Application of random forest and Sentinel-1/2 in the information extraction of impervious layers in Dongying City

LIU Chunting; FENG Quanlong; JIN Dingjian; SHI Tongguang; LIU Jiantao; ZHU Mingshui

doi:10.6046/zrzyyg.2020310

2021 Vol. 33, No. 3

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Article navigation > Remote Sensing for Natural Resources > 2021 > 33(3): 253-261

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LIU Chunting, FENG Quanlong, JIN Dingjian, SHI Tongguang, LIU Jiantao, ZHU Mingshui. 2021. Application of random forest and Sentinel-1/2 in the information extraction of impervious layers in Dongying City. Remote Sensing for Natural Resources, 33(3): 253-261. doi: 10.6046/zrzyyg.2020310

Citation:

LIU Chunting, FENG Quanlong, JIN Dingjian, SHI Tongguang, LIU Jiantao, ZHU Mingshui. 2021. Application of random forest and Sentinel-1/2 in the information extraction of impervious layers in Dongying City. Remote Sensing for Natural Resources, 33(3): 253-261. doi: 10.6046/zrzyyg.2020310

Application of random forest and Sentinel-1/2 in the information extraction of impervious layers in Dongying City

1. School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China；
2. College of Land Science and Technology, China Agriculture University, Beijing 100083, China；
3. China Aero Geophysical Survey Remote Sensing Center for Natural Resources, Beijing 100083, China

Received Date: 27 September 2020

Publish Date: 15 September 2021

Abstract

Abstract

An impervious layer is an important indicator of human activities. Timely and accurate information of impervious layers is of great significance for the protection of the ecological environment. Taking the Yellow River Delta (Dongying City) as the study area, this study explores a novel extraction method of impervious layers by combining the random forest classification with Sentinel-1/2 data. According to comparative experiments, the confusion between dark and light impervious layers and bare soil can be reduced through the combination of the random forest algorithm with surface reflectance characteristics, texture characteristics, and backscatter coefficient, thus effectively improving the estimation accuracy of impervious layers (overall accuracy: 93.37%, Kappa coefficient: 0.925 8). The results of this study reveal that the random forest algorithm combined with Sentinel-1/2 data is a promising approach in the information extraction of impervious layers, which will provide a reference for the remote sensing monitoring of the Yellow River Delta through the integration of multi-source data.
- Dongying City /
- impervious layer /
- Sentinel-2 /
- Sentinel-1 /
- texture /
- random forest /

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References

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