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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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/8/1348 |
| _version_ | 1797569745027858432 |
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| author | Victoria L. Inman Mitchell B. Lyons |
| author_facet | Victoria L. Inman Mitchell B. Lyons |
| author_sort | Victoria L. Inman |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:15:54Z |
| format | Article |
| id | doaj.art-dd365b810a544a34b8487e26adf49a8a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:15:54Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-dd365b810a544a34b8487e26adf49a8a2023-11-19T22:35:00ZengMDPI AGRemote Sensing2072-42922020-04-01128134810.3390/rs12081348Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth EngineVictoria L. Inman0Mitchell B. Lyons1Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW 2052, AustraliaCentre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW 2052, AustraliaAccurate 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.https://www.mdpi.com/2072-4292/12/8/1348Okavango Deltainundation mapsinundation extentLandsatGoogle Earth Engineautomated time series |
| spellingShingle | Victoria L. Inman Mitchell B. Lyons Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine Remote Sensing Okavango Delta inundation maps inundation extent Landsat Google Earth Engine automated time series |
| title | Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine |
| title_full | Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine |
| title_fullStr | Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine |
| title_full_unstemmed | Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine |
| title_short | Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine |
| title_sort | automated inundation mapping over large areas using landsat data and google earth engine |
| topic | Okavango Delta inundation maps inundation extent Landsat Google Earth Engine automated time series |
| url | https://www.mdpi.com/2072-4292/12/8/1348 |
| work_keys_str_mv | AT victorialinman automatedinundationmappingoverlargeareasusinglandsatdataandgoogleearthengine AT mitchellblyons automatedinundationmappingoverlargeareasusinglandsatdataandgoogleearthengine |