Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzania.

Ernest William Mauya,Liviu Theodor Ene,Terje Gobakken,Rogers Ernest Malimbwi,Ole Martin Bollandsås,Erik Næsset,Eliakimu Zahabu

doi:10.1186/s13021-015-0037-2

Ernest William Mauya, Liviu Theodor Ene + Show 5 more

Open Access

https://doi.org/10.1186/s13021-015-0037-2

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Abstract

BackgroundAirborne laser scanning (ALS) has emerged as one of the most promising remote sensing technologies for estimating aboveground biomass (AGB) in forests. Use of ALS data in area-based forest inventories relies on the development of statistical models that relate AGB and metrics derived from ALS. Such models are firstly calibrated on a sample of corresponding field- and ALS observations, and then used to predict AGB over the entire area covered by ALS data. Several statistical methods, both parametric and non-parametric, have been applied in ALS-based forest inventories, but studies that compare different methods in tropical forests in particular are few in number and less frequent than studies reported in temperate and boreal forests. We compared parametric and non-parametric methods, specifically linear mixed effects model (LMM) and k-nearest neighbor (k-NN).ResultsThe results showed that the prediction accuracy obtained when using LMM was slightly better than when using the k-NN approach. Relative root mean square errors from the cross validation was 46.8 % for the LMM and 58.1 % for the k-NN. Post-stratification according to vegetation types improved the prediction accuracy of LMM more as compared to post-stratification by using land use types.ConclusionAlthough there were differences in prediction accuracy between the two methods, their accuracies indicated that both of methods have potentials to be used for estimation of AGB using ALS data in the miombo woodlands. Future studies on effects of field plot size and the errors due to allometric models on the prediction accuracy are recommended.

Highlights

Airborne laser scanning (ALS) has emerged as one of the most promising remote sensing technologies for estimating aboveground biomass (AGB) in forests
In the post-strata such as forest, or production and protection forests where the distribution of AGB is characterized by trees with high canopy cover and more uniform stems, the root mean square error from cross validation (RMSECV) % were relatively smaller compared to other categories
The higher RMSECV % value in the agricultural and other land uses might be attributed to the fact that most of the trees in this category are scattered with sparse canopy, and the tree crowns are smaller with some appearing to be in a degraded form

Summary

Introduction

Airborne laser scanning (ALS) has emerged as one of the most promising remote sensing technologies for estimating aboveground biomass (AGB) in forests. Use of ALS data in area-based forest inventories relies on the development of statistical models that relate AGB and metrics derived from ALS. Such models are firstly calibrated on a sample of corresponding field- and ALS observations, and used to predict AGB over the entire area covered by ALS data. Several statistical methods, both parametric and non-parametric, have been applied in ALS-based forest inventories, but studies that compare different methods in tropical forests in particular are few in number and less frequent than studies reported in temperate and boreal forests. The use of remotely sensed data as auxiliary information is considered as an option towards developing a cost-efficient MRV system in the country

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