Search Results (9,119)

Ecological security is integral to national security strategies, making the construction of ecological security patterns essential for mitigating ecological risks. However, predictive research on ecological security patterns (ESPs) remains limited. This study integrates the Patch-generating Land Use Simulation (PLUS) model with ecological security pattern analysis to provide scientific insights into spatial governance and optimization in the Poyang Lake Ecological and Economic Zone (PLEEZ). First, the PLUS model simulated land use changes in 2030 under three scenarios: natural development (ND), economic development (ED), and ecological protection (EP). Based on these projections, ecological security patterns were constructed using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, the Morphological Spatial Pattern Analysis (MSPA) method, Conefor 2.6, the Minimum Cumulative Resistance (MCR) model, and resistance theory. The results indicate: (1) 19, 18, and 21 ecological source areas were identified under different scenarios, covering 6093.16 km², 5973.21 km², and 6702.56 km², respectively, with 9, 8, and 10 important source sites, primarily in the north. (2) 37, 35, and 43 ecological corridors were delineated, exhibiting a spiderweb-like distribution. (3) 94, 62, and 107 ecological pinch points and 116, 121, and 104 ecological barrier points were detected. The Ecological Node Aggregation Area was identified as a critical zone for targeted ecological protection and restoration. Finally, the ecological zoning management strategy of “Four Cores, Two Zones, and One Belt” was proposed. This study offers valuable insights for sustainable land use planning and ecological risk mitigation. Full article

Modern cities are rapidly evolving in terms of urban morphology, driven by exponential population growth that accelerates the urbanisation process. The changes in land use have increased urban area and density, intensifying the urban heat island (UHI) effect, which poses one of the biggest threats to human health and well-being, especially in metropolitan regions. One of the most effective strategies to counter urban heat is the implementation of green infrastructure and the use of suitable building materials that help reduce heat stress. However, access to green spaces and the affordability of efficient building materials are not the same among citizens. This paper aims to identify the socio-economic characteristics of communities in Melbourne, Australia, that contribute to their vulnerability to urban heat under local conditions. This study employs remote sensing and geographical information systems (GIS) to conduct a macro-scale analysis, to investigate the correlation between urban heat patterns and socio-economic characteristics, taking into account factors such as vegetation cover, built-up areas, and land use types. The results from the satellite images and the geospatial data reveal that Deer Park, located in the western suburbs of Melbourne, has the highest land surface temperature (LST) at 32.54 °C, a UHI intensity of 1.84 °C, a normalised difference vegetation index (NDVI) of 0.11, and a normalised difference moisture index (NDMI) of −0.081. The LST and UHI intensity indicate a strong negative correlation with the NDVI (r = −0.42) and NDMI (r = −0.6). In contrast, the NDVI and NDMI have a positive correlation with the index of economic resources (IER) with r values of 0.29 and 0.24, indicating that the areas with better finance resources tend to have better vegetation coverage or plant health with less water stress, leading to lower LST and UHI intensity. This study helps to identify the most critical areas in the Greater Melbourne region that are vulnerable to the risk of urban heat and extreme heat events, providing insights for the local city councils to develop effective mitigation strategies and urban development policies that promote a more sustainable and liveable community. Full article

As climate change accelerates and urbanization intensifies, mitigating the Urban Heat Island (UHI) effect has become crucial for sustainable urban planning. This study evaluated the role of four key urban indicators—buildings, greenery, streets, and pedestrian paths—in reducing air temperature and improving energy efficiency within the Kartal District of Istanbul. To ensure accurate and data-driven results, multiple advanced software tools were integrated throughout the research process. QGIS, Google Earth, and OpenStreetMap were used to generate high-resolution land use/land cover (LULC) maps, while Meteoblue climate data and the Global Heat Island Map provided essential climatic parameters. The InVEST Urban Cooling Model was employed to simulate temperature reduction effects, and eQuest energy simulation software assessed the impact of building modifications on energy consumption. The study tested multiple UHI mitigation scenarios, including green roofs, increased street tree cover, grass-covered pedestrian paths, and high-albedo pavement, comparing their individual and combined effects. The results indicated that integrating all strategies achieved the most significant cooling impact, reducing air temperatures by 1.14 °C and improving energy efficiency by 61%. Among the individual interventions, green roofs provided the highest building energy savings (28% reduction), while grass-covered pedestrian paths homogenized the district-wide temperature distribution. These findings underscore the importance of combining GIS-based spatial analysis, climate modeling, and energy simulation tools to develop reliable, scalable, and effective urban heat mitigation strategies. Future urban planning should prioritize a multi-software approach to enhance sustainability, optimize energy efficiency, and improve urban resilience. Full article

