Keeping it simple: Monitoring flood extent in large data-poor wetlands using MODIS SWIR data
Author(s) -
Piotr Wolski,
Mike MurrayHudson,
Kgalalelo Thito,
Lin Cassidy
Publication year - 2017
Publication title -
international journal of applied earth observation and geoinformation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.623
H-Index - 98
eISSN - 1872-826X
pISSN - 1569-8432
DOI - 10.1016/j.jag.2017.01.005
Subject(s) - flood myth , wetland , remote sensing , thresholding , underlay , environmental science , geography , delta , raw data , hydrology (agriculture) , ecology , computer science , geology , engineering , image (mathematics) , telecommunications , artificial intelligence , signal to noise ratio (imaging) , geotechnical engineering , archaeology , aerospace engineering , biology , programming language
Characterising inundation conditions for flood-pulsed wetlands is a critical first step towards assessment of flood risk as well as towards understanding hydrological dynamics that underlay their ecology and functioning. In this paper, we develop a series of inundation maps for the Okavango Delta, Botswana, based on the thresholding of the SWIR band (b7) MODIS MCD43A4 product. We show that in the Okavango Delta, SWIR is superior to other spectral bands or derived indices, and illustrate an innovative way of defining the spectral threshold used to separate inundated from dry land. The threshold is determined dynamically for each scene based on reflectances of training areas capturing end-members of the inundation spectrum. The method provides a very good accuracy and is suitable for automated processing.
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