Land Use Cover Classification Research Articles

Analyzing the Spatio-temporal Changes in the Hasdeo Subbasin in Chhattisgarh Using Sentinel-2 Imagery

Understanding the dynamics of the world's landscapes is made possible by the multi-temporal change detection analysis made possible by Remote Sensing (RS) and GIS technologies. The current study provides an illustration of the spatiotemporal changes in land use land cover (LULC) of the Hasdeo subwatershed, Chhattisgarh, India, between 2016 and 2019 using Sentinel-2 imagery. To quantify the changes in the LULC area from 2016 to 2019, Sentinel-2 images from 2016 and 2019 were obtained. With the ERDAS Imagine software, the supervised classification method has been used. Eight different LULC classes, including forest, cultivated land, fallow land, water body, barren land, built-up land, mining and sand, were created using the image data of the research area. The outcome shows that between 2016 and 2019, there was an increase in built-up area and water body, while there was a decline in cultivated land, forest, and barren land. Kappa Coefficient accuracy is 88.00% and overall accuracy is 92.1% according to the LULC accuracy assessment for the data analysis. The paper's data base emphasises the significance of change detection approaches for spotting significant changes in land cover and for tracking the effects of human-induced change on the hydrological environment, behaviour, and setting of the Hasdeo sub watershed.

Open Habitats under Threat in Mountainous, Mediterranean Landscapes: Land Abandonment Consequences in the Vegetation Cover of the Thessalian Part of Mt Agrafa (Central Greece)

Land abandonment is one of the main drivers of land use/land cover (LULC) change across Europe, which has already led to a significant loss of open habitats, threatening species hosted in them. We investigated LULC changes for a period of 70 years in a mountainous area of central Greece (Mt Agrafa) by mapping its land cover for the years 1945, 1996 and 2015, calculating transition matrices of land cover classes and performing intensity analysis at different levels. Subareas of the study area, with different population trends, were compared in regard to their LULC change trends. Possible drivers of LULC changes were explored by means of Random Forest modeling, and landscape metrics were calculated to assess their trends. Our results showed great changes in LULC class cover, significant shrinkage of open habitats, accelerated rates of change in the recent period and no differences in LULC change patterns in relation to different population trends. Variables expressing favorability of ecological conditions for forest establishment or probability of farmland abandonment were found as more important drivers of the spatiotemporal distribution of LULC classes, while landscape metrics revealed certain trends. Our main conclusion is that land abandonment and the subsequent vegetation succession are going through a semifinal stage, before their completeness and the almost absolute dominance of the forest, and repopulation of the countryside cannot unconditionally ensure any halting effect on the land abandonment process.

GIS-based Valuation of Ecosystem Services in the Lower Delta Plain of West Bengal with Special Reference to Indian Sundarbans

The Indian Sundarbans partly hosts the world’s largest mangrove forest and support the livelihood of almost five million people. The Holocene delta of Indian Sundarbans was formed by the river Ganges. The decay of the distributaries of the river Ganges in deltaic West Bengal began with the eastward avulsion of the river Ganges. The rivers of Sundarbans now stand disconnected from their sources and therefore do not get a significant amount of freshwater from upstreams. The people of Sundarbans depend on the services of the Sundarbans ecosystem which is directly linked to the upstream freshwater flow, rainfall and groundwater. The scarcity of freshwater during dry seasons, high salinity of surface water and soil, siltation and drainage congestion are major constraints on livelihood security in this area. The biodiversity of Sundarbans is also threatened by water stress. To ensure the sustainable conservation of the rich biodiversity and well-being of the people of Sundarbans, improving ecosystem services in the region is extremely essential. From this perspective, the present study aims to identify major flow-dependent ecosystem services in the region and estimate the value of those ecosystem services using geoinformatics. The land use land cover (LULC) maps have been prepared for the years 1990, 2000, 2010 and 2020 from Landsat images for the valuation of major ecosystem services. Accuracy assessment has been performed using high-resolution satellite images and field data. Change detection analysis has been done to understand the changing land use pattern in the study area. The LULC-based valuation of the ecosystem services has been carried out for the years 1990, 2000, 2010 and 2020. The area under each LULC class has been multiplied by the global Ecosystem Service Value (ESV) of that class to calculate the total ESV of the specific LULC class. The study reveals that the ESVs are the highest for freshwater bodies including rivers, and estuaries followed by forests. With the decreasing upstream freshwater flow and deteriorating mangrove health, the services of flow-dependent ecosystems have changed drastically.

