Search Results (823)

This study investigates the spatial heterogeneity of carbon pools in young Betula sp. stands on former arable lands in the southern Moscow region, Russia. The findings could be useful for the current estimates and predictions of the carbon balance in such forest ecosystems. The research focuses on understanding the interactions between plant cover and the environment, i.e., how environmental factors such as stand density, tree diameter and height, light conditions, and soil properties affect ecosystem carbon pools. We also studied how heterogeneity in edaphic conditions affects the formation of plant cover, particularly tree regeneration and the development of ground layer vegetation. Field measurements were conducted on a permanent 50 × 50 m sampling plot divided into 5 × 5 m subplots, in order to capture variability in vegetation and soil characteristics. Key findings reveal significant differences in carbon stocks across subplots with varying stand densities and light conditions. This highlights the role of the spatial heterogeneity of soil properties and vegetation cover in carbon sequestration. The study demonstrates the feasibility of indirect estimation of carbon stocks using stand parameters (density, height, and diameter), with results that closely match direct measurements. The total ecosystem carbon stock was estimated at 80.47 t ha⁻¹, with the soil contribution exceeding that of living biomass and dead organic matter. This research emphasizes the importance of accounting for spatial heterogeneity in carbon assessments of post-agricultural ecosystems, providing a methodological framework for future studies. Full article

Landslides are destructive geological hazards, making accurate landslide susceptibility assessment essential for disaster prevention and mitigation. However, existing studies often lack scientific rigor in negative sample construction and have unclear model applicability. This study focuses on Ya’an City, Sichuan Province, China, and proposes an innovative approach to negative sample construction using Geographically Weighted Regression (GWR), which is then integrated with Tabular Network (TabNet), a deep learning architecture tailored to structured tabular data, to assess landslide susceptibility. The performance of TabNet is compared against Random Forest, Light Gradient Boosting Machine, deep neural networks, and Residual Networks. The experimental results indicate that (1) the GWR-based sampling strategy substantially improves model performance across all tested models; (2) TabNet trained using the GWR-based negative samples achieves superior performance over all other evaluated models, with an average AUC of 0.9828, exhibiting both high accuracy and interpretability; and (3) elevation, land cover, and annual Normalized Difference Vegetation Index are identified as dominant predictors through TabNet’s feature importance analysis. The results demonstrate that combining GWR and TabNet substantially enhances landslide susceptibility modeling by improving both accuracy and interpretability, establishing a more scientifically grounded approach to negative sample construction, and providing an interpretable, high-performing modeling framework for geological hazard risk assessment. Full article

Accurate phenological information on sugarcane is crucial for guiding precise cultivation management and enhancing sugar production. Remote sensing offers an efficient approach for large-scale phenology retrieval, but most studies have primarily focused on staple crops. The methods for retrieving the sugarcane phenology—the germination, tillering, elongation, and maturity stages—remain underexplored. This study addresses the challenge of accurately monitoring the sugarcane phenology in complex terrains by proposing an optimized strategy integrating spatiotemporal fusion data. Ground-based validation showed that the change detection method based on the Double-Logistic curve significantly outperformed the threshold-based approach, with the highest accuracy for the elongation and maturity stages achieved at the maximum slope points of the ascending and descending phases, respectively. For the germination and tillering stages with low canopy cover, a novel time-windowed change detection method was introduced, using the first local maximum of the third derivative curve (denoted as Point A) to establish a temporal buffer. The optimal retrieval models were identified as 25 days before and 20 days after Point A for germination and tillering, respectively. Among the six commonly used vegetation indices, the NDVI (normalized difference vegetation index) performed the best across all the phenological stages. Spatiotemporal fusion using the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) significantly improved the monitoring accuracy in heterogeneous agricultural landscapes, reducing the RMSE (root-mean-squared error) by 21–46%, with retrieval errors decreasing from 18.25 to 12.97 days for germination, from 8.19 to 4.41 days for tillering, from 19.17 to 10.78 days for elongation, and from 19.02 to 15.04 days for maturity, highlighting its superior accuracy. The findings provide a reliable technical solution for precision sugarcane management in heterogeneous landscapes. Full article

Tree height is a critical indicator for estimating forest stock and can be effectively acquired by UAV-LiDAR. Ground filtering works to classify ground points and non-ground points and can impact the tree height extraction results, while the points classification quality obtained by ordinary filtering methods is limited in complex forest conditions. A partitioned cloth simulation filtering (PCSF) method based on different vegetation cover was proposed in this study to improve the classification accuracy, and tree heights were extracted to demonstrate the effectiveness of the proposed method. UAV-LiDAR data and field measurements collected from the Lutou experimental forest farm in the southern subtropical forest region of China were used for validation, and the slope-based filtering, progressive triangulated irregular network densification filtering (PTD), moving surface fitting filtering (MSFF), and CSF were adopted for comparisons. The results showed that the proposed method yielded the best ground filtering effect, reducing the filtering total error by 2.12%–4.22% compared with other methods, and the relative root mean squared error (rRMSE) of extracted tree heights was reduced by 1.24%–3.84%, respectively. The proposed method can achieve a satisfactory filtering effect and tree height extraction result, which provides a methodological basis to precisely extract tree heights in large-scale forests. Full article

Photogrammetry can be a valuable tool for understanding landscape evolution and natural hazards such as landslides. However, factors such as vegetation cover, shadows, and unstable ground can limit its effectiveness. Using photos across time to monitor an area with unstable or changing ground conditions results in fewer tie points between images across time, and often leads to low comparative accuracy if single-epoch (i.e., classical) photogrammetric processing approaches are used. This paper presents a study evaluating the co-alignment approach applied to fixed terrestrial timelapse photos at an active landslide site. The study explores the comparative accuracy of reconstructed surface models and the location and behavior of tie points over time in relation to increasing levels of global change due to landslide activity and rockfall. Building upon previous work, this study demonstrates that high comparative accuracy can be achieved with a relatively low number of inter-epoch tie points, highlighting the importance of their distribution across stable ground, rather than the total quantity. High comparative accuracy was achieved with as few as 0.03 percent of the overall co-alignment tie points being inter-epoch tie points. These results show that co-alignment is an effective approach for conducting change detection, even with large degrees of global changes between surveys. This study is specific to the context of geoscience applications like landslide monitoring, but its findings should be relevant for any application where significant changes occur between surveys. Full article

Rice is one of the three primary staple crops worldwide. The accurate monitoring of its key growth stages is crucial for agricultural management, disaster early warning, and ensuring food security. The effective collection of ground reference data is a critical step for monitoring rice growth stages using satellite imagery, traditionally achieved through labor-intensive field surveys. Here, we propose utilizing UAVs as an alternative means to collect spatially continuous ground reference data across larger areas, thereby enhancing the efficiency and scalability of training and validation processes for rice growth stage mapping products. The UAV data collection involved the Nanchuan, Yongchuan, Tongnan, and Kaizhou districts of Chongqing City, encompassing a total area of 377.5 hectares. After visual interpretation, centimeter-level high-resolution labels of the key rice growth stages were constructed. These labels were then mapped to Sentinel-2 imagery through spatiotemporal matching and scale conversion, resulting in a reference dataset of Sentinel 2 data that covered growth stages such as jointing and heading. Furthermore, we employed 30 vegetation index calculation methods to explore 48,600 spectral band combinations derived from 10 Sentinel-2 spectral bands, thereby constructing a series of novel vegetation indices. Based on the maximum relevance minimum redundancy (mRMR) algorithm, we identified an optimal subset of features that were both highly correlated with rice growth stages and mutually complementary. The results demonstrate that multi-feature modeling significantly enhanced classification performance. The optimal model, incorporating 300 features, achieved an F1 score of 0.864, representing a 2.5% improvement over models based on original spectral bands and a 38.8% improvement over models using a single feature. Notably, a model utilizing only 12 features maintained a high classification accuracy (F1 = 0.855) while substantially reducing computational costs. Compared with existing methods, this study constructed a large-scale ground-truth reference dataset for satellite imagery based on UAV observations, demonstrating its potential as an effective technical framework and providing an effective technical framework for the large-scale mapping of rice growth stages using satellite data. Full article

The Cerrado-Amazon Transition (CAT) in Brazil represents one of the most ecologically complex and dynamic tropical ecotones globally; however, it remains insufficiently characterized at high spatial resolution, primarily due to its intricate vegetation mosaics and the limited availability of reliable ground reference data. Accurate land cover maps are urgently needed to support conservation and sustainable land-use planning in this frontier region, especially for distinguishing critical vegetation types such as Amazon rainforest, Cerradão (dense woodland), and Savanna. In this study, we produce the first high-resolution land cover map of the CAT by integrating PlanetScope optical imagery, Sentinel-2 multispectral data, and Sentinel-1 SAR data within a U-net deep learning framework. This data fusion approach enables improved discrimination of ecologically similar vegetation types across heterogeneous landscapes. We systematically compare classification performance across single-sensor and fused datasets, demonstrating that multi-source fusion significantly outperforms single-source inputs. The highest overall accuracy was achieved using the fusion of PlanetScope, Sentinel-2, and Sentinel-1 (F1 = 0.85). Class-wise F1 scores for the best-performing model were 0.91 for Amazon Forest, 0.76 for Cerradão, and 0.76 for Savanna, indicating robust model performance in distinguishing ecologically important vegetation types. According to the best-performing model, 50.3% of the study area remains covered by natural vegetation. Cerradão, although ecologically important, covers only 8.4% of the landscape and appears highly fragmented, underscoring its vulnerability. These findings highlight the power of deep learning and multi-sensor integration for fine-scale land cover mapping in complex tropical ecotones and provide a critical spatial baseline for monitoring ecological changes in the CAT region. Full article

