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Characterizing Uncertainty in Forest Remote Sensing Studies

Persson, Henrik and Ståhl, Göran (2020). Characterizing Uncertainty in Forest Remote Sensing Studies. Remote Sensing. 12 , 1-21
[Research article]

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This discussion paper addresses (1) the challenge of concisely reporting uncertainties in forest remote sensing (RS) studies, primarily conducted at plot and stand level, and (2) the influence of reference data errors and how corrections for such errors can be made. Different common ways of reporting uncertainties are discussed, and a parametric error model is proposed as a core part of a comprehensive approach for reporting uncertainties (compared to, e.g., conventional reporting of root mean square error (RMSE)). The importance of handling reference data errors is currently increasing since estimates derived from RS data are becoming increasingly accurate; in extreme cases the accuracies of RS- and field-based estimates are of equal magnitude and there is a risk that reported RS accuracies are severely misjudged due to inclusion of errors from the field reference data. Novel methods for correcting for some types of reference data errors are proposed, both for the conventional RMSE uncertainty metric and for the case when a parametric error model is applied. The theoretical framework proposed in this paper is demonstrated using real data from a typical RS study where airborne laser scanning and synthetic aperture radar (SAR) data are applied for estimating biomass at the level of forest stands. With the proposed correction method, the RMSE for the RS-based estimates from laser scanning was reduced from 50.5 to 49.5 tons/ha when errors in the field references were properly accounted for. The RMSE for the estimates from SAR data was reduced from 28.5 to 26.1 tons/ha.

Authors/Creators:Persson, Henrik and Ståhl, Göran
Title:Characterizing Uncertainty in Forest Remote Sensing Studies
Series Name/Journal:Remote Sensing
Year of publishing :2020
Page range:1-21
Number of Pages:21
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 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 > 1 Natural sciences > 101 Mathematics > 10106 Probability Theory and Statistics
Keywords:uncertainty, error, analysis, remote sensing, field, sampling, modeling, reference
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Additional ID:
Type of IDID
Web of Science (WoS)000515393800164
ID Code:16898
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:05 May 2020 08:55
Metadata Last Modified:15 Jan 2021 19:45

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