Trends in habitat quality and habitat degradation in terrestrial protected areas.

Protected areas are typically considered a cornerstone of conservation programs and play a fundamental role in protecting natural areas and biodiversity. Human-driven land-use and land-cover (LULC) changes lead to habitat loss and biodiversity loss inside protected areas, impairing their effectiveness. However, the global dynamics of habitat quality and habitat degradation in protected areas remain unclear. We used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model based on global annual remotely sensed data to examine the spatial and temporal trends in habitat quality and degradation in global terrestrial protected areas. Habitat quality represented the ability of habitats to provide suitable conditions for the persistence of individuals and populations, and habitat degradation represented the impacts on habitats from human-driven LULC changes in the surrounding landscape. Based on a linear mixed-effects modeling method, we also explored the relationship between habitat degradation trends and protected area characteristics, biophysical factors, and socioeconomic factors. Habitat quality declined by 0.005 (0.6%) and habitat degradation increased by 0.002 (11%) from 1992 to 2020 globally, and similar trends occurred even in remote or restrictively managed protected areas. Habitat degradation was attributed primarily to nonirrigated cropland (62%) and urbanization (27%) in 2020. Increases in elevation, gross domestic production per capita, and human population density and decreases in agricultural suitability were associated with accelerated habitat degradation. Our results suggest that human-induced LULC changes have expanded from already-exploited areas into relatively undisturbed areas, and that in wealthy countries in particular, degradation is related to rapid urbanization and increasing demand for agricultural products.

Tendencias en la calidad y degradación del hábitat en áreas protegidas terrestres Resumen Las áreas protegidas suelen considerarse la piedra angular de los programas de conservación y desempeñan un papel fundamental en la protección de los espacios naturales y la biodiversidad. Los cambios en el uso y la cobertura del suelo (CUCS) provocados por el hombre conducen a la pérdida de hábitats y de biodiversidad dentro de las áreas protegidas, lo que merma su eficacia. Sin embargo, la dinámica global de la calidad y la degradación del hábitat en las áreas protegidas sigue sin estar clara. Utilizamos el modelo de valoración integrada de servicios ambientales y compensaciones (InVEST), basado en datos anuales mundiales obtenidos por teledetección, para examinar las tendencias espaciales y temporales de la calidad y degradación del hábitat en las áreas terrestres protegidas de todo el mundo. La calidad del hábitat representó su capacidad para proporcionar condiciones adecuadas para la persistencia de individuos y poblaciones, y la degradación del hábitat representó los impactos sobre los hábitats de los cambios CUCS provocados por el hombre en el paisaje circundante. Con base en un método de modelo lineal de efectos mixtos, también exploramos la relación entre las tendencias de degradación del hábitat y las características de las áreas protegidas, los factores biofísicos y los factores socioeconómicos. La calidad del hábitat disminuyó en un 0.005 (0,6%) y la degradación del hábitat aumentó en un 0.002 (11%) entre 1992 y 2020 a nivel mundial, y se produjeron tendencias similares incluso en áreas protegidas remotas o gestionadas de forma restrictiva. La degradación del hábitat se atribuyó principalmente a las tierras de cultivo sin irrigación (62%) y a la urbanización (27%) en 2020. El aumento de la altitud, del producto interno bruto per cápita y de la densidad de población humana, así como la disminución de la idoneidad agrícola, se asociaron a una aceleración de la degradación del hábitat. Nuestros resultados sugieren que los cambios en el CUCS inducidos por el hombre se han extendido desde zonas ya explotadas a zonas relativamente inalteradas, y que, en particular en los países ricos, la degradación está relacionada con la rápida urbanización y la creciente demanda de productos agrícolas.

Future land use and land cover simulations with cellular automata-based artificial neural network: A case study over Delhi megacity (India).

According to United Nations projections, future global urban growth will mostly occur in Asian megacities. In this study, a Cellular Automata based Artificial Neural Network (CA-ANN) model is used to simulate the future land use and land cover (LULC) over Delhi megacity (India). Delhi, projected to become the world's most populated city by 2030, is an example of a data poor city in Asia, having millions of climate vulnerable people. The CA-ANN model of Modules for Land Change Simulation (MOLUSCE), an open-source plugin, is first tested to simulate the LULC for 2009. Based on good validation results-structural similarity (SSIM; 0.8288), overall accuracy (79.78 %), kappa index of agreement (KIA; 77.25 %), and minimum validation overall error (0.0379), the same model set-up is used to carry out LULC simulation for 2030. This model is found to be simple, efficient, and computationally less expensive tool, and can be used to model future LULCs with a minimal set of inputs, a constraint often found in data poor cities. Results show continued increase in built-up area from 38.3 % (2014) to 53.8 % (2030), at the expense of cultivable areas, forests, and wastelands. The study incorporates past and future LULC change trajectories to highlight the changing LULC dynamics of the megacity from 1977 to 2030. Rate of urban sprawl, calculated using compound annual growth rate (CAGR) is projected to be 2.51 % for 2014-2030, substantially higher than the estimates for 2006-2014 (0.62 %). Further, the past and future urban growth patterns for Delhi are found to mimic other big Asian cities. The database generated from the present study has wide applicability for scientific research community, governmental bodies, profit and non-profit organizations for topics concerning-future urban climate research, climate risk and adaption policy frameworks, climate finance budgeting, future town planning, etc.

