Land Use And Land Cover Patterns Research Articles

Change analyses and prediction of land use and land cover changes in Bernam River Basin, Malaysia

Land use and land cover (LULC) change is a dynamic process which is significantly influenced by anthropogenic activities. Analysing historical LULC trends and predicting future dynamics is critical to provide insights for decision-makers and planners aiming for sustainable land management and development. This study focuses on the Bernam River Basin (BRB). It employs an integrated approach that combines the Multi-Layer Perceptron (MLP), the Cellular Automata (CA)-Markov algorithm, remote sensing, and Geographical Information System (GIS) techniques. Using multi-temporal 10m resolution Sentinel-2 Landsat imagery from 2010, 2020, and 2022, the study classified LULC into seven categories: water, forest, wetlands, agriculture, urban, barren, and rangeland areas. Change analysis from 2010 to 2020 was conducted, with 2022 validating predicted LULC transitions. The MLP model, trained on land change driver variables, facilitated the generation of transition potentials for simulating future LULC changes. A spatially explicit CA-Markov model implemented LULC change projections for 2022, 2025, 2050, and 2075, based on the transition potentials. The analysis reveals an annual increase of 0.24% in water, 0.61% in forest, and 2.11% in urban areas, while wetlands (2.69%), agriculture (2.47%), barren (3.51%), and rangeland (4.58%) experienced declines. The CA-Markov approach accurately predicted LULC transitions for 2022, validated through an error matrix with an overall accuracy of 91.56% based on 450 sampling points. Predictions for 2025–2075 indicate rising trends in water (1.76%), wetlands (29.18%), agriculture (60.08%), urban (96.53%), barren (0.59%), and rangeland areas (3.57%). Forests are expected to decrease by 12% (261.52 km2). The study identified agriculture and urban expansion as the primary drivers of LULC changes in the river basin. These findings provide critical information for regional authorities to formulate evidence-based policies and management strategies, ensuring the environmental sustainability of BRB. Furthermore, these predicted LULC patterns can be integrated into complementary models, such as the Soil and Water Assessment Tool, to assess the impacts of LULC changes on water resources.

Knowing the lay of the land: changes to land use and cover and landscape pattern in village tank cascade systems of Sri Lanka

Social-ecological systems (SESs) possess a great diversity of land use and land cover (LULC) types with unique assemblages of biodiversity and ecosystem services. However, LULC changes due to landscape fragmentation are emerging as major threats to the system productivity of SESs around the world. This study examined changes to LULC extent and landscape patterns in the Village Tank Cascade Systems (VTCSs) of Sri Lanka using satellite imagery and GIS techniques between 1994 and 2021. Multispectral Landsat images (5 TM and 8 OLI/TIRS) obtained from Google Earth Engine were classified using machine learning algorithms. Overall accuracies obtained were 85.9% (1994) and 88.6% (2021). The LULC change matrix and spatial pattern metrics were used to examine LULC and landscape pattern change dynamics over the VTCS landscapes. LULC change matrix results revealed that forest, which is the dominant LULC class covering 73.7% of the total land area was reduced by 206,725 ha due to transformation into agricultural (70.43%) and scrub (24.33%) lands between 1994 and 2021. Over this time landscape pattern of the VTCS has gradually changed from forest to agricultural land-dominated landscape, with forest and agricultural land types showing a significant negative correlation (p < 0.001; R2 > 0.868), particularly in the southeastern region. Landscape patterns were analysed based on eight spatial metrics calculated at both the landscape and class levels using FRAGSTATS spatial pattern analysis software. At the landscape level, the structure became more dispersed and complex in shape. Heterogeneity was noted to have gradually increased with weakening connectivity, whereas the fragmentation process had gradually accelerated. At the class level, the dominance of forest patches decreased, fragmentation and isolation increased, and connectivity and shape complexity reduced leading to the loss of fragmented forest habitats. The number of patches within the agricultural class increased and became more aggregated and complex in shape. Landscape performance indicators show that VTCSs have experienced a gradual loss of environmental sustainability. Assessment of LULC along with fragmentation can help to monitor the spatial pattern impacts that determine ecological integrity. Thus, the study provides scientific guidance for ecological restoration in degraded VTCSs to effectively improve ecological productivity.

