Impacts of irrigation tank restoration on water bodies and croplands in Telangana State of India using Landsat time series data and machine learning algorithms. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Gumma, Murali Krishna; Panjala, Pranay; Deevi, Kumara Charyulu; Bellam, Pavan Kumar; Dheeravath, Venkateswarlu; Mohammed, Ismail
• Is Part Of: Geocarto international. Volume 38:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Land use change -- irrigated areas -- Mission Kakatiya -- Telangana -- Landsat -- irrigation tanks and NDVI; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2023.2186493
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: 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 0.6 M ha and 0.2 M 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.