Application of Multi-Temporal Landsat Images to Analyze the Relationship Between the Land Surface Temperature (LST) and the Land Use Land Cover (LULC) in Ho Chi Minh City

Urban sprawl has changed the land use land cover (LULC) in Ho Chi Minh City (HCMC). It has caused the gradual decline of the natural vegetation and rapid increase of the impervious surface, which leads to the heat storage capacity of the urban surface being on the rise. There is a land surface temperature (LST) difference between the urban center and surrounding areas and it creates the urban heat island phenomenon (UHI). This study used Landsat 4, 5, and Landsat 8 satellite image data from 1990 to 2020 with the GIS algorithms on ENVI and ArcGIS to analyze and estimate the correlation between the LULC and the LST in HCMC. The Kappa coefficients of the LULC classifications were all over 0.8 in 1990 – 2020 period. The total area of impervious surface has increased by 57,338 hectares over the past 30 years; while the total area of natural green has decreased by 19,116 hectares. The LST has remarkably increased in the areas of construction lands, industrial zones, and impervious surfaces, with an average increase from 22°C to 27°C. While the LST in the others such as the vegetation surface and water surface were slower, from 20°C to 23°C. The solution of enhancing the urban green space was suggested to mitigate the increasing LST impact on HCMC.

Application of Free Satellite Imagery to Map Ecosystem Services in Ungwana Bay, Kenya

A major obstacle to mapping Ecosystem Services (ES) and the application of the ES concept has been the inadequacy of data at the landscape level necessary for their quantification. This study takes advantage of free satellite imagery to map and provide relevant information regarding ES and contribute to the sustainable management of natural resources in developing countries. The aim is to assess the flow of ES in mangrove ecosystem of Ungwana Bay, located on the northern coast of Kenya, by adopting the Land Use Land Cover (LULC) matrix approach. This study characterized LULC classes present in the study area, identified the most important ES, and collected data on expert opinions via a survey on ES flow supplied by the mangrove ecosystem. A qualitative and quantitative analysis of the expert scoring produced a LULC matrix which, when integrated with the LULC maps, showed the spatial distribution of ES flow. The assessment indicates very high flow (5.0) for the regulating and supporting services, high flow (4.0) for the cultural services, and medium flow (3.0) for the provisioning services as supplied by mangroves. In addition, the analysis indicates there are sixteen major ES supplied by the mangrove ecosystem of Ungwana bay as of the year 2021. This study highlights the importance of mangroves as a coastal ecosystem and how the visualization of the spatial distribution of ES flow using maps can be useful in informing natural resource management. In addition, the study shows the possibilities of using freely accessible satellite imagery and software to bolster the ES assessment studies lacking in developing countries.

Land use and land cover classification with hyperspectral data: A comprehensive review of methods, challenges and future directions

Recently, many efforts have been concentrated on land use land cover (LULC) classification due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and climate change. However, various aspects have attracted hyperspectral imaging due to there being informative discriminative features, such as spectral-spatial features. To this end, this paper is a comprehensive and systematic review of LULC classification using hyperspectral images by reviewing four significant research investigations. Moreover, the four investigations have addressed the following points: (1) the main components of the hyperspectral imaging, the modes of hyperspectral imaging with data acquisition methods, and the intrinsic differences between hyperspectral image and multispectral image, (2) the role of machine learning in LULC classification, and the standard deep learning methods: Convolution Neural Network (CNN), Stacked Autoencoder (SAE), Deep Belief Network (DBN), Recurrent Neural Network (RNN), and Generative Adversarial Network (GAN), (3) the standard benchmark hyperspectral datasets and the evaluation criteria, (4) the main challenges of LULC classification with the possible solutions for the limited training samples issue, the promising future directions, and finally the recent applications for LULC classification.

