Aspen detection in boreal forests: Capturing a key component of biodiversity with airborne hyperspectral, lidar, and UAV data using machine learning and 3D convolutional neural networks

Abstract:

Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. Importance of biodiversity is increasingly highlighted as an essential part of sustainable forest management. 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.

Our study area, located in Evo, Southern Finland, covers approximately 83km², 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 unmanned aerial vehicle (UAV) data with RGB and multispectral sensors. Our 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.

Airborne hyperspectral and lidar data gave excellent results using machine learning and deep learning methods (Mäyrä et al 2020 & Viinikka et al 2020). UAV data analysis also gave good results in aspen detection (Kuzmin et al 2020). Altogether these different data sources and methods provides a possibility to produce a spatially explicit map of aspen occurrence and abundance that can contribute to biodiversity management and conservation efforts in boreal forests.