Remote sensing insights for sustainable development: Water quality and landscape dynamics in Mirik Lake, Darjeeling District, West Bengal, India

The study employs remote sensing and GIS techniques to assess the water quality dynamics of Mirik Lake, located in the Darjeeling Himalayas, West Bengal, India. To analyse the impact of land use and land cover (LULC) changes on the water quality of Mirik Lake from 1993 to 2023. Landsat imagery spanning from 1993 to 2023 was used to detect significant alterations in LULC patterns. Remote sensing and GIS techniques were utilised to analyse the data, focusing on changes in LULC and their implications for water quality. The results indicate a steady increase in total phosphorus (TP), total nitrogen (TN), and Biological Oxygen Demand (BOD) levels, attributed to anthropogenic activities such as urbanisation and tourism development. LULC change analysis highlights the expanding built-up areas and agricultural lands surrounding the lake, contributing to nutrient loading and organic pollution. The spatial distribution of pollution categories underscores the influence of tourist infrastructure on water quality degradation. Integrated watershed management and sustainable development strategies are recommended to mitigate anthropogenic impacts and preserve the ecological integrity of Mirik Lake.

Sentinel-2 Perspective: Land Use and Land Cover Dynamics in East and West Karbi Anglong

This study delves into the dynamics of land use and land cover (LULC) in East and West Karbi Anglong using Sentinel-2 satellite imagery. In response to escalating environmental changes, remote sensing technologies, particularly Sentinel-2, offer crucial insights for sustainable land management. Through an analysis of LULC patterns, the study aims to inform evidence-based decisions for environmental conservation and resource management in the region. The present study highlights the utility of remote sensing in LULC assessments and acknowledges the necessity for customized approaches in regions with distinctive features such as Karbi Anglong. Methodologically, temporally stratified Sentinel-2 datasets were utilized, with meticulous pre-processing and accuracy assessments ensuring reliability. Results indicate notable shifts in LULC classes, including urban expansion and vegetation decline. Despite limitations, such as potential inaccuracies in data processing, the study emphasizes the importance of monitoring environmental changes and advocating for sustainable land management strategies.

Modeling future land use and land cover under different scenarios using patch-generating land use simulation model. A case study of Ndola district

Accurate predictions of changes in Land-use and Land-cover (LULC) are crucial in climate modeling, providing valuable insights into the possible effects of land-use alterations on Earth’s intricate system. This study focuses on forecasting and examining future LULC changes in the Ndola district from 2022 to 2042, considering three scenarios: Traditional mode (TM), Ecological protection (EP), and Economic Development (ED). TM reflects past land use changes, EP prioritizes environmental conservation, and ED emphasizes economic growth and urbanization. Using the patch-generating land use simulation (PLUS) model, we achieved precise predictions of LULC changes in Ndola district. The model, which combines LEAS rule-extraction with a CA model using CARS, addresses limitations of previous models like CLUE-S, CA-Markov, and FLUS by accurately simulating scattered LULC patterns and the mutual attraction and evolution of open space and urban land under different policies. Using LULC data from the livingatlas platform for the base period (2017–2022), the model demonstrated a Kappa coefficient of 78% and a FoM value of 0.34. Key findings indicate significant trends, such as reductions in forest and agricultural lands in the TM and ED scenarios, with rangeland expanding consistently across all scenarios, particularly in the ED scenario. The decline in agricultural and forest lands raises concerns about household food security, habitat fragmentation, biodiversity loss, and diminished ecosystem services. Urban sprawl onto other land uses could further strain urban infrastructure and public services. Future research should incorporate uncertainty analysis methods such as fuzzy logic or Bayesian methodologies to quantify and differentiate uncertainties related to modeling simulations.