Urban expansion impacts on agricultural land and thermal environment in Larkana, Pakistan

Urban area expansion and the increase in the built-up area are major threats to agriculture, the natural environment, and ecology throughout the world. The population of the city of Larkana is rapidly increasing due to natural growth and uncontrolled migration from the surrounding areas, which have resulted in a haphazard increase in the built-up area over the fertile agricultural land and affected the city’s thermal environment. This research aims to evaluate the impacts of urban area expansion on agricultural land and the thermal environment of the study area by assessing the change in land use/ land cover (LULC) from 1990 to 2020 and land surface temperature (LST). For this purpose, the Random Forest (RF) algorithm was applied, and LST was calculated in Google Earth Engine (GEE) using Landsat imageries for the years 1990, 2000, 2010, and 2020. For the RF classifier algorithm, 3500 reference random points were generated for each year, which were then randomly divided into two datasets, i.e., a training sample consisting of 70% of the points and a validation dataset (30% of the points). After LULC classification, the results were validated for 1990, 2000, 2010, and 2020, and the accuracy was 88.3%, 89%, 90.01%, and 90.8%, respectively. The study results showed that the built-up area increased from 12.31 to 43.83 km2while the barren land in the study region decreased from 56.51 km2to 11.62 km2. The agricultural land was 66.66 km2in 1990, expanded to 101.38 km2, and then decreased to 79.49 km2in 2020. The results also revealed that most of the urban expansion in the last decade (2010-2020) took place on agricultural land. The urban thermal environment also showed a gradual increase in surface temperatur,e as recorded by the LST results. The LST results revealed that the maximum LST was 33.4°C in 1990, which increased to 36.1°C in 2020, and similarly, the minimum LST was 25.1°C, which also increased to 26.6°C in 2020. This study provides useful results for stakeholders to devise better policies and plans to control further haphazard urban expansion on fertile agricultural land in the study area.

Evaluating resampled and fused Sentinel-2 data and machine-learning algorithms for mangrove mapping in the northern coast of Qeshm island, Iran

Mangrove forests, as an essential component of the coastal zones in tropical and subtropical areas, provide a wide range of goods and ecosystem services that play a vital role in ecology. Mangroves are globally threatened, disappearing, and degraded. Consequently, knowledge on mangroves distribution and change is important for effective conservation and making protection policies. Developing remote sensing data and classification methods have proven to be suitable tools for mapping mangrove forests over a regional scale. Here, we scrutinized and compared the performance of pixel-based and object-based methods under Support Vector Machine (SVM) and Random Forest (RF) algorithms in mapping a mangrove ecosystem into four main classes (Mangrove tree, mudflat, water, and sand spit) using resampled and fused Sentinel-2 images. Additionally, landscape metrics were used to identify the differences between spatial patterns obtained from different classification methods. Results showed that pixel-based classifications were influenced heavily by the effect of salt and pepper noise, whereas in object-based classifications, boundaries of land use land cover (LULC) polygons were smoother and visually more appealing. Object-based classifications, with an excellent level of kappa, distinguished mudflat and sand spit from each other and from mangrove better than the pixel-based classifications which obtained a fair-to-good level of kappa. RF and SVM performed differently under comparable circumstances. The results of landscape metrics comparison presented that the classification methods can be affected on quantifying area and size metrics. Although the results supported the idea that fused Sentinel images may provide better results in mangrove LULC classification, further research needs to develop and evaluate various image fusion approaches to make use of all Sentinel’s fine resolution images. Our results on the mapping of mangrove ecosystems can contribute to the improvement of management and conservation strategies for these ecosystems being impacted by human activities.

