Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia

SN Applied Sciences, Journal Year: 2021, Volume and Issue: 4(1)

Published: Dec. 20, 2021

Abstract Mapping and quantifying the status of Land use/Land cover (LULC) changes drivers change are important for identifying vulnerable areas designing sustainable ecosystem services. This study analyzed LULC key last 30 years through a combination remote sensing GIS with surveying local community understanding patterns in Gubalafto district, Northeastern Ethiopia. Five major types (cultivated settlement, forest cover, grazing land, bush land bare land) from Landsat images 1986, 2000, 2016 were mapped. The results demonstrated that cultivated settlement constituted most extensive type area increased by 9% extent. It also revealed substantial expansion during past years. On other hand, classes has high environmental importance such as have reduced drastically time expanding same period. 1986 was about 11.1% total area, it had decreased to 5.7% 2016. In contrast, 45.6% 49.5% Bush 14.8 21% period, while declined 8.9 2% root causes this particular include population growth, tenure insecurity, common property rights, persistent poverty, climate change, lack public awareness. Therefore, be controlled, resources use is essential; else, these scarce natural resource bases will soon lost no longer able play their contribution Article Highlights Forest lands rapidly. Fluctuating trends land. Population pressure associated demand main behind area.

Language: Английский

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A comprehensive systematic review: Impact of Land Use/ Land Cover (LULC) on Land Surface Temperatures (LST) and outdoor thermal comfort

Building and Environment, Journal Year: 2023, Volume and Issue: 249, P. 111130 - 111130

Published: Dec. 21, 2023

Language: Английский

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Addressing the impact of land use land cover changes on land surface temperature using machine learning algorithms

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 13, 2024

Over the past two and a half decades, rapid urbanization has led to significant land use cover (LULC) changes in Kabul province, Afghanistan. To assess impact of LULC on surface temperature (LST), province was divided into four classes applying Support Vector Machine (SVM) algorithm using Landsat satellite images from 1998 2022. The LST assessed data thermal band. Cellular Automata-Logistic Regression (CA-LR) model applied predict future patterns for 2034 2046. Results showed classes, as built-up areas increased about 9.37%, while bare soil vegetation decreased 7.20% 2.35%, respectively, analysis annual revealed that highest mean LST, followed by vegetation. simulation results indicate an expected increase 17.08% 23.10% 2046, compared 11.23% Similarly, indicated area experiencing class (≥ 32 °C) is 27.01% 43.05% 11.21% increases considerably decreases, revealing direct link between rising temperatures.

Language: Английский

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Analysis of the vertical structure of atmospheric thermal balance over Central Africa

Bulletin of Atmospheric Science and Technology, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 24, 2025

Language: Английский

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Land surface dynamics and meteorological forcings modulate land surface temperature characteristics

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 101, P. 105072 - 105072

Published: Nov. 21, 2023

This study examines the effect of land cover, vegetation health, climatic forcings, elevation heat loads, and terrain characteristics (LVCET) on surface temperature (LST) distribution in West Africa (WA). We employ fourteen machine-learning models, which preserve nonlinear relationships, to downscale LST other predictands while preserving geographical variability WA. Our results showed that random forest model performs best downscaling predictands. is important for sub-region since it has limited access mainframes power multiplex algorithms. In contrast northern regions, southern regions consistently exhibit healthy vegetation. Also, areas with unhealthy coincide hot clusters. The positive Normalized Difference Vegetation Index (NDVI) trends Sahel underscore rainfall recovery subsequent Sahelian greening. southwesterly winds cause upwelling cold waters, lowering WA highlighting cooling influence water bodies LST. Identifying elevated paramount prioritizing greening initiatives, our underscores importance considering LVCET factors urban planning. Topographic slope-facing angles, diurnal anisotropic all contribute variations LST, emphasizing need a holistic approach when designing resilient sustainable landscapes.

Language: Английский

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Assessment of land surface temperature and land cover variability during winter: A spatio-temporal analysis of Pabna municipality in Bangladesh

Environmental Challenges, Journal Year: 2021, Volume and Issue: 4, P. 100167 - 100167

Published: June 2, 2021

Monitoring the change of land use and cover (LULC) surface temperature (LST) at different spatio-temporal scales is vital for evaluating landscape dynamics thermal environment. This study investigates decadal LULC winter LST on Pabna municipality over period between 1990 2020 using Landsat images (TM, ETM+ OLI). The further explores distribution classes explanatory power various indicators in LST. A supervised maximum likelihood classification (MLC) technique was used mapping area. results showed that built-up areas were increasing rapidly while water bodies, bare lands vegetation decreased. area expanded by 358% 2020, with occupied rising from 1.44 km2 to 6.60 km2. To obtain reliable results, average values obtained multiple each year used. mean season has risen 0.63 °C last 30 years. variation separate days same increased significantly, although small. Statistical analysis revealed NDVI, NDBI NDBaI have significant describe scenarios. explain rise time cooling capacity NDVI declining. had a moderate positive correlation weak negative NDVI.

