Search Results (1,928)

Rapid urbanization and land-use changes may affect the intensity of urban heat islands (UHIs). However, research on the eastern Chinese city of Yangzhou is lacking. Using land cover data and the InVest Urban Cooling model, this study evaluated the spatiotemporal heterogeneity of the UHI effect from 1990 to 2020 and its socioecological drivers in Yangzhou City. Landscape pattern indices such as patch area (CA), percentage of landscape (PLAND), number of patches, patch density, and aggregation index were created using Fragstats 4.2 software. Several social indicators, such as gross domestic product (GDP), night-light index, and population density, were considered to explore their correlation with UHI indicators. During the past three decades, rapid urbanization in Yangzhou has intensified the UHI effect, with the cooling capacity (cc park) and heat mitigation index (HMI) decreasing by ~9.6%; however, the mixed air temperature (T air) has increased by 0.14 °C. The main heat island areas are concentrated in southern Yangzhou, including the Hanjiang and Guangling districts, and have expanded over time. T air was positively correlated with GDP, night-light index, and population density. Moreover, for the impervious land use type, cc park and HMI were negatively correlated with CA and PLAND (p < 0.01). This study contributes to a deeper understanding of the dynamics of UHIs and provides valuable insights for policymakers, urban planners, and researchers striving to create sustainable and climate-resilient cities in Yangzhou. Full article

In the 21st century, the rapid urbanization process has led to increasingly severe urban heat island effects and other urban thermal environment issues, posing significant challenges to urban planning and environmental management. This study focuses on Zhengzhou, China, utilizing Landsat remote sensing imagery data from five key years between 2000 and 2020. By applying atmospheric correction methods, we accurately retrieved the land surface temperature (LST). The study employed a gravity center migration model to track the spatial changes of heat island patches and used the geographical detector method to quantitatively analyze the combined impact of surface characteristics, meteorological conditions, and socio-economic factors on the urban heat island effect. Results show that the LST in Zhengzhou exhibits a fluctuating growth trend, closely related to the expansion of built-up areas and urban planning. High-temperature zones are mainly concentrated in built-up areas, while low-temperature zones are primarily found in areas covered by water bodies and vegetation. Notably, the Normalized Difference Built-up Index (NDBI) and the Normalized Difference Vegetation Index (NDVI) are the two most significant factors influencing the spatial distribution of land surface temperature, with explanatory power reaching 42.7% and 41.3%, respectively. As urban development enters a stable stage, government environmental management measures have played a positive role in mitigating the urban heat island effect. This study not only provides a scientific basis for understanding the spatiotemporal changes in land surface temperature in Zhengzhou but also offers new technical support for urban planning and management, helping to alleviate the urban heat island effect and improve the living environment quality for urban residents. Full article

Understanding the interrelationships between land use, climate change, and regional water yield is critical for effective water resource management and ecosystem protection. However, comprehensive insights into how water yield evolves under different land use scenarios and climate change remain elusive. This study employs the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models, Patch-generating Land Use Simulation (PLUS) model, and Geodetector within a unified framework to evaluate the dynamics of land use, water yield, and their relationships with various factors (meteorological, social, economic, etc.). To forecast the land use/cover change (LUCC) pattern of the Yellow River Basin by 2030, three scenarios were considered: economic development priority (Scenario 1), ecological development priority (Scenario 2), and cropland development priority (Scenario 3). Climate change scenarios were constructed using CMIP6 data, representing low-stress (SSP119), medium-stress (SSP245), and high-stress (SSP585) conditions. The results show the following: (1) from 2000 to 2020, cropland was predominant in the Yellow River Basin, Henan Province, with significant land conversion to impervious land (construction land) and forest land; (2) water yield changes during this period were primarily influenced by meteorological factors, with land use changes having negligible impact; (3) by 2030, the water yield of Scenario 1 is highest among different land use scenarios, marginally surpassing Scenario 2 by 1.60 × 10⁸ m³; (4) climate scenarios reveal significant disparities, with SSP126 yielding 54.95 × 10⁸ m³ higher water yield than SSP245, driven predominantly by precipitation; (5) Geodetector analysis identifies precipitation as the most influential single factor, with significant interactions among meteorological and socio-economic factors. These findings offer valuable insights for policymakers and researchers in formulating land use and water resource management strategies. 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

