Search Results (1,153)

Optical remote sensing images acquired in rugged mountains are affected by reflectance distortion caused by both topography and shadows. Most topographic correction (TC) methods normalize sunlit slopes but can become unstable in self shadow and cast shadow, where little or no direct solar radiation reaches the surface. This study builds on the topographic shadow effect correction (TSEC) model and develops TSEC+TC, a partitioned framework for horizontal equivalent normalization. Using a shadow mask extended to penumbra, the framework integrates TSEC for shadowed pixels with conventional TC for sunlit pixels. Both branches target horizontal equivalent reflectance, enabling simultaneous correction of topographic and shadow effects across the scene. We implemented TSEC+TC with path length correction (PLC) and SCS with C (SCSC) models and evaluated it using ten multi-temporal Landsat 8 OLI scenes under different illumination conditions. The results showed that TSEC+TC reduced terrain-related brightness variation and improved land cover classification in the auxiliary comparison relative to uncorrected and TC-only results. For TSEC+SCSC, the R² values between corrected reflectance and cosi were below 0.025 for both Red and SWIR1 bands, and the coefficient of variation of reflectance across aspects was consistently lower than the corresponding values for SE and SCSC, with a maximum of 36.00%. Shadow area analyses indicated that TSEC+TC compensated reflectance distortion in self shadow and cast shadow areas, reduced TC-induced outliers, and better preserved spectral patterns than TC-only correction. Tests using Sentinel-2 MSI and GF-1 WFV imagery provided preliminary evidence of applicability to other sensors. Accounting for the topographic shadow effect improved TC performance in complex mountainous areas. Full article

Open-water mapping of flooded areas using multispectral remote sensing still presents significant challenges, particularly in urban environments, where high spectral mixing leads to substantial commission and omission errors in water classifications. This study proposes FLOODISI (Flood Detection Integrating Spectral Water Indices), a framework that integrates multiple spectral water indices through adaptive thresholding to generate an Integrated Water Map (IWM). The method was applied to Landsat-8/OLI imagery to map an extreme flood event in southern Brazil. The approach evaluates 12 spectral indices and iteratively adjusts threshold values to minimize false positives while preserving true positives. The results indicate that the Normalized Difference Flood Index (NDFI₂), using adaptive thresholding, achieved the best individual performance, with an overall accuracy of 91.8%, whereas the IWM increased this value to 93.5%, substantially reducing omission errors and improving open-water detection in urban areas. In comparison, the Random Forest classification achieved an overall accuracy of 95.0%, but exhibited similar precision and specificity, with a slight increase in commission errors and a modest reduction in omission errors relative to the IWM. In general, the integration of multiple spectral indices with adaptive thresholds through FLOODISI improved the robustness of open-water detection by reducing the dependence on individual spectral indices and providing a scalable, reproducible, and computationally efficient solution for rapid open-water mapping of flooded areas. Full article

Lakes on the Tibetan Plateau are sensitive indicators of regional climate change and hydrological variability. Comprehensive analyses of the area, level, and volume of typical lakes across multiple time scales remain limited. In this study, based on the Google Earth Engine platform, we integrated Landsat TM/ETM+/OLI and Sentinel-2 imagery, nine satellite altimetry datasets (Jason-1, Jason-2, Jason-3, Cryosat-2, Sentinel-3A, Sentinel-3B, ICESat, ICESat-2, and GEDI), and four machine learning methods (Lasso, SVM, Random Forest, and XGBoost) to investigate multi-timescale (annual, seasonal, and monthly) variations in lake area, level, and volume of Qinghai Lake, Nam Co, and Bangong Co, from 2000 to 2022. All three lakes showed overall increases in lake area, level, and volume, but with different magnitudes and temporal patterns. Qinghai Lake showed the most pronounced expansion, Nam Co experienced moderate growth, characterized by temporal fluctuations, whereas Bangong Co showed a relatively small but persistent increase. Seasonal analysis showed that lake area expansion was generally strongest in autumn, whereas monthly lake volume peaked in October for Qinghai Lake and Nam Co and in September for Bangong Co. Climate analyses indicated that Qinghai Lake may be mainly influenced by increased precipitation and runoff as well as reduced evaporation, Nam Co may be mainly influenced by precipitation, runoff, and delayed hydrological responses to glacier meltwater, and Bangong Co was likely associated with runoff and cryospheric water supply, while basin characteristics may have also contributed to its long-term hydrological response. These results highlight regional differences in lake responses to climate variability across the Tibetan Plateau. Full article

