Search

The information on land use/land cover (LULC) is indispensable in regional planning, policy formulation and tracking land use/land cover changes (LULCC). The rapid urbanization of hilly terrains, driven by population growth, has significant implications for landslide risk reduction. Recognizing the need for an innovative approach for extracting LULC information, the present study uses a random forest (RF) classifier to develop a novel, pre-trained and universal tool that automatically generates LULC classification maps based on natural colour satellite imagery without any training input from the end-user. The proposed framework with an overall accuracy of 0.75 and an area under the curve (AUC) score of 0.95 in the receiver operating characteristic curve (ROC) approach was used for mapping built-up area expansion in regions susceptible to rainfall-induced landslides in Idukki block panchayat (administrative division), Kerala, India. By comparing the LULC information for the years 2012 and 2022, it was understood that the built-up area in the location has increased from 12.76% of the total area in 2012 to 26.48% in 2022. It is important to consider the rapid increase in built-up area expansion in the 'very high' landslide susceptibility zones in the study area. This clearly demonstrates the need for hazard inclusive planning and tracking of LULCC, for disaster risk reduction. Research Highlights: A pre-trained Land Use/Land Cover (LULC) classification tool is developed using the Random Forest (RF) classifier. Based on natural colour satellite imagery, the tool automatically generates LULC maps for various landscapes worldwide. The tool demonstrates a satisfactory performance, achieving an overall accuracy of 0.75 and an overall ROC AUC score of 0.95. The tool was used to understand the LULC changes in Idukki block panchayat between 2012 and 2022. [ABSTRACT FROM AUTHOR]

The Himalayan region has been experiencing stark impacts of climate change, demographic and livelihood pattern changes. The analysis of land use and land cover (LULC) change provides insights into the shifts in spatial and temporal patterns of landscape. These changes are the combined effects of anthropogenic and natural/climatic factors. The present study attempts to monitor and comprehend the main drivers behind LULC changes (1999–2021) in the Himalayan region of Pithoragarh district, Uttarakhand. Pithoragarh district is a border district, remotely located in the north-east region of Uttarakhand, India. The study draws upon primary and secondary data sources. A total of 400 household surveys and five group discussions from 38 villages were conducted randomly to understand the climate perception of the local community and the drivers of change. Satellite imagery, CRU (Climatic Research Unit) climate data and climate perception data from the field have been used to comprehensively comprehend, analyze, and discuss the trends and reasons for LULC change. GIS and remote sensing techniques were used to construct LULC maps. This multifaceted approach ensures comprehensive and corroborated information. Five classes were identified and formed viz- cultivation, barren, settlement, snow, and vegetation. Results show that vegetation and built-up have increased whereas cultivation, barren land, and snow cover have decreased. The study further aims to elucidate the causes behind LULC changes in the spatially heterogeneous region, distinguishing between those attributed to human activities, climate shifts, and the interconnected impacts of both. The study provides a comprehensive picture of the study area and delivers a targeted understanding of local drivers and their potential remedies by offering a foundation for formulating sustainable adaptation policies in the region. [ABSTRACT FROM AUTHOR]

Land use land cover (LULC) assessment is essential for monitoring naturally available land resources through spatio-temporal changing pattern to protect land resources sustainably. The primary objective is to identify the changing pattern in LULC over last 10 years in Imphal-Iril catchment. Multi-spectral datasets of Landsat 7 and 8 were used for five different time periods (2011, 2013, 2015, 2017 and 2019). Supervised classification method was employed to generate the classified LULC maps using the maximum likelihood classifier approach in ArcGIS 10.3. In this assessment, five major land use classes have been classified as vegetation, agriculture, settlement, barren land and water body, and the dominant land cover is vegetation. The classified images obtained from this technique were checked for their accuracy using statistical kappa co-efficient and overall accuracy. According to this study, settlement and barren areas have been significantly increased by 72.25% (39.03 km2) and 22.60% (118.29 km2) during the last 10 years, whereas areas assigned to agriculture, vegetation and water bodies have decreased by 27.26% (72.16 km2), 7.59% (73.22 km2) and 60.39% (11.94 km2), respectively. LULC map for the year 2030 was also predicted using MOLUSCE model in QGIS. The results of this assessment will be significant to the policy implicates for sustainable LULC management in the region. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF)), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC(2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape. [ABSTRACT FROM AUTHOR]

Groundwater plays an important part in protecting the ecological values of the earth's systems. Assessment of groundwater (GW) recharge due to change in land use/land cover (LULC) and climate variables is essential for integrated water management and adaptation. This study is an attempt to check the combined influence of change in LULC and climate variables on GW recharge in Kallada river basin, Kerala, India. The LULC map is predicted for the year 2030 based on LULC maps during the years 1985, 1995, 2005 and 2015 using Markov chain and multilayer perceptron model. The major LULC category in the study area is plantation with an area of 797.8 km2 (49.6% in 2015) and dense forest with an area of 366.5 km2 (22.8% in 2015). Considering the period 1985–2005, a decrease of 7.43% dense forest and an increase of 2.55% built area were observed. The predicted LULC shows that there is a reduction in plantation (3.77%) and dense forest (1.28%) and increase in built-up land (4.12%) by 2030 as compared to the year 2015. The CMIP5 General Circulation Model is used for future projections of climate variables, namely daily precipitation, maximum temperature and minimum temperature under RCP 4.5 and RCP 8.5 scenarios. Multilayer perceptron neural network model was used for statistical downscaling of GCM projections. The SWAT model was used to estimate the groundwater recharge for the year 2030 using the predicted LULC map and hydro-meteorological variables. The results illustrate a significant drop in GW recharge in Kallada river basin by the year 2030. Average GW recharge in the Vettikavala and kottarakkara, which is in the western region of the basin, is reduced to 4.6% and 9.3% under RCP 4.5 and RCP 8.5 scenarios, compared to the average GW recharge in 2005, which was 24.8% of rainfall. The decline in the groundwater recharge in the western regions may be due the increase in built-up area and decrease in plantation. The proposed model results provide a reliable insight for water resources planners in conducting future research on groundwater resources. [ABSTRACT FROM AUTHOR]

Rapid urbanization creates complexity, results in dynamic changes in land and environment, and influences the land surface temperature (LST) in fast-developing cities. In this study, we examined the impact of land use/land cover (LULC) changes on LST and determined the intensity of urban heat island (UHI) in New Town Kolkata (a smart city), eastern India, from 1991 to 2021 at 10-a intervals using various series of Landsat multi-spectral and thermal bands. This study used the maximum likelihood algorithm for image classification and other methods like the correlation analysis and hotspot analysis (Getis–Ord Gi* method) to examine the impact of LULC changes on urban thermal environment. This study noticed that the area percentage of built-up land increased rapidly from 21.91% to 45.63% during 1991–2021, with a maximum positive change in built-up land and a maximum negative change in sparse vegetation. The mean temperature significantly increased during the study period (1991–2021), from 16.31°C to 22.48°C in winter, 29.18°C to 34.61°C in summer, and 19.18°C to 27.11°C in autumn. The result showed that impervious surfaces contribute to higher LST, whereas vegetation helps decrease it. Poor ecological status has been found in built-up land, and excellent ecological status has been found in vegetation and water body. The hot spot and cold spot areas shifted their locations every decade due to random LULC changes. Even after New Town Kolkata became a smart city, high LST has been observed. Overall, this study indicated that urbanization and changes in LULC patterns can influence the urban thermal environment, and appropriate planning is needed to reduce LST. This study can help policy-makers create sustainable smart cities. [ABSTRACT FROM AUTHOR]

