The project is aimed at the development of new methods for the estimation of hemiboreal forest resources at a stand level (species, tree height and diameter at breast height (DBH), undergrowth and forest floor compartments, dead wood, leaf area CO2 removals, evapotranspiration) from very high resolution (VHR) remote sensing (RS) data collected using unmanned aerial vehicles (UAV).
The main objectives of the project are:
- To develop a new classification method of tree species from hyperspectral (HS) data. Species which are common in hemiboreal forests and important for Latvian forestry companies will be targeted: Scots pine (Pinus sylvestris), Norway spruce (Picea abies), birch (Betula pendula Roth and Betula pubescens Ehrh.), grey alder (Alnus incana Moench), black alder (Alnus glutinosa Gaertn.), Eurasian aspen (Populus tremula), pedunculate oak (Quercus robur) and European ash (Fraxinus excelsior) with “dry tree” and “other” classes added for completeness. The method will be based on the 3D convolutional neural network (3D-CNN) using a few-shot learning concept enabling classification with a limited amount of learning data
- To develop a methodology for incorporating UAV-based imagery and LiDAR data for estimation of forest stand parameters, including accuracy assessment compared to traditional visual stand-level forest inventory
- To enhance the accuracy of these estimates, address the impacts of species-specific canopy cover density on a tree and stand attribute estimation uncertainty based on effectiveness analysis and differences in the image and LiDAR-based canopy height models (CHM)
- To analyse and compare newly developed methods during this study with the popular methods used for tree species classification and estimation of numerical forest inventory parameters
Assess the effectiveness and performance of the developed methods for the specifics of long-term application using UAV time series
Participating scientists