Terrestrial ecosystems play a crucial role in global carbon cycling by sequestering carbon from the atmosphere and storing it primarily in living biomass and soil. Monitoring terrestrial carbon stocks is essential for understanding the impacts of changes in land use on carbon sequestration. This study investigates the potential of remote sensing techniques and the Google Earth Engine to map and monitor changes in the forests of Calabria (Italy) over the past two decades. Using satellite-sourced Corine land cover datasets and the InVEST model, changes in Land Use Land Cover (LULC), and carbon concentrations are analyzed, providing insights into the carbon dynamics of the region. Furthermore, cellular automata and Markov chain techniques are used to simulate the future spatial and temporal dynamics of LULC. The results reveal notable fluctuations in LULC; specifically, settlement and bare land have expanded at the expense of forested and grassland areas. These land use and land cover changes significantly declined the overall carbon stocks in Calabria between 2000 and 2024, resulting in notable economic impacts. The region experienced periods of both decline and growth in carbon concentration, with overall losses resulting in economic impacts up to EUR 357.57 million and carbon losses equivalent to 6,558,069.68 Mg of CO${ }_2$ emissions during periods of decline. Conversely, during periods of carbon gain, the economic benefit reached EUR 41.26 million, with sequestered carbon equivalent to 756,919.47 Mg of CO${ }_2$ emissions. This research aims to highlight the critical role of satellite data in enhancing our understanding and development of comprehensive strategies for managing carbon stocks in terrestrial ecosystems. Full article

Small-scale open-pit, non-artisanal mining of low-value ores is an understudied practice despite its widespread occurrence and potential impact on freshwater resources due to mining-induced land-use/cover changes (LUCCs). This research investigates the long-term impacts of andesite mining in Pasuruan, Indonesia, on the Umbulan Spring’s water discharge within its watershed. System Dynamics (SD) modeling captures the systemic and systematic impact of mining-induced LUCCs on discharge volumes and groundwater recharge. Agricultural and reservoir-based land reclamation scenarios then reveal post-mining temporal dynamics. The no-mining scenario sees the spring’s discharge consistently decrease until an inflection point in 2032. With mining expansion, reductions accelerate by ~1.44 million tons over two decades, or 65.31 thousand tons annually. LUCCs also decrease groundwater recharge by ~2.48 million tons via increased surface runoff. Proposed post-mining land interventions over reclaimed mining areas influence water volumes differently. Reservoirs on reclaimed land lead to ~822.14 million extra tons of discharge, 2.75 times higher than the agricultural scenario. Moreover, reservoirs can restore original recharge levels by 2039, while agriculture only reduces the mining impact by 28.64% on average. These findings reveal that small-scale non-artisanal andesite mining can disrupt regional hydrology despite modest operating scales. Thus, evidence-based guidelines are needed for permitting such mines based on environmental risk and site water budgets. Policy options include discharge or aquifer recharge caps tailored to small-scale andesite mines. The varied outputs of rehabilitation scenarios also highlight evaluating combined land and water management interventions. With agriculture alone proving insufficient, optimized mixes of revegetation and water harvesting require further exploration. Full article

Surface albedo plays a pivotal role in the Earth’s energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen’s slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI. Full article

