Search Results (290)

African hydrological systems are incredibly complex and highly sensitive to climate variability. This review synthesizes observational data, remote sensing, and climate modeling to understand the interactions between fluvial processes, water cycle dynamics, and anthropogenic pressures. Currently, these systems are experiencing accelerating warming (+0.3 °C/decade), leading to more intense hydrological extremes and regionally varied responses. For example, East Africa has shown reversed temperature–moisture correlations since the Holocene onset, while West African rivers demonstrate nonlinear runoff sensitivity (a threefold reduction per unit decline in rainfall). Land-use and land-cover changes (LULCC) are as impactful as climate change, with analysis from 1959–2014 revealing extensive conversion of primary non-forest land and a more than sixfold increase in the intensity of pastureland expansion by the early 21st century. Future projections, exemplified by studies in basins like Ethiopia’s Gilgel Gibe and Ghana’s Vea, indicate escalating aridity with significant reductions in surface runoff and groundwater recharge, increasing aquifer stress. These findings underscore the need for integrated adaptation strategies that leverage remote sensing, nature-based solutions, and transboundary governance to build resilient water futures across Africa’s diverse basins. Full article

Corporate environmental claims often neglect the substantial ecological impact of land-use changes. This case study examines the spatial dimension of retail-driven land-use transformation by analyzing supermarket expansion in the Veneto region (northern Italy), with a focus on a large grocery retailer. We evaluated its corporate environmental claims by assessing land consumption patterns from 1983 to 2024 using Geographic Information Systems (GIS). The GIS-based methodology involved geocoding 113 Points of Sale (POS—individual retail outlets), performing photo-interpretation of historical aerial imagery, and classifying land-cover types prior to construction. We applied spatial metrics such as total converted surface area, land-cover class frequency across eight categories (e.g., agricultural, herbaceous, arboreal), and the average linear distance between afforestation sites and POS developed on previously rural land. Our findings reveal that 65.97% of the total land converted for Points of Sale development occurred in rural areas, primarily agricultural and herbaceous lands. These landscapes play a critical role in supporting urban biodiversity and providing essential ecosystem services, which are increasingly threatened by unchecked land conversion. While the corporate sustainability reports and marketing strategies emphasize afforestation efforts under their “We Love Nature” initiative, our spatial analysis uncovers no evidence of actual land-use conversion. Additionally, reforestation activities are located an average of 40.75 km from converted sites, undermining their role as effective compensatory measures. These findings raise concerns about selective disclosure and greenwashing, driving the need for more comprehensive and transparent corporate sustainability reporting. The study argues for stronger policy frameworks to incentivize urban regeneration over greenfield development and calls for the integration of land-use data into corporate sustainability disclosures. By combining geospatial methods with content analysis, the research offers new insights into the intersection of land use, business practices, and environmental sustainability in climate-vulnerable regions. Full article

This study aimed to investigate land-use and land-cover change and the associated change in the ecological stability of the model area Dobrá–Opatová (district of Trenčín, Slovakia), where increasing landscape transformation has raised concerns about declining ecological resilience. Despite the importance of sustainable land management, few studies in this region have addressed long-term landscape dynamics in relation to ecological stability. This research fills that gap by evaluating historical and recent LULC changes and their ecological consequences. Four time horizons were analysed: 1850, 1949, 2009, and 2024. Although the selected time periods are irregular, they reflect key milestones in the region’s land development, such as pre-industrial land use, post-war collectivisation, and recent land consolidation. These activities significantly altered the structure of the landscape. To assess future trends, we used the MOLUSCE plug-in in QGIS to simulate ecological stability for the future. The greatest structural landscape changes occurred between 1850 and 1949. Significant transformation in agricultural areas was observed between 1949 and 2009, when collectivisation reshaped small plots into large block structures and major water management projects were implemented. The 2009–2024 period was marked by land consolidation, mainly resulting in the construction of gravel roads. These structural changes have contributed to a continuous decrease in ecological stability, calculated using the coefficient of ecological stability derived from LULC categories. To explore future trends, we simulated ecological stability for the year 2030 and the simulation confirmed a continued decline in ecological stability, highlighting the need for sustainable land-use planning in the area. Full article