Illinois is a leading state for agricultural production in the United States, and corn production in the state has rapidly increased since the 1970s. Intensification of agriculture has been shown to have impacts on the atmosphere by altering humidity, and changes in land cover and soil moisture have resulted in changes in stability and temperature in the planetary boundary layer. Using descriptive statistics and regression analysis, this study assessed changes in temperature and dew point across different land cover classes, parts of the growing season, and by the geographic location of the station (north vs. south) in Illinois from 2005–2022 using data from 58 hourly weather stations. Overall, dew points are not increasing more rapidly in cultivated agriculture areas compared to other land cover classes in the state. Dew points are increasing across land cover classifications, particularly in the later part of the growing season. Temperatures are not as consistent, with decreases in temperature observed in cultivated agricultural areas and during the peak of the growing season. While dew points are increasing in both the northern and southern regions of the state, temperature increases are only found in the north. Dew point increases in Illinois do not appear to be driven by changing agricultural practices. However, future work should examine additional regions inside and outside of the Corn Belt to determine if changes in land cover and agricultural practices have impacts on the climates of those regions. Full article

This study presents a multi-temporal analysis of vegetation cover changes in the Guayepo stream watershed (Cartagena de Indias, Colombia) for 2000, 2010, and 2020 and their impact on surface runoff generation. Hydrological data from 1974 to 2019 were processed to model intensity–duration–frequency (IDF) curves and simulate heavy rainfall events using six storms of nine-hour duration. Following the Soil Conservation Service guidelines, these were used to estimate runoff flows for return periods of 25, 50, and 100 years via the curve number method in HEC-HMS. Vegetation cover was assessed using the CORINE land cover methodology applied to official land use maps. The analysis revealed a significant loss of natural vegetation: dense forest cover declined dramatically from 14.38% in 2000 to 0% in 2020, and clean pastures were reduced by 46%. In contrast, weedy pastures and pasture mosaics with natural areas increased by 299% and 136%, respectively, reflecting a shift towards more degraded land cover types. As a result of these changes, total runoff flows of the model increased by 9.7% and 4.3% under antecedent moisture conditions I and II, respectively, for the 100-year return period. These findings reveal ongoing degradation of the watershed’s natural cover, linked to expanding agricultural uses and changes in vegetation structure. The decline in forested areas has increased surface runoff, elevating flood risk and compromising the watershed’s hydrological regulation. The study suggests that integrated land management and ecological restoration strategies could be key in preserving hydrological ecosystem services and reducing the negative impacts of land use change. Full article

Plantation forests (PFs) play a crucial role in China’s climate change mitigation strategy due to their significant capacity to sequestrate carbon (C). Understanding the long-term trend in PFs’ C uptake capacity and the key drivers influencing it is crucial for optimizing PF management and planning for climate mitigation. In this study, we quantified the long-term (1981–2019) C sequestration of PFs in Jiangsu Province, where PFs have expanded considerably in recent decades, particularly since 2015. Seasonal and interannual variations in gross primary productivity (GPP), net primary productivity (NPP), and net ecosystem productivity (NEP) were assessed using the boreal ecosystem productivity simulator (BEPS), a process-based terrestrial biogeochemical model. The model integrates multiple sources of remote-sensing datasets, such as leaf area index and land cover data, to simulate the critical biogeochemical processes governing land surface dynamics, enabling the quantification of vegetation and soil C stocks and nutrient cycling patterns. The results indicated a significant increasing trend in GPP, NPP, and NEP over the past four decades, suggesting enhanced C sequestration by PFs across the study region. The interannual variability in these indicators was associated with that of nitrogen (N) deposition in recent years, implying that nutrient availability could be a limiting factor for plantation productivity. Seasonal GPP and NPP exhibited peak values in spring (April to May) or late summer (August to September), with increases in growing season productivity in recent years. In contrast, NEP peaked in spring (April to May) but declined to negative values in early summer (July to August), indicating a seasonal C source–sink transition. All three indicators showed a general negative correlation with late-growing-season temperature (August to September), suggesting that summer droughts probably highly constrained the C sequestration of the existing PFs. These findings provide insights for the strategic implementation and management of PFs, particularly in regions with a warm temperate climate undergoing afforestation expansion. Full article

