U.S. Department of Agriculture: Forest Service -- National Agroforestry Center


Date of this Version



Remote Sensing of Environment 95 (2005) 532–548; doi:10.1016/j.rse.2005.01.010


Estimation of the amount of carbon stored in forests is a key challenge for understanding the global carbon cycle, one which remote sensing is expected to help address. However, carbon storage in moderate to high biomass forests is difficult to estimate with conventional optical or radar sensors. Lidar (light detection and ranging) instruments measure the vertical structure of forests and thus hold great promise for remotely sensing the quantity and spatial organization of forest biomass. In this study, we compare the relationships between lidar measured canopy structure and coincident field measurements of forest stand structure at five locations in the Pacific Northwest of the U.S.A. with contrasting composition. Coefficient of determination values (r2) ranged between 41% and 96%. Correlations for two important variables, LAI (81%) and above ground biomass (92%), were noteworthy, as was the fact that neither variable showed an asymptotic response.

Of the 17 stand structure variables considered in this study, we were able to develop eight equations that were valid for all sites, including equations for two variables generally considered to be highly important (aboveground biomass and leaf area index). The other six equations that were valid for all sites were either related to height (which is most directly measured by lidar) or diameter at breast height (which should be closely related to height). Four additional equations (a total of 12) were applicable to all sites where either Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla) or Sitka spruce (Picea sitchensi) were dominant. Stand structure variables in sites dominated by true firs (Abies sp.) or ponderosa pine (Pinus ponderosa) had biases when predicted by these four additional equations. Productivity-related variables describing the edaphic, climatic and topographic environment of the sites where available for every regression, but only two of the 17 equations (maximum diameter at breast height, stem density) incorporated them. Given the wide range of these environmental conditions sampled, we conclude that the prediction of stand structure is largely independent of environmental conditions in this study area.

Most studies of lidar remote sensing for predicting stand structure have depended on intensive data collections within a relatively small study area. This study indicates that the relationships between many stand structure indices and lidar measured canopy structure have generality at the regional scale. This finding, if replicated in other regions, would suggest that mapping of stand structure using lidar may be accomplished by distributing field sites extensively over a region, thus reducing the overall inventory effort required.