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The development of sustainable tourism planning is comprised of various interrelated components. As a result, the system is complex, with each factor involved having its own unique goals and management strategies. This phenomenon triggers unexpected conflicts among stakeholders. Given the dynamic and complex challenges in sustainable tourism development, the ability to identify them is required. Therefore, the study aims to delve deeper into factors that influence the implementation of sustainable smart sports tourism planning. It also explores strategies required to analyze the dynamic causal relationships of these factors using a system approach method with the causal loop diagram (CLD) model and produce a new multi criteria decision analysis (MCDA) dynamic computational model which represents conditions in terms of tourist visit rates and economic and environmental improvements that describe the interrelationships of interacting factors. This model is implemented by maximizing service quality, maximizing marketing, maximizing regional income, minimizing implementation costs, maximizing the use of smart/ICT, minimizing environmental damage, minimizing tourist destination promotion, and maximizing investment. maximizing art and cultural activities. Smart and sustainable sports tourism planning and development use the model as a consideration and decision-making tool. [ABSTRACT FROM AUTHOR]

Multi-criteria decision making (MCDM) is necessary for choosing one from the available alternatives (or from the Pareto-optimal solutions obtained by multi-objective optimization), where the performance of each alternative is quantified against several criteria (or objectives). This paper presents a comprehensive review of the application of MCDM methods in chemical and process engineering. It systematically outlines the essential steps in MCDM including the various normalization, weighting, and MCDM methods that are critical to decision making. The review draws on published papers identified through a search in the Scopus database, focusing on works by authors with more contributions to the field and on highly cited papers. Each selected paper was analyzed based on the MCDM, normalization, and weighting methods used. Additionally, this paper introduces two readily available programs for performing MCDM calculations. In short, it provides insights into the MCDM steps and methods, highlights their applications in chemical and process engineering, and discusses the challenges and prospects in this area. [ABSTRACT FROM AUTHOR]

Rapid damage assessment after an earthquake is crucial for allocating and prioritizing emergency actions. Building damage due to an earthquake depends on the seismic hazard and the building's strength. While it is now possible to promptly access acceleration data as seismic input through online strong motion networks in urban areas, good models are necessary to evaluate the damage in different zones of the affected area. This study aims to present a rapid method for such an urban-scale building collapse evaluation by conducting a nonlinear dynamic analysis of modeled buildings. Based on the Nagato and Kawase model, this study estimates the yield shear strength of 3-story steel buildings, 3-story reinforced concrete buildings, and 1-story masonry buildings in Sarpol-e-Zahab City after the 2017 Mw7.3 earthquake. The damage ratio is calculated through nonlinear dynamic analyses using estimated records from the main earthquake shock in different city zones. The research found that the seismic yield shear strength of steel and reinforced concrete buildings might be weaker than that of the Iranian seismic code's standard value. Conversely, masonry-building resistance is stronger than the guidelines assumed. The constructed numerical models can be used for the rapid building damage assessment immediately after a damaging earthquake. [ABSTRACT FROM AUTHOR]

The diversified innovative strategies adopted by the new retail format in urban spaces have significantly driven retail transformation and innovation. The combination of online platforms and physical stores provides a substantial advantage in market competition. This paper takes "Freshippo", a typical representative of China's new retail, as an example. Based on multi-source data and using tools such as GIS spatial analysis, statistical analysis, and geographical detectors, this study comprehensively examines the spatial clustering characteristics and influencing factors of Freshippo physical stores in Shanghai. The findings show that Freshippo has significantly expanded in the Shanghai fresh food market by innovatively opening various types of stores. However, there are substantial differences in the proportions of different types of stores, with 94% of the stores having online retail capabilities. Each offline store in the new retail format presents a multi-level "complementary" spatial distribution feature across the urban space, with distinctive clusters in the urban central districts, urban periphery areas, and outer suburban districts. The radiation range of logistics and distribution services exhibits characteristics of "central agglomeration and multi-point distribution", providing residents with diverse and accurate services. Additionally, the comparison of multiple model results shows that the location selection of various types of new retail stores is significantly influenced by multiple factors, especially the nonlinear amplification effect of factor interactions on store agglomeration. These findings provide an important scientific reference for understanding the development of new retail formats and offer new ideas that promote the transformation and innovation of the retail industry, thereby achieving sustainable development. [ABSTRACT FROM AUTHOR]

Regular detection of land-use and land-cover (LULC) changes with high accuracy is necessary for natural resources management and sustainable urban planning. The produced LULC maps from Google Earth Engine (GEE) also illustrate the transformation of the LULC for the respective landscape over time. The selected study area, Cottbus City and the Spree-Neisse district in northeastern Germany, has undergone significant development over the past decades due to various factors, including urbanization and industrialization; also, the landscape has been converted in some areas for post-mining activities. Detection of LULC changes that have taken place over the last few decades thus plays a vital role in quantifying the impact of these factors while improving the knowledge of these developments and supporting the city planners or urban management officials before implementing further long-term development initiatives for the future. Therefore, the study aims to (i) detect LULC changes for the time slices 2002 and 2022, testing machine learning (ML) algorithms in supervised and unsupervised classification for Landsat satellite imageries, and (ii) validate the newly produced LULC maps with the available regional database (RDB) from the federal and state statistical offices, Germany, and the Dynamic World (DW) near real-time 10 m global LULC data set powered by artificial intelligence (AI). The results of the Random Forest (RF) and the Smilecart classifiers of supervised classification using Landsat 9 OLI-2/TIRS-2 in 2022 demonstrated a validation accuracy of 88% for both, with Kappa Index (KI) of 83% and 84%, respectively. Moreover, the Training Overall Accuracy (TOA) was 100% for both years. The wekaKMeans cluster of the unsupervised classification also illustrated a similar transformation pattern in the LULC maps. Overall, the produced LULC maps offered an improved representation of the selected region's various land-cover classes (i.e., vegetation, waterbodies, built areas, and bare ground) in the last two decades (20022 to 2022). [ABSTRACT FROM AUTHOR]

