Expansion monitoring of construction land based on SAR time series: A case study of Xinbei District, Changzhou
SUN Chao1,2(), CHEN Zhenjie1,2(), WANG Beibei1,2
1. School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China 2. Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation, Nanjing 210023, China
With the acceleration of urbanization process, the size of the city is growing, and hence it is of great importance to grasp the change of construction land quickly and accurately for the sustainable development of cities. Because SAR images are not affected by the weather, it is possible to use SAR time series to study the expansion of construction land. There are two kinds of time series structures in SAR, which are named “Z” structure and “V” structure in this paper. In view of the previous studies that only consider the “Z” structure but not the “V” structure, this study proposes a construction land extension method based on time series adaptive segmentation. The original time series is segmented in an adaptive manner, the average value of the segments is used as the characteristic value, and the extended area of construction land is extracted by the decision tree. The accuracy and completeness of the method are 89.60% and 92.73% respectively. The results are as follows: ① The method proposed in this paper can effectively monitor the expansion of construction land. Compared with that of the dynamic time warping(DTW) method, the accuracy is increased by 1.80 percentage points and the integrity rate is increased by 1.27 percentage points. ② From 2015 to 2019, construction land in Xinbei District of Changzhou increased by 557.96 hectares, mainly in the south and the southeast.
孙超, 陈振杰, 王贝贝. 基于SAR时间序列的建设用地扩展监测——以常州市新北区为例[J]. 国土资源遥感, 2020, 32(4): 154-162.
SUN Chao, CHEN Zhenjie, WANG Beibei. Expansion monitoring of construction land based on SAR time series: A case study of Xinbei District, Changzhou. Remote Sensing for Land & Resources, 2020, 32(4): 154-162.
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2020, Vol. 32 
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