Augmenting forest inventory attributes with geometric optical modelling in support of regional susceptibility assessments to bark beetle infestations

Assessment of the susceptibility of forests to mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation is based upon an understanding of the characteristics that predispose the stands to attack. These assessments are typically derived from conventional forest inventory data; however, this information often represents only managed forest areas. It does not cover areas such as forest parks or conservation regions and is often not regularly updated resulting in an inability to assess forest susceptibility. To address these shortcomings, we demonstrate how a geometric optical model (GOM) can be applied to Landsat-5 Thematic Mapper (TM) imagery (30 m spatial resolution) to estimate stand-level susceptibility to mountain pine beetle attack. Spectral mixture analysis was used to determine the proportion of sunlit canopy and background, and shadow of each Landsat pixel enabling per pixel estimates of attributes required for model inversion. Stand structural attributes were then derived from inversion of the geometric optical model and used as basis for susceptibility mapping. Mean stand density estimated by the geometric optical model was 2753 (standard deviation ± 308) stems per hectare and mean horizontal crown radius was 2.09 (standard deviation ± 0.11) metres. When compared to equivalent forest inventory attributes, model predictions of stems per hectare and crown radius were shown to be reasonably estimated using a Kruskal–Wallis ANOVA (p < 0.001). These predictions were then used to create a large area map that provided an assessment of the forest area susceptible to mountain pine beetle damage

Inventory strategies for monitoring and evaluation of forest damage

Under global change, increasing stresses on forests require strategies for monitoring and mitigation of damages caused by pests and diseases. While the threats to forests increase, so do the possibilities to set up efficient monitoring programmes and detect forest damage by utilising new technologies. This thesis focuses on strategies for forest damage inventories where different auxiliary data are combined to improve information for pest mitigation programmes. First, the efficiency of National Forest Inventories (NFIs; or similar inventories) for detecting and estimating state and change of forest damage across large regions was evaluated. NFIs were found efficient for assessing widely distributed damage, but unable to detect clustered and local outbreaks with adequate precision. Second, targeted forest damage inventories directed to areas with potential or suspected damage were investigated. It was found that two-phase sampling for stratification taking the first phase information from existing NFIs was an efficient strategy. Remotely-sensed auxiliary information and post-stratification was shown to further improve the precision. Third, the use of a new sampling design was evaluated: the local pivotal method (LPM), which spreads the sample in the multi-dimensional space of available auxiliary data. The LPM was found to be more efficient than simple random sampling in all scenarios and, depending on the allocation of the sample and the properties of the auxiliary data, it sometimes outperformed two-phase sampling for stratification. Thus, the LPM may be a valuable tool for practical forest damage inventories. Fourth, the cost-plus-loss method was applied to evaluate inventory strategies in a pest mitigation context. If inventory costs are large, it is especially important to quantify the inventory efforts necessary to evaluate the need for mitigation. The optimal sampling effort necessary for deciding whether or not a defoliator outbreak should be treated was quantified. Double sampling was found to be a cost-effective sampling strategy, i.e. the size of the second phase sample was determined based on the estimates from a small first phase sample. As an overall conclusion, the thesis points out the importance of making use of existing information in setting up effective inventories of forest damage and of using appropriate sampling strategies for making use of the information in the best possible way

Fire

Vegetation plays a crucial role in regulating environmental conditions, including weather and climate. The amount of water and carbon dioxide in the air and the albedo of our planet are all influenced by vegetation, which in turn influences all life on Earth. Soil properties are also strongly influenced by vegetation, through biogeochemical cycles and feedback loops (see Volume 1A—Section 4). Vegetated landscapes on Earth provide habitat and energy for a rich diversity of animal species, including humans. Vegetation is also a major component of the world economy, through the global production of food, fibre, fuel, medicine, and other plantbased resources for human consumptio