Search Results (574)

Different countries face significant challenges in managing water-related natural hazards, such as floods and shortages, while ensuring adequate water quality and quantity to satisfy human needs and preserve ecosystems. Climate change projections exacerbate this situation by intensifying the hydrological cycle, resulting in substantial changes in precipitation patterns, evapotranspiration, and groundwater storage. This study reviews water security challenges across 43 countries, drawing on 128 articles obtained from databases including EBSCOHOST, Scopus and ResearchGate, as well as specific journals. Key search terms included “water security”, “water security and climate change”, “water scarcity”, “water risk index”, “water balance”, “water assessment”, and “land use and land cover change”. The analysis reveals the main water security issues present in 43 countries (flash floods, drought and water quality), and the response measures identified these challenges to water security. All the countries studied face one or more critical water-related effects. Afghanistan, Bangladesh, India, and Mexico were identified as the most severely affected, dealing with a combination of water scarcity, flooding, and water pollution. The most suggested strategies for improving water security include sustainable urban planning, improving consumption efficiency, strategic land-use planning, applying technologies to predict availability of water resources and planning according to variations in resource availability over time. In addition, other general actions include enhancing water storage infrastructure, improving consumption efficiency and adopting sustainable urban planning. Full article

Desertification presents major environmental challenges in Central Asia, driven by climatic and anthropogenic factors. The present study quantifies desertification risk through an integrated approach using Bayesian networks and the ESAS model, offering a holistic perspective on desertification dynamics. Four key variables—vegetation cover, precipitation, land-use intensity, and soil quality—were incorporated into a Bayesian model to evaluate their influence on desertification. A probabilistic model was developed to gauge desertification intensity, with simulations conducted at 200 geospatial points. Hazard maps for 2030–2050 were produced under climate scenarios SSP245 and SSP585, incorporating projected land-use changes. All procedures for desertification risk assessment, land-use mapping, and climate downscaling were performed using the Google Earth Engine platform. The findings suggest a 4% increase in desertification risk under SSP245 and an 11% increase under SSP585 by 2050, with the greatest threats observed in western regions such as Kazakhstan, Uzbekistan, and Turkmenistan. Sensitivity analysis indicated that vegetation quality exerts the strongest influence on desertification, reflected by a Vegetation Quality Index (VQI) ranging from 1.582 (low in Turkmenistan) to 1.692 (very low in Kazakhstan). A comparison of the Bayesian and ESAS models revealed robust alignment, evidenced by an R² value of 0.82, a Pearson correlation coefficient of 0.76, and an RMSE of 0.18. These results highlight the utility of Bayesian networks as an effective tool for desertification assessment and scenario analysis, underscoring the urgency of targeted land management and proactive climate adaptation. Although reclaimed land presents opportunities for afforestation and sustainable agriculture, carefully considering potential trade-offs with biodiversity and ecosystem services remains essential. Full article

Economic pressure on agricultural land is generating major changes in affected territorial systems. The development of methodologies to analyze the pressure on agricultural land is one of the main concerns regarding food security and how to provide fresh produce to large cities. The methodology used uses the Corine Land Cover database, provided by Copernicus Land Monitoring Services (CLMS), from 1990–2018. Data processing and analysis was performed using the open-source software package QGIS, a process that started by reprojecting the data into the national coordinate reference system Pulkovo 1942(58)/Stereo 70, EPSG: 3844. The methodology used was able to highlight the transformations that have taken place in land use, highlighting when and how the land was transformed. Our results show that quantitative and land-use changes due to the socio-economic pressures generated by the transition to a different type of economy can be highlighted. Urban sprawl has led to dramatic changes in land use, with agricultural land being the category that has seen the largest reductions in area. Full article

Human activities and climate change have accelerated land use and land cover change (LUCC) globally, diminishing the ecosystem service value (ESV) in ecologically fragile areas such as highly mountainous canyons and disrupting the human–nature balance. However, existing research lacks analysis on the impact of land use changes on ecosystem service value in typical counties with highly mountainous canyon regions. Therefore, we aim to address this gap by analyzing land use changes and their driving factors in Chayu County using multi-year land use data, calculating the ecosystem service value (ESV) for different periods, and estimating its spatial correlation and stability. The results showed the following: (1) Forestland and grassland were the predominant land-use types, with notable conversions between grassland and water bodies, grassland and unused land, and water bodies and unused land. (2) The total ESV increased steadily from 2003 to 2023, with higher values in the north and west and lower values in the central east. Forestland and water areas were the primary contributors to ESV changes, and ESV sensitivity to LUCC steadily increased from 0.46% to 2.49%. (3) Moran’s I ESV shows an overall increase, with a heightened correlation and enhanced stability. Spatially, the ESV exhibited a general high–high and low–low clustering pattern, with localized high–low and low–high clusters. These changes, driven by natural resource endowments and climate change, provide essential support for ecological protection and sustainable development in highly mountainous canyons and similar regions. Full article

