Arizona Space Grant Consortium Statewide Symposium Arizona Space Grant Consortium Statewide Symposium Light Detection and Ranging (LiDAR) Survey of a Sky.

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Presentation transcript:

Arizona Space Grant Consortium Statewide Symposium Arizona Space Grant Consortium Statewide Symposium Light Detection and Ranging (LiDAR) Survey of a Sky Island Forest Thanks to Tyson Swetnam, M.S. School of Natural Resources, University of Arizona Stephen Yool, Ph.D Department of Geography and Regional Development, University of Arizona Jimmy Mack

2 Uncertainty regarding quantity and distribution of forest biomass (above ground carbon stores). LiDAR is becoming more available, shown to be highly accurate at quantifying forest structure (Anderson et al. 2005, Reutebuch et al. 2005). Unprecedented accuracy (typical R 2 > 0.90) (McGaughey et al. 2006), becoming more cost effective (<$0.50/acre). Perspective and Significance

3 Objectives

4

5 LiDAR data covering 31,857 hectares of forest land Original purchase by Pima County Flood control Santa Catalinas “Sky Island” Aspen (2003) and Bullock Fires (2002)

6 Data and Methods Converted Log ASCII Standard (LAS) files to X,Y,Z format using Fusion Digital Terrain Models Canopy Height Models

Results and Discussion

Results and Discussion continued

Vegetation and Surface Model Elimination of Surface Returns (0.7m and below) Surface Elevation Subtracted from Vegetation

Results and Discussion continued Calculation of Both Plot and Individual Tree Metrics (i.e. Canopy Diameter and Tree Height)

Ongoing Work Summer field work to validate/reference LiDAR measurements Stratification of vegetation by type, estimate biomass in kg/m 2 Inclusion of Pinaleño Mountains and other southwestern “sky islands”

References Andersen, H.-E., S.E. Reutebuch, and R.J. McGaughey Chapter 3: Active remote sensing. In Computer Applications in Sustainable Forest Management, Edited by G. Shao and K. Reynolds. Springer-Verlag, Dordrecht. Andersen, H.-E., R.J. McGaughey, and S.E. Reutebuch Estimating forest canopy fuel parameters using LIDAR data. Remote Sensing of Environment 94(4): McGaughey, R.J., H.-E. Andersen, and S.E. Reutebuch Considerations for planning, acquiring, and processing LIDAR data for forestry applications. In Proceedings of the eleventh Biennial USDA Forest Service Remote Sensing Applications Conference, Salt Lake City, ID, April 24-28, American Society of Photogrammetry and Remote Sensing, Bethesda, MD. McGaughey, R.J., W.W. Carson, S.E. Reutebuch, and H.-E. Andersen Direct measurement of individual tree characteristics from LIDAR data. Proceedings of the Annual ASPRS Conference, Denver, May 23-28, American Society of Photogrammetry and Remote Sensing, Bethesda, MD. Popescu, S.C., R.H. Wynne, and R.F. Nelson Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. Canadian Journal of Remote Sensing 29(5): Reutebuch, S.E., H.-E. Andersen, and R.J. McGaughey Light detection and ranging (LIDAR): An emerging tool for multiple resource inventory. Journal of Forestry 103(6):