Search Results (265)

Groundwater accounting in arid and semi-arid well-irrigated areas is often constrained by difficulties in water-meter installation and maintenance, as well as variability in well–pump operation, thereby limiting refined agricultural water-use management. The electricity-to-water conversion coefficient (Tc) can be used to estimate groundwater abstraction from electricity consumption; however, the applicability of empirical models developed for plain irrigation districts remains uncertain in plateau regions characterized by pronounced topographic relief and complex aquifer conditions. This study examined 56 typical irrigation wells in Chayouzhongqi on the Inner Mongolia Plateau. Based on pumping-test data and using correlation analysis, structural equation modeling, redundancy analysis, and random forest analysis, we investigated the spatial distribution of Tc and its associated mechanisms. Tc ranged from 0.08 to 3.88 m³ kWh⁻¹, with a mean of 1.62 m³ kWh⁻¹, and exhibited a pattern of higher values in the north, lower values in the south, and the lowest values in the western part of the study area. Electricity consumption, rated flow rate, and actual discharge were the principal associated variables, with relative importance values of 43.0%, 21.6%, and 15.7%, respectively. Topographic and aquifer conditions imposed regional constraints on spatial variation in Tc by influencing well–pump operating states. These findings indicate that Tc estimation in plateau well-irrigation districts should not directly adopt empirical relationships developed for plains, but should instead be calibrated according to regional hydrogeological and engineering operating conditions, thereby providing a basis for improved groundwater accounting and water-saving management in arid and semi-arid regions. Full article

Rapid and reliable landslide mapping from satellite observations is essential for hazard assessment, emergency response, and reservoir-area risk management, yet automatic segmentation remains challenging in mountainous regions because landslide scars are spectrally heterogeneous, terrain-constrained, morphologically irregular, and frequently confused with other exposed surfaces. This study proposes GeoSlide-XMamba, a terrain-conditioned spectral-topographic boundary-aware state-space network for pixel-wise landslide semantic segmentation. The model first separates Sentinel-2 spectral bands and DEM/slope-derived topographic layers into modality-specific branches, integrates them through spectral-topographic adaptive fusion (STAF++), and then performs terrain-conditioned selective state-space scanning in the XMamba bottleneck. Unlike direct token concatenation, the proposed bottleneck uses terrain descriptors to dynamically weight directional selective scan branches so that long-range feature propagation is guided by slope-related morphology. Boundary-aware decoding, signed-distance supervision, and hard-negative mining are further introduced to improve inventory-oriented geometric quality and suppress common false positives. Experiments were conducted on the Landslide4Sense benchmark using 14-channel multispectral-topographic inputs. Among the compared methods, GeoSlide-XMamba achieved the highest validation performance under a unified five-seed protocol, with precision = 0.729, recall = 0.626, F1-score = 0.673, IoU = 0.507, kappa = 0.666, Boundary-F1 = 0.466, and HD95 = 3.45 pixels. Five-seed experiments produced F1 = 0.673 ± 0.003, IoU = 0.507 ± 0.002, Boundary-F1 = 0.466 ± 0.002, and HD95 = 3.45 ± 0.13 pixels, with a 95% CI of [0.670, 0.676] for F1. Relative to the strong 14-channel concatenation baseline, the proposed model improves mean F1 by 0.045 and reduces HD95 by 1.42 pixels. Expanded qualitative inference on Jinsha River patches indicates that the learned spectral-topographic representation transfers plausibly to high-relief reservoir-canyon terrain. These results show that terrain-conditioned state-space modeling can improve both segmentation accuracy and boundary geometry for remote sensing landslide mapping. Full article

