Land Use And Land Cover Types Research Articles

Predicting ixodid tick distribution in Tamil Nadu domestic mammals using ensemble species distribution models

Abstract Background Tick borne diseases are re-emerging around the world, including India. Information about the occurrence of the tick vectors in different geographical locations is essential for controlling the diseases. Tick surveys have not been conducted in many parts of India and information on the current prevalence of tick vectors is not available in all states of India. Many studies have been carried out utilizing modelling methods to predict the distribution of tick species in other countries. The MaxEnt model is widely used for predicting tick species distribution using bioclimatic variables. Lyme disease vectors such as Ixodes sp., Amblyomma sp., and Dermacentor sp. are the most commonly predicted tick species. However, very few studies have been carried out to predict the distribution of tick species in India. Haemaphysalis spinigera, the primary Kyasanur Forest Disease vector, was predicted along the Western Ghats using the MaxEnt model. Rhipicephalus (Boophilus) microplus was predicted across India using the generalized linear model (GLM). Identifying the tick vectors in transmitting the infection through conventional survey and identification methods is cumbersome due to the less number of experienced persons available. Prediction of tick vectors of public health concern, including other tick species in different geographical regions of Tamil Nadu, India, is essential for the prevention and control of tick-borne disease in humans and domestic animals. The present study adopts the package ‘SSDM’ (stacked species distribution models) with R software containing ensemble species distribution models to predict the distribution of tick species using different available environmental and climatic data. Results The categorical variables such as land use and land cover (LULC), soil type, elevation, Bio1, Bio10, Bio15, Bio19 and Bio8 contributed more to modelling the distribution of tick species. MaxEnt, GLM, GBM and GAM are suitable models for predicting the tick species distribution in the present study. Among these models, MaxEnt is the most suitable model for predicting tick species distribution in Tamil Nadu, India. Conclusions Our results suggest that MaxEnt is a suitable model for predicting the distribution of tick species. Both environmental factors such as LULC, elevation and soil type and bioclimatic factors such as temperature and precipitation contribute significantly to predicting tick species distribution in domestic animals in Tamil Nadu. The SSDM package is very useful and user-friendly graphical user interface for modelling the distribution of tick species. However, the package can be further improved by using higher resolution raster variables in larger areas, which is not currently supported. The predicted elevation range of Ha. spinigera distribution could not be provided due to software limitations.

Impact of Land Use Land Cover Changes on Ecosystem Service Values: Implication for Landscape Management

Abstract Globally growing human activity has had an enormous effect on natural ecosystems and the services they provide. Estimating the effects of land use and land cover (LULC) on ecosystem service values (ESVs) is crucial for determining how land use changes affect human well-being. To ensure long-term sustainability of the ecosystems, considering the trade-offs and synergies between ecosystem services, tracking changes in ecosystem services, and implementing effective land use planning and conservation strategies, we can ensure the long-term sustainability. This study aimed to find out how ESV in the Borkena watershed of the Awash basin was impacted by LULC changes during the last three decades (1993–2023). The study quantifies the spatiotemporal variation of ESV of the Borkena watershed in relation with LULC changes. Six LULC types were identified using supervised image classification following maximum likelihood method in ArcGIS 10.8 environment. The ESV was estimated following benefit transfer methods based on the coefficients published by Costanza and his team in 2014. The results demonstrated that between 1993 and 2023, there was a continuous increase in the amount of land under cultivation and built-up area, as well as a decrease in the amount of forest and shrub land. The study shown that the significant expansion of cultivated land and built-up area and decreasing of forest and shrub land cover during 1993-2023 periods, has reduced the total ESV from US$ 640.74 in 1993 to US $603.52 in 2003 and US $607.05 in 2013 and US $625.45 in 2023. It is urgently necessary to take action to improve the sustainability of ecosystem service value at the landscape level with proper land management methods in light of the decline in both the total and individual ESV in the study area and period. The results of this study could be useful to raise public awareness of the state of ESV, landscape management and support policy-making processes.

