Search Results (399)

Wet areas, which are locations in the landscape that consistently retain moisture, and channel networks are important landscape compartments, with key hydrological and ecological functions. Hence, defining their spatial boundaries is an important step towards sustainable watershed management. In catchments of developing countries, wet areas and small order channels of river networks are rarely mapped, although they represent a crucial component of local livelihoods and ecosystems. In this study, topographic attributes generated with a 30 m SRTM DEM were used to map wet areas and stream networks of two tropical catchments in Central Brazil. The topographic attributes for wet areas were the local slope and the slope curvature, and the Topographic Wetness Index (TWI) was used to delineate the stream networks. Threshold values of the selected topographic attributes were calibrated in the Santa Maria catchment, comparing the synthetically generated wet areas and drainage networks with corresponding reference (map) features, and validated in the nearby Santa Maria basin. Drainage network and wet area delineation accuracies were estimated using random basin transects and multi-criteria and confusion matrix methods. The drainage network accuracies were 67.2% and 70.7%, and wet area accuracies were 72.7% and 73.8%, for the Santa Maria and Gama catchments, respectively, being equivalent or higher than previous studies. The mapping errors resulted from model incompleteness, DEM vertical inaccuracy, and cartographic misrepresentation of the reference topographic maps. The study’s novelty is the use of readily available information to map, with simplicity and robustness, wet areas and channel initiation in data-scarce, tropical environments. Full article

This study assesses the accuracy of flood extent predictions in five U.S. watersheds. We generated flood maps for four return periods using various digital elevation models (DEMs)—FABDEM, SRTM, ALOS, and USGS 3DEP—and two versions of the GEOGLOWS River Forecast System (RFS) hydrography. These comparisons are notable because they build on operational global hydrology models so subsequent work can develop global modeled flood products. Models were made using the Automated Rating Curve (ARC) and Curve2Flood tools. Accuracy was measured against USGS reference maps using the F-statistic. Our results show that flood map accuracy generally increased with higher return periods. The most consistent and reliable improvements in accuracy occurred when both the DEM and hydrography datasets were upgraded to higher-resolution sources. While DEM improvements generally had a greater impact, hydrography refinements were more important for lower return periods when flood extents were the smallest. Generally, DEM resolution improved accuracy metrics more as the return period increased and hydrography and bare earth DEMs mattered more as the return period decreased. There was a 38.9% increase in the mean F-statistic between the two principal pairings of interest (FABDEM-RFS2 and SRTM 30 m DEM-RFS1). FABDEM’s bare-earth representation combined with RFS2 sometimes outperformed higher-resolution non-bare-earth DEMs, suggesting that there remains a need for site-specific investigation. Using ARC and Curve2Flood with FABDEM and RFS2 is a suitable baseline combination for general flood extent application. Full article

Amidst the intensification of global climate change and the increasing impacts of human activities, ecosystem patterns and processes have undergone substantial transformations. The distribution and evolutionary dynamics of mountain ecosystems have become a focal point in ecological research. The Surkhan River Basin is located in the transitional zone between the arid inland regions of Central Asia and the mountain systems, where its unique physical and geographical conditions have shaped distinct patterns of vertical zonation. Utilizing Landsat imagery, this study applies a hierarchical classification approach to derive land cover classifications within the Surkhan River Basin. By integrating the NDVI (normalized difference vegetation index) and DEM (digital elevation model (30 m SRTM)), an “NDVI-DEM-Land Cover” scatterplot is constructed to analyze zonation characteristics from 1980 to 2020. The 2020 results indicate that the elevation boundary between the temperate desert and mountain grassland zones is 1100 m, while the boundary between the alpine cushion vegetation zone and the ice/snow zone is 3770 m. Furthermore, leveraging DEM and LST (land surface temperature) data, a potential energy analysis model is employed to quantify potential energy differentials between adjacent zones, enabling the identification of ecological transition areas. The potential energy analysis further refines the transition zone characteristics, indicating that the transition zone between the temperate desert and mountain grassland zones spans 1078–1139 m with a boundary at 1110 m, while the transition between the alpine cushion vegetation and ice/snow zones spans 3729–3824 m with a boundary at 3768 m. Cross-validation with scatterplot results confirms that the scatterplot analysis effectively delineates stable zonation boundaries with strong spatiotemporal consistency. Moreover, the potential energy analysis offers deeper insights into ecological transition zones, providing refined boundary identification. The integration of these two approaches addresses the dimensional limitations of traditional vertical zonation studies, offering a transferable methodological framework for mountain ecosystem research. Full article

Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. In addition, a canopy height model, constructed from a digital surface model (DSM) derived from Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and an ICESat-2-corrected SRTM DEM, is incorporated to quantify its impact on the accuracy of AGB estimation. The results indicate the following: (1) The incorporation of multi-source remote sensing data significantly improves the accuracy of AGB estimation, among which the RF model performs the best (R² = 0.69, RMSE = 24.26 t·ha⁻¹) compared with the single-source model. (2) The canopy height model (CHM) obtained from InSAR-LiDAR effectively alleviates the signal saturation effect of optical and SAR data in high-biomass areas (>200 t·ha⁻¹). When FCH is added to the RF model combined with multi-source remote sensing data, the R² of the AGB estimation model is improved to 0.74. (3) In 2018, AGB in Chenzhou City shows clear spatial heterogeneity, with a mean of 51.87 t·ha⁻¹. Biomass increases from the western hilly part (32.15–68.43 t·ha⁻¹) to the eastern mountainous area (89.72–256.41 t·ha⁻¹), peaking in Dongjiang Lake National Forest Park (256.41 t·ha⁻¹). This study proposes a comprehensive feature integration framework that combines red-edge spectral indices for capturing vegetation physiological status, SAR-derived texture metrics for assessing canopy structural heterogeneity, and canopy height metrics to characterize forest three-dimensional structure. This integrated approach enables the robust and accurate monitoring of carbon storage in subtropical forests. Full article

Accurate estimation of forest aboveground carbon stock (AGC) is critical for climate change mitigation and ecological management. This study develops a high-resolution AGC estimation workflow for the Pinglu Canal basin, integrating Sentinel-2, Sentinel-1, ALOS PALSAR, and SRTM data with field survey measurements. Feature selection via Recursive Feature Elimination and modeling with a Random Forest algorithm—optimized through hyperparameter tuning—yielded high predictive accuracy under the ALL data combination (R² = 0.818, RMSE = 11.126 tC/ha), enabling the generation of a 10 m-resolution AGC map. The total AGC in 2024 was estimated at 2.26 × 10⁶ tC. To evaluate human-induced changes, we established a baseline scenario based on historical AGC trends (2002–2021) and climate data. Comparisons revealed that afforestation and vegetation restoration during canal construction led to higher AGC values than projected under natural conditions. This positive deviation highlights the effectiveness of targeted ecological interventions in mitigating carbon loss and promoting forest recovery. Our results demonstrate a cost-effective, scalable method for AGC mapping using freely accessible remote sensing data and machine learning. The findings also provide insights into balancing large-scale infrastructure development with ecosystem conservation. Full article

High thematic resolution vegetation mapping is essential for monitoring wetland ecosystems, supporting conservation, and guiding water management. However, producing accurate, fine-scale vegetation maps in large, heterogeneous floodplain wetlands remains challenging due to complex hydrology, spectral similarity among vegetation types, and the high cost of extensive field surveys. This study addresses these challenges by developing a scalable vegetation classification framework that integrates cluster-guided sample selection, Random Forest modelling, and multi-source remote-sensing data. The approach combines multi-temporal Sentinel-1 SAR, Sentinel-2 optical imagery, and hydro-morphological predictors derived from LiDAR and hydrologically enforced SRTM DEMs. Applied to the Great Cumbung Swamp, a structurally and hydrologically complex terminal wetland in the lower Lachlan River floodplain of Australia, the framework produced vegetation maps at three hierarchical levels: formations (9 classes), functional groups (14 classes), and plant community types (PCTs; 23 classes). The PCT-level classification achieved an overall accuracy of 93.2%, a kappa coefficient of 0.91, and a Matthews correlation coefficient (MCC) of 0.89, with broader classification levels exceeding 95% accuracy. These results demonstrate that, through targeted sample selection and integration of spectral, structural, and terrain-derived data, high-accuracy, high-resolution wetland vegetation mapping is achievable with reduced field data requirements. The hierarchical structure further enables broader vegetation categories to be efficiently derived from detailed PCT outputs, providing a practical, transferable tool for wetland monitoring, habitat assessment, and conservation planning. Full article

