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Assessing Error Correlations in Remote Sensing-Based Estimates of Forest Attributes for Improved Composite Estimation

Ehlers, Sarah and Saarela, Svetlana and Lindgren, Nils and Lindberg, Eva and Nyström, Mattias and Persson, Henrik and Olsson, Håkan and Ståhl, Göran (2018). Assessing Error Correlations in Remote Sensing-Based Estimates of Forest Attributes for Improved Composite Estimation. Remote Sensing. 10 , 667
[Research article]

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Abstract

Today, non-expensive remote sensing (RS) data from different sensors and platforms can be obtained at short intervals and be used for assessing several kinds of forest characteristics at the level of plots, stands and landscapes. Methods such as composite estimation and data assimilation can be used for combining the different sources of information to obtain up-to-date and precise estimates of the characteristics of interest. In composite estimation a standard procedure is to assign weights to the different individual estimates inversely proportional to their variance. However, in case the estimates are correlated, the correlations must be considered in assigning weights or otherwise a composite estimator may be inefficient and its variance be underestimated. In this study we assessed the correlation of plot level estimates of forest characteristics from different RS datasets, between assessments using the same type of sensor as well as across different sensors. The RS data evaluated were SPOT-5 multispectral data, 3D airborne laser scanning data, and TanDEM-X interferometric radar data. Studies were made for plot level mean diameter, mean height, and growing stock volume. All data were acquired from a test site dominated by coniferous forest in southern Sweden. We found that the correlation between plot level estimates based on the same type of RS data were positive and strong, whereas the correlations between estimates using different sources of RS data were not as strong, and weaker for mean height than for mean diameter and volume. The implications of such correlations in composite estimation are demonstrated and it is discussed how correlations may affect results from data assimilation procedures.

Authors/Creators:Ehlers, Sarah and Saarela, Svetlana and Lindgren, Nils and Lindberg, Eva and Nyström, Mattias and Persson, Henrik and Olsson, Håkan and Ståhl, Göran
Title:Assessing Error Correlations in Remote Sensing-Based Estimates of Forest Attributes for Improved Composite Estimation
Series Name/Journal:Remote Sensing
Year of publishing :2018
Volume:10
Article number:667
Number of Pages:19
Publisher:MDPI
Language:English
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 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
Keywords:airborne LiDAR, Composite estimators, forest inventory, SPOT-5 HRG, TanDEM-X
URN:NBN:urn:nbn:se:slu:epsilon-p-95900
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-95900
Additional ID:
Type of IDID
DOI10.3390/rs10050667
Web of Science (WoS)000435198400009
ID Code:23358
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:27 Apr 2021 11:26
Metadata Last Modified:27 Apr 2021 11:31

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