The Tigris and Euphrates Basin (TEB) encompasses a wide area, and due to its different geographical and political conditions, each environmental factor in different conditions has different effects on the process of surface water changes. Accordingly, in this research, we aim to evaluate the trend of surface water changes in this basin in 2001-2021 by using the time series of 16 different parameters and the products available on the Google Earth Engine (GEE) platform. Based on the findings, the general trend of surface water changes is increasing, and the water area has reached 8605.9 km2 in 2001 to 10021.8 km2 in 2021. Nevertheless, the spatial-temporal changes of water have been different because the extent of lakes and wetlands in the southern areas of the basin has decreased drastically. On the contrary, it has increased upstream of the basin due to the expansion of various dams and channels. In addition, our findings indicated a high correlation between climatic variables, especially evapotranspiration, and temperature, with temporal changes in water in the region. Thus, the impact of global climate changes on the hydrology and environment of the basin highlights the importance and high sensitivity of the major lakes in the region, such as Razazeh, Tharthar, Hamrin, and Habbaniyah, to climate changes. The present research results may be used to assess surface water in other regions and provide valuable information for the planning and management of global surface water resources. [ABSTRACT FROM AUTHOR]

The development of ecotourism in protected areas faces the challenge of balancing conservation and ecotourism. Ecotourism suitability assessments are essential tools for managing tourism in these areas. However, current assessments often overlook biological factors, leading to adverse effects on wildlife. This study uses the Northeast China Tiger and Leopard National Park as a case study to establish a comprehensive assessment system that integrates ecotourism suitability with tiger and leopard habitat suitability, thereby linking ecotourism with wildlife conservation. The primary research methods include ecotourism suitability analysis based on the entropy weight method and habitat suitability analysis using the MaxEnt model. Based on the zoning results of ecotourism and habitat suitability, a comprehensive ecotourism suitability zoning map was produced. This map indicates that areas of very high suitability account for 45.62% of the total area, covering approximately 6152.563 km2, and are primarily located on the edges of village clusters. These areas can be prioritized for developing tourism infrastructure. The comprehensive ecotourism assessment system can balance the development of ecotourism with wildlife conservation, contributing significantly to the coordinated development of economic, social, and environmental objectives. [ABSTRACT FROM AUTHOR]

The article delves into the development of a comprehensive concept for the sustainable development of regions characterized by intensive environmental management. This concept is rooted in the interplay of several key factors, including the environmental component, social attractiveness, and infrastructural accessibility. By meticulously analyzing these factors and juxtaposing them with the geographical distribution of recreational facilities, the authors propose the innovative concept of a recreational and functional zone. This concept aims to harmonize environmental, economic, and social considerations to foster sustainable development. In this study, particular attention is given to three districts within the steppe zone of the Aktobe region, which are notable for their high levels of economic development and intensive environmental management practices. The analysis encompasses a thorough examination of the ecological, economic, and sociological dimensions of these areas. The authors explore the intricate dynamics between these components to understand the challenges and opportunities they present for sustainable regional development. Building on this analysis, the article offers specific recommendations and strategies designed to optimize environmental management practices. These recommendations are not only aimed at mitigating potential environmental impacts but also at enhancing the development of recreational areas as a pivotal element of the region's sustainable development strategy. The authors argue that such an approach is crucial for ensuring the long-term viability and resilience of the region, balancing economic growth with the preservation of its natural and social environments. [ABSTRACT FROM AUTHOR]

This review explores the comparative utility of machine learning (ML) and deep learning (DL) in land system science (LSS) classification tasks. Through a comprehensive assessment, the study reveals that while DL techniques have emerged with transformative potential, their application in LSS often faces challenges related to data availability, computational demands, model interpretability, and overfitting. In many instances, traditional ML models currently present more effective solutions, as illustrated in our decisionmaking framework. Integrative opportunities for enhancing classification accuracy include data integration from diverse sources, the development of advanced DL architectures, leveraging unsupervised learning, and infusing domain-specific knowledge. The research also emphasizes the need for regular model evaluation, the creation of diversified training datasets, and fostering interdisciplinary collaborations. Furthermore, while the promise of DL for future advancements in LSS is undeniable, present considerations often tip the balance in favor of ML models for many classification schemes. This review serves as a guide for researchers, emphasizing the importance of choosing the right computational tools in the evolving landscape of LSS, to achieve reliable and nuanced land-use change data. [ABSTRACT FROM AUTHOR]

Accurate land use-land cover mapping is essential to policymakers for future planning. This study aims to assess the land use-land cover dynamics and estimate its future projection in the Jagatsinghpur district of Odisha state from India. In recent years, cloud-based platforms like Google Earth Engine and domains like machine learning have attracted considerable attention from researchers. In this study, five machine learning algorithms, such as Classification and Regression Tree, Naive Bayes, Support Vector Machine, Gradient Tree Boost and Random Forest are experimented on the multitemporal Sentinel-1 C-band dataset from Google Earth Engine. The results are evaluated based on metrics like overall accuracy and Kappa statistics. The performance metrics indicate that Random Forest with 60 trees outperforms others. Next, the land use-land cover maps of the study area are generated with Random Forest classifier for the years 2017 and 2021. The results are compared to ESRI land cover maps and ESA world cover maps. The 2017 and 2021 maps are exported to QGIS, and these maps are used to generate a simulation map for 2021. The simulated land use-land cover map for 2021 indicates promising results with an overall Kappa value of 0.97 and a percentage of correctness of 98.21%. The simulated map is validated against a factual map. Finally, future projections of land-use changes are forecasted for the years 2030 and 2050 using QGIS-MOLUSCE. The predicted maps project a significant rise in agricultural and built-up areas. These findings will assist policymakers in future planning. [ABSTRACT FROM AUTHOR]

For proper planning of urban infrastructures such as road networks, pipelines, and other linear engineering structures, it is necessary to construct precise land use and land cover maps. Multiple attempts to develop land use land cover classification techniques have been made using various methods ranging from surveying to date image interpretation using remote sensing techniques. Land use land cover classification remains an intricate and challenging task due to the spectral and spatial complexity of the imagery. This work generates a labelled dataset called Sen-2 LULC () using QGIS for land use land cover classification to train the state-of-the-art available convolutional neural networks with the backbone for pixel-wise classification of seven classes. This work deals with five models: UNet with ResNet50 backbone, UNet with ResNet152 backbone, UNet with DenseNet169, FPN with VGG16, and LinkNet with MobileNetv2. UNet-ResNet50 gives the best overall accuracy of 94.10% and Matthews correlation coefficient value of 0.69. [ABSTRACT FROM AUTHOR]