Decadal Change in Land Use and Land Cover Dynamics: A Comprehensive Assessment of the Kharun Catchment

Land Use and Land Cover (LULC) changes exert profound influences on environmental and resource dynamics, particularly in developing regions experiencing rapid urbanization and agricultural expansion. This study investigates LULC changes over a 30-year period (1992–2022) in the Kharun catchment, part of the upper Mahanadi basin in Chhattisgarh, India. Encompassing 4,143.17 km² across six districts, the catchment plays a critical role in supporting agricultural and urban activities. Landsat imagery from 1992, 2002, 2012, and 2022 was analyzed using supervised classification with Remote Sensing (RS) and Geographic Information System (GIS) tools to categorize six LULC types: water bodies, forests, urban areas, upland paddy fields, lowland paddy fields, and barren land. The results reveal dynamic and significant LULC changes. In 1992, water bodies (2.00%), forests (4.86%), and urban areas (1.92%) occupied smaller portions of the landscape, while barren land accounted for 28.64%. By 2002, water bodies slightly increased to 2.12%, forests decreased to 3.98%, urban areas expanded to 3.21%, and barren land reduced to 27.80%. Urban expansion became pronounced by 2012, with urban areas rising to 6.42%, while forests further declined to 3.11%, and barren land reduced to 26.30%. By 2022, water bodies expanded to 2.63%, forests declined significantly to 2.77%, and urban areas surged to 9.35%, indicative of extensive deforestation and urbanization. Barren land continued to decrease, covering 24.15%. These findings highlight the urgent need for integrated land-use management strategies that balance developmental needs with environmental conservation. Policy frameworks should prioritize reforestation initiatives, sustainable urban planning, and soil and water conservation practices to mitigate the adverse effects of deforestation and land degradation. Regular and systematic monitoring of LULC dynamics at the catchment scale is essential to support informed decision-making, ensuring ecological integrity and socio-economic sustainability in rapidly developing regions.

Multi-temporal analysis of land use and land cover change detection in Binh Duong province, Vietnam using geospatial techniques

Understanding changes in land use and land cover (LULC) is crucial for effective land management, environmental planning, and decision-making. It helps identify areas of environmental concern, assess the impacts of human activities on ecosystems, and develop strategies for conservation efforts and sustainable land use. In this study, remote sensing and geographic information systems (GIS) were used to monitor LULC changes in Binh Duong province, Vietnam from 1988 to 2023. The supervised classification method in ArcGIS 10.8 software was applied to Landsat satellite data (Landsat 5-TM for 1988 and 2004, and Landsat 9-OLI/TIRS for 2023) to detect and classify five main LULC types: arable land, barren land, built-up areas, forests and waterbodies. The classification accuracy was evaluated using kappa coefficients, which were 0.877, 0.894 and 0.908 for 1988, 2004, and 2023, respectively. During the period of 1988–2023, the forest, barren land, and waterbodies class areas decreased by 560.55 km2, 200.04 km2, and 19.68 km2, respectively. Meanwhile, the arable land and built-up areas classes increased by 343.80 km2 and 436.47 km2, respectively. Furthermore, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) were used to quickly assess changes in LULC, and their trends were found to be consistent with the supervised classification results. These changes in LULC pose significant threats to the environment and the findings of this study can serve as valuable resources for future land management and planning in the region.

Drivers of mangrove area change and suppression in Brazil from 2000 to 2020.

