Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Mapping of the Canopy Openings in Mixed Beech-Fir Forest at Sentinel-2 Subpixel level Using UAV and Machine Learning Approach

Version 1 : Received: 30 October 2020 / Approved: 2 November 2020 / Online: 2 November 2020 (11:23:12 CET)

A peer-reviewed article of this Preprint also exists.

Pilaš, I.; Gašparović, M.; Novkinić, A.; Klobučar, D. Mapping of the Canopy Openings in Mixed Beech–Fir Forest at Sentinel-2 Subpixel Level Using UAV and Machine Learning Approach. Remote Sens. 2020, 12, 3925. Pilaš, I.; Gašparović, M.; Novkinić, A.; Klobučar, D. Mapping of the Canopy Openings in Mixed Beech–Fir Forest at Sentinel-2 Subpixel Level Using UAV and Machine Learning Approach. Remote Sens. 2020, 12, 3925.

Journal reference: Remote Sens. 2020, 12, 3925
DOI: 10.3390/rs12233925

Abstract

The presented study demonstrates the bi-sensor approach suitable for rapid and precise up-to-date mapping of forest canopy gaps for the larger spatial extent. The approach makes use of UAV RGB images on smaller areas for highly precise forest canopy mask creation. Sentinel-2 was used as a scaling platform for transferring information from UAV to a wider spatial extent. The various approaches of the improvement of the predictive performance were examined: (I) the highest R2 of the single satellite index was up to 0.57, (II) the highest R2 using multiple features obtained from the single date, S-2 image was 0.624 and, (III) the highest R2 on the multi-temporal set of S-2 images, was 0.697. Satellite indices such as ARVI, IPVI, NDI45, PSSRa, MCARI, CI, RI, and NDTI were the dominant predictors in most of the ML algorithms. The more complex ML algorithms such as SVM, Random Forest, GBM, XGBoost, and Catboost that provided the best performance on the training set exhibited weaker generalization capabilities. Therefore, a simpler and more robust Elastic Net algorithm was chosen for the final map creation.

Subject Areas

Sentinel-2; UAV; machine learning; forest canopy; canopy gaps; canopy openings percentage; satellite indices; Elastic Net; beech-fir forests

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