Spatial targeting of irrigation development and water resource management to mitigate vector-borne disease.

Scaling irrigated agriculture is a global strategy to mitigate food insecurity concerns. While expanding irrigated agriculture is critical to meeting food production demands, it is important to consider how these land use and land cover changes (LULCC) may alter the water resources of landscapes and impact the spatiotemporal epidemiology of disease. Here, a generalizable method is presented to inform irrigation development decision-making aimed at increasing crop production through irrigation while simultaneously mitigating malaria risk to surrounding communities. Changes to the spatiotemporal patterns of malaria vector (Anopheles gambiae s.s.) suitability, driven by irrigated agricultural expansion, are presented for Malawi's rainy and dry seasons. The methods presented may be applied to other geographical areas where sufficient irrigation and malaria prevalence data are available. Results show that approximately 8.60% and 1.78% of Malawi is maximally suitable for An. gambiae s.s. breeding in the rainy and dry seasons, respectively. However, the proposed LULCC from irrigated agriculture increases the maximally suitable land area in both seasons: 15.16% (rainy) and 2.17% (dry). Proposed irrigation development sites are analyzed and ranked according to their likelihood of increasing malaria risk for those closest to the schemes. Results illustrate how geospatial information on the anticipated change to the malaria landscape driven by increasing irrigated agricultural extent can assist in altering development plans, amending policies, or reassessing water resource management strategies to mitigate expected changes in malaria risk.

Evaluating the impact of urbanization on the urban heat islands through integrated radius and non-linear regression approach.

Urban heat islands (UHIs) are a significant environmental problem, exacerbating the urban climate and affecting human health in the Asir region of Saudi Arabia. The need to understand the spatio-temporal dynamics of UHI in the context of urban expansion is crucial for sustainable urban planning. The aim of this study was to quantify the changes in land use and land cover (LULC) and urbanization, assess the expansion process of UHI, and analyze its connectivity in order to develop strategies to mitigate UHI in an urban context over a 30-year period from 1990 to 2020. Using remote sensing data, LULC changes were analyzed with a random forest model. LULC change rate (LCCR), land cover intensity (LCI), and landscape expansion index (LEI) were calculated to quantify urbanization. The land surface temperature for the study period was calculated using the mono-window algorithm. The UHI effect was analyzed using an integrated radius and non-linear regression approach, fitting SUHI data to polynomial curves and identifying turning points based on the regression derivative for UHI intensity belts to quantify the expansion and intensification of UHI. Landscape metrics such as the aggregation index (AI), landscape shape index (LSI), and four other matrices were calculated to assess UHI morphology and connectivity of the UHI. In addition, the LEI was adopted to measure the extent of UHI growth patterns. From 1990 to 2020, the study area experienced significant urbanization, with the built-up area increasing from 69.40 to 338.74 km2, an increase of 1.923 to 9.385% of the total area. This expansion included growth in peripheral areas of 129.33 km2, peripheral expansion of 85.40 km2, and infilling of 3.80 km2. At the same time, the UHI effect intensified with an increase in mean LST from 40.55 to 46.73 °C. The spatial extent of the UHI increased, as shown by the increase in areas with an LST above 50 °C from 36.58 km2 in 1990 to 133.52 km2 in 2020. The connectivity of the UHI also increased, as shown by the increase in the AI from 38.91 to 41.30 and the LSI from 56.72 to 93.64, reflecting a more irregular and fragmented urban landscape. In parallel to these urban changes, the area classified as UHI increased significantly, with the peripheral areas expanding from 23.99 km2 in the period 1990-2000 to 80.86 km2 in the period 2000-2020. Peripheral areas also grew significantly from 36.42 to 96.27 km2, contributing to an overall more pronounced and interconnected UHI effect by 2020. This study provides a comprehensive analysis of urban expansion and its thermal impacts. It highlights the need for integrated urban planning that includes strategies to mitigate the UHI effect, such as improving green infrastructure, optimizing land use, and improving urban design to counteract the negative effects of urbanization.

Analyses of LULC dynamics in a socio-ecological system of the Bale Mountains Eco Region of Southeast Ethiopia.

