Search Results (320)

This study examines the influence of oil rents on territorial dynamics and urban expansion in Maricá (Rio de Janeiro, Brazil) between 2012 and 2024, grounded in debates on the resource curse and extractive economies. It investigates the relationship between oil-driven fiscal expansion and land-use change using spatial analysis and Land Change Modeler (LCM) techniques. Results show sustained, predominantly horizontal urban growth, mainly through the conversion of anthropized land, especially Mosaic of Uses and Pasture classes. Between 2012 and 2024, the Urban Area increased from 53.31 km² to 74.17 km², corresponding to a net expansion of 20.86 km² (39.14%). Expansion follows a spatially selective pattern linked to accessibility, land availability, and public investments financed by oil revenues. Although direct causality cannot be fully isolated, the temporal association between rising oil revenues and accelerated urbanization indicates that the expansion of municipal fiscal capacity occurred contemporaneously with significant territorial transformations. Even when occurring over altered land, urban expansion generates indirect pressures on environmentally sensitive areas, reinforcing the need to integrate extractive revenues into long-term territorial planning. In the context of the energy transition, these findings highlight the territorial and sustainability challenges associated with continued dependence on fossil-fuel revenues and underscore the importance of integrating low-carbon development perspectives into long-term territorial planning. Full article

Accurate mapping of rice cropping patterns is fundamental to sustainable agricultural management and regional food security assessment. In this study, we developed a high-precision mapping framework for major rice cropping patterns in the Yangtze River Delta region, China, using the Google Earth Engine (GEE) cloud platform. A multi-temporal and multi-source feature set was constructed by integrating Sentinel-1 radar backscatter (VV/VH polarization), Sentinel-2 optical indices, including the normalized difference vegetation index (NDVI) and land surface water index (LSWI), and topographic factors (DEM and slope), to classify three predominant cropping systems, namely, wheat–rice rotation, double rice, and rapeseed–rice rotation. A total of 548 field-surveyed sample points collected during the 2024–2025 growing season were used for model training and validation. Three classical classifiers—Random Forest (RF), A Gradient Boosting Tree (GBTREE), and a Support Vector Machine (SVM)—were systematically compared. The ablation experiment demonstrated that the fusion of Sentinel-1 and Sentinel-2 outperformed both the Sentinel-1-only and Sentinel-2-only configurations across all three classifiers. Among them, the GBTREE achieved the highest overall accuracy (93.8%), Kappa coefficient (0.87), and macro-average F1 score (88.3%) in this specific experiment. Notably, it also performed best on the more challenging double-rice class. The SHapley Additive exPlanations (SHAP)-based feature importance analysis revealed that multi-temporal NDVI phenological features were the primary drivers of classification accuracy, while radar backscatter and water indices provided essential complementary information, and topographic factors served as spatial constraints at the regional scale. The spatial distribution derived from the GBTREE classification exhibited clear patterns: wheat–rice rotation dominated the northern plains (northern Jiangsu, northern Anhui, and the Hangjiahu Plain); double rice was concentrated in the southern Zhejiang hills and scattered valley plains; and rapeseed–rice rotation showed a scattered, mosaic distribution. Overall, this study demonstrates that integrating multi-source remote sensing data on the GEE platform with the GBTREE classifier enables effective and scalable high-precision mapping of rice cropping patterns in complex agricultural landscapes. This approach provides a reliable technical foundation for regional agricultural structure analysis, crop rotation assessment, and sustainable agricultural monitoring. Full article

Tropical protected areas face pressures from smallholder agriculture and illegal mining, yet many monitoring approaches reduce these dynamics to net forest loss, obscuring whether change is alternating or persistent. In the Okapi Wildlife Reserve, Democratic Republic of the Congo, where the zoning plan allows only nonperennial crops in agriculture zones and forbids mining throughout the reserve, we analyzed annual Joint Research Centre–Tropical Moist Forest maps from 2000 to 2024 at 30 m spatial resolution. We applied Trajectory Analysis and DynamicPATCH across three study zones: agriculture, mining, and hunting–conservation. Trajectory Analysis classifies each pixel’s twenty-five-year non-forest time series into categories such as Gain without Alternation and Gain with Alternation, distinguishing persistent change from alternating change. Meanwhile, DynamicPATCH decomposes annual differences into eight patch-transition types. Trajectory Analysis summarizes each zone’s change into components of Quantity, Exchange, and Alternation, expressed as an annual percentage of each zone’s unified size to enable comparison among zones of various extents. The Quantity component most clearly distinguishes the zones as it accounts for 45% of the change in the agriculture zone, 98% of the change in the mining zone, and 27% of the change in the hunting–conservation zone. The agriculture study zone is characterized by many alternating trajectories and frequent patch transitions, but after 2022 agriculture evolves toward Gain without Alternation and more expansive transitions, suggesting a move from rotational mosaics toward more persistent non-forest, partly associated with prohibited perennial crops. The mining study zone shows consistent dominance of Gain without Alternation and Expanding or Merging transitions, reflecting progressive, and recently accelerating, expansion of illegal mining fronts. The hunting–conservation study zone remains largely stable, with only sparse and low-magnitude changes. This combined approach distinguishes land-use pattern signatures, shows that full annual series are essential for capturing patch dynamics that endpoint analyses miss, and provides actionable insight into where zoning rules are being followed, stretched, or broken. Full article

Land-cover (LC) and land-use (LU) mapping is essential for environmental monitoring, yet supervised multi-decadal classification remains constrained by heterogeneous agroforestry mosaics, uneven observation quality, and limited consistent reference data. This study presents a Landsat-based workflow for robust multi-temporal LC/LU classification, combining quality-focused preprocessing, radiometric harmonization, predictor-set evaluation, and Random Forest (RF) hyperparameter-stability assessment. Implemented in south-central Chile, the workflow combines masks with a locally calibrated cloud–snow index and applies per-band histogram matching for color balancing. It also tests progressive predictor designs (seasonal spectral bands, spectral indices, and territorial variables) under sample scarcity. Classification used Google Earth Engine and an RF grid search over hyperparameter ranges. Performance was evaluated through validation-kappa (κ) distributions and a hyperparameter-dispersion metric assessing accuracy and stability. Color balancing improved cross-period consistency and can partly offset the absence of spectral indices, but high performance was achieved only with the full predictor set, even under limited observations. High-performing treatments showed lower hyperparameter dispersion, supporting model selection that jointly considers accuracy and stability rather than one tuned configuration. Under limited reference data, classification depends more on predictor design and robustness-based selection than on increasing sample size alone. Full article

Synthetic Aperture Radar (SAR) backscatter serves as a key tool for tracking surface water dynamics; however, single-source data dependencies introduce systematic bias tied to the specific physical limitations of the signal. A primary challenge in SAR analysis is the backscatter ambiguity created by ‘water look-alike’ surfaces, which frequently result in false-positive water detections. We show that integrating interferometric repeat-pass coherence significantly enhances the robustness of hydrological mapping in environments where backscatter is prone to signal ambiguity. Using global-scale C-band Sentinel-1 (S1) VV-polarized one-year mosaics (December 2019 to November 2020), we first analyzed normalized backscatter and coherence signatures across major land cover and land use (LULC) classes. To benchmark the complementary value of these data streams, a tile-based minimum-error thresholding approach was applied to detect permanent water surfaces across five challenging global test sites. This evaluation was conducted without post-processing or masking to isolate the fundamental strengths of each dataset. The results indicate that coherence is an optimal complement to backscatter in arid and bare soil regions, where it vastly outperforms backscatter in mapping inland water surfaces. Crucially, since the spatial overlap of False Positives and False Negatives between datasets is minimal, the inherent complementarity of the datasets is proven here via a logical AND fusion rule, which significantly mitigates commission errors and yields substantial improvements in the aggregated F1-score and IoU performance. Analysis-ready L-band NISAR products could contribute to a more comprehensive approach for operational, large-scale surface water assessments. Full article

Ecological sensitivity zoning is widely used in conservation planning, yet the interaction between land-use structure and landscape ecological risk across different sensitivity levels remains underexplored. We propose a dual-coupling framework—integrating (i) land-use composition and (ii) landscape patterns with ecological sensitivity—to assess landscape ecological risk. The Analytic Hierarchy Process (AHP) weighted sensitivity factors across five units (Levels 1, 3, 5, 7, and 9). The entropy weight method derived the Landscape Ecological Risk Index (LERI), while ridge regression and partial least squares regression (PLSR) identified key explanatory land-use variables. When land use is included in the sensitivity zoning, unexpectedly, the low-sensitivity zone (Level 3) exhibited the highest LERI (0.7588), whereas the non-sensitive zone (Level 1) recorded the lowest (0.1732). Ridge regression at the sensitivity-level scale revealed grassland (β = 0.255, VIP = 1.596) as the strongest positive correlate of LERI. Construction land showed a negative association at this scale (β = −0.433, VIP = 1.299). Grid-scale validation, however, indicated that the construction-land relationship is scale dependent, with grassland remaining the only factor consistently positive across both scales. PLSR validated these sensitivity-level trends, with grassland ranking highest in VIP across both models. Mechanistically, low-sensitivity zones feature a mixed mosaic of cropland, grassland, and construction land, intensifying landscape fragmentation. Conversely, non-sensitive zones, dominated by uniform construction land, exhibit landscape homogenization and correspondingly lower risk. This study challenges the traditional assumption that high ecological sensitivity inherently dictates high risk, emphasizing that land-use-driven landscape fragmentation in low-sensitivity zones warrants priority in ecological risk assessments. Full article