In recent years, particularly in 2024, there has been an escalation in the frequency and intensity of megafires in the state of São Paulo, Brazil. This state, the most industrialized in the country, has seen extensive land-use changes in recent decades, with agriculture extending upon areas previously dedicated to other uses and forests. The practice of family farming, which is distinguished by its smaller operational areas and the majority involvement of the family that owns the land, has the potential to influence the occurrence of fires, but few studies have explored the link between agricultural practices (especially the difference between family and other farming types) and fire intensity. This study aims to assess whether the higher presence of family-farming establishments in different municipalities reduces fire incidents. The results indicate that the municipalities with the highest presence of family farming present lower percentages of burned areas. The increased diversity in crop types and the presence of forest cover within these municipalities have been identified as contributing factors to this reduced fire rate and burned areas. These findings underscore the need for public policies that support family farming as a strategy to reduce fires and protect vulnerable farmers in rural landscapes. Full article

Soil carbon sequestration (SCS) is a nature-based, low-cost climate mitigation strategy that also contributes to the climate adaptation of agricultural systems. Some land-use and land-management practices potentially lead to an enhancement of the soil organic carbon (SOC) sink, such as no-till, the use of cover crops, leaving residues on fields, improving the variety of legume species in grasslands and reducing grazing intensity. However, uncertainties remain both in estimating and measuring the impact of the application of certain practices, as these vary with the soil, climate and historic land use. IPCC (Intergovernmental Panel on Climate Change) guidelines are commonly used to estimate SOC and SOC sequestration potentials at different tiers. Here, the IPCC’s tier 1 methodology was applied to estimate (1) the sequestration potential of nine mitigation practices and (2) the emission or sequestration potential of four current land-change trends for n = 7092 unique agricultural sites in mainland Portugal. The conversion of irrigated crops to improved grasslands resulted in the highest average unit sequestration (1.05 tC ha⁻¹ yr⁻¹), while cropland conversion to poor degraded pasture (abandonment) resulted in the highest unit SOC loss (−0.08 tC ha⁻¹ yr⁻¹). The abandonment of cropland results in a national SOC loss of up to 0.09 MtC yr⁻¹, while the improvement of poor degraded pastures has the highest national sequestration potential, equal to 0.6 MtC yr⁻¹ (2.2 MtCO_2eq yr⁻¹), about 4% of Portugal’s emissions in 2021, if applied in all managed areas. The results enable a comparison between different practices and land uses; however, to enhance accuracy, a higher tier methodology tailored to the Portuguese context should be developed. 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

Land cover mapping is essential to understanding global land-use patterns and studying biodiversity composition and the functioning of eco-systems. The introduction of remote sensing technologies and artificial intelligence models made it possible to base land cover mapping on satellite imagery in order to monitor changes, assess ecosystem health, support conservation efforts, and reduce monitoring time. However, significant challenges remain in managing large, complex satellite imagery datasets, acquiring specialized datasets due to high costs and labor intensity, including a lack of comparative studies for the selection of optimal deep learning models. No less important is the scarcity of aerial datasets specifically tailored for agricultural areas. This study addresses these gaps by presenting a methodology for semantic segmentation of land covers in agricultural areas using satellite images and deep learning models with pre-trained backbones. We introduce an efficient methodology for preparing semantic segmentation datasets and contribute the “Land Cover Aerial Imagery” (LICAI) dataset for semantic segmentation. The study focuses on the Franciacorta area, Lombardy Region, leveraging the rich diversity of the dataset to effectively train and evaluate the models. We conducted a comparative study, using cutting-edge deep-learning-based segmentation models (U-Net, SegNet, DeepLabV3) with various pre-trained backbones (ResNet, Inception, DenseNet, EfficientNet) on our dataset acquired from Google Earth Pro. Through meticulous data acquisition, preprocessing, model selection, and evaluation, we demonstrate the effectiveness of these techniques in accurately identifying land cover classes. Integrating pre-trained feature extraction networks significantly improves performance across various metrics. Additionally, addressing challenges such as data availability, computational resources, and model interpretability is essential for advancing the field of remote sensing, in support of biodiversity conservation and the provision of ecosystem services and sustainable agriculture. Full article

