FHYL: Field spectral libraries, airborne hyperspectral images and topographic and bathymetric LiDAR data for complex coastal mapping

Author(s):  
Andrea Taramelli ◽  
Emiliana Valentini ◽  
Carlo Innocenti ◽  
Sergio Cappucci
Author(s):  
Carl Legleiter ◽  
Brandon Overstreet

The Snake River is an essential feature of Grand Teton National Park, and this dynamic fluvial system maintains diverse habitats while actively shaping the landscape. The complex, ever-changing nature of the river make effective characterization difficult, however; traditional field methods are illsuited for this task. Remote sensing provides an appealing alternative that could facilitate resource management while providing novel insight on the controls of channel form and behavior. This study continued our ongoing assessment of the potential to measure the morphology and dynamics of large, complex rivers such as the Snake via remote sensing (Figure 1). More specifically, we acquired hyperspectral images and bathymetric LiDAR data in August 2012 and are now comparing the depth retrieval capabilities of these sensors; in situ observations of water column optical properties inform this analysis as well. In addition to bathymetry, we are investigating the feasibility of using these data to infer bottom reflectance and hence delineate various substrates, such as gravel and submerged aquatic vegetation. Another new aspect of our research focuses on estimating flow velocities from the hyperspectral images and high-resolution digital aerial photography acquired simultaneously. Extensive field measurements of velocity will help us develop this approach. Similarly, measurements of sediment grain size on exposed bar surfaces will be used to assess whether particle size can be inferred from the highresolution photography. Remotely sensed data also are being used to identify areas of erosion and deposition and hence quantify the sediment flux associated with changes in channel morphology. Additional hyperspectral and bathymetric LiDAR data will be acquired in 2013, along with field measurements of depth, velocity, and bottom type.


2019 ◽  
Vol 11 (13) ◽  
pp. 1556 ◽  
Author(s):  
Maxim Okhrimenko ◽  
Craig Coburn ◽  
Chris Hopkinson

Multi-spectral (ms) airborne lidar data are enriched relative to traditional lidar due to the multiple channels of intensity digital numbers (DNs), which offer the potential for active Spectral Vegetation Indices (SVIs), enhanced classification, and change monitoring. However, in case of SVIs, indices should be calculated from spectral reflectance values derived from intensity DNs after calibration. In this paper, radiometric calibration of multi-spectral airborne lidar data is presented. A novel low-cost diffuse reflectance coating was adopted for creating radiometric targets. Comparability of spectral reflectance values derived from ms lidar data for coniferous stand (2.5% for 532 nm, 17.6% for 1064 nm, and 8.4% for 1550 nm) to available spectral libraries is shown. Active vertical profiles of SVIs were constructed and compared to modeled results available in the literature. The potential for a new landscape-level active 3D SVI voxel approach is demonstrated. Results of a field experiment with complex radiometric targets for estimating losses in detected lidar signals are described. Finally, an approach for estimating spectral reflectance values from lidar split returns is analyzed and the results show similarity of estimated values of spectral reflectance derived from split returns to spectral reflectance values obtained from single returns (p > 0.05 for paired test).


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