High-precision estimation of forest aboveground biomass (AGB) is crucial for global carbon cycle monitoring and sustainable forest management. However, existing machine learning-based approaches often suffer from high-dimensional feature redundancy, uneven spatial distribution of training samples, and inefficient hyperparameter optimization, which collectively limit estimation accuracy and computational efficiency. To address these issues, this study proposes a synergistic feature-sample optimization framework (DFS) for high-precision forest AGB estimation. First, with the involvement of forestry experts, we constructed the Hunan and Hubei datasets covering typical subtropical forest types through multi-source remote sensing and ground plot sampling. Second, we propose the Dual-Criteria Adaptive Feature Selection (DCAFS) method, integrating ReliefF and mutual information criteria to adaptively select key features highly correlated with AGB, eliminating spectral redundancy while preserving biomass-sensitive information. Next, we introduce a Bidirectional Active Learning Sample Optimization mechanism, called BALSO, and in its forward step, plots with high uncertainty and representativeness are given priority, so samples with high AGB variability can be captured effectively; in the backward step, spatially redundant samples and feature-redundant samples are removed through density peak clustering, and by doing this, sample selection and spatial distribution are optimized at the same time, so plot balance gets improved. Finally, the framework brings in a parameter tuning structure based on Dream Optimization Algorithm, namely DOA, and through staged exploration together with local fine-tuning, DOA makes model hyperparameters and AGB data distribution characteristics align in an adaptive manner, which helps improve convergence efficiency and estimation stability. Input variables comprise Landsat 8 OLI spectral bands, GLCM texture features, vegetation indices, and Sentinel-1/2 data. On the Hunan dataset, the framework achieved an $R^2$ of 0.83 and an RMSE of 25.6 Mg·ha⁻¹; on the Hubei dataset, it achieved an $R^2$ of 0.86 and an RMSE of 26.8 Mg·ha⁻¹. The framework was further validated on an independent public dataset from Inner Mongolia. These results demonstrate that the DFS framework provides an effective and feasible approach for regional-scale forest AGB estimation and carbon monitoring. Full article

Farm field units are the fundamental management units of modern precision agriculture, and accurate identification of their geographic boundaries is essential for the data management of farmland soil and nutrients. However, traditional manual vectorization methods are inefficient and costly. To address this issue, this study proposes an automated farm field unit identification framework based on remote sensing image segmentation and parameter optimization. Starting from the framework of Geographic Object-Based Image Analysis (GEOBIA), the framework employs the multiresolution segmentation (MRS) algorithm combined with intra-segment homogeneity and inter-segment heterogeneity indicators, and iteratively determines the optimal scale parameters through three unsupervised segmentation parameter optimization (USPO) methods, namely the methods proposed by Espindola et al. (EUSPO), Zhang et al. (ZUSPO), and Johnson et al. (JUSPO), to achieve automatic identification of farm field information. The framework was evaluated on eleven production teams of the Tenihe Farm, a large-scale intensive farm in Hulunbuir, China, using two scenes of Landsat 8 OLI imagery acquired on 18 September 2019 (multispectral bands pan-sharpened to 15 m, with the red, green, blue, and near-infrared bands used for segmentation), taking field-investigation-verified farm field vector maps as the reference. The results show that the identification accuracy of farm field units generally reaches over 80%. The main contribution of this study lies in extending USPO methods from segmentation quality evaluation to automated farm field vector extraction in precision agriculture, and in providing a systematic horizontal comparison of three USPO combination strategies under large-scale heterogeneous agricultural landscapes, which reveals that the JUSPO method, with its F-measure fusion strategy, achieves the most robust performance in most teams. These findings verify the effectiveness and application potential of the proposed framework. Full article

