Change Detection for LULC Classification Using Satellite Imagery

Abstract Change Detection for LULC Classification Using Satellite Imagery. To maintain the present natural resources and to understand the causes and consequences of over exploitation of soil and water resources the land use, a land cover mapping and monitoring was done in the study area i.e. Sakoli Taluka. In this study, satellite images for 2015 and 2025 were used for LULC (Land Use/ Land Cover) Supervised / Visually classification. For the classification purposes, Five LULC classes are derived. 1. Agriculture Land, 2.Forest Land and 3.Transportation 4.Urban built-up, 5.Water body For Accuracy Assessment Classification error matrix analysis was done. Change Detection between both the images for all the land use and land cover classes were computed. One decade Study for Change detection for Sakoli taluka district bhandara using scale 1: 10000 on Landsat 8 imagery between 2015 to 2025. Change Detection for LULC Classification for five classes Agriculture Land Decrease 2025 in 0.95% as compare 2015, Forest Land Decrease 2025 in 1.93% as compare 2015 and Urban built-up Land Increases 0.43% as compare 2015 and Water body Increases 0.66% as compare 2015.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-12-31
DOI
https://doi.org/10.5281/zenodo.18113705
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

Change Detection for LULC Classification Using Satellite Imagery

Shushma Damodare, Pradip Shahare
Zenodo (CERN European Organization for Nuclear Research)
Remote Sensing in Agriculture
article

Change Detection for LULC Classification Using Satellite Imagery

Shushma Damodare, Pradip Shahare
article en

Abstract

Abstract Change Detection for LULC Classification Using Satellite Imagery. To maintain the present natural resources and to understand the causes and consequences of over exploitation of soil and water resources the land use, a land cover mapping and monitoring was done in the study area i.e. Sakoli Taluka. In this study, satellite images for 2015 and 2025 were used for LULC (Land Use/ Land Cover) Supervised / Visually classification. For the classification purposes, Five LULC classes are derived. 1. Agriculture Land, 2.Forest Land and 3.Transportation 4.Urban built-up, 5.Water body For Accuracy Assessment Classification error matrix analysis was done. Change Detection between both the images for all the land use and land cover classes were computed. One decade Study for Change detection for Sakoli taluka district bhandara using scale 1: 10000 on Landsat 8 imagery between 2015 to 2025. Change Detection for LULC Classification for five classes Agriculture Land Decrease 2025 in 0.95% as compare 2015, Forest Land Decrease 2025 in 1.93% as compare 2015 and Urban built-up Land Increases 0.43% as compare 2015 and Water body Increases 0.66% as compare 2015.

Zenodo (CERN European Organization for Nuclear Research)
Rashtrasant Tukadoji Maharaj Nagpur University (IN)
Life in Land
Openalex Percentile: Top 28%
Remote Sensing in Agriculture
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Change Detection for LULC Classification Using Satellite Imagery — Shushma Damodare, Pradip Shahare · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS