The project investigates a range of remote sensing data types and methods for evaluation of their ability to measure forest health. The focus here is primarily on leaf area index (LAI) and defoliation as general forest health variables. The main objective is to find the most appropriate methods for forest health monitoring at the national scale.

 

NR is responsible for two parts of the project. The first is the use of multi-frequency SAR. The aim of this work is to develop a model for defoliation and LAI based on SAR imagery. The data available for model development will be TerraSAR X-band data and Radarsat-2 and ENVISAT ASAR C-band data. Ground truth is available from field measurements with an LAI-2000 instrument, estimates of LAI based on LIDAR, as well as detailed data from a number of sample trees. The second part of the project, which NR is responsible of, is the use of hyperspectral data from airborne sensors. The aim of this work is to develop a model for defoliation and LAI based on imaging spectrometer data. The data available for model development will be airborne hyperspectral imagery from NEO HySpex VNIR-1600 sensor with 160 bands in the range of 0.4-1.0 μm.

 

The main objective of the REM-FOR project is to find the most appropriate method for forest health monitoring by remote sensing at the national scale. The project will try to estimate leaf area index (LAI) and forest damage with a range of remote sensing data and methods. The project shall compare and rank the various types of remote sensing data, and various methods, according to their ability to detect forest damage and estimate LAI. The project shall also evaluate the ability of the methods for early detection.