Rapid urban expansion and agricultural development have substantially altered land use/land cover (LULC), surface thermal regimes, and ecosystem conditions in semi-arid cities. This study investigates the spatiotemporal dynamics of LULC and evaluates their associations with land surface temperature (LST), vegetation health, soil moisture, and drought conditions in Ghazni City, Afghanistan, between 2013 and 2023 using a Google Earth Engine (GEE)-based framework. Landsat 8 OLI/TIRS imagery was classified using a Random Forest (RF) algorithm, while the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), and LST were derived to evaluate environmental responses. To provide a comprehensive evaluation of urban–climate interactions, monthly Landsat-derived LST time series were analyzed and compared to MODIS and ERA5 datasets through a multi-source consistency assessment framework. The RF classification achieved overall accuracies of 95.56% (2013) and 97.77% (2023), with Kappa coefficients of 0.89 and 0.94, respectively. Results revealed a substantial expansion of built-up areas (5.4%) and vegetation/agricultural land (7.2%), accompanied by a decline in bare land. Urban and barren surfaces consistently exhibited higher LST values, whereas vegetated areas demonstrated a pronounced cooling effect. NDVI and SAVI analyses indicated improving vegetation conditions and soil moisture status over the study period. LST exhibited strong seasonal variability, with summer maxima reaching 49.74 °C and winter minima declining to −8.39 °C. Comparisons among the Landsat, MODIS, and ERA5 datasets demonstrated strong agreement, with a high correlation between Landsat- and MODIS-derived LST (R = 0.84), supporting the reliability of the Landsat-derived LST estimates. Generally, the findings demonstrate the critical role of vegetation in moderating surface temperatures and enhancing urban climate resilience, providing scientific evidence for sustainable land use planning and climate adaptation strategies in semi-arid cities. Full article

Urban blue–green spaces (UBGSs) serve as irreplaceable ecological infrastructure that underpins ecosystem service provision and human well-being improvement in densely populated urban regions. Based on 10-year (2014–2023) Landsat 8 OLI remote sensing imagery, this paper systematically investigates the temporal–spatial evolutionary characteristics and potential socioeconomic and policy-related associations with UBGS changes in the Shandong Peninsula urban agglomeration (SPUA), a pivotal coastal urban agglomeration in eastern China. The results demonstrate that the total UBGSs in the SPUA exhibited a pronounced increasing trend throughout the study period: the area of urban green spaces (UGSs) expanded from 28,311.66 km² to 30,194.39 km², while urban blue spaces (UBSs) grew from 1108.02 km² to 1699.04 km². Concurrently, the ecological quality of UGSs has markedly improved, with NDVI showing a significant upward trend in over one-third of the built-up areas, and vegetation greenness in cities such as Binzhou, Jinan, and Zibo increasing by more than 35%. Landscape pattern analysis reveals that the spatial structure of UGSs has transformed from a fragmented and scattered distribution to a centralized and contiguous layout. Specifically, the aggregation index (AI) and largest patch index (LPI) increased overall, while the landscape shape index (LSI) decreased by approximately 18.3%, indicating that the connectivity and structural integrity of urban green spaces have been substantially enhanced. National strategic policies, particularly the outline of ecological protection and high-quality development planning for the Yellow River basin, have effectively alleviated the encroachment pressure of population agglomeration and economic expansion on UBGSs, and played a decisive regulatory role in promoting the structural optimization of blue–green spaces. These findings provide empirical evidence for cross-city collaborative planning and integrated ecological governance of blue–green spaces at the urban agglomeration level, and offer valuable reference for achieving sustainable urban development in other rapidly urbanizing areas. Full article