Despite the importance of using digital technologies for resource management, Peru does not record current and estimated processed data on rural agriculture, hindering an effective management process combined with policy. This research analyzes the connotation of spatiotemporal level trends of eight different land cover types in nine rural districts representative of the three natural regions (coast, highlands, and jungle) of Peru. The effect of change over time of the COVID-19 pandemic was emphasized. Then, forecast trends of agricultural areas were estimated, approximating possible future trends in a post-COVID-19 scenario. Landsat 7, Landsat 8, and Sentinel 2 images (2017–2022) processed in the Google Earth Engine platform (GEE) and adjusted by random forest, Kappa index, and Global Accuracy. To model the forecasts for 2027, the best-fit formula was chosen according to the criteria of the lowest precision value of the mean absolute percentage error, the mean absolute deviation, and the mean squared deviation. In the three natural regions, but not in all districts, all cover types suggested in the satellite images were classified. We found advantageous situations of agricultural area dynamics (2017–2022) for the coast of up to 80.92 km² (Guadalupe, 2022), disadvantageous situations for the Sierra, and indistinct situations for the Selva: between −91.52 km² (Villa Rica, 2022) and 22.76 km² (Santa Rosa, 2022). The trend analysis allows us to confirm the effects of the COVID-19 pandemic on the extension dedicated to agriculture. The area dedicated to agriculture in the Peruvian coast experienced a decrease; in the highlands, it increased, and in the jungle, the changes were different for the districts studied. It is expected that these results will allow progress in the fulfillment of the 2030 Agenda in its goals 1, 2, and 17. Full article

Climate change will have various impacts on the survival and development of species, and it is important to explore whether plants can adapt to future climate conditions. Dipteronia dyeriana is an endangered species with a narrow distribution in Yunnan, characterized by a small population size. However, studies on its current distribution and the impact of climate change on its future survival and distribution are very limited. In this study, the current and future (2050 and 2090) potential habitats under the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios were predicted using both maximum entropy (MaxEnt) and random forest (RF) models based on the current range points of D. dyeriana, soil, topographical, land cover, and climate data. The results showed that the RF model demonstrated significantly higher AUC, TSS, and Kappa scores than the MaxEnt model, suggesting high accuracy of the RF model. Isothermality (bio_3), minimum temperature of the coldest month (bio_6), and annual precipitation (bio_12) are the main environmental factors affecting the distribution of D. dyeriana. At present, the high suitability area of D. dyeriana is mainly concentrated in the eastern part of Yunnan Province and part of southern Tibet, covering an area of 3.53 × 10⁴ km². Under future climate change scenarios, the total area suitable for D. dyeriana is expected to increase. Although, the highly suitable area has a tendency to decrease. With regards to land use, more than 77.53% of the suitable land area (29.67 × 10⁴ km²) could be used for D. dyeriana planting under different SSP scenarios. In 2090, the centroid shifts of the two models exhibit a consistent trend. Under the SSP1-2.6 scenario, the centroids transfer to the southeast. However, under the SSP3-7.0 and SSP5-8.5 scenarios, the centroids of high suitability areas migrate toward the northwest. In summary, this study enhances our understanding of the influence of climate change on the geographic range of D. dyeriana and provides essential theoretical backing for efforts in its conservation and cultivation. Full article

Land cover products provide the key inputs for terrestrial change monitoring and modeling. Numerous land cover products have been generated in the past few decades, but their performance on the southeastern Tibetan Plateau remains unclear. This study analyzed 15 land cover products for consistency through compositional similarity and overlay analyses. Additionally, 1305 validation samples from four datasets were employed to construct confusion matrices to evaluate their accuracy. The results indicate the following: (1) Land cover products exhibit relatively high consistency in 62.92% of the region. (2) Land cover products are strongly influenced by terrain fluctuations, showing lower consistency at elevation below 200 m and instability in land cover classification with increasing elevation, particularly between 2800–4400 m and 4800–5400 m. (3) The accuracy for forest, water, and snow/ice is relatively high. However, there is a relatively lower accuracy for wetland and shrubland, necessitating more field samples for reference to improve classification. (4) The average values of the four validation datasets show that the overall accuracy of the 15 products ranges from 50.97% to 73.50%. For broad-scale studies with lower resolution requirements, the CGLS-LC100 product can be considered. For studies requiring a finer scale, a combination of multiple land cover products should be utilized. ESRI is recommended as a reference for built-up land, while FROM-GLC30 can be used for cropland, although misclassification issues should be noted. This study provides valuable insights for analyzing land cover types on plateaus to refine classification. It also offers guidance for selecting suitable land cover products for future research in this region. Full article

