Search Results (265)

Current InVEST habitat quality assessments rely heavily on subjective expert judgment for parameter specification, introducing substantial uncertainty and limiting their regional applicability. To address this gap, we developed an objective, statistically rigorous framework for parameter derivation by integrating Principal Component Analysis (PCA), Structural Equation Modeling (SEM), and spatial analysis, supported by high-resolution biotope mapping. The methodology was applied to Gochang-gun, South Korea, where nine threat factors were analyzed using empirical data from 6633 sampling points. PCA identified threat groupings, SEM quantified habitat–threat relationships for sensitivity derivation, and variogram analysis determined maximum influence distances, while 1:5000 scale biotope maps incorporating 14 ecological indicators replaced conventional land cover classifications. These empirically derived parameters were directly incorporated into the InVEST Habitat Quality model, replacing default or expert-based values. As a result, the biotope-based InVEST HQ implementation achieved exceptional performance (R² = 0.892) with crops emerging as the dominant threat factor (sensitivity = 1.000, weight = 34.1%). Compared to the land use/land cover (LULC)-based approach using conventional parameterization, the biotope–PCA–SEM model demonstrated higher predictive accuracy (AUC = 0.805 vs. 0.755), stronger correlations with independent conservation indicators (protected area correlation: 0.457 vs. 0.201), and clearer ecological gradients across UNESCO Biosphere Reserve zones. This framework eliminates subjective bias while maintaining regional specificity, establishing a transferable foundation for evidence-based conservation planning. By demonstrating substantial improvements over conventional parameterization, the study highlights the inadequacy of transferred parameters and provides an objective standard for advancing InVEST applications worldwide. Full article

The satellite-derived climatic variables offer extensive spatial and temporal coverage for research; however, their inherent biases can subsequently reduce their accuracy for water balance estimate. This study evaluates the effectiveness of bias correction in improving the Tropical Rainfall Measuring Mission (TRMM) rainfall and the Global Land Data Assimilation System (GLDAS) land surface temperature (LST) data and illustrates their long-term (2000–2019) hydrological assessment. The novelty lies in coupling the bias-corrected climate variables with the Thornthwaite–Mather water balance model as well as land use land cover (LULC) for improved predictive hydrological modeling. Bias correction significantly improved the agreement with ground observations, enhancing the R² value from 0.89 to 0.96 for temperature and from 0.73 to 0.80 for rainfall, making targeted inputs ready to predict hydrological dynamics. LULC mapping showed a predominance of agricultural land (64.5%) in the area followed by settlements (20.0%), forest (7.3%), barren land (6.5%), and water bodies (1.7%), with soils being silt loam, clay loam, and clay. With these improved datasets, the model found seasonal rise in potential evapotranspiration (PET), peaking at 120.7 mm in June, with actual evapotranspiration (AET) following a similar trend. The annual water balance showed a surplus of 523.8 mm and deficit of 121.2 mm, which proves that bias correction not only enhances the reliability of satellite data but also reinforces the credibility of hydrological indicators, with a direct, positive impact on evidence-based irrigation planning and flood mitigation and drought management, especially in data-scarce regions. Full article

Urbanization, characterized by population growth and socioeconomic development, is a major driving factor of land use land cover (LULC) change. A spatio-temporal understanding of land cover change is crucial, as it provides essential insights into the pattern of urban development. This study conducted a longitudinal analysis of LULC change in order to evaluate the tradeoffs of urban growth and sustainability challenges in the Himalayan region. Landsat time-series satellite imagery from 1988 to 2024 were analyzed for two major cities in Nepal—Kathmandu metropolitan city (KMC) and Pokhara metropolitan city (PMC). The LULC classification was conducted using a machine learning support vector machine (SVM) approach. For this study period, our analysis showed that KMC and PMC witnessed urban growth of over 400% and 250%, respectively. In the next step, LULC change and urban expansion patterns were predicted based on the urban development indicator using the Cellular Automata Markov chain (CA-Markov) model for the years 2040 and 2056. Based on the CA-Markov chain analysis, the projected expansion areas of the urban area for the two future years are 282.39 km² and 337.37 km² for Kathmandu, and 93.17 km² and 114.15 km² for PMC, respectively. The model was verified using several Kappa variables (K-location, K-standard, and K-no). Based on the LULC trends, the majority of urban expansion in both the study areas has occurred at the expense of prime farmlands, which raises grave concern over the sustainability of the food supply to feed an ever-increasing urban population. This haphazard urban sprawl poses a significant challenge for future planning and highlights the urgent need for effective strategies to ensure sustainable urban growth, especially in restoring local food supply to alleviate over-reliance on long-distance transport of agro-produce in high-altitude mountain regions. The alternative planning of sustainable urban growth could involve adequate consideration for urban farming and community gardening as an integral part of the urban fabric, both at the household and city infrastructure levels. Full article