Analysis of land use and land cover (LULC) change and its drivers and impacts in the biodiversity hotspot of Bale Mountain's socio-ecological system is crucial for formulating plausible policies and strategies that can enhance sustainable development. The study aimed to analyze spatio-temporal LULC changes and their trends, extents, drives, and impacts over the last 48 years in the Bale Mountain social-ecological system. Landsat imagery data from the years 1973, 1986, 1996, 2014, and 2021 together with qualitative data were used. LULC classification scheme employed a supervised classification method with the application of the maximum likelihood algorithm technique. In the period between 1973 and 2021, agriculture, bare land, and settlement showed areal increment by 153.13%, 295.57%, and 49.03% with the corresponding increased annual rate of 1.93%, 2.86%, and 0.83%, respectively. On the contrary, forest, wood land, bushland, grass land, and water body decreased by 29.97%, 1.36%, 28.16%, 8.63%, and 84.36% during the study period, respectively. During the period, major LULC change dynamics were also observed; the majority of woodland was converted to agriculture (757.8 km2) and grassland (531.3 km2); and forests were converted to other LULC classes, namely woodland (766.5 km2), agriculture (706.1 km2), grassland (34.6 km2), bushland (31.9 km2), settlement (20.5 km2), and bare land (14.3 km2). LULC changes were caused by the expansion of agriculture, settlement, overgrazing, infrastructure development, and fire that were driven by population growth and climate change, and supplemented by inadequate policy and institutional factors. Social and environmental importance and values of land uses and land covers in the study area necessitate further assessment of potential natural resources' user groups and valuation of ecosystem services in the study area. Hence, we suggest the identification of potential natural resource-based user groups, and assessment of the influence of LULC changes on ecosystem services in Bale Mountains Eco Region (BMER) for the sustainable use and managements of land resources.

Land use and land cover dynamics in the Upper Ganga Riverine Wetland: unraveling ecosystem services over two decades.

Anthropogenic activities have drastically transformed natural landscapes, profoundly impacting land use and land cover (LULC) and, consequently, the provision and functionality of ecosystem service values (ESVs). Evaluating the changes in LULC and their influence on ESVs is imperative to protect ecologically fragile ecosystems from degradation. This study focuses on a highly sensitive Upper Ganga riverine wetland in India, covering Hapur, Amroha, Bulandshahr, and Sambhal districts, which is well-known for its significant endemic flora and fauna. The study analyzes the subtle variability in ecosystem services offered by the various LULC biomes, including riverine wetland, built-up, cropland, forest, sandbar, and unused land. LULC classification is carried out using Landsat satellite imagery 5 and 8 for the years 2000, 2010, and 2020, using the random forest method. The spatiotemporal changing pattern of ESVs is assessed utilizing the value transfer method with two distinct value coefficients: global value coefficients (C14) for a worldwide perspective and modified local value coefficients X08 for a more specific local context. The results show a significant increase in built-up and unused land, with a corresponding decrease in wetlands and forests from 2000 to 2020. The combined ESVs for all the districts are worth US $5072 million (C14) and US $2139 million (X08) in the year 2000, which declined to US $4510 million (C14) and US $1770 million (X08) in the year 2020. The sensitivity analysis reveals that the coefficient of sensitivity (CS) is below one for all biomes, suggesting the robustness of the employed value coefficients in estimating ESVs. Moreover, the analysis identifies cropland, followed by forests and wetlands, as the LULC biomes most responsive to changes. This research provides crucial insights to stakeholders and policymakers for developing sustainable land management practices aimed at enhancing the ecological worth of the Upper Ganga Riverine Wetland.

Generation and classification of patch-based land use and land cover dataset in diverse Indian landscapes: a comparative study of machine learning and deep learning models.

In the context of environmental and social applications, the analysis of land use and land cover (LULC) holds immense significance. The growing accessibility of remote sensing (RS) data has led to the development of LULC benchmark datasets, especially pivotal for intricate image classification tasks. This study addresses the scarcity of such benchmark datasets across diverse settings, with a particular focus on the distinctive landscape of India. The study entails the creation of patch-based datasets, consisting of 4000 labelled images spanning four distinct LULC classes derived from Sentinel-2 satellite imagery. For the subsequent classification task, three traditional machine learning (ML) models and three convolutional neural networks (CNNs) were employed. Despite facing several challenges throughout the process of dataset generation and subsequent classification, the CNN models consistently attained an overall accuracy of 90% or more. Notably, one of the ML models stood out with 96% accuracy, surpassing CNNs in this specific context. The study also conducts a comparative analysis of ML models on existing benchmark datasets, revealing higher prediction accuracy when dealing with fewer LULC classes. Thus, the selection of an appropriate model hinges on the given task, available resources, and the necessary trade-offs between performance and efficiency, particularly crucial in resource-constrained settings. The standardized benchmark dataset contributes valuable insights into the relative performance of deep CNN and ML models in LULC classification, providing a comprehensive understanding of their strengths and weaknesses.