Land-use and land-cover change (LULCC) is a critical environmental issue that has significant effects on biodiversity, ecosystem services, and climate change. This study examines the land-use and land-cover (LULC) spatiotemporal dynamics across a three-decade period (1998–2023) in a district area. In order to forecast the LULCC patterns, this study suggests a hybrid strategy that combines the random forest method with multi-layer perceptron (MLP) and Markov chain analysis. To predict the dynamics of LULC changes for the year 2035, a hybrid technique based on multi-layer perceptron and Markov chain model analysis (MLP-MCA) was employed. The area of developed land has increased significantly, while the amount of bare land, vegetation, and forest cover have all decreased. This is because the principal land types have changed due to population growth and economic expansion. This study also discovered that between 1998 and 2023, the built-up area increased by 468 km² as a result of the replacement of natural resources. It is estimated that 25.04% of the study area’s urbanization will increase by 2035. The performance of the model was confirmed with an overall accuracy of 90% and a kappa coefficient of around 0.89. It is important to use advanced predictive models to guide sustainable urban development strategies. The model provides valuable insights for policymakers, land managers, and researchers to support sustainable land-use planning, conservation efforts, and climate change mitigation strategies. Full article

Mangrove forests are essential for coastal protection and carbon sequestration, yet accurately mapping their distribution remains challenging due to spectral similarities with other vegetation. This study introduces a novel unsupervised learning method, the Elite Individual Adaptive Genetic Algorithm-Semantic Inference (EIAGA-S), designed for the high-precision semantic segmentation of mangrove forests using remote sensing images without the need for ground truth samples. EIAGA-S integrates an adaptive Genetic Algorithm with an elite individual’s evolution strategy, optimizing the segmentation process. A new Mangrove Enhanced Vegetation Index (MEVI) was developed to better distinguish mangroves from other vegetation types within the spectral feature space. EIAGA-S constructs segmentation rules through iterative rule stacking and enhances boundary information using connected component analysis. The method was evaluated using a multi-source remote sensing dataset covering the Hainan Dongzhai Port Mangrove Nature Reserve in China. The experimental results demonstrate that EIAGA-S achieves a superior overall mIoU (mean intersection over union) of 0.92 and an F1 score of 0.923, outperforming traditional models such as K-means and SVM (Support Vector Machine). A detailed boundary analysis confirms EIAGA-S’s ability to extract fine-grained mangrove patches. The segmentation includes five categories: mangrove canopy, other terrestrial vegetation, buildings and streets, bare land, and water bodies. The proposed EIAGA-S model offers a precise and data-efficient solution for mangrove semantic mapping while eliminating the dependency on extensive field sampling and labeled data. Additionally, the MEVI index facilitates large-scale mangrove monitoring. In future work, EIAGA-S can be integrated with long-term remote sensing data to analyze mangrove forest dynamics under climate change conditions. This innovative approach has potential applications in rapid forest change detection, environmental protection, and beyond. Full article