Differentiating Fire Regimes and Their Biophysical Drivers in Central Portugal

We characterize fire regimes in central Portugal and investigate the degree to which the differences between regimes are influenced by a set of biophysical drivers. Using civil parishes as units of analysis, we employ three complementary parameters to describe the fire regime over a reference period of 44 years (1975–2018), namely cumulative percentage of parish area burned, Gini concentration index of burned area over time, and area-weighted total number of wildfires. Cluster analysis is used to aggregate parishes into groups with similar fire regimes based on these parameters. A classification tree model is then used to assess the capacity of a set of potential biophysical drivers to discriminate between the different parish groups. The results allowed us to distinguish four types of fire regime and show that these can be significantly differentiated using the biophysical drivers, of which land use/land cover (LULC), slope, and spring rainfall are the most important. Among LULC classes, shrubland and herbaceous vegetation play the foremost role, followed by agriculture. Our results highlight the importance of vegetation type, availability, and rate of regeneration, as well as that of topography, in influencing fire regimes in the study area, while suggesting that these regimes should be subject to specific wildfire prevention and mitigation policies.

A method of estimating imperviousness for the catchment modelling of urban environments

Abstract Urban impervious surfaces, a symbol of urbanisation, have permanently changed urban hydrology behaviour and play a critical role in modelling rainfall-runoff process. The distribution pattern of impervious surfaces is intrinsically connected with functional land zoning schemes. However, estimating impervious fractions for catchment modelling is becoming increasingly difficult due to intricate land zoning categories and heterogeneous land use land cover (LULC) during urbanisation. This study demonstrates an integrated approach of deep learning (DL) and grid sampling method to overcome the challenges of LULC classification, sample standardisation and statistical sample extraction. The classified impervious features were extracted within the land zoning scope and translated into polynomial functions using a probability-fitting approach to measure the occurrence likelihood distribution of samples' impervious fraction. Then, we use the information entropy (IE) to evaluate prediction stability by quantifying the condition entropy and information gain (IG) from each functional land zones to the occurrence likelihood of different impervious fraction intervals. The DL model shows robust LULC prediction, while probability-fitting study of impervious samples reflects the distribution differential of impervious fractions under the land zoning categories. The IE stability test shows a robust approach that clarifies different confident ranges of imperviousness estimation based on land zoning information.

Impacts of irrigation tank restoration on water bodies and croplands in Telangana State of India using Landsat time series data and machine learning algorithms

In 2014, the State of Telangana in southern India began repairing and restoring more than 46,000 irrigation water tanks (artificial reservoirs) under the Mission Kakatiya project with an investment in excess of USD 2 billion. In this study, we attempted to map the temporal changes that have occurred in cropland areas and water bodies as a result of the project, using remote sensing imagery and applying land use/land cover (LULC) mapping algorithms. We used 16-day time series data from Landsat 8 to study the spatial distribution of changes in water bodies and cropland areas over the 2013–18 period. Ground survey information was used to assess the pixel-based accuracy of the Landsat-derived data. The areas served by these tanks were identified on the basis of training data and Random Forest algorithms using Google Earth Engine. Our spatial analysis revealed a substantial increase in cropped area under irrigation and expansion of water bodies over the study period. We observed a 20% increase in total tank area in 2017–18 and total cropland and irrigated area expansion of the order of 0.6 M ha and 0.2 M ha, respectively. A comparison of ground survey data and four LULC classes derived from Landsat temporal imagery showed an overall accuracy of 87%, significantly correlated with national agriculture statistics. Periodic monitoring based on remote sensing has proved to be an effective method of capturing LULC changes resulting from the Mission Kakatiya interventions. Higher-resolution satellite data can further improve the accuracy of estimates.

