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Impacts of irrigation tank restoration on water bodies and croplands in Telangana State of India using Landsat time series data and machine learning algorithms

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Relation http://oar.icrisat.org/12137/
https://www.tandfonline.com/doi/full/10.1080/10106049.2023.2186493
https://doi.org/10.1080/10106049.2023.2186493
 
Title Impacts of irrigation tank restoration on water bodies and croplands in Telangana State of India using Landsat time series data and machine learning algorithms
 
Creator Gumma, M K
Panjala, P
Deevi, K C
Bellam, P K
Dheeravath, V
Mohammed, I
 
Subject Telangana
Irrigation
 
Description In 2014, the State of Telangana in southern India began repairing and restoring more than 46,000 irrigation water tanks (artificial reservoirs) under the Mission Kakatiya project with an investment in excess of USD 2 billion. In this study, we attempted to map the temporal changes that have occurred in cropland areas and water bodies as a result of the project, using remote sensing imagery and applying land use/land cover (LULC) mapping algorithms. We used 16-day time series data from Landsat 8 to study the spatial
distribution of changes in water bodies and cropland areas over the 2013–18 period. Ground survey information was used to assess the pixel-based accuracy of the Landsat-derived data. The areas served by these tanks were identified on the basis of training data and Random Forest algorithms using Google Earth Engine. Our spatial analysis revealed a substantial increase in cropped area under irrigation and expansion of water bodies over the study period. We observed a 20% increase in total tank area in 2017–18 and total cropland and irrigated area expansion of the order of 0.6M ha and 0.2M ha, respectively. A comparison of ground survey data and four LULC classes derived from Landsat temporal imagery showed an overall accuracy of 87%, significantly
correlated with national agriculture statistics. Periodic monitoring based on remote sensing has proved to be an effective method of capturing LULC changes resulting from the Mission Kakatiya interventions. Higher-resolution satellite data can further improve the accuracy of estimates.
 
Publisher Taylor & Francis
 
Date 2023-02-26
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Rights cc_attribution
 
Identifier http://oar.icrisat.org/12137/1/Geocarto%20International_38_1_01-19_2023.pdf
Gumma, M K and Panjala, P and Deevi, K C and Bellam, P K and Dheeravath, V and Mohammed, I (2023) Impacts of irrigation tank restoration on water bodies and croplands in Telangana State of India using Landsat time series data and machine learning algorithms. Geocarto International, 38 (1). 01-19. ISSN 1010-6049