Soil moisture dynamics are a key parameter in regulating agricultural productivity and ecosystem functioning. The accurate monitoring and quantitative retrieval of soil moisture play a crucial role in optimizing agricultural water resource management. In recent years, the development of multi-source remote sensing technologies—such as high spatiotemporal resolution optical, radar, and thermal infrared sensors—has opened new avenues for efficient soil moisture retrieval. However, the accuracy of soil moisture retrieval decreases significantly when the soil is covered by vegetation. This study proposes a multi-modal remote sensing collaborative retrieval framework that integrates UAV-based multispectral imagery, Sentinel-1 radar data, and in situ ground sampling. By incorporating a vegetation suppression technique, a random-forest-based quantitative soil moisture model was constructed to specifically address the interference caused by dense vegetation during crop growing seasons. The results demonstrate that the retrieval performance of the model was significantly improved across different soil depths (0–5 cm, 5–10 cm, 10–15 cm, 15–20 cm). After vegetation suppression, the coefficient of determination (R²) exceeded 0.8 for all soil layers, while the mean absolute error (MAE) decreased by 35.1% to 49.8%. This research innovatively integrates optical–radar–thermal multi-source data and a physically driven vegetation suppression strategy to achieve high-accuracy, meter-scale dynamic mapping of soil moisture in vegetated areas. The proposed method provides a reliable technical foundation for precision irrigation and drought early warning. Full article

This study explores a method for deriving air temperature (AT) from land surface temperature (LST) based on different urban land-use types, aiming to address the accuracy of urban heat island (UHI) effect measurements. Using Wuhan as a case study, the research integrates remote sensing data with ground meteorological observations to develop various models, analyze their accuracy and applicability, and generate LST and AT maps to validate model reliability. The results indicate that when establishing the LST–AT relationship, polynomial regression performs best for water bodies (R² = 0.905), while random forest yields the highest R² for built-up areas, cropland, and vegetation at 0.942, 0.953, and 0.924, respectively. Due to the characteristics of the algorithms, it is recommended to prioritize random forest for prediction when the sample range covers the observed data range and to use BP neural networks when it does not. The generated maps reveal that in summer, using LST significantly overestimates UHI intensity in the study area, while differences between UHI intensities in winter are negligible. In resource-constrained scenarios, LST can be directly used to assess the UHI effect. Full article

Wildfires pose a significant threat to the natural and built environment and may alter the hydrologic cycle in burned areas increasing the risk of flooding, erosion, debris flows, and shallow landslides. In this paper, we investigate the feasibility of using differential interferometric synthetic aperture radar (DInSAR) to interpret changes in ground surface elevation following the 2017 Eagle Creek Wildfire in Oregon, USA. We show that DInSAR is capable of measuring ground surface displacements in burned areas not obscured by vegetation cover and that interferometric coherence can differentiate between areas that experienced different burn severities. The distribution of projected vertical displacement was analyzed, suggesting that different areas experience variable rates of change, with some showing little to no change for up to four years after the fire. Comparison of the projected vertical displacements with cumulative precipitation and soil moisture suggests that increases in precipitation and soil moisture are related to periods of increased vertical displacement. The findings of this study suggest that DInSAR may have value where in situ instrumentation is infeasible and may assist in prioritizing areas at high-risk of erosion or other changes over large geographical extents and measurement locations for deployment of instrumentation. Full article

This study investigated the synergistic effects of vegetation configurations and microclimate factors on seasonal thermal comfort in a semi-enclosed university courtyard in Wuhan, located in China’s Hot Summer and Cold Winter climate zone (Köppen: Cfa, humid subtropical). By adopting a field measurement–simulation–validation framework, spatial parameters and annual microclimate data were collected using laser distance meters and multifunctional environmental sensors. A validated ENVI-met model (grid resolution: 2 m × 2 m × 2 m, verified by field measurements for microclimate parameters) simulated 15 vegetation scenarios with varying planting patterns, evergreen–deciduous ratios (0–100%), and ground covers. The Physiological Equivalent Temperature (PET) index quantified thermal comfort improvements relative to the baseline. The optimal grid-based mixed planting configuration (40% evergreen trees + 60% deciduous trees) significantly improved winter thermal comfort by raising the PET from 9.24 °C to 15.42 °C (66.98% increase) through windbreak effects while maintaining summer thermal stability with only a 1.94% PET increase (34.60 °C to 35.27 °C) via enhanced transpiration and airflow regulation. This study provides actionable guidelines for climate-responsive courtyard design, emphasizing adaptive vegetation ratios and spatial geometry alignment. Full article