The use of Google Earth Engine (GEE), a cloud-based computing platform, in spatio-temporal evaluation studies has increased rapidly in natural sciences such as forestry. In this study, Sentinel-2 satellite imagery and Shuttle Radar Topography Mission (SRTM) elevation data and image classification algorithms based on two machine learning techniques were examined. Random Forest (RF) and Classification and Regression Trees (CART) were used to classify land use and land cover (LULC) in western Oregon (USA). To classify the LULC from the spectral bands of satellite images, a composition consisting of vegetation difference indices NDVI, NDWI, EVI, and BSI, and a digital elevation model (DEM) were used. The study area was selected due to a diversity of land cover types including research forest, botanical gardens, recreation area, and agricultural lands covered with diverse plant species. Five land classes (forest, agriculture, soil, water, and settlement) were delineated for LULC classification testing. Different spatial points (totaling 75, 150, 300, and 2500) were used as training and test data. The most successful model performance was RF, with an accuracy of 98% and a kappa value of 0.97, while the accuracy and kappa values for CART were 95% and 0.94, respectively. The accuracy of the generated LULC maps was evaluated using 500 independent reference points, in addition to the training and testing datasets. Based on this assessment, the RF classifier that included elevation data achieved an overall accuracy of 92% and a kappa coefficient of 0.90. The combination of vegetation difference indices with elevation data was successful in determining the areas where clear-cutting occurred in the forest. Our results present a promising technique for the detection of forests and forest openings, which was helpful in identifying clear-cut sites. In addition, the GEE and RF classifier can help identify and map storm damage, wind damage, insect defoliation, fire, and management activities in forest areas. Full article

Semi-Mediterranean (SM) and semi-arid (SA) regions, exemplified by the Kurdo-Zagrosian forests in western Iran and northern Iraq, have experienced frequent wildfires in recent years. This study proposes a modified Non-Negative Matrix Factorization (NMF) method for detecting fire-prone areas using satellite-derived data in SM and SA forests. The performance of the proposed method was then compared with three other already proposed NMF methods: principal component analysis (PCA), K-means, and IsoData. NMF is a factorization method renowned for performing dimensionality reduction and feature extraction. It imposes non-negativity constraints on factor matrices, enhancing interpretability and suitability for analyzing real-world datasets. Sentinel-2 imagery, the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Zagros Grass Index (ZGI) from 2020 were employed as inputs and validated against a post-2020 burned area derived from the Normalized Burned Ratio (NBR) index. The results demonstrate NMF’s effectiveness in identifying fire-prone areas across large geographic extents typical of SM and SA regions. The results also revealed that when the elevation was included, NMF_L1/2-Sparsity offered the best outcome among the used NMF methods. In contrast, the proposed NMF method provided the best results when only Sentinel-2 bands and ZGI were used. Full article

