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CART-RF Classification with Multifilter for Monitoring Land Use Changes Based on MODIS Time-Series Data: A Case Study from Jiangsu Province, China

Author

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  • Le’an Qu

    (School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
    School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China)

  • Zhenjie Chen

    (School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China)

  • Manchun Li

    (School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China)

Abstract

The periodic determination of land use changes over large areas is crucial for improving our understanding of land system dynamics. Jiangsu lies at the center of China’s Yangtze Delta and has one of the fastest-developing economies in China. However, it is also a region where serious conflicts exist between the available land resources and the human demand for land. To address these conflicts, it is important to analyze the patterns of land use change in Jiangsu, as they can serve as a useful reference for other rapidly urbanizing regions in China as well as other developing countries. In this study, we propose a method of classification and regression tree-random forest (CART-RF) classification with a multifilter based on time-series Moderate Resolution Imaging Spectroradiometer (MODIS) imaging data. The proposed method integrates the CART decision tree and the random forest algorithms (CART-RF) to obtain accurate yearly land use data for large areas from multivariate time-series remote sensing data and employs a spatial-temporal-logical filter to exclude any abnormal changes in the multivariate time-series pixel data. The obtained results indicated that (1) the CART-RF classifier is effective for land use classification based on the multivariate time-series MODIS data, with the overall classification accuracy being greater than 90%; (2) the use of the proposed combinatorial spatial-temporal-logical filtering method effectively eliminates most anomalous changes and minimizes the effects of “salt-and-pepper” noise; and (3) from 2000 to 2015, land use in Jiangsu province underwent significant and spatiotemporally heterogeneous changes on a province-wide scale, owing to various factors, such as those related to the economy, location, and government policies. These changes were manifested as continuous expansions in the built-up land at the expense of farmland. While this expansion of built-up land has been very rapid in southern Jiangsu, especially in the region close to Yangtze River Delta, it has been relatively slower in northern Jiangsu.

Suggested Citation

  • Le’an Qu & Zhenjie Chen & Manchun Li, 2019. "CART-RF Classification with Multifilter for Monitoring Land Use Changes Based on MODIS Time-Series Data: A Case Study from Jiangsu Province, China," Sustainability, MDPI, vol. 11(20), pages 1-23, October.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:20:p:5657-:d:276236
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    References listed on IDEAS

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    2. Chunyu Li & Rong Cai & Wei Tian & Junna Yuan & Xiaofei Mi, 2023. "Land Cover Classification by Gaofen Satellite Images Based on CART Algorithm in Yuli County, Xinjiang, China," Sustainability, MDPI, vol. 15(3), pages 1-18, January.

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