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Research article2017Peer reviewedOpen access

Influence of footprint size and geolocation error on the precision of forest biomass estimates from space-borne waveform LiDAR

Milenkovic, Milutin; Schnell, Sebastian; Holmgren, Johan; Ressl, Camillo; Lindberg, Eva; Hollaus, Markus; Pfeifer, Norbert; Olsson, Hakan


Space-borne LiDAR systems can potentially assist large-area assessments of forest resources, in particular when a subset of the acquired LiDAR footprints is combined with field surveys of forest stand characteristics at footprint location. When combined, space-borne LiDAR geolocation error and the footprint size may however have considerable effects on the estimation accuracy of forest stand variables, such as aboveground biomass (AGB). The aim of this study was to draw recommendations for future space-borne LiDAR systems, which should deliver data for unbiased AGB assessments. The recommendations were drawn from AGB estimations based on space borne LiDAR waveforms simulated over a 1300 ha large study site in southern Sweden. Large-footprint, nadir looking satellite waveforms were simulated by stacking individual small-footprint, airborne LiDAR waveforms observed near a predefined sampling pattern. The stacked waveforms, represented by their metrics, were used as input for a two-phase systematic sampling in combination with model-assisted estimation or hybrid inference for estimating AGB and its variance. The second-phase sample included 264 inventory plots, whereas the first-phase sample included 1010 sample locations, where satellite waveforms were simulated. After simulating satellite waveforms with different footprint sizes and analyzing the AGB variance, the recommendation is to have a footprint size that is similar to the size of the field plots used for collecting reference data, i.e. 20 m diameter in our case. For the optimal footprint size, AGB was estimated with a precision of 2.9 Mg per hectare (2.9% of the average). The results also showed that variance estimates increased constantly with increasing geolocation error. For a geolocation error of 14 m, variance estimates increased by 17%, which justifies investing additional efforts in minimizing it.


Satellite LiDAR; Biomass; Large footprint; Waveform stacking; Forest inventory; Model-assisted estimation; Hybrid inference

Published in

Remote Sensing of Environment
2017, Volume: 200, pages: 74-88