Predicting land cover driven ecosystem service value using artificial neural network model

Understanding the synergies and trade-offs of major cities' ecosystem services is vital to mitigating regional ecological and environmental risks and enhancing human well-being in this era of rapid urbanization and global climate change. This study aimed to assess and predict the land use- and land cover (LULC)-driven ecosystem service value (ESV) dynamics in Arkansas’s capital city, Little Rock. Historical LULC data were derived by applying support vector machine learning algorithms to Landsat satellite imagery. The benefit transfer method was utilized to identify nine types of ecosystem services and their corresponding economic values. A cellular automata artificial neural network model was used to simulate future potential LULC and ESV patterns. Vegetation accounted for more than 94% of total ESV over the past two decades. However, a 38.40% expansion of built-up areas resulted in a 45.28% decrease in vegetated areas, which reduced total ESV from $3619.73 × 106 to $2563.81 × 106 during 2003–2023. By 2033, the city’s urban area will expand to 72.75% of the total area and will witness further declines of 30.35 km2 in vegetation, 19.30 km2 in barren soil, and 1.69 km2 in waterbody areas. Consequently, the ESVs of these natural landscapes will decline by $708.58 × 106, $44.87 × 106, and $15.69 × 106, respectively. Provisioning services will be most affected, followed by supporting, regulating, and cultural services. The study findings provide reference information to policymakers and the local government for use in adopting sustainable land management policies, thereby promoting the ecological value of Little Rock.

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.

Novel approach for the LULC change detection using GIS & Google Earth Engine through spatiotemporal analysis to evaluate the urbanization growth of Ahmedabad city

Changes in Land Use and Land Cover (LULC) have a significant impact on both urban and environmental planning, especially in the context of rapidly urbanising areas. The largest city in the state of Gujarat, Ahmedabad, has experienced substantial growth. Utilising the powerful tools of Geographic Information System (GIS) and Google Earth Engine (GEE), this study uses a thorough methodology to track and examine changes in LULC over the last ten years. Modifications in LULC have a significant impact on both urban and environmental planning, especially in light of the fast urbanisation cycle. This study uses a comprehensive approach to monitor and analyze Landsat imagery changes in LULC over the past ten years, leveraging the powerful capabilities of Google Earth Engine and GIS. Important insights into LULC patterns are revealed by analysing Landsat imagery from Ahmedabad from 2013 to 2023. Pre-processing with GIS and GEE, accuracy evaluation, and classification of satellite images are all included in the methodology. With a notable 0.85 Kappa and 89 % accuracy in LULC classification, the resulting LULC classifications cover settlements, vegetation, water bodies, arid land, and other relevant features. Results show that over the previous ten years, Ahmedabad has seen a noteworthy decrease in agricultural lands of 13.74 % and a reduction in barren areas of 4.78 %. Meanwhile, the total LULC patterns have shown a significant expansion of 23.56 %. This study is significant because it highlights how urbanisation reduces vegetation cover. The results will helpful for urban planning strategies that take changing environmental dynamics into account.

Monitoring and predicting land use/land cover dynamics in Djelfa city, Algeria, using Google Earth Engine and a Multi Layer Perceptron Markov Chain model

Understanding the historical and projected changes in land use and land cover (LULC) in Djelfa city is crucial for sustainable land management, considering both natural and human influences. This study employs Landsat images from the Google Earth Engine and the support vector machine (SVM) technique for LULC classification in 1990, 2005, and 2020, achieving over 90% accuracy and kappa coefficients above 88%. The Land Change Modeler (LCM) was used for detecting changes and predicting future LULC patterns, with Markov Chain (MC) and Multi Layer Perceptron (MLP) techniques applied for 2035 projections, showing an average accuracy of 83.96%. Key findings indicate a substantial urban expansion in Djelfa city, from 924.09 hectares in 1990 to 2742.30 hectares in 2020, with a projected increase leading to 1.6% of nonurban areas transitioning to urban by 2035. There has been significant growth in steppe areas, while forested, agricultural, and barren lands have seen annual declines. Projections suggest continued degradation of bare land and a slight reduction in steppe areas by 2035. These insights underscore the need for reinforced policies and measures to enhance land management practices within the region to cater to its evolving landscape and promote sustainable development.

