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Climate Change Studies, Permanent Forest Observational Plots and Geospatial Modeling
Published in Shruti Kanga, Suraj Kumar Singh, Gowhar Meraj, Majid Farooq, Geospatial Modeling for Environmental Management, 2022
Forest resource information, gathered in Forest Inventory Systems at local, national and global levels, is required for planning and policy decisions. Forest Research Plot Networks provide essential data for studying ecosystem structure and dynamics. Conservation and sustainable use of biological resources based on local knowledge systems and practices is embedded in the Indian culture. India has assigned a special status and protection to biodiversity-rich areas by declaring them as national parks, wildlife sanctuaries, biosphere reserves, ecologically fragile and sensitive areas. For sustainable forest management, Permanent Observational Plots (POPs) are required to lead way to analyze and research. POPs provide service to science and society through reliable information for the identification and solution of ecological problems and sustainable development (Tewari et al., 2014).
Economic Impacts of Climatic Change on the Global Forest Sector: An Integrated Ecological/Economic Assessment
Published in Roger A. Sedjo, R. Neil Sampson, Joe Wisniewski, in Forestry, 2020
John Perez-Garcia, Linda A. Joyce, C.S. Binkley, A. D. McGuire
Although each component is complex, the logic of the analysis is simple. Increased atmospheric concentrations of CO2and other greenhouse gases affect climate. The various GCMs forecast future temperature and precipitation regimes under the hypothesized future atmospheric concentrations of these gases. These changes in climate, as well as the changes in atmospheric CO2 concentrations themselves, alter the growth of forests. TEM estimates the impacts of these modelled future climates on NPP. Changes in NPP translate directly into changes on forest growth which in turn affect forest inventories, especially over the long time periods considered in this analysis. Increases or decreases in forest inventory influence economic timber supply. The CGTM forecasts the impact of these shifts in timber supply on such key variables as forest products production, consumption, prices and trade. Our analysis depends on these three modeling systems, so it is important to review each.
Forest Inventory Using Laser Scanning
Published in Jie Shan, Charles K. Toth, Topographic Laser Ranging and Scanning, 2018
Juha Hyyppä, Xiaowei Yu, Harri Kaartinen, Antero Kukko, Anttoni Jaakkola, Xinlian Liang, Yunsheng Wang, Markus Holopainen, Mikko Vastaranta, Hannu Hyyppä
There exist several types of operational forest inventory methods ranging from national/continent-wise forest inventory to compartmentwise forest inventory. In this presentation we concentrate on compartmentwise inventory due to its high commercial impact. Compartmentwise forest inventory is a widely used method in Finland, both in public and privately owned forests. The basic unit of forest inventories is a forest stand, which is used as the management-planning unit. The size of a forest stand is normally 0.5–3 ha. The forest stand is defined as a homogenous area according to relevant stand characteristics, for example, site fertility, composition of tree species, and stand age. Forest inventory data are mostly collected with the aid of field surveys, which are both expensive and time-consuming. The compartments are typically measured separately by analyzing sample plots placed on the stands. From each plot, tree and stand attributes are measured. Finally, the standwise attributes describing the density and tree dimensions are derived from these plot measurements. The method is also sensitive to subjective measurement errors. Remote sensing is normally used for nothing more than delineation of compartment boundaries. The total costs of compartmentwise inventory in Finland were 17.9 €/ha in 2000, of which 7.9 €/ha, that is, 45% of the costs, consisted of field measurements (Uuttera et al., 2002; Holopainen and Talvitie, 2006). For the total stem volume per hectare, basal area per hectare, and mean height, the required accuracy is roughly 15%.
Would weight parity on interstate highways improve safety and efficiency of timber transportation in the US South?
Published in International Journal of Forest Engineering, 2020
Total savings within each wood basket were calculated using equation 2 combined with the number of tonnes of timber harvested annually in each wood basket from USDA Forest Service (2019) Forest Inventory and Analysis data. The number of dry tonnes harvested per year from trees ≥12.7 cm (5 inches) in diameter at breast height was calculated within 80 km (50 miles) of the center of each wood basket. An 80-km radius was selected because it is the average haul distance in the region (TimberMart-South 2020b) and almost all mills will purchase timber from at least this distance. Dry tonnes were converted to green tonnes assuming 50% moisture content on a wet basis (Baker et al. 2012; Cutshall et al. 2013; Jernigan et al. 2013). Green tonnes were converted to loads by assuming 25.4 t (28 tons) per load in Georgia and South Carolina and 27.2 t (30 tons) per load in Alabama, North Carolina, and Virginia. There was some overlap between wood baskets (Figure 2); consequently, some savings were double-counted. However, this should be more than offset by savings on loads hauled from outside the 80-km radius that were not considered in the calculation.
Analysis of timber transportation accident frequency, location, and contributing factors in Georgia, USA 2006-2016
Published in International Journal of Forest Engineering, 2019
Accident rates in the US are generally reported per 1.6 million or 160 million km (1 million or 100 million mi) travelled. Because this data was not available for logging vehicles, million tonnes of wood hauled within Georgia was used as a proxy for distance travelled. Weight of wood hauled in Georgia was estimated using USDA Forest Service Forest Inventory and Analysis Timber Product Output and Use (TPO) data (Johnson et al. 2007, 2011; Schiller et al. 2009; Bentley et al. 2014; Wall et al. 2017a, 2017b; USDA Forest Service 2018). TPO data is collected every 2 years from a census of mills (Coulston et al. 2018). For years when TPO data was not available, the average of the previous and the next year of available data were used; for example, for the year 2012, the average of 2011 and 2013 was used. Timber hauled in Georgia was the sum of timber harvested in Georgia and delivered to a Georgia mill or processing facility, timber harvested in Georgia and exported to another state, and timber harvested in another state and delivered to a Georgia mill. This approach is similar to the methodology employed by previous research (Greene et al. 1996, 2007; Cole 2018).
A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data
Published in Enterprise Information Systems, 2021
Ruei-Yuan Wang, Pao-an Lin, Jui-Yuan Chu, Yi-Huang Tao, Hsiao-Chi Ling
In this paper, we aim to apply RS Big Data and incorporate it with KBDSS to provide a meaningful design and operation aid for forest management and to improve the results of decision-making for forest inventory and monitoring flows in the Remote Sensing KBDSS (RS-KBDSS) (shown as figure 3). As mentioned previously, the system architecture of KBDSS includes RBR and CBR. As part of the RBR, we adopt a five-step scheme.