Canadian Forest Service Publications

Integrating airborne LiDAR and space-borne radar via multivariate kriging to estimate above-ground biomass. 2013. Tsui, O.W.; Coops, N.C.; Wulder, M.A.; Marshall, P.L. Remote Sensing of Environment. 139:340-352.

Year: 2013

Available from: Pacific Forestry Centre

Catalog ID: 35186

Language: English

CFS Availability: PDF (download)

Available from the Journal's Web site.
DOI: 10.1016/j.rse.2013.08.012

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Abstract

Understanding and investigating synergies between LiDAR (light detection and ranging) and SAR (synthetic aperture radar) provide new and innovative opportunities to characterize above-ground biomass. We demonstrate a spatial modeling framework that integrates above-ground biomass transects, derived from plot-based field data and small-footprint discrete return LiDAR, with complete wall-to-wall spaceborne L-band and C-band SAR to predict biomass over a larger area. Transect intervals of 2000 m, 1000 m, and 500 m were tested. Co-kriging, regression kriging, and regression co-kriging were used to extend the LiDAR-derived biomass transects. LiDAR-derived above-ground biomass and L-band backscatter (HV polarization) were moderately correlated, with a maximum semivariance distance between the LiDAR-derived biomass and SAR data of 374 m. Regression kriging at a sample interval of 500 m showed the smallest root mean squared error (RMSE) and mean absolute error (MAE) at 203.9 Mg ha− 1 and 131.6 Mg ha− 1, respectively. The mean error (ME) showed an average bias of − 14.0 Mg ha− 1. Predictions using regression co-kriging at a sample interval of 2000 m resulted in the highest RMSE and MAE values at 238.2 Mg ha− 1 and 164.6 Mg ha− 1, respectively. ME also was highest, averaging − 37.4 Mg ha− 1. Regardless of the spatial modeling technique employed, lower errors in predicted above-ground biomass were associated with smaller transect intervals. Moderate correlations between the LiDAR-derived above-ground biomass and the radar data impacted the predictive accuracy of the spatial models; however, overall variation in above-ground biomass in the study area was well represented. This study demonstrated that a sampling framework integrating LiDAR data with space-borne radar data using a spatial modeling approach can provide spatially-explicit above-ground biomass estimates for large areas. Such a sampling framework can be used in combination with ground plot and land cover data to assess carbon stocks under conditions where more common optical remote sensing approaches are difficult to implement.

Plain Language Summary

Forests help mitigate the effects of climate change by storing carbon. However, when forests are cleared much of the stored carbon is released into the atmosphere as CO2; as well, valuable carbon sinks are removed. National strategies are in place to reduce deforestation and conversion of forests to other land types, and financial incentives are proposed to reduce such land conversions; therefore a reliable method of quantifying the amount of carbon emitted and sequestered in forests is needed. Scientists can calculate changes in forest carbon stocks using maps that capture above-ground forest biomass (all living biomass above the soil). Optical data, Synthetic Aperture Radar (SAR), and Light Detection and Ranging (LiDAR) are three remote sensing methods (each with its own limitations) used to characterize above-ground biomass. The authors of this study tested integration of these three methods to produce high quality, spatially-explicit biomass maps. Field plots and LiDAR data from a study site on Vancouver Island were used as the reference data set. They demonstrate a modeling framework that integrates biomass information from the field plots and LiDAR data with SAR data. The results show that this integration can provide spatially-explicit biomass estimates for large areas and can be used to assess carbon stocks where more common remote sensing approaches are difficult to implement.

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