Assessment Of Land Cover Changes Research Articles

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.

Assessment of land cover changes using sentinel-2 satellite image data: A case study of Thanh Hoa coastal area, Viet Nam

Land use and management decisions heavily rely on the evaluating changes in land cover. Remote sensing techniques are becoming increasingly reliable and applicable, making them particularly useful for assessing and monitoring changes in land cover. In this study, the authors used Sentinel-2 MSI optical satellite image data and machine learning algorithms to classify and evaluate land cover changes in Thanh Hoa province’s coastal area between 2015 and 2023. Our research findings showed that Sentinel-2 MSI satellite image data can accurately interpret and classify land cover, with Kappa values ranging from 0.892 to 0.907. Furthermore, our findings indicated an increase in the area covered by build-up class. Meanwhile, vegetation cover and water surface class tend to decrease, especially the sharp decline of surface water. Research results help local policymakers develop land use plans in the direction of sustainable economic and social development.

Mapping alien and native forest dynamics in Chile using Earth observation time series analysis

Chile is a global biodiversity hotspot and hosts a large proportion of the southern hemisphere’s temperate forests. The Chilean Valdivian temperate forest is a vulnerable ecosystem containing a highly ecologically valuable species assemblage. Productive forest plantations have involved deforestation of, and alien species introduction into, this ecosystem. This process has already severely impacted the western part of Chile (the Coastal Range) and is now occurring in the eastern part (the Andes), with forestry plantations promoted by government subsidies between 1974 and 2012. Archive Landsat satellite imagery classification and Google Earth Engine are used to assess land cover change over a 31-year period with a focus on alien species (Pinaceae and Eucalyptus spp.) spread and native (Nothofagus spp. and Araucaria araucana) deforestation. Results show a clear land cover pattern based on elevation: a higher altitude, relatively undisturbed area dominated by native forest (the Andes), and a lower altitude area where most human activity and related land covers are located (the valley area). The valley is highly dynamic because of constant land cover change due to forestry. Overall, Araucaria araucana cover has decreased over the study period, while Nothofagus spp. has remained relatively stable. Alien Pinaceae has decreased, while Eucalyptus spp. has remained stable. However, the results indicate that change analysis over long periods conceal dynamism. For example, Eucalyptus spp. sharply decreased between the 1980s and the 1990s and surged afterwards. Also, even though Nothofagus spp. cover dominates throughout the study period, change analysis shows a high degree of change in the valley area, indicating newly established Nothofagus spp. patches. Over the study period, long rotation Pinaceae plantations for timber have given way to shorter rotation forestry (alien Eucalyptus spp., native Nothofagus spp.) for pulp and local uses resulting from discontinuation of forest subsidies. In the absence of subsidies, only large-scale plantations can engage in long rotation forestry, as smallholders need the more stable income provided by shorter rotation forestry. Although higher elevations (the Andes) are dominated by native forest, several abandoned alien forest plantations may be the source of biological invasion. In addition, native forest degradation as a result of Araucaria araucana loss and shrub encroachment is occurring. Earth observation methods are key for forest and alien species monitoring and landscape management. They can enhance traditional, ground-based forest surveys and provide continuous and even retrospective monitoring of forest change thanks to the wide availability of current and historical satellite data.

Assessment of land use land cover change and its effects using artificial neural network-based cellular automation

The challenge of urban growth and land use land cover (LULC) change is particularly critical in developing countries. The use of remote sensing and GIS has helped to generate LULC thematic maps, which have proven immensely valuable in resource and land-use management, facilitating sustainable development by balancing developmental interests and conservation measures. The research utilized socio-economic and spatial variables such as slope, elevation, distance from streams, distance from roads, distance from built-up areas, and distance from the center of town to determine their impact on the LULC of 2016 and 2019. The research integrates Artificial Neural Network with Cellular Automta to forecast and establish potential land use changes for the years 2025 and 2040. Comparison between the predicted and actual LULC maps of 2022 indicates high agreement with kappa hat of 0.77 and a percentage of correctness of 86.83%. The study indicates that the built-up area will increase by 8.37 km2 by 2040, resulting in a reduction of 7.08 km2 and 1.16 km2 in protected and agricultural areas, respectively. These findings will assist urban planners and lawmakers to adopt management and conservation strategies that balance urban expansion and conservation of natural resources leading to the sustainable development of the cities.