Remotely sensed data, including rainfall estimates and digital elevation models (DEMs), are increasingly available at various temporal and spatial scales, offering new opportunities for rainfall regionalization in regions with limited ground-based observations. We evaluate the efficacy of NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) rainfall estimates and SRTM-derived elevation data as alternative spatial covariates for regionalizing average and extreme rainfall patterns across Greece. Using the Bilinear Surface Smoothing (BSS) framework, we assess and compare the regionalization of average daily rainfall and average annual maximum rainfall across multiple timescales (0.5 h to 48 h) by leveraging both IMERG-derived estimates and the elevation data as covariates. Additionally, the BSS framework is herein extended to provide Bayesian credible intervals for the final estimates, using the posterior variance estimate and the equivalent degrees of freedom determined through the Generalized Cross Validation error minimization procedure. Elevation-based models outperformed IMERG, particularly for indices of extreme rainfall, capturing the differential effects of orography. The exploration of the orographic effect based on the BSS framework revealed that the average annual rainfall maxima at small timescales exhibit a negative relation to elevation, which becomes positive and more significant with increasing timescale. However, IMERG proved valuable for regionalizing average daily rainfall, demonstrating its utility as a complementary tool. The results also underscore the role of temporal scale in regionalization efficiency of extreme rainfall, with higher accuracy observed at longer timescales (24 h and 48 h) and greater uncertainty at finer scales. Full article

Accurate and timely flood mapping is critical for informing emergency response and risk mitigation during extreme weather events. This study presents a synthetic aperture radar (SAR)-based approach for rapid flood extent mapping using Sentinel-1 imagery, demonstrated for Tropical Storm Imelda (17–21 September 2019) in southeastern Texas. Dual-polarization Sentinel-1 SAR data (VH and VV) were processed by computing the VH/VV backscatter ratio, and the resulting ratio image was classified using a supervised Random Forest classifier to delineate water and land. All Sentinel-1 images underwent radiometric calibration, speckle noise filtering, and terrain correction to ensure precision in flood delineation. The Random Forest classifier achieved an overall flood mapping accuracy exceeding 94%, with Cohen’s kappa coefficients of approximately 0.75–0.80, demonstrating the approach’s reliability in distinguishing transient floodwaters from permanent water bodies. The spatial distribution of flooding was strongly influenced by topography and land cover. Analysis of Shuttle Radar Topography Mission (SRTM) digital elevation data revealed that low-lying, flat terrain was most vulnerable to inundation; correspondingly, the land cover types most affected were hay/pasture, cultivated land, and emergent wetlands. Additionally, urban areas with low-intensity development experienced extensive flooding, attributed to impervious surfaces exacerbating runoff. A strong, statistically significant correlation (R² = 0.87, p < 0.01) was observed between precipitation and flood extent, indicating that heavier rainfall led to greater inundation; accordingly, the areas with the highest rainfall totals (e.g., Jefferson and Chambers counties) experienced the most extensive flooding, as confirmed by SAR-based change detection. The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. Harnessing globally available Sentinel-1 data with near-real-time processing and a robust classifier, this approach provides a scalable solution for rapid flood monitoring. These findings underscore the potential of SAR-based flood mapping under adverse weather conditions, thereby contributing to improved disaster preparedness and resilience in flood-prone regions. Full article

Understanding the relationship between geological and geomorphological processes is essential for reconstructing landscape evolution. This study examines how geology and geomorphology shape landscape development in central Brazil, focusing on the Natividade Group area. Sentinel-2 and SRTM data were integrated with geospatial analyses to produce two key maps: (i) a pedo-geomorphological map, classifying landforms and soil–landscape relationships, and (ii) a predictive geological–geomorphological map, based on a machine learning-based prediction of geomorphic units, which employed a Random Forest classifier trained with 15 environmental predictors from remote sensing datasets. The predictive model classified the landscape into six classes, revealing the ongoing interactions between geology, geomorphology, and surface processes. The pedo-geomorphological map identified nine pedoforms, grouped into three slope classes, each reflecting distinct lithology–relief–soil relationships. Resistant lithologies, such as quartzite-rich metasedimentary rocks, are associated with shallow, poorly developed soils, particularly in the Natividade Group. In contrast, phyllite, schist, and Paleoproterozoic basement rocks from the Almas and Aurumina Terranes support deeper, more weathered soils. These findings highlight soil formation as a critical indicator of landscape evolution in tropical climates. Although the model captured geological and geomorphological patterns, its moderate accuracy suggests that incorporating geophysical data could enhance the results. The landscape bears the imprint of several tectonic events, including the Rhyacian amalgamation (~2.2 Ga), Statherian taphrogenesis (~1.6 Ga), Neoproterozoic orogeny (~600 Ma), and the development of the Sanfranciscana Basin (~100 Ma). The results confirm that the interplay between geology and geomorphology significantly influences landscape evolution, though other factors, such as climate and vegetation, also play crucial roles in landscape development. Overall, the integration of remote sensing, geospatial analysis, and machine learning offers a robust framework for interpreting landscape evolution. These insights are valuable for applications in land-use planning, environmental management, and geohazard assessment in geologically complex regions. Full article