Spatial Green Space Assessment in Suburbia: Implications for Urban Development

Nonthaburi, a suburban province adjacent to the Bangkok Metropolis, has experienced a reduction in green spaces due to urban expansion. This study quantified Nonthaburi’s green space through visual interpretation of land use and land cover (LULC) using THEOS and Sentinel-2. Areas of green space were extracted using remote sensing indices and pixel-based classification based on THEOS. The extracted green area was then integrated with the existing LULC patterns to align with the green space characteristic established by Thailand’s Office of Natural Resource and Environmental Policy and Planning. This includes public services, functional utility, median strips, community economics, fallow, and natural green space. The analysis of green space management and planning utilized the Urban Green Space Index (UGSI), Per Capita Green Space (PCGS), and accessibility to public green space. The results revealed that Nonthaburi comprises a green space area of 465.29 km2 or 73.06%, exhibiting a higher prevalence within its western region while displaying a relatively lower extent in the urban zone adjacent to the Bangkok Metropolis. The per capita green space is 367.71 m2 but decreases to 255.82 m2 when accounting for the latent population, meaning it still meets the World Health Organization (WHO) criteria. Currently, only six parks (single and clusters) meet the criteria for public green space. Additionally, both fallow and median strip green spaces (at road interchanges) need to be considered for their potential use in new public service. Furthermore, very high-resolution imagery from unmanned aerial vehicles (UAVs) should be used for green space planning by the organization.

Impact of land use changes on the land surface thermal environment in Nanchang, Jiangxi province, China

Throughout human history, human activities have resulted in land use and land cover changes (LUCC) and can have a direct impact on the land surface thermal environment (LSTE). In the existing studies, the holistic nature of changes in land use and land cover (LULC) has been neglected in favor of focusing on the interactions between different LULC types and the land surface thermal environment. This study used ArcGIS Pro 3.0, ENVI 5.3, and SPSS software to construct a contribution index model and stepwise regression equation to investigate the relationship between LULC type, structure, and pattern changes and LSTE changes in Nanchang City, Jiangxi Province, China, from 1990 to 2020. The findings revealed that 1) rapid urbanization has led to a surge in the area of built-up land and a decrease in the area of arable land in Nanchang; between 1990 and 2020, the area of built-up land in Nanchang increased by 433.29 km2, while the area of arable land decreased by 291.99 km2; 2) The land surface temperature (LST) was divided into five classes according to the equal spacing method, and the areas with the highest and lowest temperature classes were the high temperature zone and the low temperature zone, respectively. Over the past 30 years, the LSTE in Nanchang has gradually deteriorated, with the area of the low temperature zone shrinking by 554.2 km2 and the area of other classes appearing to increase significantly; 3) the contribution index and stepwise regression equation demonstrate that the primary reasons for the worsening of the LSTE are an increase in the scale of cultivated land and construction land. It was found that rationalization of urban LULC type, structure, and pattern can effectively reduce land surface temperature.

Contribution of multi-objective land use optimization to carbon neutrality: A case study of Northwest China

Land use and land cover (LULC) patterns fundamentally influence the carbon cycle of terrestrial ecosystems, and rational optimization of LULC patterns can contribute to achieving carbon neutrality goals. This study constructs a multi-objective LULC optimization coupled model by integrating the objective of carbon neutrality into the LULC optimization process, with Northwest China serving as the study case. This model integrated the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model with the Intergovernmental Panel on Climate Change (IPCC) inventory methodology to assess the spatiotemporal variations in carbon storage and emissions in Northwest China between 2000 and 2020, which aimed to elucidate the mechanisms through which changes in LULC affect carbon neutrality. Additionally, through the amalgamation of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the Patch-Generating Land Use Simulation (PLUS) model, optimized the quantitative structure and spatial layout of four LULC scenarios for Northwest China in 2060: natural development (ND), low carbon emissions (CE), high carbon storage (CS), and carbon neutrality (CN), projected the evolving pathways of carbon storage and emissions within these scenarios while examining the values of different LULC scenarios and their respective contributions to carbon neutrality. The study's findings underscore that the substantial expansion of built-up land development areas and farmland from 2000 to 2020 has resulted in notable carbon storage and emissions increases. Moreover, changes in carbon emissions and carbon storage show significant spatial consistency with changes in LULC patterns. In contrast to the ND scenario, the LULC patterns within the CE, CS, and CN scenarios exhibit higher LULC values and contribute more substantially to carbon neutrality. Remarkably, among these scenarios, the CN scenario stands out for achieving the maximum contribution to carbon neutrality and LULC value, thereby establishing it as the preeminent LULC pattern for the Northwest China region. As the study concludes, it recommended that future developmental strategies in Northwest China prioritize curtailing the expansion of built-up areas while bolstering the conservation of vital ecological zones such as forests and grasslands. This study addresses the inherent tension between regional economic progress and environmental preservation, offering a foundation of scientific insights for effectively managing LULC patterns and attaining carbon neutrality goals.