Refining the land use structure can boost land utilization efficiency and curtail regional carbon emissions. Nevertheless, prior research has predominantly concentrated on static linear planning analysis. It has failed to account for how future dynamic alterations in driving factors (such as GDP and population) affect simulation outcomes and how the land use spatial configuration impacts the attainment of the carbon-neutrality goal. In this research, 1 km spatial resolution LULC products were employed to meticulously simulate multiple land use scenarios across China at the national level from 2030 to 2060. This was performed by taking into account the dynamic changes in driving factors. Subsequently, an analysis was carried out on the low-carbon land use spatial structure required to reach the carbon-neutrality target. The findings are as follows: (1) When employing the PLUS (Patch—based Land Use Simulation) model to conduct simulations of various land use scenarios in China by taking into account the dynamic alterations in driving factors, a high degree of precision was attained across diverse scenarios. The sustainable development scenario demonstrated the best performance, with kappa, OA, and FoM values of 0.9101, 93.15%, and 0.3895, respectively. This implies that the simulation approach based on dynamic factors is highly suitable for national-scale applications. (2) The simulation accuracy of the PLUS and GeoSOS-FLUS (Systems for Geographical Modeling and Optimization, Simulation of Future Land Utilization) models was validated for six scenarios by extrapolating the trends of influencing factors. Moreover, a set of scenarios was added to each model as a control group without extrapolation. The present research demonstrated that projecting the trends of factors having an impact notably improved the simulation precision of both the PLUS and GeoSOS-FLUS models. When contrasted with the GeoSOS-FLUS model, the PLUS model attained superior simulation accuracy across all six scenarios. The highest precision indicators were observed in the sustainable development scenario, with kappa, OA, and FoM values reaching 0.9101, 93.15%, and 0.3895, respectively. The precise simulation method of the PLUS model, which considers the dynamic changes in influencing factors, is highly applicable at the national scale. (3) Under the sustainable development scenario, it is anticipated that China’s land use carbon emissions will reach their peak in 2030 and achieve the carbon-neutrality target by 2060. Net carbon emissions are expected to decline by 14.36% compared to the 2020 levels. From the perspective of dynamic changes in influencing factors, the PLUS model was used to accurately simulate China’s future land use. Based on these simulations, multi-scenario predictions of future carbon emissions were made, and the results uncover the spatiotemporal evolution characteristics of China’s carbon emissions. This study aims to offer a solid scientific basis for policy-making related to China’s low-carbon economy and high-quality development. It also intends to present Chinese solutions and key paths for achieving carbon peak and carbon neutrality. Full article

This study was conducted using Geographic Information Systems (GISs), Remote Sensing (RS) techniques, and the SLEUTH model based on Cellular Automata (CA) to analyze the spatial and temporal dynamics of urban growth in Sanliurfa Province and to create future projections. The model in question simulates urban sprawl by using Slope, Land Use/Land Cover (LULC), Excluded Areas, urban areas, transportation, and hill shade layers as inputs. In addition, disaster risk areas and public policies that will affect the urbanization of the city were used as input layers. In the study, the spatial pattern of urbanization in Sanliurfa was determined by using Landsat satellite images of six different periods covering the years 1985–2025. The Analytical Hierarchy Process (AHP) method was applied within the scope of Multi-Criteria Decision Analysis (MCDA). Weighting was made for each parameter. Spatial analysis was performed by combining these values with data in raster format. The results show that the SLEUTH model successfully reflects past growth trends when calibrated at different spatial resolutions and can provide reliable predictions for the future. Thus, the proposed model can be used as an effective decision support tool in the evaluation of alternative urbanization scenarios in urban planning. The findings contribute to the sustainability of land management policies. Full article