Post-processing optimization refers to the refinement of land cover products by applying specific rules or algorithms to minimize erroneous changes in land cover types caused by classification uncertainty or interannual phenological variations. Global land cover (GLC) mapping has gained significant attention over the past decade, but current GLC time-series products suffer from considerable inconsistencies in mapping results between different epochs, leading to severe erroneous changes. Here, we aimed to design a novel post-processing approach by combining multi-source data to optimize the GLC_FCS30D product, which represents a groundbreaking improvement in GLC dynamic mapping at a resolution of 30 m. First, spatiotemporal filtering with a window size of 3 × 3 × 3 was applied to reduce the “salt-and-pepper” effect. Second, a temporal consistency optimization algorithm based on LandTrendr was used to identify land cover changes across the entire time series and eliminate excessively frequent erroneous changes. Third, certain land cover transitions between easily misclassified types were optimized using logical rules and multi-source data. Specifically, the illogical wetland-related transitions (wetland–water and wetland–forest) were corrected using a simple replacement rule. To address the noticeable erroneous changes in arid and semi-arid regions, the erroneous land cover transitions involving bare areas, sparse vegetation, grassland, and shrubland were corrected by combining NDVI and precipitation data. Finally, the performance of our post-processing optimization approach was evaluated and quantified. The proposed approach successfully reduced the cumulative change area from 7537.00 million hectares (Mha) in the GLC_FCS30D product without optimization to 1981.00 Mha in the GLC_FCS30D product with optimization, eliminating 5556.00 Mha of erroneous changes across 26 epochs. Furthermore, the overall accuracy of the mapping was also improved from 73.04% to 74.24% for the Land Cover Classification System (LCCS) level-1 validation system. Erroneous changes in GLC_FCS30D were considerably mitigated with the post-processing optimization method, providing more reliable insights into GLC changes from 1985 to 2022 at a 30 m resolution. Full article

Tropical forests provide essential ecosystem services, playing a critical role in climate regulation, biodiversity conservation, and regional hydrological cycles while also supporting livelihoods. However, they are increasingly threatened by deforestation and land-use change. Accurate land cover (LC) mapping is vital to monitor these changes, but mapping tropical forests is challenging due to complex spatial patterns, spectral similarities, and frequent cloud cover. This study aims to improve LC classification accuracy in such a heterogeneous tropical forest region in Southeast Asia, namely Kulen, Cambodia, which is characterized by natural forests, regrowth forests, and agricultural lands including cashew plantations and croplands, using Sentinel-2 imagery, recursive feature elimination (RFE), and Random Forest. We generated 65 variables of spectral bands, indices, bi-seasonal differences, and topographic data from Sentinel-2 Level-2A and Shuttle Radar Topography Mission datasets. These variables were extracted from 1000 random points per 12 LC classes from reference polygons based on observed GPS points, Uncrewed Aerial Vehicle imagery, and high-resolution satellite data. The random forest models were optimized through correlation-based filtering and recursive feature elimination with hyperparameter tuning to improve classification accuracy, validated via confusion matrices and comparisons with global and national-scale products. Our results highlight the significant role of topographic variables such as elevation and slope, along with red-edge spectral bands and spectral indices related to tillage, leaf water content, greenness, chlorophyll, and tasseled cap transformation for tropical land cover mapping. The integration of bi-seasonal datasets improved classification accuracy, particularly for challenging classes like semi-evergreen and deciduous forests. Furthermore, correlation-based filtering and recursive feature elimination reduced the variable set from 65 to 19, improving model efficiency without sacrificing accuracy. Combining these variable selection methods with hyperparameter tuning optimized the classification, providing a more reliable LC product that outperforms existing LC products and proves valuable for deforestation monitoring, forest management, biodiversity conservation, and land use studies. Full article