The Lesser Grey Shrike Lanius minor is a migratory passerine whose steep decline has been linked to agricultural intensification and the loss of crop heterogeneity. Using 15 years of data (2010–2024), we sought the optimal crop type composition capable of boosting the breeding success (number of young fledged per pair) of the Lesser Grey Shrike population dwelling in province of Parma (northern Italy). We found two optimal crop type compositions: (a) heterogeneous pattern (35% of the buffer surface at alfalfa, 7% at fallow land, 12% at soybean, 46% at wheat; expected breeding success = 5.38); and (b) homogeneous pattern (70% of the buffer surface at alfalfa, 6% at fallow land, 12% at soybean, 12% at wheat; expected breeding success = 5.21). Since effective conservation of the Lesser Grey Shrike relies on targeted agri-environment payments that maintain the low-intensity farmland mosaics essential for breeding and foraging, our study delivers sharp knowledge of the requirements of this species with regard to the optimal crop type compositions that lead to an elevated breeding success. Full article

Ensuring the long-term sustainable utilisation of supplemented cropland has become a critical challenge for implementing China’s requisition–compensation balance of farmland (RCBF) policy, particularly in the fragmented hilly and mountainous regions of Southwest China. Existing studies generally evaluate land suitability and utilisation performance separately, making it difficult to identify mismatches between theoretical suitability and actual utilisation and thereby limiting targeted regulation. To address this limitation, this study proposes a suitability–utilisation deviation identification (SUDI) framework, which integrates four sequential analytical components: three-dimensional suitability assessment, suitability–utilisation deviation identification, driving mechanism analysis, and sustainable regulation. Taking Beibei District of Chongqing as a case study, supplemented cropland parcels were identified using the 2020–2024 land change survey data. A three-dimensional suitability evaluation system incorporating production, ecological, and utilisation attributes was established to quantify theoretical land suitability. Actual utilisation performance was characterised using the land economic utilisation coefficient, and suitability–utilisation deviation was identified through residual analysis between theoretical suitability and utilisation intensity. A Bayesian-optimised Extreme Gradient Boosting-SHAP (XGBoost-SHAP) model was subsequently employed to reveal the nonlinear effects and interaction mechanisms of the driving factors. The results indicate the following: (1) supplemented cropland in Beibei District is predominantly characterised by medium-to-high suitability, with high-suitability patches exhibiting a mosaic spatial pattern of local aggregation and overall dispersion; (2) suitability–utilisation deviation is dominated by under-utilised plots, whereas well-matched and over-intensified plots account for substantially smaller proportions, indicating that insufficient realisation of land suitability is the prevailing utilisation pattern; and (3) the land economic utilisation coefficient is the dominant factor driving suitability–utilisation deviation, while high-standard farmland construction and plot area exhibit significant mitigating effects. Moreover, significant interaction effects between utilisation intensity and location-related variables reveal that unfavourable spatial conditions amplify deviation risk under intensive land use. The proposed SUDI framework extends conventional suitability assessment by explicitly linking suitability evaluation with utilisation performance, driving mechanism analysis, and differentiated regulation. It provides a transferable analytical framework for diagnosing suitability–utilisation mismatch and supports dynamic management and sustainable utilisation of supplemented cropland in fragmented hilly and mountainous regions. Full article

