As the most widely distributed terrestrial ecosystem on the earth, forests provide many direct and indirect benefits to human well-being. Sustainable management of forests’ multiple functions and services requires spatially explicit information about forests state and development, which are usually acquired through forest inventories.
Although traditional field-based forest inventory can provide relatively accurate information, the process is time-consuming and labour intensive, and in some cases access to certain forest areas is not possible. Therefore, the potential of remote sensing (RS) application in forest inventory have been long recognized by both researchers and practicing foresters. Despite the availability of various RS data and great potential for their use, in Croatia as well as in many other countries, forest inventory is still based only on traditional field methods.
Therefore, the main goal of this proposal is to develop and evaluate methods and workflows for forest inventory applications based on different 3D RS data, aiming to improve efficiency and cost-effectiveness of current field-based inventory practices. Specifically, the accuracy of products (image-based point cloud, CHM, orthoimage) derived from various 3D RS data (UAS, airborne, satellite stereo images) with different spatial resolutions will be evaluated for estimating the main tree and forest stand attributes at various spatial levels. Secondly, automatic segmentation and classification of both individual trees and forest stands using above mentioned products will be investigated.
A comparison studies dealing with the performance of different 3D RS data in forest inventory are lacking, especially in the South-east European region. Therefore, this research will provide first assessments of quality and accuracy of forest information derived from different 3D RS data and its products. Thus, the results of this research will be of interests for both forestry researchers and industry.