Assessment Of Land Cover Changes Research Articles

Assessment of Land Cover Changes and Fragmentation of Mining Landscape using Geospatial Technology

Mining plays an important role in economic development of Jharkhand, India. Simultaneously, it also has significant negative impacts on the environment, ecology, and society. Geospatial technology enables the identification, delineating, and monitoring of change in the mining landscape in spatial and temporal scale. The aim of this work was to evaluate the quantitative change in the mining landscape using multi-temporal Landsat datasets of 2004 and 2024. The study showed that mining areas have increased rapidly during the study period, and the growth has occurred by depleting the agricultural and forest land. In 2004 agriculture land covered 36.41% of the study area, but by 2024, it had decrease to 19.68%. Further, the study assessed the dynamics of mining patches and their characteristics through spatial metrics analysis. %. It has been observed that number of patches (NP) and patch density (PD)has increase whereas the largest patch index (LPI) has decreased. Dense and open forest has also been decreased in this period. The rapid expansion of mining areas over the past decade has raised serious ecological concerns.

Assessment of global land cover changes using satellite data: intermittent and long-term land cover changes from 2001 to 2020

Abstract Global land cover has changed during the past decades, influencing biogeochemical cycles and the global climate system. This study aimed to improve understanding of global land cover dynamics to enable more effective future land management practices and conservation actions. This study quantified interannual changes in global land cover types from 2001 to 2020 and distinguished intermittent transitions from stable gains and losses. From the interannual perspective, we found that global barren lands, forests, shrublands, and snow-covered areas decreased by 5281, 1804, 952, and 188 kha yr−1, respectively. In contrast, grasslands, croplands, urban areas, and water bodies increased at 6529, 1407, 237, and 51 kha yr−1, respectively, from 2001 to 2020. According to the definitions provided in this paper, of the global forest areas, 75% was Stable (no change), 4% was Gain, 5% was Loss, and 16% was Unstable. Of the cropland areas, 56% was Stable, 9% was Gain, 9% was Loss, and 26% was Unstable. Hotspots for forest loss were Brazil, the Rest of South America, and Sub-Saharan Africa, and grassland was the most common land cover classification following forest loss. The global cropland expansion hotspots were Brazil, Canada, China, India, and the Rest of South America. The cropland gains were mainly converted from grasslands. On the other hand, barren areas in China and Middle Eastern and North Africa were changed to grasslands. A certain amount of shrublands were changed to forest in temperate regions. This paper provided land cover changes at a 500 m spatial resolution as a benchmark for future assessments. The findings showed that unstable pixels play an important role in determining the sources of uncertainty when assessing land cover changes using satellite data. Land cover assessments are sensitive to the time steps used for analysis and the definition of changes.

Assessment of Land Use and Land Cover Changes in Afi River Forest Reserve Cross River State, Nigeria

Forest conversion remains a pressing concern for the global environmental community. This study evaluated the impact of land use and land cover (LULC) changes on selected ecosystem components in the Afi River Forest Reserve, Cross River State, Nigeria. Spatial and temporal changes over a 30-year period (1992 to 2022) were evaluated using Landsat-4 Thematic Mapper (TM) for the 1992 images and Landsat-7 Enhanced Thematic Mapper (ETM) images for 2002 and 2022. A supervised classification method was employed to classify and map LULC types and future changes up to 2052 predicted. Six distinct land use types-dense vegetation, farmland/sparse vegetation, built-up area, bare land, rock, and water bodies were identified. A significant decline in dense vegetation from 312.14 km² in 1992 to 271.28 km² in 2022 was observed while farmland, built-up areas, and bare land increased. The decline in dense vegetation highlights the pressures from agricultural expansion, urbanization, and other anthropogenic activities. A further decline in dense vegetation to 220.32 km² by 2052 and further expansions in the areas covered by farmland, water bodies, built-up areas, and bare land, were predicted. The observed trends highlight the urgent need for immediate action to combat environmental degradation in the Afi River Forest Reserve. Prioritizing sustainable land use practices, such as agroforestry and sustainable agriculture, is essential to alleviate agricultural pressures. Strengthening community-based forest management programs to foster local participation and commitment to conservation efforts. Furthermore, continuous monitoring of LULC changes is vital for informing adaptive management strategies and implementing effective policy interventions.

