Mapping impervious surfaces with integrated method of classification and regression trees (CART) and linear spectral mixture analysis (LSMA) based on error analysis

Authors: Jin Wang*, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Zhifeng Wu, School of Geographical Sciences, Guangzhou University, Changshan Wu, Department of Geography, University of Wisconsin-Milwaukee, Paolo Tarolli , Department of Land, Environment, Agriculture and Forestry, University of Padova, Jinsong Chen, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
Topics: Urban Geography, Remote Sensing
Keywords: impervious surface; CART; LSMA; remote sensing
Session Type: Paper
Day: 4/6/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Balcony A, Marriott, Mezzanine Level
Presentation File: No File Uploaded

Classification and regression tree (CART) has been widely implemented to estimate impervious surface, an important indicator of urbanization and environmental quality. Although the CART algorithm gains higher overall accuracy than linear regression
models, only very few studies have noticed that reliability of CART is affected by systematic errors. Additionally, CART typically overestimates impervious surfaces in low-density urban areas and underestimates them in high-density urban areas. The
primary objective of this study is to develop an improved integrated method to estimate impervious surface with higher accuracy by reducing the systematic errors of CART. This integrated method was applied to three urban areas, Chicago (United States), Venice (Italy), and Guangzhou (China) to examine its effectiveness. When compared with the conventional CART, overall mean average error (MAE) and root mean square error (RMSE) of improved method are decreased by 22.64% and 20.93%, respectively, and R2 rises from 0.9 to 0.96. In high-density impervious surfaces, where intensely developed urban area is located, the MAE and RMSE for the improved method are 0.066 and 0.088, respectively, largely improved from 0.100 to 0.130. Since accurate estimation of high-density impervious surfaces is the fundamental issue for monitoring and understanding the urban environment, the improved method demonstrated in this study is significant.

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