Carbon storage estimates are essential for sustainable urban planning and development. This study examines the spatio-temporal effects of land use and land cover changes on the provision and monetary value of above- and below-ground carbon sequestration and storage during 2011, 2019, and the simulated year 2027 in Noida. The Google Earth Engine-Random Forests (GEE-RF) classifier, the Cellular Automata Artificial Neural Network (CA-ANN) model, and the InVEST-CCS model are some of the software tools applied for the analysis. The findings demonstrate that the above- and below-ground carbon storage for Noida is 23.95 t/ha. Carbon storage in the city increased between 2011 and 2019 by approximately 67%. For the predicted year 2027, a loss in carbon storage is recorded. The simulated land cover for the year 2027 indicates that if the current pattern continues for the next decade, the majority of the land will be transformed into either built-up or barren land. This predicted decline in agriculture and vegetation would further lead to a slump in the potential for terrestrial carbon sequestration. Urban carbon storage estimates provide past records to serve as a baseline and a precursor to study future changes, and therefore more such city-scale analyses are required for overall urban sustainability. Full article

The Qinghai–Tibet Plateau ecosystem is fragile, experiencing rapid changes in land cover driven by both climate change and human activities, leading to habitat fragmentation and loss and resulting in biodiversity decline. Habitat ecological networks (HA-ENs) are considered effective solutions for habitat connectivity and biodiversity conservation in response to these dual drivers. However, HA-EN studies typically rely on current or historical landscape data, which hinders the formulation of future conservation strategies. This study proposes three future scenarios—improvement, deterioration, and baseline scenarios—focused on the southeastern Qinghai–Tibet Plateau (SE-QPT). The habitats of 10 species across three classes are extracted, integrating land use and climate change data into habitat ecological network modeling to assess the long-term dynamics of HA-ENs in the SE-QPT. Finally, conservation management strategies are proposed based on regional heterogeneity. The results show the following: Climate change and human activities are expected to reduce the suitable habitat area for species, intensifying resource competition among multiple species. By 2030, under all scenarios, the forest structure will become more fragmented, and grassland degradation will be primarily concentrated in the southeastern and western parts of the study area. Compared to 1985 (71,891.3 km²), the habitat area by 2030 is projected to decrease by 12.9% (62,629.3 km²). The overlap rate of species habitats increases from 25.4% in 1985 to 30.9% by 2030. Compared to the HA-EN control in 1985, all scenarios show a decrease in connectivity and complexity, with only the improvement scenario showing some signs of recovery towards the control network, albeit limited. Finally, based on regional heterogeneity, a conservation management strategy of “two points, two cores, two corridors, and two regions” is proposed. This strategy aims to provide a framework for future conservation efforts in response to climate change and human activities. Full article

In remote sensing image processing, when categorizing images from multiple remote sensing data sources, the deepening of the network hierarchy is prone to the problems of feature dispersion, as well as the loss of semantic information. In order to solve this problem, this paper proposes to integrate a parallel network architecture HDAM-Net algorithm with a hybrid dual attention mechanism Hybrid dual attention mechanism for forest land cover change. Firstly, we propose a fusion MCA + SAM (MS) attention mechanism to improve VIT network, which can capture the correlation information between features; secondly, we propose a multilayer residual cascade convolution (MSCRC) network model using Double Cross-Attention Module (DCAM) attention mechanism, which is able to efficiently utilize the spatial dependency between multiscale encoder features: the spatial dependency between multiscale encoder features. Finally, the dual-channel parallel architecture is utilized to solve the structural differences and realize the enhancement of forestry image classification differentiation and effective monitoring of forest cover changes. In order to compare the performance of HDAM-Net, mountain urban forest types are classified based on multiple remote sensing data sources, and the performance of the model is evaluated. The experimental results show that the overall accuracy of the algorithm proposed in this paper is 99.42%, while the Transformer (ViT) is 96.92%, which indicates that the proposed classifier is able to accurately determine the cover type.The HDAM-Net model emphasizes the effectiveness in terms of accurately classifying the land, as well as the forest types by using multiple remote sensing data sources for predicting the future trend of the forest ecosystem. In addition, the land utilization rate and land cover change can clearly show the forest cover change and support the data to predict the future trend of the forest ecosystem so that the forest resource survey can effectively monitor deforestation and evaluate forest restoration projects. Full article