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Research article - Peer-reviewed, 2022

Quantify and account for field reference errors in forest remote sensing studies

Persson, Henrik Jan; Ekstrom, Magnus; Stahl, Goran

Abstract

Field inventoried data are often used as references (ground truth) in forest remote sensing studies. However, the reference values are affected by various kinds of errors, which tend to make the reported accuracies of the remote sensing-based predictions worse than they are. The more accurate the remote sensing techniques are becoming, the more pronounced this problem will be. This paper addresses the impact of uncertainties in field reference data due to measurement errors, model errors, and position errors when evaluating the accuracy of biomass predictions from airborne laser scanning at plot level. We present novel theoretical analysis methods that take the interactions of the error sources into account. Further, an error characterization model (ECM) is used to describe the error structure of the remote sensing-based predictions, and we show how the parameters of the ECM can be adjusted when field references contain errors. We also show how root mean square error (RMSE) estimates can be adjusted. Based on data from Scandinavian forests, we conclude that the field reference errors have an impact on the remote sensing-based predictions. By accounting for these errors the RMSE of the remote sensing-based predictions was reduced by 6-18%. The most influential sources of error in the field references were found to be the residual errors of the allometric biomass model and the field plot position errors. Together, these two sources accounted for 97% of the variance while measurement errors and biomass model parameter uncertainties were negligible in our study.

Keywords

Forest inventory; Uncertainty; Errors; Remote sensing

Published in

Remote Sensing of Environment
2022, volume: 283, article number: 113302
Publisher: ELSEVIER SCIENCE INC

Authors' information

Swedish University of Agricultural Sciences, Department of Forest Resource Management
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Umeå University
Swedish University of Agricultural Sciences, Department of Forest Resource Management

UKÄ Subject classification

Remote Sensing
Forest Science

Publication Identifiers

DOI: https://doi.org/10.1016/j.rse.2022.113302

URI (permanent link to this page)

https://res.slu.se/id/publ/119891