Merging Landsat and airborne LiDAR observations for continuous monitoring of floodplain water extent, depth and volume

dc.contributor.authorHou, Jiawei
dc.contributor.authorVan Dijk, Albert
dc.contributor.authorRenzullo, Luigi
dc.date.accessioned2023-07-18T04:39:59Z
dc.date.issued2022
dc.date.updated2022-05-15T08:16:52Z
dc.description.abstractThe Darling River system in Australia is under pressure from water extraction and climate change. Management interventions such as environmental flow releases require understanding of water storage dynamics and the connectivity of floodplains and wetlands. Such knowledge can be gleaned from the long observational record of the Landsat series of satellite sensors and high (<5 m) resolution digital elevation models derived from airborne light detection and ranging (LiDAR). Here, for the first time, we develop and demonstrate an approach to reconstruct 16-day floodplain water dynamics, including extent, depth, and volume for a long Landsat time series (1987 to present). Time series mapping of surface water extent at 5-m resolution was achieved by topographic downscaling of Landsat-derived surface water data. We propose a simple and effective algorithm to restore missing data in the images caused by, e.g., cloud and shadows, swath edges and the Landsat 7 Scan Line Corrector (SLC) failure, thereby increasing the number of useable images five-fold. The 5-m surface water extent maps clearly delineate the narrow river channel and the boundary of floodplain wetlands. They can capture the development, peak and retreat of flood events. By combining Landsat and airborne LiDAR observations, we produced time series of surface water depth mapping at 5-m resolution, accounting for the degree of hydraulic surface water connectivity. Based on these maps, we derived 16-day floodplain volume dynamics for 1987 to present. The correlation coefficient between upstream river flow records and floodplain volume time series was 0.88, indicating that the estimates were robust. The algorithms developed can be used for ongoing very high-resolution mapping to assist in managing human water use and environmental health in the Murray-Darling Basin.en_AU
dc.description.sponsorshipThis research was funded by NSW Environmental Trust Grant (2019-RD-0002) and NSW Department of Planning, Industry and Environment. Calculations were performed on the high-performance computing system, Gadi, from the National Computational Infrastructure (NCI), and also on the Digital Earth Australia Sandbox developed by a partnership between Geoscience Australia (GA), the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the NCI.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn0022-1694en_AU
dc.identifier.urihttp://hdl.handle.net/1885/294358
dc.language.isoen_AUen_AU
dc.publisherElsevieren_AU
dc.rights© 2022 The authorsen_AU
dc.sourceJournal of Hydrologyen_AU
dc.subjectFloodplainen_AU
dc.subjectLiDARen_AU
dc.subjectLandsaten_AU
dc.subjectWater extenten_AU
dc.subjectWater depthen_AU
dc.subjectWater volumeen_AU
dc.titleMerging Landsat and airborne LiDAR observations for continuous monitoring of floodplain water extent, depth and volumeen_AU
dc.typeJournal articleen_AU
local.contributor.affiliationHou, Jiawei, College of Science, ANUen_AU
local.contributor.affiliationVan Dijk, Albert, College of Science, ANUen_AU
local.contributor.affiliationRenzullo, Luigi, College of Science, ANUen_AU
local.contributor.authoremailu5250651@anu.edu.auen_AU
local.contributor.authoruidHou, Jiawei, u6007660en_AU
local.contributor.authoruidVan Dijk, Albert, u5250651en_AU
local.contributor.authoruidRenzullo, Luigi, u5917000en_AU
local.description.embargo2099-12-31
local.description.notesImported from ARIESen_AU
local.identifier.absfor370704 - Surface water hydrologyen_AU
local.identifier.absfor401304 - Photogrammetry and remote sensingen_AU
local.identifier.absseo180308 - Surface water quantification, allocation and impact of depletionen_AU
local.identifier.ariespublicationa383154xPUB25947en_AU
local.identifier.citationvolume609en_AU
local.identifier.doi10.1016/j.jhydrol.2022.127684en_AU
local.identifier.scopusID2-s2.0-85126147552
local.identifier.uidSubmittedBya383154en_AU
local.publisher.urlhttps://www.sciencedirect.com/en_AU
local.type.statusPublished Versionen_AU

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