Land Cover Change Research Articles

Quantitative assessment of interplay between urbanization dynamics and land surface temperature variations using generalized additive model coupled PDP for sustainable urban planning and management.

The mountainous region of Asir is experiencing rapid and unsystematic urbanization leading to an increase in land surface temperatures (LST), which poses a challenge to human well-being and ecological balance. Therefore, it is necessary to study the interaction between land use and land cover (LULC)-induced urbanization and LST using advanced geostatistical techniques. In addition, understanding the urbanization process and urban density is essential for effective urban planning and management. The aim of this study was to investigate the interaction between the urbanization process, urban density and the associated LST. Using the Random Forest Algorithm, LULC mapping was conducted for the years 1990, 2000 and 2020. Metrics such as land cover change rate (LCCR), land cover index (LCI), landscape expansion index (LEI), mean landscape expansion index (MLEI) and area-weighted landscape expansion index (AWLEI) were used to understand urbanization processes and LULC changes. Convolutional kernels were used to model urban density, and the mono-window algorithm was applied to analyse LST in the selected years. In addition, the study assessed the Surface Urban Heat Island (SUHI) contribution index to LULC and used Generalized Additive Models (GAMs) in conjunction with Partial Dependence Plots (PDPs) to understand the relationship between urbanization processes, urban density and LST. In a detailed 30-year study, the application of the RF algorithm showed significant shifts in LULC with an overall validation accuracy of over 85%. Urban areas grew dramatically from 69.40 km2 in 1990 to 338.74 km2 in 2020, while water areas decreased from 1.51 to 0.54 km2. Dense vegetation increased from 43.36 to 52.22 km2, indicating positive ecological trends. The LST analysis showed a general warming, with the mean LST increasing from 40.51°C in 1990 to 46.73°C in 2020 and the highest temperature category (50-60°C) increasing from 0.78 to 33.35 km2. The built-up area of cities tripled between 1990 and 2020, with the Landscape Expansion Index reflecting significant growth in suburban areas. The modeling of urban density shows increasing urbanization in the centre, which will expand significantly to the east by 2020. The contribution of LULC to LST and the Urban Heat Island (SUHI) effect was evident, with built-up areas showing a constant temperature increase. GAMs confirmed a statistically significant relationship between urban density and LST, with different effects for different types of urban expansion. This comprehensive study quantitatively sheds light on the complicated dynamics of urbanization, land cover change and temperature variation and provides important insights for sustainable urban development.

Warming reality of Kozhikode Urban Area: Uncovering the heat of built-up expansion and vegetation loss

Urban expansion often comes at the expense of natural landscapes, leading to increased temperatures and environmental degradation. This study examines the effects of urbanization on land cover changes and their implications for land surface temperature (LST) by analyzing normalized difference satellite indices in the Kozhikode Urban Area (KUA), a city where substantial urbanization has significantly impacted the natural environment. By investigating changes in vegetation and impervious surfaces from 1993 to 2022, the research highlights a troubling trend: while vegetation cover has sharply declined, impermeable surfaces like roads and buildings have surged, contributing to a dramatic rise in average temperatures. The study reveals that built-up areas significantly elevate LST, whereas vegetation and water bodies offer cooling effects. The results underscore the urgent need for integrating green infrastructure into urban planning to combat rising temperatures and enhance environmental sustainability. This research emphasizes the importance of considering smaller, vegetation-rich cities like Kozhikode in broader urban heat research, as they present unique challenges and opportunities for effective climate adaptation strategies. By highlighting these issues, this study aims to inform policymakers and urban planners about the critical role of urban greening in mitigating the effects of climate change and fostering sustainable urban environments.