Long-term monitoring of surface-water dynamics in hyper-arid reservoir systems requires consistent remote-sensing methods that can distinguish open water from bright desert surfaces, shallow water, wet sand, and mixed shoreline pixels. This study evaluates Landsat-derived spectral water indices for delineating surface water in Lake Nasser and the adjacent Tushka Lakes, generates a multi-decadal record of surface-water extent using Google Earth Engine, and places the resulting surface-water patterns in the context of available hydrogeological observations. Landsat TM and OLI surface reflectance imagery was used to compare seven commonly applied water indices (NDWI, EWI, NDX, WRI, AWEI_nsh, TCW, and NWI) based on mapped water area, relative area differences, and classification accuracy metrics derived from 1000 stratified reference samples. Among the tested indices, NDWI provided stable water–land separation (overall accuracy ≈ 93.6%; κ ≈ 0.898) and was selected for long-term mapping. The NDWI-based workflow was implemented in Google Earth Engine to generate quarterly composites of surface-water extent for the period 1987–2026. The resulting time series reveals stable, persistent surface water in the central and southern sectors of Lake Nasser, in contrast to pronounced seasonal and interannual variability in the shallow, intermittently connected Tushka basins. Total mapped water area increased from 2631 km² in 1987 to 8923 km² in early 2026, with Lake Nasser ranging from 2411 to 6060.7 km² and the Tushka Lakes expanding from no mapped water before 1998 to more than 3300 km² during 2025. To assess possible surface–subsurface interaction, daily lake-stage records (1965–2014) and monthly groundwater levels from 44 observation wells were used to estimate potential seepage losses from Lake Nasser to the Nubian Sandstone Aquifer System using Darcy’s law. Annual seepage estimates ranged from 15.58 × 10⁶ to 36.68 × 10⁶ m³/year, suggesting spatial variability in potential lake–aquifer seepage along the western lake margin. The combined remote-sensing and hydrogeologic results provide complementary, non-causal evidence for interpreting where surface-water persistence and estimated seepage may co-occur. Because spatial correlation analysis, calibrated ground-water modeling, full water-budget analysis, and independent field validation were not performed, the inferred seepage–surface-water relation should be regarded as a cautious hypothesis rather than proof of causality. Full article

Selective logging is an important component of forest degradation in the Brazilian Amazon. The detection and mapping of selective logging via satellite imagery remains challenging because spatial features associated with selective logging are generally small-scale, spatially heterogeneous, and short-lived disturbances in the forest. This study evaluated the potential of Sentinel-2 MSI imagery at 10 m and 20 m, and Landsat-8 OLI imagery at 30 m and pansharpened 15 m, to discriminate land-cover classes associated with selective logging in the state of Mato Grosso in the Brazilian Amazon for 2017 using the Random Forest algorithm. The resulting maps were used to characterize selective logging alerts from the Deter system and areas under Sustainable Forest Management Plans (SFMP). Sentinel-2 at 10 m achieved the highest overall accuracy, while Landsat-based products tended to estimate larger areas of exposed soil and, in some cases, regeneration. Deter polygons showed higher proportions of exposed soil and degradation and lower remaining forest cover than SFMP areas, suggesting that Deter alerts tend to capture more advanced stages of visible forest disturbance. Overall, the results indicate that differences in overall accuracy among the evaluated products were small, but class-specific performance and spatial representation patterns remain important for interpreting selective logging-related disturbance in the Amazon. Full article

The Landsat program has provided over 54 years of multispectral imagery, contributing vital information for agricultural and forestry scientific research and operational activities. Freely available Landsat data have enabled scientists to analyze land use patterns, assess ecological impacts, and develop strategies for sustainable management. We explored Landsat’s pivotal role through the lens of the United States Group on Earth Observations 2023 Earth Observation Assessment (EOA). The EOA included comprehensive surveys of more than 2000 federally supported Earth observation data products. We subsequently analyzed how Landsat satellite data and derived products support agricultural and forestry-related priorities compared to other Earth observation inputs. We evaluated both direct and indirect applications of the data, identifying key users across federal agencies and assessing how Landsat data contribute to critical products, services, and objectives. The results indicate that Landsat provides key information to support diverse activities across agriculture and forestry sectors, such as enhancing food supply, improving resilience to disaster and disturbance events, maximizing ecosystem productivity and conservation, and supporting regulatory requirements and decision-making. The Landsat OLI and TIRS sensors ranked 4th and 10th, respectively, out of 1171 Earth observation inputs identified in the study, underscoring their value to agriculture and forestry. Full article

