Remote sensing and machine learning for tree detection and classification in forestry applications

By V. Mosin, Roberto Aguilar, A. Platonov, A. Vasiliev, A. Kedrov, A. Ivanov. 2019

11 citations recorded by the source corpus.

Machine learning has been very actively used in analysis of remote sensing data and for a wide range of applications such as agriculture, land and ice surface change detection, etc. In this paper, we explore use of machine learning methods for forest inventory, since it has great impact on economic and ecological sustainability. Our approach is based on UAV data from hyperspectral and LiDAR sensors. A particular case described in the present paper showcases how aerial survey data achieves high classification accuracy of forest tree species. Moreover, applicability of different data types such as LiDAR and hyperspectral (NIR and VIS) for the task at hand is investigated. In the paper we present the solution in its full scope, starting from remote and in-situ data acquisition and pre-processing, to automatic tree species classification and recognition using machine learning algorithms, as well, as further analysis of the results. In the initial stage, during an expedition to the northern region of Arkhangelsk (Russia), airborne data (LiDAR and hyperspectral) and manually recording of trees locations and species were collected. Afterwards, an object-based classification has been performed by detecting individual tree crowns beforehand with the help of LiDAR data. Various hyperspectral features have been calculated for use in classification algorithms complemented by on-ground spectroscopic benchmark data. In this paper, we prove applicability of the proposed method and workflow in real life application. Results validation was done using data from the observation parcels, where trees were manually labeled. The next step is design of a system with finely tuned filters, which will make possible robust species classification at a cost much lower than hyperspectral imaging.

Subfields: Remote Sensing

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