Assessment Of Land Cover Changes Research Articles

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

Study regionHat Yai Basin, Thailand. Study focusThis study aimed to assess the impacts of land use and land cover changes on groundwater recharge and groundwater levels—specifically, the effects of increasing built-up areas and urban expansion. The MOLUSCE (plug-in QGIS) software was used to analyze land use and land cover changes. The groundwater recharge estimation in this study was conducted using the WetSpass model, and the groundwater level was simulated using MODFLOW. New hydrological insights for the regionThe findings indicate that the Hat Yai basin has experienced its highest increase in built-up areas and found a decrease in the average recharge rate for 2017 compared to 2007. Correspondingly, a simulation of groundwater levels indicates that the overall groundwater level has decreased. Spatially, the Hat Yai district, which exhibited the highest increase in built-up areas, experienced a decrease in groundwater levels. In contrast, the Khuan Niang district, which had the largest increase in perennial areas, demonstrated increased groundwater levels. The findings show that increasing built-up areas decreases the recharge rate, consequently lowering groundwater levels. The spatial assessment is proved to be more effective in illustrating positive and negative impacts than the basin's overall average. Such spatial assessment is valuable for water resource management planning and addressing localized water resource issues that may differ across various areas.

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.

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

Monitoring Sustainable Development Goal Indicator 15.3.1 on Land Degradation Using SEPAL: Examples, Challenges and Prospects

A third of the world’s ecosystems are considered degraded, and there is an urgent need for protection and restoration to make the planet healthier. The Sustainable Development Goals (SDGs) target 15.3 aims at protecting and restoring the terrestrial ecosystem to achieve a land degradation-neutral world by 2030. Land restoration through inclusive and productive growth is indispensable to promote sustainable development by fostering climate change-resistant, poverty-alleviating, and environmentally protective economic growth. The SDG Indicator 15.3.1 is used to measure progress towards a land degradation-neutral world. Earth observation datasets are the primary data sources for deriving the three sub-indicators of indicator 15.3.1. It requires selecting, querying, and processing a substantial historical archive of data. To reduce the complexities, make the calculation user-friendly, and adapt it to in-country applications, a module on the FAO’s SEPAL platform has been developed in compliance with the UNCCD Good Practice Guidance (GPG v2) to derive the necessary statistics and maps for monitoring and reporting land degradation. The module uses satellite data from Landsat, Sentinel 2, and MODIS sensors for primary productivity assessment, along with other datasets enabling high-resolution to large-scale assessment of land degradation. The use of an in-country land cover transition matrix along with in-country land cover data enables a more accurate assessment of land cover changes over time. Four different case studies from Bangladesh, Nigeria, Uruguay, and Angola are presented to highlight the prospect and challenges of monitoring land degradation using various datasets, including LCML-based national land cover legend and land cover data.

Land Evaluation and Assessment of Land Cover Change Using Geospatial Techniques: A Case Study in West Samlout Area, Egypt.

Land Evaluation and Assessment of Land Cover Change Using Geospatial Techniques: A Case Study in West Samlout Area, Egypt.

Assessment of Land use Land Cover Changes in West Godavari District Using Gis

Assessment of Land use Land Cover Changes in West Godavari District Using Gis

Spatio-temporal land use and land cover change assessment: Insights from the Ouémé River Basin

The rapid increase in population and urban development are exacerbating the transformation of natural environments into unnatural forms. While detailed assessment of the environment is beneficial for efficient ecosystem system management, it can also be time and resources-consuming. This study aimed to map and quantify the spatio-temporal changes in land use and land cover (LULC) using the Ouémé River Basin as a case study. The supervised classification in Google Earth Engine (GEE) cloud-computing platform was employed to distinguish Landsat images for 1986, 2000, 2015 and 2023 into forest areas, settlements/bare lands, savanna areas (woodlands), agricultural lands and water bodies. Analysis of the LULC changes revealed that savanna areas and woodlands which were predominant in the basin in 1986 have steadily declined by 24 % in area in 2023. Forest areas have diminished by 4.3 % at an annual rate of 4 %. Agricultural lands have however grown exponentially by 28 % since 1986, with a more rapid increase between 2015 and 2023 at an annual rate of 3.7 %, driven by rising food demand due to population growth within and around the basin. Settlements and bare areas tripled in area, reflecting a similar trend to Benin's urban population growth. Accuracy statistics of the LULC classification showed overall accuracy and kappa statistic values above 90 % and 86 %, respectively, indicating the admirable performance and reliability of the Simple Composite Landsat algorithm for image composition, and the Random Forest Classifier for LULC classification approach applied in this study. The approach also demonstrates the robustness and potential of LULC mapping in large and complex ecosystems using the GEE cloud-based remote sensing tool, which is underutilized in the study area. Overall, the LULC trends provide beneficial insights useful to policy-makers and any other stakeholders involved in sustainable ecosystem management planning in the basin.

Land cover change assessment in Thai Nguyen Province, Vietnam using GIS and remote sensing techniques

The expansion of agricultural production and urbanization has led to the depletion of the global forest ecosystem, resulting in land cover changes (LCCs) that pose threats to the land environment. Therefore, it is imperative to identify these LCCs as an essential step toward resolution and mitigation. This study aims to evaluate temporal LCCs in Thai Nguyen province by utilizing GIS and remote sensing techniques (RST) to analyze high-resolution satellite imagery spanning from 2001 to 2023. LCCs were identified using semi-automatic classification plugin (SCP) techniques with Landsat-8 and Sentinel-2 images in ERVI software. The accuracy of the LCC maps was validated through post-classification comparisons, yielding high precision rates ranging from 87% to 96%. The results indicate a significant reduction in forest cover area, declining by 1018 ha from 226,18 ha in 2001 to 207,22 ha in 2023. This decline primarily occurred in urban areas, highlighting the intensification of urbanization processes. Cultivated and bare land experienced a minor decrease and a substantial decrease from 18,631 ha and 105,000 ha to 16,578 ha and 86,82 ha between 2001 and 2010, followed by a slight increase and a significant increase to 5,069 ha and 24,487 ha (1.4% and 6.9%) during the 2010-2023 period. These findings underscore the escalating risk of diminishing forest cover in the study area.

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.