Flood susceptibility mapping is essential for urban planning and disaster risk management, especially in rapidly urbanizing areas exposed to extreme rainfall events. This study applies an integrated approach combining Geographic Information Systems (GIS), map algebra, and the Analytic Hierarchy Process (AHP) to assess flood-prone zones in Ananindeua, Pará, Brazil. Five geoenvironmental criteria—rainfall, land use and land cover (LULC), slope, soil type, and drainage density—were selected and weighted using AHP to generate a composite flood susceptibility index. The results identified rainfall and slope as the most influential criteria, with both contributing to over 184 km² of high-susceptibility area. Spatial patterns showed that flood-prone zones are concentrated in flat urban areas with high drainage density and extensive impermeable surfaces. CHIRPS rainfall data were validated using Pearson’s correlation (r = 0.83) and the Nash–Sutcliffe efficiency (NS = 0.97), confirming the reliability of the precipitation input. The final susceptibility map, categorized into low, medium, and high classes, was validated using flood events derived from Sentinel-1 SAR data (2019–2025), of which 97.2% occurred in medium- or high-susceptibility zones. These findings demonstrate the model’s strong predictive performance and highlight the role of unplanned urban expansion, land cover changes, and inadequate drainage in increasing flood risk. Although specific to Ananindeua, the proposed methodology can be adapted to other urban areas in Brazil, provided local conditions and data availability are considered. Full article

Landslides are among the most destructive natural hazards in the Western Ghats region of Kerala, driven by complex interactions between geological, hydrological, and anthropogenic factors. This study aims to generate a high-resolution Landslide Susceptibility Level Map (LSLM) using a machine learning (ML)-based Random Forest (RF) model integrated with Geographic Information Systems (GIS). A total of 231 historical landslide locations obtained from the Bhukosh portal were used as reference data. Eight predictive factors—Stream Order, Drainage Density, Slope, Aspect, Geology, Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), and Moisture Stress Index (MSI)—were derived from remote sensing and ancillary datasets, preprocessed, and reclassified for model input. The RF model was trained and validated using a 50:50 split of landslide and non-landslide points, with variable importance values derived to weight each predictive factor of the raster layer in ArcGIS. The resulting Landslide Susceptibility Index (LSI) was reclassified into five susceptibility zones: Very Low, Low, Moderate, High, and Very High. Results indicate that approximately 17.82% of the study area falls under high to very high susceptibility, predominantly in the steep, weathered, and high rainfall zones of the Western Ghats. Validation using Area Under the Curve–Receiver Operating Characteristic (AUC-ROC) analysis yielded an accuracy of 0.890, demonstrating excellent model performance. The output LSM provides valuable spatial insights for planners, disaster managers, and policymakers, enabling targeted mitigation strategies and sustainable land-use planning in landslide-prone regions. Full article

In recent years, the world has seen a significant increase in extreme weather events, such as floods, hurricanes, and storms, which have caused extensive damage to infrastructure and communities. These events result from natural phenomena and human-induced factors, including climate change and natural climate variations. For instance, the floods in Europe in 2024 and the hurricanes in the United States have highlighted the vulnerability of urban and rural areas. These extreme events are often unpredictable and pose considerable challenges for spatial planning and risk management. This study explores an innovative approach that employs machine learning and Markov chains to enhance spatial planning and predict flood risk areas. By utilizing data such as weather records, land use and land cover (LULC) information, topographic LIDAR data, and advanced predictive models, the study aims to identify the most vulnerable areas and provide recommendations for risk mitigation. The results indicate that integrating these technologies can improve forecasting accuracy, thereby supporting more informed decisions in land management. Given the effects of climate change and the increasing frequency of extreme events, adopting advanced forecasting and planning tools is crucial for protecting communities and reducing economic and social damage. This method was applied to the Calopinace area, also known as the Calopinace River or Fiumara della Cartiera, which crosses Reggio Calabria and is notorious for its historical floods. It can serve as part of an early warning system, enabling alerts to be issued throughout the monitored area. Furthermore, it can be integrated into existing emergency protocols, thereby enhancing the effectiveness of disaster response. Future research could investigate incorporating additional data and AI techniques to improve accuracy. Full article