This study aimed to monitor long-term land use dynamics for understanding the natural forest integrity and intactness of the Rajiv Gandhi (Nagarhole) Tiger Reserve (RTR) pre- and post-declarations as TR. We employed multi-source data from Survey of India Toposheets (1:50 k), Landsat-7, and Sentinel-2A along with Global Ecosystem Dynamics Investigation (GEDI) vegetation canopy height (10 m) data, a high-spatial resolution CORONA (1970) images and temporal Google Earth Pro images for mapping and validation. To map vegetation type, land use and land cover (LULC) transitions, and fragmentation (1980–2022) we employed a hybrid classification approach. This study also analyzed decadal forest dynamics within TRs using India's State of Forest Reports (ISFR). Findings reveal significant forest fragmentation and habitat loss due to anthropogenic activities in the TR. Mono-plantations (teak and eucalyptus) were found inside TR, while the buffer (1 km) was occupied mainly with coffee and orange plantations which indicates the prevalence of human footprint. The overall accuracy of the current period (2022) is 92.0% with a kappa coefficient value of 0.90. These plantations were established during the British colonial period (early 1900s) for commercial purposes by clearing natural forests. The present study highlights that mono-plantations have not transitioned into natural forests even after a century. This lack of transformation could potentially compromise the integrity of the native forest. Despite its ecological significance, RTR has experienced disturbance due to human footprint. Hence, there is a need for an action plan to protect this vital landscape by replacing mono-plantations with suitable species to preserve the integrity of the forest. These issues extend beyond the protected areas, impacting surrounding regions and require regular monitoring. The proposed methods can be applied to other protected areas facing similar problems in the country and world. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The current study investigates the joint impact of projected land use/land cover change (LULCC) and climate change on the discharge of river Puthimari using Soil and Water Assessment Tool (SWAT). Puthimari, flowing through part of Bhutan and the northeastern region of India, earns its significance by contributing a fairly huge amount of discharge to the mainstream Brahmaputra causing frequent floods downstream, specifically in the monsoon season. The analysis was carried out from 2025 to 2099, by dividing this entire period into three sub-periods, 2025‒2049, 2050‒2074, and 2075‒2099, each of 25 years duration. To evaluate the impact of climate change, this study considered future climate data of five different CMIP5 (Coupled Model Intercomparison Project) climate models and their ensemble for RCP4.5 and RCP8.5 (Representative Concentration Pathways). The changes in LULC were incorporated by projecting the future LULC for 2035, 2065, and 2085 for each of the periods using the CA (Cellular Automata)-Markov model. SWAT performed well for both calibration and validation. The respective Nash-Sutcliffe efficiency (NSE) values for calibration and validation were found to be 0.74 and 0.77. Also, 0.75 and 0.79 coefficient of determination (R2) values were obtained for calibration and validation, respectively. The analyses reveal a 19.76% increase in rural settlement, and a 6.30%, 16.45%, and 8.76% decrease in forest, cropland, and waterbodies in the watershed by the end of this century. The average monsoon rainfall would increase by 14.16% and 38.92%, with a corresponding increase in discharge by 34.27% and 64.67%, under RCP 4.5 and RCP 8.5, respectively. This comprehensive study represents a pioneering effort to thoroughly analyze the future hydrological dynamics of the Puthimari River. This research serves as a vital resource for policymakers and government agencies, offering valuable insights to guide both structural and non-structural measures aimed at safeguarding the river from potential flood devastation. Additionally, it provides essential information to support the implementation of effective watershed management practices. [ABSTRACT FROM AUTHOR]

The present work evaluates the impact of land use changes in one of the important capital cities of Central India to understand the relation between land use practices and urbanization on the increasing trend of land surface temperature. Therefore, quantify the spatial changes in land use land cover (LULC) and Urban Heat Island (UHI) of the Bhopal city over 12 years (2008 to 2020) carried out considering multi-temporal satellite and field data. The Landsat-5 TM and Landsat-8 OLI/TIRS sensors were used to estimate the physical changes such as land use, normalized differential vegetation index (NDVI), normalized differential built-up index (NDBI), Land Surface Temperature (LST) and co-relate their interrelationship. It was found that the built-up area increased by 12.57% from 2008 to 2020, and vegetation increased by 15.95% from 2008 to 2020. It was also found that the bare rocky region has converted to vegetation by 22.95%. The Pearson coefficient between LST with NDBI was found to be 0.55 to 0.79. Pearson coefficient for NDVI association with LST was found to be negative, which varies between − 0.4 and − 0.7. The build-up area increase has been strongly influenced by the conversion of fallow land (5.84%) and bare rocky regions (22.92). Surface Urban Heat Island (SUHI) has also been calculated and found to be 4 °C higher in 2020 than in 2008. Overall, based on these findings, it is recommended to prioritize effective urban planning and land use policies that promote sustainable development and mitigate the adverse effects of increasing LST on the urban environment. [ABSTRACT FROM AUTHOR]

In the present study, the groundwater spring potential zone (GSPZ) was identified using an integrated approach of remote sensing (RS) and geographic information system (GIS), analytic hierarchy process (AHP), and fuzzy-AHP based on multicriteria decision-making (MCDM). Thus, ten associated factors with groundwater springs have been considered: slope, drainage density, lineament density, geomorphology, lithology, soil texture, land use and land cover, rainfall, groundwater level, and spring discharge. The analysis output was categorized into low, moderate, and high. The result of the AHP model shows the area under the high potential zone (16.61%), the moderate potential zone (60.42%), and the low potential zone (22.61%). The result of the fuzzy-AHP model shows that the area falls under the high potential zone (30.40%), moderate potential zone (41.29%), and low potential zone (22.61%). The validation results showed fuzzy-AHP with the area under the curve 0.806, which is slightly better than 0.779 of AHP. So, the resulting GSPZ map confirms that the thematic layers used in the study have a significant role in groundwater spring occurrence and distribution. It was recommended that any groundwater spring rejuvenation or protection activities must be implemented in medium to very high potential zones. [ABSTRACT FROM AUTHOR]

The land use/land cover (LULC) and climate change have detrimental impacts on the water balancing component (WBC) of a river basin, especially in the humid tropical regions. The aim of this study is to assess changes in the past, present, and future WBCs by analyzing the individual and combined impacts of both climate as well as LULC changes using the soil and water assessment tool (SWAT) model for a humid tropical Pamba river basin (PRB) in Western Ghats, India. To investigate the near (2021–2040), mid (2041–2070), and far (2071–2099) future effects of changing climate and LULC on WBCs, an ensemble of five general circulation models (GCM) with representative concentration pathways (RCPs) 4.5 and 8.5 scenarios was considered. Future LULCs for 2030, 2050, 2075, and 2100 were prepared using the Land Change Modeler (LCM) considering the normal LULC growth. The results show that the effects of LULC change increase the water yield and surface runoff while decreasing evapotranspiration (ET) throughout the study period (1988 to 2100). The climate change impacts show ET, water yield, and surface runoff decreases in the future for both RCP scenarios (except water yield, which increases only in near future for both RCPs). The trend of combined impacts on ET, water yield, and surface runoff for the future time was similar to the climate change trend. The results indicate climate change is dominant on WBCs in the future in the PRB due to variation in precipitation and temperature under both RCP 4.5 and 8.5 emission scenarios. This study will be useful for the overall water resources management of the river basin considered and the methodological framework can be adapted for other similar river basins. [ABSTRACT FROM AUTHOR]

The 2004-tsunami affected the South Andaman coast experiencing dynamic changes in the coastal geomorphology making the region vulnerable. We focus on pre-and post-tsunami shoreline and Land Use Land Cover changes for the period 2004, 2005, and 2022 to analyse the dynamic change in hazard. We used GEBCO bathymetry data to calculate Run-up (m), arrival times (Min), and inundation (m) at 13 different locations using the 2004 Sumatra Earthquake source parameters. The Digital Shoreline Analysis System is used for the shoreline change estimates. The Landsat data is used to calculate shoreline and LULC change in five classes, namely Built-Up Areas, Forests, Inundation areas, Croplands, and water bodies during the above period. We examine the correlation between the LULC changes and the dynamic change in shoreline due to population flux, infrastructural growth, and Gross State Domestic Product growth. India industry estimates the Andaman & Nicobar Islands losses exceed INR 10 billion during 2004 that would see a five-fold increase in economic loss due to a doubling of built-up area, a three-fold increase in tourist inflow, and a population density growth. The unsustainable decline in the forest cover, mangroves, and cropland would affect sustainability during a disaster despite coastal safety measures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Soil erosion is a significant problem in the agriculture sector and the environment globally. Susceptible soil erosion zones must be identified and erosion rates evaluated to decrease land degradation problems and increase crop productivity by protecting soil fertility. Therefore, a research study has been carried out in the Ponnaniyar River basin, an ungauged tributary of the Cauvery basin in India, primarily used for agriculture. The main purpose of this study is to assess soil erosion (SE) and sediment yield (SY) for the future in an ungauged basin by utilizing the projected land use/land cover (LULC) map of the study area. Additionally, Landsat 8 satellite dataset was only used for the classification and prediction of LULC to eliminate the variation between the resolution, bands and its wavelength of different satellites datasets. To achieve the goals of this study, three phases were followed. First, the LULC of the study area was classified using a Random Trees Classifier (RTC), a machine learning technique, followed by the projection of land cover using a Cellular Automata-based Artificial Neural Network (CA-ANN) model. The driving factors for this model include digital elevation model (DEM), slope, distance to roads, settlements, and water bodies. The accuracy level of the projected LULC map was determined by comparing it with the classified LULC map of the study area, and the results showed an overall accuracy (OA) of 85.35 percentage and a kappa coefficient (K) of 0.74, respectively. Second, the projected LULC map was used in the land management factor (C) and conversation practice factor (P) of the Revised Universal Soil Loss Equation (RUSLE) model to assess soil erosion. The model was integrated with the sediment delivery ratio (SDR) to estimate sediment yield within the study area. The accuracy of the generated erosion map based on the classified and projected LULC for the year 2022 was determined using the receiver operating characteristic curve (ROC) curve, and it was found to be in satisfactory agreement. Finally, for effective soil and water conservation measures, the basin was divided into 13 sub-watersheds (SWs) using terrain analysis in geographical information system (GIS). The SWs were prioritized based on the mean soil loss in the 4-year interval from 2014 to 2030 and integrated using the weighted average method to determine the final prioritization. From these findings, SW 11, SW 9, SW 12, and SW 1 are extremely affected by soil erosion, and immediate implementation of water harvesting structures is required for soil conservation. Also, this research might be useful for decision-makers and policymakers in land management. [ABSTRACT FROM AUTHOR]

The current high rate of urbanization in developing countries and its consequences, like traffic congestion, slum development, scarcity of resources, and urban heat islands, raise a need for better Land Use Land Cover (LULC) classification mapping for improved planning. This study mainly deals with two objectives: 1) to explore the applicability of machine learning-based techniques, especially the Random forest (RF) algorithm and Support Vector Machine (SVM) algorithm as the potential classifiers for LULC mapping under different scenarios, and 2) to prepare a better LULC classification model for mountain terrain by using different indices with combination of spectral bands. Due to differences in topography, shadows, spectral confusion from overlapping spectral signatures of different land cover types, and a lack of access for ground verification, classification in mountainous terrain is difficult task compared to plain terrain classification. An enhanced LULC classification model has been designed using two popular machine learning (ML) classifier algorithms, SVM and RF, explicitly for mountainous terrains by taking into consideration of a study area of Gopeshwer town in the Chamoli district of Uttarakhand state, India. Online-based cloud platform Google Earth Engine (GEE) was used for overall processing. Four classification models were built using Sentinel 2B satellite imagery with 20m and 10m resolutions. Two of these models (Model 'i' based on RF algorithm and Model 'ii' based on SVM algorithm) were designed using spectral bands of visible and infrared wavelengths, and the other two (Model 'iii' based on RF algorithm and Model 'iv' based on SVM algorithm) with the addition of indices with spectral bands. The accuracy assessment was done using the confusion matrix based on the output results. Obtained result highlights that the overall accuracy for model 'i' and model 'ii' were 82% and 86% respectively, whereas these were 87.17% and 87.2% for model 'iii' and model 'iv' respectively. Finally, the study compared the performance of each model based on different accuracy metrics for better LULC mapping. It proposes an improved LULC classification model for mountainous terrains, which can contribute to better land management and planning in the study area. [ABSTRACT FROM AUTHOR]

Land use and land cover changes have greatly contributed to global warming and increased urbanization during the last two decades. It is crucial to track alterations of the globe's surface's usage of land as well as cover in order to obtain accurate information about the study region for all development planning scenarios. Land cover categorization is critical for identifying changes, managing development, and tracking and assessing the ground's surface area. Along with RS and GIS methodologies, machine learning algorithms have recently gained popularity in land use and land cover change detection studies. This present study was conducted to detect land use and land cover (LULC) in a heterogeneous landscape of Pauri Garhwal district of Uttarakhand, India by using different machine learning classifiers, i.e. (i) Random forest. (ii) SmileCart (iii) SmileGradientTreeBoost and (iv) SVM. Overall accuracy and kappa coefficient found for RF, SVM, CART and GTB were (0.98, 0.96), (0.97, 0.95), (0.80, 0.75) and (0.89, 0.80) respectively. Results show that Random Forest (RF) provided highest classification accuracy among all four classifier. [ABSTRACT FROM AUTHOR]

The frequency of mass wasting events in Kashmir Himalaya has increased manifold leading to the degeneration of its paleoenvironment. The primary aim of the current study is to demonstrate a comparative evaluation of three distinct statistical models which include frequency ratio (FR), information value (Inv), and analytical hierarchy process (AHP), for developing landslide susceptible map (LSM) in and around Gool area of Ramban district in Jammu and Kashmir union territory, India. With total of 365 spotted landslides, 11 causative factors were evaluated for generating LSM which include slope, aspect, curvature, drainage density, relative relief, distance to fault, distance to road, stream power index, land use land cover, topographic wetness index, and lithology. Four of the eleven analyzed factors-slope, relative relief, drainage density, and distance to road play an influential role in landslide distribution. The sensitivity analysis of the receiver operator curve (ROC) is used to estimate the accuracy of the models. The ROC results revealed an accuracy of 0.80, 0.80, and 0.74 for Inv, FR, and AHP respectively. The accuracy and efficiency findings suggest that all models can provide a credible landslide susceptibility zonation for the region; however, the FR and Inv models are somewhat more accurate than AHP model. [ABSTRACT FROM AUTHOR]