Landslides are recognised as one of the most widespread mass-wasting processes that modify the surface of Mars. Understanding the distribution of these processes is essential for identifying areas where slope failure conditions may occur and the factors that most strongly influence their occurrence. This study utilises a Frequency Ratio (FR) landslide susceptibility method to a landslide inventory in Valles Marineris, considering three landslide types. The analysis involves conditioning factors derived from topographic and structural data. The results underline the influential role of morphometric parameters in controlling landslide occurrence, with steep slope classes and high local relief values showing the strongest positive correlations with landslide distribution. The predictive performance of the susceptibility models is supported by Area Under the Curve (AUC) values of 0.82 for Slumps, 0.78 for Rock Avalanches, and 0.75 for Debris Flows, indicating good model reliability. Proximity to tectonic structures appears to contribute to landslide occurrence, suggesting that structurally weakened rock masses or past seismic activity may influence slope instability in the region. Overall, the results display the potential of statistical landslide susceptibility approaches for analysing slope instability processes in planetary environments and provide a new toolkit for future investigations on Mars. Full article

To track land use and land cover transformation in Makkah, techniques that allow steep relief, spectral confusion, and dense sacred–commercial mosaics, and can be justified in terms of planning, should be used. Satellite images are tedious and prone to uneven labeling on mixed-pixel boundaries, particularly in urban regions and Haram borders. Using multi-temporal Landsat-8 data (2013 and 2024), a hybrid deep learning architecture comprising U-Net, DenseNet201, and a Vision Transformer was trained. U-Net retained the geometry of the boundaries, DenseNet201 reinforced feature transfer across heterogeneous textures, and the transformer modeled long-range context. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to incorporate interpretability during spatial attention mapping, and Shapley Additive exPlanations (SHAP) during spectral topographic attribution, after which paired class-level statistical tests were performed. Modern residential increased from 15% to 20% (180 million to 240 million m²); roads from 5% to 10% (60 million to 120 million m²); industrial facilities from 3% to 5% (36 million to 60 million m²). The vegetation expanded by 1 to 5% (an addition of 48 million m²), and agriculture declined by 2 to 1% (a loss of 12 million m²). Its tension with urban development and preservation of productive land was growing. The proposed U-Net–DenseNet201–ViT hybrid system achieved over 98% overall accuracy on the test data for both study years, with kappa coefficients of 0.978 and 0.981 for 2013 and 2024, respectively. Grad-CAM identified attention focused on development fronts and transport corridors, whereas SHAP identified SWIR, thermal response, and slope as the main drivers. Significant class-level gains were statistically validated (p < 0.01), confirming an interpretable and auditable account of land transformation in Makkah. Full article

The position of urban road networks along the spectrum from geometric order to organic growth reflects the long-term tension between institutional planning and adaptive accommodation to local conditions, yet systematic quantification of this continuum in large historical urban datasets remains absent. This study digitizes road network data for 256 Qing Dynasty county cities using GIS methods and proposes a Geometric–Organic Index (GOI), a composite measure formed by equal-weight averaging of four sub-indicators: cross-junction ratio (CrossR), orientation regularity (OrientR), block regularity (BlockR), and connectivity ratio (ConnR). The results show that the GOI follows a continuous unimodal distribution (mean 0.346, median 0.341, n = 220, representing cities for which all four sub-indicators including block regularity could be computed), confirming that the geometric–organic dimension constitutes a continuum rather than a binary classification. Across the 182 cities for which both GOI values and external city wall regularity scores are simultaneously available, wall regularity correlates significantly with road network GOI (Pearson r = 0.320, p < 0.001). Mediation analysis reveals that topographic relief influences road network structure indirectly through wall regularity (Sobel z = −2.15, p = 0.032; Bootstrap 90% CI excludes zero), establishing city walls as morphological templates through which environmental constraints are transmitted to the urban interior. Primal-graph-based syntax-style validation on 173 cities (the 182-city set restricted to n_nodes ≥ 10 and available road files) further shows that GOI correlates significantly with intelligibility (r = 0.403, p < 0.001) and synergy (r = 0.463, p < 0.001), while mean local integration correlates negatively with GOI (r = −0.349), revealing a structural trade-off between global order and local efficiency. The equal-weight GOI scheme proves robust across 624 weighting combinations (Kendall $\tau$ = 0.928 for near-equal-weight combinations), and a global spatial autocorrelation test (Moran’s I = 0.137, p = 0.001) indicates weak spatial clustering without undermining the principal conclusions. This study provides the first large-sample empirical test of the morphological transmission hypothesis linking a city’s outer boundary to its interior road network, and offers a transferable quantitative framework for urban morphological typology. Full article

