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Advanced airborne hyperspectral remote sensing to support forest management (HYPERFOREST)

Research project SR/00/134 (Research action SR)


Persons :

  • Prof. dr.  COPPIN Pol - Katholieke Universiteit Leuven (K.U.Leuven)
    Coordinator of the project
    Financed belgian partner
    Duration: 1/12/2009-31/12/2013
  • Dr.  KNAEPS Els - Vlaamse Instelling voor Technologisch Onderzoek (VITO)
    Financed belgian partner
    Duration: 1/12/2009-31/12/2013
  • Dr.  HOFFMANN Lucien - CRP - Gabriel Lippmann (LIPPM)
    Financed foreign partner
    Duration: 1/12/2009-31/12/2013

Description :

The HyperForest project – a consortium of K.U.Leuven, UGent, VITO, GLI, INBO, RSL - aims at providing foresters with detailed spatial explicit data on forest vitality, species composition and stand diversity based on airborne hyperspectral and LiDAR data. The complex nature of hyperspectral data sets urge producers to set up a complete imagery pre- processing chain to perform standard corrections for radiometric, geometric and atmospheric effects which might corrupt the data. Moreover, bidirectional effects caused by the heterogeneous character of terrestrial targets, such as forests which have pronounced vegetation structures, are affecting the captured hyperspectral signal.

Objective

This project aims at:

- developing an advanced airborne hyperspectral imagery pre-processing chain (e.g. APEX) that considers vegetation structure (bidirectional) effects of the reflected signal,
- delivering of a robust methodology to extract optimized vegetation indices quantifying forest diversity from this pre-processed imagery.
- organizing intensive interactions with end-users by considering their feedback facilitating the supply of tuned and more end-user oriented forest thematic products.
Basically, with this project we want to identify forest canopies components that contribute the most to the captured reflectance values of airborne sensors.

Method

In general, this project contains of four steps:

- Data collection and processing on three forest sites: full dendrometric inventories, leaf biochemical analysis, hyperspectral leaf spectra, terrestrial LiDAR measurements, airborne hyperspectral and LiDAR data sets.
- Produce fully processed hyperspectral imagery, by first assessing the canopy structure elements that affects the hyperspectral signal the most based on the LiDAR and radiative transfer modeling (e.g. DART), and then by converting these information in a format useful for operational imagery pre-processing.
- Incorporate the acquired RS data and pre-processing techniques in the development of optimized hyperspectral feature extraction to better estimate forest vitality, species composition, and stand diversity.
- End-user potential check of the developed algorithms for extracting forest parameters by receiving feedback from potential end-users such as scientists, forest administration and forest policy to the image processing community.

Result

The main outcome will be algorithms that strive
- to analyze the effect of structure on the hyperspectral signal
- to translate radiative transfer model results into an operational hyperspectral pre-processing chain (e.g. APEX),
- to derive forest vitality, species composition, and stand diversity from remote sensing data.

- A successful airborne flight campaign of both hyperspectral as well as laser scanning with high quality imagery.
- Optimization the structure of the hyperspectral imagery pre-processing chain by analyzing the hierarchy of the different processing steps and analyzing the sensitivity to errors in the input data.
- The evaluation of the cost-benefit of combining airborne hyperspectral with laser scanning remote sensing data for deriving forest thematic output.


Documentation :

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