In today's world, by integrating remote sensing technology and modern state-of-the-art machine learning techniques, obtaining Land Use Land Cover (LULC) maps has become easier in comparison to traditional manual methods. The performance of a Machine Learning classifier is influenced by various factors. The objective of this study is to evaluate the impact of sampling design in rough complex terrain located in the Northern Himalayan region in Uttarakhand state, India, where reference data is often limited due to the geographical characteristics of the study area. Three sampling design strategies have been incorporated in this study, namely, stratified random sampling with a proportional number of samples (SRS)proportional, stratified random sampling with an equal number of samples (SRS)equivalent and stratified systematic sampling with an equal number of samples with a minimum distance of 10 m between the consecutive samples (SSS)D = 10 m for the LULC classification. In this study, Sentinel-2 data of 10 m spatial resolution for the study area of Dehradun district, Uttarakhand, India, has been selected. The following conclusions can be drawn from the results of this study (i) (SRS)proportional achieved the highest Overall Accuracy (OA) among all the three sampling techniques. The OA and kappa score (ka) using (SRS)proportional are OA = 90.25 and ka = 0.874 by Random Forest, OA = 88.84 and ka = 0.856 by Support Vector Machine and k Nearest Neighbours (kNN) obtained OA = 87.72 and ka = 0.842, respectively. (ii) It was found that in the case of (SRS)proportional, the majority classes like the deciduous forest, evergreen forest and cropland achieved higher recall and precision values in comparison to those obtained from the other two sampling strategies, i.e. (SRS)equivalent and (SSS)D = 10 m. (iii) The results showed that while switching from (SRS)proportional to (SRS)equivalent or from (SRS)proportional to (SSS)D = 10 m, there was a slight reduction in the precision and recall values for the majority classes and a slight increase for a few of the minority classes. [ABSTRACT FROM AUTHOR]

The dynamics of land use and land cover are profoundly affected by the growth, mobility, and demand of people. Thematic maps of land use and land cover (LULC) help planners account for conservation, concurrent uses, and land-use compressions by providing a reference for analysis, resource management, and prediction. The purpose of this research is to identify the transition of land-use changes in the Saroor Nagar Watershed between 2008 and 2014 using the Modules for Land Use Change Evaluation (MOLUSCE) plugin (MLP-ANN) model and to forecast and establish potential land-use changes for the years 2020 and 2026. To predict how these factors affected LULC from 2008 to 2014, MLP-ANN was trained with maps of DEM, slope, distance from the road, and distance to a waterbody. The projected and accurate LULC maps for 2020 have a Kappa value of 0.70 and a correctness percentage of 81.8%, indicating a high degree of accuracy. Changes in LULC are then predicted for the year 2026 using MLP-ANN, which shows a 17.4% increase in built-up area at the expense of vegetation and barren land. The results contribute to the development of sustainable plans for land use and resource management. [ABSTRACT FROM AUTHOR]

A proper understanding of soil parameters under different production systems of the world is necessary for efficient soil management. We, therefore, carried out the present study to assess the status of some selected soil properties (soil pH and electrical conductivity (EC)), phyto-available nutrients (available nitrogen (AN), available potassium (AK), exchangeable calcium (Ex. Ca), exchangeable magnesium (Ex. Mg), available sulfur (AS), and soil organic carbon (SOC) pools (SOC, total organic carbon (TOC), very labile C, labile C, less labile C, and non-labile C) and to establish relationships among the measured soil parameters at different depths of Vertisols of India under various land uses. A total of 150 composite soil samples (from 25 plots including nine from agricultural land, nine from horticultural land, three from forest land, and four from grassland) were collected from 6 soil depths viz, 0–10, 10–20, 20–40, 40–60, 60–80, and 80–100 cm under agriculture, horticulture, forest, and grassland land uses present in Central India and analyzed. The values of soil pH, EC, AN, AK, Ex. Ca, Ex. Mg, and AS in various soil depths under different land uses varied widely. The values of SOC (0.19 to 1.00%), TOC (0.58 to 2.42%), very labile C (0.14 to 0.83%), labile C (0.05 to 0.25%), less labile C (0.05 to 0.26%) and non-labile C (0.23 to 1.42%) in various soil depths under different land uses also varied significantly. Forest and grassland land uses had higher levels of SOC, TOC, very labile, and non-labile C content in all the soil depths in comparison to SOC, TOC, very labile, and non-labile C content in different soil depths under agriculture and horticulture land use. The levels of SOC, TOC, very labile, and non-labile C content under all the land uses decreased with increasing soil depths. SOC was positively and significantly correlated with AN, AK, AS, and estimated SOC pools in surface soil layers. Principal component analysis (PCA) of soil parameters in different soil depths resulted in 5 principal components (PCs) with > 1 eigenvalue and accounting for > 75% variability. This information could be used for managing SOC status and phyto-available nutrients in Vertisols under different land uses. [ABSTRACT FROM AUTHOR]

In modern days, a sustainable urban planning system requires a balance of vegetation, water, and settlement. The proportions of these surface features directly influence the land surface temperature (LST) in an urban area. LST primarily depends on the emittance of land use/land cover (LULC) categories. In an urban area, changes in LULC categories as well as local warming are the prime regulators of LST change. The study analyses the LULC change and its impact on LST in Imphal City, India. Landsat satellite data for the summer season and winter seasons for 1991, 2001, 2011, and 2021 have been used in this study. Results show that the mean LST of the study area increased at >1% rate/decade. The green area and water area decreased the LST values whereas the built-up area and fallow lands increased the LST values. The study indicates the consequences of proper land conversion to regulate the LST change. Moreover, the influence of population on LST is also determined. The continuous rising trend of population is a positive factor of increasing LST. The study may help the town and country planners to generate sustainable urban land. [ABSTRACT FROM AUTHOR]

Climate change and land use land cover (LULC) change are two major factors influencing river basin hydrology. This study explored these drivers' isolated and combined impacts on the ecologically relevant flow in the Achencoil basin, Kerala, India. The LULC classification in the study is carried out with the Random Forest (RF) algorithm in the Google Earth Engine (GEE) platform, and Land Change Modeler (LCM) is incorporated for change detection and projection. The future climate data from the National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) is used for climate change impact assessment. The Soil and Water Assessment Tool (SWAT) is employed to simulate streamflow under LULC and climate change scenarios. The historical and projected future LULC change in the basin revealed an increase in the built-up and barren land, with a significant decrease in agricultural and forest areas. The results show that the projected future precipitation will decrease under the RCP 4.5 and increase under the RCP 8.5 scenario. The projected average maximum and minimum temperature are expected to increase under both scenarios in the basin. The LULC 2050 scenario shows the most significant rise in average annual streamflow, at 7.5%. Whereas in the climate change scenarios, the average annual flow decreases under RCP 4.5 and increases under RCP 8.5. The combined impacts of climate change and LULC change are relatively higher than the isolated effects of these drivers in the basin. The study outcomes are expected to help policymakers consider the effect of climate change and LULC change on the river's hydrology so as to implement the management activities that account for the riverine ecosystem. Changes in land use land cover and meteorological parameters are important environmental issues that must be pointed out. These changes will affect the river flow and eventually affect the river ecosystem. The current research investigates the combined effects of land use land cover and climate change in the flow of the Achencoil basin in India. This study area is one of the most frequently flooded in the state. As a result, the projected climate and land use land cover provide an idea of future streamflow in the basin. This research enables a better understanding of the response of streamflow components to climate variability and land use land cover changes in the Achencoil basin, which might help policymakers develop strategies for regional water resource management. Implementing these policies and management strategies will necessitate collaboration and coordination across various levels of government, private sector, and local communities in order to mitigate the risk and support a sustainable riverine ecosystem. [ABSTRACT FROM AUTHOR]

Predictive distribution models are widely used in species conservation planning. However, identifying specific sites for undertaking conservation action such as reintroduction pose a practical challenge for practitioners. We demonstrate that the potential habitat distribution models intersected with existing land use and land cover (LULC) map helped in identifying the specific conservation areas with greater confidence. We used a case of Magnolia punduana Hk. f & Th. (Magnoliaceae)—a threatened and rare tree species distributed in the Khasi, Jaintia and Garo Hills of northeast India at an elevation range of 800–1600 m asl. We modeled the distribution of potential habitats of the species using maximum entropy (Maxent) software, moderate resolution imaging spectroradiometer (MODIS) imageries, and ASTER-based digital elevation data. Amongst the environmental variables, elevation and the EVI for the months of May, June and July had a collective contribution of > 70%, indicating their dominant role in determining the potential habitat. The predicted potential habitat of the species covered ~ 2.88% area of the state in the southern part of the Khasi and Jaintia hills with a few patches in Garo hills. However, intersection of the potential distribution area map with the existing forest cover map of the study area showed ~ 3853 ha of the state, has high suitability. The ground-truthing of the high suitability regions revealed that they are similar to the species' original habitat and may support conservation-related activities such as habitat protection, restoration, and species (re)introduction. [ABSTRACT FROM AUTHOR]

