vegetation on SWS in the upstream area is mainly the water storage function. In some areas of the middle and lower reaches, vegetation intensifies the consumption of SWS. Our study provides valuable insights into the response mechanism between vegetation restoration and SWS changes, facilitating better coordination between water resource management and ecological conservation in the YRB, thereby achieving sustainable regional economic and ecological development. Full article

Global climate change is closely tied to CO₂ emissions, and implementing conservation-agricultural systems can help mitigate emissions in the Amazon. By maintaining forest cover and integrating sustainable agricultural practices in pasture, these systems help mitigate climate change and preserve the carbon stocks in Amazon forest soils. In addition, these systems improve soil health, microclimate regulation, and promote sustainable agricultural practices in the Amazon region. This study aimed to evaluate the CO₂ emission dynamics and its relationship with soil attributes under different uses in the Amazon. The experiment consisted of four treatments (Degraded Pasture—DP; Managed Pasture—MP; Native Forest—NF; and Livestock Forest Integration—LF), with 25 replications. Soil CO₂ emission (FCO₂), soil temperature, and soil moisture were evaluated over a period of 114 days, and the chemical, physical, and biological attributes of the soil were measured at the end of this period. The mean FCO₂ reached values of 4.44, 3.88, 3.80, and 3.14 µmol m⁻² s⁻¹ in DP, MP, NF, and LF, respectively. In addition to the direct relationship between soil CO₂ emissions and soil temperature for all land uses, soil bulk density indirectly influenced emissions in NF. The amount of humic acid induced the highest emission in DP. Soil organic carbon and carbon stock were higher in MP and LF. These values demonstrate that FCO₂ was influenced by the Amazon land uses and highlight LF as a low CO₂ emission system with a higher potential for carbon stock in the soil. Full article

With the advancement of polarimetric synthetic aperture radar (PolSAR) imaging technology and the growing demand for image interpretation, extracting meaningful land cover information from PolSAR images has become a key research focus. To address the segmentation challenge, we propose an innovative method. First, features from co-polarization and cross-polarization channels are separately used as dual inputs, and a cross-attention mechanism effectively fuses these features to capture correlations between different polarization information. Second, a diffusion framework is employed to jointly model target features and class probabilities, aiming to improve segmentation accuracy by learning and fitting the probabilistic distribution of target labels. Finally, experimental results demonstrate that the proposed method achieves superior performance in PolSAR image segmentation, effectively managing complex polarization relationships while offering robustness and broad application potential. Full article

The Amazon region is becoming more vulnerable to wildfires occurring in the dry season, a crisis amplified by climate change, which affects biomass burning across a wide range of forest environments. In this study, we examined the impact of seasonal fire on greenhouse (GHG) emissions over the study region during the last two decades of the 21st century by integrating calibrated and validated satellite-derived products of estimations of burned biomass area, land cover, vegetation greenness, rainfall, land surface temperature (LST), carbon monoxide (CO), and nitrogen dioxide (NO₂) through geospatial techniques. The results revealed a strong impact of fire activity on GHG emissions, with abrupt changes in CO and NO₂ emission factors between early and middle dry season fires (July–September). Among these seven variables analyzed, we found a positive relationship between the total biomass burned area and fire-derived GHG emission factors (r² = 0.30) due to the complex dynamics of plant moisture and associated CO and NO₂ emissions generated by fire. Nevertheless, other land surface drivers showed the weakest relationships (r²~0.1) with fire-derived GHG emissions due to other factors that drive their regional distribution. Our analysis suggests the importance of continued research on the response of fire season to other land surface characteristics that represent the processes driving fire over the study region such as fuel load, composition, and structure, as well as prevailing weather conditions. These determinants drive fire-related GHG emissions and fire-related carbon cycling relationships and can, therefore, appropriately inform policy fire-abatement guidelines. Full article

