Remote Sensing to Characterize River Floodplain Structure and Function
Advancing understanding of the complexities and expansive spatial scales of river ecology can be enhanced through the application of remote sensing. We obtained satellite (Quickbird) and airborne (LIDAR, hyperspectral, multispectral, and thermal) imagery data of an alluvial gravel-bed river floodpla...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/5/1132 |
| _version_ | 1797473914181386240 |
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| author | F. Richard Hauer Mark S. Lorang Tom Gonser |
| author_facet | F. Richard Hauer Mark S. Lorang Tom Gonser |
| author_sort | F. Richard Hauer |
| collection | DOAJ |
| description | Advancing understanding of the complexities and expansive spatial scales of river ecology can be enhanced through the application of remote sensing. We obtained satellite (Quickbird) and airborne (LIDAR, hyperspectral, multispectral, and thermal) imagery data of an alluvial gravel-bed river floodplain in western Montana to quantify both riparian and aquatic habitats and processes. LIDAR data provided a detailed bare earth DEM and vegetation canopy DEM. We classified river hydraulics and aquatic habitats using a combination of the satellite multispectral, airborne hyperspectral, and LIDAR data coupled with spatially-explicit acoustic Doppler velocity profile data of water depth and velocity. Velocity, depth, and Froude classifications were aggregated into similar hydraulic zones of river habitat classes. Thermal imagery data were coupled with field measurements of temperature and radon gas tracer to identify patterns of water exchange between the alluvial aquifer and the surface. We found a high complexity of aquatic surface temperatures and radon tracer linked to groundwater discharge from the alluvial aquifer. Airborne hyperspectral data were used to identify “hot spots” of periphyton production, which coincided with the complex nature of groundwater–surface water exchange. Airborne hyperspectral data provided differentiation of vegetation patches by dominant species. When the hyperspectral data were coupled to LIDAR first return metrics, we were able to determine vegetation canopy height and relative vegetation patch age classes. The integration of these various remote sensing sources allowed us to characterize the distribution and abundance of floodplain aquatic and riparian species and model processes of change through space and time. |
| first_indexed | 2024-03-09T20:22:55Z |
| format | Article |
| id | doaj.art-03a3f14d868d4fd7b0e0fe8188574d9e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T20:22:55Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-03a3f14d868d4fd7b0e0fe8188574d9e2023-11-23T23:41:58ZengMDPI AGRemote Sensing2072-42922022-02-01145113210.3390/rs14051132Remote Sensing to Characterize River Floodplain Structure and FunctionF. Richard Hauer0Mark S. Lorang1Tom Gonser2Flathead Lake Biological Station, University of Montana, Polson, MT 59864, USAFlathead Lake Biological Station, University of Montana, Polson, MT 59864, USACenter for Ecology, Evolution and Biogeochemistry (CEEB), Department of Aquatic Ecology, EAWAG, 6047 Kastanienbaum, SwitzerlandAdvancing understanding of the complexities and expansive spatial scales of river ecology can be enhanced through the application of remote sensing. We obtained satellite (Quickbird) and airborne (LIDAR, hyperspectral, multispectral, and thermal) imagery data of an alluvial gravel-bed river floodplain in western Montana to quantify both riparian and aquatic habitats and processes. LIDAR data provided a detailed bare earth DEM and vegetation canopy DEM. We classified river hydraulics and aquatic habitats using a combination of the satellite multispectral, airborne hyperspectral, and LIDAR data coupled with spatially-explicit acoustic Doppler velocity profile data of water depth and velocity. Velocity, depth, and Froude classifications were aggregated into similar hydraulic zones of river habitat classes. Thermal imagery data were coupled with field measurements of temperature and radon gas tracer to identify patterns of water exchange between the alluvial aquifer and the surface. We found a high complexity of aquatic surface temperatures and radon tracer linked to groundwater discharge from the alluvial aquifer. Airborne hyperspectral data were used to identify “hot spots” of periphyton production, which coincided with the complex nature of groundwater–surface water exchange. Airborne hyperspectral data provided differentiation of vegetation patches by dominant species. When the hyperspectral data were coupled to LIDAR first return metrics, we were able to determine vegetation canopy height and relative vegetation patch age classes. The integration of these various remote sensing sources allowed us to characterize the distribution and abundance of floodplain aquatic and riparian species and model processes of change through space and time.https://www.mdpi.com/2072-4292/14/5/1132riverfloodplainsatellite imageryairborne imagerythermal imageryLIDAR |
| spellingShingle | F. Richard Hauer Mark S. Lorang Tom Gonser Remote Sensing to Characterize River Floodplain Structure and Function Remote Sensing river floodplain satellite imagery airborne imagery thermal imagery LIDAR |
| title | Remote Sensing to Characterize River Floodplain Structure and Function |
| title_full | Remote Sensing to Characterize River Floodplain Structure and Function |
| title_fullStr | Remote Sensing to Characterize River Floodplain Structure and Function |
| title_full_unstemmed | Remote Sensing to Characterize River Floodplain Structure and Function |
| title_short | Remote Sensing to Characterize River Floodplain Structure and Function |
| title_sort | remote sensing to characterize river floodplain structure and function |
| topic | river floodplain satellite imagery airborne imagery thermal imagery LIDAR |
| url | https://www.mdpi.com/2072-4292/14/5/1132 |
| work_keys_str_mv | AT frichardhauer remotesensingtocharacterizeriverfloodplainstructureandfunction AT markslorang remotesensingtocharacterizeriverfloodplainstructureandfunction AT tomgonser remotesensingtocharacterizeriverfloodplainstructureandfunction |