Analysis of forest areas by advanced remote sensing systems based on hyperspectral and LIDAR data

Forest management is an important and complex process, which has significant implications on the envi-ronment (e.g. protection of biological diversity, climate mitigation) and the economy (e.g. estimation of timber volume for commercial usage). An efficient management requires a very detailed knowledge of forest attributes such as species composition, trees stem volume, height, etc. Hyperspectral and LIDAR remote sensing data can provide useful information to the identification of these attributes: hyperspectral data with their dense sampling of the spectral signatures are important for the classification of tree spe-cies, while LIDAR data are important for the study and estimation of quantitative parameters of forests (e.g. stem height, volume). This thesis presents novel systems for the exploitation of hyperspectral and LIDAR data in forest applica-tion domain. In particular, the novel contributions to the existing literature are on both the development of new systems for data processing and the analysis of the potentialities of these data in forestry. In greater detail the main contribution of this thesis are: i) an empirical analysis on the relationship be-tween spectral resolution, classifier complexity and classification accuracy in the study of complex forest areas. This analysis is very important for the design of future sensors and the better exploitation of the existing ones; ii) a novel system for the fusion of hyperspectral and LIDAR remote sensing data in the classification of forest areas. The system proposed exploits the complementary information of these data in order to obtain accurate and precise classification maps; iii) an analysis on the usefulness of different LIDAR returns and channels (elevantion and intensity) in the classification of forest areas; iv) an empiri-cal analysis on the use of multireturn LIDAR data for the estimation of tree stem volume. This study in-vestigates in detail the potentialities of variables extracted from LIDAR returns (up to four) for the esti-mation of tree stem volume; v) a novel system for the estimation of single tree stem diameter and volume with multireturn LIDAR data. A comparative analysis on the use of three different variable selection me-thods and three different estimation algorithms is also presented; vi) a system for the fusion of hyperspec-tral and LIDAR remote sensing data in the estimation of tree stem diameters. This system is able to ex-ploit hyperspectral and LIDAR data combined and separated: this is very important as the experimental analysis carried out with this system shows that hyperspectral data can be used for rough estimations of stem diameters when LIDAR data are not available. The effectiveness of all the proposed systems is confirmed by quantitative and qualitative experimental results.