Recently, the earth's climate has changed considerably, leading to several hazards, including flash floods (FFs). This study aims to introduce an innovative approach to mapping and identifying FF exposure in the city of Tetouan, Morocco. To address this problem, the study uses different machine learning methods applied to remote sensing imagery within the Google Earth Engine (GEE) platform. To achieve this, the first phase of this study was to map land use and land cover (LULC) using Random Forest (RF), a Support Vector Machine (SVM), and Classification and Regression Trees (CART). By comparing the results of five composite methods (mode, maximum, minimum, mean, and median) based on Sentinel images, LULC was generated for each method. In the second phase, the precise LULC was used as a related factor to others (Stream Power Index (SPI), Topographic Position Index (TPI), Slope, Profile Curvature, Plan Curvature, Aspect, Elevation, and Topographic Wetness Index (TWI)). In addition to 2024 non-flood and flood points to predict and detect FF susceptibility, 70% of the dataset was used to train the model by comparing different algorithms (RF, SVM, Logistic Regression (LR), Multilayer Perceptron (MLP), and Naive Bayes (NB)); the rest of the dataset (30%) was used for evaluation. Model performance was evaluated by five-fold cross-validation to assess the model's ability on new data using metrics such as precision, score, kappa index, recall, and the receiver operating characteristic (ROC) curve. In the third phase, the high FF susceptibility areas were analyzed for two-way validation with inundated areas generated from Sentinel-1 SAR imagery with coherent change detection (CDD). Finally, the validated inundation map was intersected with the LULC areas and population density for FF exposure and assessment. The initial results of this study in terms of LULC mapping showed that the most appropriate method in this research region is the use of an SVM trained on a mean composite. Similarly, the results of the FF susceptibility assessment showed that the RF algorithm performed best with an accuracy of 96%. In the final analysis, the FF exposure map showed that 2465 hectares were affected and 198,913 inhabitants were at risk. In conclusion, the proposed approach not only allows us to assess the impact of FF in this study area but also provides a versatile approach that can be applied in different regions around the world and can help decision-makers plan FF mitigation strategies. [ABSTRACT FROM AUTHOR]

In recent decades, the depletion of surface water resources within the Lake Urmia Basin (LUB), Iran, has emerged as a significant environmental concern. Both anthropogenic activities and climate change have influenced the availability and distribution of surface water resources in this area. This research endeavors to provide a comprehensive evaluation of the impacts of climate change and anthropogenic activities on surface water resources across the LUB. Various critical climatic and anthropogenic factors affecting surface water bodies, such as air temperature (AT), cropland (CL), potential evapotranspiration (PET), snow cover, precipitation, built-up areas, and groundwater salinity, were analyzed from 2000 to 2021 using the Google Earth Engine (GEE) cloud platform. The JRC-Global surface water mapping layers V1.4, with a spatial resolution of 30 m, were employed to monitor surface water patterns. Additionally, the Mann–Kendall (MK) non-parametric trend test was utilized to identify statistically significant trends in the time series data. The results reveal negative correlations of −0.56, −0.89, −0.09, −0.99, and −0.79 between AT, CL, snow cover, built-up areas, and groundwater salinity with surface water resources, respectively. Conversely, positive correlations of 0.07 and 0.12 were observed between precipitation and PET and surface water resources, respectively. Notably, the findings indicate that approximately 40% of the surface water bodies in the LUB have remained permanent over the past four decades. However, there has been a loss of around 30% of permanent water resources, transitioning into seasonal water bodies, which now account for nearly 13% of the total. The results of our research also indicate that December and January are the months with the most water presence over the LUB from 1984 to 2021. This is because these months align with winter in the LUB, during which there is no water consumption for the agriculture sector. The driest months in the study area are August, September, and October, with the presence of water almost at zero percent. These months coincide with the summer and autumn seasons in the study area. In summary, the results underscore the significant impact of human activities on surface water resources compared to climatic variables. [ABSTRACT FROM AUTHOR]

Landscape change is intricately linked to natural resource utilization. Landscape dynamics are closely tied to land use and land cover (LULC), serving as a representation of ecosystems and human activities. In the Citarum River Basin, Indonesia, a comprehensive approach is necessary to comprehend landscape dynamics as a manifestation of human interaction with the environment. This research aims to analyze landscape dynamics and its factors that can significantly drive changes. We focused on the Cirasea Watershed, which serves as an upper region of the Citarum River Basin. Data was acquired from Landsat-series imageries from 1993 to 2023, and LULC analyses were conducted using classification and regression trees (CART), random forest (RF), and support vector machine (SVM). We analyzed seven independent variables, including slope (X1), elevation (X2), main river (X3), population (X4), central business district (X5), distance from the past settlements (X6), and accessibility (X7) using multivariate analysis. This research found that RF stands out as the optimal choice for LULC analysis over CART and SVM because it had the highest overall accuracy and overall kappa (0.91-0.92, 0.88-0.89). Notably, there was a substantial 273.43% increase in built-up areas, coupled with significant declines in plantations and horticultures. LULC changes was more pronounced in the lower areas near Bandung City. LR model highlighted X1, X3 and X6 as the significant driving forces for built-up areas expansion (r-square 0.44 with p-value < 0.01 and 95% confidence level). Without effective spatial planning, flat areas near rivers and past settlements have the greatest potential for LULC changes. [ABSTRACT FROM AUTHOR]

Traffic accident prediction is crucial to preventive measures against accidents and effective traffic management. Identifying hotspots can facilitate the selection of the most critical survey points to note the contributing features. In this research, an effort has been made to identify hotspots and predict traffic accident occurrences in an urban area. Accident data was obtained from the Rescue 1122 Emergency Services of Faisalabad, and hotspots were identified using Moran’s I in ArcGIS. Results showed that most hotspots were located around the General Transport Stand (GTS) area due to the maximum number of road users. The temporal investigations showed that the accident occurrence was significant from 1 to 2 p.m. The identified hotspots were further investigated by conducting a field survey. Essential features such as road geometric features, road furniture, and traffic data were used for developing Machine Learning Algorithms for accident prediction. Using Computer Vision, traffic data was extracted from recorded videos. Random forest, linear regression, and Decision tree algorithms were developed using Python in the Jupyter Notebook environment. The decision tree algorithm showed a maximum accuracy of 84.4%. The analysis of contributing factors revealed that road measurements had the maximum effect on accident occurrence. [ABSTRACT FROM AUTHOR]

This study proposes a two-dimensional analytical framework based on urban spatial form and spatial service perspectives, utilizing data on buildings and points of interest (POIs). It integrates fishnet analysis, kernel density analysis, the categorization of POI functionalities, and mixture calculations to enhance our understanding of urban spatial form and function. Taking the main urban area of Zhengzhou as an example, this study identifies image elements that can describe urban spatial characteristics through the results of two-dimensional analysis and enriches the city image in the form of a portrait. The experimental findings demonstrate that the elements of the annular layer, functional landmarks, ring line boundaries, and special districts can fully convey the spatial picture of Zhengzhou City. The performance of the four types of image elements has a high degree of matching with the content of the urban spatial planning of Zhengzhou City, which can effectively identify the urban multi-center structure and development pattern. This paper explores and tests the development status of the city from a new perspective, which can provide an effective reference for the future planning and sustainable development of the city. [ABSTRACT FROM AUTHOR]