Mangrove area loss is increasing globally, and drivers of loss differ depending not only on natural conditions but also on national and regional policies. Some countries with the most mangrove area, for instance, Brazil, lack broad systematic quantification of specific drivers of mangrove land-use and land-cover (LULC) change dynamics. We investigated the direct conversion (i.e., replacement) of mangrove forests due to changes in 21 types of LULC across Brazil from 2000 to 2020 based on annual LULC maps developed by the MapBiomas project. We quantified the area changes at national, regional, and state scales. We also determined and quantified mangrove forest conversion for each of the 21 LULC types with a pixel comparison analysis and identified temporal trends with a time-series analysis. The total conversion of mangrove area (3429km2) was offset by a gain that was twice as large (6776km2). Forest formations and water bodies, which may be interpreted as natural or indirect anthropogenic changes, were associated with most of the areas where mangrove cover was lost. Land-use modifications, mainly creation of pastures, accounted for 4% of direct mangrove conversions. We found that changes in LULC categories and patterns of gain and loss of mangrove areas differed among Brazilian states and regions. Based on other research, they also differ between Brazil and other countries. Thus, integrated mangrove forest conservation and management efforts that transcend political boundaries are essential to effectively address negative impacts on mangrove forests. We provide an interactive map to allow qualitative assessments of mangrove conversion drivers by different stakeholders, such as managers, policymakers, and nongovernmental organizations.

The Impact of Bamboo on Rainfall-Triggered Landslide Distribution at the Regional Scale: A Case Study from SE China

It is widely accepted that land use and land cover (LULC) is an important conditioning factor for landslide occurrence, especially when considering the role of tree roots in stabilizing slopes and consolidating the soil. However, it is still difficult to assess the impacts of a specific LULC type on landslide distribution. The objective of the present study is to reveal the relationship between bamboo and landslide distribution at the regional scale. We aim to answer the following question: do the areas covered by bamboo have a higher susceptibility to landslides? Wenzhou City in SE China was taken as the study area, and a landslide inventory containing 1725 shallow landslides was constructed. The generalized additive model (GAM) was employed to assess the significance of LULC and nine additional factors, all of which were generated using the GIS platform. The frequency ratio (FR) method was used to analyze and compare the landslide density in each LULC category. Machine learning models were applied to perform landslide susceptibility mapping of the region. The results show that in the Wenzhou region, LULC is the second most important factor for landslide occurrences after the slope factor, whereas bamboo has a relatively higher FR value than most other LULC categories. The accuracies of the landslide susceptibility maps obtained from the random forest and XGBoost models were 79.6% and 85.3%, respectively. Moreover, 23.8% and 25.5% of the bamboos were distributed in very-high- and high-susceptibility-level areas. The incidents and density of landslides in bamboo areas were significantly higher than those with debris flow and rock collapses, indicating a promotional effect of bamboo on slope failure in the study area. This work will improve our understanding regarding the role of geological and ecological conditions that affect slope stability, which may provide useful guidance for land use planning and landslide risk assessment and mitigation at the regional scale.

Impact of Land Use and Land Cover Change on Soil Erosion in Dondor Watershed, Blue Nile Basin, Northwestern Ethiopia

Understanding how land use and land cover (LULC) changes affect soil erosion is essential for effective management of watershed areas. This study used Geographic Information Systems (GISs) and the Revised Universal Soil Loss Equation (RUSLE) model to analyze the impact of LULC changes on soil erosion in the Dondor Watershed. Remote sensing data, including Landsat and Sentinel-2 satellite images, alongside field surveys, topographic data, rainfall, and soil data were used. The results showed agricultural land as the primary LULC type, increasing from 43.49% in 2002 to 59.10% in 2023. Forest and built-up areas also expanded, while grassland decreased. Soil erosion estimates revealed that more than 85% of the watershed experienced very slight erosion though the average annual soil loss increased from 4.98 t ha⁻1 year⁻1 in 2002 to 7.96 t ha⁻1 year⁻1 in 2023. Agriculture and built-up areas were identified as the primary contributors to erosion. This study underscores the importance of monitoring LULC dynamics for responsible land management and conservation efforts in the watershed.

