Search Results (899)

Quantitative analysis of LULC changes and their effects on carbon stock and sequestration is important for mitigating climate change. Therefore, this study examines carbon stock and sequestration in relation to LULC changes using the Land Change Modeler (LCM) and Ecosystem Services Modeler (ESM) in tropical dry deciduous forests of West Bengal, India. The LULC for 2006, 2014, and 2021 were classified using Google Earth Engine (GEE), while LULC changes and predictions were analyzed using LCM. Carbon stock and sequestration for present and future scenarios were estimated using ESM. The highest carbon was stored in forest land (124.167 Mg/ha), and storage outside the forest declined to 13.541 Mg/ha for agricultural land and 0–8.123 Mg/ha for other lands. Carbon stock and economic value decreased from 2006 to 2021, and are likely to decrease further in the future. Forest land is likely to contribute to 94% of future carbon loss in the study region, primarily due to its conversion into agricultural land. The implementation of multiple-species plantations, securing tenure rights, proper management practices, and the strengthening of forest-related policies can enhance carbon stock and sequestration. These spatial-temporal insights will aid in management strategies, and the methodology can be applied to broader contexts. Full article

The Ebinur Lake Basin (ELB), which is a typical watershed in an arid region, has an extremely delicate natural ecosystem. Rapid urbanisation and economic growth have triggered substantial ecological and environmental transformations in this key economic hub of Xinjiang. However, a comprehensive and systematic knowledge of the evolving ecological conditions in the ELB remains limited. Therefore, this study modelled the landscape ecological risk index (LERI) using land use/land cover (LULC) data from 1985 to 2022 and assessed the drivers of landscape ecological risk (LER) using a geographical detector model (GDM). The findings revealed that (1) from 1985 to 2022, the construction land, cropland, and forestland areas in the ELB increased, whereas those of water bodies, grasslands, and barren land decreased. (2) Between 1985 and 2022, LER in the ELB showed a downward trend. Spatially, LER was predominantly characterised by lower and lowest risk levels. The higher and highest risk status has been around Ebinur lake and has continued to improve each year. (3) Climatic factors, particularly temperature and precipitation, were identified as the most significant drivers of the LER change from 1985 to 2022. The findings provide crucial scientific knowledge for advancing sustainable development and maintaining ecological security in the ELB. Full article

The ecosystems in the mountainous region of Southwest China are exceptionally fragile and constitute one of the global hotspots for wildfire occurrences. Understanding the complex interactions between wildfires and their environmental and anthropogenic factors is crucial for effective wildfire modeling and management. Despite significant advancements in wildfire modeling using machine learning (ML) methods, their limited explainability remains a barrier to utilizing them for in-depth wildfire analysis. This paper employs Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models along with the MODIS global fire atlas dataset (2004–2020) to study the influence of meteorological, topographic, vegetation, and human factors on wildfire occurrences in the mountainous region of Southwest China. It also utilizes Shapley Additive exPlanations (SHAP) values, a method within explainable artificial intelligence (XAI), to demonstrate the influence of key controlling factors on the frequency of fire occurrences. The results indicate that wildfires in this region are primarily influenced by meteorological conditions, particularly sunshine duration, relative humidity (seasonal and daily), seasonal precipitation, and daily land surface temperature. Among local variables, altitude, proximity to roads, railways, residential areas, and population density are significant factors. All models demonstrate strong predictive capabilities with AUC values over 0.8 and prediction accuracies ranging from 76.0% to 95.0%. XGBoost outperforms LR and RF in predictive accuracy across all factor groups (climatic, local, and combinations thereof). The inclusion of topographic factors and human activities enhances model optimization to some extent. SHAP results reveal critical features that significantly influence wildfire occurrences, and the thresholds of positive or negative changes, highlighting that relative humidity, rain-free days, and land use land cover changes (LULC) are primary contributors to frequent wildfires in this region. Based on regional differences in wildfire drivers, a wildfire-risk zoning map for the mountainous region of Southwest China is created. Areas identified as high risk are predominantly located in the Northwestern and Southern parts of the study area, particularly in Yanyuan and Miyi, while areas assessed as low risk are mainly distributed in the Northeastern region. Full article

