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Open AccessArticle

Removing the Scaling Error Caused by Allometric Modelling in Forest Biomass Estimation at Large Scales

1 and 2,3,*
1
Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1N 6N5, Canada
2
Research Center for Ecological Forecasting and Global Change, Northwest A&F University, Yangling 712100, China
3
Ecological Modeling and Carbon Science, Department of Biology Science, University of Quebec at Montreal, Montreal, QC H3C 3P8, Canada
*
Author to whom correspondence should be addressed.
Forests 2019, 10(7), 602; https://doi.org/10.3390/f10070602
Received: 14 June 2019 / Accepted: 20 July 2019 / Published: 22 July 2019
(This article belongs to the Section Forest Inventory, Quantitative Methods and Remote Sensing)
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Abstract

To estimate the responses of forest ecosystems, most relationships in biological systems are described by allometric relationships, the parameters of which are determined based on field measurements. The use of existing observed data errors may occur during the scaling of fine-scale relationships to describe ecosystem properties at a larger ecosystem scale. Here, we analyzed the scaling error in the estimation of forest ecosystem biomass based on the measurement of plots (biomass or volume per hectare) using an improved allometric equation with a scaling error compensator. The efficiency of the compensator on reducing the scaling error was tested by simulating the forest stand populations using pseudo-observation. Our experiments indicate that, on average, approximately 94.8% of the scaling error can be reduced, and for a case study, an overestimation of 3.6% can be removed in practice from a large-scale estimation for the biomass of Pinus yunnanensis Franch. View Full-Text
Keywords: aggregation error; allometric equation; error compensation; scaling error; variable allometric ratio aggregation error; allometric equation; error compensation; scaling error; variable allometric ratio
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Zhou, C.; Zhou, X. Removing the Scaling Error Caused by Allometric Modelling in Forest Biomass Estimation at Large Scales. Forests 2019, 10, 602.

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