Using liDAR to estimate the total aboveground live biomass of redwood stands in South Fork Caspar Creek Watershed, Jackson Demonstration State Forest, Mendocino, California

Thesis (M..)--Humboldt State University, Natural Resources: Forest, Watershed and Wildland Sciences, 2014The overall objective of this study is to develop a method for estimating total aboveground live (ABGL) biomass of redwood stands in South Fork Caspar Creek Watershed (SFCCW), Jackson Demonstration State Forest (JDSF), Mendocino, California using airborne LiDAR data. The study focused on two major species: redwood (Sequoia sempervirens or SESE) and Douglas-fir (Pseudotsuga menziesii or PSME). Specifically, the objective includes developing statistical models for tree diameter at breast height (DBH) on LiDAR-derived height for both species. From twenty-three 0.1-ha plots randomly selected within the study area, field measurements (DBH and tree coordinates) were collected for a total of 429 trees of SESE and PSME. Field measurements were taken for all trees having DBH equal to or greater than 25.4cm. In case of LiDAR-derived tree the height, a minimum height of 15m was used for this study. Software programs TreeVaW and FUSION/LDV were used to develop Canopy Height Models (CHM), from which tree heights were extracted. Based on LiDAR-derived height and ground-based DBH, linear regression models were developed. The linear regression models explained 62.65% of the total variation associated with redwood???s DBH and 82.58% of Douglas fir???s DBH. The predicted DBH was used to estimate the ABGL biomass using Jenkins??? formula (Jenkins et al., 2003A). At a single tree level, the average ABGL biomass of 257 SESE trees using predicted DBH was underestimated by about 10.1% compared with that of ABGL biomass using the ground-based DBH. The average ABGL biomass of 172 PSME trees using predicted DBH was underestimated by about 8.0% compared with that of using ground-based DBH. For both species, there was a statistically significant difference in the mean ABGL-biomass between using predicted DBH and ground-based DBH. In case of the twelve randomly-sampled plots, biomass estimates for both species on the rough terrain (\ud ??? 15% slope) were significantly lower and more varied than those on the flat terrain (< 15% slope). The 95% confidence interval for the mean ABGL-biomass of the two species combined was 369.5??128.8 ton/ha while that of all species included was 583.1?? 165.5 ton/ha. This study demonstrates that LiDAR data plays an important role in estimating the ABGL biomass of the second-growth redwood stands and Douglas fir. Thus, this method can make a significant contribution to forestry inventory by reducing time and labor cost in the timber industry