Characteristics and drivers of the soil multifunctionality under different land use and land cover types in the drylands of China

The drylands of China cover approximately 6.6×106 km2 and are home to approximately 5.8×108 people, providing important ecosystem services for human survival and development. However, dryland ecosystems are extremely fragile and sensitive to external environmental changes. Land use and land cover (LULC) changes significantly impact soil structure and function, thus affecting the soil multifunctionality (SMF). However, the effect of LULC changes on the SMF in the drylands of China has rarely been reported. In this study, we investigated the characteristics of the SMF changes based on soil data in the 1980s from the National Tibetan Plateau Data Center. We explored the drivers of the SMF changes under different LULC types (including forest, grassland, shrubland, and desert) and used structural equation modeling to explore the main driver of the SMF changes. The results showed that the SMF under the four LULC types decreased in the following descending order: forest, grassland, shrubland, and desert. The main driver of the SMF changes under different LULC types was mean annual temperature (MAT). In addition to MAT, pH in forest, soil moisture (SM) and soil biodiversity index in grassland, SM in shrubland, and aridity index in desert are crucial factors for the SMF changes. Therefore, the SMF in the drylands of China is regulated mainly by MAT and pH, and comprehensive assessments of the SMF in drylands need to be performed regarding LULC changes. The results are beneficial for evaluating the SMF among different LULC types and predicting the SMF under global climate change.

Patterns of change, driving forces and future simulation of LULC in the Fuxian Lake Basin based on the IM-RF-Markov-PLUS framework

Studying land use and land cover (LULC) patterns, identifying driving forces, and simulating future scenarios are vital for grasping the complex connection between human actions and the environment. This helps in shaping sustainable land management strategies and preparing for the impacts of climate change. However, there is a necessity for a comprehensive system modeling framework that can accurately capture spatial and temporal changes in LULC, analyze driving mechanisms, and provide an integrated analysis of future simulations. In this paper, a comprehensive IM-RF-Markov-PLUS analysis framework is developed, focusing on the Fuxian Lake Basin (FLB) as a study area. The study aims to achieve accurate prediction of LULC by combining the microscopic LULC change trend and the contribution of macroscopic driving forces. The results show that: (1) The IM-RF-Markov-PLUS framework can explore the change patterns and driving mechanisms of LULC in the FLB, and accurately predict the LULC in the FLB. Compared with the PLUS model, its accuracy is improved by 2 %. (2) IM analysis reveals that LULC transformation in the FLB is both general and specific. Although the area of grassland, buildings, roads and structures converted to desert and bare land is minimal, it shows relatively tendentious and specific change characteristics. (3) Different land types are significantly affected by driving factors, with the expansion of LULCs is constrained by major factors. The distance to the lake has the most significant impact on the distribution of garden land, while the primary road has the greatest impact on the distribution of forestland. (4) Under different scenarios, the spatial heterogeneity of LULC patterns is obvious. In 2035, under baseline and economic development scenarios, cultivated land will decrease, while other LULC types will increase. Under the cultivated land protection scenario, cultivated land is protected, with an increase of 5.85 %. Under the ecological protection scenario, there is an increase in ecological land. The largest increase is in forestland, which increases by a total of 3.46 %. The ecological protection scenario presents a viable approach for ensuring the sustainable development of the FLB. The results of this paper may serve as a reliable foundation for implementing LULC strategies in the FLB and offer guidance for crafting sustainable development regulations at the regional level.

Assessment of urbanization impacts on soil erosion and groundwater recharge in Enugu, Southeastern Nigeria

