Thesis (Ph. D.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 158-169). Also available on the World Wide Web.

Deforestation of flooded (várzea) and non-flooded (terra firme) forests caused by firewood consumption at tile factories (olaria) was investigated in Abaetetuba Island at the Amazon estuary. Várzea is spatially limited, the area is only 3% of the whole Amazon, however, it is heavily influenced by human activities, especially by the cultivation of acaí palm (Euterpe oleracea Mart.). The trees are cut down for the olarias. The number of tree species are small, and they have less wood density than terra firme tree species because the várzea is flooded twice a day throughout the year. Terra firme forests, which are also secondary forests, receive less human impact, and have more tree species and more individual trees with a growth extension that exceeds the species in the várzea forests. The deforestation was examined by comparing forest biomass in a unit area to firewood consumption at olarias. The annual area of deforestation was estimated by using the combination of tree species in the firewood and human impact in the várzea forests. Then the estimation was extended to the whole island, assuming that the forests were rotatively cleared every 25 to 30 years. The results indicated that the area of deforestation was 6,870ha/25 years to 8,337ha/30 years, and that it was smaller than the island. However, logging is not only for fuel at olarias. If Belém's economic influence becomes stronger, and electric energy is not diffused throughout the island, the lumber consumption will accelerate and the increase might make the forest disappear faster than estimated.

South Africa lags behind the rest of the world with regard to the availability of allometric biomass information. There is a complete lack of site specific allometric data for Pinus radiata in the southern Cape region, impeding investment in the renewable energy sector. This shortcoming was addressed by developing up-scalable, single tree biomass models. These models quantify the aboveground biomass of rotation age P. radiata grown in the southern Cape across a range of site conditions. The models use diameter at breast height (DBH) to predict the aboveground component biomass. A nutrient loss risk potential was assigned to each biomass component. Nineteen trees were destructively harvested using a full fresh weight sampling approach. Basic density was determined using a water displacement method, while Newton’s volume equation was used for stemwood volume determination. Log linear models were simultaneously regressed through seemingly unrelated regression (SUR) using the “Systemfit” R statistical package to force component additivity. A categorical variable was applied to the models, grouping the data into two Site Index (SI) based categories, namely “Low” SI and “Medium to High” SI, to account for inter-site variability. The corrected Akaike Information Criteria (AICc) and coefficient of determination (R2) was used to determine the goodness of fit of the models. The McElroy R2 for the SUR system was 0.95. Biomass models were developed that are able to predict various tree component masses at high levels of certainty within site and stand attribute ranges similar to this study. The importance of accurate, site specific wood basic density was demonstrated by its substantial weighting on stem and hence total biomass. Results showed that the stemwood mean basic density range was between 503 kg m-3 and 517 kg m-3 for the “Low” SI sites and 458 kg m-3 for the “Medium to High” SI sites. Site quality can have a major impact on the models, particularly on poorer sites where stemwood production is proportionally less than other tree components. Total aboveground biomass was estimated to range between 58.61 odt ha-1 and 70.85 odt ha-1 for “Low” SI sites, and 185.31 odt ha-1 to 266.58 odt ha-1 for “Medium to High” SI sites. Stemwood biomass accounted for 65% of the total aboveground biomass for “Low” SI sites and 70% for “Medium to High” SI sites.

