Preprint Article Version 1 This version is not peer-reviewed

Towards a Strategy to Implement the China Wetland Mapping Using Landsat TM

Version 1 : Received: 11 May 2017 / Approved: 11 May 2017 / Online: 11 May 2017 (08:03:34 CEST)

How to cite: Zhang, H.; Niu, Z.; Zheng, Y. Towards a Strategy to Implement the China Wetland Mapping Using Landsat TM. Preprints 2017, 2017050098 (doi: 10.20944/preprints201705.0098.v1). Zhang, H.; Niu, Z.; Zheng, Y. Towards a Strategy to Implement the China Wetland Mapping Using Landsat TM. Preprints 2017, 2017050098 (doi: 10.20944/preprints201705.0098.v1).

Abstract

Wetlands are among the most bio-diverse and highest productivity ecosystems on earth, making their monitoring a high priority to conservation, protection and management interests. Although visual interpretation of satellite images is generally precise for monitoring wetlands, recent works have emphasized computerized classification methods because of the reduction in analyst time. However, it is difficult to automatically identify wetland solely based on spectral characteristics due to the complexity of wetland ecosystems. The ability to extract wetland information rapidly and accurately is the basis and the key to wetland mapping at a large scale. Here we propose an operational method to map China wetlands based on Landsat TM data and ancillary data. On the basis of theoretical analysis of wetland automatic classification, we developed a revised multi-layer wetland classification scheme and a rule-based classification model. In the latter, supervised classification (SVM and decision tree) and unsupervised classification (ISODATA) methods were tested. Four Landsat TM images, representing various wetland eco-regions in China (i.e. the Sanjiang Plain in the northeast China, the North China Plain, the Zoige Plateau in the southwest China and the Pearl River Estuary in southeast China), were automatically classified. The overall classification accuracies were 86.57%, 96.00%, 84.51% and 88.30%, respectively, which we considered to be satisfactory accuracy. Our results indicate that issues such as the resolution of geographic data and the understanding of wetland samples should be carefully addressed in the future.

Subject Areas

rule-based classification model; wetland remote sensing; SVM; TC-Wetness; China

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