The impact of land use and land cover changes (LULCC) is one of the major contributors to increasing greenhouse gas emissions to the atmosphere. At the terrestrial surface, the impact of LULCC is realized in altered hydrology. Conversion of cultivable lands into fallow lands severely affects crop production in agriculturally dominant basins such as the Vaigai River Basin (VRB) in Tamil Nadu, India. Considered as a granary of South Tamil Nadu, any LULCC in VRB results in uncertainty in food production. Therefore, in this study, Landsat images were used to evaluate changes in land use and MODIS NDVI images to estimate changes in browning and greening in VRB during 2001-2020. We also analyzed the rainfall and river discharge in the basin to understand the variations from 2001 to 2019 concerning LULCC. The results showed an increase of seventy-seven percent in fallow lands between 2010 and 2020 and a forty-one to fifty-nine percent increase in urban settlements between 2001 and 2020 in the basin. The impacts of LULCC were realized in monsoon rainfall with no change in river discharge in the lower Vaigai Basin. The study results will aid regulated land use planning and encourage further research on feedback between terrestrial and atmospheric water fluxes for ensuring food security. [ABSTRACT FROM AUTHOR]

Amid global concerns regarding climate change and urbanization, understanding the interplay between land use/land cover (LULC) changes, the urban heat island (UHI) effect, and land surface temperatures (LST) is paramount. This study provides an in-depth exploration of these relationships in the context of the Kamrup Metropolitan District, Northeast India, over a period of 22 years (2000–2022) and forecasts the potential implications up to 2032. Employing a high-accuracy supervised machine learning algorithm for LULC analysis, significant transformations are revealed, including the considerable growth in urban built-up areas and the corresponding decline in cultivated land. Concurrently, a progressive rise in LST is observed, underlining the escalating UHI effect. This association is further substantiated through correlation studies involving the normalized difference built-up index (NDBI) and the normalized difference vegetation index (NDVI). The study further leverages the cellular automata–artificial neural network (CA-ANN) model to project the potential scenario in 2032, indicating a predicted intensification in LST, especially in regions undergoing rapid urban expansion. The findings underscore the environmental implications of unchecked urban growth, such as rising temperatures and the intensification of UHI effects. Consequently, this research stresses the critical need for sustainable land management and urban planning strategies, as well as proactive measures to mitigate adverse environmental changes. The results serve as a vital resource for policymakers, urban planners, and environmental scientists working towards harmonizing urban growth with environmental sustainability in the face of escalating global climate change. [ABSTRACT FROM AUTHOR]

The main aim of this study is to comprehensively analyze the dynamics of land use and land cover (LULC) changes in the Bathinda region of Punjab, India, encompassing historical, current, and future trends. To forecast future LULC, the Cellular Automaton–Markov Chain (CA) based on artificial neural network (ANN) concepts was used using cartographic variables such as environmental, economic, and cultural. For segmenting LULC, the study used a combination of ML models, such as support vector machine (SVM) and Maximum Likelihood Classifier (MLC). The study is empirical in nature, and it employs quantitative analyses to shed light on LULC variations through time. The result indicates that the barren land is expected to shrink from 55.2 km2 in 1990 to 5.6 km2 in 2050, signifying better land management or increasing human activity. Vegetative expanses, on the other hand, are expected to rise from 81.3 km2 in 1990 to 205.6 km2 in 2050, reflecting a balance between urbanization and ecological conservation. Agricultural fields are expected to increase from 2597.4 km2 in 1990 to 2859.6 km2 in 2020 before stabilizing at 2898.4 km2 in 2050. Water landscapes are expected to shrink from 13.4 km2 in 1990 to 5.6 km2 in 2050, providing possible issues for water resources. Wetland regions are expected to decrease, thus complicating irrigation and groundwater reservoir sustainability. These findings are confirmed by strong statistical indices, with this study's high kappa coefficients of Kno (0.97), Kstandard (0.95), and Klocation (0.97) indicating a reasonable level of accuracy in CA prediction. From the result of the F1 score, a significant issue was found in MLC for segmenting vegetation, and the issue was resolved in SVM classification. The findings of this study can be used to inform land use policy and plans for sustainable development in the region and beyond. [ABSTRACT FROM AUTHOR]

The sole purpose of this review paper is to reveal the land use and land cover (LULC) changes in the study region (Northern India) from 2017 to 2021. The researchers discovered that no systematic review of the LULC dynamics of the study region had been conducted so far. This study used a novel systematic review procedure (i.e., the ICA framework) to conduct the review process. The data for this study were retrieved from the Web of Science of Clarivate Analytics, using keywords related to LULC. A thorough review of 29 full-text articles was done to examine the primary land use classes (LUC), techniques used, factors responsible, and dynamics of the major land classes. Most articles included agriculture, built-up, water bodies, and plantations as the main LULC categories. Most of them used on-screen digitisation and supervised algorithms to classify LULC classes and incorporated accuracy assessment to validate their results. It was also found that almost all the papers cited climate change, increasing population, urbanisation, and economic considerations as the main drivers responsible for the changes that occurred in different LULCs. The findings of the various studies revealed that horticulture and built-up areas have increased while agricultural, forest cover and open spaces have decreased to a greater extent. Environmental negatives of LULC dynamics of the region suggested that LULC change is not favourable everywhere. Thus, to control the haphazard LULC change, the government must come up with a substantial land conversion policy in the region with ground-level implantation that will restrict the people from blind land conversion. [ABSTRACT FROM AUTHOR]

Peri-urban area around a city is a dynamic zone that undergoes considerable changes over time in terms of its functional land use. Analogous to other Indian cities, these changes are also evident in the peri-urban villages of Burdwan city, which has seen intense land use changes between 1987 and 2017. These changes are gauged by using LANDSAT-5 Thematic Mapper (TM) for 1987; LANDSAT-7 Enhanced Thematic Mapper (ETM +) for 2002 and LANDSAT 8 (OLI) for 2017. This study makes a unique contribution by combining the use of remote sensing and intensity analysis to evaluate changes in land use and land cover. The results showed that both the 1987–2002 and 2002–2017 time periods experience rapid change and the land use transformation has been accelerated over the whole period. The profound changes in the vegetation and water bodies are amplified by rampant reclamation for built up and commercial purposes. Results show that the proportion of vegetation and waterbodies decreased by 72.3% and 56.3%, respectively, whereas built-up area has increased by 182.8%. The research also revealed a built up pattern mostly towards the north-west, north, North-East and south-east direction. The current study attempts to analyse the variables causing land transformation and the many actors and their roles in driving these alterations. Finally, the extensive explanation and spatio-temporal differentiation maps and tables created with geospatial data will undoubtedly aid in understanding the peri-urban growth dynamics process and changing form of land-use land cover and aid the decision-making process of local planners, stakeholders, and academicians. [ABSTRACT FROM AUTHOR]