The study of Land Use and Land Cover (LULC) in Enugu, southeastern Nigeria, is crucial for understanding its effects on erosion and groundwater recharge. Different LULC types, coupled with varying slope gradients and soil types, create a complex interaction that influences soil stability and water infiltration in the region. This research aims to assess the distribution of LULC types, slope gradients, and soil types in Enugu, and to evaluate their impacts on erosion rates and groundwater recharge potential. Data for LULC, slope gradients, and soil types were collected and analyzed to determine their spatial distribution and characteristics. The study employed GIS techniques to map these variables and assess their interactions. Erosion potential and groundwater recharge capacity were evaluated based on the characteristics of the LULC types, slopes, and soils. The analysis revealed that trees cover the largest area (3709.74 km²), playing a significant role in stabilizing the soil and reducing erosion through their extensive root systems and canopy cover. Rangeland (2631.81 km²) also contributes to soil stability, although less effectively than forested areas. Crops (192.40 km²) have mixed impacts on erosion depending on agricultural practices. Built areas (1162.69 km²) present challenges due to impervious surfaces, which increase surface runoff and reduce groundwater recharge. Slope gradients were found to correlate with erosion processes and groundwater dynamics. Gentle slopes (0 - 1.81 degrees) cover 1870.15 km² and facilitate infiltration, enhancing groundwater recharge. Moderate slopes (1.81 - 4.06 degrees), covering 3275.46 km², are more prone to erosion, while steeper slopes (4.06 - 11.73 degrees) covering 2176.54 km² experience accelerated runoff and increased erosion rates. The steepest slopes (11.73 - 44.06 degrees) are the most erosion-prone areas, requiring significant intervention. The soil analysis showed that Dystric Nitosols (4052.80 km²) with the lowest K-factor (0.0178) are least prone to erosion and have high infiltration capacity, making them beneficial for groundwater recharge. Plinthic Acrisols (2732.53 km²) and Ferric Acrisols (102.36 km²) exhibit moderate erosion susceptibility. Dystric Fluvisols (793.92 km²) with the highest K-factor (0.0223) are highly erosion-prone. Gleysols (20.69 km²) have low to moderate erosion susceptibility. The interplay between LULC types, slope gradients, and soil types significantly influences erosion and groundwater recharge in Enugu. The study highlights the need for targeted land management practices, such as afforestation, contour farming, terracing, and the use of cover crops to mitigate erosion and enhance groundwater recharge. This research provides a comprehensive analysis of the relationships between LULC, slope gradients, and soil types in Enugu, offering valuable insights for developing effective land management strategies to address erosion and groundwater recharge challenges.

Modeling of land use and land cover changes using google earth engine and machine learning approach: implications for landscape management

A precise and up-to-date Land Use and Land Cover (LULC) valuation serves as the fundamental basis for efficient land management. Google Earth Engine (GEE), with its numerous machine learning algorithms, is now the most advanced open-source global platform for rapid and accurate LULC classification. Thus, this study explores the dynamics of the LULC changes between 1993 and 2023 using Landsat imagery and the machine learning algorithms in the Google Earth Engine (GEE) platform. Focus group discussion and key informant interviews were also used to get further data regarding LULC dynamics. Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART) were demonstrated for LULC classification. Six LULC types (agricultural land, grazingland, shrubland, built-up area, forest and bareland) were identified and mapped for 1993, 2003, 2013, and 2023. The overall accuracy and kappa coefficient demonstrated that the RF using images comprising auxiliary variables (spectral indices and topographic data) performed better than SVM and CART. Despite being the most common type of LULC, agricultural land shows a trend of shrinking during the study period. The built-up area and bareland exhibits a trend of progressive expansion. The amount of forest and shrubland has decreased over the last 20 years, whereas grazinglands have exhibited expanding trends. Population growth, agricultural land expansion, fuelwood collection, charcoal production, built-up areas expansion, illegal settlement and intervention are among causes of LULC shifts. This study provides reliable information about the patterns of LULC in the Robit watershed, which can be used to develop frameworks for watershed management and sustainability.

Seasonal-scale intercomparison of SMAP and fused SMOS-SMAP soil moisture products