Rapid urbanization in developing countries leads to significant land-use and land-cover change (LULCC), which contributes to increased carbon dioxide (CO₂) emissions and the degradation of carbon storage. Studying spatio-temporal changes in carbon storage is crucial for guiding sustainable urban development toward carbon neutrality. This study integrates machine-learning random forest algorithm, CA–Markov, and InVEST models to predict carbon storage distribution in Shenzhen, China, under various scenarios. The findings indicate that, over the past two decades, Shenzhen has experienced significant land-use changes. The transformation from high- to low-carbon-density land uses, particularly the conversion of forestland to construction land, is the primary cause of carbon storage loss. Forestland is mainly influenced by natural factors, such as digital elevation model (DEM) and precipitation, while other land-use and land-cover (LULC) types are predominantly affected by socio-economic and demographic factors. By 2030, carbon storage is projected to vary significantly across different development scenarios, with the greatest decline expected under the natural development scenario (NDS) and the least under the ecological priority scenario (EPS). The RF-CA–Markov model outperforms the traditional CA–Markov model in accurately simulating land use, particularly for small and scattered land-use types. Our conclusions can inform future low-carbon city development and land-use optimization. Full article

In response to economic and environmental challenges like sea-level rise, salinity intrusion, groundwater extraction, sand mining, and sinking delta phenomena, the demand for solutions to adapt to changing conditions in riverine environments has increased significantly. High-quality analyses of land use and land cover (LULC) dynamics play a critical role in addressing these challenges. This study introduces a novel high-spatial resolution satellite-based approach to identify sub-seasonal LULC dynamics in the Mekong River Delta (MRD), employing a three-year (2021–2023) Sentinel-1 and Sentinel-2 satellite data time series. The primary obstacle is discerning detailed vegetation dynamics, particularly the seasonality of rice crops, answered through quantile mapping, harmonic regression with Fourier transform, and phenological metrics as inputs to a random forest machine learning classifier. Due to the substantial data volume, Google’s cloud computing platform Earth Engine was utilized for the analysis. Furthermore, the study evaluated the relative significance of various input features. The overall accuracy of the classification is 82.6% with a kappa statistic of 0.81, determined using comprehensive reference data collected in Vietnam. While the purely pixel-based approach has limitations, it proves to be a viable method for high-spatial resolution satellite image time series classification of the MRD. Full article

This study presents a comprehensive flood vulnerability assessment for Freetown, Sierra Leone, spanning the period from 2001 to 2022. The objective of this research was to assess the temporal and spatial changes in the flood vulnerability using Geographic Information System (GIS) tools and AHP-based Multi-Criteria Decision-Making (MCDM) analysis. This study identified the flood-vulnerable zones (FVZs) by integrating critical factors such as the rainfall, NDVI, elevation, slope, drainage density, TWI, distance to road, distance to river, and LULC. The analysis reveals that approximately 60% of the study area is classified as having medium to high vulnerability, with a significant 20% increase in the flood risk observed over the past two decades. In 2001, very-high-vulnerability zones covered about 68.84 km² (10% of the total area), with high-vulnerability areas encompassing 137.68 km² (20%). By 2020, very-high-vulnerability zones remained constant at 68.84 km² (10%), while high-vulnerability areas decreased to 103.26 km² (15%), and medium-vulnerability zones expanded from 206.51 km² (30%) in 2001 to 240.93 km² (35%). The AHP model-derived weights reflect the varied significance of the flood-inducing factors, with rainfall (0.27) being the most critical and elevation (0.04) being the least. A consistency ratio (CR) of 0.068 (< 0.1) confirms the reliability of these weights. The spatial–temporal analysis highlights the east and southeast regions of Freetown as consistently vulnerable over the years, while infrastructure improvements in other areas have contributed to a general decrease in very-high-vulnerability zones. This research highlights the urgent need for resilient urban planning and targeted interventions to mitigate future flood impacts, offering clear insights into the natural and human-induced drivers of the flood risk for effective hazard mitigation and sustainable urban development. Full article