Investigating the changing land use patterns and their impacts on ecosystem in the Teesta River Basin

This study investigated the changing land use patterns and their impacts on ecosystem in the Teesta River Basin of northwestern Bangladesh. Although anthropocentric land use patterns, including agricultural land use, settlements, built areas, and waterbody loss, have been increasing in the Nilphamari district, by negatively affecting local ecosystems, they have not been identified by prior research. Limitations of contemporary literature motivated me to work on this crucial ground in the Teesta River Basin in Northwestern Bangladesh. This study applied a mixed research approach to identify the study objectives. Firstly, the land use and land cover (LULC) changes which occurred between 2000 and 2020 were detected using satellite imagery and supervised classification method. In addition to the detection of LULC changes, the study explored the people’s perceptions and experiences about the ecosystem changes resulted from the LULC changes over the last 20 years, conducting stakeholders’ consultations and household surveys utilizing a semi-structured questionnaire. The findings indicated that waterbodies in Nilphamari district have significantly decreased from 378 km2 in 2000 to 181 km2 in 2020. In the same way, the vegetation coverage has reduced 187 km2 between the years 2000 and 2020. On the contrary, agricultural lands (croplands) have increased from 595 km2 to 905 km2 and settlements have increased from 81 km2 to 206 km2 between the years 2000 and 2020. From the chi-square test, it was found a significant association between ecosystem change and biodiversity loss. It was further identified that waterbody decreases have significant impacts on aquatic ecosystems. The results of this study also indicated that due to the introduction of foreign tree species, local and native species have been significantly decreasing over the time. This study emphasizes the non-anthropocentric and inclusive land use policy implications for protecting life on land and preserving the aquatic ecosystem in Bangladesh.

Fire-sensitive and threatened plants in the Upper Paraguay River Basin, Brazil: Identifying priority areas for Integrated Fire Management and ecological restoration

Extreme wildfires present an ever-increasing problem in different landscapes around the world. Here, we explore recent wildfires to prioritize recovery efforts and mitigate effects of future wildfires in the Pantanal, the largest continuous wetland in the world. Using ecological niche modeling (ENM), we categorize affected areas based on potential levels of degradation by fire and the predicted suitability for threatened and fire-sensitive plants. Based on this information, we identify key areas for fire prevention. Under land cover change scenarios predicted for 2050, approximately 25 % of the land cover under future threat will have potentially high biomass accumulation. We identified 1272 km2 in priority areas with forest formation, with restoration cost of at least US$ 576,189. This case study provides an analysis workflow that can be used to assess other wildfire-threatened parts of the world. These results can inform public policy to establish new fire brigades, conservation efforts using Integrated Fire Management, and restoration areas to mitigate biodiversity loss.

Assessing the impact of land use and land cover change on the Densu Delta wetland using Markov chain modeling and artificial neural networks

This study investigates the dynamics of land use and land cover (LULC) changes in the Densu Delta wetlands, a critical ecosystem in Ghana. Here, satellite images spanning from 1998 to 2023 were used to analyse the spatio-temporal patterns of LULC changes and their implications for water bodies, wetlands, vegetations, bare lands and urban areas in the Densu Delta wetland. Employing advanced techniques such as Markov chain modelling and artificial neural networks (ANNs), the research assesses and predicts LULC alterations. Significantly, the largest loss of LULC is observed in the Densu Delta wetland, where wetlands transition to waterbody cover type (14.02 km²). Model validation for 2023 attests to the accuracy of the model, boasting a correctness percentage of 70% and a kappa value of 0.74. In-depth analyses explore regional variations in the Densu Delta wetlands, revealing distinct patterns in the rates of LULC change before and after 2013. Notably, urbanization emerges as a prominent factor post-2013, with urban areas experiencing remarkable rates of change in the wetland. Transition matrices underscore the intricate interplay of different land cover classes over the years. Simulated LULC predictions for 2033 and 2043 highlight the urban land cover type as having the highest positive change, recording approximately 0.39% for the Densu Delta wetland. The wetland land cover in the Densu Delta wetland exhibit negative changes of about −0.52%. The synthesis of LULC data enhances our understanding of the complex interactions shaping these critical ecosystems. This research offers valuable insights for sustainable environmental conservation, emphasizing the pivotal role of informed urban planning policies. It also unveils potential challenges posed by climate change, advocating for a holistic approach to preserve these vital wetland ecosystems.