Community-based assessment of the dynamics of urban landscape characteristics and ecosystem services in the rainforest and guinea savanna ecoregions of Nigeria.

Understanding the dynamics of urban landscapes and their impacts on ecological well-being is crucial for developing sustainable urban management strategies in times of rapid urbanisation. This study assesses the nature and drivers of the changing urban landscape and ecosystem services in cities located in the rainforest (Akure and Owerri) and guinea savannah (Makurdi and Minna) of Nigeria using a combination of remote sensing and socioeconomic techniques. Landsat 8 datasets provided spatial patterns of the normalised difference vegetation index (NDVI) and normalised difference built-up index (NDBI). A household survey involving the administration of a semi-structured questionnaire to 1552 participants was conducted. Diminishing NDVI and increasing NDBI were observed due to the rising trend of urban expansion, corroborating the perception of over 54% of the respondents who noted a decline in landscape ecological health. Residential expansion, agricultural practices, transport and infrastructural development, and fuelwood production were recognised as the principal drivers of landscape changes. Climate variability/change reportedly makes a 28.5%-34.4% (Negelkerke R2) contribution to the changing status of natural landscapes in Akure and Makurdi as modelled by multinomial logistic regression, while population growth/in-migration and economic activities reportedly account for 19.9%-36.3% in Owerri and Minna. Consequently, ecosystem services were perceived to have declined in their potential to regulate air and water pollution, reduce soil erosion and flooding, and mitigate urban heat stress, with a corresponding reduction in access to social services. We recommend that urban residents be integrated into management policies geared towards effectively developing and enforcing urban planning regulations, promoting urban afforestation, and establishing sustainable waste management systems.

Spatio-temporal classification of land use and land cover and its changes in Kerala using remote sensing and machine learning approach.

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices.

Ecosystem service valuation and multi-scenario simulation in the Ebinur Lake Basin using a coupled GMOP-PLUS model.

The Ebinur Lake Basin is an ecologically sensitive area in an arid region. Investigating its land use and land cover (LULC) change and assessing and predicting its ecosystem service value (ESV) are of great importance for the stability of the basin's socioeconomic development and sustainable development of its ecological environment. Based on LULC data from 1990, 2000, 2010, and 2020, we assessed the ESV of the Ebinur Lake Basin and coupled the grey multi-objective optimization model with the patch generation land use simulation model to predict ESV changes in 2035 under four scenarios: business-as-usual (BAU) development, rapid economic development (RED), ecological protection (ELP), and ecological-economic balance (EEB). The results show that from 1990 to 2020, the basin was dominated by grassland (51.23%) and unused land (27.6%), with a continuous decrease in unused land and an increase in cultivated land. In thirty years, the total ESV of the study area increased from 18.62 billion to 67.28 billion yuan, with regulation and support services being the dominant functions. By 2035, cultivated land increased while unused land decreased in all four scenarios compared with that in 2020. The total ESV in 2035 under the BAU, RED, ELP, and EEB scenarios was 68.83 billion, 64.47 billion, 67.99 billion, and 66.79 billion yuan, respectively. In the RED and EEB scenarios, ESV decreased by 2.81 billion and 0.49 billion yuan, respectively. In the BAU scenario, provisioning and regulation services increased by 6.05% and 2.93%, respectively. The ELP scenario, focusing on ecological and environmental protection, saw an increase in ESV for all services. This paper can assist policymakers in optimizing land use allocation and provide scientific support for the formulation of land use strategies and sustainable ecological and environmental development in the inland river basins of arid regions.

Identifying critical source areas of non-point source pollution to enhance water quality: Integrated SWAT modeling and multi-variable statistical analysis to reveal key variables and thresholds.