Land use and land cover (LULC) in Northern Bangladesh have undergone substantial transformations due to both anthropogenic and natural drivers. This study examines historical LULC changes (1990–2022) and projects future trends for 2030 and 2054 using remote sensing and the Cellular Automata-Artificial Neural Network (CA-ANN) model. Multi-temporal Landsat imagery was classified with 80.75–86.23% accuracy (Kappa: 0.75–0.81). Model validation comparing simulated and actual 2014 data yielded 79.98% accuracy, indicating a reasonably good performance given the region’s rapidly evolving and heterogeneous landscape. The results reveal a significant decline in waterbodies, which is projected to shrink by 34.4% by 2054, alongside a 1.21% reduction in cropland raising serious environmental and food security concerns. Vegetation, after an initial massive decrease (1990–2014), increased (2014–2022) due to different forms of agroforestry practices and is expected to increase by 4.64% by 2054. While the model demonstrated fair predictive power, its moderate accuracy highlights challenges in forecasting LULC in areas characterized by informal urbanization, seasonal land shifts, and riverbank erosion. These dynamics limit prediction reliability and reflect the region’s ecological vulnerability. The findings call for urgent policy action particularly afforestation, water resource management, and integrated land use planning to ensure environmental sustainability and resilience in this climate-sensitive area. Full article

Monitoring urban growth through simulation models is becoming increasingly vital for the sustainable management of cities. Although various raster-based models have been developed over the past three decades, the irregular, fragmented, and heterogeneous geometric structure of urban areas poses significant challenges to effectively modeling complex land use and land cover (LULC) transitions. To address these limitations, this study proposes a novel urban growth simulation model based on vector cellular automata (VCA). In this model, dynamic neighborhood relationships are flexibly established using an algorithm called growth vectors (GVs). Open-access data from four time periods between 1990 and 2018 were utilized for three major European metropolitan areas: Istanbul, Berlin, and Madrid. During the calibration phase, the model was trained using three machine learning algorithms: Random forest, support vector machine, and multi-layer perceptron. For the simulation phase, an adaptive VCA-based urban growth model was developed to predict LULC changes through to 2040. The results demonstrate that the proposed algorithm can achieve a satisfactory level of accuracy in modeling urban growth. Full article

In a world experiencing rapid urbanization, the phenomenon of land surface temperature (LST) variation has invited substantial attention due to its profound impact on the environment and human well-being. Changes in land use and land cover (LULC) within urban areas significantly influence the dynamics of LST and are a major driver of urban eco-environmental change. The complex connections between LULC dynamics, LST, and climate change are investigated in this systematic review, with a focus on the combined effects of these variables and the use of Machine Learning (ML) techniques. The data in this study, based on peer-reviewed publications from the past 25 years, were obtained from Science Direct and Web of Science databases. Based on our findings, Landsat is the most widely used dataset for analyzing the impacts of LULC on LST. Additionally, built-up areas, vegetation, and population density had the biggest effects on LST values based on focused studies. This systematic review reveals that Artificial Neural Networks (ANNs), Cellular Automata-Markov (CA-Markov), and Random Forest (RF) are the most used ML techniques in predicting LULC and LST. The study finds that NDBI and NDVI are recognized as the key LULC indices that have strong correlations with LST. We also highlight key LULC classes that have the most impact on LST variation. To validate the results, these studies employ Pearson correlation, the NDVI and NDBI index, and other linear regression methods. This review concludes by highlighting future research directions and the current need for interdisciplinary efforts to address the intricate dynamics of LULC and the Earth’s surface temperature. Full article

With the severe challenges resulting from global climate change, the role of land use/land cover (LU/LC) optimization in mitigating carbon emissions and promoting carbon cycle balance has gained increasing attention. This study takes Jiangsu Province as a case study, analyzing the changes in LU/LC from 1995 to 2020 and their impacts on carbon emissions and carbon storage. For Jiangsu Province’s five development scenarios in 2030 (business-as-usual, carbon emission, carbon storage, and carbon neutrality scenarios), objective functions and constraints were constructed. The PLUS model was employed to simulate land use for 2030, predicting carbon storage, economic benefits, and ecological benefits under each scenario and evaluating the impact of each scenario on achieving carbon peaking and carbon neutrality goals. The findings indicate that (1) from 1995 to 2020 there were great changes in land use types in Jiangsu Province, with an overall downward trend in carbon storage. (2) The simulated land use quantity structure and spatial patterns for 2030 under different scenarios exhibited significant differences. Compared with the Business-as-Usual Scenario, the other four optimized scenarios achieved a better balance between economic and ecological values. (3) The Integrated Scenario realized optimal synergy between farmland protection, ecological expansion, and economic output, representing the best compromise under multiple objectives. Full article