Spatial distribution of rice cultivated area and its future projections are indispensable for providing food security, irrigation water management, greenhouse gas emission, ecosystem, and environmental protection. Accurate simulation of future rice cultivation is challenging in the context of climate change involving spatial modeling. This study introduced a hydrological data-based prediction approach integrating cellular automata-Markov chain model with multi-criteria decision analysis. Three decadal transitions of different land use land cover (LULC) in an agricultural intensified canal irrigated area were analyzed using Random Forest algorithm. The Landsat satellite imageries were classified with an overall accuracy of more than 90%. The results revealed that the area under water sources, plantation, and fallow land decreased by 53.1%, 33.9%, and 63.6%, respectively, while habitation and Boro rice cultivated area increased significantly by 1243.3% and 211%, respectively, during 1988–2018. The kappa coefficient (0.86), allocation disagreement (10.9%), and quantity disagreement (1.16%) revealed good agreement between the predicted and classified LULC map of 2018. The probable distribution of the LULC map in 2028 and 2038 indicated 54.5% and 59.3% of the command area under Boro rice cultivation as a dominant land cover, respectively. Further, the predicted LULC map anticipated that Boro rice cultivation was likely to expand at the expense of plantation and fallow land which may lead to massive water scarcity in the near future. The proposed approach covered up the limitations of spatial modeling on agricultural growth trends by integrating significant driving factors with quantitative and spatiotemporal aspects of LULC transition. These findings will provide a baseline map for sustainable policy reformation and contemplation on the repercussions that local LULC transition has on global environmental change. [ABSTRACT FROM AUTHOR]

Land Use/Land Cover (LULC) changes have a significant impact on Land Surface Temperature (LST). The LST is an important parameter in various environmental and climatological studies, as it plays a crucial role in understanding the Earth's surface–atmosphere interactions. The LULC changes can modify the surface energy balance and alter the radiation budget, leading to changes in LST. Urbanization, deforestation, and agricultural land use changes are some of the primary drivers of LULC change that have a significant impact on LST. Deforestation and agricultural land use changes result in a reduction in evapotranspiration, leading to an increase in LST. The main objective of the study is to analyze the spatio-temporal change in Land Use/Land Cover (LULC) and its effect on Land Surface Temperature (LST), as well as to establish a correlation of LST with the Normalized Difference Vegetation Index (NDVI) and Normalize Difference Snow Index (NDSI). Understanding the impact of LULC on LST is essential for developing effective land use policies that can mitigate the adverse effects of LULC change on the environment and human health. [ABSTRACT FROM AUTHOR]

The spatial monitoring of variation in land cover and its mapping support the local authorities and decision-makers in strengthening comprehensive and sustainable plans for future urbanization and sustainability of the city. This research work was carried out to evaluate the change in the land cover pattern and its impact on the land surface temperature of Prayagraj city from 2000 to 2020. This study also examined the correlation between land surface temperature (LST) and other improved urban biophysical indices, such as soil-adjusted vegetation index (SAVI) and enhanced built-up and bareness index (EBBI), to determine the significant factors affecting the LST of the urban area. The result indicates that the built-up area and cropland have been escalated by 10 and 6.5%, whereas tree cover, water bodies and fallow land were reduced by 12, 1.38 and 1.27%, respectively. LST was increased by 1.47°C between 2000 and 2020 due to an increase in the built-up area. LST showed a positive correlation with EBBI but a negative one with vegetation indices (normalized difference vegetation index, enhanced vegetation index and SAVI) and water indices (MNDWI). The results indicate that the land use pattern governs LST and any change in land use will ultimately change the LST. [ABSTRACT FROM AUTHOR]

The relationship between humans and nature must be taken into account as LULC changes in order to map and monitor the natural resources accessible on earth's surface. The amalgamation of remote sensing and GIS advancements in technology has made it easier to track variations in land use and cover from times gone by to the today. To properly represent the city, decision makers and policy makers frequently need accurate and updated LULC data. By utilizing remote sensing and GIS methodologies, this study piece illustrates the changes to the LULC status and driving factors associated to it in study area from 2010 to 2020. Machine learning Random Forest (RF) classifier was used to categorize LANDSAT photos in Google Earth Engine. The results of this analysis show that the Pauri Garhwal district in the state of Uttarakhand has experienced a rapid LULC changes from 2010 to 2020 using GIS and remote sensing methods. The fundamental reason for these changes in the study area is the conversion of forest, agricultural land, and arid land into urban areas. Therefore, it is crucial to control the factors causing LULC changes for sustainable ecosystem; otherwise, these LULC classes lose their ability to provide essential services for a healthy ecosystem. [ABSTRACT FROM AUTHOR]

Urbanization in India is having a significant impact on the environment and human health, as built-up areas are expanding while vegetation and water bodies are declining. To mitigate the negative effects of urbanization, there is an urgent need to monitor and manage the growth of cities through the creation of smart cities. Accurately identifying and mapping urban expansion patterns is crucial to achieving this goal, but this is currently lacking in India. To address this gap, this study takes a comprehensive approach to examining the processes of urban expansion in English Bazar Municipality, Malda, India. The study employs the random forest (RF) method for land use and land cover (LULC) mapping for the years 2001, 2011, and 2021 and then uses the frequency approach, landscape homogeneity, and fragmentation to model urban expansion over the same time period. The study also employs several landscape matrices to quantify urban expansion. The results of the study reveal a substantial increase in built-up areas from 601.26 hectares in 2001 to 859.13 hectares in 2021, accompanied by a corresponding decline in vegetation, cropland, and open land. This land use transition process has also led to increased fragmentation of the landscape. The spatiotemporal landscape homogeneity analysis identifies two transition zones (zone 2 and zone 1) surrounding the stable zone, which capture the new, small, isolated built-up areas and show a progression of urban expansion from 2001 to 2021. Overall, the study provides valuable insights into the changes in urban expansion and fragmentation in English Bazar Municipality. To promote sustainable urban development and enhance the quality of life for residents, it is important for stakeholders and the government to prioritize the conservation and creation of green and blue spaces. This can be accomplished through the incorporation of green infrastructure, smart city principles, community engagement and education, and partnerships with local businesses. [ABSTRACT FROM AUTHOR]

Identification and prediction of future land use and land cover (LULC) changes and their drivers are required for land resources management, formulation of better policy, practicing sustainable management strategies, and modeling future LULC. The present study has focused on the prediction of future LULC and assessment of local drivers of LULC change in the hill agroecosystem Senapati district of Manipur, Northeast India. The prediction of future LULC for 2029 was achieved based on LULC data of 1999, 2009, and 2019 satellite data using multi-layer perceptron Markov change (MLP-MC) of Land Change Modeler (LCM). The socio-economic survey and field observation were performed to identify local drivers of LULC changes in the study area. The total area covered by open and dense forests in 2029 was 69.06% of the total geographical area of the district, a slight increase from the 2019 assessment of 61.01 km2. The drivers of LULC were categorized by assigning rank. The proximate drivers directly influence land use dynamics, such as increases in the settlement area, firewood collection, and construction. The underlying drivers—population growth and poverty—have also indirectly influenced LULC change in the district. It was observed that the livelihoods of local communities depend on forest products and agriculture. Therefore, the study finding will help create a future LULC information database and identification of local drivers of LULC. It will encourage the communities to participate in proper land resource management and create awareness for the sustainable use of the land. [ABSTRACT FROM AUTHOR]

