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Article: Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine

TitleIntegrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine
Authors
KeywordsGoogle Earth Engine
Inundation
LiDAR
Surface water
Topographic depressions
Wetland hydrology
Issue Date2019
Citation
Remote Sensing of Environment, 2019, v. 228, p. 1-13 How to Cite?
AbstractThe Prairie Pothole Region of North America is characterized by millions of depressional wetlands, which provide critical habitats for globally significant populations of migratory waterfowl and other wildlife species. Due to their relatively small size and shallow depth, these wetlands are highly sensitive to climate variability and anthropogenic changes, exhibiting inter- and intra-annual inundation dynamics. Moderate-resolution satellite imagery (e.g., Landsat, Sentinel) alone cannot be used to effectively delineate these small depressional wetlands. By integrating fine spatial resolution Light Detection and Ranging (LiDAR) data and multi-temporal (2009–2017) aerial images, we developed a fully automated approach to delineate wetland inundation extent at watershed scales using Google Earth Engine. Machine learning algorithms were used to classify aerial imagery with additional spectral indices to extract potential wetland inundation areas, which were further refined using LiDAR-derived landform depressions. The wetland delineation results were then compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and existing global-scale surface water products to evaluate the performance of the proposed method. We tested the workflow on 26 watersheds with a total area of 16,576 km2 in the Prairie Pothole Region. The results showed that the proposed method can not only delineate current wetland inundation status but also demonstrate wetland hydrological dynamics, such as wetland coalescence through fill-spill hydrological processes. Our automated algorithm provides a practical, reproducible, and scalable framework, which can be easily adapted to delineate wetland inundation dynamics at broad geographic scales.
Persistent Identifierhttp://hdl.handle.net/10722/329559
ISSN
2023 Impact Factor: 11.1
2023 SCImago Journal Rankings: 4.310
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWu, Qiusheng-
dc.contributor.authorLane, Charles R.-
dc.contributor.authorLi, Xuecao-
dc.contributor.authorZhao, Kaiguang-
dc.contributor.authorZhou, Yuyu-
dc.contributor.authorClinton, Nicholas-
dc.contributor.authorDeVries, Ben-
dc.contributor.authorGolden, Heather E.-
dc.contributor.authorLang, Megan W.-
dc.date.accessioned2023-08-09T03:33:40Z-
dc.date.available2023-08-09T03:33:40Z-
dc.date.issued2019-
dc.identifier.citationRemote Sensing of Environment, 2019, v. 228, p. 1-13-
dc.identifier.issn0034-4257-
dc.identifier.urihttp://hdl.handle.net/10722/329559-
dc.description.abstractThe Prairie Pothole Region of North America is characterized by millions of depressional wetlands, which provide critical habitats for globally significant populations of migratory waterfowl and other wildlife species. Due to their relatively small size and shallow depth, these wetlands are highly sensitive to climate variability and anthropogenic changes, exhibiting inter- and intra-annual inundation dynamics. Moderate-resolution satellite imagery (e.g., Landsat, Sentinel) alone cannot be used to effectively delineate these small depressional wetlands. By integrating fine spatial resolution Light Detection and Ranging (LiDAR) data and multi-temporal (2009–2017) aerial images, we developed a fully automated approach to delineate wetland inundation extent at watershed scales using Google Earth Engine. Machine learning algorithms were used to classify aerial imagery with additional spectral indices to extract potential wetland inundation areas, which were further refined using LiDAR-derived landform depressions. The wetland delineation results were then compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and existing global-scale surface water products to evaluate the performance of the proposed method. We tested the workflow on 26 watersheds with a total area of 16,576 km2 in the Prairie Pothole Region. The results showed that the proposed method can not only delineate current wetland inundation status but also demonstrate wetland hydrological dynamics, such as wetland coalescence through fill-spill hydrological processes. Our automated algorithm provides a practical, reproducible, and scalable framework, which can be easily adapted to delineate wetland inundation dynamics at broad geographic scales.-
dc.languageeng-
dc.relation.ispartofRemote Sensing of Environment-
dc.subjectGoogle Earth Engine-
dc.subjectInundation-
dc.subjectLiDAR-
dc.subjectSurface water-
dc.subjectTopographic depressions-
dc.subjectWetland hydrology-
dc.titleIntegrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.rse.2019.04.015-
dc.identifier.scopuseid_2-s2.0-85064317905-
dc.identifier.volume228-
dc.identifier.spage1-
dc.identifier.epage13-
dc.identifier.isiWOS:000470050500001-

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