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Remote Sens. 2016, 8(3), 246;

Automated Extraction and Mapping for Desert Wadis from Landsat Imagery in Arid West Asia

1,2,3,* , 4
Department of Geographic Information Science, Nanjing University, Nanjing 210023, China
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Center for Earth System Science, Tsinghua University, Beijing 100084, China
Author to whom correspondence should be addressed.
Academic Editors: Magaly Koch and Prasad S. Thenkabail
Received: 17 February 2016 / Revised: 7 March 2016 / Accepted: 11 March 2016 / Published: 16 March 2016
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Wadis, ephemeral dry rivers in arid desert regions that contain water in the rainy season, are often manifested as braided linear channels and are of vital importance for local hydrological environments and regional hydrological management. Conventional methods for effectively delineating wadis from heterogeneous backgrounds are limited for the following reasons: (1) the occurrence of numerous morphological irregularities which disqualify methods based on physical shape; (2) inconspicuous spectral contrast with backgrounds, resulting in frequent false alarms; and (3) the extreme complexity of wadi systems, with numerous tiny tributaries characterized by spectral anisotropy, resulting in a conflict between global and local accuracy. To overcome these difficulties, an automated method for extracting wadis (AMEW) from Landsat-8 Operational Land Imagery (OLI) was developed in order to take advantage of the complementarity between Water Indices (WIs), which is a technique of mathematically combining different bands to enhance water bodies and suppress backgrounds, and image processing technologies in the morphological field involving multi-scale Gaussian matched filtering and a local adaptive threshold segmentation. Evaluation of the AMEW was carried out in representative areas deliberately selected from Jordan, SW Arabian Peninsula in order to ensure a rigorous assessment. Experimental results indicate that the AMEW achieved considerably higher accuracy than other effective extraction methods in terms of visual inspection and statistical comparison, with an overall accuracy of up to 95.05% for the entire area. In addition, the AMEW (based on the New Water Index (NWI)) achieved higher accuracy than other methods (the maximum likelihood classifier and the support vector machine classifier) used for bulk wadi extraction. View Full-Text
Keywords: wadi extraction; water index; Gaussian matched filtering; local adaptive threshold segmentation wadi extraction; water index; Gaussian matched filtering; local adaptive threshold segmentation

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Liu, Y.; Chen, X.; Yang, Y.; Sun, C.; Zhang, S. Automated Extraction and Mapping for Desert Wadis from Landsat Imagery in Arid West Asia. Remote Sens. 2016, 8, 246.

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