This study explores the relationship between land use and land cover (LULC) changes and a significant cyclogenesis event that occurred in Alabama’s Black Belt region from 6 to 7 October 2021. Utilizing the Weather Research and Forecasting (WRF) model, two scenarios were analyzed: the WRF Control Run, which maintained unchanged LULC, and the WRF Sensitivity Experiment, which converted 56.5% of forested areas into cropland to assess the impact on storm dynamics. Quantitative comparisons of predicted rainfall from both simulations were conducted against observed data. The control run demonstrated a Root Mean Square Error (RMSE) of 1.64, indicating accurate rainfall predictions. In contrast, the modified scenario yielded an RMSE of 2.01, suggesting lower reliability. The Mean Bias (MB) values were 1.32 for the control run and 1.58 for the modified scenario, revealing notable discrepancies in accuracy. The coefficient of determination (R²) was 0.247 for the control run and 0.270 for the modified scenario. The Nash–Sutcliffe Efficiency (NSE) value was 0.1567 for the control run but dropped to −0.2257 following LULC modifications. Sensitivity analyses revealed a 60% increase in heat flux and a 36% rise in precipitation, underscoring the significant impact of LULC on meteorological outcomes. While this study concentrated on the Black Belt region, the methodologies employed could apply to various other areas, though caution is advised when generalizing these results to different climates and socio-economic contexts. Further research is necessary to enhance the model’s applicability across diverse environments. Full article

Land use and land cover (LULC) in coastal areas is critical in shaping the ecological systems, regional economy, and livelihood of indigenous communities. This study analyzes LULC changes (LULCC) in Soc Trang Province, Vietnam Mekong Delta, from 2010 to 2020 and simulates future LULC for 2030 under four scenarios: natural growth (business as usual, BAU), climate change challenges, profit optimization, and adaptation strategies. Satellite-based LULC maps and geospatial datasets were integrated into a LULC simulation model based on a Markov Chain and Cellular Automata to predict LULC in 2030 under disparate scenarios. Simultaneously, this study also estimates economic values and ecosystem service values as proxies to evaluate benefits and trade-offs between the scenarios. The research findings reveal that the critical LULCC observed during 2010–2020 are transitions from triple rice crops to double rice crops, rice–shrimp to brackish aquaculture, and expansion of perennial plantations. These transitional trends will persist at a modest rate under the BAU scenario in 2030. The climate change challenge scenario will intervene up to 24.2% of the total area, with double rice crops reaching the most extensive area compared to other scenarios, about 106,047 ha. The profit optimization scenario will affect 16.03% of the total area, focusing on aquaculture expansion to the maximum shared proportion of 34% (approximately 57,000 ha). Adaptive solutions will emphasize reducing triple rice crops while expanding double rice crops and reviving rice–shrimp to different extents depending on development pathways. Economic evaluations show a growth trend across scenarios, with maximum returns under profit optimization. Yet, ecosystem service values notably highlight ecological trade-offs, raising concerns about balancing economic benefits and ecological trade-offs in land use planning. The research findings recommend a comprehensive and multitarget approach to land use planning that integrates ecosystem services into initial assessments to balance benefits and trade-offs in coastal areas commonly affected by LULCC. By adopting well-informed and strategic land use plans that minimize ecological and social impacts, local sustainability and resilience to climate change can be significantly enhanced. Full article

The Colombian Coffee Cultural Landscape (CCLC), a UNESCO World Heritage site, faces conservation threats due to changes in land use and land cover (LULC). This study analyzed and predicted the spatiotemporal dynamics of LULC in the CCLC from 2014 to 2034, assessing its effects on the landscape structure. The analyses identified negative impacts and provided insights for developing conservation and land use planning strategies aimed at comprehensive landscape management. A supervised classification methodology using the Random Forest algorithm was implemented by integrating multispectral (Landsat 8) and Synthetic Aperture Radar (SAR) data (Sentinel-1), achieving an overall accuracy of 87.88% and a Kappa coefficient of 84.20%. Future projections were conducted using a hybrid Cellular Automata and Artificial Neural Network model (CA-ANN), reaching an accuracy of 88.12% and a Kappa coefficient of 0.84. The results indicate urban expansion, increasing from 1.46% in 2014 to 15.64% by 2034, accompanied by a forest cover loss of 77.8% and a reduction in coffee-growing areas from 77.91% in 2019 to 68.40% by 2034. Landscape metric analysis revealed increased fragmentation and spatial heterogeneity. The integration of multisensor remote sensing, hybrid predictive models, and landscape metrics within the CCLC provides a quantitative methodological framework to evaluate the transformation of cultural landscapes under anthropogenic pressures. Full article