Citation: Zhang Z.Y.,Kazakova A.N. and Moskal L.M Integrating LIDAR with Hyperspectral Data for Tree Species Classification in Urban Ecosystems. Factsheet # 25. Remote Sensing and Geospatial Application Laboratory, University of Washington, Seattle, WA. Digital version of the fact sheet can be downloaded at: THE ISSUE: The identification of tree species is a key issue for forest inventory.The advent of aerial LiDAR and aerial hyperspectral imaging provided efficient and spatially explicit approaches to species detection. THE KEY QUESTIONS: How well can LiDAR and hyper- spectral data integrated to distinguish tree species for forest inventory? ⓒ RSGAL 2013 Integrating LIDAR with Hyperspectral Data for Tree Species Classification in Urban Ecosystems Factsheet # 29 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS This research was funded by the Mclntire-stennis Cooperative Forestry Research Program Introduction Tree species inventory is important for understanding many ecological ecosystem processes and improving natural resource management. Traditionally,information on tree species has been acquired from field plots inventory and aerial image interpretation. The processes can be very labor- and time- intensive. This study aims to integrate LiDAR and Hyperspectral Data to provide a fast and cost-effective approach to identify tree species in urban ecosystems. Data LiDAR Hyperspectral Hymap field collected tree data Orthoimagery Fig.1 Object based image analysis processes Left:Original LiDAR data Middle:Extraction of tree canopies Right:Sementation Result Predicted speciessweetgumsycamore western redcedar Japanese zelkova red mapleDouglas firEuropean larch Producer's Accuracy sweetgum % sycamore % western redcedar % Japanese zelkova % red maple % Douglas fir % European larch % User's Accurcy88.6%81.0%66.7%90.0%80.0%34.8%81.0%74.2% Table.1 Confusion Matrix for classification of seven species with Selected bands Segmentation Method With Canopy Height Models derived from LiDAR segments of individual tree crowns. were generated with a region growing algorithm using height increments. Centroids of tree segments waere found and used as a “center” of each tree. Stem map was validated using hyperspatial orhtoimagery and data collected in the field. Classification Methods The hyperspectral values of each band were extracted for each tree using tree locations as regions of interest in ENVI Consistency Subset Evaluator eliminated autocorrelations of predictor variables (bands) The variances of featured spectral signature values within individual species was used in object based classification by Multi Class Classifier. Results Band Selection 23 from 117 bands were selected as input to classification Coniferous & Broadleaved Overall Accuracy=91.2% Kappa = 82.1% Seven Individual Tree Species Overall Accuracy=74.2% Kappa=69% Conclusion Certain species can be identified by applying the spectral characteristics of hyperspectral data based on crown delineation form LiDAR Compared our results to our previous work,the best approaches should unite both the spectral content of hyperspectral data and the spatial content inherent in the LiDAR Fig.3 Mean hyperspectral values of seven species