By integrating soil and water assessment tool (SWAT) modeling and land use and land cover (LULC) based multi-variable statistical analysis, this study aimed to identify driving factors, potential thresholds, and critical source areas (CSAs) to enhance water quality in southern Alabama and northwest Florida's Choctawhatchee Watershed. The results revealed the significance of forest cover and of the lumped developed areas and cultivated crops ("Source Areas") in influencing water quality. The stepwise linear regression analysis based on self-organizing maps (SOMs) showed that a negative correlation between forest percent cover and total nitrogen (TN), organic nitrogen (ORGN), and organic phosphorus (ORGP), highlighting the importance of forests in reducing nutrient loads. Conversely, Source Area percentage was positively correlated with total phosphorus (TP) loads, indicating the influence of human activities on TP levels. The receiver operating characteristic (ROC) curve analysis determined thresholds for forest percentage and Source Area percentage as 37.47 % and 20.26 %, respectively. These thresholds serve as important reference points for identifying CSAs. The CSAs identified based on these thresholds covered a relatively small portion (28 %) but contributed 47 % of TN and 50 % of TP of the whole watershed. The study underscores the importance of considering both physical process-based modeling and multi-variable statistical analysis for a comprehensive understanding of watershed management, i.e., the identification of CSAs and the associated variables and their tipping points to maintain water quality.

Availability of the current and future water resources in Equatorial Central Africa: case of the Nyong forest catchment in Cameroon.

To anticipate disasters (drought, floods, etc.) caused by environmental forcing and reduce their impacts on its fragile economy, sub-Saharan Africa needs a good knowledge of the availability of current water resources and reliable hydroclimatic forecasts. This study has an objective to quantify the availability of water resources in the Nyong basin and predict its future evolution (2024-2050). For this, the SWAT (Soil and Water Assessment Tool) model was used. The performance of this model is satisfactory in calibration (2001-2005) and validation (2006-2010), with R2, NSE, and KGE greater than 0.64. Biases of - 11.8% and - 13.9% in calibration and validation also attest to this good performance. In the investigated basin, infiltration (GW_RCH), evapotranspiration (ETP), surface runoff (SURQ), and water yield (WYLD) are greater in the East, probably due to more abundant rainfall in this part. The flows and sediment load (SED) are greater in the middle zone and in the Southwest of the basin, certainly because of the flat topography of this part, which corresponds to the valley floor. Two climate models (CCCma and REMO) predict a decline in water resources in this basin, and two others (HIRHAM5 and RCA4) are the opposite. However, based on a statistical study carried out over the historical period (2001-2005), the CCCma model seems the most reliable. It forecasts a drop in precipitation and runoff, which do not exceed - 19% and - 18%, respectively, whatever the emission scenario (RCP4.5 or RCP8.5). Climate variability (CV) is the only forcing whose impact is visible in the dynamics of current and future flows, due to the modest current (increase of + 102 km2 in builds and roads) and future (increase of + 114 km2 in builds and roads) changes observed in the evolution of land use and land cover (LULC). The results of this study could contribute to improving water resource management in the basin studied and the region.

Spatiotemporal analysis of national carbon emission and regional carbon simulation in China.

Land use and land cover (LULC) will cause large flows of carbon sources and sinks. As the world's largest carbon emitter with a complicated LULC, China's carbon emissions have profound implications for its ecological environment and future development. In this paper, we account for the land-use changes and carbon emissions of 30 Chinese provinces and cities in China from 2000 to 2020. Furthermore, the spatial correlation of carbon emissions among the study areas is explored. Four typical regions with spatial association (Beijing, Hebei, Sichuan, and Anhui) are selected, and their land-use change trends in 2025 and 2030 are simulated to predict the total carbon emissions in the future. The results show that the distribution of land-use in China is mainly cultivated and woodland, but the growth of urban built-up (UBL) land area indirectly leads to the continuous increase of carbon emissions. Total carbon emissions have increased over the past two decades, albeit at a slower growth rate, with some provinces experiencing no further growth. In the typical regional carbon emission simulation, it is found that the carbon emissions of the four provinces would show a downward trend in the future. The main reason is the reduction in indirect carbon emissions from fossil energy in UBL, while the other part is the influx of carbon sinks due to grassland, woodland, etc. We recommended that future carbon reduction measures should focus and prioritize controlling fossil energy and mitigating carbon emissions from UBL. Simultaneously, the significant contribution of forests and other land types as carbon sinks should be acknowledged to better implement China's carbon neutral commitment.