Global forests cover approximately 30% of land’s surface storing around 45% of above-ground terrestrial carbon. This carbon storage is constantly endangered by anthropogenic activities. Especially, tropical regions like the Amazon rainforest suffer from deforestation taking a great share in global CO2 emissions. In addition, forest dynamics are affected by climatic change like more frequent drought events. Quantifying the impact and feedback mechanisms of such climatic and anthropogenic changes on the global carbon cycle is still a great challenge. In this thesis, we developed a regionalization scheme to apply a forest gap model on the entire Amazon rainforest. Such a forest model has the advantage that it calculates forest growth at the individual tree level. It considers different successional states, that evolve form natural forest dynamics and disturbances, including information on tree height and species. The regionalized forest model thereby allows for integrating forest structure and species compositions into large-scale carbon analyses. The approach is independent of spatial scale and the simulation results can be linked to measurements from field inventory, eddy covariance, and remote sensing at local to continental scales. In a first study (chapter 2), we tested the capability of the forest model FORMIND to simulate gross primary production (GPP), respiration, and net ecosystem exchange (NEE) at daily and yearly time scales. The forest model was applied to spruce forests in Germany in order to analyze how the variability in environmental factors affects simulated carbon fluxes. Simulation results were compared to 6 years of eddy covariance (EC) data at a daily scale. The analysis shows that the forest model described the seasonal cycle of the carbon fluxes correctly, but estimated GPP differed from the observed data on days with extreme climatic conditions. Based on these findings, we developed two new parameterizations. One resulted from a numerical calibration against EC data. The other parameterization resulted from a method where EC data is filtered to extract the limiting factors for productivity. Thereby, new parameter values and even a new function for the temperature limitation of photosynthesis were found. The adopted forest model was then tested successfully at another spruce forest for cross validation. In general, the forest model reproduced the observed carbon fluxes of a forest ecosystem quite well. Although the overall performance of the calibrated model version was best, the filtering approach showed that calibrated parameter values did not necessarily correctly display the individual functional relations. The study has shown that the concept of simulating forest dynamics at the individual tree level is a valuable approach for simulating the NEE, GPP, and respiration of forest ecosystems. The focus of the second study (chapter 3) lied on the simulation of forest structure and above-ground biomass in the Amazon region with the forest model FORMIND. Estimating the spatial variation of biomass in the Amazon rainforest is challenging and, hence, a source of substantial uncertainty in the assessment of the global carbon cycle. On the one hand, estimates need to consider small-scale variations of forest structures due to natural tree mortality. On the other hand, it requires large-scale information on the state of the forest that can be detected by remote sensing. We, here, introduced a novel method that considered both aspects by linking the forest model and a wall-to-wall canopy height map derived from LIDAR remote sensing. The forest model was applied to estimate above-ground biomass stocks across the Amazon rainforest. This allowed for the direct comparison of simulated and observed canopy heights from remote sensing. The comparison enabled the detection of disturbed forest states from which we derived a biomass map of the Amazon rainforest at 0.16 ha resolution. Simulated biomass varied between 20 and 490 t(dry mass) ha-1 across 7.8 Mio km² of the Amazon rainforest (elevation < 1000 m). That equals a total above-ground biomass stock of 76 GtC with a strong spatial variation (coefficient of variation = 63%). The estimated biomass values fit estimates, that had been observed in 114 field inventories, well (deviation of only 15%). Beside biomass, the forest model allowed for estimating additional forest attributes such as basal area and stem density. The linkage of a forest model with a canopy height map allows for capturing forest structures at the individual to large scale. The approach is flexible and can also be combined with measurements of future satellite missions like ESA Biomass or GEDI. Hence, the study sets a basis for large-scale analyses of the heterogeneous structure of tropical forests and their carbon cycle. In a third study (chapter 4), we analyzed the interactions of productivity, biomass, and forest structure that are essential for understanding ecosystem’s response to climatic and anthropogenic changes. We here applied the forest model on the Amazon rainforest, combined simulation results with remotely-sensed data as in chapter 3, and additionally simulated ecosystem carbon fluxes. We found that the successional state of a forest has a strong influence on mean annual net ecosystem productivity (NEP), woody above-ground net primary production (wANPP), and net ecosystem productivity (NEP). These relations were used to derive maps of carbon fluxes at 0.16 ha resolutions (current state of the Amazon rainforest under spatial heterogenic environmental conditions). The Amazon was estimated to be a sink of atmospheric carbon with a mean NEP of 0.73 tC ha-1 a-1. Mean wANPP equals 4.16 tC ha-1 a-1 and GPP 25.2 tC ha-1 a-1. We found that forests in intermediate successional states are the most productive. Under current conditions, the Amazon rainforest takes up 0.59 PgC per year. This third study shows that forest structure and species compositions substantially influence productivity and biomass, and should not be neglected when estimating current carbon budgets or climate change scenarios for the Amazon rainforest. The findings of this thesis set a fundament for future analyses on carbon storage and fluxes of forests. Simulating at the tree level has the potential to investigate carbon dynamics from individual to continental scales. The regionalized forest model allows for the integration of different types of remotely sensed data in order to improve the spatial accuracy of estimates. The insights, we have gained from the eddy covariance study (chapter 2), help to investigate carbon dynamics of forests at continental scale also under changing climate. In combination with the regionalization approach (chapter 3 and 4), the findings of this thesis may be used to complement studies on drought events in forests and to understand feedback mechanisms caused by anthropogenic disturbances.

