Canadian Forest Service Publications
Integration of GLAS and Landsat TM data for aboveground biomass estimation. 2010. Duncanson, L.; Niemann, K.O.; Wulder, M.A. Canadian Journal of Remote Sensing 36(2): 129-141.
Issued by: Pacific Forestry Centre
Catalog ID: 31871
Availability: PDF (download)
Current regional aboveground biomass estimation techniques, such as those that require extensive fieldwork or airborne light detection and ranging (lidar) data for validation, are time and cost intensive. The use of freely available satellite-based data for carbon stock estimation mitigates both the cost and the spatial limitations of field-based techniques. Spaceborne lidar data have been demonstrated as useful for aboveground biomass (AGBM) estimation over a wide range of biomass values and forest types. However, the application of these data is limited because of their spatially discrete nature. Spaceborne multispectral sensors have been used extensively to estimate AGBM, but these methods have been demonstrated as inappropriate for forest structure characterization in high-biomass mature forests. This study uses an integration of ICESat Geospatial Laser Altimeter System (GLAS) lidar and Landsat data to develop methods to estimate AGBM in an area of south-central British Columbia, Canada. We compare estimates with a reliable AGBM map of the area derived from high-resolution airborne lidar data to assess the accuracy of satellite-based AGBM estimates. Further, we use the airborne lidar dataset in combination with forest inventory data to explore the relationship between model error and canopy height, AGBM, stand age, canopy rugosity, mean diameter at breast height (DBH), canopy cover, terrain slope, and dominant species type. GLAS AGBM models were shown to reliably estimate AGBM (R2 = 0.77) over a range of biomass conditions. A partial least squares AGBM model using Landsat input data to estimate AGBM (derived from GLAS) had an R2 of 0.60 and was found to underestimate AGBM by an average of 26 Mg/ha per pixel when applied to areas outside of the GLAS transect. This study demonstrates that Landsat and GLAS data integration are most useful for forests with less than 120 Mg/ha of AGBM, less than 60 years of age, and less than 60% canopy cover. These techniques have high associated error when applied to areas with greater than 200 Mg/ha of AGBM.