Predicting Growing Stock Volume of Boreal Forests Using Very Long Time Series of Sentinel-1 Data

Shaojia Ge, Erkki Tomppo, Yrjo Rauste, Weimin Su, Hong Gu, Jaan Praks, Oleg Antropov

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

In this study, we assess the potential of long time series of Sentinel-1 SAR data in forest growing stock volume (GSV) estimation. The study site with 17,762 forest stands is located near the Hyytiälä forestry field station in Finland and represents the boreal coniferous forest. Altogether 96 images spanning more than three years of observations have been studied using linear and random forest regression approaches. Our analysis demonstrates considerable decrease in the prediction errors of GSV as the the number of input scenes increases. The use of feature extraction and dimensionality reduction techniques allows to achieve to nearly optimal performance already with 10 scenes. While the GSV prediction errors using individual Sentinel-1 scenes varied considerably from 86 to 93 m3/ha, the prediction accuracy with combined scenes improved to 76 m3/ha (44.9%) RMSE.

Original languageEnglish
Title of host publication2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Subtitle of host publicationProceedings
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages4509-4512
ISBN (Electronic)978-1-7281-6374-1
ISBN (Print)978-1-7281-6375-8
DOIs
Publication statusPublished - 17 Feb 2020
MoE publication typeA4 Article in a conference publication
EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020: Online - Virtual, Waikoloa, United States
Duration: 26 Sep 20202 Oct 2020

Publication series

SeriesIEEE International Geoscience and Remote Sensing Symposium Proceedings
ISSN2153-6996

Conference

ConferenceIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
CountryUnited States
CityWaikoloa
Period26/09/202/10/20

Keywords

  • boreal forest
  • forest stem volume
  • random forest regression
  • Sentinel-1
  • support vector regression
  • synthetic aperture radar

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