Detection of short-term urban land use changes by combining SAR time series images and spectral angle mapping

Abstract

Rapid urban sprawl and re-construction of old towns have been leading to great changes of land use in cities of China. To witness short-term urban land use changes, rapid or real time remote sensing images and effective detection methods are required. With the availability of short repeat cycle, relatively high spatial resolution, and weather-independent Synthetic Aperture Radar (SAR) remotely sensed data, detection of short-term urban land use changes becomes possible. This paper adopts newly released Sentinel-1 SAR data for urban change detection in Tianhe District of Guangzhou City in Southern China, where dramatic urban redevelopment practices have been taking place in past years. An integrative method that combines the SAR time series data and a spectral angle mapping (SAM) was developed and applied to detect the short-term land use changes. Linear trend transformations of the SAR time series data were first conducted to reveal patterns of substantial changes. Spectral mixture analysis was then conducted to extract temporal endmembers to reflect the land development patterns based on the SAR backscattering intensities over time. Moreover, SAM was applied to extract the information of significant increase and decrease patterns. The results of validation and method comparison showed a significant capability of both the proposed method and the SAR time series images for detecting the short-term urban land use changes. The method received an overall accuracy of 78%, being more accurate than that using a bi-temporal image change detection method. The results revealed land use conversions due to the removal of old buildings and their replacement by new construction. This implies that SAR time series data reflects the spatiotemporal evolution of urban constructed areas within a short time period and this study provided the potential for detecting changes that requires continuously short-term capability, and could be potential in other landscapes.

Keywords

Sentinel-1 SAR / time series images / urban land use change detection / temporal endmember / spectral angle mapping

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Acknowledgments:

This research was supported by Key Program of the National Natural Science Foundation of China (Grant No. U1301253), Guangdong Provincial Science and Technology Project (Nos. 2017A050501031 and 2017A040406022), Guangzhou Science and Technology Projects (Nos. 201807010048 and 201804020034), and the International Postdoctoral Exchange Fellowship Program 2017 (No. 20170029). The authors would like to express their thanks to European Space Agency for providing Sentinel-1 SAR data as well as ESA-SNAP software in conducting research, our colleagues Haiyan Deng and Li Zhao for their assistance in collecting field validation, and processing images, and the colleagues from the Guangzhou Urban Renewal Bureau for their good suggestions. We also would like to thank the editors and anonymous reviewers for their instructive comments.

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