Hyperforest
Description
|
Hyperforest |
| Advanced airborne hyperspectral remote sensing to support forest management |
| Keywords |
forest, hyperspectral, LIDAR, structure, BRDF |
Project Objectives
This project targets (i) the development of an advanced airborne hyperspectral imagery pre-processing chain (e.g. APEX) that considers vegetation structure effects and hence bidirectional effects on the captured signal, (ii) the delivery of a robust methodology to extract forest thematic products from this pre-processed imagery, and (iii) intensive interactions with end-users by considering their feedback facilitating the supply of tuned and more end-user oriented forest thematic products.
Methodology
First of all, this requires the determination of forest structure parameters (for instance crown density, vertical LAI distribution, etc) at the forest test sites (three plot locations in Flanders: Wijnendalebos, Aelmoeseneiebos, Kersselaerspleyn as indicated on the map of form 7) derived from full dendrometric inventories, fine spatial scale terrestrial and coarser scale airborne LiDAR measuring campaigns.
In order to identify the most contributing structure parameters to the hyperspectral signal, radiative transfer models will be used. Reference forest canopy spectral data will be collected using field spectroradiometers in the Aelmoeseneiebos (where a measuring tower is available). Canopy leaf picking and leaf biochemical analysis (chlorophyll, dry matter and water content) will be conducted since they are crucial inputs in these radiative transfer models.
The analysis of the effects of vegetation structure on hyperspectral signatures will be accomplished using a bottom-up (frog’s eye view with the terrestrial LiDAR) and top-down (bird’s eye view with the airborne LiDAR and the terrestrial LiDAR mounted on the measuring tower) approach. The bottom-up approach initiates with implementing gradually coarser vegetation structure data (from high to less detail) - the structure which is most affecting the hyperspectral signal - in the radiative transfer models. From this analysis, the minimum required level of canopy structure info that can also be obtained from airborne LIDAR data is assessed at spatially explicit scale.
Once the most contributing structure parameters are available from airborne LiDAR data, combined with its quantified effect on hyperspectral signals, a procedure can be developed to build an advanced hyperspectral imagery pre-processing chain (for APEX data) that considers the impact of vegetation structure and its bidirectional effects on the captured signal. This procedure will be based on comparison of the original APEX signals with simulated ones from radiative transfer models with airborne derived vegetation structure parameters as inputs.
Finally, a methodology based on deep belief neural networks will be developed to produce forest parameters from the remote sensing thematic data derived from the traditional and advanced hyperspectral imagery pre-processing chains.
(Expected) Results
- Identification and listing of the vegetation elements most contributing to hyperspectral reflectance of forest canopies
- An operational algorithm, an add-in in current pre-processing chain that accounts for vegetation structure effects in airborne hyperspectral data.
- A methodology/algorithm to produce thematic forest thematic products (in terms of species composition, forest vitality and forest diversity) from preprocessed airborne hyperspectral remote sensing.
VITO Contribution
Improving current processing chain with new improved algorithms for BRDF and visibility estimation.
Integrate a structure correction in the processing chain.
Partners
KUL (Belgium)
UGent (Belgium)
CRP-GL (Luxembourg)
INBO (Belgium)
RSL (Switzerland)
Illustration
Contact:
Els Knaeps
Tel. + 32 14 33 68 64
Fax + 32 14 32 27 95
Send a message to Els Knaeps
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