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Estimation of impervious surface percentage of river network regions using an ensemble leaning of CART analysis |
| LI Xiaoning1, ZHANG Youjing1,2, SHE Yuanjian1, CHEN Liwen1, CHEN Jingxin3 |
1. School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China;
2. State Key Laboratory of Hydrology-Water Resource and Hydraulic Engineering, Hohai University, Nanjing 210098, China;
3. Surveying Products Quality Supervision Station of Jiangsu Province, Nanjing 210013, China |
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Abstract The rapid expansion of impervious surface has become a major factor affecting ecosystem health of the high density river network. This paper provides an approach to estimate impervious surface percent (ISP) through the ensemble leaning of CART analysis based on variable precision rough sets (VPRS). Landsat TM and ALOS imagery were utilized to construct the ISP predictive model; then, in order to get the best attribute variables of CART decision tree, the authors adopted VPRS to extract optimum feature subset from multi-source feature sets. The results illustrate the validity of this ensemble leaning, and prove that this method can obtain estimated accuracy better than the traditional single CART method. However, in the initial estimation results, ISP's high value area is underestimated relatively seriously. The authors have discovered that the temperature vegetation dryness index (TVDI) and ISP have an intensive relationship with each other: the increase of ISP will cause the increase of local TVDI significantly. Therefore, the post-processing rule extracted from the relationship is used to improve the results. According to the verification results, the method combined with VPRS reduction and post-processing rule in CART algorithm has fairly higher analysis precision than the traditional single CART learning algorithm. The root mean square error between estimated ISP value and reference ISP is 10.0%, with the correlation coefficient being 0.89, so it can be used to estimate the ISP in plain river network region.
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| Keywords
UAV images
local enhancement
match
variable window
gradient difference
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Issue Date: 21 October 2013
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2013,
Vol. 25
