Author: Warren D. Devine
Publisher:
Published: 2013
Total Pages: 29
ISBN-13:
DOWNLOAD EBOOK →We sampled trees grown with and without competing vegetation control in an 11-year-old Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco) plantation on a highly productive site in southwestern Washington to create diameter- based allometric equations for estimating individual-tree bole, branch, foliar, and total aboveground biomass. We used these equations to estimate per-hectare aboveground biomass, nitrogen (N), and carbon (C) content, and compared these results to (1) estimates based on biomass equations published in other studies, and (2) estimates made using the mean-tree method rather than allometric equations. Component and total-tree biomass equations were not influenced by the presence of vegetation control, although per-hectare biomass, C, and N estimates were greater where vegetation control was applied. Our biomass estimates differed from estimates using previously published biomass equations by as much as 23 percent. When using the mean-tree biomass estimation approach, we found that incorporating a previously published biomass equation improved accuracy of the mean-tree diameter calculation.
Author: Susanna L. Melson
Publisher:
Published: 2004
Total Pages: 436
ISBN-13:
DOWNLOAD EBOOK →Concentration of carbon dioxide (CO2) in the atmosphere has increased over the past 150 years. Because CO2 is one of a number of radiatively active gases, there is concern that global temperatures will rise and climatic conditions will change. Recent research indicates northern hemisphere forests may currently be accumulating carbon (C) from the atmosphere. Live trees hold a large proportion of forest C, however, live-tree C can only be measured indirectly and therefore estimates of live-tree C are subject to numerous uncertainties. The objectives of this research were to estimate how live-tree C stores changed in the Pacific Northwest (Oregon and Washington west of the Cascade crest) between 1963-91 and to assess the factors introducing uncertainty into the estimate of live-tree C storage. The first objective was accomplished by using data from the Forest Service Forest Inventory and Analysis Program (FIA), combined with western Oregon and western Washington annual timber harvest data. The study produced live-tree C estimates for all timberland by land-ownership group. Between 1963-91, C on all timberland in the Pacific Northwest decreased from 1636 to 1392 Tg, or by 15% of the 1963 total. National forest, other public (other federal, state, and local government), forest industry, and miscellaneous private land lost 15, 5 (non-significant), 24, and 18% of their 1963 total timberland live-tree C by 1991, respectively. All landowners except industry experienced significant declines in total timberland area. C density (live-tree C per area) on all timberland dropped by 13% on national forests and by 30% on forest industry, but rose by 1% (non-significant) on other public and 26% on miscellaneous private land. For the Pacific Northwest as a whole, C density on all timberland decreased by 8% over the 28-year study period. C density declined most dramatically between 1963 and 1974. Since 1974, increasing C density on other public and miscellaneous private land balanced declining C density on national forest and forest industry land, resulting a C density ranging between 135-136 Mg C ha−1 on all timberland. The live-tree C estimate is subject to uncertainty arising from sampling, regression, measurement, and model error. We created and implemented a method for assessing uncertainty arising from model error. Volume equations, densities, biomass equations, and C:biomass ratios were compiled for the five major tree species in northwest Oregon: Picea sitchensis, Pseudotsuga menziesii, Tsuga heterophylla, Acer macrophyllum, and Alnus rubra. Volume equations were transformed into biomass by multiplying predicted volume with a range of species-specific measured densities. Biomass derived from volume equations multiplied by densities or from biomass equations was converted to C using a range of C:biomass ratios. For each tree component, species, and diameter at breast height, the maximum and minimum C predicted by equations was captured and stored as lookup tables. Component lookup tables were summed to create estimates of tree total C under three assumptions about within-dbh class correlation between components: perfect positive, zero, or perfect negative correlation. Application of lookup table bounds to individual tree data from the FIA program produced estimates of minimum and maximum C for the five target species in northwest Oregon. The above methods resulted in a base-case live-tree C estimate for northwest Oregon ranging from 28-210 Tg C (±76% uncertainty) assuming perfect positive correlation, and 67-1 54 Tg C (±40% uncertainty) for perfect negative correlation. When height variation was incorporated, C storage uncertainty rose to ±91% for positive and ±51% for negative correlation. A gain in precision was realized when species-specific equations were applied. Replacement of diameter-distribution data by quadratic mean diameter for each species reduced the absolute value of uncertainty, but created a bias when compared to the base case. Our attempt to incorporate regression standard error produced extremely large uncertainties for some equations and therefore was not pursued further. Results indicate that the most substantial reductions in uncertainty could be obtained by accurately assigning individual trees to suitable equations. The magnitude of model error produced by our methods currently precludes determination of significant differences between live-tree C stores of most landowners in the Pacific Northwest, and renders impossible the precise determination of the amount of live-tree C in a given forest area. Nevertheless, this does not necessarily preclude meaningful comparisons of C flux. Results of this study indicate uncertainty from model error in live-tree C could be extremely large. However, by accurately assigning appropriate volume or biomass prediction equations to trees, uncertainty could be greatly reduced.
Author: Raffaele Spinelli
Publisher: MDPI
Published: 2018-09-19
Total Pages: 417
ISBN-13: 3038971847
DOWNLOAD EBOOK →This book is a printed edition of the Special Issue "Forest Operations, Engineering and Management" that was published in Forests
Author: Krishna Prasad Poudel
Publisher:
Published: 2015
Total Pages: 128
ISBN-13:
DOWNLOAD EBOOK →The issue of global climate change and an increasing interest in the reduction of fossil fuel carbon dioxide emissions by using forest biomass for energy production has increased the importance of quantifying forest biomass in recent years. The official U.S. forest carbon reporting is based on the forest biomass estimates obtained from the equations, sample tree measurements, and forest area estimates of the U.S. Forest Service, Forest Inventory and Analysis (FIA). These biomass estimates differ from the estimates obtained from regional and other commonly used biomass equations and the difference is more evident in the component biomass estimates. In this dissertation, I assessed the efficiency of different sampling strategies to estimate crown biomass using data collected destructively from sampled trees. In terms of bias and root mean squared errors (RMSE), the stratified random sampling with probability proportional to branch basal diameter was better than other methods when 3 or 6 branches per tree are sampled but a systematic sampling with ratio estimation technique produced the smallest RMSE when 9 or 12 branches per tree are sampled. Total and component aboveground biomass estimates were obtained using the existing approaches and locally fitted equations based on the data collected in this study. The use of existing equations resulted in biased component biomass estimates along with higher RMSE. The locally fitted system of component biomass equations with seemingly unrelated regression (SUR) provided better estimates than existing equations. The need to use other explanatory variables in addition to the diameter at breast height (DBH) to estimate component biomass was justified by decrease in RMSE. Beta, Dirichlet, and multinomial loglinear regressions that predict proportion of biomass in each component were unbiased and produced lower RMSEs compared to the SUR methods for most of the species-component combinations. Three different methods for adjusting regional volume and component biomass equations were applied. All the adjustment methods were able to improve the performance of regional equations. Based on the leave one out cross validation, the RMSEs in cubic volume including top and stump (CVTS) and component biomass estimation were similar for the adjustments from a correction factor based on ordinary least square (OLS) regression through origin and an inverse approach. The adjustment based on OLS with intercept did not perform as well as the other two adjustment methods. When only one tree is available for calibration of regional models, we found it useful to use the tree with maximum DBH to calibrate regional CVTS and bark biomass equations and the dominant tree to calibrate bole, foliage, and branch biomass rather than to use randomly selected one tree.