There is growing interest in airborne lidar for forest carbon accounting and precision forestry purposes. Airborne lidar systems offer a cost-effective, versatile, operationally flexible and robust sampling tool for forest managers. The objective of this study was to develop and test lidar canopy surface enhancement and segmentation processes for estimating dominant above-ground biomass (DAB) in a harvested eucalypt forest on the Central Coast of New South Wales (Australia). The Crown Infill, Trim and Smooth (CITS) process, incorporating a series of filters, algorithms, and selective multi-stage smoothing, was used to enhance lidar canopy surfaces prior to segmentation. Canopy segmentation was achieved using a vertical crown template approach termed the Spatially and Morphologically Isolated Crest (SMIC) process. SMIC delineates dominant tree crowns by detecting elevated crown crests within a 3D lidar canopy surface. Consolidated crown units constitute the basic sampling, analysis and reporting units for wall-to-wall forest inventory. The performance, sensitivity and limitations of these procedures were evaluated using a combination of simulated forest models and actual lidar forest data. Automated crown polygons were used as a sampling template to extract dominant tree height values which were converted to DAB estimates via height-to-biomass relationships derived from field survey and on-site destructive sampling. Results were compared with field based tree height and biomass estimates. Compared against a manually derived crown map from a 2ha field plot, canopy segmentation results revealed a producer???s accuracy of 76% and overall accuracy of 67%. Results indicated a trend toward greater crown splitting (fragmentation) as trees increase in age, height, stem diameter and crown size. Extracted dominant tree height values were highly correlated with ground survey height estimates (r2 0.95 for precision survey and r2 0.69 for standard survey). There was also no significant difference between SMIC and manual crown height estimates. SMIC units overestimated ground-based DAB by 5%; this increased to 36% with the inclusion of segmentation errors. However, SMIC estimation of total plot above-ground biomass (AGB) was within 9% of the ground-based estimate. Results are encouraging considering the mixed-species, multi-aged composition of the forest, and the combined effects of SMIC segmentation and lidar height errors.
| Identifer | oai:union.ndltd.org:ADTP/234313 |
| Date | January 2006 |
| Creators | Turner, Russell Sean, School of Biological, Earth & Environmental Science, UNSW |
| Publisher | Awarded by:University of New South Wales. School of Biological, Earth and Environmental Science |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | Copyright Russell Sean Turner, http://unsworks.unsw.edu.au/copyright |
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