Spatiotemporal analysis of land use and land cover changes, LST and NDVI in Thatta district, Sindh, Pakistan

The purpose of this work is to determine land-use and land-cover (LULC) patterns, land surface temperature (LST), and normalized difference vegetation index (NDVI) changes in Thatta district using Landsat data from 1991 to 2021 and evaluate the relationship between LST and NDVI. The research process employed the selection of the study area, data acquisition, preprocessing, and classification of remotely sensed images for the estimation of the land use land cover change (LULC), vegetation index (NDVI), and evaluation of LST using thermal bands in the Landsat dataset. The study revealed the area under built-up structures has increased from 1991 to 2021. Although the vegetation cover showed an increase, the bare soil showed a decreasing pattern, indicating a constant change in the LULC patterns in the region. The confusion matrix method for accuracy valuation of LULC data of 2021 revealed an overall accuracy of 88.24%, with a Kappa coefficient of 84.22%, while the Artificial Neural Network Multilayer Perceptron (ANN-MLP) model had a Kappa validation of 0.95 for 2021. The highest maximum temperature is observed for 2021, indicating a positive relationship between LST and built-up structures, while regression analysis found a negative correlation between LST and NDVI. This study provides a valuable monitoring framework to help resource managers develop strategies to manage land resources.

Land use transformations in the Brazilian Savanna: A decade of soil erosion and runoff measurements

Changes in land cover and land use (LULC) are one of the main drivers of erosion and runoff. How ever, most research has relied on short-term observations and only focused on one or two land cover types. We investigated the long-term trade-off between common agricultural land covers (sugarcane, pasture, and soybean), runoff, and soil loss rates. We compared these to native forest (wooded Cerrado) and bare soil. The field observations were done in 100 m2 experimental plots in Brazil maintained during the past 10 years. The paper provides three main contributions: (1) long-term runoff and soil loss rates of plots under different LULCs, (2) comparison of runoff, soil loss, and pedological characteristics between plots constructed 10 years apart, and (3) analysis of the trade-off between different LULCs. When ranking land covers based on runoff and soil loss rates, there is a shift in ranking positions, making it difficult to determine which one is more environmentally harmful. However, it is evident that whatever agricultural practice is used, there is a significant impact when compared to native forest. For example, the area converted to pasture resulted in almost 20 times higher runoff, while conversion to sugarcane resulted in 5 times higher soil loss. Not only land cover plays a major influencing factor, but also weathering exposing time. Areas under the same land cover and environmental conditions had different rates of soil loss and runoff due to long-term exposure effects such as soil crusting. Our findings have high relevance for the hydrological and agricultural community by demonstrating (i) the magnitude of trade-off in terms of soil loss and runoff due to land cover changes and (ii) that soil loss should not be assumed to be a linear process over time, as it is commonly assumed.

Evaluation and Optimization of Landscape Spatial Patterns and Ecosystem Services in the Northern Agro-Pastoral Ecotone, China

The alteration of landscape spatial patterns (LSPs) and ecosystem services (ESs) in watersheds can have detrimental effects on the local environment and community. However, a comprehensive understanding of the current state of LSPs and ESs in watersheds around Winter Olympic venues in China is limited. Here, we assessed current LSPs and ESs and developed optimization strategies for the Xigou watershed around Winter Olympic venues in the northern agro-pastoral ecotone of China. The results indicated that the main land use type was grassland in the Xigou watershed, and landscape types were relatively homogenous. All three ESs (water yield, sediment retention, and carbon storage) generally improved from 2004 to 2020. For ESs, there was the lowest total volume of water yield in 2004 (637.44 × 104 m3). But sediment retention (10.54 × 106 t, 18.13 × 106 t, 13.28 × 106 t, and 16.85 × 106 t) had an upward, then downward, then upward trend before and after ERP. Carbon storage grew steadily. Correlation analysis suggested that the three ESs were closely related to the landscape spatial indices of average patch area (AREA_MN), contagion index (CONTAG), and Shannon’s evenness index (SHEI). AREA_MN, CONTAG, and SHEI in the eastern part of the study area promoted sediment retention and carbon storage, while in the southwestern part of the study area, they inhibited water yield and sediment retention. The results suggest that improving sediment retention by optimizing land use and cover change (LUCC) and LSPs is the main approach to further enhance ESs in the study area. Our study suggests that the inclusion of multiple landscape pattern indices can provide a more comprehensive representation of regional ecosystem service.