Climate and Land Use/Land Cover Changes within the Sota Catchment (Benin, West Africa)

Climate and land cover changes are key factors in river basins’ management. This study investigates on the one hand 60-year (1960 to 2019) rainfall and temperature variability using station data combined with gridded data, and on the other hand land cover changes for the years 1990, 2005, and 2020 in the Sota catchment (13,410 km2, North Benin, West Africa). The climate period is different from the chosen land use change period due to the unavailability of satellite images. Standardized anomaly index, break points, trend analysis, and Thiessen’s polygon were applied. Satellite images were processed and ground truthing was carried out to assess land cover changes. The analyses revealed a wet period from 1960 to 1972, a dry period from 1973 to 1987, and another wet period from 1988 to 2019. The annual rainfall decreases from the south to the north of the catchment. In addition, rainfall showed a non-significant trend over the study period, and no significant changes were identified between the two normals (1960–1989 and 1990–2019) at catchment scale, although some individual stations exhibited significant trends. Temperatures, in contrast, showed a significant increasing trend over the study period at catchment scale, with significant break points in 1978, 1990, and 2004 for Tmax, and 1989 for Tmin. An increase of 0.4 °C and 1.2 °C is noted, respectively, for Tmax and Tmin between the two normals. The study also revealed increases in agricultural areas (212.1%), settlements (76.6%), waterbodies (2.9%), and baresoil (52%) against decreases in woodland (49.6%), dense forest (42.2%), gallery forest (21.2%), and savanna (31.9%) from 1990 to 2020. These changes in climate and land cover will have implications for the region. Appropriate adaptation measures, including Integrated Water Resources Management and afforestation, are required.

Assessing the extent of land degradation in the eThekwini municipality using land cover change and soil organic carbon

ABSTRACT More than 75% of the global land has already suffered degradation, leading to the recognition of land degradation as one of the foremost challenges society faces. This recognition stems from its profound adverse impacts on natural ecosystem functioning, biodiversity, soil productivity, and food availability. Consequently, understanding the spatial distribution of land degradation across all scales becomes imperative. This study employed land cover change and soil organic carbon (SOC) stock assessments to analyse land degradation within the eThekwini Municipality beyond the baseline period (2000–2015). Utilizing remote sensing and machine learning techniques, this research examined land degradation within the eThekwini Municipality over the period spanning 2000 to 2022. Landsat 7 (Enhanced Thematic Mapper Plus – ETM+), Landsat 8 (Operational Land Imager 1 - OLI1), and Landsat 9 (Operational Land Imager 2 - OLI2) images were employed to extract variables for both land cover change and SOC stock prediction through XGBoost, LightGBM, Random Forest (RF), and Support Vector Machine (SVM) models. Among these models, LightGBM demonstrates superior performance, achieving an overall accuracy of 80.646 in land cover predictions and 77.869 in SOC stock predictions. Analysis of land cover change within the eThekwini Municipality unveiled a shift from forests and shrubland landscapes to cropland and built-up areas. This shift results in the municipality encountering losses in SOC stock between 2015 and 2022. The model predicted that most SOC stock losses occur at the 20–50 cm depth (9.27%), in comparison to the 7.21% loss at the 0–20 cm depth. These findings underscore the pivotal role of remote sensing and machine learning in aiding policymakers to assess land degradation and implement pertinent measures to enhance the landscape.

Long-term land cover changes assessment in the Jiului Valley mining basin in Romania