Against the dual objectives of food security and sustainable agriculture, this study examines how agricultural machinery services—China’s primary organized mode of agricultural production—affect agricultural carbon emissions. Using panel data covering 30 provinces in China from 2010 to 2022, this study applies two-way fixed effects, mediation, and moderation models to investigate the effects of these services on carbon emissions as well as the mechanisms involved. The results show: (1) Both carbon emissions and the level of machinery services in China differ by region and over time. Carbon emissions are stabilizing, while machinery services are steadily improving. Both variables cluster in certain areas. (2) Machinery services exhibit a significant inverted U-shaped impact on carbon emissions. As the level of machinery services grows, emissions first rise, then fall. (3) The emission reduction impact of machinery services varies widely. It differs across topographic relief, farmland types, and grain crop types, but the inverted U-shaped relationship remains in most cases. (4) The efficiency of the division of labor and agricultural chemical input intensity partly explain the effect. They help reduce emissions by enabling labor substitution and lower input levels. (5) Large-scale agricultural operations strongly influence how machinery services affect carbon emissions. To accelerate the low-carbon sustainable transformation of Chinese agriculture, efforts should prioritize establishing a differentiated, regionally tailored agricultural machinery socialized service system, improving service efficiency and green development capacity, and optimizing large-scale land management structures. Full article

Landslides constitute a globally pervasive and highly destructive natural hazard. Although artificial intelligence (AI)-driven landslide susceptibility mapping has emerged as an effective tool for delineating high-risk zones, its predictive performance is frequently constrained by inherent data noise and insufficient characterization of landslide triggering factors, restricting the credibility of the mapping results. In this study, to remedy this limitation, we adopt fractal analysis to extract latent inherent information from topographic features. Specifically, the box-counting method and multifractal analysis are applied to excavate the intrinsic nonlinear characteristics embedded in eight topographic factors, and an improved K-means algorithm is utilized to perform feature selection and construct a dedicated fractal feature dataset, which is fed to advanced AI models. Our results indicate that the information dimension ($D_1$) of the slope gradient, the correlation dimension ($D_2$) of aspect, land relief, the $D_2$ of roughness, the $D_2$ of plan curvature, the multifractal spectrum width ($∆\mathsf{\alpha }$) of profile curvature, the $D_2$ of elevation, and the surface cutting depth were the most effective features, demonstrating superior performance in capturing landslide targets. Comparative performance evaluations reveal that AI models trained on fractal features demonstrate substantially superior predictive capabilities compared to AI models trained on raw features. This superiority is consistently evidenced across key evaluation metrics, including overall accuracy, kappa coefficient, F1-score, and predictive efficiency, demonstrating that the integration of fractal characteristics significantly augments model robustness and predictive efficacy. To mitigate the ‘black-box’ problem of AI modeling, Shapley additive explanations were employed to quantify individual feature contributions and elucidate the underlying predictive mechanisms. Our findings indicate that the integration of fractal analysis yields highly discriminative and robust feature representations, thereby expanding the representational capacity of the models and improving predictive accuracy. Furthermore, a joint assessment of spatial uncertainty and susceptibility maps demonstrates that these models exhibit low predictive variance and high spatial stability when delineating high-susceptibility zones. Notably, models utilizing fractal-derived features achieve superior spatial capture efficiency. The resultant topographic features characterized by fractal representation and selected via the improved K-means algorithm can significantly improve the predictive performance of trained AI models in landslide susceptibility mapping tasks, offering a scientific and viable technical approach for future landslide prediction and prevention. Full article