Spatial transformation of land use and land cover and identification of hotspots using geospatial technology: a case of major industrial zone of eastern India.

Technology-driven population expansion is closely linked to land use change. Unregulated mining, urbanization, industrialization, and forest clearing threaten land use and cover. This study used GIS and statistical methods to examine land use and cover changes in eastern India's Asansol-Durgapur Development Authority (ADDA). The Kappa coefficient was used to validate each year's LULC map accuracy. This region is changing rapidly due to industrial and urban development, which might cause environmental issues. Thus, this area is ideal for a scientific land-use change study. The central hypothesis of this study is that the LULC of an industrial area is spatially heterogeneous and that the number of hotspots is gradually increasing in response to the dynamicity of land use change over time and space. Three years (1992, 2007, and 2022) were used to determine the estimated transition rate. Hotspots of land use change were identified using autocorrelation statistics for LULC clustering using Moron's I and Gi Z statistics. The proportion of land encompassed by natural vegetation experienced a decline from 12% in 1992 to 4% in 2022. Similarly, the extent of land occupied by agricultural activities decreased from 47 to 38% during the period spanning from 1992 to 2022. The industrial and coal mining sectors experienced a modest growth rate of 1% during the period spanning from 1992 to 2022. If the current rate of land use change persists, it will gradually and consistently alter the existing landscape. This study's findings can potentially inform strategies to mitigate the adverse impacts of industrialization and urbanization on the region's natural resources.

GIS based method for mapping actual LULC by combining seasonal LULCs.

One of the most significant applications of remote sensing data is to prepare land use and land cover (LULC) maps. LULC maps are always affected by seasonality and a single LULC map of a particular month is prepared to represent a year in most of the research, especially in change detection research. This does not represent the real view of the landscape because the seasonal variation of different LULC types is always overlooked. Considering the issue, the current method aims to solve the problem by incorporating seasonal LULC using the raster overlay method to remove the seasonality effect on LULC classification. To apply this method, a minimum of two seasonal LULC maps is required for a single study year. The map needs to overlay and then reclassify according to the stable and rotational LULC pattern of the study area. This method will replicate the actual LULC pattern of a study area from satellite images. Summary of the method is as follows:•LULC of each season was classified using image classification technique.•LULC of each seasons are coded and combined using overlay technique.•Combined map is reclassified to prepare the actual LULC map.

Accurate classification of land use and land cover using a boundary-specific two-level learning approach augmented with auxiliary features in Google Earth Engine.

Land use land cover (LULC) classification using remote sensing images is a valuable resource in various fields such as climate change, urban development, and land degradation monitoring. The city of Madurai in India is known for its diverse geographical elements and rich heritage, which includes the cultural sport of "Jallikattu": whose main competitor, the zebusare deeply affected by the conversion of their waterbodies and pastures into concrete jungles. Hence, monitoring land degradation is vital in preserving the geography and cultural heritage of the study area, Madurai. The "Landsat 8 Operational Land Imager tier_2 collection_2 Level_2 Surface Reflectance" image was taken for this study. The LULC classification is performed based on the following classes: forest, agriculture, urban, water bodies, uncultivated land, and bare land. The objective of the study is to incorporate auxiliary features to spectral and textural features along with a simple non-iterative clustering (SNIC) segmentation algorithm and implement a boundary-specific two-level learning approach based on support vector machines (SVM) and k nearest neighbors (kNN) classification algorithms. The overall accuracy (OA) of 95.78% and 0 .94 Kappa score (K) were obtained using a boundary-specific two-level model augmented with auxiliary feature and SNIC algorithm in comparison to PB, OB, and OBS, which achieve OA (K) of 81% (0.76), 91% (0.89), and 94.42% (0.92), respectively. The results demonstrate a notable enhancement in overall classification accuracy when augmenting the features and refining classification decisions using a boundary-specific two-level learning approach.

Using multilayer perceptron and similarity-weighted machine learning algorithms to reconstruct the past: A case study of the agricultural expansion on grasslands in the Uruguayan savannas.