The pressing need to address climate change and advance global sustainable development has heightened the emphasis on ecosystem services, especially carbon sequestration. This research assesses the supply and demand dynamics of carbon sequestration services on Hainan Island, China, highlighting its significant contributions to global biodiversity conservation and carbon balance. The analysis considers the spatial distribution and interrelation of these services in light of recent land use and ecological policy changes. The methodology incorporates land use and land cover data, the Normalized Difference Vegetation Index (NDVI), meteorological data, and soil data. A gravity model is employed to elucidate the supply–demand relationship for carbon sequestration services, examining the flow across different regions and identifying spatial connections and their intensities. The results indicate a notable increase in carbon sequestration supply in Hainan from 2000 to 2020, particularly in the central mountainous areas. Conversely, the demand for these services has risen, especially in the northern plains’ urban areas and southern coastal towns. The gravity model reveals a strong spatial interdependence between the central mountainous supply zones and the high-demand urban locales. This study underscores the disparities in carbon sequestration supply and demand on Hainan, emphasizing the need for the strategic management of these elements. It provides critical data for ecological compensation policies and offers insights into the roles of regional ecosystems in climate change mitigation. The research highlights the necessity of incorporating ecosystem services into land-use planning and decision-making to foster sustainable development and strengthen climate resilience. Full article

Spatial and temporal information about cropping patterns of single and multiple crops is important for monitoring crop production and land-use intensity. We used time-series MODIS NDVI 8-day composite data to develop annual cropping pattern products at a 250 m spatial resolution for China, covering the period from 2001 to 2023. To address the potential impacts of varying parameters in both data pre-processing and the peak detection algorithm on the accuracy of cropping pattern mapping, we employed a grid-search method to fine-tune these parameters. This process focused on optimizing the Savitzky–Golay smoothing window size and the peak width parameters using a calibration dataset. The results highlighted that an optimal combination of a five to seven MODIS composite window size in Savitzky–Golay smoothing and a peak width of four MODIS composites achieved good overall mapping accuracy. Pixel-wise accuracy assessments were conducted for the selected mapping years of 2001, 2011, and 2021. Overall accuracies were between 89.7% and 92.0%, with F1 scores ranging from 0.921 to 0.943. Nationally, this study observed a fluctuating trend in multiple cropping percentages, with a notable increase after 2013, suggesting shifts toward more intensive agricultural practices in recent years. At a finer spatial scale, the combination of Mann–Kendall and Sen’s slope analyses revealed that approximately 12.9% of 3 km analytical windows exhibited significant changes in cropping intensity. We observed spatial clusters of increasing and decreasing crop intensity trends across provinces such as Hebei, Shandong, Shaanxi, and Gansu. This study underscores the importance of data smoothing and peak detection methods in analyzing high temporal resolution remote sensing data. The generation of annual single/multiple cropping pattern maps at a 250 m spatial resolution enhances our comprehension of agricultural dynamics through time and across different regions. Full article

Economic corridors play a crucial role in promoting economic growth and facilitating coordinated regional development. However, land use changes associated with the development of emerging economic corridors have become a prominent source of conflict in regional integration in the Global South. This study takes the Laos Economic Corridor as a case study to explore the characteristics and driving mechanisms of land use changes in emerging economic corridor regions. Using global land cover data from 2000 to 2020 (GlobeLand30) and employing spatial statistical analysis, the Random Forest (RFC) algorithm, and the CA-Markov model, the study follows a Pattern–Process–Mechanism–Trend analytical framework to reveal the spatial distribution characteristics and transformation paths of land use within the corridor. The study results indicate that (1) The land use pattern in the Laos Economic Corridor has gradually shifted from a “single-core radial” structure to a “dumbbell-shaped” structure, promoting coordinated regional economic development. (2) A significant unidirectional flow of land use has been established, with forestland being converted into cultivated land and cultivated land being further converted into artificial surfaces. (3) In addition to the natural geographical constraints, the transport infrastructure and the spatial layout of industries are the main drivers for the expansion of ecological land, agricultural land, and built-up land. (4) Spatial planning interventions are essential and urgent: the establishment of land management rules based on the principles of forest conservation and intensive development can effectively control the uncontrolled expansion of artificial areas, significantly reduce the loss of forestland, and ensure the rational allocation of land resources for long-term development. The findings of this study offer valuable insights and reference points for the Global South, enhancing understanding of the spatial development dynamics of economic corridors, informing the optimization of land-use policies, and supporting efforts to promote regional integration and sustainable development. 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