The examination of changes in land use and land cover (LULC) holds a pivotal role in advancing our comprehension of underlying processes and mechanisms. The advancement of sophisticated earth observation programs has opened unprecedented opportunities to meticulously observe geographical areas, courtesy of the vast array of satellite imagery available across time. However, effectively analyzing this wealth of data to process LULC information remains a significant challenge within remote sensing. Recent times have witnessed the introduction of diverse techniques for scrutinizing satellite images, encompassing remote sensing technologies and machine/deep learning (M/DL) methods. This research endeavors to explore the transformation of LULC within the N'fis watershed, situated in the Western High Atlas region of Morocco, covering the timeline from 1984 to 2022. By harnessing remote sensing technologies, we have traced alterations in dams, forests, agriculture, and soil over this duration. Moreover, we have conducted comparisons among multiple machine and deep learning (M/DL) models to simulate and forecast LULC changes specifically for the year 2030. Our study outcomes manifest remarkable accuracy in LULC classification, consistently ranging between 91% and 97% for most years, with the kappa coefficient maintaining a range between 89% and 95%. Regarding predictive analysis, the Random Forest (RF) model emerges as the most precise, displaying an accuracy rate of 91%. [ABSTRACT FROM AUTHOR]

Road traffic accidents have increased globally, which has led to significant challenges to urban safety and public health. This concerning trend is also evident in California, where major cities have seen a rise in accidents. This research conducts a spatio-temporal analysis of traffic accidents across the four major Californian cities—Los Angeles, Sacramento, San Diego, and San Jose—over five years. It achieves this through an integration of Geographic Information System (GIS) functionalities (space–time cube analysis) with non-parametric statistical and spatial techniques (DBSCAN, KDE, and the Getis-Ord Gi* method). Our findings from the temporal analysis showed that the most accidents occurred in Los Angeles over five years, while San Diego and San Jose had the least occurrences. The severity maps showed that the majority of accidents in all cities were level 2. Moreover, spatio-temporal dynamics, captured via the space–time cube analysis, visualized significant accident hotspot locations. The clustering of accidents using DBSCAN verified the temporal and hotspot analysis results by showing areas with high accident rates and different clustering patterns. Additionally, integrating KDE with the population density and the Getis-Ord Gi* method explained the relationship between high-density regions and accident occurrences. The utilization of GIS-based analytical techniques in this study shows the complex interplay between accident occurrences, severity, and demographic factors. The insight gained from this study can be further used to implement effective data-driven road safety strategies. [ABSTRACT FROM AUTHOR]

Reliable and up-to-date training reference samples are imperative for land cover (LC) classification. However, such training datasets are not always available in practice. The sample migration method has shown remarkable success in addressing this challenge in recent years. This work investigated the application of Sentinel-1 (S1) and Sentinel-2 (S2) data in training sample migration. In addition, the impact of various spectral bands and polarizations on the accuracy of the migrated training samples was also assessed. Subsequently, combined S1 and S2 images were classified using the Support Vector Machines (SVM) and Random Forest (RF) classifiers to produce annual LC maps from 2017 to 2021. The results showed a higher accuracy (98.25%) in training sample migrations using both images in comparison to using S1 (87.68%) and S2 (96.82%) data independently. Among the LC classes, the highest accuracy in migrated training samples was found for water, built-up, bare land, grassland, cropland, and wetland. Inquiries on the efficiency of different spectral bands and polarization used in training sample migration showed that bands 4 and 8 and VV polarization in the water class were more important, while for the wetland class, bands 5, 6, 7, 8, and 8A together with VV polarization showed superior performance. The results showed that the RF classifier provided better performance than the SVM (higher overall, producer, and user accuracy). Overall, our findings suggested that shared use of S1 and S2 data can be used as a suitable means for producing up-to-date and high-quality training samples. [ABSTRACT FROM AUTHOR]

This study delves into the integration of analytic hierarchy process (AHP) and geographic information system (GIS) techniques to identify suitable areas for urban development in six districts within the Mersin Metropolitan Area of Turkey. The specific aim is to generate an urban land use suitability map, in order to facilitate informed decision-making for urban development. Drawing on open Landsat satellite imagery and employing the random forest (RF) algorithm, the study spans a fifteen-year period, over which land use/land cover (LULC) changes are measured. Furthermore, a novel approach is introduced by incorporating the urban land use suitability map into an urban growth simulation model developed using a logistic regression (LR) algorithm. This simulation forecasts urban growth up to 2027, enabling planners to evaluate potential development areas against suitability criteria. Findings reveal spatial patterns of land suitability and projected urban growth, aiding decision-makers in selecting optimal areas for development while preserving ecological integrity. Notably, the study emphasizes the importance of considering various factors such as topography, accessibility, soil capability, and geology in urban planning processes. The results showcase significant proportions of the study area as being moderately to highly suitable for urban development, alongside notable shifts in LULC classes over the years. Additionally, the overlay analysis of simulated urban growth and land suitability maps highlights areas with contrasting suitability levels, offering valuable insights for sustainable urban growth strategies. By overlaying the urban land suitability map with a simulated LULC map for 2027, it is revealed that 2247.3 hectares of potential new urbanization areas demonstrate very high suitability for settlement, while 7440.12 hectares exhibit very low suitability. By providing a comprehensive framework for assessing urban land suitability and projecting future growth, this research offers practical implications for policymakers, urban planners, and stakeholders involved in Mersin's development trajectory, ultimately fostering more sustainable and resilient urban landscapes. [ABSTRACT FROM AUTHOR]

High-resolution land cover mapping is crucial in various disciplines but is often hindered by the lack of accurately matched labels. Our study introduces an innovative deep learning methodology for effective land cover mapping, independent of matched labels. The approach comprises three main components: (1) An advanced fully convolutional neural network, augmented with super-resolution features, to refine labels; (2) The application of an instance-batch normalization network (IBN), leveraging these enhanced labels from the source domain, to generate 2-m resolution land cover maps for test sites in the target domain; (3) Noise assessment tests to evaluate the impact of varying noise levels on the model's mapping accuracy using external labels. The model achieved an overall accuracy of 83.40% in the target domain using endogenous super-resolution labels. In contrast, employing exogenous, high-precision labels from the National Land Cover Database in the source domain led to a notable accuracy increase of 2.55%, reaching 85.48%. This improvement highlights the model's enhanced generalizability and performance during domain shifts, attributed significantly to the IBN layer. Our findings reveal that, despite the absence of native high-precision labels, the utilization of high-quality external labels can substantially benefit the development of precise land cover mapping, underscoring their potential in scenarios with unmatched labels. [ABSTRACT FROM AUTHOR]

