Sentinel-1 Time Series for Predicting Growing Stock Volume of Boreal Forest: Multitemporal Analysis and Feature Selection

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Remote Sensing, Volume 15, issue 14
Copernicus Sentinel-1 images are widely used for forest mapping and predicting forest growing stock volume (GSV) due to their accessibility. However, certain important aspects related to the use of Sentinel-1 time series have not been thoroughly explored in the literature. These include the impact of image time series length on prediction accuracy, the optimal feature selection approaches, and the best prediction methods. In this study, we conduct an in-depth exploration of the potential of long time series of Sentinel-1 SAR data to predict forest GSV and evaluate the temporal dynamics of the predictions using extensive reference data. Our boreal coniferous forests study site is located near the Hyytiälä forest station in central Finland and covers an area of 2500 km2 with nearly 17,000 stands. We considered several prediction approaches and fine-tuned them to predict GSV in various evaluation scenarios. Our analyses used 96 Sentinel-1 images acquired over three years. Different approaches for aggregating SARimages and choosing feature (predictor) variables were evaluated. Our results demonstrate a considerable decrease in the root mean squared errors (RMSEs) of GSV predictions as the number of images increases. While prediction accuracy using individual Sentinel-1 images varied from 85 to 91 m3/ha RMSE, prediction accuracy with combined images decreased to 75.6 m3/ha. Feature extraction and dimension reduction techniques facilitated the achievement of near-optimal prediction accuracy using only 8–10 images. Examined methods included radiometric contrast, mutual information, improved k-Nearest Neighbors, random forests selection, Lasso, and Wrapper approaches. Lasso was the most optimal, with RMSE reaching 77.1 m3/ha. Finally, we found that using assemblages of eight consecutive images resulted in the greatest accuracy in predicting GSV when initial acquisitions started between September and January.
Funding Information: This study was supported by the National Natural Science Foundation of China (Grant No. 61801221, 62001229) and by the China Postdoctoral Science Foundation (Grant No. 2020M681604), as well as by Aalto University. O.A. was supported by the ESA Forest Carbon Monitoring project funded by the European Space Agency. Publisher Copyright: © 2023 by the authors.
boreal forests, growing stock volume, random forests regression, Sentinel-1, support vector regression, synthetic aperture radar
Ge , S , Tomppo , E , Rauste , Y , McRoberts , R E , Praks , J , Gu , H , Su , W & Antropov , O 2023 , ' Sentinel-1 Time Series for Predicting Growing Stock Volume of Boreal Forest: Multitemporal Analysis and Feature Selection ' , Remote Sensing , vol. 15 , no. 14 , 3489 .