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Mapping Riparian Vegetation Functions Using 3D Bispectral LiDAR Data
Riparian zones experience many anthropic pressures and are the subject of European legislation to encourage their monitoring and management, to attenuate these pressures. Assessing the effectiveness of management practices requires producing indicators of ecological functions. Laser Detection and Ranging (LiDAR) data can provide valuable information to assess the ecological status of riparian zones. The objective of this study was to evaluate the potential of LiDAR point clouds to produce indicators of riparian zone status. We used 3D bispectral LiDAR data to produce several indicators of a riparian zone of a dammed river in Normandy (France). The indicators were produced either directly from the 3D point clouds (e.g., biomass overhanging the channel, variation in canopy height) or indirectly, by applying the Random Forest classification algorithm to the point clouds. Results highlight the potential of 3D LiDAR point clouds to produce indicators with sufficient accuracy (ca. 80% for the number of trunks and 68% for species composition). Our results also reveal advantages of using metrics related to the internal structure of trees, such as penetration indexes. However, intensity metrics calculated using bispectral properties of LiDAR did not improve the quality of classifications. Longitudinal analysis of the indicators revealed a difference in attributes between the reservoir and areas downstream from it.
Mapping Riparian Vegetation Functions Using 3D Bispectral LiDAR Data
Riparian zones experience many anthropic pressures and are the subject of European legislation to encourage their monitoring and management, to attenuate these pressures. Assessing the effectiveness of management practices requires producing indicators of ecological functions. Laser Detection and Ranging (LiDAR) data can provide valuable information to assess the ecological status of riparian zones. The objective of this study was to evaluate the potential of LiDAR point clouds to produce indicators of riparian zone status. We used 3D bispectral LiDAR data to produce several indicators of a riparian zone of a dammed river in Normandy (France). The indicators were produced either directly from the 3D point clouds (e.g., biomass overhanging the channel, variation in canopy height) or indirectly, by applying the Random Forest classification algorithm to the point clouds. Results highlight the potential of 3D LiDAR point clouds to produce indicators with sufficient accuracy (ca. 80% for the number of trunks and 68% for species composition). Our results also reveal advantages of using metrics related to the internal structure of trees, such as penetration indexes. However, intensity metrics calculated using bispectral properties of LiDAR did not improve the quality of classifications. Longitudinal analysis of the indicators revealed a difference in attributes between the reservoir and areas downstream from it.
Mapping Riparian Vegetation Functions Using 3D Bispectral LiDAR Data
Marianne Laslier (author) / Laurence Hubert-Moy (author) / Simon Dufour (author)
2019
Article (Journal)
Electronic Resource
Unknown
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