As direct monitoring of biodiversity is not possible, proxy variables have been used to indicate site's species richness and quality. In boreal forests, European aspen (Populus tremula L.) is one of the most significant proxies for biodiversity.
Aspen is a keystone species, hosting a range of endangered species, hence having a high importance in maintaining forest biodiversity. Still, reliable and fine-scale spatial data on aspen occurrence remains scarce and incomprehensive. Although remote sensing-based species classification has been used for decades for the needs of forestry, commercially less significant species (e.g., aspen) have typically been excluded from the studies.This creates a need for developing general methods for tree species classification covering also ecologically significant species. Our study area, located in Evo, Southern Finland, covers approximately 83 km2, and contains both managed and protected southern boreal forests. The main tree species in the area are Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst), and birch (Betula pendula and pubescens L.), with relatively sparse and scattered occurrence of aspen.
Along with a thorough field data, airborne hyperspectral and LiDAR data have been acquired from the study area. We also collected ultra high resolution UAV data with RGB and multispectral sensors. The aim is to gather fundamental data on hyperspectral and multispectral species classification, that can be utilized to produce detailed aspen data at large scale. For this, we first analyze species detection at tree-level. We test and compare different machine learning methods (Support Vector Machines, Random Forest, Gradient Boosting Machine) and deep learning methods (3D Convolutional Neural Networks), with specific emphasis on accurate and feasible aspen detection.
The results will show, how accurately aspen can be detected from the forest canopy, and which bandwidths have the largest importance for aspen. This information can be utilized for aspen detection from satellite images at large scale.
Read more at https://ui.adsabs.harvard.edu/abs/2020EGUGA..2221268K/abstract