Changes in land use and land cover (LULC) have significant implications for biodiversity, ecosystem functioning, and deforestation. Modeling LULC changes is crucial to understanding anthropogenic impacts on environmental conservation and ecosystem services. Although previous studies have focused on predicting future changes, there is a growing need to determine past scenarios using new assessment tools. This study proposes a methodology for LULC past scenario generation based on transition analysis. Aiming to hindcast LULC scenario in 1970 based on the transition analysis of the past 35 years (from 1985 to 2020), two machine learning algorithms, multilayer perceptron (MLP) and similarity weighted (SimWeight), were employed to determine the driver variables most related to conversions in LULC and to simulate the past. The study focused on the Aristida spp. grasslands in the Uruguayan savannas, where native grasslands have been extensively converted to agricultural areas. Land use and land cover data from the MapBiomas project were integrated with spatial variables such as altimetry, slope, pedology, and linear distances from rivers, roads, urban areas, agriculture, forest, forestry, and native grasslands. The accuracy of the predicted maps was assessed through stratified random sampling of reference images from the Multispectral Scanner (MSS) sensor. The results demonstrate a reduction of approximately 659 934 ha of native grasslands in the study area between 1985 and 2020, directly proportional to the increase in cultivable areas. The MLP algorithm exhibited moderate performance, with notable errors in classifying agriculture and grassland areas. In contrast, the SimWeight algorithm displayed better accuracy, particularly in distinguishing grassland and agriculture classes. The modeled map using SimWeight accurately represented the transitions between grassland and agriculture with a high level of agreement. By modeling the 1970s scenario using the SimWeight model, it was estimated that the Aristida spp. grasslands experienced a substantial reduction in grassland coverage, ranging from 9982.31 to 10 022.32 km2 between 1970 and 2020. This represents a range of 60.8%-61.07% of the total grassland area in 1970. These findings provide valuable insights into the driving factors behind land use change in the Aristida spp. grasslands and offer useful information for land management, conservation, and sustainable development in the region. The study's main contribution lies in the hindcasting of past LULC scenarios, utilizing a tool used primarily for forecasting future scenarios. Integr Environ Assess Manag 2023;00:1-16. © 2023 SETAC.

Mapping and spatiotemporal dynamics of land-use and land-cover change based on the Google Earth Engine cloud platform from Landsat imagery: A case study of Zhoushan Island, China.

Land resources are an essential foundation for socioeconomic development. Island land resources are limited, the type changes are particularly frequent, and the environment is fragile. Therefore, large-scale, long-term, and high-accuracy land-use classification and spatiotemporal characteristic analysis are of great significance for the sustainable development of islands. Based on the advantages of remote sensing indices and principal component analysis in accurate classification, and taking Zhoushan Archipelago, China, as the study area, in this work long-term satellite remote sensing data were used to perform land-use classification and spatiotemporal characteristic analysis. The classification results showed that the land-use types could be exactly classified, with the overall accuracy and Kappa coefficient greater than 94% and 0.93, respectively. The results of the spatiotemporal characteristic analysis showed that the built-up land and forest land areas increased by 90.00 km2 and 36.83 km2, respectively, while the area of the cropland/grassland decreased by 69.77 km2. The areas of the water bodies, tidal flats, and bare land exhibited slight change trends. The spatial coverage of Zhoushan Island continuously expanded toward the coast, encroaching on nearby sea areas and tidal flats. The cropland/grassland was the most transferred-out area, at up to 108.94 km2, and built-up land was the most transferred-in areas, at up to 73.31 km2. This study provides a data basis and technical support for the scientific management of land resources.

Landscape resistance index aiming at functional forest connectivity.