Assessing changes in the ecosystem service value in response to land use and land cover dynamics in Malawi.

Land use and land cover (LULC) changes are inevitable outcomes of socioeconomic changes and greatly affect ecosystem services. Our study addresses the critical gap in the existing literature by providing the first comprehensive national analysis of LULC changes and their impacts on ecosystem service values (ESVs) in Malawi. We assessed changes in ecosystem service values (ESVs) in response to LULC changes using the benefit transfer method in ArcGIS 10.6 software. Our findings revealed a significant increase in grasslands, croplands, and urban areas and a notable decline in forests, shrubs, wetlands, and water bodies. Grassland, cropland, and built-up areas expanded by 52%, 1%, and 23.2%, respectively. In contrast, permanent wetlands, barren land, and water bodies declined by 27.6%, 34.3%, and 1%, respectively. The ESV declined from US$90.87 billion in 2001 to US$85.60 billion in 2022, marking a 5.8% reduction. Provisioning services increased by 0.5% while regulating, supporting, and cultural ecosystem service functions declined by 12.2%, 3.16%, and 3.22%, respectively. The increase in provisioning services was due to the expansion of cropland. However, the loss of regulating, supporting, and cultural services was mainly due to the loss of natural ecosystems. Thus, environmental policy should prioritise the conservation and restoration of natural ecosystems to enhance the ESV of Malawi.

Landslide dynamic susceptibility mapping in urban expansion area considering spatiotemporal land use and land cover change.

Landslides pose a noteworthy threat in urban settlements globally, especially in areas experiencing extreme climate and rapid engineering. However, researches focusing on the long-term uninterrupted land use and land cover change (LULCC) impacted on landslide susceptibility mapping (LSM) in rapid urban expansion areas remains limited, let alone different temporal scenarios adjacency thresholds. This work aims to refine the temporal LSM considering spatiotemporal land use and land cover (LULC) and to provide decision makers with governing factors in landslides control during urbanization in mountainous areas. Herein, annual LULC data and landslide inventory spanning from 1992 to 2022 were utilized to map dynamic landslide susceptibility in Wanzhou District of the Three Gorges Reservoir Area, China. Initially, the landslide-related factors were filtered as input features of random forest (RF) model before diagnosis via multicollinearity test and Pearson Correlation Coefficient (PCC). The advanced patch-generating land use simulation (PLUS) model was then invited to fuel temporal susceptibility prediction powered by LULCC projections. Finally, the performance of various scenarios was evaluated using Receiver Characteristic Curve (ROC) curves and Shapley Additive exPlanation (SHAP) technique, with discussions on LULCC temporal adjacency thresholds and mutual feedback mechanism between territorial exploitation and landslide occurrences. The results indicate that the precision of LSM is positively correlated with the time horizon, acted by incorporating the latest LULC and LULCC achieving an area under the curve (AUC) of 0.920. The transition of land from forest to cropland and impervious areas should be avoided to minimize the increase in landslide susceptibility. Moreover, a one-year adjacency threshold of LULCC is recommended for optimal model accuracy in future LSM. This dynamic LSM framework can serve as a reference for decision makers in future landslide susceptibility mitigation and land resources utilization in rapid urban expansion areas worldwide.

Comprehensive spatiotemporal evaluation of urban growth, surface urban heat island, and urban thermal conditions on Java island of Indonesia and implications for urban planning.

Urban heat island (UHI) and thermal comfort conditions are among the impacts of urbanization, which have been extensively studied in most cities around the world. However, the comprehensive studies in Indonesia in the context of urbanization is still lacking. This study aimed to classify land use and land cover (LULC) and analyse urban growth and its effects on surface urban heat islands (SUHIs) and urban thermal conditions as well as contributing factors to SUHI intensity (SUHII) using remote sensing in the western part of Java Island and three focused urban areas: the Jakarta metropolitan area (JMA), the Bandung and Cimahi Municipalities (BC), and the Sukabumi Municipality (SKB). Landsat imagery from three years was used: 2000, 2009, and 2019. Three types of daytime SUHII were quantified, namely the SUHII of urban central area and two SUHIIs of urban sprawl area. In the last two decades, urban areas have grown by more than twice in JMA and SKB and nearly 1.5 times in BC. Along with the growth of the three cities, the SUHII in the urban central area has almost reached a magnitude of 6 °C in the last decade. Rates of land surface temperature change of the unchanged urban pixels have magnitudes of 0.25, 0.15, and 0.14 °C/year in JMA, SKB, and BC, respectively. The urban thermal field variance index (UTFVI) and discomfort index (DI) showed that the strongest SUHI effect was most prevalent in urban pixels and the regions were mostly in the very hot and hot categories. Anthropogenic heat flux and urban ratio have positive contributions to SUHII variation, while vegetation and water ratios are negative contributors to SUHII variation. For each city, the contributing factors have a unique magnitude that can be used to evaluate SUHII mitigation options.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.