LIDAR remote sensing for estimation of biophysical vegetation parameters

Abstract

Wildland fires pose an immensive threat to social and economic values in many countries around the world. These fires are ecological phenomena subject to boundary con- ditions which vary spatially and temporally. Airborne laser scanning (ALS) is an active remote sensing methodology capable of directly measuring the location of reflecting points on the earth’s surface. ALS systems scan the earth surface with a laser beam, resulting in a three-dimensional point cloud containing structural aspects of the vege- tation canopy. Thus, it was hypothesized that ALS could provide structural information relating to the spatial arrangement of fuels to assess either the risk or the potential impact of wildland fires. The presented dissertation focuses on the derivation of such structural parameters from ALS data in its raw form and attempts to develop, implement and validate robust methods for biophysical vegetation parameter estimation. An approach for the estimation of the canopy geometry at the scale of single trees from ALS data was implemented and validated using field data. It was shown that the tree geometry, including position, tree height and crown diameter, of single trees could be derived from the laser point cloud with an accuracy that matches the one of traditional field work. A methodology for the derivation of canopy density measures such as leaf area index (LAI) and fractional cover (fCover) was implemented using a physically based ALS estimator related to different echo types inside the canopy as a predictor variable. The validation was done using field data being geolocated with centimeter precision. This allowed for matching field data and ALS estimates for very small areas, opposed to more commonly used stand wise approaches. The methods developed provide a high degree of automation, once they were calibrated with field measurements and were found to be robust in respect to changes of ALS scanning angles used in this study, while changing the flying altitude significantly affects the methods for derivation of vegetation density. In such cases, a recalibration of the methodologies for different flying altitudes is needed, as long as the dependence of flying altitude is not well enough understood to allow for a direct correction. The methodologies developed for deriving structural and density related information of the fuel bed sustain the large potential that ALS data has for the derivation of biophys- ical vegetation properties. One key finding of this thesis is that if one wants to exploit this potential even further, one will need to consider the interaction of the laser beam and the canopy more thoroughly.