In this study, the impacts of changes in land use/ land cover (LULC) and climate on surface runoff are investigated in the Kadalundi river basin, Western Ghats, India. Land Change Modeller (LCM) was applied to project the future LULC scenarios in 2030 and 2050. Climate change scenarios of the future time period of near (2011−2040), mid (2041−2070), and far (2071−2099) were procured for Representative Concentration Pathway (RCP) 4.5 and 8.5 emission scenarios from five different general circulation models of coordinated regional downscaled experiment project. The 1988 and 2000 LULC were used for projection of 2014 LULC and validated with actual 2014 LULC. Further, 2000 and 2014 LULC were used for projection of 2030 LULC, and 2014 and 2030 LULC were used to project 2050 LULC. Changes in LULC from 2000 to 2050 are calculated using the transition matrix approach. The impacts of changes in LULC and climate on surface runoff are investigated by the Soil and Water Assessment Tool (SWAT) model. The results showed a decrease in forest and grassland and an increase in agriculture, and urban areas from 2000 to 2050, with an increase in the mean annual surface runoff. For climate change scenarios assessed, mean annual surface runoff in the near, mid, and far would be decreasing under both RCP 4.5 and 8.5 while RCP 8.5 showing more worse condition than RCP 4.5 in the future. The combined effects of changes in LULC and climate showed a decreasing trend in the surface runoff for the near and mid future. Furthermore, the projected results indicate that the surface runoff would be higher in both RCP scenarios during winter and summer; while the monsoon period largely demonstrates a reverse trend. The analysis of combined impacts reveals that the decreasing of surface runoff under RCP 4.5 and 8.5 would be higher in near compared to mid future. [ABSTRACT FROM AUTHOR]

The present study performs landcover modelling using the SLEUTH model. The urban land use changing factors are calibrated to predict the Land Use Land Cover (LULC) for a densely populated and developing smart city, Prayagraj, India. This research aims to use the SLEUTH model for simulating the future urban growth with the help of historical LULC (1990–2020), road network and elevation data. The influence of road gravity and slope resistant coefficients is very significant in this study's outcome. The built-up area of the region increased from 40.22 km2 (5.10% of total area) in 1990 to 85.89 km2 (10.89%) in 2020. According to prediction results, in the next 20 years, the built-up growth rate would be 1.9% and approximated built-up area would be 118.66 km2 (14.98%) in the year 2040. The quality of the result has been quantified in terms of best fit value of Optimal SLEUTH Metric (OSM) and validated against the existing LULC. The study utilises a spatially explicit urban growth model with 30 m resolution remote sensing data and provides future landuse of Prayagraj city. [ABSTRACT FROM AUTHOR]

We use Soil and Water Assessment Tool (SWAT) to simulate the combined effects of land use/land cover (LU/LC) and climate change on the hydrological response of the Upper Betwa River Catchment (UBRC), a semi-arid region in Central India. We execute this model for two different time periods, 1982–2000 and 2001–2018, using the LU/LC data of 1990 and 2018, respectively. We classified the Landsat satellite images of 1990 and 2018 to obtain the dominant LU/LC classes (water body, built-up, forest, agriculture, and open land) in the catchment. The water body, built-up areas, and cropland have increased by 63%, 65%, and 3%, respectively, whereas forest cover and open land decreased by 16% and 23% in the UBRC from 1990 to 2018. The observed climate data in UBRC shows an increase in the average temperature and decrease in the total rainfall during the period between 1980 to 2018. Once the model is set up, we perform the calibration and validation by using the SWAT Calibration Uncertainty Program (SWAT-CUP). We considered two time periods (1991–1994 and 2001–2007) for the calibration and (1995–1998 and 2008–2014) for the validation. For both these time periods, the calibration and validation result of our model is satisfactory. The output of our calibrated model shows a relative decrease in rainfall (12%), surface runoff (21%), and percolation (9%) in the catchment during the period between 2001–2018 as compared to 1982–2000. Finally, we simulate the surface runoff and percolation in the UBRC using the future climate change scenario. We used the bias-corrected multi-model ensemble of CMIP6 GCMs for four different climate scenarios (2023–2100) by assuming no change in the existing LU/LC. We do this for two different time slices: one from 2023–2060 and the other from 2061–2100. For all the climate scenarios, rainfall and surface runoff in the catchment are expected to decrease by 15–40% and 50–79% as compared to the baseline period of 1982–2018. Percolation in the catchment will have a mixed response. It is expected to decrease by 18% in the middle part of the catchment and increase about 25% in the remaining parts of the catchment. [ABSTRACT FROM AUTHOR]

Silchar City and its surrounding areas have witnessed massive population growth in the last few decades due to local and migrated peoples, which led to the rapid transformation of its land use/land cover (LU/LC) pattern. The spatio‐temporal very high‐resolution satellite data (VHRS) and on‐screen visual interpretation techniques were used for detailed large‐scale LU/LC analysis. The LU/LC classification of the City has been prepared for 2005, 2011, and 2018 with five primary and a further 17 sub‐classes. In the present study use was made of the Census of India reports from 1991 to 2011, and the geometrical increase method was used to evaluate the ward‐level population, population density, and land requirements for residential purposes; these have been predicted from 2021 to 2051 with every 10 years interval. The ity's wards have been classified into five zones, and the Shannon entropy model has been applied to analyze the degree of randomness and trends of urban built‐up growth. The obtained results indicate that Silchar City is compact near the center, which follows the infill built‐up growth trend. However, as the distance increased from its center, the scattering with an infill growth pattern was observed. The accuracy assessment techniques are used to validate the results of the classification. The results of this study can provide detailed information for land‐use planners, researchers, policy‐decision makers, and municipal authorities. [ABSTRACT FROM AUTHOR]

Keeping in mind the detrimental effects of sprawl on the urban landscape, an accurate and thorough analysis of the spatiotemporal growth pattern is necessary for well-organized and sustainable urban progression. This study mainly focuses on urban sprawl and land use/land cover changes (LULCC) in Hisar City of Haryana, India. The extent of urban sprawl and changes in different land utilization over 30 years have been assessed using Shannon's entropy technique. Additionally, the land consumption rate and land absorption coefficient are introduced to support the quantitative evaluation of changes. Finally, the accuracy assessment (producer's, user's, and overall) with the Kappa coefficient was applied for spatial accuracy valuation to confirm the land use/land cover (LULC) reliability based on Landsat multi-temporal satellite data from 1991 to 2021. The study deals with water bodies, open/bare land, vegetation, agricultural lands and built-up area as LULC categories for changes evaluations. As per findings, the built-up area has increased more than twice (2,826.72 hectares) by the total area throughout the studied period, whereas farming land has lost almost half part (3,497.94 hectares). The urban sprawl and LULC change mechanisms have directly influenced other natural resources, including farming land. The increasing entropy values also confirm the sprawl or fragmented development in the city, mainly from the centre to the periphery. This unrestricted expansion has changed the geography of the concerned region and surpassed the land use plans. The suggested policy options may assist decision-makers in resolving the urban impediments, minimizing the additional loss of peripheral landscape, and achieving longterm urban growth planning in the study area. [ABSTRACT FROM AUTHOR]

Land use land cover (LULC) mapping and temporal observations are indispensable drivers for sustainable development. This research showed the growth trends and land use transition for the Prayagraj district in the last three decades. Supervised classification of Landsat images was performed on 5-year temporal intervals using a maximum likelihood classifier. All satellite images were organized into six major LULC feature classes viz agriculture/open land, barren land, built-up, forest, sand, and water. The overall accuracy of LULC classification was achieved by more than 89% in all seven temporal points. Furthermore, the accuracy of the classified maps was estimated through area-based error matrix. The Land Change Modeler tool of TerrSet 2020 software was used to analyze the transition of classes and to incorporate the multi-layer perceptron–Markov chain (MLP-MC) technique. The transition potentials were included in MLP-MC with the help of sensitive explanatory variables and significant transitions of classes. Furthermore, these transition potentials and the Markov chain transition matrix were used to predict the future LULC dynamics and vulnerability. The change analysis revealed that a significant portion of the agriculture/open land gradually decreased and got converted to built-up land. The results depicted that agriculture/open land was reduced by 8.03% in the last three decades while the built-up region was grown by 199.61%. Forest area was continuously decreasing while the sand area increased due to river meandering. Overall, more than 75% of accuracy was achieved in MLP. The prediction model was first validated with observed data, and then the LULC scenario of 2035 and 2050 was simulated. LULC of 2050 showed that the built-up area would likely reach 13.90% of district area whereas the forest area would remain only 0.79%. The prediction model has given the output in the form of future LULC map along with projected potential transition maps. This would be useful for sustainable urban planning to deal with the alarming rate of built-up growth and agriculture/open land shrinkage. [ABSTRACT FROM AUTHOR]

