Home About Browse Search
Svenska


Data Assimilation of Growing Stock Volume Using a Sequence of Remote Sensing Data from Different Sensors

Lindgren, Nils and Olsson, Håkan and Nyström, Kenneth and Nyström, Mattias and Ståhl, Göran (2022). Data Assimilation of Growing Stock Volume Using a Sequence of Remote Sensing Data from Different Sensors. Canadian Journal of Remote Sensing, 48 (2). TAYLOR AND FRANCIS INC
[Book (author)]

[img] PDF
3MB

Abstract

Airborne Laser Scanning (ALS) has implied a disruptive transformation of how data are gathered for forest management planning in Nordic countries. We show in this study that the accuracy of ALS predictions of growing stock volume can be maintained and even improved over time if they are forecasted and assimilated with more frequent but less accurate remote sensing data sources like satellite images, digital photogrammetry, and InSAR. We obtained these results by introducing important methodological adaptations to data assimilation compared to previous forestry studies in Sweden. On a test site in the southwest of Sweden (58 degrees 27 ' N, 13 degrees 39 ' E), we evaluated the performance of the extended Kalman filter and a proposed modified filter that accounts for error correlations. We also applied classical calibration to the remote sensing predictions. We evaluated the developed methods using a dataset with nine different acquisitions of remotely sensed data from a mix of sensors over four years, starting and ending with ALS-based predictions of growing stock volume. The results showed that the modified filter and the calibrated predictions performed better than the standard extended Kalman filter and that at the endpoint the prediction based on data assimilation implied an improved accuracy (25.0% RMSE), compared to a new ALS-based prediction (27.5% RMSE).

Authors/Creators:Lindgren, Nils and Olsson, Håkan and Nyström, Kenneth and Nyström, Mattias and Ståhl, Göran
Title:Data Assimilation of Growing Stock Volume Using a Sequence of Remote Sensing Data from Different Sensors
Series Name/Journal:Canadian Journal of Remote Sensing
Year of publishing :2022
Volume:48
Number:2
Page range:127-143
Number of Pages:17
Publisher:TAYLOR AND FRANCIS INC
ISSN:0703-8992
Language:English
Publication Type:Book (author)
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
URN:NBN:urn:nbn:se:slu:epsilon-p-114173
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-114173
Additional ID:
Type of IDID
DOI10.1080/07038992.2021.1988542
Web of Science (WoS)000707887100001
Scopus2-s2.0-85117190083
ID Code:27910
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:16 May 2022 17:07
Metadata Last Modified:17 May 2022 05:01

Repository Staff Only: item control page

Downloads

Downloads per year (since September 2012)

View more statistics

Downloads
Hits