Estimating vegetation height and canopy cover from remotely sensed data with machine learning

6 High quality information on forest resources is important to forest ecosystem management. Tra7 ditional ground measurements are labor and resource intensive and at the same time expensive 8 and time consuming. For most of the Slovenian forests, there is extensive ground-based infor9 mation on forest properties of selected sample locations. However there is no continuous infor10 mation of objectively measured vegetation height and canopy cover at appropriate resolution. 11 Currently, Light Detection And Ranging (LiDAR) technology provides detailed measure12 ments of different forest properties because of its immediate generation of 3D data, its accuracy 13 and acquisition flexibility. However, existing LiDAR sensors have limited spatial coverage and 14 relatively high cost of acquisition. Satellite data, on the other hand, are low-cost and offer broader 15 spatial coverage of generalized forest structure, but are not expected to provide accurate infor16 mation about vegetation height. 17 Integration of LiDAR and satellite data promises to improve the measurement, mapping, and 18 monitoring of forest properties. The primary objective of this study is to model the vegetation 19 height and canopy cover in Slovenia by integrating LiDAR data, Landsat satellite data, and the 20 use of machine learning techniques. This kind of integration uses the accuracy and precision of 21 LiDAR data and the wide coverage of satellite data in order to generate cost effective realistic 22 estimates of the vegetation height and canopy cover, and consequently generate continuous forest 23 vegetation map products to be used in forest management and monitoring. 24 Several machine learning techniques are applied to this task: they are evaluated and their 25 performance is compared by using statistical significance tests. Ensemble methods perform sig26 nificantly better than single and multi-target regression trees and are further used for the gen27 eration of forest maps. Such maps are used for land-cover and land-use classification, as well 28 as for monitoring and managing ongoing forest processes (like spontaneous afforestation, forest 29 reduction and forest fires) that affect the stability of forest ecosystems. 30