Following European directives, asbestos–cement corrugated roofing tiles must be eliminated by 2025. Therefore, identifying asbestos–cement rooftops is the first necessary step to proceed with their removal. Unfortunately, asbestos detection is a challenging task. Current procedures for identifying asbestos require human exploration, which is costly and slow. This has motivated the interest of governments and companies in developing automatic tools that can help to detect and classify these types of materials that are dangerous to the population. This paper explores multiple computer vision techniques based on Deep Learning to advance the automatic detection of asbestos in aerial images. On the one hand, we trained and tested two classification architectures, obtaining high accuracy levels. On the other, we implemented an explainable AI method to discern what information in an RGB image is relevant for a successful classification, ensuring that our classifiers' learning process is guided by the right variables—color, surface patterns, texture, etc.—observable on asbestos rooftops. [ABSTRACT FROM AUTHOR]

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The study and analysis of urban water consumption habits in different regions contribute to the development of strategies aimed at secure water reduction and distribution. Within this context, knowledge of global water availability and the analysis of factors that influence consumption in different regions in distinct situations become extremely important. Several studies have been carried out in a number of countries and describe different approaches. The objective of this article is to learn about the strategies used in water consumption forecast and analysis. Most of the studies analysed seek to understand the factors influencing consumption in different building types. When it comes to residential buildings, the number of residents and the influence of economic issues on water consumption have an important role in this matter. In this context, pieces of research present the use of awareness campaigns as a strategy towards water use reduction. As a contribution, this article presents a systemic view of the pieces of research conducted and their contribution to forecasting water consumption in different regions. In conclusion, one observes the importance of analysing the factors influencing water consumption in different regions and scenarios, such as during the COVID-19 pandemic. This article can help managers and researchers understand the main factors that influence water consumption and how this consumption takes place in different regions. [ABSTRACT FROM AUTHOR]

The sudden decrease in water levels in Urmia Lake has elevated the environmental risk to both nearby and distant communities. For the people living in this region, particularly the villagers, the crisis has brought about a number of negative effects, including economic, social, environmental, and production-related. The current study was carried out with the purpose of evaluating the economic and productive effects of this problem from the viewpoint of the rural residents in the eastern part of the lake. The eastern of the lake's including 87,049 rural households that made up the statistical population of this study from which 230 households were chosen and analyzed using a stratified sampling method. The formula proposed by Cochran was used to determine the sample size. A questionnaire and face-to-face interviews were the data collection tool and method. By employing the opinions of experts and determining Cronbach's alpha coefficient the validity and reliability of the research instrument were assessed. The data analysis was done using SPSS statistical software. According to the findings, among the most significant effects that the villagers agreed upon were the decrease in the area used to cultivate agricultural products, the reduction of water resources and the rise in costs associated with the supply of agricultural water, and the decline in the quality of surface and ground water. According to the results of the exploratory factor analysis, the various economic-production effects of the water reduction of this lake can be summarized under four groups including "quantitative and qualitative reduction of agricultural production and income", "decrease in the quantity and quality of water resources", "increase in costs and threat to job security and income", and "reduction of non-agricultural activities". Around 69% of the variance in the results' overall variance was explained by these four variables. Among the recommendations made by this study to lessen the effects of water depletion in this lake are encouraging the production and introduction of agricultural and horticultural crops suitable for the eastern area of Urmia Lake, developing new irrigation systems, and altering the pattern of irrigated agricultural crops. [ABSTRACT FROM AUTHOR]

Identifying urban buildings in high-resolution RGB images presents challenges, mainly due to the absence of near-infrared bands in UAVs and Google Earth imagery and the diversity in building attributes. Deep learning (DL) methods, especially Convolutional Neural Networks (CNNs), are widely used for building extraction but are primarily pixel-based. Geographic Object-Based Image Analysis (GEOBIA) has emerged as an essential approach for high-resolution imagery. However, integrating GEOBIA with DL models presents challenges, including adapting DL models for irregular-shaped segments and effectively merging DL outputs with object-based features. Recent developments include tabular DL models that align well with GEOBIA. GEOBIA stores various features for image segments in a tabular format, yet the effectiveness of these tabular DL models for building extraction still needs to be explored. It also needs to clarify which features are crucial for distinguishing buildings from other land-cover types. Typically, GEOBIA employs shallow learning (SL) classifiers. Thus, this study evaluates SL and tabular DL classifiers for their ability to differentiate buildings from non-building features. Furthermore, these classifiers are assessed for their capacity to handle roof heterogeneity caused by sun exposure and roof materials. This study concludes that some SL classifiers perform similarly to their DL counterparts, and it identifies critical features for building extraction. [ABSTRACT FROM AUTHOR]

In Protected Areas (PAs), zones of ecotourism development need to be carefully chosen to minimize environmental impacts and maximize socio-economic outcomes. The excessive growth of ecotourism in PAs and insufficient monitoring have caused a decrease in ecological connectivity (EC) and unsustainability in these areas. This research has evaluated environmental potential to determine suitable zones for ecotourism development in Lar National Park (NP) and Kavdeh Wildlife Refuge (WR) and also evaluated landscape ecology changes to investigate ecological function (EF) and status of EC in these areas. To achieve this purpose, environmental potential and landscape ecology changes have been examined, using multi-criteria decision analysis (MCDA) model and fuzzy set theoretic approach, as well as mathematical modeling. Our mixed-method approach allowed us to identify and prepare map zones with moderate to high potential for intensive and extensive recreational development in Kavdeh. Conversely, because of the high ecological sensitivity and restrictions for human activity, Lar was not deemed suitable for ecotourism development. In addition, the landscape ecology assessment raised concerns about the increasing human activities and ecotourism development in the study areas, impacting ecological function and connectivity. Accordingly, the results demonstrated that in Lar and Kavdeh Ecological Connectivity Index (ECI) has the highest decreasing trend. The findings of this study can be used as a blueprint for modeling land potential for sustainable ecotourism development in PAs with similar geographical conditions. These findings can also help policymakers and planners to prevent habitat fragmentation and protect landscape ecology. [ABSTRACT FROM AUTHOR]