Accurate land-use classification is critical for urban planning and environmental monitoring, yet effectively integrating heterogeneous data sources such as hyperspectral imagery and laser radar (LiDAR) remains challenging. To address this, we propose MixtureRS, a compact multimodal network that effectively integrates hyperspectral imagery and LiDAR data for land-use classification. Our approach employs a 3-D plus heterogeneous convolutional stack to extract rich spectral–spatial features, which are then tokenized and fused via a cross-modality transformer. To enhance model capacity without incurring significant computational overhead, we replace conventional dense feed-forward blocks with a sparse Mixture-of-Experts (MoE) layer that selectively activates the most relevant experts for each token. Evaluated on a 15-class urban benchmark, MixtureRS achieves an overall accuracy of 88.6%, an average accuracy of 90.2%, and a Kappa coefficient of 0.877, outperforming the best homogeneous transformer by over 12 percentage points. Notably, the largest improvements are observed in water, railway, and parking categories, highlighting the advantages of incorporating height information and conditional computation. These results demonstrate that conditional, expert-guided fusion is a promising and efficient strategy for advancing multimodal remote sensing models. Full article

Mangrove ecosystems are crucial coastal habitats that support life and regulate the Earth’s atmosphere. However, these ecosystems face prominent threats due to anthropogenic activities and environmental constraints. For instance, the Saudi Arabian coast is particularly vulnerable to species extinction and biodiversity loss due to the fragility of the ecosystem; this highlights the need to understand the spatial and temporal dynamics of mangrove forests in desert environments. Hence, this is the first national study to quantify mangrove forests and analyze at-risk zone-based land use along Saudi Arabian coasts over 40 years. Thus, the primary contents of this research were (1) to produce a new long-term dataset covering the entire Saudi coastline, (2) to identify the patterns, analyze the trends, and quantify the change of mangrove areas, and (3) to determine vulnerability zoning of mangrove area-based land use and transportation networks. This study used Landsat satellite imagery via Google Earth Engine for national-scale mangrove mapping of Saudi Arabia between 1985 and 2024. Visible and infrared bands and seven spectral indices were employed as input features for the random forest classifier. The two classes used were mangrove and non-mangrove; the latter class included non-mangrove land-use and land-cover areas. Then, the study employed the output mangrove mapping to delineate vulnerable mangrove forest-based land use. The overall results showed a substantial increase in mangrove areas, ranging from 27.74 to 59.31 km² in the Red Sea and from 1.05 to 8.65 km² in the Arabian Gulf between 1985 and 2024, respectively. However, within this decadal trend, there were noticeable periods of decline. The spatial coverage of mangroves was larger on Saudi Arabia’s western coasts, especially the southwestern coasts, than on its eastern coasts. The overall accuracy, conducted annually, ranged between 91.00% and 98.50%. The results also show that expanding land uses and transportation networks within at-risk zones of mangrove forests may have a high potential effect. This study aimed to benefit the government, conservation agencies, coastal planners, and policymakers concerned with the preservation of mangrove habitats. Full article

The Lijiang River Basin (LRB) is a karst ecosystem that presents unique challenges for agricultural land planning. Evaluating cultivated land suitability based on natural factors is critical for ensuring food security in this region. This study was based on the cultivated land distribution data of the LRB in the China Land-Use and Land-Cover Chang dataset, selecting 22 restriction factors across five dimensions: climate, topography, soil, hydrology, and social conditions, and the suitability of cultivated land (paddy fields and drylands) in the LRB was evaluated using the MaxEnt model to further identify the main restricting factors affecting the spatial distribution. The research showed that (1) For paddy fields, high-suitability areas covered 2875.05 km², medium-suitability 1670.58 km², low-suitability 3187.25 km², and non-suitable 9368.46 km². The main restriction factors were distance to villages, slope, surface gravel content, soil thickness, soil pH, and total phosphorus content. (2) For drylands, high-suitability areas covered 3282.3 km², medium-suitability 2260.93 km², low-suitability 4536.27 km², and non-suitable 6836.85 km². The main restriction factors were soil thickness, distance to roads, surface gravel content, elevation, soil pH, and soil texture. This research can provide a scientific basis for the layout of food security and planning agricultural land use in the LRB. Full article