Anthropogenic land use constitutes a key driver of land cover change, exerting profound impacts on terrestrial carbon/water cycles and ecosystems across global to regional scales. Consequently, long-term land cover datasets serve as fundamental data infrastructure for ecological effect assessment and paleoclimate modeling. Considering the dominant role of historical anthropogenic disturbances in vegetation change across the Hehuang Valley, we developed a 1 km × 1 km potential vegetation prior to land reclamation map through integrating remotely sensed land-use patterns with Random Forest-modeled vegetation predictions. Then, we derived changes in forest and grassland areas and their spatial patterns over the past two millennia by subtracting the spatially explicit cropland cover for six agricultural expansion stages. Finally, we compared our results with representative historical LUCC datasets. The main conclusions are as follows: (1) The potential vegetation of the Hehuang Valley was predominantly grassland, with forest occurring as a patchy and linear mosaic. Grassland and forest covered approximately 2.6 × 10⁴ km² and 0.7 × 10⁴ km², respectively. (2) Over the past two millennia, during the Han (202 BCE–220 CE), Tang (618–907 CE), Song (960–1279 CE), Ming (1368–1644 CE), and Qing (1644–1912 CE) dynasties, as well as the Republic of China period (1912–1949 CE), cropland reclamation reduced forest and grassland cover by 6% and 18%, respectively. Forest area decreased by 23.58–100.03 km², while grassland area decreased by 310.72–1513.05 km² across these periods. Grassland reduction was concentrated in valleys, whereas forest reduction was spatially scattered but locally intensive. These findings highlight the need to promote sustainable cropland development through technological innovation, improved management, and agricultural specialization rather than ecosystem conversion. (3) Compared with the HYDE 3.2 dataset and a national-scale reconstruction dataset, our framework better reflects the regional characteristics of the Hehuang Valley and provides a more detailed reconstruction of long-term forest and grassland changes. Full article

Transposable elements (TEs) are rarely used as phylogenetic markers because of their high copy number, frequent recombination, and potential for horizontal transfer. However, their long-term coexistence with host genomes suggests that some TE lineages may preserve information about the evolutionary history of their genomic environment. Here, we investigate whether Tat LTR retrotransposons of the Ty3/Gypsy superfamily retain a phylogenetic signal informative for deep plant evolution. We reconstructed the phylogeny of Tat reverse transcriptase domains among representative lineages of land plants, including bryophytes, lycophytes, ferns, gymnosperms, and basal angiosperms. The analysis revealed stable clusters characterized by both structural specificity, determined by the position of the additional ribonuclease H domain, and taxonomic specificity. In many cases, the Tat subclusters reproduced established host relationships at the genus and family levels, particularly within conifers, indicating a predominantly vertical mode of inheritance. The distribution of Tat lineages also preserved signals relevant to unresolved questions of plant phylogeny. Among seed plants, different Tat lineages reflected aspects of existing alternative hypotheses, including the association of gnetophytes both with conifers II and angiosperms. Interestingly, the strong separation between two major conifer groups, pines and cypress with yews, was observed. Among non-seed plants, the topology of Tat lineages highlighted the unique position of lycophytes by the preservation of multiple ancient transposon lineages associated with early diversification after aRNH domain acquisition. It also suggested that the origins of mosses and hornworts involved different patterns of lineage elimination from a common ancestor that carried all structures found in lycophytes. We propose a conceptual framework in which TE clusters are interpreted as collections of partially independent evolutionary lineages rather than as a single species tree. Under this view, Tat retrotransposons provide an additional layer of phylogenetic information that complements conventional molecular markers and reflects the mosaic nature of plant genome evolution. The present work, therefore, represents the first implementation of this approach rather than its final methodological form. Full article

Agricultural intensification in the steppe zone of Central Asia has increasingly exacerbated the tension between food security and the conservation of natural ecosystems. This study proposes an integrated landscape planning methodology for Kazakhstan by adapting the German Landschaftsplanung approach and the Chinese concept of “ecological red lines” to the conditions of post-Soviet land use. The focus is on the analysis of soil degradation and biodiversity loss. It is applied to the Ayyrtau district of the North Kazakhstan region, an area characterized by a heterogeneous landscape mosaic composed of arable land, a substantial share of degraded land, vulnerable steppe ecosystems, and woodlands. Using GIS analysis and remote sensing data, we identify nine types of landscape units, which are operational units for evaluating landscape functions and planning priorities. The result of this work is a map of the zoning of the planning area, which defines seven modes of eco-oriented management, ranging from strict protection to active agricultural production. The study demonstrates that the transition from an extensive monocultural system to a landscape-adaptive strategy can improve the spatial coordination between agricultural use and ecological protection, strengthen the regional ecological framework, and enhance ecotourism potential. For the first time, an integrated zoning system is presented, designed for use by local authorities as a decision support tool aimed at preventing land degradation. Full article