Understanding the likely impacts of climate change (CC) and Land Use Land Cover (LULC) on water resources (WR) is critical for a water basin's mitigation. The present study intends to quantify the impact of (CC) and (LULC) on the streamflow (SF) of the Parvara Mula Basin (PMB) using SWAT. The SWAT model was calibrated and validated using the SWAT Calibration Uncertainty Program (SWAT-CUP) for the two time periods (2003–2007 and 2013–2016) and (2008–2010 and 2017–2018), respectively. To evaluate the model's performance, statistical matrices such as R2, NSE, PBIAS, and RSR were computed for both the calibrated and validated periods. For both these periods, the calibrated and validated results of the model were found to be very good. In this study, three bias-corrected CMIP6 GCMs (ACCESS-CM2, BCC-CSM2-MR, and CanESM5) under three scenarios (ssp245, ssp370, and ssp585) have been adopted by assuming no change in the existing LULC (2018). The results obtained from the SWAT simulation at the end of the century show that there will be an increase in streamflow (SF) by 44.75% to 53.72%, 45.80% to 77.31%, and 48.51% to 83.12% according to ACCESS-CM2, BCC-CSM2-MR, and CanESM5, respectively. A mean ensemble model was created to determine the net change in streamflow under different scenarios for different future time projections. The results obtained from the mean ensembled model also reveal an increase in the SF for the near future (2020–2040), mid future (2041–2070), and far future (2071–2100) to be 64.19%, 47.33%, and 70.59%, respectively. Finally, based on the obtained results, it was concluded that the CanESM5 model produces better results than the ACCESS-CM2 and BCC-CSM2-MR models. As a result, the streamflow evaluated with this model can be used for the PMB's future water management strategies. Thus, this study's findings may be helpful in developing water management strategies and preventing the pessimistic effect of CC in the PMB. [ABSTRACT FROM AUTHOR]

The alteration of land use and land cover caused by human activities on a global scale has had a notable impact on ecosystem services at regional and global levels, which are crucial for the survival and welfare of human beings. Merbil, a small freshwater wetland located in the Brahmaputra basin in Assam, India, is not exempt from this phenomenon. In the present study, we have estimated and shown a spatio-temporal variation of ecosystem service values in response to land use and land cover alteration for the years 1990, 2000, 2010, and 2021, and predicted the same for 2030 and 2040. Supervised classification and the CA-Markov model were used in this study for land-use and land-cover classification and future projection, respectively. The result showed a significant increase in built-up areas, agricultural land, and aquatic plants and a decrease in open water and vegetation during 1990–2040. The study area experienced a substantial rise in ecosystem service values during the observed period (1990–2021) due to the rapid expansion of built-up areas and agricultural and aquatic land. Although the rise of built-up and agricultural land is economically profitable and has increased the study site's overall ecosystem service values, decreasing the area under open water and vegetation cover may have led to an ecological imbalance in the study site. Hence, we suggest that protecting the natural ecosystem should be a priority in future land-use planning. The study will aid in developing natural resource sustainability management plans and provide useful guidelines for preserving the local ecological balance in small wetlands over the short to medium term. [ABSTRACT FROM AUTHOR]

Diurnal temperature range (DTR) which reflects the difference between the daily maximum (Tmax) and minimum temperature (Tmin) is an important indication of changing climate and a critical thermal metric to assess the impact on agriculture, biodiversity, water resources, and human health. The major aim of this study is to assess the probable future spatio-temporal changes in the Tmax, Tmin, and DTR and their long-term warming trend from 2006 to 2099 under two representative concentration pathways (hereafter RCP4.5 and RCP8.5) over diverse agroclimatic regions of India. The observed data from India Meteorological Department (IMD) was used to evaluate the performance of climate models (1970–2005). The result shows a very slight underestimation in DTR by models compared to the observed. In future projections, we found a reduction in DTR (0.001 to 0.020 °C/year) partly linked to the substantial increase in Tmin (0.020 to 0.071 °C/year) than Tmax (0.031 to 0.060 °C/year) that was stronger in far twenty-first-century future under RCP8.5. The decline in DTR was profound and consistent over northern India (up to 3 °C) surrounding the Indo-Gangetic Plain, western dry region, and part of central India with the highest decline observed in winter and pre-monsoon season. However, a decline in DTR was also anticipated over the plateau, coastal, and eastern Himalayas region. Change in land use land cover (LULC) also complimented the decline in DTR. The main findings of the study advocate implementation of a robust framework for climate change adaptation strategies to mitigate adverse consequences to the natural ecosystem and human health over specific regions arising due to declining DTR. [ABSTRACT FROM AUTHOR]

Channel dynamics is one of the important features of the Ganga River. It has become a major concern for floodplain residents as well as for policymakers interested in riverine planning and management. The present study used remote sensing datasets for a period of about 46 years (1972 to 2018) and explored the spatial and temporal migration of the Ganga River channel in the middle Ganga plain (MGP), India. The raster datasets were obtained from the United States Geological Survey (USGS) Earth Explorer. Various features were extracted manually, and supervised classification was performed for land use and land cover (LU/LC) analysis. This study also used conversion maps to outline the changes within and among different LU/LC classes. The results show that a significant portion of land along both banks of the Ganga River has changed from 1972 to 2018. This research pinpoints five main sites indicating active channel migration: (i) MS1, (ii) MS2, (iii) MS3, (iv) MS4, and (v) MS5. All these five sites highlight a significant increase in the built-up area and vegetation cover. Fallow land and waterbodies have declined at all these five sites. MS1 was the most affected site by the migration of the Ganga River channel. The results indicate that channel migration and improvements in geomorphic units considerably affect LU/LC. [ABSTRACT FROM AUTHOR]

This study assesses the sensitivity of Land Use Land Cover (LULC) representation on the evolution of mesoscale convective systems over Bhubaneswar, a rapidly growing city (~ 77% growth in the last two decades) in India. In this study, three types of LULC maps have been prepared using supervised machine learning (ML) methods such as Classification and Regression Trees (CART), Naive Bayes (NB), and Support Vector Machine (SVM) on Google Earth Engine (GEE) platform using Landsat 8 for 2014. A high accuracy score (87%) and kappa coefficient (84%) revealed the best performance of CART in generating the LULC map. The Weather Research and Forecasting (WRF) model at 6 and 2 km horizontal resolution is forced with these LULC maps. Model results highlight that the CART experiment exhibits relatively less bias in 2 m relative humidity (~ – 10% to – 5%), 2 m temperature (~ 2.5 °C to ~ 0 °C), and 10 m wind speed (– 1 to ~ 1.8 m s−1) up to peak stage of the thunderstorms. The CART performs better with less rainfall error (~ – 16 mm) than CNTL (~ – 33 mm), NB (~ – 37 mm), and SVM (~ – 38 mm) and is supported by the quantitative statistical analysis, viz. less false alarm ratio, critical success index for different thresholds. LULC class-wise analysis indicates a higher variation in surface and lower atmospheric parameters over urban, shrubland, and cropland while less variation over barren, forest, and water. Thus, the study highlights the credibility of ML models in representing LULC information to input the high-resolution models. [ABSTRACT FROM AUTHOR]