With advancements in cloud computing and machine learning algorithms, an increasing number of cropland datasets have been developed, including the China land-cover dataset (CLCD) and GlobeLand30 (GLC). The unique climatic conditions of the Tibetan Plateau (TP) introduce significant differences and uncertainties to these datasets. Here, we used a quantitative and visual integrated assessment approach to assess the accuracy and spatial consistency of five cropland datasets around 2020 in the TP, namely the CLCD, GLC30, land-use remote sensing monitoring dataset in China (CNLUCC), Global Land Analysis and Discovery (GLAD), and global land-cover product with a fine classification system (GLC_FCS). We analyzed the impact of terrain, climate, population, and vegetation indices on cropland spatial consistency using structural equation modeling (SEM). In this study, the GLAD cropland area had the highest fit with the national land survey (R² = 0.88). County-level analysis revealed that the CLCD and GLC_FCS underestimated cropland areas in high-elevation counties, whereas the GLC and CNLUCC tended to overestimate cropland areas on the TP. Considering overall accuracy, GLC and GLAD performed the best with scores of 0.76 and 0.75, respectively. In contrast, CLCD (0.640), GLC_FCS (0.640), and CNLUCC (0.620) exhibited poor overall accuracy. This study highlights the significantly low spatial consistency of croplands on the TP, with only 10.60% consistency in high and complete agreement. The results showed substantial differences in spatial accuracy among zones, with relatively higher consistency observed in low-altitude zones and notably poorer accuracy in zones with sparse or fragmented cropland. The SEM results indicated that elevation and slope directly influenced cropland consistency, whereas temperature and precipitation indirectly affected cropland consistency by influencing vegetation indices. This study provides a valuable reference for implementing cropland datasets and future cropland mapping studies on the TP region. Full article

Understanding land-use and land-cover (LULC) changes is essential for sustainable land management, particularly in regions experiencing rapid urbanization and agricultural expansion. This study analyzes the LULC dynamics in the Upper Kafue River Basin, Zambia, from 1989 to 2019, using remote-sensing data, Geographic Information Systems (GISs), and advanced analytical techniques such as intensity analysis and directional gradient analysis. The findings indicate a notable decline in forest cover, primarily driven by agricultural expansion, while built-up areas increased, reflecting urban growth. Forest-to-agriculture conversion emerged as the dominant driver of change, with significant transitions also occurring across multiple land categories. The results highlight a dynamic and complex landscape shaped by overlapping socio-economic and environmental pressures, emphasizing the need for targeted policy interventions to mitigate environmental degradation. These insights provide valuable guidance for policymakers and land managers seeking to balance development with conservation in Zambia and similar regions. Full article

Farmland covers present diverse characteristics, methods, and techniques to monitor and evaluate crops in other geographic areas. This study systematically reviews Land Use/Land Cover Change (LULCC) in agricultural land in Chile through a systematic review of the scientific literature. Using the PRISMA 2020 method, the Web of Science (WOS) database was consulted using the keywords “Landuse”, “Landcover”, “Agriculture”, and “Chile”. We applied six exclusions criteria and constructed a matrix to select relevant aspects, such as title, year of publication, study area and period, methods used, and principal results. In our review, we identified four studies that focused specifically on agricultural land dynamics, mainly in south-central Chile. Chile was selected as the study area due to its geographical diversity, which poses significant challenges for decision-making in land use regulation. These results underscore the need for more spatially informed data on farmland dynamics to inform decision-making, particularly during the alternatives evaluation stage. In this phase, it is essential to assess the impacts on and potential of the territory in order to define suitable economic activities. Although there are numerous studies on LULCC, most emphasize changes in native forests, underscoring the need to address LULCC more comprehensively by considering other land categories, such as agricultural land, shrublands, grasslands, and others. This evidence is crucial for designing practical land management tools and identifying areas that have been extensively studied but lack sufficient research. Full article

