Integration of geospatial technologies with multiple regression model for urban land use land cover change analysis and its impact on land surface temperature in Jimma City, southwestern Ethiopia

Moisa, Mitiku Badasa; Dejene, Indale Niguse; Gemeda, Dessalegn Obsi

doi:10.1007/s12518-022-00463-x

Cited by 10 publications

(2 citation statements)

References 51 publications

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“…Addiotnally, a negative correlation between vegetation cover and LST, indicating that vegetation loss contributes to increased LST. The study proposes raising local community awareness for environmental conservation and the establishment of urban green spaces to mitigate UHI and enhance urban thermal comfort [23].…”

Section: Literature Reviewsmentioning

confidence: 99%

Exploring the Effects of Land Use/Land Cover (LULC) Modifications and Land Surface Temperature (LST) in Pune, Maharashtra with Anticipated LULC for 2030

2024

IJG

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Land surface temperature (LST) is a crucial parameter influencing the thermal environment of urban and natural areas. Land use/land cover (LULC) patterns significantly impact LST, highlighting the need for a comprehensive understanding of their relationship. This study delves into the intricate interplay between LULC and LST in Pune city, India, employing a spatiotemporal analysis approach. The study utilizes satellite imagery from 2000, 2010, and 2022 to map LULC changes with Maximum likelihood, support vector machine and Random tree classification and their evaluation with accuracy and Kappa coefficient. Additionally, projected LULC for 2030 is prepared to assess future LST scenarios. Winter and summer LST data are analyzed to capture the seasonal dynamics of LST-LULC interactions. Pune has undergone significant LULC transformations, with the proportion of built-up areas increasing from 7% in 2000 to 47% in 2022, while barren land has decreased from 41% to 37%. Built-up areas exhibit consistently lower LST values during winter, while barren land tends to have the highest LST. In contrast, during summer, built-up and barren areas experience higher LST values, while water bodies and vegetation display relatively lower LST values due to their cooling effects. Mean LST in Pune has increased from 34.59°C in 2000 to 39.38°C in 2022, reflecting the changing thermal dynamics associated with LULC alterations. The expansion of built-up areas has contributed to temperature increases of up to 5.5°C in specific locations, highlighting the urban heat island effect. The study's findings emphasize the need for sustainable land use planning and development strategies to mitigate the adverse impacts of urbanization on LST. Policymakers, urban planners, and environmentalists can utilize these insights to develop informed land use policies, zoning regulations, and climate-smart urban development strategies.

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Section: Literature Reviewsmentioning

confidence: 99%

Exploring the Effects of Land Use/Land Cover (LULC) Modifications and Land Surface Temperature (LST) in Pune, Maharashtra with Anticipated LULC for 2030

2024

IJG

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“…Thus, comprehensive LULC cartography is indispensable for meticulous monitoring, land management, and hazard assessment (Jat Baloch et al, 2022;Acharya et al, 2023). Reducing vegetation cover emerges as a significant driver of elevated land surface temperatures, underscoring the urgent need for proactive land cover management (Moisa et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

Nigar,

Li,

Jat Baloch

et al. 2024

Front. Environ. Sci.

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Classifying land use and land cover (LULC) is essential for various environmental monitoring and geospatial analysis applications. This research focuses on land classification in District Sukkur, Pakistan, employing the comparison between machine and deep learning models. Three satellite indices, namely, NDVI, MNDWI, and NDBI, were derived from Landsat-8 data and utilized to classify four primary categories: Built-up Area, Water Bodies, Barren Land, and Vegetation. The main objective of this study is to evaluate and compare the effectiveness of comparison of machine and deep learning models. The machine learning models including Random Forest achieved an overall accuracy of 91.3% and a Kappa coefficient of 0.90. It accurately classified 2.7% of the area as Built-up Area, 1.9% as Water Bodies, 54.8% as Barren Land, and 40.4% as Vegetation. While slightly less accurate, Decision Tree model provided reliable classifications. Deep learning models showed significant accuracy, of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The CNN model achieved an impressive overall accuracy of 97.3%, excelling in classifying Water Bodies with User and Producer Accuracy exceeding 99%. The RNN model, with an overall accuracy of 96.2%, demonstrated strong performance in categorizing Vegetation. These findings offer valuable insights into the potential applications of machine learning and deep learning models for perfect land classifications, with implications for environmental monitoring management and geospatial analysis. The rigorous validation and comparative analysis of these models contribute to advancing remote sensing techniques and their utilization in land classification tasks. This research presents a significant contribution to the field and underscores the importance of precise land classification in the context of sustainable land management and environmental conservation.

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Investigating land cover changes and their impact on land surface temperature in Tay Ninh province, Vietnam

Thien,

Phuong,

Huong

2024

Environ Monit Assess

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Integration of geospatial technologies with multiple regression model for urban land use land cover change analysis and its impact on land surface temperature in Jimma City, southwestern Ethiopia

Cited by 10 publications

References 51 publications

Exploring the Effects of Land Use/Land Cover (LULC) Modifications and Land Surface Temperature (LST) in Pune, Maharashtra with Anticipated LULC for 2030

Exploring the Effects of Land Use/Land Cover (LULC) Modifications and Land Surface Temperature (LST) in Pune, Maharashtra with Anticipated LULC for 2030

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

Investigating land cover changes and their impact on land surface temperature in Tay Ninh province, Vietnam

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