A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, and five-class land cover using two dates (winter and non-winter) of a Sentinel-2 granule across seven international sites. The approach uses a series of spectral, textural, and distance decision functions combined with modified ancillary layers (such as global impervious surface and global tree cover) to create binary masks from which to generate a balanced set of training data applied to a random forest classifier. For the land cover masks, stepwise threshold adjustments were applied to reflectance, spectral index values, and Euclidean distance layers, with 62 combinations evaluated. Global (all seven scenes) and regional (arid, tropics, and temperate) adaptive thresholds were computed. An annual 95th and 5th percentile NDVI composite was used to provide temporal corrections to the decision functions, and these corrections were compared against the original model. The accuracy assessment found that the regional adaptive thresholds for both the two-date land cover and the temporally corrected land cover could accurately map land cover type within nine-class (68.4% vs. 73.1%), six-class (79.8% vs. 82.8%), and five-class (80.1% vs. 85.1%) schemes. Lastly, the five-class and six-class models were compared with a manually labeled deep learning model (Esri), where they performed with similar accuracies (five classes: Esri 80.0 ± 3.4%, region corrected 85.1 ± 2.9%). The results highlight not only performance in line with an intensive deep learning approach, but also that reasonably accurate models can be created without a full annual time series of imagery. [ABSTRACT FROM AUTHOR]

Recent droughts worldwide have significantly affected ecosystems in various regions. Among these affected areas, the Lake Urmia Basin (LUB) has experienced substantial effects from both drought and human activity in recent years. Lake Urmia, known as one of the hypersaline lakes globally, has been particularly influenced by these activities. The extraction of water since 1995 has resulted in an increase in the extent of salty land, leading to the frequent occurrence of salt storms. To address this issue, the current study utilized various machine learning algorithms within the Google Earth Engine (GEE) platform to map the probability of saline storm occurrences. Landsat time-series images spanning from 2000 to 2022 were employed. Soil salinity indices, Ground Points (GPs), and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products were utilized to prepare the training data, which served as input for constructing and running the models. The results demonstrated that the Support Vector Machine (SVM) performed effectively in identifying the probability of saline storm occurrence areas, achieving high R2 values of 91.12%, 90.45%, 91.78%, and 91.65% for the years 2000, 2010, 2015, and 2022, respectively. Additionally, the findings reveal an increase in areas exhibiting a very high probability of saline storm occurrences from 2000 to 2022. In summary, the results of this study indicate that the frequency of salt storms is expected to rise in the near future, owing to the increasing levels of soil salinity resources within the Lake Urmia Basin. [ABSTRACT FROM AUTHOR]

This study investigated the dynamics of land use and land cover (LULC) modelling, mapping, and assessment in the Kegalle District of Sri Lanka, where policy decision-making is crucial in agricultural development where LULC temporal datasets are not readily available. Employing remotely sensed datasets and machine learning algorithms, the work presented here aims to compare the accuracy of three classification approaches in mapping LULC categories across the time in the study area primarily using the Google Earth Engine (GEE). Three classifiers namely random forest (RF), support vector machines (SVM), and classification and regression trees (CART) were used in LULC modelling, mapping, and change analysis. Different combinations of input features were investigated to improve classification performance. Developed models were optimised using the grid search cross-validation (CV) hyperparameter optimisation approach. It was revealed that the RF classifier constantly outstrips SVM and CART in terms of accuracy measures, highlighting its reliability in classifying the LULC. Land cover changes were examined for two periods, from 2001 to 2013 and 2013 to 2022, implying major alterations such as the conversion of rubber and coconut areas to built-up areas and barren lands. For suitable classification with higher accuracy, the study suggests utilising high spatial resolution satellite data, advanced feature selection approaches, and a combination of several spatial and spatial-temporal data sources. The study demonstrated practical applications of derived temporal LULC datasets for land management practices in agricultural development activities in developing nations., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Agricultural intensification and urban sprawl have led to significant alterations in riverscapes, and one of the critical consequences is the deterioration of water quality with significant implications for public health. Therefore, the objectives of this study were the assessment of the water quality of the Suquía River, the assessment of LULC change at different spatial scales, and the analysis of the potential seasonal correlation among LULC change and Water Quality Index (WQI). The Sample Sites (SS) 1 and 2 before Cordoba city had the highest WQI values while from SS3 the WQI decreased, with the lowest WQI close to the wastewater treatment plant (SS7) after Cordoba city. From SS8 in a agricultural context, the WQI increases but does not reach the original values. In light of analysis carried out, the correlation between water quality variables and the different LULC classes at the local and regional scales demonstrated that WQI is negatively affected by agricultural and urban activities, while natural classes impacted positively. The spatialization of the results can help strongly in assessing and managing the diffusion of point and non-point pollution along the riverscape. The knowledge gained from this research can play a crucial role in water resources management, which supports the provision of river ecosystem services essential for the well-being of local populations. [ABSTRACT FROM AUTHOR]

Tourist maps provide tourists with destination information that reflects their unique characteristics and cultural connotations and play an important role in attracting tourists and serving marketing purposes. However, existing designs of tourist maps often ignore the importance of cultural resource selection and the relationship between maps and structural linguistics, thereby affecting the narrative function and representativeness of tourist maps. This study utilizes the local chronicle as a data source and proposes a novel visual narrative framework (VNF) for tourist maps. The VNF combines Todorov's narrative hierarchy and Roth's visual storytelling tropes to establish a mapping between map elements and narrative elements. To demonstrate the effectiveness of the VNF, the Songshan Scenic Area was selected as a case study. By applying the VNF, highly characteristic and meaningful colors, figurative hand-painted symbols, and scene symbols are selected and integrated into the map design to enhance the artistic value and narrative of the map. This framework reveals the potential cultural value of local chronicles and can serve as a reference for other historical tourist cities, contributing to the preservation of local cultural heritage. [ABSTRACT FROM AUTHOR]

This article analyzes the development of ecotourism in the Ulytau Nature Park using innovative Geographi c information systems technologies. The main purpose of the study is to create a favorable and innovative environment for the development of the tourist experience, including the search and discovery of historical sites, the development of optimal routes and infrastructure improvements. The use of GIS maps in ecological tourism contributes to the development of optimal routes, improvement of tourist infrastructure and provision of informative services. The analysis of the study makes it possible to identify recommendations for public and private organizations in the field of using GIS technologies for the sustainable development of ecotourism. The created GIS map provides information about the park's territory, the location of objects and routes, which contributes to a more informative and oriented tourist experience. The experience of working with GIS technologies enhances the ability of tourists to navigate, discover and obtain information about historical sites and attractions in ecotourism and help enrich the tourist experience. [ABSTRACT FROM AUTHOR]