Language: Английский

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Assessment of urban thermal field variance index and thermal comfort level of Addis Ababa metropolitan city, Ethiopia

Heliyon, Journal Year: 2022, Volume and Issue: 8(8), P. e10185 - e10185

Published: Aug. 1, 2022

Land use land cover (LULC) conversion around urban areas is the root cause for increasing trend of surface temperature (LST) in many cities. The increase LST driven by replacement vegetation and other LULC impervious surface. This study aimed to assess extent thermal field variance index (UTFVI) comfort level Addis Ababa city using geospatial techniques linear regression model. Landsat image 1990 TM, 2000 ETM+ 2020 OLI/TIRS are used analyze Urban Heat Islands (UHI) assessing UTFVI level. results showed that UHI over substantial increased past decades. reveled has 7.9 °C due decline expansion built-up area. Results show about 225 km2 (42.7%) excellent resident while 241.4 (45.8%) categorized as worst ecological evaluation index, which discomfort dwellers. key findings from this crucial informing administrators planners reduce heat islands investing on green open spaces.

Language: Английский

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Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102738 - 102738

Published: July 28, 2024

Monitoring and evaluation of landscape fragmentation is important in numerous research areas, such as natural resource protection management, sustainable development, climate change. One the main challenges image classification intricate selection parameters, optimal combination significantly affects accuracy reliability final results. This aimed to analyze change northwestern Peru. We utilized accurate land cover use (LULC) maps derived from Landsat imagery using Google Earth Engine (GEE) ArcGIS software. For this, we identified best dataset based on its highest overall accuracy, kappa index; then performed an analysis variance (ANOVA) assess differences accuracies among datasets, finally, obtained LULC analyzed them. generated 31 datasets resulting spectral bands, indices vegetation, water, soil clusters. Our revealed that 19, incorporating bands along with water indices, emerged choice. Regarding number trees classification, determined between 10 400 decision Random Forest doesn't affect or Kappa index, but observed a slight cumulative increase metrics when 100 trees. Additionally, 1989 2023, categories Artificial surfaces, Agricultural Scrub/ Herbaceous vegetation exhibit positive rate change, while Open spaces little no display decreasing trend. Consequently, areas patches perforated have expanded terms area units, contributing reduction forested (Core 3) due fragmentation. As result, smaller than 500 acres 1 2) increased. Finally, our provides methodological framework for assessment fragmentation, crucial information makers current agricultural zone

Language: Английский

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Assessment of the environmental impacts of conflict-driven Internally Displaced Persons: A sentinel-2 satellite based analysis of land use/cover changes in the Kas locality, Darfur, Sudan

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(5), P. e0304034 - e0304034

Published: May 30, 2024

Internal displacement of populations due to armed conflicts can substantially impact a region's Land Use and Cover (LULC) the efforts towards achievement Sustainable Development Goals (SDGs). The objective this study was determine effects conflict-driven Internally Displaced Persons (IDPs) on vegetation cover environmental sustainability in Kas locality Darfur, Sudan. Supervised classification change analysis were performed Sentinel-2 satellite images for years 2016 2022 using QGIS software. Level 2A data analysed Random Forest (RF) Machine Learning (ML) classifier. Five land types successfully classified (agricultural land, cover, built-up area, sand, bareland) with overall accuracies more than 86% Kappa coefficients greater 0.74. results revealed 35.33% (-10.20 km2) decline area over six-year period, equivalent an average annual loss rate -5.89% (-1.70 cover. In contrast, agricultural areas increased by 17.53% (98.12 60.53% (5.29 respectively between two years. trends changes among different LULC classes suggest potential influences human activities especially IDPs, natural processes, combination both area. This highlights impacts IDPs resources patterns conflict-affected region. It also offers pertinent that support decision-makers restoring affected preventing further degradation sustainability.

Language: Английский

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Leveraging Machine Learning for Analyzing the Nexus Between Land Use and Land Cover Change, Land Surface Temperature And Biophysical Indices in an Eco-Sensitive Region of Brahmani-Dwarka Interfluve

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 102854 - 102854

Published: Sept. 1, 2024

Language: Английский

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