Deep learning models have shown great potential in scientific research, particularly in remote sensing for monitoring natural resources, environmental changes, land cover, and land use. Deep semantic segmentation techniques enable land cover classification, change detection, object identification, and vegetation health assessment, among other applications. However, their effectiveness relies on large labeled datasets, which are costly and time-consuming to obtain. Domain adaptation (DA) techniques address this challenge by transferring knowledge from a labeled source domain to one or more unlabeled target domains. While most DA research focuses on single-target single-source problems, multi-target and multi-source scenarios remain underexplored. This work proposes a deep learning approach that uses Domain Adversarial Neural Networks (DANNs) for deforestation detection in multi-domain settings. Additionally, an uncertainty estimation phase is introduced to guide human review in high-uncertainty areas. Our approach is evaluated on a set of Landsat-8 images from the Amazon and Brazilian Cerrado biomes. In the multi-target experiments, a single source domain contains labeled data, while samples from the target domains are unlabeled. In multi-source scenarios, labeled samples from multiple source domains are used to train the deep learning models, later evaluated on a single target domain. The results show significant accuracy improvements over lower-bound baselines, as indicated by F1-Score values, and the uncertainty-based review showed a further potential to enhance performance, reaching upper-bound baselines in certain domain combinations. As our approach is independent of the semantic segmentation network architecture, we believe it opens new perspectives for improving the generalization capacity of deep learning-based deforestation detection methods. Furthermore, from an operational point of view, it has the potential to enable deforestation detection in areas around the world that lack accurate reference data to adequately train deep learning models for the task. 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

Free trade zones are key regions experiencing rapid economic growth, urbanization, and a sharp increase in population density. During the development of free trade zones, these areas undergo drastic transformations in landscape types, large-scale urban construction, heightened resource consumption, and other associated challenges. These factors have led to severe landscape ecological risk (LER). Therefore, conducting comprehensive assessments and implementing effective management strategies for LER is crucial in advancing ecological civilization and ensuring high-quality development. This study takes Hainan Island (HI), China, as a case study and utilizes multi-source data to quantitatively evaluate land use and land cover change (LULCC) and the evolution of the LER in the study area from 2015 to 2023. Additionally, it examines the spatial patterns of LER under three future scenarios projected for 2033: a natural development scenario (NDS), an economic priority scenario (EPS), and an ecological conservation scenario (ECS). Adopting a spatiotemporal dynamic perspective framed by the “historical–present–future” approach, this research constructs a zoning framework for LER management to examine the temporal and spatial processes of risk evolution, its characteristics, future trends, and corresponding management strategies. The results indicate that, over an eight-year period, the area of built-up land expanded by 40.31% (504.85 km²). Specifically, between 2015 and 2018, built-up land increased by 95.85 km², while, from 2018 to 2023, the growth was significantly larger at 409.00 km², highlighting the widespread conversion of cropland into built-up land. From 2015 to 2023, the spatial distribution of LER in the study area exhibited a pattern of high-risk peripheries (central mountainous areas) and low-risk central regions (coastal areas). Compared to 2023, projections for 2033 under different scenarios indicate a decline in cropland (by approximately 17.8–19.45%) and grassland (by approximately 24.06–24.22%), alongside an increase in forestland (by approximately 4.5–5.35%) and built-up land (by approximately 23.5–41.35%). Under all three projected scenarios, high-risk areas expand notably, accounting for 4.52% (NDS), 3.33% (ECS), and 5.75% (EPS) of the total area. The LER maintenance area (65.25%) accounts for the largest proportion, primarily distributed in coastal economic development areas and urban–rural transition areas. In contrast, the LER mitigation area (7.57%) has the smallest proportion. Among the driving factors, the GDP (q = 0.1245) and year-end resident population (q = 0.123) were identified as the dominant factors regarding the spatial differentiation of LER. Furthermore, the interaction between economic factors and energy consumption further amplifies LER. This study proposes a policy-driven dynamic risk assessment framework, providing decision-making support and scientific guidance for LER management in tropical islands and the optimization of regional land spatial planning. Full article