Landscape risk analysis of the 2003 Barkal-Rangamati (5.7 Mw) earthquake in Bangladesh using geospatial techniques and people’s perceptions

Planning and urban sustainability depend equally on understanding land use and land cover (LULC) change, variability, and landscape risk (LR) assessment in earthquake-prone areas. This paper considered Barkal town and its environs (BTEN), an earthquake-prone area close to the 2003 Barkal-Rangamati Earthquake (5.7 Mw) for the LR change analysis from 2001–2024. Six different multi-temporal data for the years 2001, 2003, 2011, 2021, and 2024 are used for the detailed analysis to assess the spatial and temporal LULC and LR changes. Further, for LR mapping, Geographical Information System (GIS), and multi-criteria evaluation (MCE) based Analytical Hierarchy Process (AHP) approaches were used. The LR mapping of the study region was prepared using various tools and approaches of geospatial techniques i.e. lineament density (LD), LR analysis, the local geology, and the LULC of various timelines. Furthermore, the ROC-AUC curve considered to validate the LD image with the LR maps considered as a novel technique in this study. The LR in the earthquake-affected areas, people’s risk perception and surface and subsurface rupture estimation also found suitable in the present study. Our detailed results will ultimately help the planners to assess better the changing state of 2003 Barkal-Rangamati earthquake-prone areas in a sustainable manner.

Assessing Land Cover Changes Using the LUCAS Database and Sentinel Imagery: A Comparative Analysis of Accuracy Metrics

Classification of remote sensing images using machine learning models requires a large amount of training data. Collecting this data is both labor-intensive and time-consuming. In this study, the effectiveness of using pre-existing reference data on land cover gathered as part of the Land Use–Land Cover Area Frame Survey (LUCAS) database of the Copernicus program was analyzed. The classification was carried out in Google Earth Engine (GEE) using Sentinel-2 images that were specially prepared to account for the phenological development of plants. Classification was performed using SVM, RF, and CART algorithms in GEE, with an in-depth accuracy analysis conducted using a custom tool. Attention was given to the reliability of different accuracy metrics, with a particular focus on the widely used machine learning (ML) metric of “accuracy”, which should not be compared with the commonly used remote sensing metric of “overall accuracy”, due to the potential for significant artificial inflation of accuracy. The accuracy of LUCAS 2018 at Level-1 detail was estimated at 86%. Using the updated LUCAS dataset, the best classification result was achieved with the RF method, with an accuracy of 83%. An accuracy overestimation of approximately 10% was observed when reporting the average accuracy ACC metric used in ML instead of the overall accuracy OA metric.

AGRICULTURAL OASIS EXPANSION AND URBAN GROWTH IN TOLGA SOUTHEASTERN ALGERIA: A GEOSPATIAL STUDY

The assessment of land use and land cover (LULC) changes is crucial to understanding its impacts on the natural environment and resources. The dynamics of LULC might be a result of national legislation or unplanned development. This study utilizes remote sensing data to evaluate the LULC in Tolga Oasis resulting from the promulgation of agricultural development law in 1983. Four Landsat images from 1985, 2000, 2015, and 2023 were classified using the Support Vector Machine (SVM) and ArcGIS Pro software. The findings showed a continuous change in the built-up area and vegetation area. The increase in built-up area was in conjunction with the rise in vegetation area. A spatial direction approach and concentric circle approach were used to assess the change in each direction and to identify the zone experiencing the most change in the built-up and agricultural oasis expansion. Shannon’s entropy model was used to measure the dispersion and the compactness of urban growth. The overall outcomes revealed that all directions showed an increase in built-up and vegetation area. Total Shannon’s entropy values showed compact urban growth in 1985, while, a dispersed development was recorded in 2000, 2015, and 2023. Statistical analysis demonstrated a high correlation between date palm plantations and vegetation area by 0,928%, as well as a significant correlation between built-up areas and population growth by 0,926%. These results can be helpful for the local authorities and planners in order to make a sustainable urban development and protect the fragile oasis ecosystem.

ESTIMATION OF LAND COVER CHANGE USING LANDSAT SATELLITE IMAGERY AND THE RANDOM FOREST CLASSIFIER

The quantification of land cover change facilitates natural resource management. In general, these changes are determined at large scales but less frequently at a regional level. In this research, changes in land area covered by vegetation (V), agricultural use (A), grassland (G), and urban-rural (U) were estimated for the period 2002–2021 in the municipality of Huehuetla, in the Northern Sierra of Puebla, Mexico (39.5 km2), based on Landsat satellite images and the random forest classifier (RF). The latter was trained and evaluated with two datasets consisting of three spectral bands (red, green, and blue) and seven vegetation indices. RF performed well in classifying the four cover types at the beginning and end of the evaluation period. RF obtained an overall correct classification accuracy of 92.5 % in 2002 and 92.3 % in 2021. At the land cover level, RF identified vegetation cover with an F1 score of 100 % in 2002 and 98.2 % in 2021; however, it identified the urban-rural cover less effectively, with an F1 of 71.5 % in 2002 and 81.8 % in 2021. In the period analyzed, the urban area increased from 1.7 to 6.4 % (an increase of 4.7 % of the total area), and the vegetation area from 48.1 to 68.7 % (an increase of 15.6 %), at the expense of a reduction in the grassland area (19.8 %), while the agricultural area remained stable (reduction of 0.5 %). This study illustrates the importance of using machine learning techniques and satellite images to assess land cover changes at the regional level as a viable and low-cost alternative.