Land-Cover and Land-Use Change (LCLUC) is a dynamic process affected by the combination and mutual interaction of climatic and socioeconomic drivers. Field studies and surveys, which are typically time- and resource-consuming, have been employed by researchers to better understand LCLUC drivers. However, remotely sensed data may provide the same trustworthy outcomes with less time and expense. This study aimed to assess the relationship between LCLUC and changes in socioeconomic and climatic factors in the Dallas-Fort Worth (DFW) metropolitan area, Texas, USA, between 2000 and 2020. The LCLU, socioeconomic, and climatic data were obtained from the National Land Cover Database of Multi-Resolution Land Characteristics Consortium, NASA’s Socioeconomic Data and Applications Center (SEDAC), and the global climate and weather data website (WorldClim), respectively. Change detection calculated from these data was used to analyze spatial and statistical relationships between LCLUC and changes in socioeconomic and climatic factors. Results showed that LCLUC was significantly predicted by population change, housing and transportation, household and disability change, socioeconomic status change, monthly average minimum temperature change, and monthly mean precipitation change. While socioeconomic factors played a predominant role in driving LCLUC in this study, the influence of climatic factors should not be overlooked, particularly in regions where climate sensitivity is more pronounced, such as arid or transitional zones. These findings highlight the importance of considering regional variability when assessing LCLUC drivers. Full article

Understanding the changes in land use and land cover (LULC) in national parks and their corresponding ecosystem service value (ESV) shifts is crucial for shaping future management policies and directions. However, comprehensive analyses in this research area that integrate tourism development perspectives are lacking. Therefore, this interdisciplinary study considers Akan-Mashu National Park in Japan as a case study. Using remote sensing data, LULC maps for the past 10 years were generated using the Google Earth Engine. The benefit transfer method was employed to calculate the corresponding ESV for each year, followed by a qualitative analysis of local tourism policy documents to explore how the park ecosystem has changed in the context of promoting tourism development. The results showed that LULC changes in Akan-Mashu National Park have been relatively stable over the past decade, with the most noticeable changes occurring in built-up areas. The results also confirm that tourism development has not had a significant negative impact on the ESV of the Akan-Mashu National Park. The recommendations proposed in this study can also be applied to other similar national parks or protected areas worldwide to achieve a dynamic balance between environmental protection and tourism development. Full article

Urban expansion reshapes spatial patterns over time, leading to complex challenges such as environmental degradation, resource scarcity, and socio-economic inequality. It is critical to anticipate these transformations in order to devise proactive urban policies and implement sustainable planning practices that minimize negative impacts on ecosystems and human livelihoods. This study investigates LULC changes in the rapidly urbanizing Manisa metropolitan area of Turkey using Sentinel-2 satellite imagery and advanced machine learning algorithms. High-accuracy LULC maps were generated for 2018, 2021, and 2024 using Random Forest, Support Vector Machine, k-Nearest Neighbors, and Classification and Regression Trees algorithms. Among these, the Random Forest algorithm demonstrated superior accuracy and consistency in distinguishing complex land-cover classes. Future LULC scenarios for 2027 and 2030 were simulated using the Cellular Automata–Artificial Neural Network model and the QGIS MOLUSCE plugin. The results indicate significant urban growth, with built-up areas projected to increase by 23.67% between 2024 and 2030, accompanied by declines in natural resources such as bare land and water bodies. This study highlights the implications of urban expansion regarding ecological balance and demonstrates the importance of integrating machine learning and simulation models to forecast land use changes, enabling sustainable urban planning and resource management. Overall, effective policies must be developed to manage the negative environmental impacts of urbanization and conduct land use planning in a balanced manner. Full article