Navigating Urban Sustainability: Urban Planning and the Predictive Analysis of Busan’s Green Area Dynamics Using the CA-ANN Model

While numerous studies have employed deep learning and high-resolution remote sensing to predict future land use and land cover (LULC) changes, no study has integrated these predictive tools with the current urban planning context to find a potential issues for sustainability. This study addresses this gap by examining the planning context of Busan Metropolitan City (BMC) and analyzing the paradoxical objectives within the city’s 2040 Master Plan and the subordinate 2030 Busan Master Plan for Parks and Greenbelts. Although the plans advocate for increased green areas to enhance urban sustainability and social wellbeing, they simultaneously support policies that may lead to a reduction in these areas due to urban development. Using the CA-ANN model in the MOLUSCE plugin, a deep learning-based LULC change analysis, we forecast further urban expansion and continued shrinkage of natural green areas. During 1980–2010, Busan Metropolitan City (BMC) underwent high-speed urban expansion, wherein the urbanized areas almost doubled and agricultural lands and green areas, including forests and grassland, reduced considerably. Forecasts for the years 2010–2040 show continued further expansion of urban areas at the expense of areas for agriculture and green areas, including forest and grasslands. Given the master plans, these highlight a critical tension between urban growth and sustainability. Despite the push for more green spaces, the replacement of natural landscapes with artificial parks and green areas may threaten long-term sustainability. In view of these apparently conflicting goals, the urban planning framework for BMC would have to take up increasingly stronger conservation policies and adaptive planning practices that consider environmental preservation on a par with economic development in the light of the planning context and trajectory of urbanization.

Using Neural Networks for Sustainable Land Use Prediction in Sumbawa Regency, Indonesia

Agriculture is vital to Sumbawa Regency's economy, with key activities such as rice cultivation, corn production, onion farming, and cattle rearing. This study applies artificial neural networks (ANN) to predict land cover changes, focusing on agricultural land expansion. Using land cover datasets from ESRI, digital elevation model, and topographical maps, we analyzed land cover changes from 2017 to 2023 and generated future projections for 2050 with the MOLUSCE plugin in qGIS. The predictive model achieved an 85% accuracy rate when comparing 2023 actual data with predictions. Results indicate a significant increase in agricultural land cover by 2050. The key finding is that over a long-term period, the simulation of land use and land cover (LULC) change in Sumbawa reveals an increase of crop areas in the Lunyuk and Labangka Districts. This study highlights the effectiveness of ANN in land cover prediction and emphasizes the need for sustainable practices to balance agricultural expansion. AI-driven insights can aid policymakers in opti-mizing resource allocation and ensuring long-term environmental and economic stability in Sumbawa Regency. Future research should refine models and incorporate additional factors for improved accuracy.

Effects of Climate Change and Changes in Land Use and Cover on Water Yield in an Equatorial Andean Basin

Ecosystem services contribute significantly to human development, with water production being a crucial component. Climate and land use changes can impact water availability within a basin. In this context, researching water-related areas is essential for formulating policies to protect and manage hydrological services. The objective of this study was to estimate water yield in the sub-basins of the Tabacay and Aguilán rivers under climate change scenarios in 2030, 2040, and 2050, combined with scenarios of changes in land cover and land use. The InVEST model was employed to analyze water yield. The results show that crop areas were identified as the lowest water yield in future scenarios, and forested areas, particularly the region where the Cubilán Protected Forest is located, contribute the most to water yield in the subbasin. Besides, water yield has increased in the historic period (2016–2018) due to the conservation and reforestation initiatives carried out by the Municipal Public Service Company for Drinking Water, Sewerage, and Environmental Sanitation of the city of Azogues in 2018, the so-called Reciprocal Agreements for Water. Additionally, an increase in water yield is projected for future scenarios. This study can serve as a basis for decision-makers to identify areas that should prioritize protection and conservation.