This study investigates land-cover changes along riparian zones in South Carolina, focusing on intermittent and perennial streams to assess the impact of urbanization, forest loss, and impervious surface expansion on sensitive ecosystems. South Carolina’s diverse geography, ranging from coastal marshes to the Blue Ridge Mountains, and subtropical humid climate, offers a rich context for understanding environmental changes. The research utilizes various geospatial datasets, including the National Land Cover Database (NLCD), National Hydrography Dataset (NHD), and National Agricultural Imagery Program (NAIP) imagery, to evaluate changes in forest cover, urbanization, and impervious surfaces from 2011 to 2021 as a decade of transition. The study areas were divided into buffer zones around intermittent and perennial streams, following South Carolina’s riparian management guidelines. The results indicate significant land-cover transitions, including a total of 3184.56 hectares of non-urban areas converting to forest within the 100 m buffer around intermittent streams. In contrast, 137.43 hectares of forest transitioned to urban land in the same buffer zones, with Spartanburg and Greenville leading the change. Intermittent stream buffers exhibited higher imperviousness (4.6–5.5%) compared to perennial stream buffers (3.3–4.5%), highlighting the increased urban pressure on these sensitive areas. Furthermore, tree canopy loss was significant, with counties such as Greenwood and Chesterfield experiencing substantial reductions in canopy cover. The use of high-resolution NAIP imagery validated the land-cover classifications, ensuring accuracy in the results. The findings emphasize the need for effective land-use management, particularly in the riparian zones, to mitigate the adverse impacts of urban expansion and to safeguard water quality and biodiversity in South Carolina’s streams. Full article

This study examines climate change impacts on evapotranspiration in Inner Mongolia, analyzing potential (PET) and actual (AET) evapotranspiration shifts across diverse land-use classes using the SEBAL model and SSP2-4.5 and SSP5-8.5 projections (2030–2050) relative to a 1985–2015 baseline. Our findings reveal substantial PET increases across all LULC types, with Non-Vegetated Lands consistently showing the highest absolute PET values across scenarios (931.19 mm under baseline, increasing to 975.65 mm under SSP5-8.5) due to limited vegetation cover and shading effects, while forests, croplands, and savannas exhibit the most pronounced relative increases under SSP5-8.5, driven by heightened atmospheric demand and vegetation-induced transpiration. Monthly analyses show pronounced PET increases, particularly in the warmer months (June–August), with projected SSP5-8.5 PET levels reaching peaks of over 500 mm, indicating significant future water demand. AET increases are largest in densely vegetated classes, such as forests (+242.41 mm for Evergreen Needleleaf Forests under SSP5-8.5), while croplands and grasslands exhibit more moderate gains (+249.59 mm and +167.75 mm, respectively). The widening PET-AET gap highlights a growing vulnerability to moisture deficits, particularly in croplands and grasslands. Forested areas, while resilient, face rising water demands, necessitating conservation measures, whereas croplands and grasslands in low-precipitation areas risk soil moisture deficits and productivity declines due to limited adaptive capacity. Non-Vegetated Lands and built-up areas exhibit minimal AET responses (+16.37 mm for Non-Vegetated Lands under SSP5-8.5), emphasizing their limited water cycling contributions despite high PET. This research enhances the understanding of climate-induced changes in water demands across semi-arid regions, providing critical insights into effective and region-specific water resource management strategies. Full article

Rapid population growth accelerates changes in land use and land cover (LULC), straining natural resource availability. Monitoring LULC changes is essential for managing resources and assessing climate change impacts. This study focused on extracting LULC data from 1993 to 2024 using the classification and regression tree (CART) method on the Google Earth Engine (GEE) platform in Qena Governorate, Egypt. Moreover, the cellular automata (CA) Markov model was used to anticipate the future changes in LULC for the research area in 2040 and 2050. Three multispectral satellite images—Landsat thematic mapper (TM), enhanced thematic mapper (ETM+), and operational land imager (OLI)—were analyzed and verified using the GEE code editor. The CART classifier, integrated into GEE, identified four major LULC categories: urban areas, water bodies, cultivated soils, and bare areas. From 1993 to 2008, urban areas expanded by 57 km², while bare and cultivated soils decreased by 12.4 km² and 42.7 km², respectively. Between 2008 and 2024, water bodies increased by 24.4 km², urban areas gained 24.2 km², and cultivated and bare soils declined by 22.2 km² and 26.4 km², respectively. The CA-Markov model’s thematic maps highlighted the spatial distribution of forecasted LULC changes for 2040 and 2050. The results indicated that the urban areas, agricultural land, and water bodies will all increase. However, as anticipated, the areas of bare lands shrank during the years under study. These findings provide valuable insights for decision makers, aiding in improved land-use management, strategic planning for land reclamation, and sustainable agricultural production programs. Full article