Land-use and land-cover (LULC) changes in the Amazon biome are key processes that influence the environment and societies at local, national, and global scales. Numerous studies have already relied on land-cover and land-use maps to analyze change processes. This study presents a new dataset created by calculating the time required for deforested areas to transition to agriculture (annual and permanent crops) in the Brazilian Amazon biome. The calculations were performed over MapBiomas land-cover data (version 7), which range from 1985 to 2021, at a spatial resolution of 30 m. The method consists of basic algebraic operation and recursion to identify every conversion from forest to agriculture between 1985 and 2021. The results show a correlation between environmental policies and the time required for the conversion to be completed, such as the adoption of the soy moratorium and the New Forest Code, that were followed by a search for old cleared areas for the establishment of new agricultural sites. The new data can be useful in interdisciplinary studies focused on land-use and land-cover change analysis in Brazil, such as planning of forest restoration initiatives, and the evaluation of carbon stocks according to conversion length. Our accuracy assessment shows an opportunity to improve conversion length calculations by reducing errors in the classification of agriculture establishment. The major innovation of this study is the establishment of explicit links between the deforestation year of a given pixel and its respective year of agriculture establishment, which can provide new insights into understanding long-term land-use conversion processes in tropical ecosystems. Full article

The remote sensing community benefits from new sensors and easier access to Earth Observation data to frequently released new land-cover maps. The propagation of such independent and heterogeneous products offers promising perspectives for various scientific domains and for the implementation and monitoring of land-use policies. Yet, it may also confuse the end-users when it comes to identifying the most appropriate product to address their requirements. Data fusion methods can help to combine competing and/or complementary maps in order to capitalize on their strengths while overcoming their limitations. We assessed the potential of the Dempster–Shafer Theory (DST) to enhance oil palm mapping in Sumatra (Indonesia) by combining four land-cover maps, hereafter named DESCALS, IIASA, XU, and MAPBIOMAS, according to the first author’s name or the research group that published it. The application of DST relied on four steps: (1) a discernment framework, (2) the assignment of mass functions, (3) the DST fusion rule, and (4) the DST decision rule. Our results showed that the DST decision map achieved significantly higher accuracy (Kappa = 0.78) than the most accurate input product (Kappa = 0.724). The best result was reached by considering the probabilities of pixels to belong to the OP class associated with DESCALS map. In addition, the belief (i.e., confidence) and conflict (i.e., uncertainty) maps produced by DST evidenced that industrial plantations were detected with higher confidence than smallholder plantations. Consequently, Kappa values computed locally were lower in areas dominated by smallholder plantations. Combining land-use products with DST contributes to producing state-of-the-art maps and continuous information for enhanced land-cover analysis. Full article

Environmental policy and the expansion of the bioeconomy sector has led to increased demand for wood and wood products, pressuring forests. In order to meet additional demand, foresters must either intensify forest management practices or alter forest area. One of the more observable shifts in forest management comes in the form of changes in land use or land cover. Yet despite the many short- and long-term consequences of land-use change, the environmental impacts of it are less explored in forestry than in agriculture. In this paper, we conduct a literature review over the period between 1993 and 2022 to better understand how the notions of land-use and land-cover change are included in environmental impact assessments related to the production of wood and wood products. Specifically, we identified five categories of impacts studied and found a surprising dichotomy in terminology between land-use/land-cover change and changes in forest management practices. We present general trends in the methods and indicators used and discuss potential methodological and conceptual challenges inherent to this literature. Our results are particularly important in light of the growing popularity of land-use and land-cover data in research, as we highlight how they have been integrated into existing environmental impact assessment methods and how we can improve them going into the future. Full article