Two L-band passive microwave satellite sensors, onboard the Soil Moisture and Ocean Salinity (SMOS) launched in 2009 and Soil Moisture Active Passive (SMAP) launched in 2015, are specifically designed for surface soil moisture (SM) monitoring. The first global continuous fused L-band satellite SM product based on SMOS and SMAP observations (SMOS-SMAP-INRAE-BORDEAUX, the so-called Fused-IB) was recently released to the public. Currently, the performance of Fused-IB has only been evaluated collectively over the entire data records in the study period, without specific evaluation for individual seasons. To fill this gap, this study intercompared the Fused-IB and the enhanced SMAP-L3 version 6 (SMAP-E) SM products against in situ SM data from the International Soil Moisture Network (ISMN) from 2016 to 2020 regarding the whole period and different seasons. We aim to investigate the performance of these two products at different time scales and to explore the potential eco-hydrological factors (i.e., precipitation and vegetation) driving their seasonal variations. Results show that both SM products are in good agreement with the in situ measurements, demonstrating high median correlation (R) and low ubRMSD (median R = 0.70 and ubRMSD = 0.058 m3/m3 for Fused-IB vs. R = 0.68 and ubRMSD = 0.059 m3/m3 for SMAP-E) during 2016–2020. For most land use and land cover (LULC) types, Fused-IB outperformed SMAP-E with higher accuracy and lower errors, particularly in forests, partly due to the advantage of the robust SMAP-IB (SMAP-INRAE-BORDEAUX) algorithm used to generate Fused-IB in forests, which avoids the pronounced saturation effects of vegetation optical depth caused by relying on optical information. Besides, both products had superior performances across most LULC types in summer (JJA) and autumn (SON), yet increased uncertainties were observed in forests, grasslands, and croplands during spring (MAM) and winter (DJF). These uncertainties could be mainly attributed to the effects of vegetation growth in forests, grasslands and croplands, and the interception of water from rainfall events in grasslands. The results of this study can serve as a reference for algorithm developers to enhance the accuracy of SM and thus promote hydro-meteorological applications that benefit from L-band radiometer soil moisture products.

Changing Pattern and Drivers of Land Use and Land Cover in Bagmati Province of Nepal

Land use and land cover (LULC) represents the physical component of the Earth's environment, and often changes as a result of natural and anthropogenic activities. This study carried out changing patterns and orientations of LULC types of Bagmati province of Nepal by using satellite images between 1996 and 2016. The study applied a classification scheme through support vector machines (SVM) for the period from 1996 to 2006, and 2006 to 2016. Our study explored that built-up area expansion was the major change witnessed which increased from 85.25 km2 in 1996 and sharply increased and labeled 250.06 km2 in 2016. Similarly, the forest land of the province increased by 329.02 km2 between 1996 and 2016. The status of cultivated land was found slightly decreasing trend, which was noted at 6113.50 km2 in 1996 and labeled at 5725.82 km2 in 2016. The shrubland of the province declined by 272.03 km2 from 1851.67 km2 in 1996 to 1579.64 km2 in 2016. However, the status of snow/ ice cover found at fluctuated form, which was noted 774.27 km2 in 1996, 1297.43 km2 in 2006, and 828.92 km2 in 2016. The increasing population trend is the main driver of the high rate of built-up area expansion and contraction of the cultivated land. Community forestry programs highly supported the expansion of forest cover. The proper implementation and improvement of the existing policy are the major concerns for sustainable LULC management in the province.

Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India.

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.

Change in Bhutan’s Land Use and Land Cover from 1930 - 2020

The land is a fundamental component of Bhutan's geographical makeup, characterized by natural features and human activities. Bhutan's land can be divided into land use and land cover (LULC), where human activities contribute to land use, and natural elements define land cover. This paper analyzes Bhutan's LULC patterns, emphasizing the evolving trends in major LULC types, strengths and challenges of sustainable land use management, and envisioning its future trajectory. In recent years, Bhutan's land-use pattern has displayed a predominant forest cover, encompassing a significant portion of the total land area. Similarly, the built-up area in Bhutan is gradually increasing, reflecting the country's ongoing urbanization and development activities. A long-term analysis reveals dynamic shifts in agricultural land in Bhutan. While there has been a historical expansion of agricultural areas, recent trends may suggest a potential slowdown or decline, influenced by factors such as urban growth and changing economic landscapes. Like community forestry practices in Bhutan, successful conservation efforts may also influence land-use changes. In the eastern Himalayas, Bhutan faces challenges related to snow/glacial cover impacted by climate change. Increasing temperatures in the region contribute to alterations in snow/glacial patterns, necessitating a focus on environmental conservation and sustainable practices to preserve these vital resources. The land tenure system and land use policies in Bhutan have evolved and been shaped by socioeconomic and political dynamics. Bhutan needs to adapt and formulate effective policies to address contemporary challenges and promote sustainable LULC management. Implementing specific LULC zones, as outlined in Bhutan's land use policies, is crucial for ensuring sustainable land management practices.