GIS based method for mapping actual LULC by combining seasonal LULCs

One of the most significant applications of remote sensing data is to prepare land use and land cover (LULC) maps. LULC maps are always affected by seasonality and a single LULC map of a particular month is prepared to represent a year in most of the research, especially in change detection research. This does not represent the real view of the landscape because the seasonal variation of different LULC types is always overlooked. Considering the issue, the current method aims to solve the problem by incorporating seasonal LULC using the raster overlay method to remove the seasonality effect on LULC classification. To apply this method, a minimum of two seasonal LULC maps is required for a single study year. The map needs to overlay and then reclassify according to the stable and rotational LULC pattern of the study area. This method will replicate the actual LULC pattern of a study area from satellite images. Summary of the method is as follows:•LULC of each season was classified using image classification technique.•LULC of each seasons are coded and combined using overlay technique.•Combined map is reclassified to prepare the actual LULC map.

Assessment of Land Use Land Cover Changes and Future Predictions Using CA-ANN Simulation for Gazipur City Corporation, Bangladesh

Anthropogenic activities have a significant influence on land use and land cover (LULC) changes, especially in rapidly growing areas. Among several models, the combination of a cellular automata–artificial neural network (CA-ANN) model is being widely used for assessing future LULC changes using satellite images. This study aimed to investigate LULC changes in Gazipur City Corporation (GCC), Bangladesh, and the changes in LULC patterns over the last two decades (2002 to 2022). In this study, the maximum likelihood supervised classification technique was used for processing the available satellite images. The results show that the urban area and vegetation coverage increased by 150% and 22.78%, whereas the bare land and waterbody decreased by 7.02% and 78.9%, respectively, from 2002 to 2022 inside the GCC area. For future LULC predictions, the CA-ANN model was developed, the accuracy percentage of which was 86.49%, and the kappa value was 0.83. The future LULC prediction model results show that the urban area will increase by 47.61%, whereas the bare land and waterbody are supposed to decrease by 24.17% and 67.23%, respectively, by 2042. The findings of this study could be useful for future sustainable urban planning and management, as well as enabling decision making by authorities for improvements in environmental and ecological conditions in the study area.

Land use changes impact on selected chemical denudation element and components of water cycle in small mountain catchment using SWAT model

Land use changes are extremely important in the context of land and water management. This study examines impact of land use and land cover (LULC) changes on nitrate nitrogen, outflow and surface runoff in the agriculture catchment in the Polish Carpathians using the Soil and Water Assessment Tool (SWAT). The study covers 25-year (1995–2019) and was divided into three periods relating to different LULC patterns (1995–2004, 2005–2012, 2013–2019). The LULC maps used were based on aerial photos from 1997, 2009, and 2016. The SWAT was calibrated and applied to periods with variable LULC (in simulation 1) and by keeping the LULC constant during the period 1995–2019 (simulation 2; the LULC from 1997) to determine the impact of LULC changes on the analysed variables. The ongoing LULC changes were found to have resulted in a reduction of cultivated land area (by 72 %) and an increase of grassland (by 45 %), forest (by 2 %) and urbanised areas (by 17 %). The SWAT model achieved reasonable accuracy. The model results tend to overestimate the load of nitrate nitrogen and underestimate outflow and surface runoff. The LULC changes resulted in a decrease in outflow between 1995 and 2004 of 24 % in comparison to 2013–2019, and a decrease in surface runoff of 12 %. At the same time, there was a significant decrease in nitrate nitrogen load of 68 % (from 13.0 kg ha−1 in 1995–2004 to 4.2 kg ha−1 in 2013–2019). If the LULC of 2013–2019 was restored to 1997 conditions (as in simulation 2), the outflow and load of NO3-N would be higher by 16 % (12 % in simulation 1) and 67 % (55 % in simulation 1) in relation to the observed values. The impact of LULC on the analysed parameters was spatially differentiated between subbasins depending on the scale of the land use changes. The research conducted here indicates that planning and managing the effects of LULC changes is of key importance for sustainable development in terms of the quantity and quality of water.

