Remote sensing and DEM-based approach for updating acid sulphate soil distribution: an example in Pearl River Estuary, South China

Shuisen Chen; Qinhuo Liu; Liangfu Chen; Juan Wang

doi:10.1117/12.559365

9 November 2004 Remote sensing and DEM-based approach for updating acid sulphate soil distribution: an example in Pearl River Estuary, South China

Shuisen Chen, Qinhuo Liu, Liangfu Chen, Juan Wang

Author Affiliations +

Proceedings Volume 5544, Remote Sensing and Modeling of Ecosystems for Sustainability; (2004) https://doi.org/10.1117/12.559365
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

Abstract

There is more population but less cultivated land in the Pearl River estuary region South China. The commissariat provision in the region is not enough for a long amount of time. The distribution of acid sulphate soil (ASS) is constantly changing for the Pearl River Delta as it expands seaward. It is important to research the ASS for land use planning and crop potentiality in appraising the Pearl River estuary. As more than 20 years have passed since the soil type maps and reports of the 2nd national soil investigation were made, soil quality problems have appeared. These problems can be solved by modern remote sensing and GIS techniques. The ASS units are updated by the analysis of the new remote sensing images and the application of digital elevation model (DEM). Changes in the ASS area are also produced by GIS. Finally, a practical distribution of ASS is presented in the map. The authors found out that the results of remote sensing and the GIS methods are of high reliability and spatial conclusiveness by comparing the result data with the past investigation report of soil and some field exploration data.

Citation Download Citation

Shuisen Chen, Qinhuo Liu, Liangfu Chen, and Juan Wang "Remote sensing and DEM-based approach for updating acid sulphate soil distribution: an example in Pearl River Estuary, South China", Proc. SPIE 5544, Remote Sensing and Modeling of Ecosystems for Sustainability, (9 November 2004); https://doi.org/10.1117/12.559365

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