The Soil and Water Assessment Tool (SWAT) was used to quantify the potential impacts of clearcutting on hydrologic and water quality components, and assess potential forest biomass production and perform sensitivity analysis of crop parameters to predict forest biomass in the Upper Pearl River Watershed. Results based on clearcutting indicated that the hydrologic and water quality components increases with increase in percentage of forest area harvested. The most significant effect was observed from 55% and 75% harvesting scenarios. The results based on SWAT’s performance to simulate potential forest biomass production showed satisfactory performance and revealed that the watershed has the potential to produce approximately 49 tons/ha of annual forest biomass. The results also revealed that predicted forest biomass was sensitive to three out of the seven crop parameters: Fraction of maximum leaf area index, corresponding to 1stpoint on the optimal leaf area development curve, Radiation use efficiency and maximum LAI.

Thesis (M.S.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 51-56). Also available on the World Wide Web.

Thesis (M.S.)--Mississippi State University. Department of Forestry. / Title from title screen. Includes bibliographical references.

Improved monitoring of forest biomass is needed to quantify natural and anthropogenic effects on the terrestrial carbon cycle. Landsat's temporal and spatial coverage, fine spatial grain, and long history of earth observations provide a unique opportunity for measuring biophysical properties of vegetation across large areas and long time scales. However, like other multi-spectral data, the relationship between single-date reflectance and forest biomass weakens under certain canopy conditions. Because the structure and composition of a forest stand at any point in time is linked to the stand's disturbance history, one potential means of enhancing Landsat's spectral relationships with biomass is by including information on vegetation trends prior to the date for which estimates are desired. The purpose of this research was to develop and assess a method that links field data, airborne lidar, and Landsat-derived disturbance and recovery history for mapping of forest biomass and biomass change. Our study area is located in eastern Oregon (US), an area dominated by mixed conifer and single species forests. In Chapter 2, we test and demonstrate the utility of Landsat-derived disturbance and recovery metrics to predict current forest structure (live and dead biomass, basal area, and stand height) for 51 field plots, and compare the results with estimates from airborne lidar and single-date Landsat imagery. To characterize the complex nature of long-term (insect, growth) and short-term (fire, harvest) vegetation changes found in this area, we use annual Landsat time series between 1972 and 2010. This required integrating Landsat data from MSS (1972-1992) and TM/ETM+ (1982-present) sensors. In Chapter 2, we describe a method to bridge spectral differences between Landsat sensors, and therefore extent Landsat time-series analyses back to 1972. In Chapter 3, we extend and automate our approach and develop maps of current (2009) and historic (1993-2009) live forest biomass. We use lidar data for model training and evaluate the results with forest inventory data. We further conduct a sensitivity analysis to determine the effects of forest structure, time-series length, terrain and sampling design on model predictions. Our research showed that including disturbance and recovery trends in empirical models significantly improved predictions of forest biomass, and that the approach can be applied across a larger landscape and across time for estimating biomass change. / Graduation date: 2012 / Access restricted to the OSU Community at author's request from Nov. 29, 2011 - Nov. 29, 2012