While numerous studies have employed deep learning and high-resolution remote sensing to predict future land use and land cover (LULC) changes, no study has integrated these predictive tools with the current urban planning context to find a potential issues for sustainability. This study addresses this gap by examining the planning context of Busan Metropolitan City (BMC) and analyzing the paradoxical objectives within the city’s 2040 Master Plan and the subordinate 2030 Busan Master Plan for Parks and Greenbelts. Although the plans advocate for increased green areas to enhance urban sustainability and social wellbeing, they simultaneously support policies that may lead to a reduction in these areas due to urban development. Using the CA-ANN model in the MOLUSCE plugin, a deep learning-based LULC change analysis, we forecast further urban expansion and continued shrinkage of natural green areas. During 1980–2010, Busan Metropolitan City (BMC) underwent high-speed urban expansion, wherein the urbanized areas almost doubled and agricultural lands and green areas, including forests and grassland, reduced considerably. Forecasts for the years 2010–2040 show continued further expansion of urban areas at the expense of areas for agriculture and green areas, including forest and grasslands. Given the master plans, these highlight a critical tension between urban growth and sustainability. Despite the push for more green spaces, the replacement of natural landscapes with artificial parks and green areas may threaten long-term sustainability. In view of these apparently conflicting goals, the urban planning framework for BMC would have to take up increasingly stronger conservation policies and adaptive planning practices that consider environmental preservation on a par with economic development in the light of the planning context and trajectory of urbanization. Full article

Terrestrial ecosystems play a critical role in the global carbon cycle, and their carbon sequestration capacity is vital for mitigating the impacts of climate change. Changes in land use and land cover (LULC) dynamics significantly alter this capacity. This study scrutinizes the LULC evolution within the Beijing metropolitan region from 1992 to 2022, evaluating its implications for ecosystem carbon storage. It also employs the Patch-Generating Land Use Simulation (PLUS) model to simulate LULC patterns under four scenarios for 2035: an Uncontrolled Scenario (UCS), a Natural Evolution Scenario (NES), a Strict Control Scenario (SCS), and a Reforestation and Wetland Expansion Scenario (RWES). The InVEST model is concurrently used to assess and forecast ecosystem carbon storage under each scenario. Key insights from the study are as follows: (1) from 1992 to 2022, Beijing’s LULC exhibited a phased developmental trajectory, marked by an expansion of urban and forested areas at the expense of agricultural land; (2) concurrently, the region’s ecosystem carbon storage displayed a fluctuating trend, peaking initially before declining, with higher storage in the northwest and lower in the central urban zones; (3) by 2035, ecosystem carbon storage is projected to decrease by 1.41 Megatons under the UCS, decrease by 0.097 Megatons under the NES, increase by 1.70 Megatons under the SCS, and increase by 11.97 Megatons under the RWES; and (4) the study underscores the efficacy of policies curtailing construction land expansion in Beijing, advocating for sustained urban growth constraints and intensified afforestation initiatives. This research reveals significant changes in urban land use types and the mechanisms propelling these shifts, offering a scientific basis for comprehending LULC transformations in Beijing and their ramifications for ecosystem carbon storage. It further provides policymakers with substantial insights for the development of strategic environmental and urban planning initiatives. Full article

