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Article Navigation >  Chinese Geographical Science  > 2017  >  27(4): 614-625

HU Deyong, CHEN Shanshan, QIAO Kun, CAO Shisong. Integrating CART Algorithm and Multi-source Remote Sensing Data to Estimate Sub-pixel Impervious Surface Coverage: A Case Study from Beijing Municipality, China[J]. Chinese Geographical Science, 2017, 27(4): 614-625. doi: 10.1007/s11769-017-0882-x
Citation: HU Deyong, CHEN Shanshan, QIAO Kun, CAO Shisong. Integrating CART Algorithm and Multi-source Remote Sensing Data to Estimate Sub-pixel Impervious Surface Coverage: A Case Study from Beijing Municipality, China[J]. Chinese Geographical Science, 2017, 27(4): 614-625. doi: 10.1007/s11769-017-0882-x
shu

Integrating CART Algorithm and Multi-source Remote Sensing Data to Estimate Sub-pixel Impervious Surface Coverage: A Case Study from Beijing Municipality, China

doi: 10.1007/s11769-017-0882-x
  • 1.

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;

  • 2.

    College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China

Funds:  Under the auspices of National Natural Science Foundation of China (No. 41671339)
More Information
  • Corresponding author: CHEN Shanshan.E-mail:amchenshanshan@163.com
  • Received Date: 2016-05-02
  • Rev Recd Date: 2016-09-01
  • Publish Date: 2017-08-27
  • The sub-pixel impervious surface percentage (SPIS) is the fraction of impervious surface area in one pixel, and it is an important indicator of urbanization. Using remote sensing data, the spatial distribution of SPIS values over large areas can be extracted, and these data are significant for studies of urban climate, environment and hydrology. To develop a stabilized, multi-temporal SPIS estimation method suitable for typical temperate semi-arid climate zones with distinct seasons, an optimal model for estimating SPIS values within Beijing Municipality was built that is based on the classification and regression tree (CART) algorithm. First, models with different input variables for SPIS estimation were built by integrating multi-source remote sensing data with other auxiliary data. The optimal model was selected through the analysis and comparison of the assessed accuracy of these models. Subsequently, multi-temporal SPIS mapping was carried out based on the optimal model. The results are as follows: 1) multi-seasonal images and nighttime light (NTL) data are the optimal input variables for SPIS estimation within Beijing Municipality, where the intra-annual variability in vegetation is distinct. The different spectral characteristics in the cultivated land caused by the different farming characteristics and vegetation phenology can be detected by the multi-seasonal images effectively. NLT data can effectively reduce the misestimation caused by the spectral similarity between bare land and impervious surfaces. After testing, the SPIS modeling correlation coefficient (r) is approximately 0.86, the average error (AE) is approximately 12.8%, and the relative error (RE) is approximately 0.39. 2) The SPIS results have been divided into areas with high-density impervious cover (70%-100%), medium-density impervious cover (40%-70%), low-density impervious cover (10%-40%) and natural cover (0%-10%). The SPIS model performed better in estimating values for high-density urban areas than other categories. 3) Multi-temporal SPIS mapping (1991-2016) was conducted based on the optimized SPIS results for 2005. After testing, AE ranges from 12.7% to 15.2%, RE ranges from 0.39 to 0.46, and r ranges from 0.81 to 0.86. It is demonstrated that the proposed approach for estimating sub-pixel level impervious surface by integrating the CART algorithm and multi-source remote sensing data is feasible and suitable for multi-temporal SPIS mapping of areas with distinct intra-annual variability in vegetation.
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Integrating CART Algorithm and Multi-source Remote Sensing Data to Estimate Sub-pixel Impervious Surface Coverage: A Case Study from Beijing Municipality, China

doi: 10.1007/s11769-017-0882-x
  • 1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;
  • 2. College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China
Funds:  Under the auspices of National Natural Science Foundation of China (No. 41671339)
    Corresponding author: CHEN Shanshan.E-mail:amchenshanshan@163.com
  • Received Date: 2016-05-02
  • Rev Recd Date: 2016-09-01
  • Publish Date: 2017-08-27

Abstract: The sub-pixel impervious surface percentage (SPIS) is the fraction of impervious surface area in one pixel, and it is an important indicator of urbanization. Using remote sensing data, the spatial distribution of SPIS values over large areas can be extracted, and these data are significant for studies of urban climate, environment and hydrology. To develop a stabilized, multi-temporal SPIS estimation method suitable for typical temperate semi-arid climate zones with distinct seasons, an optimal model for estimating SPIS values within Beijing Municipality was built that is based on the classification and regression tree (CART) algorithm. First, models with different input variables for SPIS estimation were built by integrating multi-source remote sensing data with other auxiliary data. The optimal model was selected through the analysis and comparison of the assessed accuracy of these models. Subsequently, multi-temporal SPIS mapping was carried out based on the optimal model. The results are as follows: 1) multi-seasonal images and nighttime light (NTL) data are the optimal input variables for SPIS estimation within Beijing Municipality, where the intra-annual variability in vegetation is distinct. The different spectral characteristics in the cultivated land caused by the different farming characteristics and vegetation phenology can be detected by the multi-seasonal images effectively. NLT data can effectively reduce the misestimation caused by the spectral similarity between bare land and impervious surfaces. After testing, the SPIS modeling correlation coefficient (r) is approximately 0.86, the average error (AE) is approximately 12.8%, and the relative error (RE) is approximately 0.39. 2) The SPIS results have been divided into areas with high-density impervious cover (70%-100%), medium-density impervious cover (40%-70%), low-density impervious cover (10%-40%) and natural cover (0%-10%). The SPIS model performed better in estimating values for high-density urban areas than other categories. 3) Multi-temporal SPIS mapping (1991-2016) was conducted based on the optimized SPIS results for 2005. After testing, AE ranges from 12.7% to 15.2%, RE ranges from 0.39 to 0.46, and r ranges from 0.81 to 0.86. It is demonstrated that the proposed approach for estimating sub-pixel level impervious surface by integrating the CART algorithm and multi-source remote sensing data is feasible and suitable for multi-temporal SPIS mapping of areas with distinct intra-annual variability in vegetation.

HU Deyong, CHEN Shanshan, QIAO Kun, CAO Shisong. Integrating CART Algorithm and Multi-source Remote Sensing Data to Estimate Sub-pixel Impervious Surface Coverage: A Case Study from Beijing Municipality, China[J]. Chinese Geographical Science, 2017, 27(4): 614-625. doi: 10.1007/s11769-017-0882-x
Citation: HU Deyong, CHEN Shanshan, QIAO Kun, CAO Shisong. Integrating CART Algorithm and Multi-source Remote Sensing Data to Estimate Sub-pixel Impervious Surface Coverage: A Case Study from Beijing Municipality, China[J]. Chinese Geographical Science, 2017, 27(4): 614-625. doi: 10.1007/s11769-017-0882-x
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