Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height

Abstract

Light detection and ranging (lidar) data provide accurate measurements of forest canopy structure in the vertical plane; however, current lidar sensors have limited coverage in the horizontal plane. Landsat data provide extensive coverage of generalized forest structural classes in the horizontal plane but are relatively insensitive to variation in forest canopy height. It would, therefore, be desirable to integrate lidar and Landsat data to improve the measurement, mapping, and monitoring of forest structural attributes. We tested five aspatial and spatial methods for predicting canopy height, using an airborne lidar system (Aeroscan) and Landsat Enhanced Thematic Mapper (ETM+) data: regression, kriging, cokriging, and kriging and cokriging of regression residuals. Our 200-km 2 study area in western Oregon encompassed Oregon State University's McDonald–Dunn Research Forest, which is broadly representative of the age and structural classes common in the region. We sampled a spatially continuous lidar coverage in eight systematic patterns to determine which lidar sampling strategy would optimize lidar–Landsat integration in western Oregon forests: transects sampled at 2000, 1000, 500, and 250 m frequencies, and points sampled at these same spatial frequencies. The aspatial regression model results, regardless of sampling strategy, preserved actual vegetation pattern, but underestimated taller canopies and overestimated shorter canopies. The spatial models, kriging and cokriging, produced less biased results than regression but poorly reproduced vegetation pattern, especially at the sparser (2000 and 1000 m) sampling frequencies. The spatial model predictions were more accurate than the regression model predictions at locations <200 m from sample locations. Cokriging, using the ETM+ panchromatic band as the secondary variable, proved slightly more accurate than kriging. The integrated models that kriged or cokriged regression residuals were preferable to either the aspatial or spatial models alone because they preserved the vegetation pattern like regression yet improved estimation accuracies above those predicted from the regression models alone. The 250-m point sampling strategy proved most optimal because it oversampled the landscape relative to the geostatistical range of actual spatial variation, as indicated by the sample semivariograms, while making the sample data volume more manageable. We concluded that an integrated modeling strategy is most suitable for estimating and mapping canopy height at locations unsampled by lidar, and that a 250-m discrete point sampling strategy most efficiently samples an intensively managed forested landscape in western Oregon.