Spatial assessment of land use and land cover change impacts on groundwater recharge and groundwater level: A case study of the Hat Yai basin

Spatial assessment of land use and land cover change impacts on groundwater recharge and groundwater level: A case study of the Hat Yai basin

Spatial Based Assessment of Land Use and Land Cover Change on the Different Landscape Patterns in the Wakatobi National Park, Indonesia

Spatial Based Assessment of Land Use and Land Cover Change on the Different Landscape Patterns in the Wakatobi National Park, Indonesia

Evaluating floodplain vegetation after valley‐scale restoration with unsupervised classification of National Agriculture Imagery Program data in semi‐arid environments

AbstractMonitoring vegetation response to valley‐scale floodplain restoration to evaluate effectiveness can be costly and time‐consuming. We used publicly available National Agriculture Imagery Program (NAIP) data and commonly used ArcGIS software to assess land cover change over time at five study sites located in semi‐arid environments of eastern Oregon and north‐central California. Accuracy assessments of our unsupervised classifications were used to evaluate effectiveness. Overall accuracy across sites and years ranged from 64.2% to 89.2% with mean and median accuracy of 79.1% and 80.6%, respectively. Further, we compared our classifications with high‐resolution uncrewed aerial systems (UAS)‐based data collected in the same timeframe. Restored areas classified as dense vegetation were within 4% of the UAS study, water was within 6%, and post‐restoration classifications of sparse vegetation and bare ground classes were within 6% and 4% of the UAS study, respectively. This comparison demonstrates that our unsupervised NAIP data classification of land cover change across entire valley‐scale restoration projects can be used to monitor riparian vegetation change over time as accurately as UAS‐based methods, but at lower cost. Additionally, our methods leverage existing fine‐resolution, pre‐restoration vegetation density data that were not collected as part of project planning.

An Assessment of Land Use and Land Cover Changes on Urban Flooding: A Case Study in Sri Jayewardenepura Kotte

This study investigates the relationship between Land Use and Land Cover (LULC) changes and urban flooding in the Sri Jayewardenepura Kotte Divisional Secretariat Division (DSD). The research objectives include analysing flooding patterns, mapping LULC changes, and assessing the correlation between these changes and flooding occurrences. Additionally, a temporal rainfall variation analysis was conducted to provide further context. The study employed Geographic Information System (GIS) technology, specifically utilising ArcGIS for LULC classification and Inverse Distance Weighting (IDW) interpolation technique for rainfall data. The analysis reveals a clear association between heightened precipitation and major flood events. In 2010, the flood inundation area was 5.762 km² during the major flood in May, despite a monthly rainfall of over 650mm. However, in 2016, with over 700 mm of rainfall in May, the flood-inundated area was reduced to 4.046 km², highlighting the role of other factors such as wetland recovery. Moreover, it identifies significant LULC changes, emphasising rapid urbanisation and wetland decline. From 2009 to 2018, built-up areas increased from 11.49 km² to 14.18 km², while wetlands decreased from 2.29 km² in 2009 to 1.19 km² in 2015, before recovering slightly to 1.58 km² by 2018. The expansion of built-up areas is found to slightly increase flood risks, while wetland recovery acts as a natural flood buffer. This research underscores the importance of targeted interventions for urban flood resilience, such as flood-resistant infrastructure and wetland management. The findings provide critical insights for evidence-based urban planning and flood management strategies, aiming to create more resilient and sustainable urban environments.

Integrating Traditional Ecological Knowledge into Land Use and Land Cover Change Assessments, Pastoralist Communities in Northwest Inner Mongolia China

Integrating Traditional Ecological Knowledge into Land Use and Land Cover Change Assessments, Pastoralist Communities in Northwest Inner Mongolia China

Assessment of Land Use and Land Cover (LULC) Changes in the Hydrological Parameters in Teesta Catchment, Bangladesh

Assessment of Land Use and Land Cover (LULC) Changes in the Hydrological Parameters in Teesta Catchment, Bangladesh

Spatiotemporal variations in land surface temperature within an urban ecosystem: A comprehensive assessment of land cover change in Düzce, Türkiye

Spatiotemporal variations in land surface temperature within an urban ecosystem: A comprehensive assessment of land cover change in Düzce, Türkiye

GIS-Based Land Use and Land Cover Change Assessment Around Assosa District, Upper Blue Nile Basin, Ethiopia