Post-disaster recovery has been addressed in the literature by different sectoral perspectives and scientific communities. Nevertheless, studies providing holistic approaches to recovery, integrating reconstruction procedures and socio-economic impacts, are still lacking. Additionally, there is a gap in disaster recovery research addressing the additional challenges posed by the effect of complex, multiple, and interacting risks on highly interconnected urban areas. Furthermore, recovery has only been marginally explored from a pre-disaster perspective in terms of planning and actions to increase urban resilience and recoverability. This paper provides a critical review of existing literature and guidelines on multi-risk disaster recovery with the twofold aim of identifying current gaps and providing the layout to address multi-risk recovery planning tools for decision-making. The literature on disaster recovery is investigated in the paper by focusing on the definition of the recovery phase and its separation or overlapping with other disaster risk management phases, the different destinations and goals that an urban system follows through recovery pathways, the requirements to implement a holistic resilience-based recovery roadmap, the challenges for shifting from single-risk to multi-risk recovery approaches, and the available tools for optimal decision-making in the recovery planning. Finally, the current challenges in multi-risk recovery planning are summarized and discussed. This review can be a ground basis for new research directions in the field of multi-risk recovery planning to help stakeholders in decision-making and optimize their pre-disaster investments to improve the urban system's recoverability. [ABSTRACT FROM AUTHOR]

Controlling and extinguishing spreading forest fires is a challenging task that often leads to irreversible losses. Moreover, large-scale forest fires generate smoke and dust, causing environmental pollution and posing potential threats to human life. In this study, we introduce a modified deep convolutional neural network model (MDCNN) designed for the recognition and localization of fire in video imagery, employing a deep learning-based recognition approach. We apply transfer learning to refine the model and adapt it for the specific task of fire image recognition. To combat the issue of imprecise detection of flame characteristics, which are prone to misidentification, we integrate a deep CNN with an original feature fusion algorithm. We compile a diverse set of fire and non-fire scenarios to construct a training dataset of flame images, which is then employed to calibrate the model for enhanced flame detection accuracy. The proposed MDCNN model demonstrates a low false alarm rate of 0.563%, a false positive rate of 12.7%, a false negative rate of 5.3%, and a recall rate of 95.4%, and achieves an overall accuracy of 95.8%. The experimental results demonstrate that this method significantly improves the accuracy of flame recognition. The achieved recognition results indicate the model's strong generalization ability. [ABSTRACT FROM AUTHOR]

Although Sistan and Baluchestan Province is renowned for its handicrafts and has the potential to contribute significantly to the local economy, the industry has not yet met expectations. The downturn in demand in the handicraft market has not only eroded the incentive to enter this industry, but also made some people unemployed in this area and had other side effects, such as an increase in rural-to-city migration and the lack of production of some handicrafts. In this regard, the purpose of this study is to evaluate and rate barriers to market development in the handicraft cooperatives of this province. The current research adopts an applied and quantitative approach, gathering essential information through questionnaires and interviews with practitioners and experts in the field within Sistan and Baluchestan province. A total of 40 questionnaires were collected in 2022 and subjected to analysis employing both descriptive and inferential statistics methodologies. The results show that the limited holding of national or international exhibitions of handicrafts, investors' unwillingness to invest in handicrafts due to restrictive mechanisms and inadequate public propaganda to recognize the effects of handicrafts (especially tourists) are the most important barriers to the development of handicraft market are. The lack of design and use of integrated and scientific marketing systems for selling products by manufacturing companies, and the inability to sell handmade products directly by manufacturers, are also serious barriers to the lack of development of the province's handicraft market. However, the mismatch of provincial handicraft production with consumer tastes, diversity and product attractiveness is not a serious obstacle to the development of crafts markets. [ABSTRACT FROM AUTHOR]

Background: Given the impact of high intake of sugar-sweetened beverages on type 2 diabetes, intervention to reduce their consumption can be a top priority for any health system. Thus, the purpose of the present study is to simulate the impact of policy options related to reduce consumption of sugar-sweetened beverages (SSBs) on the prevalence and mortality of type 2 diabetes in Iranian men and women. Methods: A discrete event simulation (DES) model was used to predict the effect of several policy options on the prevalence and death from type 2 diabetes in Azar Cohort Databases. Population age- and sex-specific prevalence and incidence rate of diagnosed diabetes were derived from the national health data. The Preventable Risk Integrated Model (PRIME) model was used for coding the input parameters of simulation using R and Python software. Results: The prevalence and mortality rate of type 2 diabetes under the scenario of reduced consumption of SSBs indicated that the highest and the lowest prevalence and mortality rates of type 2 diabetes for men and women were related to no policy condition and replacing SSBs with healthy drinks, like water, respectively. Also, the maximum "number of deaths postponed/ prevented" from type 2 diabetes was related to replacing SSBs with water (n=2015), and an integration of reformulation and applying 10% tax on SSBs (n=1872), respectively. Conclusion: Simulating the effect of different policy options on reducing the consumption of SSBs showed "replacing of SSBs with water" as the most effective policy option in Iranian setting. [ABSTRACT FROM AUTHOR]