Hill-shading, also referred to as relief shading, is an important visualization method in small- and medium-scale topographic mapping. Although analytical hill-shading methods offer high generation efficiency, they often suffer from terrain detail overload. Some deep learning generative models have introduced terrain priors, but they still have deficiencies in the controllable representation and reasonable simplification of terrain structures. This study proposes a Swiss-style hill-shading method based on hierarchical terrain structure lines. By introducing ridgelines with hierarchical semantics as priors, a synergistic regulation mechanism of enhancement and suppression is constructed to emphasize major terrain structures while moderately weakening secondary details. The model adopts a hybrid architecture integrating a Transformer encoder and a convolutional decoder to model global terrain structures. In the testing phase, by adjusting the combinations of different levels of ridgelines, the controllable adjustment of the level of detail and structural significance in hill-shading representation is realized. Multi-region experimental results show that the proposed method can generate hill-shading results with clear structural hierarchies and distinct primary–secondary relationships. Compared with other models, the proposed method demonstrates more stable performance in achieving reasonable hierarchical representation of terrain structures and detail simplification, approaching the visual style of Swiss-style manual hill-shading. Full article

Worldwide, flash floods are among the most unpredictable and hazardous hydrological phenomena, particularly in arid and semi-arid regions such as the Kingdom of Saudi Arabia, where sudden heavy rainfall follows prolonged periods of drought. This work presents an effective integrated model for flood hazard evaluation in the Hanefah Catchment, a socioeconomically vital area in the central part of Saudi Arabia that includes the capital city, Riyadh. Using high-resolution ALOS PALSAR 12.5 m Digital Elevation Model spatial data, we extracted and investigated indicative linear, areal, and relief morphometric keys of 64 sub-catchments. This paper employs a dual-method concept that integrates a multi-criteria ranking method and the El-Shamy approach in conjunction with morphotectonic analysis to model flood-susceptibility zones. Furthermore, this paper suggests a comparative assessment of low-cost morphometric models under data-scarce conditions, assessing the multi-criteria ranking method against El-Shamy’s approach, using the topographic position index (TPI) as an internal terrain scale benchmark. The ranking method successfully assigned 85.7% of the historically recorded flood locations to the high-hazard zone that covers ~24.22% of the Hanefah catchment. In contrast, the El-Shamy approach systematically underestimated flood susceptibility because regional tectonic activity increases bifurcation ratios, resulting in just ~42.9% of the historical floods being assigned to the high-hazard zone. The final results highlight the northern and northwestern parts of the catchment as high-hazard zones, characterized by high drainage density and steep relief. This study provides a refined, cost-effective model that aligns with the strategic objectives of Saudi Vision 2030 for sustainable water resources management and significant urban development. Full article

Karst regions are characterized by fragmented topography and significant micro-relief mosaics, leading to prominent spectral aliasing of land features, which can result in insufficient spatial representativeness of remote sensing samples for Land Use and Land Cover (LULC). The accuracy of LULC data directly affects the scientific basis of decision-making for rocky desertification control and ecological conservation. This study selected the Beipanjiang River Basin in Guizhou Province, a typical karst region, as the study area. The study selected the SOS, LOS, OM, and EOS indices from the 2001–2020 MODIS MCD12Q2 phenological dataset, combined with topographic zoning data. This study developed a sample spatial optimization scheme for complex karst terrain by integrating Spearman’s correlation analysis, SKATER spatially constrained clustering, statistical tests, adaptive stratified sampling, and Random Forest classification. The scheme was designed to test a phenology–landform joint stratification strategy for spatial sample allocation. The results indicate that (1) the study area was divided into six phenological pattern subregions, with significant spatial differentiation observed among them; (2) the “phenology–landform joint stratification + dual-weighted sample allocation” method was associated with improved sample representativeness and greater internal homogeneity within sample strata under the current experimental setting; and (3) compared to simple random sampling, the remote sensing phenological pattern-driven spatial optimization scheme improved overall accuracy from 71.33% to 77.55% and increased the Kappa coefficient from 0.43 to 0.62. These results suggest that, under the current study-area, sample-size, and validation settings, the phenology–landform joint stratification and dual-weighted allocation scheme can improve the spatial organization of training samples and classification performance over complex karst terrain, although weakly vegetated or bare classes remain difficult to separate. Full article