Mangrove forests are vital ecosystems with the highest global carbon absorption capacity, playing a crucial role in climate change mitigation. Therefore, their conservation and management are essential. However, as mangroves are primarily found in tropical regions, frequent cloud cover and limited accessibility pose significant challenges to effective monitoring using optical satellite imagery. In addition, many developing countries with extensive mangrove coverage face challenges in conducting precise monitoring due to limited technological infrastructure. To overcome these limitations, this study integrated open-access synthetic aperture radar (SAR) data with optical imagery to enhance the classification accuracy of mangrove forests in the Bali Denpasar–Badung region. The Sentinel-1 and Sentinel-2 datasets were used, and the U-Net deep learning model was employed for training and classification. A digital elevation model (DEM) from the Shuttle Radar Topography Mission (SRTM) was applied to exclude areas higher than 10 m above sea level, thereby improving the classification accuracy. Additionally, a time-series analysis was performed to assess changes in the mangrove distribution over the past decade, revealing a consistent increase in mangrove extent in the study area. The classification performance was evaluated using a confusion matrix, demonstrating that the combined SAR-optical model outperformed single-source models across all key metrics including precision, accuracy, recall, and F1-score. The findings highlight the effectiveness of integrating SAR and optical data for capturing the complex ecological and geographical characteristics of mangrove forests. Notably, SAR imagery, which is resistant to cloud cover, shows considerable potential for independent application in tropical mangrove monitoring, warranting further research to explore its capabilities in greater depth. Full article

Soil erosion poses a significant global environmental challenge, causing land degradation, deforestation, river siltation, and reduced agricultural productivity. Although the Revised Universal Soil Loss Equation (RUSLE) has been widely applied in Brazil, its use in the tropical river basins of the Amazon remains limited. This study aimed to apply a GIS-integrated RUSLE model and compare its soil loss estimates with multiple linear regression (MLR) models based on terrain attributes, aiming to identify priority areas and key geomorphometric drivers of soil erosion in a tropical Amazonian river basin. A digital elevation model based on Shuttle Radar Topography Mission (SRTM) data, land use and land cover (LULC) maps, and rainfall and soil data were applied to the GIS-integrated RUSLE model; we then defined six risk classes—slight (0–2.5 t ha⁻¹ yr⁻¹), slight–moderate (2.5–5), moderate (5–10), moderate–high (10–15), high (15–25), and very high (>25)—and identified priority zones as those in the top two risk classes. The Caeté River Basin (CRB) was classified into six erosion risk categories: low (81.14%), low to moderate (2.97%), moderate (11.88%), moderate to high (0.93%), high (0.03%), and very high (3.05%). The CRB predominantly exhibited a low erosion risk, with higher erosion rates linked to intense rainfall, gentle slopes covered by Arenosols, and human activities. The average annual soil loss was estimated at 2.0 t ha⁻¹ yr⁻¹, with a total loss of 1005.44 t ha⁻¹ yr⁻¹. Additionally, geomorphological and multiple linear regression (MLR) analyses identified seven key variables influencing soil erosion: the convergence index, closed depressions, the topographic wetness index, the channel network distance, and the local curvature, upslope curvature, and local downslope curvature. These variables collectively explained 26% of the variability in soil loss (R² = 0.26), highlighting the significant role of terrain characteristics in erosion processes. These findings indicate that soil erosion control efforts should focus primarily on areas with Arenosols and regions experiencing increased anthropogenic activity, where the erosion risks are higher. The identification of priority erosion areas enables the development of targeted conservation strategies, particularly for Arenosols and regions under anthropogenic pressure, where the soil losses exceed the tolerance threshold of 10.48 t ha⁻¹ yr⁻¹. These findings directly support the formulation of local environmental policies aimed at mitigating soil degradation by stabilizing vulnerable soils, regulating high-impact land uses, and promoting sustainable practices in critical zones. The GIS-RUSLE framework is supported by consistent rainfall data, as verified by a double mass curve analysis (R² ranging from 0.64 to 0.77), and offers a replicable methodology for soil conservation planning in tropical basins with similar erosion drivers. This approach offers a science-based foundation to guide soil conservation planning in tropical basins. While effective in identifying erosion-prone areas, it should be complemented in future studies by dynamic models and temporal analyses to better capture the complex erosion processes and land use change impacts in the Amazon. Full article

