A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Tracking land use land cover changes in the twin cities of Odisha, India using a machine learning based Google Earth Engine approach
The current study is based on analyzing the land use land cover (LULC) changes and its corresponding effects on water and land surface temperature (LST) on the twin cities of Odisha, i.e. Bhubaneswar and Cuttack using a machine learning based Google Earth Engine (GEE) platform. A random forest (RF) classification model was adopted due to its simplicity and high popularity for providing accurate results. For the study, Landsat 8 (OLI/TRIS) and Sentinel 2 were accessed via GEE. With an overall accuracy of about 99% using an RF algorithm, the results indicate an alarming situation for the cities, especially Cuttack where there has been a reduction in water by about 59% in response to increments in the built-up area by 90% and LST by 1.5%. With an expanding city radius, Bhubaneswar faced a reduction in water by 28% in response to the built-up area and LST increase by about 17% and 3.4%. respectively.
Tracking land use land cover changes in the twin cities of Odisha, India using a machine learning based Google Earth Engine approach
The current study is based on analyzing the land use land cover (LULC) changes and its corresponding effects on water and land surface temperature (LST) on the twin cities of Odisha, i.e. Bhubaneswar and Cuttack using a machine learning based Google Earth Engine (GEE) platform. A random forest (RF) classification model was adopted due to its simplicity and high popularity for providing accurate results. For the study, Landsat 8 (OLI/TRIS) and Sentinel 2 were accessed via GEE. With an overall accuracy of about 99% using an RF algorithm, the results indicate an alarming situation for the cities, especially Cuttack where there has been a reduction in water by about 59% in response to increments in the built-up area by 90% and LST by 1.5%. With an expanding city radius, Bhubaneswar faced a reduction in water by 28% in response to the built-up area and LST increase by about 17% and 3.4%. respectively.
Tracking land use land cover changes in the twin cities of Odisha, India using a machine learning based Google Earth Engine approach
Nayak, Abhayaa (author) / Kar, Anil Kumar (author)
Urban Water Journal ; 22 ; 291-312
2025-03-16
22 pages
Article (Journal)
Electronic Resource
English
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