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Forests 2017, 8(5), 170; doi:10.3390/f8050170

Comparing Empirical and Semi-Empirical Approaches to Forest Biomass Modelling in Different Biomes Using Airborne Laser Scanner Data

1
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway
2
Department of Forest Mensuration and Management, Sokoine University of Agriculture, P.O. Box 3013, Chuo Kikuu, Morogoro, Tanzania
3
Department of Forest Management and Applied Geoinformatics, Mendel University, Zemědělská 3, 613 00 Brno, Czech Republic
*
Author to whom correspondence should be addressed.
Academic Editors: Christian Ginzler and Lars T. Waser
Received: 10 March 2017 / Revised: 7 May 2017 / Accepted: 12 May 2017 / Published: 16 May 2017
(This article belongs to the Special Issue Optimizing Forest Inventories with Remote Sensing Techniques)
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Abstract

Airborne laser scanner (ALS) data are used operationally to support field inventories and enhance the accuracy of forest biomass estimates. Modelling the relationship between ALS and field data is a fundamental step of such applications and the quality of the model is essential for the final accuracy of the estimates. Different modelling approaches and variable transformations have been advocated in the existing literature, but comparisons are few or non-existent. In the present study, two main approaches to modelling were compared: the empirical and semi-empirical approaches. Evaluation of model performance was conducted using a conventional evaluation criterion, i.e., the mean square deviation (MSD). In addition, a novel evaluation criterion, the model error (ME), was proposed. The ME was constructed by combining a MSD expression and a model-based variance estimate. For the empirical approach, multiple regression models were developed with two alternative transformation strategies: square root transformation of the response, and natural logarithmic transformation of both response and predictors. For the semi-empirical approach, a nonlinear regression of a power model form was chosen. Two alternative predictor variables, mean canopy height and top canopy height, were used separately. Results showed that the semi-empirical approach resulted in the smallest MSD in three of five study sites. The empirical approach resulted in smaller ME in the temperate and boreal biomes, while the semi-empirical approach resulted in smaller ME in the tropical biomes. View Full-Text
Keywords: airborne laser scanning; biomass modelling; light detection and ranging (LiDAR); model-based variance; model error airborne laser scanning; biomass modelling; light detection and ranging (LiDAR); model-based variance; model error
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MDPI and ACS Style

Hansen, E.H.; Ene, L.T.; Mauya, E.W.; Patočka, Z.; Mikita, T.; Gobakken, T.; Næsset, E. Comparing Empirical and Semi-Empirical Approaches to Forest Biomass Modelling in Different Biomes Using Airborne Laser Scanner Data. Forests 2017, 8, 170.

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