Continuous Monitoring of the Mining Activities, Restoration Vegetation Status and Solar Farm Growth in Coal Mine Region Using Remote Sensing Data

Abstract Land reclamation of previously mined regions has been incorporated in the mining process as awareness of environmental protection has grown. In this study, we used the open-pit coal mine Oslomej in R. N. Macedonia to demonstrate the activities related to the monitoring process of the study area. We combined the Google Earth Engine (GEE) computing platform with the Landsat time-series data, Normalized Difference Vegetation Index (NDVI), Random Forest (RF) algorithm, and the LandTrendr algorithm to monitor the mining impacts, land reclamation, and the solar farm growth of the coalfield region between 1984 and 2021. The data from the sequential Landsat archive that was used to construct the spatiotemporal variability of the NDVI over the Oslomej mine site (1984-2021) and the pixel-based trajectories from the LandTrendr algorithm were used to achieve accurate measurements and analysis of vegetation disturbances. The different land use/land cover (LULC) classes herbaceous, water, mine, bare land, and solar farm in the Oslomej coalfield area were identified, and the effects of LULC changes on the mining environment were discussed. The RF classification algorithm was capable of separating these LULC classes with accuracies exceeding 90 %. We also validated our results using random sample points, field knowledge, imagery, and Google Earth. Our methodology, which is based on GEE, effectively captured information on mining, reclamation, and solar farm change, providing annual data (maps and change attributes) that can help local planners, policymakers, and environmentalists to better understand environmental influences connected to the ongoing conversion of the mining areas.

High Heterogeneity LULC Classification in Ujung Kulon National Park, Indonesia: A Study Testing 11 Indices, Random Forest, Sentinel-2 MSI, and GEE-based Cloud Computing

The Ujung Kulon National Park (UKNT) is one of the national parks on the island of Java and has an essential role in saving endemic species in Indonesia. As a form of national park conservation effort, the completeness of LULC spatial data is a primary database that is indispensable in determining national park management policies. Therefore, this research was conducted to map the LULC (Land Use - Land Cover) in the forest landscape with high heterogeneity in UKNT. Sentinel-2 MSI (Multispectral Instrument) image data were classified using the Random Forest (RF) classification algorithm and tested using 11 index algorithms. The classification process takes place on a cloud computing-based geospatial platform, Google Earth Engine (GEE). This test resulted in 10 LULC classes; water had the broadest percentage of 45.44%. Meanwhile, the primary forest has an area of 21,868.41 or about 19.53% of the total area of the national park. However, there are some discrepancies in the spatial information generated by this classification process, so it is considered necessary to evaluate the combination of indexes, training data, and classification algorithms to limit the classification area. Therefore, this study is expected to be considered for further research related to LULC in high-heterogeneity landscapes.

Assessing the link between land use/land cover changes and land surface temperature in Himalayan urban center, Srinagar.

The rapidly growing urbanization and the consequent land use/land cover (LULC) changes have resulted in unsustainable growth of cities in Indian subcontinent especially in Himalayan region which are highly sensitivity to condition like climate change. Using multi-temporal and multi-spectral satellite datasets, this study analyzes the impact of LULC changes on land surface temperature (LST) in the Himalayan city of Srinagar from 1992 to 2020. For LULC classification, the maximum likelihood classifier technique was utilized, and to extract LST from Landsat 5 (TM) and Landsat 8 (TM) (OLI), spectral radiance was employed. The results show that, among various LULC classes, built-up area has seen a maximum increase of 14% while agriculture has decreased by about 21%. On the whole, Srinagar city has witnessed an increase in LST by 4.5°C with maximum increase of 5.35°C especially over marshes and a minimum increase of 4°C on agriculture landscape. Other LULC categories of built-up, water bodies, and plantation saw an increase in LST by 4.19°C, 4.47°C, and 5.07°C, respectively. The transformation of marshes into built-up saw a maximum increase in LST by 7.18°C followed by water body to built-up (6.96°C) and water body to agriculture (6.18°C) while minimum increase was seen in the conversion of agriculture to marshes by about 2.42°C followed by agriculture to plantation (3.84°C) and plantation to marshes (3.86°C). The findings may be useful to urban planners and policymakers in terms of land use planning and city thermal environment control.

