Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine

Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper, we automate a method (thresholding of the short-wave infrared band) for classifying peak inundation in the Okavango Delta, northern Botswana, using Landsat imagery a...
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Gespeichert in:

Autor*in:	Victoria L. Inman [verfasserIn] Mitchell B. Lyons [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Okavango Delta inundation maps inundation extent Landsat Google Earth Engine automated time series

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 12(2020), 8, p 1348
Übergeordnetes Werk:	volume:12 ; year:2020 ; number:8, p 1348

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs12081348

Katalog-ID:	DOAJ061158127

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author	Victoria L. Inman
spellingShingle	Victoria L. Inman misc Okavango Delta misc inundation maps misc inundation extent misc Landsat misc Google Earth Engine misc automated time series misc Science misc Q Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine
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topic_title	Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine Okavango Delta inundation maps inundation extent Landsat Google Earth Engine automated time series
topic	misc Okavango Delta misc inundation maps misc inundation extent misc Landsat misc Google Earth Engine misc automated time series misc Science misc Q
topic_unstemmed	misc Okavango Delta misc inundation maps misc inundation extent misc Landsat misc Google Earth Engine misc automated time series misc Science misc Q
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title	Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine
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title_sort	automated inundation mapping over large areas using landsat data and google earth engine
title_auth	Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine
abstract	Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper, we automate a method (thresholding of the short-wave infrared band) for classifying peak inundation in the Okavango Delta, northern Botswana, using Landsat imagery and Google Earth Engine. Inundation classification in the Okavango Delta is complex owing to the spectral overlap between inundated areas covered with aquatic vegetation and dryland vegetation classes on satellite imagery, and classifications have predominately been implemented on broad spatial resolution imagery. We present the longest time series to date (1990–2019) of inundation maps for the peak flood season at a high spatial resolution (30 m) for the Okavango Delta. We validated the maps using image-based and in situ data accuracy assessments, with overall accuracy ranging from 91.5% to 98.1%. Use of Landsat imagery resulted in consistently lower (on average, 692 km<sup<2</sup<) estimates of inundation extent than previous studies that used Moderate Resolution Imaging Spectroradiometer (MODIS) and National Oceanic and Atmospheric Administration Advanced Very-High-Resolution Radiometer (NOAA AVHRR) imagery, likely owing to the increased number of mixed pixels that occur when using broad spatial resolution imagery, which can lead to overestimations of the size of inundated areas. We provide the inundation maps and Google Earth Engine code for public use. This classification method can likely be adapted for inundation mapping in other regions.
abstractGer	Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper, we automate a method (thresholding of the short-wave infrared band) for classifying peak inundation in the Okavango Delta, northern Botswana, using Landsat imagery and Google Earth Engine. Inundation classification in the Okavango Delta is complex owing to the spectral overlap between inundated areas covered with aquatic vegetation and dryland vegetation classes on satellite imagery, and classifications have predominately been implemented on broad spatial resolution imagery. We present the longest time series to date (1990–2019) of inundation maps for the peak flood season at a high spatial resolution (30 m) for the Okavango Delta. We validated the maps using image-based and in situ data accuracy assessments, with overall accuracy ranging from 91.5% to 98.1%. Use of Landsat imagery resulted in consistently lower (on average, 692 km<sup<2</sup<) estimates of inundation extent than previous studies that used Moderate Resolution Imaging Spectroradiometer (MODIS) and National Oceanic and Atmospheric Administration Advanced Very-High-Resolution Radiometer (NOAA AVHRR) imagery, likely owing to the increased number of mixed pixels that occur when using broad spatial resolution imagery, which can lead to overestimations of the size of inundated areas. We provide the inundation maps and Google Earth Engine code for public use. This classification method can likely be adapted for inundation mapping in other regions.
abstract_unstemmed	Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper, we automate a method (thresholding of the short-wave infrared band) for classifying peak inundation in the Okavango Delta, northern Botswana, using Landsat imagery and Google Earth Engine. Inundation classification in the Okavango Delta is complex owing to the spectral overlap between inundated areas covered with aquatic vegetation and dryland vegetation classes on satellite imagery, and classifications have predominately been implemented on broad spatial resolution imagery. We present the longest time series to date (1990–2019) of inundation maps for the peak flood season at a high spatial resolution (30 m) for the Okavango Delta. We validated the maps using image-based and in situ data accuracy assessments, with overall accuracy ranging from 91.5% to 98.1%. Use of Landsat imagery resulted in consistently lower (on average, 692 km<sup<2</sup<) estimates of inundation extent than previous studies that used Moderate Resolution Imaging Spectroradiometer (MODIS) and National Oceanic and Atmospheric Administration Advanced Very-High-Resolution Radiometer (NOAA AVHRR) imagery, likely owing to the increased number of mixed pixels that occur when using broad spatial resolution imagery, which can lead to overestimations of the size of inundated areas. We provide the inundation maps and Google Earth Engine code for public use. This classification method can likely be adapted for inundation mapping in other regions.
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title_short	Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine
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Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine

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