Urban heat mitigation in megacities depends not only on cooling sources, but also on the connectivity through which cooling effects are transmitted across heterogeneous landscapes. However, existing studies have mainly focused on the static patterns of urban cold islands (UCIs), while the connectivity and disturbance response of urban cooling systems remain poorly understood. Taking Landsat-based summer thermal observations in Beijing, this study developed an integrated framework to assess the structure and resilience of the urban cold island network (CIN) by combining thermal source identification, resistance-surface construction, connectivity modeling, and disturbance simulations. Land surface temperature (LST) was extracted from Landsat 8 OLI/TIRS Collection 2 Level-2 surface temperature products acquired in July–August 2022, and cold island core sources (CICS) were subsequently identified by integrating thermal conditions with land-use characteristics. GeoDetector was used to quantify the explanatory power and interaction effects of natural, land-use, and socio-economic factors on LST spatial heterogeneity, serving as an attribution tool for interpreting thermal-environment drivers. These factors were then integrated into a resistance surface for circuit-theory-based connectivity analysis. Under the summer heat-stress scenario, 202 CICS covering 6416.95 km² were identified, mainly concentrated in peripheral mountainous areas. A total of 401 corridors were identified, including 70 primary corridors forming the structural backbone of the CIN. This spatial distribution reveals a mountain–plain cooling structure in Beijing, in which mountainous CICS constitute the regional cooling-supply base, while potential cooling transmission toward the urban core mainly depends on a limited number of backbone corridors. LULC was the dominant driver of LST, and its interactions with PD, NTL, and vegetation-related factors substantially enhanced explanatory power. Compared with random disturbance, targeted node removal led to an earlier and sharper decline in network resilience, with substantial deterioration already evident after approximately 20–30% of critical nodes were removed. These summer-based findings provide spatially explicit evidence for prioritizing cooling corridors, critical nodes, and restoration areas in connectivity-oriented urban heat mitigation and climate-responsive planning, thereby supporting hierarchical maintenance and restoration strategies based on their relative importance within the cooling network. Full article

Climate change threatens ecosystem stability in arid Central Asia, yet regional vegetation responses remain poorly resolved at the operational scale of land-use policy. We integrated long-term meteorological records (2000–2024) from Kazhydromet with Landsat surface-reflectance imagery for four epochs (2010, 2015, 2020, 2025) across the five administrative regions of Southern Kazakhstan (≈710,000 km²). After cross-sensor harmonization of Landsat 5 TM and Landsat 8 OLI, dense vegetation cover (NDVI > 0.4) increased modestly across all regions, with the cumulative area growing from 9.09 to 9.60 million hectares (+5.6%) and a transient 2020 minimum linked to the 2018–2020 drought. Per-region OLS trend slopes were not statistically significant at p < 0.05, given the four-epoch sampling (n = 4). A composite Biodiversity–Climate Sensitivity Index (BCSI), constructed from four normalized components (temperature trend, precipitation deficit, NDVI trend, and the coefficient of variation of dense-vegetation cover as a biodiversity–vulnerability proxy), identifies the lower Syr Darya floodplain and former Aral Sea margins as the most sensitive territories and the Northern Tien Shan as the most resilient. The framework provides an operational evidence base for climate-adaptive conservation aligned with SDG 13 and SDG 15. Full article

This paper presents an integrated approach to the mapping and structural-tectonic interpretation of lineaments in the Mangystau region using multispectral Landsat-8 OLI data and the medium-resolution Airbus WorldDEM4Ortho digital elevation model. Automatic extraction of linear structures has enabled the identification of over 35,000 lineaments of varying length and orientation, forming a network of intersecting zones that influence the distribution of sedimentary thicknesses, drainage directions, and the location of karst-suffosion depressions. The most prominent are the north-western and sub-latitudinal systems, closely correlated with zones of fracturing and faults, which confirms their tectonic origin. The spatial concentration of lineaments coincides with areas of increased permeability in carbonate and gypsum-bearing rocks and localizes the pathways of groundwater circulation, contributing to the development of karst-suffosion processes. The obtained results demonstrate the significance of structural influences on the region’s current geomorphological and hydrogeological conditions and also have practical importance for engineering-geological surveys, the assessment of geological risks, and the planning of sustainable land use. Full article