Disentangling the effects of climate variability and forest management on catchment hydrology remains a major challenge in temperate plantation landscapes. We analyzed 21 hydrological years (1997/98–2017/18) of precipitation and runoff records from two experimental catchments in the Chilean Coastal Range with contrasting silvicultural histories to characterize changes in low-flow behavior. Hydroclimatic and low-flow indices were evaluated using the monotonic trends test, Sen’s slope estimates, change point detection, and standardized inter-catchment anomaly differences. Annual and seasonal precipitation indices, rainfall frequency, and maximum dry-spell duration showed no significant monotonic trends, whereas maximum 5-day precipitation declined at LP. The two catchments nevertheless exhibited divergent low-flow trajectories. Los Pinos, managed through partial harvesting and thinning within a forest mosaic, showed decreasing normalized low-flow availability and longer low-flow exposure during the latter part of the record. La Reina, clearcut in 1999/2000 and subsequently reforested, showed a progressive increase in low-flow magnitude and normalized low-flow availability, together with declining flow variability and fewer below-threshold events. Standardized inter-catchment comparisons confirmed a temporal divergence in low-flow behavior. They also revealed a concurrent shift in inter-catchment precipitation anomalies. These results indicate contrasting long-term hydrological trajectories that are consistent with differences in forest management histories; however, the non-paired study design, limited pre-harvest observations at La Reina, and differential precipitation forcing preclude formal attribution to silvicultural effects alone. This study highlights the value of long-term experimental catchments for evaluating low-flow dynamics under interacting climatic and land-management influences. Full article

Accurate land cover mapping is a fundamental tool for environmental management and ecosystem monitoring. This study presents a comparative evaluation of four deep learning architectures, namely a Convolutional Neural Network (CNN), UNet++, ConvNeXt and Vision Transformer (ViT), for land cover classification into five primary classes: water, cropland, forest, low/natural vegetation and built-up. The broader Lake Kerkini basin was selected as the primary training and evaluation area. The multispectral input data were generated through Google Earth Engine and consisted of multi-seasonal Sentinel-2 composite mosaics for the 2021 mapping year, covering winter, spring, summer and autumn. To obtain a more reliable performance estimate and mitigate the effects of spatial autocorrelation, a four-fold spatial cross-validation approach was implemented. Under this spatial validation framework, the convolution-based architectures achieved the strongest performance. CNN obtained the highest numerical fold-mean performance, with an overall accuracy of 81.53% and a macro-averaged F1 score (Macro-F1) of 80.09%, followed closely by UNet++ and ConvNeXt. Non-parametric repeated-measures statistical testing indicated a significant overall architecture effect, with CNN, UNet++ and ConvNeXt showing broadly comparable fold-level Macro-F1 performance, while the tested ViT configuration trained from scratch ranked last across all spatial folds. Regional transferability was further evaluated in the nearby independent Lake Doirani region, where the convolutional architectures, particularly CNN and UNet++, showed strong agreement with the WorldCover-derived reference labels without fine-tuning. Finally, feature-importance analysis indicated that specific spectral-seasonal channels, especially the Blue band (B2) in winter and the Short-Wave Infrared band (B12) in summer, were consistently influential in the models’ predictions. Overall, under the tested 2021 Mediterranean case-study conditions, the results highlight the importance of spatially rigorous validation and show that the evaluated convolution-based configurations achieved stronger performance than the tested ViT configuration trained from scratch. Full article

Traditional grazing maintains essential ecosystem services, yet this activity is rapidly disappearing across Europe. Understanding the geobotanical features of traditionally grazed areas is critical for predicting biodiversity shifts driven by pastoral decline. This study provides a geobotanical characterisation of traditional sheep farms in north-western Spain. We integrated bioclimatic, phytosociological, and biogeographical approaches with spatial autocorrelation analyses, including global Moran’s I, Local Indicators of Spatial Association (LISA), and join-count tests, to assess spatial patterns in vegetation richness and plant community organisation. The results indicate that 28.22% of the studied farms were located in the Castilian Duero sector, 93.45% within the supramediterranean thermotype, and 75.46% within the subhumid ombrotype. A high diversity of vegetation was recorded, with 111 plant communities identified. These include several priority habitats of community interest within the European Union, notably belonging to the phytosociological classes Molinio-Arrhenatheretea, Festuco-Brometea, and Poetea bulbosae. This spatial approach characterises the vegetation mosaics within a fixed buffer around the holdings, although it does not directly measure actual forage use. As a key scientific novelty, this work provides, for the first time, a macro-regional and quantitatively validated integration that explicitly links broad environmental filters with localized pastoral vegetation mosaics. By providing a statistically robust diagnosis of landscape aggregation and segregation, this geobotanical characterisation serves as a fundamental tool for land managers and shepherds, contributing directly to the conservation and sustainable management of endangered traditional pastoral landscapes under changing environmental conditions. 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