Identifying land use land cover dynamics using machine learning method and GIS approach in Karaivetti, Tamil Nadu

<p>An important analytical tool for tracking, mapping, and quantifying changes in land use and land cover (LULC) across time serves as the use of machine learning techniques. The environment and human activities both have the potential to change how land is used and covered. Classifying LULC types at different spatial scales has been effectively achieved by models like classification and regression trees (CART), support vector machines (SVM), extreme gradient boosting (XGBoost), and random forests (RF). To prepare images from Landsat before sending and analysis for an aspect of our research, we employed the Google Earth Engine. High-resolution imagery from Google Earth images were used to assess each kind of method and field data collection. Utilizing Geographic Information System (GIS) techniques, LULC fluctuations between 2015 and 2020 were assessed. According to our results, XGBoost, SVM, and CART models proved superior by the RF model regarding categorization precision. Considering the data, we collected between 2015 and 2020, from 11.57 hectares (1.74%) in 2015 to 184.19 hectares (27.65%) in 2020, the barren land experienced the greatest variation, that made an immense effect. Utilizing the support of satellite imagery from the Karaivetti Wetland, our work combines novel GIS techniques and machine learning strategies to LULC monitoring. The created land cover maps provide a vital benchmark that will be useful to authorities in formulating policies, managing for sustainability, and keeping track of degradation.</p>

Sustainable flood hazard mapping with GLOF: A Google Earth Engine approach

This study aims to evaluate the efficacy of Google Earth Engine (GEE) in mapping floods and their aftermath, focusing on the recent event caused by cloud burst rainfall and glacial lake outburst flood (GLOF) of Lhonak glacier lake in the Teesta River basin, North Sikkim. The objective is to utilize GEE, coupled with Sentinel-1 Synthetic Aperture Radar (SAR) data and Landsat 9 imagery, for precise remote sensing analysis, flood mapping, and Land Use and Land Cover (LULC) classification. The study employs a comprehensive methodology within the GEE platform, involving the acquisition and preprocessing of Sentinel-1 SAR data to create pre- and post-flood images. The difference between these images is calculated to generate flood maps at five-day intervals, providing a temporal evolution of the flood extent. Additionally, LULC mapping is conducted using Landsat 9 data, contributing to an understanding of pre-flood landscape characteristics. The results and discussion reveal significant impacts on various LULC types, with barren rocks, dense and medium forests, settlements, and agricultural lands experiencing notable effects. This research not only enhances our understanding of GLOFs but also serves as a critical tool for informing disaster management strategies, emphasizing the importance of accurate hazard assessment and the need for holistic approaches to mitigate the cascading effects of such events.

Quantifying Ecosystem Services and Environmental Dynamics in Lagos State's Coastal Zones

Coastal regions are experiencing rapid urbanization, leading to substantial land use and land cover (LULC) transformations that profoundly impact the environment, livelihoods, and ecosystem services, and this has become a concern for environmentalists. However, research linking dynamics to ecosystem service valuation in this critically important ecoregion is rarely studied. This study attempts to bridge this gap by quantifying the ecosystem services in Lagos, Nigeria's rapidly urbanizing coastal region. A multi-temporal Landsat imagery of 2003, 2013, and 2023 was employed to map LULC categories in the region. Furthermore, the Value transfer method was employed to estimate the ecosystem services in each LULC type. The estimated ecosystem service values revealed significant transitions, with an overall decline of $985.01 million from 2003-2013; built-up areas experienced the most significant increase, rising with $47.06 million, while mangrove forests saw the most significant decline, dropping to $ 602.86 million. Between 2013 and 2023, built-up continue to exhibit the most significant increase in value, with $15.72 million. Conversely, mangroves experienced a sharp decline, decreasing with $153.52 million. The valuation of LULC-associated ecosystem services in Lagos's coastal zone highlights the urgent need to integrate environmental accounting into coastal management.

Transfer-Ensemble Learning: A Novel Approach for Mapping Urban Land Use/Cover of the Indian Metropolitans

Land use and land cover (LULC) classification plays a significant role in the analysis of climate change, evidence-based policies, and urban and regional planning. For example, updated and detailed information on land use in urban areas is highly needed to monitor and evaluate urban development plans. Machine learning (ML) algorithms, and particularly ensemble ML models support transferability and efficiency in mapping land uses. Generalization, model consistency, and efficiency are essential requirements for implementing such algorithms. The transfer-ensemble learning approach is increasingly used due to its efficiency. However, it is rarely investigated for mapping complex urban LULC in Global South cities, such as India. The main objective of this study is to assess the performance of machine and ensemble-transfer learning algorithms to map the LULC of two metropolitan cities of India using Landsat 5 TM, 2011, and DMSP-OLS nightlight, 2013. This study used classical ML algorithms, such as Support Vector Machine-Radial Basis Function (SVM-RBF), SVM-Linear, and Random Forest (RF). A total of 480 samples were collected to classify six LULC types. The samples were split into training and validation sets with a 65:35 ratio for the training, parameter tuning, and validation of the ML algorithms. The result shows that RF has the highest accuracy (94.43%) of individual models, as compared to SVM-RBF (85.07%) and SVM-Linear (91.99%). Overall, the ensemble model-4 produces the highest accuracy (94.84%) compared to other ensemble models for the Kolkata metropolitan area. In transfer learning, the pre-trained ensemble model-4 achieved the highest accuracy (80.75%) compared to other pre-trained ensemble models for Delhi. This study provides innovative guidelines for selecting a robust ML algorithm to map urban LULC at the metropolitan scale to support urban sustainability.

Temporal and spatial variations of carbon storage and carbon sink improvement strategy at the district and county level based on PLUS-InVEST model: Taking Yanqing District as an example

Under the background of the carbon peaking and carbon neutrality goals, the evolution of the spatiotemporal pattern of carbon storage has recently emerged as a research hotspot. The change in land use and land cover (LULC) is the primary driver of carbon storage changes. Understanding the spatiotemporal variations of LULC and carbon storage at the small scale of district and county level and proposing strategies to improve carbon sink, will contribute to the ecological conservation, restoration and sustainable development of districts or counties. With Yanqing District in Beijing as an example, we calculated carbon storage from 1990 to 2020 based on the InVEST model and used the PLUS model to predict LULC type changes under three scenarios (natural growth, ecological conservation and economic development) from 2020 to 2050. We further predicted the carbon storage and proposed mea-sures to improve carbon sink. The results showed that the key LULC change in Yanqing between 1990 and 2020 were the conversion of 88.9% of grassland to forest, 50.1% of farmland to forest, and 39.5% of cropland to impervious surface. The total carbon storage showed an upward trend, with an increase of 3.34×106 Mg. The spatial distribution of carbon storage presented "high in the northeast, low in the southwest, and high in the mountainous areas, low in the riverine areas." The increase in forest and the decrease in grassland were the main reasons for the increase and decrease in carbon storage, respectively. Between 2020 and 2050, the ecological restoration efforts under the ecological protection scenario increased, and the probability of other LULCs transforming into forest increased, resulting in a 5.8% increase in carbon storage, which had the highest increase and carbon storage under the three scenarios. High-value carbon storage areas were concentrated in the northeast, northwest, and south of Yanqing District, basically corresponding to the mountainous regions of Yanqing with high forest coverage, and the low-value areas generally corresponded to the plains with high development intensity and low forest coverage. We could implement comprehensive ecological protection and restoration measures, including forest and grassland ecosystem protection, water environment ecological restoration, farmland ecological restoration, to promote sustainable development in Yanqing District and to achieve the "dual carbon" goal.