Wildlife habitat mapping using Sentinel-2 imagery of Mehao Wildlife Sanctuary, Arunachal Pradesh, India

Mehao Wildlife Sanctuary, situated in the state of Arunachal Pradesh, is part of an important biodiversity hotspot in the north-eastern part of India in the Himalayas. The current study deals with the identification of important wildlife habitats in the sanctuary. We used a supervised classification technique to delineate these habitats in the sanctuary, which are used by several mammals and bird species encountered during camera trap and sign surveys conducted between November 2017 and May 2020. Satellite images from Sentinel – 2A were used to classify the land use land cover (LULC) of the sanctuary. The LULC information was generated by using a maximum likelihood classifier. We classified a total of thirteen LULC classes, i.e., water, built-up, agriculture, orchard, grassland, bamboo forest, bamboo-mixed forest, riverbed, barren land, snow, wild banana, riverine forest and mixed forest. LULC classification reveals a high percentage of mixed forest, about 69.9%, followed by wild bananas at 7.2%. The commission and omission error rates, however, are high for riverbed and agriculture (0.5) and bamboo forest (0.5), respectively. The accuracy assessment showed an overall classification accuracy of 88.5% with a Kappa coefficient of 0.87. The abundance of mammals was high in the mixed forest, but Ivlev's electivity index shows that species generally avoided this habitat and preferred specialized forest habitats, such as bamboo forest, bamboo-mixed forest, grassland, riverbed and riverine forest. Our LULC map will provide a baseline for potential planning and monitoring changes of wildlife habitats in Mehao WLS.

Land Use Land Cover Mapping in Dhamangaon Railway, MH with the help of GIS Software and Remote Sensing

Mapping land use land cover changes at regional scales is essential for a wide range of application, including, landslide, erosion, land planning, global warming etc. The aim of the project is to develop an action plan for land use cover mapping is the process of creating and implementing plans. To maintain the present natural resources and to understand to causes and consequences of over exploitation of soil and water resources the land use, a land cover mapping and monitoring was done in the study area i.e., Dhamanagaon railway. In this study satellite image for January 2015 and February 2019were used for LULC (land use land cover) supervised classification. For the classification purposes, seven LULC classes were decided change detection between both the image for al the land use and land cover classed were computer.

Quantitative Analysis of Land Use and Land Cover Dynamics using Geoinformatics Techniques: A Case Study on Kolkata Metropolitan Development Authority (KMDA) in West Bengal, India

One of the most valuable approaches in spatial analysis for a better understanding of the hydrological response of a region or a watershed is certainly the analysis of the well-known land use land cover (LULC) dynamicity. The present case study delves deeper into the analysis of LULC dynamicity by using digital Landsat TM and Landsat OLI data to classify the Kolkata Metropolitan Development Authority (KMDA) into seven classes with over 90% classification accuracy for decadal level assessments of 30 years (for the years 1989, 1999, 2009, and 2019). The change index, the Dematel method for analyzing the cause-effect relationship among the LULC classes, the Jaccard Similarity Index for measuring the nature of similarity among the LULC classes, and the Adherence Index for measuring the consistency of the LULC classes after the transition was used in this study to analyze the LULC transformation. In more detail, the present study considers how urban land use is altering at the expense of other land uses. Besides the shifting pattern of mean centers of the LULC classes through time, also gives a very significant insight into the LULC dynamics over 30 years of span. The current study of LULC dynamicity and transformation patterns over the 30 years of the KMDA area is expected to assist land and urban planners, engineers, and administrators in sustainable decisions and policies to ensure inclusive urbanization that accommodates population growth while minimizing the impact on potential natural resources within the whole study area.

ANALYZING THE EFFECTS OF LAND USE / COVER CHANGE (LULCC) SIMULATION ON FLOODING: A CASE STUDY IN LAS NIEVES, AGUSAN DEL NORTE, MINDANAO, PHILIPPINES