Analysis of The Impact of Climate Change on The Vulnerability of Coastal Areas and Small Islands in Gili Air, North Lombok

This research analyzes the impact of climate change on the vulnerability of coastal areas and small islands in Gili Air, North Lombok. Climate change, including rising temperatures and sea levels, is affecting coastal ecosystems, such as coral reefs which are vulnerable to bleaching and death. This study identifies a trend of increasing built-up land on Gili Air in the 2013-2022 period, which is caused by tourism promotion policies. The impact of this policy causes an increase in the number of tourists and local residents, which then increases the need for tourism supporting facilities. These land cover changes and development pressures increase Gili Air's environmental and economic vulnerability to climate change. These findings emphasize the importance of comprehensive adaptation and mitigation strategies to preserve the sustainability of ecosystems and the livelihoods of local residents.

Assessing Land-Cover Change Trends, Patterns, and Transitions in Coalfield Counties of Eastern Kentucky, USA

Surface coal mining and reclamation have greatly reshaped eastern Kentucky’s landscape affecting its socioeconomic, environmental and climatic aspects. This study examined the land-cover changes, trends and patterns in Floyd, Knott, Letcher, Magoffin, Martin, Perry, and Pike counties from 2004 to 2019. Using a random forest classifier, land cover was categorized into seven major classes, i.e., water, barren land, developed land, forest, shrubland, herbaceous, and planted/cultivated, majorly based on Landsat images. The Kappa accuracy ranged from 75 to 89%. The results showed a notable increase in forest area from 5052 sq km to 5305 sq km accompanied by a substantial decrease in barren land from 179 sq km to 91 sq km from 2004 to 2019. These findings demonstrated that reclamation activities positively impacted the forest expansion and reduced the barren land of the study area. Key land-cover transitions included barren land to shrubland/herbaceous, forest to shrubland, and shrubland to forest, indicating vegetation growth from 2004 to 2019. An autocorrelation analysis indicated similar land-cover types clustered together, showing effective forest restoration efforts. As surface coal mining and reclamation significantly influenced the landscapes of the coalfield counties in eastern Kentucky, this study provides a holistic perspective for understanding the repercussions of these transformations, including their effects on humans, society, and environmental health.

Spatiotemporal Variation and Prediction of Carbon Storage in Terrestrial Ecosystems at Multiple Development Stages in Beijing City Based on the Plus and Integrated Valuation of Ecosystem Services and Tradeoffs Models

Terrestrial ecosystems play a critical role in the global carbon cycle, and their carbon sequestration capacity is vital for mitigating the impacts of climate change. Changes in land use and land cover (LULC) dynamics significantly alter this capacity. This study scrutinizes the LULC evolution within the Beijing metropolitan region from 1992 to 2022, evaluating its implications for ecosystem carbon storage. It also employs the Patch-Generating Land Use Simulation (PLUS) model to simulate LULC patterns under four scenarios for 2035: an Uncontrolled Scenario (UCS), a Natural Evolution Scenario (NES), a Strict Control Scenario (SCS), and a Reforestation and Wetland Expansion Scenario (RWES). The InVEST model is concurrently used to assess and forecast ecosystem carbon storage under each scenario. Key insights from the study are as follows: (1) from 1992 to 2022, Beijing’s LULC exhibited a phased developmental trajectory, marked by an expansion of urban and forested areas at the expense of agricultural land; (2) concurrently, the region’s ecosystem carbon storage displayed a fluctuating trend, peaking initially before declining, with higher storage in the northwest and lower in the central urban zones; (3) by 2035, ecosystem carbon storage is projected to decrease by 1.41 Megatons under the UCS, decrease by 0.097 Megatons under the NES, increase by 1.70 Megatons under the SCS, and increase by 11.97 Megatons under the RWES; and (4) the study underscores the efficacy of policies curtailing construction land expansion in Beijing, advocating for sustained urban growth constraints and intensified afforestation initiatives. This research reveals significant changes in urban land use types and the mechanisms propelling these shifts, offering a scientific basis for comprehending LULC transformations in Beijing and their ramifications for ecosystem carbon storage. It further provides policymakers with substantial insights for the development of strategic environmental and urban planning initiatives.