Beavers (Castor fiber L.) are recognized as keystone ecological engineers who shape freshwater ecosystems by modifying hydrology, sediment dynamics, and biodiversity. Although beaver populations have recovered across Europe, including Romania, understanding the environmental factors driving their dam distribution remains limited. This study aimed to (i) characterize the physical and compositional features of beaver dams in the Râul Negru basin, Romania, (ii) model the environmental variables influencing the dam distribution using MaxEnt, and (iii) evaluate the implications for broader conservation strategies. Over a five-year survey covering 353.7 km of watercourses, 135 beaver families were identified, with an estimated population of 320–512 individuals. The dam dimensions showed strong correlations with the river slope, channel width, and wetness index. Predictive models based on LIDAR data achieved over 90% accuracy, outperforming SRTM-based models. The results reveal that topographic wetness, flow accumulation, and valley morphology are the strongest predictors of dam presence. These findings contribute to proactive beaver management strategies, highlighting areas of potential future expansion and offering data-driven guidance for balancing ecosystem restoration with human land use, contributing to the development of conservation strategies that balance ecosystem engineering by beavers with human land-use needs in Romania and across Europe. Full article

Landscape fragmentation in mountainous resource-based cities has become increasingly serious, particularly in blue-green spaces. This study aims to establish a quantitative theoretical foundation for constructing an ecological network using the integrated morphological spatial pattern analysis (MSPA)–Conefor–minimum cumulative resistance (MCR) model. It employs multiple data sets, including land use data, remote sensing images, Shuttle Radar Topography Mission (SRTM) digital elevation, vegetation coverage data, etc., to conduct the quantitative analysis. Five groups of spatial resolution datasets (i.e., 30 m, 60 m, 90 m, 150 m, and 300 m) are employed for comparison and selection through MSPA to identify and analyze core landscape types. Connectivity analysis uses Conefor 2.6 software, and ecological sources are selected accordingly. Subsequently, the MCR model is applied to construct ecological corridors. Moreover, 153 ecological corridors are delineated, comprising 78 primary and 58 secondary corridors. The results show that most ecological core patches are fragmented and dispersed, while ecological corridors are vulnerable to disruption by external interference. This study also identifies 470 ecological breakpoints, mainly concentrated in the northeast, central, and southwestern areas characterized by high corridor density and intense anthropogenic activity. Additionally, 39 biological resting points are primarily located in the central urban area, and peripheral areas show few or no such points. This suggests establishing additional biological resting points to facilitate species migration and diffusion and complement the ecological network. This research addresses a significant gap in ecological network modeling within mountainous resource-based cities by developing a blue-green ecological network model. The findings encourage ecological governance bodies and technical professionals to recognize the interdependent relationship between blue and green spaces. This study supports the formulation of targeted planning strategies and helps maintain the potential connectivity essential for ecological balance. Full article