Water Information Extraction Based on Multi-Model RF Algorithm and Sentinel-2 Image Data

Zhiqi Jiang, Yijun Wen, Gui Zhang and Xin Wu
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Zhiqi Jiang: School of Forestry, Central South University of Forestry and Technology, Changsha 410004, China
Yijun Wen: Faculty of Science, Central South University of Forestry and Technology, Changsha 410004, China
Gui Zhang: National Forest Fire Prevention Virtual Simulation Laboratory Teaching Center, Changsha 410004, China
Xin Wu: Key Laboratory of Digital Dongting Lake of Hunan Province, Changsha 410004, China

Sustainability, 2022, vol. 14, issue 7, 1-19

Abstract: For the Sentinel-2 multispectral satellite image remote sensing data, due to the rich spatial information, the traditional water body extraction methods cannot meet the needs of practical applications. In this study, a random forest-based RF_16 optimal combination model algorithm is proposed to extract water bodies. The research process uses Sentinel-2 multispectral satellite images and DEM data as the basic data, collected 24 characteristic variable indicators (B2, B3, B4, B8, B11, B12, NDVI, MSAVI, B5, B6, B7, B8A, NDI45, MCARI, REIP, S2REP, IRECI, PSSRa, NDWI, MNDWI, LSWI, DEM, SLOPE, SLOPE ASPECT), and constructed four combined models with different input variables. After analysis, it was determined that RF_16 was the optimal combination for extracting water body information in the study area. Model. The results show that: (1) The characteristic variables that have an important impact on the accuracy of the model are the improved normalized difference water index (MNDWI), band B2 (Blue), normalized water index (NDWI), B4 (Red), B3 (Green), and band B5 (Vegetation Red-Edge 1); (2) The water extraction accuracy of the optimal combined model RF_16 can reach 93.16%, and the Kappa coefficient is 0.8214. The overall accuracy is 0.12% better than the traditional Relief F algorithm. The RF_16 method based on the optimal combination model of random forest is an effective means to obtain high-precision water body information in the study area. It can effectively reduce the “salt and pepper effect” and the influence of mixed pixels such as water and shadows on the water extraction accuracy.

Keywords: random forest; Sentinel-2; red-edge remote sensing data; water extraction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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