Change in Land Use Affects Soil Organic Carbon Dynamics and Distribution in Tropical Systems

Anthropogenic land cover change is directly responsible for the deforestation and degradation of tropical forests. In this context, assessing soil organic carbon (SOC) stocks is key to understanding the impact of anthropogenic activities on SOC so that we can implement management practices that effectively reduce emissions or promote carbon sequestration. Our objective was to assess the effect of land-use change on the dynamics and distribution of SOC in three systems (agriculture, pasture and agroforestry) after 40 years of deforestation in a tropical dry forest in the central–eastern region of Honduras. For this purpose, the bulk density, percentage of coarse fragments (>2 mm) and soil organic carbon content were determined at three depths (0.00–0.10 m, 0.10–0.20 m and 0.20–0.30 m). The results showed an increase in bulk density for all new uses, although soil compaction had not yet occurred. In terms of total soil organic carbon (TOC) stocks, deforestation caused a decrease from 17% to 48% in agricultural and agroforestry soils, respectively; on the other hand, grasslands did not show significant differences compared to tropical dry forest, suggesting that they have a high potential as carbon sinks in deforested tropical areas. However, this did not imply a better state of the system, as the greatest increases in bulk density were found in pastures.

Disproportionate contributions of land cover and changes to ecosystem functions in Kazakhstan and Mongolia

Land use and land cover change (LULCC) have profoundly altered land surface properties and ecosystem functions, including carbon and water production. While mapping these changes from local to global scales has become more achievable due to advancements in earth observations and remote sensing, linking land cover changes to ecosystem functions remains challenging, especially at regional scale. Our study attempts to fill this gap by employing a computationally efficient method and two types of widely used high-resolution satellite images. We first investigated the contribution of landscape composition to ecosystem function by examining how land cover and proportion affected gross primary production (GPP) and evapotranspiration (ET) at six macro-landscapes in Mongolia and Kazakhstan. We hypothesized that both ecosystem and landscape GPP and ET are disproportionate to their composition and, therefore, changes in land cover will have asymmetrical influences on landscape functions. We leveraged a computational-friendly linear downscaling approach to align the coarse spatial resolution of MODIS (500 m) with a fine-grain and localized land cover map developed from Landsat (30 m) for six provinces in countries where intensive LULCC occurred in recent decades. By establishing two metrics—function to composition ratio (F/C) and function to changes in composition change (ΔF/ΔC)—we tested our hypothesis and evaluated the impact of land cover change on ecosystem functions within and among the landscapes. Our results show three major themes. (1) The five land cover types have signature downscaled ET and GPP that appears to vary between the two countries as well as within each country. (2) F/C of ET and GPP of forests is statistically greater than 1 (i.e., over-contributing), whereas F/C of grasslands and croplands is close to or slightly less than 1 (i.e., under-contribution). F/C of barrens is clearly lower than 1 but greater than zero. Specifically, a unit of forest generates 1.085 unit of ET and 1.123 unit of GPP, a unit of grassland generates 0.993 unit of ET and GPP, and a unit of cropland produces 0.987 unit of ET and 0.983 unit of GPP. The divergent F/C values among the land cover classes support the hypothesis that landscape function is disproportionate to its composition. (3) ΔET/ΔC and ΔGPP/ΔC of forests and croplands showed negative values, while grasslands and barrens showed positive values, indicating that converting a unit of forest to other land cover leads to a decrease in ET and GPP, while converting units of grassland or barren to other land cover classes will result in increased ET and GPP. This linear downscaling approach for calculating F/C and ΔF/ΔC is labor-saving and cost-effective for rapid assessment on the impact of land use land cover change on ecosystem functions.