High-altitude mountainous regions are highly vulnerable to climate and environmental shifts, with the current global climate change exerting a profound influence on the ecological landscape of the Tianshan Mountains in China. This study assesses the ecological security quality in the Tianshan Mountains of China from 2001 to 2020 by employing various remote sensing techniques such as the Remote Sensing Ecological Index (RSEI) for evaluation, Normalized Difference Vegetation Index (NDVI) for fractional vegetation cover (FVC) analysis, the CASA model for estimating vegetation primary productivity (NPP), and a carbon source/sink model for calculating the net ecosystem productivity (NEP) of vegetation. The research also delves into the evolutionary trends and impact mechanisms on the ecological environment using land use and meteorological data. The findings reveal that the RSEI’s principal component (PC1) exhibits significant explanatory power, showing a notable increase of 5.90% from 2001 to 2020. Despite relatively stable changes in the RSEI over the past two decades covering 61.37% of the study area, there is a prevalent anti-persistence pattern at 72.39%. Notably, NDVI, FVC, and NPP display upward trends in vegetation characteristics. While most areas in the Tianshan Mountains continue to emit carbon, there is a marked increase in NEP, signifying an enhanced carbon absorption capacity. The partial correlation coefficients between the RSEI and temperature, as well as precipitation, demonstrate statistically significant relationships (p < 0.05), encompassing 6.36% and 1.55% of the study area, respectively. Temperature displays a predominantly negative correlation in 98.71% of the significantly correlated zones, while precipitation exhibits a prevalent positive correlation. An in-depth analysis of how climate change affects the quality of the ecological environment provides crucial insights for strategic interventions to enhance regional environmental protection and promote ecological sustainability. Full article

Mountains support many kinds of ecosystem services (ESs) for human beings, emphasizing the need to understand the characteristics and drivers of ES changes in mountainous regions. In this study, Nanling, the most significant mountains of southern China, was selected as a case study. Utilizing the GlobeLand30 dataset, we employed InVEST, Geodetector and MGWR to identify the spatio-temporal characteristics and drivers of ES changes, investigate trade-offs and synergies between ESs, and examine the relationship between ESs and the landscape ecological risk index (LERI) to provide a new perspective for ecosystem management in vulnerable mountain regions. The results showed that carbon storage (CS) and habitat quality (HQ) slightly decreased, while the water yield (WY) increased slightly. Soil conservation (SC) significantly decreased, but the total ES (TES) slightly increased. All ES bundles demonstrated a synergistic relationship, but most of the synergies exhibited a decreasing trend. The ESs in the study area were mainly affected by climate factors, and anthropogenic factors also had a significant impact on ESs. LERI exhibited a negative correlation with the provision of ESs and demonstrated a high explanatory power for ES changes, especially for CS, HQ and TES, suggesting that areas with more stable landscape patterns are likely to harbor greater levels of ESs. The results provide insights into the analysis of the characteristics of ES change in vulnerable mountainous areas, also providing the practical implications for introducing LERI as a driver for ES change. Full article

The frequent occurrence of floods puts additional pressure on people to change their activities and alter land use practices, consequently making exposed lands more vulnerable to floods. It is thus crucial to investigate dynamic changes in flood exposures and conduct quantitative evaluations of flood risk-reduction strategies to minimize damage to exposed items. This study quantitatively assessed dynamics of flood exposure and flood risk, and evaluated the effectiveness of flood control measures in the Bengawan Solo River basin, Indonesia. The Water and Energy Budget-Based Rainfall–Runoff–Inundation Model was employed for flood simulation for different return periods, and then dynamics of flood exposures and flood risk were assessed. After that, the effectiveness of flood control measures was quantitively evaluated. The results show that settlement/built-up areas and population are increasing in flood-prone areas. The flood-exposed paddy field and settlement areas for 100-year flood were estimated to be more than 950 and 212.58 km², respectively. The results also show that the dam operation for flood control in the study area reduces the flood damage to buildings, contents, and agriculture by approximately 21.2%, 20.9%, and 25.1%, respectively. The river channel improvements were also found effective to reduce flood damage in the study area. The flood damage can be reduced by more than 60% by implementing a combination of a flood control dam and river channel improvements. The findings can be useful for planning and implementing effective flood risk reduction measures. Full article