Gully erosion is a major driver of irreversible soil loss in Northeast China’s Mollisol belt, a region that supplies roughly one-quarter of the national grain output. Existing susceptibility assessments in this region have rarely combined multi-model comparison with spatially explicit cross-validation, and the predictive contribution of composite anthropogenic indicators such as the Human Footprint Index (HFI) has not been quantitatively benchmarked against conventional topographic variables. This study addresses these gaps for the Heihe region by combining an inventory of 4020 gully polygons supported by field checks in Xunke County, 16 VIF-screened environmental factors, three tree-based ensemble models and a logistic regression baseline. Under stratified random splitting, XGBoost achieved the highest discrimination (AUC = 0.95, $\kappa$ = 0.74); under leave-one-district-out spatial cross-validation all tree-based models retained AUC above 0.83, confirming that random-split metrics overestimate discrimination by approximately 0.11 AUC units due to spatial autocorrelation and inter-district covariate shift. SHAP analysis identified LULC and HFI as the dominant predictors, exceeding all topographic variables, while slope gradient contributed least—consistent with the low-relief, intensively cultivated character of the study area. Susceptibility was highest in the southwestern agricultural lowlands. A one-factor sensitivity test in which only NDVI was increased by 20% suggested a reduction in modelled high-susceptibility area of approximately 12%, although co-occurring land-cover and hydrological changes were not simulated. The multi-model framework, integrating spatial cross-validation and post hoc interpretability, provides an explicit estimate of conventional evaluation optimism and supports spatially differentiated erosion management. Full article

Snow avalanches are complex geomorphological processes that represent major natural hazards in mountain environments and result from the interaction between topographic, climatic, and human factors. This study represents the first attempt in Romania to analyze the role and contribution of these controlling factors in triggering avalanches within Bucegi Natural Park (Southern Carpathians). Two distinct sectors were identified based on their geographical characteristics. The High Glacial Sector, located in the northern part of the park, exceeds 2400–2500 m a.s.l. and is characterized by steep slopes, rugged terrain, and harsh climatic conditions, and hosts ski touring and mountaineering activity. The second sector, the Sinaia ski area, lies between 1400 and 2100 m a.s.l., with cuesta relief and milder climate, where human influence is more pronounced in the form of ski touring and off-piste skiing. This study aims to analyze avalanche-influencing factors; identify differences between the two sectors; and evaluate risk management measures. The methodology integrates morphometric analysis, correlation of terrain attributes, hazard classification, and statistical analysis of avalanche events using mountain rescue data and climatic variables. The results indicate that avalanches in the high-altitude mountain sector are mainly controlled by natural factors, while in the Sinaia ski area, they are often triggered by human activity. Risk management measures remain limited and unevenly distributed. Full article

Cultivated Land “Non-grain” Rectification is reshaping crop allocation across China, yet whether the policy promotes or impedes agricultural growth remains contested. This paper argues that the same uniform regulation generates spatially heterogeneous outcomes along a continuous topographic relief: strict enforcement on contiguous plain farmland raises compliance costs for horticultural production and displaces it toward higher-elevation counties, where land-use rules bind less tightly and micro-climates favor cash crops. Using a panel of 2077 Chinese counties from 2019 to 2023, we construct a municipal-level measure of rectification intensity from government work reports and examine how its effect varies with county-level terrain relief. The results show that the marginal effect of policy intensity on agricultural value added rises monotonically with terrain, turning from negative in flat plains to increasingly positive beyond 0.5–1.0 km of relief; at the sample mean a one-standard-deviation increase in policy intensity raises agricultural value added by about 0.36 percent, and at 2 km of relief by 1.16 percent. The mechanism is spatial reallocation, not land expansion. Rectification shrinks horticultural area in plains and expands it in mountains. A Moran’s I test confirms this: counties with very different terrain show opposite changes in orchard cover. Further heterogeneity tests indicate that rectification primarily promotes the relocation and expansion of fruit orchards toward higher-relief counties. The growth effect is stronger where transport networks are denser, whereas water endowment does not significantly moderate the effect. Results are robust to alternative keyword classifications, concurrent-policy controls, and two instrumental-variable strategies. Full article