Impact of land cover changes on land surface temperature variations in Adama City, Ethiopia

ABSTRACT Land surface temperature (LST) is becoming a serious environmental issue, since it is an essential controller of city climate. Currently, addressing this challenge in urban planning has gained worldwide importance. The study aims to analyze the effect of land cover dynamics on LST using geographic information system and remote sensing techniques. In this study, Landsat 7 ETM+ (2002) and Landsat 8 TIRS (2022) data were used. The LST was retrieved from Landsat datasets. The correlation analyses were conducted on LST, normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI). The findings suggested that there was a negative correlation between LST and the NDVI, while a positive correlation existed with the NDBI. Moreover, NDBI was found to be a better predictor of LST than NDVI. Additionally, it was revealed that the proportion of dense and sparse vegetation cover was reduced from 24.14 km2 (17.47%) in 2002 to 18.17 km2 (13.15%) in 2022. Similarly, the average LST increased from 28.25 to 31.78°C. This indicated that the rapid growth of urban development was the primary factor behind the rise in LST. Therefore, it was deemed crucial to create a smart urban land use plan to mitigate the impacts of microclimate change.

Monitoring Land Use and Land Cover Changes in the Lower Mara River Basin, using Google Earth Engine

Monitoring Land Use and Land Cover Changes in the Lower Mara River Basin, using Google Earth Engine

Assessing Urbanization Dynamics Through Land Use/Land Cover Change Detection in Zunheboto Sadar, Nagaland, India

Providing a systematic overview of the environment is crucial for identifying environmental changes and understanding human-related factors and feedback in research on land use and land cover (LULC) changes and the incorporation of LULC data facilitates pinpointing particular areas undergoing changes, discerning the character of these changes, and gaining insights into the evolving nature of the land. Utilizing Remote Sensing & Geographic Information System (GIS) and methodologies, this study aims to examine variation LULC patterns between 2014 and 2023 in Zunheboto Sadar, the highest populated region in the Zunheboto district of Nagaland with area covering about 123 Km2 and altitude encompassing from 642 m to 1989 m above mean sea level. Landsat-8 OLI data was employed to produce a map depicting LULC in the study region. The application of Supervised classification method, specifically the Maximum Likelihood Classifier (MLC), were utilised in producing LULC maps. Through this comprehensive analysis, the research aims to identify specific areas undergoing alterations, discern the nature and extent of these changes, and explore the underlying causes and human responses. Eight classes were delineated namely Jhum cultivation, Moderately Dense Forest, Open Forest, Settlements, Shrub, Terrace Cultivation, Very Dense Forest, and Water Bodies. In the year 2023, Jhum cultivation has decreased by 68.36%, Very Dense Forest by 16.96%, Water Bodies by 6.53%, Shrub by 52.36%, and Terrace Cultivation by 32.83%. Conversely, there is an increase in Moderately Dense Forest by 21.39%, Open Forest by 111.25%, and Settlements by 38.41%. The Kappa accuracy assessment technique was chosen for evaluating the accuracy of the categorized map. The categorized map of 2023 exhibits an Overall Classification Accuracy of 86.50%, coupled with a Kappa Coefficient of 0.84, indicating an almost perfect classification. Similarly, for 2014, the Overall Classification Accuracy stands at 78.50%, with a Kappa Coefficient of 0.75, also falling into the Substantial classification category.

Land Use Land Cover Dynamics around a Western Himalayan Wetland using Geospatial Techniques

Despite having multitude of environmental advantages, wetlands are facing serious anthropogenic threats due to changes in the surrounding area. Therefore, the present investigation has been conducted to evaluate the landuse land cover (LULC) around a 6 km buffer strip of Nowgam wetland located in Kashmir valley. The detection of Land Use and Land Cover (LULC) changes was conducted using Landsat imagery within ArcGIS, spanning a 22-year period from 2000 to 2022. The Landsat imageries of three years (2000, 2010 and 2022) were classified using the supervised classification algorithm (maximum likelihood classification) in ArcGIS. Five LULC classes, viz., water, agriculture, settlement, vegetation and bareland were identified in the study region. The exploration of the classified images revealed the area under water decreased by about 56% while as area under agriculture and settlement increased by 44.7% and 30.9%, respectively. The area under vegetation decreased by 7.3% and area under bareland increased by 8.0%. The outcomes of the present discourse reveal that the changes in LULC around the Nowgam wetland are mostly anthropogenic which may pose serious threat to wetland ecosystem in near future. The present study provides a baseline data regarding ecosystem transformations and acts as an important stimulus for all the stakeholders of wetland in planning and implementation of the strategic conservational measures in Nowgam wetland (Bandipora).