Introduction: Highlighting and assessing land cover changes in a heterogeneous landscape, such as those with surface mining activities, allows for understanding the dynamics and status of the analyzed area. This paper focuses on the long-term land cover changes in the Jiului Valley, the largest mining basin in Romania, using Landsat temporal image series from 1988 to 2017.Methods: The images were classified using the supervised Support Vector Machine (SVM) algorithm incorporating four kernel functions and two common algorithms (Maximum Likelihood Classification - MLC) and (Minimum Distance - MD). Seven major land cover classes have been identified: forest, pasture, agricultural land, built-up areas, mined areas, dump sites, and water bodies. The accuracy of every classification algorithm was evaluated through independent validation, and the differences in accuracy were subsequently analyzed. Using the best-performing SVM-RBF algorithm, classified maps of the study area were developed and used for assessing land cover changes by post-classification comparison (PCC).Results and discussions: All three algorithms displayed an overall accuracy, ranging from 76.56% to 90.68%. The SVM algorithms outperformed MLC by 4.87%–8.80% and MD by 6.82%–10.67%. During the studied period, changes occurred within analyzed classes, both directly and indirectly: forest, built-up areas, mined areas, and water bodies experienced increases, whereas pasture, agricultural land, and dump areas saw declines. The most notable changes between 1988 and 2017 were observed in built-up and dump areas: the built-up areas increased by 110.7%, while the dump sites decreased by 53.0%. The mined class showed an average growth of 6.5%. By highlighting and mapping long-term land cover changes in this area, along with their underlying causes, it became possible to analyze the impact of land management and usage on sustainable development and conservation effort over time.

Assessment of land cover changes in Andijan region for 1997-2020 using remote sensing


 
 
 Remote sensing (RS) plays a vital role in mapping of land use/land cover (LU/LC) and in quantitatively assessing the effect of human intervention on natural resources. In this study, the land cover analysis and correlation method for a certain period of time was used to classify the land cover of Andijan region with Landsat satellite images of 1997-2009-2020. A classification method combined with clustering was used to determine the land cover change, and a study was conducted using Google Earth software through 30 classes. The analysis shows that compared to 1997 (566.6 km2), the area of buildings and structures in the study area has expanded to 750 km2 in 2009 and in 2020 it was 770,4 km2, an increase of almost 1.4 times. Another aspect is that there is a decrease in the class of forested areas, gardens and cultivated fields. In particular, in 1997, the area of this class was 3047 km2, and by 2009 it had decreased to 2,430 km2. Thanks to the measures aimed at improving the geo-ecological situation, by 2020 the amount of the «green cover» was 2,644 km2. In Andijan region, empty lands (bare soils and barren lands) amounted to 678.8 km2 in 1997 and had increased to 1,115 km2 (1.6 times) by 2009. In 2020, the area of degraded lands had decreased by 858 km2. The results of remote sensing studies show that the expansion of construction and bare land has led to a reduction in green space. The amount of dust in the atmosphere rises when green space decreases. Another object representing the geo-ecological situation is water bodies, which has in- creased in the studied area. Initially, water basins were initially 9.65 km2 in 1997, decreased to 7.77 km2 by 2009, and reached 29.63 km2 by 2020. This study was carried out in order to determine the formation of agglomeration in the small and densely populated Andijan region, to study the effective use of limited land resources.
 
 

Spatiotemporal assessment of land use land cover change, driving forces, and consequences using geospatial techniques: The case of Naqamte city and hinterland, western Ethiopia

Land use and land cover change (LULCC) is the result of both anthropogenic and natural activities. LULCC has caused biodiversity and climate change in the study area. Land use and land cover (LULC) datasets for the investigation site were created from Landsat images in 1987, 2004, and 2021 using the maximum likelihood method. After LULCC the cross-tabulation and trend-surface analyses were performed to identify dominant land transitions in post-classification maps. The driving forces that caused LULCC and its consequences were examined using qualitative data. Study results show that spatial and temporal LULC classes were significantly changed except for waterbodies. The estimated aerial extent of forest land has decreased dramatically by 14.5 % over the last 34 years (1987–2021), while bare land, built-up land, and agricultural land have increased by 6.83 %, 4.33 %, and 3.21 %, respectively. According to this study, the main causes of LULCC in the district were population increase, growing cropland, the need for firewood, building supplies, and coal, as well as the ineffective use of natural resources and land monitoring systems. Forest deterioration, extinction of plant and animal species, soil degradation, and a lack of animal feed were the primary effects of changes in LULC. The overall accuracy of the three classified images of the years 1987, 2004, and 2021 was 86 %, 94 %, and 97 %, respectively. Lastly, our work used Markov chain analysis to examine LULCC in a spatiotemporal manner. These findings would provide decision support for policymakers to frame future sustainable land use policies.