Previous research has found higher temperature trends at urban observatories. This study examines in depth the features of the urban environment, the thermal behaviour of land use and land cover, and the changes that have taken place in five urban areas of the Spanish Mediterranean. The CORINE Land Cover database was used to delimit the primary land use land cover (LULC) and its changes between 1990 and 2018. Once this had been established, land surface temperatures (LSTs) between 1985 and 2023 were retrieved from the Landsat database available on the Climate Engine website. There has been a significant advance in artificial land uses, which have become the main uses in the urban areas in Valencia and Alicante. An analysis of the primary land cover showed the greatest thermal increase in artificial surfaces, especially in the industrial, commercial, and transport units that are common on their outskirts, without exception in any urban area. The results are less clear for urban fabrics and agricultural areas due to their diversity and complexity. The density of vegetation is a key factor in the magnitude of the UHI, which is higher in the urban areas with more vegetated agriculture areas, therefore showing lower LST than both industrial units and urban fabrics. Another important conclusion is the role of breezes in limiting or eliminating the strength of the UHI. Sea breezes help to explain the monthly variation of UHIs. Both bodies of water and areas of dense tree vegetation provided the lowest LST, a fact of special interest for mitigating the effects of heat waves in increasingly large urban areas. This study also concludes the different effect of each LULC on the temperatures recorded by urban observatories and enables better decision-making when setting up weather stations for a more detailed time study of the urban heat island (UHI). Full article

Land use and land cover (LULC) changes driven by ecological restoration and protection projects play a pivotal role in reshaping landscape patterns. However, the specific impacts of these projects on landscape structure remain understudied. In this research, we applied geographically weighted regression (GWR) to analyze the spatial relationships between typical land use expansion and landscape pattern characteristics in the Lesser Khingan Mountains–Sanjiang Plain region between 2017 and 2022. Our results indicate three key findings: (1) Significant spatial heterogeneity exists in the relationship between landscape patterns and land use expansion, which varies across geographic locations; (2) Ecological restoration projects generally reduce fragmentation, dominance, and heterogeneity while enhancing connectivity, particularly in forest and farmland regions. However, excessive land use expansion in certain areas may reverse these positive effects; (3) Landscape complexity increases in high-altitude mountainous regions due to land use expansion but decreases in plains, particularly in forest-to-farmland conversions. These findings provide new insights into how landscape patterns respond to ecological restoration efforts and offer actionable guidance for improving future land use planning and policy decisions. Our study highlights the need to consider local geomorphological factors when designing ecological projects, ensuring that restoration efforts align with regional landscape dynamics to maintain landscape integrity. Full article

Land Use and Land Cover Change (LUCC) represents the interaction between human societies and the natural environment. Studies of LUCC simulation allow for the analysis of Land Use and Land Cover (LULC) patterns in a given region. Moreover, these studies enable the simulation of complex future LUCC scenarios by integrating multiple factors. Such studies can provide effective means for optimizing and making decisions about the future patterns of a region. This review conducted a literature search on geographic models and simulations in the Web of Science database. From the literature, we summarized the basic steps of spatiotemporal dynamic simulation of LUCC. The focus was on the current major models, analyzing their characteristics and limitations, and discussing their expanded applications in land use. This review reveals that current research still faces challenges such as data uncertainty, necessitating the advancement of more diverse data and new technologies. Future research can enhance the precision and applicability of studies by improving models and methods, integrating big data and multi-scale data, and employing multi-model coupling and various algorithmic experiments for comparison. This would support the advancement of land use spatiotemporal dynamic simulation research to higher levels. Full article

Polycentric development facilitates urban–rural spatial reshaping and land use/land cover (LULC) protection. Previous studies have predominantly focused on urban areas, with spatial delineation methods biased towards the macro-level, lacking a holistic perspective that situates them within the urban–rural spatial framework. This study proposes a spatial delineation framework that is applicable to the polycentric structure, taking into account the social, economic, and natural characteristics of urbanization. It employs semivariance analysis and spatial continuous wavelet transform (SCWT) to analyze the effects of polycentric development on the urban–rural space of Wuhan from 2012 to 2021 and applies a land use transition matrix, landscape indices, and bivariate spatial autocorrelation to quantify the responses and differences of LULC within urban–rural space. The results indicate that $600\mathrm{m}\times 600\mathrm{m}$ is the best scale for exhibiting the multidimensional characterization of urbanization. The polycentric structure alleviates the compact development of the central city, and it drives rapid expansion at the urban–rural fringe, exacerbating the spatial heterogeneity in LULC change pattern, spatial configuration, and urbanization response within urban–rural spaces. The overall effects of urbanization on LULC are relatively weak along the urban–rural gradient, experiencing a transition from positive to negative and back to positive. This study employs a novel spatial delineation framework to depict the polycentric transformation of metropolitan areas and provides valuable insights for regional planning and ecological conservation in the urban–rural fringe. Full article