Uncontrolled human activity and nature are causing the deterioration of Saint Martin Island, Bangladesh's only tropical island, necessitating sustainable land use strategies and ecological practices. Therefore, the present study measures the land use/cover transition from 1974 to 2021, predicts 2032 and 2042, and constructs the spatiotemporal features of the Landscape Ecological Risk Index based on land use changes. The study utilized Maximum Likelihood Classification (MLC) on Landsat images from 1974, 1988, 2001, 2013, and Sentinel 2B in 2021, achieving ≥ 80% accuracy. The MLP-MC approach was also used to predict 2032 and 2042 LULC change patterns. The eco-risk index was developed using landscape disturbance and vulnerability indices, Bayesian Kriging interpolation, and spatial autocorrelations to indicate spatial clustering. The research found that settlements increased from 2.06 to 28.62 ha between 1974 and 2021 and would cover 41.22 ha in 2042, causing considerable losses in agricultural areas, waterbodies, sand, coral reefs, and vegetation. The area under study showed a more uniform and homogenous environment as Shannon's diversity and evenness scores decreased. The ecological risk of Saint Martin Island increased from 4.31 to 31.05 ha between 1974 and 2042 due to natural and human factors like erosion, tidal bores, population growth, coral mining, habitat destruction, and intensive agricultural practices and tourism, primarily in Nazrul Para, Galachipa, and Western Dakhin Para. The findings will benefit St. Martin Island stakeholders and policymakers by providing insights into current and potential landscape changes and land eco-management., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Forest fire risk assessment plays a crucial role in the environmental management of natural hazards, serving as a key tool in the prevention of forest fires and the protection of various species. As these risks continue to evolve with environmental changes, the pertinence of contemporary research in this field remains undiminished. This review constructs a comprehensive taxonomic framework for classifying the existing body of literature on forest fire risk assessment within forestry studies. The developed taxonomy categorizes existing studies into 8 primary categories and 23 subcategories, offering a structured perspective on the methodologies and focus areas prevalent in the domain. We categorize a sample of 170 articles to present recent trends and identify research gaps in forest fire risk assessment literature. The classification facilitates a critical evaluation of the current research landscape, identifying areas in need of further exploration. Particularly, our review identifies underrepresented methodologies such as optimization modeling and some advanced machine learning techniques, which present routes for future inquiry. Moreover, the review underscores the necessity for model development that is tailored to specific regional data sets but also adaptable to global data resources, striking a balance between local specificity and broad applicability. Emphasizing the dynamic nature of forest fire behavior, we advocate for models that integrate the burgeoning field of machine learning and multi-criteria decision analysis to refine predictive accuracy and operational effectiveness in fire risk assessment. This study highlights the great potential for new ideas in modeling techniques and emphasizes the need for increased collaboration among research communities to improve the effectiveness of assessing forest fire risks., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The conversion of large-scale agricultural land into urban areas poses a significant challenge to achieving national and global food security targets, as outlined in Sustainable Development Goal number 2, which aims to eradicate hunger. Indonesia has experienced a significant decline in rice field areas, with a reduction of approximately 650 thousand hectares within a year (2017-2018), the largest being in Java. Hence, this study aims to examine the impact of urban expansion on agricultural land in the north coast region of West Java Province from 2013 to 2020 and develop a predictive model for 2030 to support sustainable land use planning. The primary methods employed were random forest (RF) analysis using Google Earth Engine, intensity analysis, multilayer perceptron-neural network (MLP-NN), Markov chains-cellular automata (Markov-CA), and stakeholder interviews. The model also evaluated the influence of "distance to tollgates" as a previously unexplored driving factor in existing land use modeling studies. Landsat image classification results using the RF algorithm showed 87-88% accuracy. Cropland has historically been and is projected to remain the primary target for the expansion of built-up areas. Spatial planning irregularities were found in the growth of these areas that adversely affected farmers' socioeconomic and environmental conditions. Evaluation of land use models using MLP-NN and Markov-CA demonstrated an accuracy rate of 86.29-86.23%. The distance to tollgates factor significantly impacts the models, albeit less than population density. The 2030 intervention scenario, which implements a firm policy for sustainable agricultural land use, offers the potential to maintain the predicted cropland loss compared to business as usual., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Mizoram (India) is part of UNESCO's biodiversity hotspots in India that is primarily populated by tribes who engage in shifting agriculture. Hence, the land use land cover (LULC) pattern of the state is frequently changing. We have used Landsat 5 and 8 satellite images to prepare LULC maps from 2000 to 2020 in every 5 years. The atmospherically corrected images were pre-processed for removal of cloud cover and then classified into six classes: waterbodies, farmland, settlement, open forest, dense forest, and bare land. We applied four machine learning (ML) algorithms for classification, namely, random forest (RF), classification and regression tree (CART), minimum distance (MD), and support vector machine (SVM) for the images from 2000 to 2020. With 80% training and 20% testing data, we found that the RF classifier works best with the most accuracy than other classifiers. The average overall accuracy (OA) and Kappa coefficient (KC) from 2000 to 2020 were 84.00% and 0.79 when the RF classifier was used. When using SVM, CART, and MD, the average OA and KC were 78.06%, 0.73; 78.60%, 0.72; and 73.32%, 0.65, respectively. We utilised three methods of topographic correction, namely, C-correction, SCS (sun canopy sensor) correction, and SCS + C correction to reduce the misclassification due to shadow effects. SCS + C correction worked best for this region; hence, we prepared LULC maps on SCS + C corrected satellite image. Hence, we have used RF classifier for LULC preparation demi-decadal from 2000 to 2020. The OA for 2000, 2005, 2010, 2015, and 2020 was found to be 84%, 81%, 81%, 85%, and 89%, respectively, using RF. The dense forest decreased from 2000 to 2020 with an increase in open forest, settlement, and agriculture; nevertheless, when Farmland was low, there was an increase in the barren land. The results were significantly improved with the topographic correction, and misclassification was quite less., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Monitoring the dynamics of land use and land cover (LULC) is imperative in the changing climate and evolving urbanization patterns worldwide. The shifts in land use have a significant impact on the hydrological response of watersheds across the globe. Several studies have applied machine learning (ML) algorithms using historical LULC maps along with elevation data and slope for predicting future LULC projections. However, the influence of other driving factors such as socio-economic and climatological factors has not been thoroughly explored. In the present study, a sensitivity analysis approach was adopted to understand the effect of both physical (elevation, slope, aspect, etc.) and socio-economic factors such as population density, distance to built-up, and distance to road and rail, as well as climatic factors (mean precipitation) on the accuracy of LULC prediction in the Brahmani and Baitarni (BB) basin of Eastern India. Additionally, in the absence of the recent LULC maps of the basin, three ML algorithms, i.e., random forest (RF), classified and regression trees (CART), and support vector machine (SVM) were utilized for LULC classification for the years 2007, 2014, and 2021 on Google earth engine (GEE) cloud computing platform. Among the three algorithms, RF performed best for classifying built-up areas along with all the other classes as compared to CART and SVM. The prediction results revealed that the proximity to built-up and population growth dominates in modeling LULC over physical factors such as elevation and slope. The analysis of historical data revealed an increase of 351% in built-up areas over the past years (2007-2021), with a corresponding decline in forest and water areas by 12% and 36% respectively. While the future predictions highlighted an increase in built-up class ranging from 11 to 38% during the years 2028-2070, the forested areas are anticipated to decline by 4 to 16%. The overall findings of the present study suggested that the BB basin, despite being primarily agricultural with a significant forest cover, is undergoing rapid expansion of built-up areas through the encroachment of agricultural and forested lands, which could have far-reaching implications for the region's ecosystem services and sustainability., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)