Temporal Transferability of Tree Species Classification in Temperate Forests with Sentinel-2 Time Series

Author:

Verhulst Margot1,Heremans Stien12ORCID,Blaschko Matthew B.3ORCID,Somers Ben14ORCID

Affiliation:

1. Division of Forest, Nature and Landscape (FNL), Department of Earth and Environmental Sciences, KU Leuven, Celestijnenlaan 200E, 3001 Leuven, Belgium

2. Research Institute for Nature and Forest (INBO), Havenlaan 88, 1000 Brussels, Belgium

3. Center for Processing Speech and Images (PSI), Department of Electrical Engineering, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium

4. KU Leuven Plant Institute (LPI), KU Leuven, Kasteelpark Arenberg 31, 3001 Leuven, Belgium

Abstract

Detailed information on forest tree species is crucial to inform management and policy and support environmental and ecological research. Sentinel-2 imagery is useful for obtaining spatially explicit and frequent information on forest tree species due to its suitable spatial, spectral, and temporal resolutions. However, classification workflows often do not generalise well to time periods that are not seen by the model during the calibration phase. This study investigates the temporal transferability of dominant tree species classification. To this end, the Random Forest, Support Vector Machine, and Multilayer Perceptron algorithms were used to classify five tree species in Flanders (Belgium) with regularly spaced Sentinel-2 time series from 2018 to 2022. Cross-year single-year input scenarios were compared with same-year single-year input scenarios to quantify the temporal transferability of the five evaluated years. This resulted in a decrease in overall accuracy between 2.30 and 14.92 percentage points depending on the algorithm and evaluated year. Moreover, our results indicate that the cross-year classification performance could be improved by using multi-year training data, reducing the drop in overall accuracy. In some cases, gains in overall accuracy were even observed. This study highlights the importance of including interannual spectral variability during the training stage of tree species classification models to improve their ability to generalise in time.

Funder

Research Foundation Flanders

Publisher

MDPI AG

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