The spatial heterogeneity and dynamic changes in soil organic matter (SOM) are key indicators for assessing cultivated land quality and the carbon cycle. Currently, large-scale SOM monitoring relies primarily on limited ground sampling, making it difficult to capture continuous spatiotemporal variation patterns. Taking Anhui Province, China as the study area, this research integrates multi-source remote sensing and geostatistical methods to construct a multi-source collaborative SOM inversion model and analyze its spatiotemporal evolution patterns, thereby achieving high-precision, continuous spatiotemporal monitoring of SOM. A total of 3026 sampling points in Huangshan, Chuzhou and Fuyang cities in Anhui Province were selected as model training samples. The study divided the terrain into three elevation zones (<20 m, 20–40 m, >40 m) and employed the Synthetic Minority Oversampling Technique (SMOTE) method to optimize sample distribution. Based on MODIS data, this study screened spectral bands and key phenological periods significantly correlated with SOM. By integrating spectral information from Landsat 8/9 OLI imagery, meteorological data and topographic factors, a random forest (RF) inversion model incorporating multi-source environmental variables was constructed. The results indicate that (1) the RF-based SOM inversion model exhibits moderate predictive accuracy acceptable for regional-scale SOM mapping, with a coefficient of determination (R²) of 0.55 and a root-mean-square error (RMSE) of 3.3 g/kg, effectively enabling the quantitative estimation of SOM at a regional scale. (2) The model’s inversion results reflect the spatial distribution of SOM in cultivated land in Anhui Province for the years 2019, 2022 and 2024. The provincial average SOM value shows an upward trend, with SOM content exhibiting a pattern of higher levels in the south and lower levels in the north, higher levels in the west and lower levels in the east, as well as a tendency to cluster. (3) Analysis using GeoDetector indicates that topography and precipitation are the primary drivers influencing SOM distribution, and the interaction between these two factors provides significantly greater explanatory power for SOM distribution than either factor alone. Through the integration of multi-source remote sensing data and model optimization, this study has validated the feasibility of multi-scale remote sensing-based SOM inversion, revealed the spatial differentiation characteristics and driving mechanisms of SOM in Anhui Province’s cultivated land, and provided a scientific basis for improving cultivated land quality and soil carbon sink management. Full article

Bark beetle infestations present a considerable risk to coniferous forest ecosystems, resulting in substantial ecological and economic losses. Monitoring and mapping vegetation health using remote sensing techniques is an important step in identifying and controlling areas of susceptibility, especially for bark beetles that cause significant damage to forest ecosystems. This study assessed the statistical discriminative efficacy of vegetation indices obtained from Landsat 8 OLI data at the stand level in discriminating between forest stands impacted and unaffected by Ips sexdentatus damage. Eighty forest stands (40 forest stands, both with and without Ips sexdentatus damage) selected through fieldwork in the Araç Forest Directorate, Kastamonu, were studied. Five frequently utilized vegetation indices (NDVI, NDMI, MSI, TCW, and RGI) were applied, and the minimum, maximum, and average values were computed for each stand. Given the non-normal distribution of the data, the Mann–Whitney U test was utilized, revealing significant differences (p < 0.001) between stands with and without beetle damage across all indices except TCW(max.). NDVI and NDMI values decreased in damaged stands, whereas MSI and RGI values increased. MANOVA results indicated substantial distinction among groups (Pillai’s Trace = 0.870, p < 0.001), whereas PCA demonstrated significant differentiation, accounting for 75.4% of the total variance. The mean values of NDVI and NDMI showed the greatest discriminatory potential among the indices. In summary, the Landsat 8 vegetation indicators tested in this study showed substantial discriminating potential. Full article