Resistance models may quantify the ability of the landscape to impede species' movement and represent suitable habitats. Moreover, the performance of resistance models parameterized by land-use/land cover attributes evidence that the integrity of the environments subject to urban sprawl is poorly understood. In this sense, the study assumed we could identify the forest functional connectivity in a landscape considering the disparity in the landscape mosaic. In this context, we sought to develop a landscape resistance index through structural equation modeling (SEM), supported by the criteria of heat emission, biomass, and anthropogenic barriers, obtained by remote sensing, called observed variables. The landscape studied in the Green Belt Biosphere Reserve of São Paulo has significant remnants of the Atlantic Forest, a biodiversity hotspot. However, our results indicated criteria variability in the landscape modeled through the SEM, obtaining a significant adjustment of the landscape resistance index, with comparative fit index (CFI) of 1.00 and root mean square error of approximation (RMSEA) of 0.00. The index reflects the resistance levels of the land use/land cover, expressed by the class interval, ranging from 0% (1.73) to 100% (493.88), with the highest values associated with the anthropized uses and forest isolation. Thus, our index based on environmental attributes reflects the structure of functional forest connectivity and offers a framework to design forest corridors across landscapes.

Quantifying the policy-driven large scale vegetation restoration effects on evapotranspiration over drylands in China.

Evapotranspiration (ET) is a key variable in the water cycle and reflects the ecosystem's feedback into the climate system. However, quantitative studies on the response of ET to large-scale vegetation restoration projects and climate change are still lacking, especially in drylands. To address this deficiency, this research examined the variation in ET since the implementation of restoration projects in the drylands of China in 2000-2018, and utilized quantitative analysis methods to investigate the effects of six environmental factors, including temperature (TEM), precipitation (PRE), solar radiation (RAD), vapour pressure deficit (VPD), soil moisture (SM), and leaf area index (LAI) on ET. Furthermore, a new method was proposed to detect the ET change caused by land use and land cover change (LUCC). The results indicated that ET showed a significant increasing trend (3.54 mm yr-1) during 2000-2018, and PRE was identified as a main influential factor with an ET contribution rate of more than 50%, especially in areas with insignificant vegetation greening. Additionally, the LAI had a major positive impact on ET in the areas of significant vegetation greening, and the contribution rate was nearly 40%. Furthermore, large-scale vegetation restoration expanded the area of high-transpiration vegetation types, and the ΔET (net variable quantity of ET caused by LUCC) increased obviously especially for the changes from cropland and grassland to forest, and barren land to grassland. These findings provide a new perspective for future assessments and further decision making regarding vegetation restoration projects in drylands.

The land finance and eco-product value nexus: Evidence from fiscal decentralization in China.

This study aimed to shed new light on the land finance and eco-product value nexus from the perspective of fiscal decentralization, using data collected from 276 Chinese prefectures between 2005 and 2020. We employed a two-way fixed effects model to explore land finance, fiscal decentralization, and the eco-product value nexus. Our findings revealed that land finance has a noticeable disincentive influence on eco-product value. The impact of land finance on the ecological value of wetlands is much higher than on that of other land types. Additionally, fiscal expenditure decentralization plays a negative regulatory role between land finance and eco-product value. This effect is further strengthened with an increase in the fiscal decentralization level. Our findings suggest that standardizing local government land-granting behavior and making land finance more ecologically friendly through policy implementation will effectively contribute to the sustainable development of China.

How does water diversion affect land use change and ecosystem service: A case study of Baiyangdian wetland, China.

Baiyangdian wetland is the biggest plant-dominated shallow freshwater wetland in Huabei Plain, providing a wide range of ecosystem services. In the past few decades, the water scarcity and eco-environmental problems resulted from climate changes and human activities have become more and more serious. To relieve the pressure of water scarcity and ecological degradation, the government has implemented ecological water diversion projects (EWDPs) since 1992. In this study, land use and land cover change (LUCC) caused by EWDPs over three decades was analyzed to quantitatively assess the impact of EWDPs on ecosystem services. Coefficients of ecosystem service value (ESV) calculation were improved for regional ESV evaluation. The results showed that the area of construction, farmland and water increased by 6171, 2827, 1393 ha, respectively, and the total ESV increased by 8.04 × 108 CNY primarily due to the increase of regulating service with water area expansion. Redundancy analysis and socio-economic comprehensive analysis showed that EWDPs impacted water area and ESV with threshold and time effect. When the water diversion exceeded the threshold, the EWDPs affected the ESV through influencing LUCC; otherwise, the EWDPs affected the ESV through influencing net primary productivity or social-economic benefits. However, the impact of EWDPs on ESV gradually weakened as time passed, which could not keep sustainability. With the establishment of Xiong'an New Area in China and implementation of carbon neutrality policy, rational EWDPs will become crucial to achieve goals of ecological restoration.