Quantifying and modeling processes involved in the global carbon cycle is important to evaluate the temporal and spatial variability of these processes and understand the effect of this variability on future response to changing climate and land use patterns. Biomass accumulation and Net Primary Productivity (NPP) are large components of ecosystem carbon exchange with the atmosphere and thus are the focus of many modeling efforts. When scaling estimates of NPP temporally from days to years and spatially from square meters to landscapes and regions the spatial coherence of these processes through time must be taken into account. Spatial coherence is the degree to which pairs of sites across space are synchronous (i.e., correlated) through time with respect to a given process or variable. In this thesis I determined the spatial coherence of a major component of NPP, tree bole productivity (NPP[subscript B]), and examine how it influences scaling and our ability to predict NPP and forecast change of this flux. In Chapter 2 I developed and tested a method modeling radial tree increment growth from sub-sampled trees and estimating annual site-level biomass accumulation that allows quantification of the uncertainty in these estimates. Results demonstrated that a simple model using the mean and standard deviation of growth increments underestimated bole biomass increment in all three age classes examined by 1% at the largest sample sizes and up to 15% at the smallest sample sizes. The long term average NPP[subscript B] and inter-annual variability were also underestimated by as much as 10% and 22%, respectively. Stratification of trees by size in sampling and modeling methods increased accuracy and precision of estimates markedly. The precision of both models was sufficient to detect patterns of inter-annual variability. To estimate bole biomass accumulation with acceptable levels of accuracy and precision our results suggest sampling at least 64 trees per site, although one site required a sample size of more than 100 trees. In Chapter 3 I compared year to year variability of NPP for tree boles (NPP[subscript B]) for two adjacent small watersheds (second-growth and old-growth) in the western Cascades of Oregon using the methods developed in Chapter 2. Spatial coherence of NPP[subscript B] within and between watersheds was assessed using multivariate analysis techniques. NPP[subscript B] was found to be less coherent between watersheds than within watersheds, indicating decreased spatial coherence with differences in age class and increased spatial scale. However, a larger degree of spatial coherence existed within the old-growth watershed compared to the second-growth watershed, which may be a result of the smaller degree of variation in environmental characteristics in the former watershed. Within a watershed, potential annual direct incident radiation and heat load were more strongly associated with the variation of NPP[subscript B] than climate. Climatic factors correlated with the temporal variation of annual NPP[subscript B] varied between the two watersheds. Results suggest that inter-annual variability and spatial coherence of forest productivity is a result of both internal (e.g., environment and stand dynamics) and external (climate) factors. An unexpected conclusion was that spatial coherence was not consistent and changed through time. Therefore, the coherence of sites over time is not a simple relationship. Instead the patterns of spatial coherence exhibit complex behaviors that have implications for scaling estimates of productivity. This result also indicates that a correlation coefficient alone may not capture the complexity of change through time across space. In Chapter 4 I estimated year to year variation of NPP[subscript B] for eleven sites of varying age, elevation, moisture, and species composition in the Western Cascades of Oregon. Spatial coherence of tree growth within sites and NPP[subscript B] between sites was assessed using Pearson's product-moment correlation coefficient (r). Results suggest that spatial coherence is highly variable between sites (r=-O.18 to 0.92). The second-growth sites exhibited the greatest temporal variability of annual NPP[subscript B] due to the large accumulation of biomass during stand initiation, but old-growth sites exhibited the greatest variation of coherence of NPP[subscript B] between sites. In some years all sites behaved similarly, but for other years some sites were synchronous while others were not. As growth of individual trees and NPP[subscript B] at the site scale increased, inter-annual variability of those variables increased. Climate in part affected annual NPP[subscript B], but intrinsic factors and spatial proximity also affected the coherence between sites in this landscape. / Graduation date: 2006

Both transportation costs and market values of woody biomass are strongly linked to the amount of moisture in the woody biomass. Therefore, managing moisture in the woody biomass well can lead to significant advantages in the woody biomass energy business. In this study, two prediction models were developed to estimate moisture content for Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and hybrid poplar (Populus spp.) woody biomass. Experimental data for the Douglas-fir model were collected over four different seasons at two different in-forest study sites in Oregon (Corvallis and Butte Falls) between December 2010 and December 2011. Three woody biomass bundles consisting of 3-meter length logs (30 to 385 mm diameter) were built each season at each study site; a total of 24 Douglas-fir bundles (1,316 to 3,621 kg weight) were built over the period. Experimental data for the hybrid poplar model were collected in two drying trials at two off-forest study sites in Oregon (Clatskanie and Boardman) between April 2011 and January 2012. Two types of woody bundles consisting of 3-meter length logs were built each trial: small (28 to 128 mm diameter, 2,268 to 5,389 kg weight) and large (75 to 230 mm diameter, 3,901 to 7,013 kg weight). A total of eight hybrid poplar bundles were built over the period. These data were used to develop linear mixed effects multiple regression models for predicting the moisture content of Douglas-fir and hybrid poplar biomass, respectively. The major factors considered in this study for predicting woody biomass moisture content change were cumulative precipitation, evapotranspiration (ET₀), and biomass piece size. The Food and Agriculture Organization (FAO) Penman-Monteith method, which requires temperature, solar radiation, wind, and relative humidity data, was used to calculate ET₀. The developed models can be easily applied to any location where historic weather data are available to calculate estimated air-drying times for Douglas-fir and hybrid poplar biomass at any time of the year. Oregon has been split into nine climate zones. Use of the model was demonstrated for four climate zones, two in which air-drying data were collected, and two in which it was not collected. Considerable differences in predicted drying times were observed between the four climate zones. / Graduation date: 2013