A determination of forest characteristics across broad areas is of great concern to the forest industry in the southern United States, as timber supply decisions can be based on opportunities, or lack of thereof, across all wood procurement areas. This is important in areas such as the southern United States, where the land ownership distribution is highly fragmented and where no general comprehensive source of forest data exists other than the low-intensity USDA Forest Service FIA surveys. In an effort to describe forest characteristics along the lower Coastal Plain of the State of Georgia (USA), we utilized a time series of Landsat data and an algorithm that assesses an integrated forest Z score. The methodology was used to create disturbance maps for over 30 years that represent the year of disturbance for specific locations. The overall accuracy was 52% when all years were considered, and approximately 70% from 1991 forward. Preliminary findings showed moderate levels of accuracy when determining ages for current forests, most of which are even-aged nature stands. Further modifications to the process were necessary to adapt to the unique conditions of study region. The modeling process also prompted several areas for future refinement, including improvement of the temporal resolution of the analysis by using all the available Landsat imagery and detection of the regeneration that normally occurs several years after disturbances.
While every growth behaviour can be described by a growth function with longitudinal independent variables, modeling longitudinal growth behaviour is complex and requires a strategic search for an appropriate growth function. Existing literature shows that several growth functions have been derived and applied to suitable growth behaviour. This implies that there is always the possibility of the existence of a unique growth function that fits a given growth data. In this paper, we propose a new statistical procedure to seek the "best" growth function as well as the "best" clusters of growth patterns. This new procedure involves a mixture of growth function selection and k-means method. Although our experiments were limited, using a real data set of sugi (Cryptomeria japonica) tree growth and hypothetical observations, our results show that the proposed method is promising. It allowed us to choose the "best" growth function that matches the "best" growth patterns at the same time.
A nondestructive, practical and efficient in-situ tree measurement approach is desperately needed to address the problem of large data volume, enhancing data quality and improving our understanding of stem volume estimation in Okinawa. This issue has become important because of two forestry related policy issues that have received lots of attention in Okinawa. The two issues are the introduction of carbon certification program and the nomination of northern Okinawa Island as a World Natural Heritage site. In this study, our attention was turned to a more recent emerging low cost approach for estimating stem volume with minimum environmental impact on the site, that is, the terrestrial close-range photogrammetry. We explored the usefulness and evaluated the limitations of the approach. For examination of the accuracy of terrestrial photogrammetry, we compared the stem volume estimated from photogrammetry with the stem volume computed based on direct measurement using 3D magnetic motion tracker. The error of the estimated volume was 0.0053 m3 root mean square error (RMSE) and the estimated surface was 0.0714 m2. In order to explore the efficiency of the photogrammetry, we extended our approach to include the simultaneous reconstruction of 3D images of multiple tree stems within a plot. Using one of the latest image-processing software, we successfully reconstructed 3D models of 11 stems within a plot and computed stem volume for each. The error of the estimated volume and surface were 0.0128 m3 and 0.1940 m2, respectively.
Time variant distribution of stochastic price dynamics models, plays an important role in evaluating the risk of forest management at any given point in time. In this paper, we present demonstrative results on the use of time variant distribution for risk evaluation, using a geometric mean-reverting stochastic model and log price data. The data used for this study comes from national monthly statistical data of 4m long sugi (Cryptomeria japonica) log prices, and the parameters of the model were derived from a pseudo-likelihood approach, using discretization by the Euler method. The time variant distribution for the stochastic model was numerically computed by applying the method of lines to the Fokker-Planck equation. The results of this study showed that when the management risk is defined by the probability that a price falls below a given threshold price to sustain forest management, the risk increase over time. This was true for all scenarios with different reverted mean values. It was also revealed through this study that the management risk under a higher threshold price tends to reach the risk neutral point of 50 % for sustaining forest management, earlier than those scenarios with a lower threshold price.
Adoption of pro-poor strategies through participatory approaches is believed to ensure the benefits of socially disadvantaged groups within the community. This does not only address inequities but also helps with resource conservation. By comparing pro-poor strategies and analyzing the policies of four different community groups in Nepal, this study examined how their policies contributed or hindered the translations of assets into livelihoods outcomes. The study is based on empirical data collected through direct observation, social and resource mappings, key informant interviews, focus group discussions and transects walks. A checklist was prepared to standardize the information gathered from each area. Results show that the pro-poor strategies incorporated in the policies of the local community groups lead to higher level of awareness of the people of their rights. The pro-poor strategies, though not economically harmful to the poor, are not sufficient to address the existing social and economic inequities. There is a need for household level support to pro-poor approaches of the community groups that will directly help the poor translating their assets to sustainable livelihoods.