Hydrological processes can be highly influenced by changes in land use land cover (LULC), which can make hydrological modelling also very sensitive to land cover characterization. Therefore, obtaining up-to-date LULC data is a crucial process in hydrological modelling, and as such, different sources of LULC data raises questions on their quality and applicability. This is especially true with new data sources, such as citizen science-based land cover maps. Therefore, this research aims to explore the influence of LULC data sources on hydrological models via their parameterization and by performing sensitivity analyses. Kiffissos catchment, in Greece, a poorly gauged and highly urbanized basin including the city of Athens, is the case study area. In total, 12 continuous hydrological models were developed by mainly varying their structure and parametrization (lumped and gridded) and using three LULC datasets: coordination of information on the environment (CORINE), Urban Atlas and Scent (citizen-based). It was found that excess precipitation is negligibly contributed to by soil saturation and is dominated by the runoff over impervious areas. Therefore, imperviousness was the main parameter influencing both sensitivity to land cover and parameterization. Lastly, although the parametrization as lumped and gridded models affected the representation of hydrological processes in pervious areas, it was not relevant in terms of excess precipitation. Full article

Caatinga is a typical Brazilian biome facing severe threats despite its ecological and socio-economic importance. Conservation strategies are essential in protecting ecosystems and ensuring natural resource sustainability. Mata da Pimenteira State Park (PEMP), launched in 2012, is an example of such a strategy. The current study aims to use orbital remote sensing techniques to assess human impacts on changes in land use and land cover (LULC) after the establishment of PEMP in the semi-arid region known as Caatinga, in Pernambuco State. The effects of this unit on vegetation preservation were specifically analyzed based on using data from the MapBiomas Brasil project to assess trends in LULC, both in and around PEMP, from 2002 to 2020. Man–Kendall and Pettitt statistical tests were applied to identify significant changes, such as converting forest areas into pastures and agricultural plantations. Trends of the loss and gain of LULC were observed over the years, such as forest areas’ conversion into pasture and vice versa, mainly before and after PEMP implementation. These findings highlight the importance of developing conservation measures and planning to help protecting the Caatinga, which is a vital biome in Brazil. Full article

Water yield (WY) service is the cornerstone of ecosystem functionality. Predicting and assessing the impact of land use/land cover (LULC) changes on WY is imperative for a nation’s food security, regional economic development, and ecological environmental protection. This study aimed to evaluate the water yield (WY) service in Henan Province, China, using high-resolution (30 m) remote sensing land use monitoring data from four study years: 1990, 2000, 2010, and 2020. It also utilized the PLUS model to predict the characteristics of LULC evolution and the future trends of WY service under four different development scenarios (for 2030 and 2050). The study’s results indicated the following: (1) From 1990 to 2020, the Henan Province’s WY first increased and then decreased, ranging from 398.56 × 10⁸ m³ to 482.95 × 10⁸ m³. The southern and southeastern parts of Henan Province were high-value WY areas, while most of its other regions were deemed low-value WY areas. (2) The different land use types were ranked in terms of their WY capacity, from strongest to weakest, as follows: unused land, cultivated land, grassland, construction land, woodland, and water. (3) The four abovementioned scenarios were ranked, from highest to lowest, based on the Henan’s total WY (in 2050) in each of them: high-quality development scenario (HDS), business-as-usual scenario (BAU), cultivated land protection scenario (CPS), and ecological protection scenario (ES). This study contributes to the advancement of ecosystem services research. Its results can provide scientific support for water resource management, sustainable regional development, and comprehensive land-use planning in Henan Province. Full article