Spatiotemporal modeling of the value of carbon sequestration under changing land use/land cover using InVEST model: a case study of Nour-rud Watershed, Northern Iran

The current paper aims to assess the effects of landscape change in a mountain river basin in the north of Iran through quantifying, mapping, and assessing carbon storage. The analyses were performed based on previous alterations in land use and land cover (LULC) (1988–2018) and on expected changes determined by three LULC alteration setups for 2048. The Landsat imagery from 2018, 2008, 1998, and 1988 was used for evaluating and predicting the spatiotemporal distributions of LULC changes. The future LULC image prediction has been generated using Land Change Modeler (LCM) module of TerrSet software for the years 2028, 2038, and 2048. Validation was carried out by overlaying the actual and projected to 2018 map. We integrated the Markov Chain (MC) and InVEST Carbon Storage and Sequestration (InVEST-CSS) models for simulating the ecosystem carbon storage and the long-term monetary valuation. In this process, we considered social costs/economic value because of the area’s loss and gain of stored carbon. The results show that forests and rangelands with good and poor conditions decreased by 631.2, 10,374, and 10,254 ha, respectively, from 1988 to 2018. Overall, modeling and mapping LULC changes showed a descending trend in forests (0.66%), agriculture (0.1%), and rangelands (4.1%) in 2048. In addition, carbon storage has already been lost by 9.9 million tons (76.98 ha−1) from 1988 to 8.8 million tons (68.86 ha−1) in 2018 and is expected to have an 8.4 million tons (65.25 ha−1) loss by 2048. Monitoring the economic value of carbon storage from 1988 to 2018 shows a loss of $US 15684338 (121.8 ha−1) and estimates a loss of $US6972622 (54.18 ha−1) by 2048. Therefore, spatiotemporal design of InVEST model by estimating the carbon value over time focuses on continuous monitoring actions for both the carbon pools dynamic and LULC pattern. This consideration causes reduction the uncertainty of estimated models and also increases the continuous cost of those changes. This will help government and decision makers for long-term and accurate carbon sequestration strategies for ecosystem.

Monitoring Land Use & Land Cover Change in an Urban Landscape: A Case Study of Siliguri, India

Information related to Land use and Land Cover (LULC) change is important for conservation of natural resources and sustainable development of an area. Siliguri is one of the populated metropolitan of West Bengal, India that has witnessed rapid urbanization. The major challenge is to identify the reason and pattern of urbanization due to inadequate and insufficient data in small cities like Siliguri. This paper has mainly assessed the LU & LC change of Siliguri municipality for the period of 1991-2001, 2001-2011 and 2011-2021. The main aim of the study is to determine the pattern and direction of future expansion. Satellite images from Landsat 4-5(1991-2011) and Landsat 8(2021) were used to extract LU & LC map of the municipality. Supervised classification using maximum likelihood classification was used to developed LULC maps. The supervised classification produced good results with overall accuracy of more than 80%. The change of the LULC pattern was obtained using change detection method. Four major classes viz. Vegetation, water body, sand and built-up have been identified. The analysis shows that maximum transformation has happened in the buildup environment from other land surface features.

