Urban tree species mapping using hyperspectral and lidar data fusion

In this study we fused high-spatial resolution (3.7 m) hyperspectral imagery with 22 pulse/m2 lidar data at the individual crown object scale to map 29 common tree species in Santa Barbara, California, USA. We first adapted and parallelized a watershed segmentation algorithm to delineate individual crowns from a gridded canopy maxima model. From each segment, we extracted all spectra exceeding a Normalized Difference Vegetation Index (NDVI) threshold and a suite of crown structural metrics computed directly from the three-dimensional lidar point cloud. The variables were fused and crowns were classified using canonical discriminant analysis. The full complement of spectral bands along with 7 lidar-derived structural metrics were reduced to 28 canonical variates and classified. Species-level and leaf-type level maps were produced with respective overall accuracies of 83.4% (kappa = 82.6) and 93.5%. The addition of lidar data resulted in an increase in classification accuracy of 4.2 percentage points over spectral data alone. The value of the lidar structural metrics for urban species discrimination became particularly evident when mapping crowns that were either small or morphologically unique. For instance, the accuracy with which we mapped the tall palm species Washingtonia robusta increased from 29% using spectral bands to 71% with the fused dataset. Additionally, we evaluated the role that automated segmentation plays in classification error and the prospects for mapping urban forest species not included in a training sample. The ability to accurately map urban forest species is an important step towards spatially explicit urban forest ecosystem assessment.