Prediction of Aboveground Biomass from Low-Density LiDAR Data: Validation over P. radiata Data from a Region North of Spain

Tojal Tojal,Barret Barret,Bastarrika Bastarrika,Graña Graña,Espeso Espeso,Lopez-Guede Lopez-Guede

doi:10.3390/f10090819

Abstract

Estimation of forestry aboveground biomass (AGB) by means of aerial Light Detection and Ranging (LiDAR) data uses high-density point sampling data obtained in dedicated flights, which are often too costly for available research budgets. In this paper we exploit already existing public low-density LiDAR data obtained for other purposes, such as cartography. The challenge is to show that such low-density data allows accurate biomass estimation. We demonstrate the approach on data available from plantations of Pinus radiata in the Arratia-Nervión region, located in Biscay province located in the North of Spain. We use public data gathered from the low-density (0.5 pulse/m2) LiDAR flight conducted by the Basque Government in 2012 for cartographic production. We propose a linear regression model based on explanatory variables obtained from the LiDAR point cloud data. We calibrate the model using field data from the Fourth National Forest Inventory (NFI4), including the selection of the optimal model variables. The results revealed that the best model depends on two variables extracted from LiDAR data: One directly related with tree height and a second parameter with the canopy density. The model explained 80% of its variability with a standard error of 0.25 ton/ha in logarithmic units. We validate the predictions against the biomass measurements provided by the government institutions, obtaining a difference of 8%. The proposed approach would allow the exploitation of the periodic available low-density LiDAR data, collected with territorial and cartographic purposes, for a more frequent and less expensive control of the forestry biomass.

Highlights

Assessing forest resources has gained increased attention by governments worldwide in the last few decades due to increased awareness about global climate change, and a greater appreciation of the ecosystem services provided by forests [1]
Estimated P. radiata carbon storage in some regions [4] serve as an indication of the magnitude of environmental services that P. radiata plantations can provide by terrestrial carbon sinks to offset carbon emissions
The resulting biomass value was corrected adding 4.04% of the obtained volume, in order to account for the tree branches and upper part of the trunk discarded for wood production reasons

Summary

Introduction

Assessing forest resources has gained increased attention by governments worldwide in the last few decades due to increased awareness about global climate change, and a greater appreciation of the ecosystem services provided by forests [1]. Don) is native to the Californian coastal environment It is the most extensively planted coniferous species in the Southern Hemisphere covering a total area exceeding 4.3 million ha and still expanding. It is a major species cultivated for timber production in Australia, Chile, New Zealand, South Africa, and Spain [3]. The worldwide P. radiata forest resource provides substantial carbon storage through continued atmospheric carbon sequestration as plantations grow accumulating biomass. Estimated P. radiata carbon storage in some regions [4] serve as an indication of the magnitude of environmental services that P. radiata plantations can provide by terrestrial carbon sinks to offset carbon emissions

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Discussion

Conclusion