Abstract. The municipality of Las Nieves, located in Agusan del Norte, Mindanao, Philippines, has experienced floods and flood-related damages in the floodplain region in recent years. Despite the best intentions of planning and implementing projects anchored in its comprehensive land use plans, recurring flooding significantly impacts its residents. Due to its growing population, changes in land cover are inevitable. The changes can result in increased overland flow and decreased infiltration rates. This study was conducted to determine and quantify the effect of changing the land use/land cover (LULC) on the flooding condition in the municipality. Base LULC map was generated using Sentinel-2 image captured in 2021. Support Vector Machine classifier was used in the classification resulting in an accuracy of 94.07%. The 2021 LULC map was the reference for predicting future LULCs for 2025 and 2029 implemented in MOLUSCE and was utilized for flood simulation. HEC-HMS and HEC-RAS were used to generate flood depth maps of different extreme rainfall scenarios. The results showed that as the rainfall event increased, the extent of affected LULC areas also increased. Based on the rainfall impact assessment results, the annual cropland was the most impacted LULC class across the various LULC classes. The class open forest is the least affected class for 2021, 2025, and 2029. However, the barren was the least affected in the scenario-based build-up increase. These assessments are especially pertinent because storm-related rains have been increasingly severe recently and will continue due to climate change. The simulations and flood mapping knowledge can inform and empower the Local Government Unit of Las Nieves. It will guide them in developing informed decision frameworks for mitigating significant land surface variabilities and adapting effective future land use plans to reduce the adverse effects of flooding.

Impact of land use land cover on variation of urban heat island characteristics and surface energy fluxes using WRF and urban canopy model over metropolitan cities of India

The present study focuses on the assessment of urban heat island (UHI) and urban energy fluxes over Indian metropolitan cities. For this purpose, a high-resolution numerical Weather Research and Forecasting (WRF) model and a coupled single-layer urban canopy model (WRF-UCM) has been implemented over Delhi, Kolkata and Hyderabad to evaluate the performance of these models in capturing the impact of urban areas in simulating the UHI over different land use land cover (LULC) classes. The model initial conditions are obtained from NCEP-FNL reanalysis data. The model simulations reveal that the air temperature at 2 m height (T2) and UHI intensity are overestimated by WRF whereas the WRF-UCM simulations are matching well with ERA5-Land reanalysis data considered as observations. For the WRF-UCM simulation, the statistical errors in T2 and UHI intensity are within the desirable limits and the index of agreement (IoA) is more than 0.8 over the built-up areas (BAs). By comparing with the satellite observations, the relative surface UHI is noticed to be better captured over the BA by WRF-UCM simulation. The sensible heat flux simulated by WRF is highly overestimated whereas WRF-UCM is in good agreement with the satellite observation over the BA. Interestingly, the latent heat flux is reasonably well simulated over all the LULC classes. The results derived from the present study have shown the performance of WRF-UCM in simulating the spatial variation of UHI and fluxes of sensible and latent heat-related varied LULC classifications over metropolitan cities of India.

Exploring the nexus between land use land cover (LULC) changes and population growth in a planned city of islamabad and unplanned city of Rawalpindi, Pakistan

For the last three decades, Islamabad - a planned city, and Rawalpindi - an unplanned city, have experienced massive land use and land cover changes. The main objective of this study was a comparative assessment and quantification of LULC changes in relation to population growth and urbanization from 1990 to 2021 with the help of satellite imagery and population data in planned and unplanned cities. For classification four land-use land cover classes: built-up, vegetation, bare land, and water were selected. Maximum likelihood algorithm and confusion matrix were employed for classification and accuracy assessment. Results revealed that built-up increased from 5.7% (52 km2) to 25.7% (233 km2) and 3.7% (60 km2) to 14.1% (228 km2) from 1990 to 2021 for Islamabad and Rawalpindi, respectively. Wherein the bare land decreased from 42.2% (382 km2) to 18.1% (164 km2) in Islamabad and 65.5% (1058 km2) to 32.1% (518 km2) in Rawalpindi. Vegetation showed an increment of 4.7% for Islamabad and 24.5% for Rawalpindi. Surface water bodies decreased in both study areas. Population growth showed a strong positive correlation with the built-up class and a strong negative correlation with the bare land class for both cities. The outcomes of this study may be helpful in policymaking for better planning and management of land use land cover and urban sprawl in the context of sustainable development goals.