Accurate Land Use and Land Cover (LULC) mapping in high-latitude mountain regions faces critical challenges from persistent cloud cover and complex topography, which limit the utility of passive optical sensors. To address the absence of evidence-based guidelines for these data-scarce environments, this study employs a systematic ablation design to quantify the marginal and synergistic contributions of optical data (Sentinel-2), Synthetic Aperture Radar (Sentinel-1 SAR), topography, and intra-seasonal phenological metrics within the Aysén River basin, Chilean Patagonia, developing a geospatial workflow with high transferability potential. Using a Random Forest classifier, five progressive configurations were compared: a seasonal optical baseline (A), and configurations incorporating intra-seasonal percentiles (A + P), topography (A + T), SAR (A + R), and their full integration (A + P + T + R). The baseline model achieved an Overall Accuracy (OA) of 89.2% and a Macro-F1 of 80.5%; the fully integrated model reached OA = 92.5% and Macro-F1 = 86.0%. Macro-F1 was adopted as the primary metric because it assigns equal weight to all 11 classes regardless of spatial prevalence, capturing gains in minority but ecologically critical classes that OA would mask. SAR and topographic variables were the largest contributors, generating non-redundant improvements in structurally complex and relief-conditioned classes, respectively. Furthermore, annual SAR composites demonstrated superior cartographic spatial consistency over seasonal aggregations, which introduced purely cartographic geometric artifacts at class ecotones despite achieving marginally higher point-based statistical metrics, a divergence explained by the spatial blindness of confusion-matrix validation to boundary-zone classification errors. Full article

Digital elevation models (DEMs) are key fundamental inputs in hydrological modelling, yet the influence of spatial resolution on basin delineation and process representation remains insufficiently understood, particularly in small catchments. This study investigates the influence of DEM resolution on topographic characterization and hydrological response in a small agroforestry basin in central Portugal. Three DEMs with resolutions of 5 m, 10 m, and 30 m were generated from contour data and satellite sources and processed using the TOPAZ-based TopAGNPS delineation framework. The sensitivity of basin structure to delineation parameters—critical source area (CSA) and minimum source channel length (MSCL)—was assessed, and the resulting configurations were used as inputs to the AnnAGNPS model. Results show that DEM resolution strongly influences the representation of hydrological cells and stream reaches. Increasing resolution from 30 m to 5 m leads to a nearly doubling of average cell slope and increases reach slope by more than four times, with corresponding changes in drainage network density and connectivity. Log-linear relationships were identified between slope and contributing area, as well as between slope and reach length, consistent with established geomorphic scaling laws. Hydrological simulations further indicate that resolution-dependent delineation significantly influences runoff, erosion, and peak discharge estimates, with finer resolutions increasing sensitivity to parametrization. Among land-cover scenarios, desertified conditions generate substantially higher runoff and peak flows compared to naturalized and forested conditions. Overall, the findings demonstrate that DEM resolution, together with preprocessing and delineation choices, exerts a critical control on hydrological model outputs. These effects are particularly pronounced in low-relief, human-influenced catchments, where coarse-resolution DEMs may lead to systematic underestimation of hydrological responses. The study highlights the need for resolution-aware modelling strategies and careful parametrization to improve the reliability and transferability of hydrological simulations. Full article