Retention forestry creates anthropogenic ecotones that diversify forest landscapes in terms of age and biomass. Such diversification can have ambiguous ecological impacts, raising uncertainties, particularly for black alder swamp woodlands, which are considered sensitive and are prioritized in EU conservation policy. This study aimed to examine the effects of adjacent clear-cutting on ground cover vegetation in 12 black alder stands in the hemiboreal zone in Latvia 11 to 120 years since the harvest. Ground cover vegetation was recorded by species along 40 m transects. The effects of the time since adjacent stand harvesting and exposure to the edge on species richness and Shannon diversity were assessed using linear mixed-effects models. A detrended correspondence analysis was used to explore the main environmental gradients. A total of 103 species were recorded: 15 in the tree and shrub layer, 66 in the herbaceous layer, and 22 in the moss and lichen layer. The exposure to the adjacent stand had a moderate positive effect on species diversity, while the effects of edge age were complex and varied by stand type. The scale of disturbance (the absolute length of the analyzed edge), rather than edge age or exposure, had the most pronounced effect on ground cover vegetation composition, suggesting persistent secondary edge effects that should be considered in forest management and conservation planning. Full article

As many areas of the world continue to grow, it is important to detect areas that are urbanizing at paces above the norm and predict future urban areas, so that optimal city planning can occur. However, methods to detect rapid urbanization are currently absent. Additionally, methods that predict future urban areas often rely on deep learning algorithms, which can be computationally expensive and require a large data volume. Furthermore, prediction methods are typically developed in a single location and are not evaluated across diverse geographies. In this study, rapid and future urbanization algorithms are developed, which are based on methods that use an ensemble of built-up spectral indices and a random forest classifier to detect built-up land cover in Sentinel-2 imagery, across ten sites that vary in their climate and population. Results show that the rapid urbanization algorithm can highlight anomalous urban growth. The future urbanization algorithm had an average overall accuracy of 0.66 (±0.11) and an average F1-score of 0.46 (±0.23). However, the method performed well in areas without seasonal vegetation changes and bare ground surroundings with overall accuracy values and F1-scores near or over 0.80. Overall, these methods provide an automated global approach to identifying rapid and future urban areas with minimal data and computational resources needed, which can enable urban planners to obtain information quickly so that decision making for city planning can be completed faster. Full article

The occurrence of hurricanes in the southern U.S. is on the rise, and assessing the damage caused to forests is essential for implementing protective measures and comprehending recovery dynamics. This work aims to create a novel data integration framework that employs LANDSAT 8, drone-based images, and geographic information system data for change detection analysis for different forest types. We propose a method for change vector analysis based on a unique spectral mixture model utilizing composite spectral indices along with univariate difference imaging to create a change detection map illustrating disturbances in the areas of McDowell County in western North Carolina impacted by Hurricane Helene. The spectral indices included near-infrared-to-red ratios, a normalized difference vegetation index, Tasseled Cap indices, and a soil-adjusted vegetation index. In addition to the satellite imagery, the ground truth data of forest damage were also collected through the field investigation and interpretation of post-Helene drone images. Accuracy assessment was conducted with geographic information system (GIS) data and maps from the National Land Cover Database. Accuracy assessment was carried out using metrics such as overall accuracy, precision, recall, F score, Jaccard similarity, and kappa statistics. The proposed composite method performed well with overall accuracy and Jaccard similarity values of 73.80% and 0.6042, respectively. The results exhibit a reasonable correlation with GIS data and can be employed to assess damage severity. Full article

Being an agricultural country, Pakistan requires lots of water for irrigation. A major portion of its water resources is located in the upper indus basin (UIB). The snowmelt runoff generated from high-altitude areas of the UIB provides inflow into the Indus river system that boosts the water supply. Snow accumulation during the winter period in the highlands in the watershed(s) becomes a source of water inflow during the snow-melting period, which is described according to characteristics like snow depth, snow density, and snow water equivalent. Snowmelt water release (SWE) and snowmelt water depth (SD) maps are generated by tracing snow occurrence from MODIS-based images of the snow-cover area, evaluating the heating degree days (HDDs) from MODIS-derived images of the land surface temperature, computing the solar radiation, and then assimilating all the previous data in the form of the snowmelt model and ground measurements of the snowmelt water release (SWE). The results show that the average snow-cover area in the Astore river basin, in the upper indus basin, ranges from 94% in winter to 20% in summer. The maps reveal that the annual average values of the SWE range from 150 mm to 535 mm, and the SD values range from 600 mm to 2135 mm, for the snowmelt period (April–September) over the years 2010–2020. The areas linked with vegetation experience low SWE accumulation because of the low slopes in the elevated regions. The meteorological parameters and basin characteristics affect the SWE and can determine the SD values. Full article