Plant species classification in semi-arid mining areas is of great significance in assessing the environmental impacts and ecological restoration effects of coal mining. However, in semi-arid mining areas characterized by mixed arbor–shrub–herb vegetation, the complex vegetation distribution patterns and spectral features render single-sensor approaches inadequate for achieving fine classification of plant species in such environments. How to effectively fuse hyperspectral images (HSI) data with light detection and ranging (LiDAR) to achieve better accuracy in classifying vegetation in semi-arid mining areas is worth exploring. There is a lack of precise evaluation regarding how these two data collection approaches impact the accuracy of fine-scale plant species classification in semi-arid mining environments. This study established two experimental scenarios involving the synchronous and asynchronous acquisition of HSI and LiDAR data. The results demonstrate that integrating LiDAR data, whether synchronously or asynchronously acquired, significantly enhances classification accuracy compared to using HSI data alone. The overall classification accuracy for target vegetation increased from 71.7% to 84.7% (synchronous) and 80.2% (asynchronous), respectively. In addition, the synchronous acquisition mode achieved a 4.5% higher overall accuracy than asynchronous acquisition, with particularly pronounced improvements observed in classifying vegetation with smaller canopies (Medicago sativa L.: 17.4%, Pinus sylvestris var. mongholica Litv.: 11.7%, and Artemisia ordosica Krasch.: 7.5%). This study can provide important references for ensuring classification accuracy and error analysis of land cover based on HSI-LiDAR fusion in similar scenarios. Full article

The increasing availability of satellite data and advances in machine learning (ML) have significantly enhanced land use image classification for environmental monitoring. However, the primary challenge in land use classification using satellite imagery lies in the presence of cloud cover, variations in data resolution, and seasonal changes, which impact classification accuracy and reliability. This paper aims to improve the assessment of land cover changes by proposing a hybrid ML, cloud interpolation, and vegetation indices-based approach. The proposed approach was implemented by using a random forest (RF) classifier, combined with cloud interpolation and vegetation indices, to classify land use Sentinel-2 satellite imagery in the Baltic States. The experimental results demonstrate that the proposed approach achieves an accuracy rate above 90%, effectively demonstrating its capacity to distinguish between various land use types. We believe that this study and its results will inspire researchers and practitioners to further work towards land use classification by applying ML algorithms and offer valuable insights for future classification tasks involving noise digitalization and research. Full article

This study presents a spatial methodology for integrating climate change (CC) risks and ecosystem service (ES) assessments into strategic spatial planning, applied to the Metropolitan Plan of the Province of Rimini (Emilia-Romagna, Italy). The proposed approach combines IPCC-aligned climate vulnerability analysis with ecosystem service mapping based on the methodology developed by CREN. Climate risks, including urban heat islands, droughts, and urban floods, were assessed using satellite-derived indices such as Land Surface Temperature (LST), Vegetation Health Index (VHI), and hydraulic modeling. For ESs, nine key services were evaluated and mapped by integrating land use, forest cover, and habitat data with biophysical modulation factors (e.g., slope, carbon stock, infiltration capacity). The results highlight priority areas where climate adaptation and ecological functions converge, enabling targeted interventions. This integrated workflow offers a replicable and scalable planning tool to support evidence-based decision-making at the metropolitan level. Its adoption is recommended by other local and regional authorities to strengthen the climate and ecological responsiveness of spatial planning instruments. Full article

Flash floods rank among the most devastating natural hazards, causing widespread socio-economic, environmental, and infrastructural damage globally. Hence, innovative management approaches are required to mitigate their increasing frequency and intensity, driven by factors such as climate change and urbanization. Accordingly, this study introduced an integrated flood assessment approach (IFAA) for sustainable management of flood risks by integrating the analytical hierarchy process-weighted linear combination (AHP-WLC) and fuzzy-ordered weighted averaging (FOWA) methods. The IFAA was applied in South Khorasan Province, Iran, an arid and flood-prone region. Fifteen controlling factors, including rainfall (RF), slope (SL), land use/land cover (LU/LC), and distance to rivers (DTR), were processed using the collected data. The AHP-WLC method classified the region into flood susceptibility zones: very low (10.23%), low (23.14%), moderate (29.61%), high (17.54%), and very high (19.48%). The FOWA technique ensured these findings by introducing optimistic and pessimistic fuzzy scenarios of flood risk. The most extreme scenario indicated that 98.79% of the area was highly sensitive to flooding, while less than 5% was deemed low-risk under conservative scenarios. Validation of the IFAA approach demonstrated its reliability, with the AHP-WLC method achieving an area under curve (AUC) of 0.83 and an average accuracy of ~75% across all fuzzy scenarios. Findings revealed elevated flood dangers in densely populated and industrialized areas, particularly in the northern and southern regions, which were influenced by proximity to rivers. Therefore, the study also addressed challenges linked to sustainable development goals (SDGs), particularly SDG 13 (climate action), proposing adaptive strategies to meet 60% of its targets. This research can offer a scalable framework for flood risk management, providing actionable insights for hydrologically vulnerable regions worldwide. Full article