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Article

New ICESat-2 Satellite LiDAR Data Allow First Global Lowland DTM Suitable for Accurate Coastal Flood Risk Assessment

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Data for Sustainability, 4571 AK Axel, The Netherlands
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Deltares, P.O. Box 177, 2600 MH Delft, The Netherlands
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NUS Environmental Research Institute (NERI), National University of Singapore, 1 Engineering Drive, Singapore 117411, Singapore
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(17), 2827; https://doi.org/10.3390/rs12172827
Received: 31 July 2020 / Revised: 28 August 2020 / Accepted: 30 August 2020 / Published: 31 August 2020
(This article belongs to the Section Environmental Remote Sensing)
No accurate global lowland digital terrain model (DTM) exists to date that allows reliable quantification of coastal lowland flood risk, currently and with sea-level rise. We created the first global coastal lowland DTM that is derived from satellite LiDAR data. The global LiDAR lowland DTM (GLL_DTM_v1) at 0.05-degree resolution (~5 × 5 km) is created from ICESat-2 data collected between 14 October 2018 and 13 May 2020. It is accurate within 0.5 m for 83.4% of land area below 10 m above mean sea level (+MSL), with a root-mean-square error (RMSE) value of 0.54 m, compared to three local area DTMs for three major lowland areas: the Everglades, the Netherlands, and the Mekong Delta. This accuracy is far higher than that of four existing global digital elevation models (GDEMs), which are derived from satellite radar data, namely, SRTM90, MERIT, CoastalDEM, and TanDEM-X, that we find to be accurate within 0.5 m for 21.1%, 12.9%, 18.3%, and 37.9% of land below 10 m +MSL, respectively, with corresponding RMSE values of 2.49 m, 1.88 m, 1.54 m, and 1.59 m. Globally, we find 3.23, 2.12, and 1.05 million km2 of land below 10, 5, and 2 m +MSL. The 0.93 million km2 of land below 2 m +MSL identified between 60N and 56S is three times the area indicated by SRTM90 that is currently the GDEM most used in flood risk assessments, confirming that studies to date are likely to have underestimated areas at risk of flooding. Moreover, the new dataset reveals extensive forested land areas below 2 m +MSL in Papua and the Amazon Delta that are largely undetected by existing GDEMs. We conclude that the recent availability of satellite LiDAR data presents a major and much-needed step forward for studies and policies requiring accurate elevation models. GLL_DTM_v1 is available in the public domain, and the resolution will be increased in later versions as more satellite LiDAR data become available. View Full-Text
Keywords: ICESat-2; global lowland DTM; GLL_DTM; coastal flood risk ICESat-2; global lowland DTM; GLL_DTM; coastal flood risk
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MDPI and ACS Style

Vernimmen, R.; Hooijer, A.; Pronk, M. New ICESat-2 Satellite LiDAR Data Allow First Global Lowland DTM Suitable for Accurate Coastal Flood Risk Assessment. Remote Sens. 2020, 12, 2827. https://doi.org/10.3390/rs12172827

AMA Style

Vernimmen R, Hooijer A, Pronk M. New ICESat-2 Satellite LiDAR Data Allow First Global Lowland DTM Suitable for Accurate Coastal Flood Risk Assessment. Remote Sensing. 2020; 12(17):2827. https://doi.org/10.3390/rs12172827

Chicago/Turabian Style

Vernimmen, Ronald, Aljosja Hooijer, and Maarten Pronk. 2020. "New ICESat-2 Satellite LiDAR Data Allow First Global Lowland DTM Suitable for Accurate Coastal Flood Risk Assessment" Remote Sensing 12, no. 17: 2827. https://doi.org/10.3390/rs12172827

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