Temporal changes in Land Use and Land Cover (LULC) and local climate in the Krueng Peusangan Watershed (KPW) area, Aceh, Indonesia

Watersheds are important sources of various ecosystem services. The Krueng Peusangan Watershed (KPW), one of the watersheds in Aceh Province, is expected to become the "lungs" of the ecology of the north-central part of Aceh. Currently, the KPW is one of the watersheds that suffers from severe or critical damage, especially that caused by changes in forest cover. Spatiotemporal monitoring of changes in landscape patterns (composition and configuration) is needed to inform policy and support planning for sustainable watershed management. This study aims to: 1) determine the pattern of changes in LULC in KPW in two decades (1999–2009 and 2009–2019, and 2) determine the impact of changes in the LULC pattern on the local climate. Landsat Satellite Imagery for three years (1999, 2009, and 2019) is used to classify LULC. Geographic Information System (GIS) technology and remote sensing are used to analyze it. Each satellite image is classified into six categories: built-up area, forest, agriculture, bare land, wetland, and water body. This classification resulted in LULC maps for 1999, 2009, and 2019 with kappa coefficients of 0.84, 0.88, and 0.84. It is found that between 1999–2009 and 2009–2019, there has been a consistent decrease in forest cover area and an increase in built-up area. Local climate change is also occurring in this KPW. Continuous monitoring of LULC changes in KPW is also necessary to keep management planning up to date.

Climate Change Impacts on LULC in the Jarmet Wetland and its Surrounding Areas in Western Ethiopia

The concerns over land use and land cover (LULC) change have emerged on the global stage due to realization that changes occurring on the land surface also influence climate, ecosystem and its services. This study aimed to map the temporal dynamic of LULC patterns and LST in the Jarmet wetland in Ethiopia. The dynamics and pattern of changes for a period of 21 years (2000-2021) were analyzed using geospatial techniques. Multi-temporal satellite images from Landsat ETM+ and Landsat-8 OLI sensor data were used to extract land-cover maps. The Land Surface Temperature (LST) trend of the study areas was computed using MODIS satellite imagery (2000-2021). Supervised classification using a Maximum Likelihood Classifier (MLC) was applied to prepare LULC maps of the watershed. The accuracy of the classified map was assessed using high-resolution data, and ground realities have been verified and ascertained through field observations. The results revealed a decreased trend in wetland, forest, shrubland and grassland in the period of 21 years (2000-2021) by -1148.71ha, -1073.26 ha, -1480.1 ha, and -87.73 ha, respectively. On the other hand, farmland and plantation areas followed an increasing trend. LST revealed decreasing trend in terms of mean and minimum with a fraction change of -0.018 and -0.073, whereas the maximum LST value shows an increasing trend with 0.021. The overall accuracy was 84.41%, with Kappa index of 76.13%. The analysis and findings of the study highlight important policy implications for sustainable LULC management in the study area. The study suggests the design and implementation of a guided natural resource policy, stopping the illegal expansion of farmland and educating society about the value of the sustainable management of habitat reserves.

Land Use/ Cover Change Detection in High-Altitude Mountain Landscapes: A Case of Pangi Valley, Western Himalaya (India).

Recently, mountains are going to widely threatened by developmental activities and global climate change leading to changing shift of land uses/covers across the globe. The present study area has witnessed very significant changes in land use and land cover (LULC) especially after the implementation of Mahatma Gandhi National Rural Employment Guarantee Scheme (MNREGS) in the form of rapid developmental and constructional activities. As a result, rapid local roads and infrastructure construction has lead to increased hazards further putting pressure on the fragile physical landscape. Therefore, the present study is an attempt to evaluate changes and transformation in the LULC patterns in the Pangi valley, Western Himalaya (India). The LULC has been analyzed using remote sensing imagery from LANDSAT for the period 1992-2021. We conduct a supervised classification by using Maximum Likelihood Classifier (MLC) to prepare LULC maps. In short, seven LULC classes have been delineated. The result shows that barren land, built-up land, water bodies and agriculture land have increased by 97.5 per cent, 98.5 per cent, 14.4 per cent and 13.2 per cent, while snow cover pasture land and forest cover have drastically decreased by 68.1 per cent, 21.1 per cent and 20.9 per cent respectively. The LULC transformation result illustrates that the area under snow cover, pasture land and forest cover converted into barren land by 217.8 km2, 175.3 km2 and 47.3 km2 respectively out of the total 1520 km2 area of the Pangi valley. It reflects human influence in terms of developmental activities and impact of global climate change in the transformation of the physical landscape in the study area. The outcome derived from the current research will be helpful for policy makers and spatial planners for sustainable landscape planning and management of the high-altitude mountain landscapes.