Land, with its intricate blend of geological, topographical, hydrological, and ecological elements, reflects the interplay of natural processes and human activities. In Ethiopia, traditional agricultural practices often lack systematic planning based on land suitability and physical characteristics, hindering agricultural productivity and economic progress. Understanding shifts in land use and cover is crucial for comprehending broader global transformations. Early studies have unveiled significant alterations in land use and cover between 2009 and 2013. Our investigation extends from 2013 to 2023, employing Landsat 8 imagery at three-year intervals (2013, 2018, and 2023) to analyze five key land cover categories: forests, farmland, built-up areas, barren land, and water bodies. Notably, farmland and built-up areas witnessed substantial expansion over these periods, juxtaposed with a significant decline in forest cover. Forests decreased by 27% from 2013 to 2018, with an additional 7% decrease from 2018 to 2023. Conversely, farmland expanded by 19% from 2013 to 2018 and by 2% from 2018 to 2023, while built-up areas saw respective increases of 4% and 10% over the same periods. These trends underscore potential environmental challenges if unaddressed. Effective land management strategies are crucial to mitigate adverse impacts on ecosystems, agricultural productivity, and overall socio-economic stability. By integrating scientific insights with sustainable practices, we can navigate the intricate dynamics of land use, paving the way for a resilient and prosperous future.

Land Cover Change and Carbon Potential in Mangrove Ecosystems at The Social Forestry Area (Study Case: Indramayu Regency, Indonesia)

This study investigates land cover change and carbon potential in mangrove ecosystems within the social forestry area of Indramayu Regency. The research aims to assess land cover changes from 2014 to 2020 and estimate the carbon potential stored in mangrove ecosystems. Field surveys and satellite images analyze land cover change patterns and quantify mangrove carbon storage potential. The research findings reveal that aquaculture land cover dominates the study area (reaching 90%). The study did not find significant changes in land cover within the social forestry area. Only minor changes were noted, with mangroves converting to aquaculture and vice versa. The carbon potential is obtained from biomass calculated based on tree diameter within the research area. According to the calculations, it was found that the three research areas have different potentials due to varying tree diameters and densities. The carbon potential from thepermitted areas of Karya Wana Tiris, Babadan Lestari, and Hijau Mandiri are 24.54 tons, 18.33 tons, and 24.87 tons. The highest carbon potential occurred in 2020 (2,419.69 tons), while the lowest was in 2017 (1,414.06 tons).

Assessment of land cover changes and endangered tree species in Rurum ward of Rano Local Government Area of Kano State

Assessment of land cover changes and endangered tree species in Rurum ward of Rano Local Government Area of Kano State

An Assessment of Land Use Land Cover Changes and its Impact on Land Surface Temperature using Geoinformatic Techniques

An Assessment of Land Use Land Cover Changes and its Impact on Land Surface Temperature using Geoinformatic Techniques

A fuzzy analytical hierarchy process -GIS approach to flood susceptibility mapping in NEOM, Saudi Arabia

The Kingdom of Saudi Arabia is undergoing massive and rapid urbanization as part of Vision 2030. This includes development projects along Saudi Arabia’s coastline across the Red Sea. Coastal areas, especially the ones along Saudi’s western regions are susceptible to natural disasters such as flooding. NEOM, a futuristic city currently being developed in the northwest of Saudi Arabia, exemplifies a potential flooding hazard due to its geographic location and proposed urbanization plans. This research aims to enhance flood hazard assessment in NEOM by applying the Fuzzy Analytical Hierarchy Process (FAHP) in combination with Geographic Information System (GIS). Acknowledging traditional limitations related to data availability and parameter selection consensus, the study carefully selects parameters such as drainage density, elevation, slope, rainfall, land use/land cover (LULC), soil type, normalized difference vegetation index (NDVI), and topographic wetness index (TWI). The 30 m DEM was used to derive Drainage Density, Slope, and TWI while LULC data helped assess land cover changes. Rainfall data and soil type information are integrated to evaluate their impact on flood susceptibility. NDVI is employed to analyze vegetation cover. Utilizing ArcGIS Pro’s weighted overlay model, the criteria were combined to generate the final flood susceptibility map. The research outcomes manifest in a flood susceptibility map categorizing areas into seven distinct susceptibility classes, ranging from ‘very low’ to ‘very high.’ A quantitative breakdown in a summary table provides insights into the proportional distribution of flood risk. Results indicate a significant portion of NEOM falls within varying degrees of moderate susceptibility range with relatively limited distribution of flood susceptibility on the extremes, equating to areas with ‘low to moderate’ susceptibility is 4,322.8 km2, areas with ‘moderate’ susceptibility is 5,109.69 km2, areas with ‘moderate to high’ is 4,081.39 km2. The flood susceptibility map developed in this study can shed insights on potential optimum areas for flood mitigation measures (i.e., optimum locations for establishing stormwater collection points).

Assessment of Land Cover Change Due to Stone Quarrying in Narok Town Ward

Assessment of Land Cover Change Due to Stone Quarrying in Narok Town Ward