Retrieval of Forest Aboveground Biomass and Stem Volume with Airborne Scanning LiDAR

Minna Räty,Hannu Hyyppä,Petteri Alho,Ville Kankare,Xiaowei Yu,Risto Viitala,Mikko Vastaranta,Markus Holopainen,Juha Hyyppä

doi:10.3390/rs5052257

Abstract

Airborne scanning LiDAR is a promising technique for efficient and accuratebiomass mapping due to its capacity for direct measurement of the three-dimensionalstructure of vegetation. A combination of individual tree detection (ITD) and an area-basedapproach (ABA) introduced in Vastaranta et al. [1] to map forest aboveground biomass(AGB) and stem volume (VOL) was investigated. The main objective of this study was totest the usability and accuracy of LiDAR in biomass mapping. The nearest neighbourmethod was used in the ABA imputations and the accuracy of the biomass estimation wasevaluated in the Finland, where single tree-level biomass models are available. The relativeroot-mean-squared errors (RMSEs) in plot-level AGB and VOL imputation were 24.9%and 26.4% when field measurements were used in training the ABA. When ITDmeasurements were used in training, the respective accuracies ranged between 28.5%–34.9%and 29.2%–34.0%. Overall, the results show that accurate plot-level AGB estimates can beachieved with the ABA. The reduction of bias in ABA estimates in AGB and VOL wasencouraging when visually corrected ITD (ITDvisual) was used in training. We conclude that itis not feasible to use ITDvisual in wall-to-wall forest biomass inventory, but it could provide acost-efficient application for acquiring training data for ABA in forest biomass mapping.

Highlights

Remote sensing (RS) methods, such as optical and microwave satellite imaging, digital aerial photography and light detection and ranging (LiDAR), are highly useful in various forest-monitoring tasks
The results showed that LiDAR is capable of retrieving aboveground biomass (AGB) accurately at the plot level
The results show that spatially accurate AGB

Summary

Introduction

Remote sensing (RS) methods, such as optical and microwave satellite imaging, digital aerial photography and light detection and ranging (LiDAR), are highly useful in various forest-monitoring tasks. Knowledge of forest biomass and its changes has been based on ground measurements and coarse or medium resolution satellite images. The accuracy of biomass estimations, especially at the local level (e.g., forest stands or sample plots), is poor. Stand biomass is highly correlated with tree heights that can be determined accurately by means of LiDAR, e.g., [2]. It is expected that LiDAR applications will enhance the accuracy of forest biomass estimates at all levels from single-tree to nationwide inventory applications. LiDAR measurements can be divided into profiling and scanning approaches.

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