conditions, and microclimatic differences. Dynamic growth models of varying complexity aid in developing a long-range yield plan for a site.
Annual remote sensing images, like LANDSAT Thematic Mapper and aerial photography are frequently used to assess reforestation success, erosion, competition from shrubs, and tree mortality. This use is predicted to increase as the next generation of satellites becomes available. The new satellites will permit information extraction about canopy condition beyond properties related to total foliage display. High spatial resolution radar, LIDAR (an acronym for light detection and ranging), and optical sensors will obtain information about forest structure and biomass distribution. More frequent temporal coverage will permit earlier detection of insects and other environmental stresses.
Many charismatic, endangered, and sensitive wildlife species require forests with late successional structural features to provide forage, nesting, and perching locations. Forests with standing and down dead trees, open gaps and spatially distributed forest patches, corridors for migration, vertical and horizontal crown complexity, and other pattern features may be mapped and tracked in a GIS. In addition, GIS based models using remotely sensed information provide a mechanism for evaluating the impact of site-specific logging on wildlife habitat conditions necessary for protection of these species. Other forestry applications for site-specific methods include mapping the spread of insects and fungal pathogens, to regional impacts of air pollution, like acid deposition and ozone, on forest health and species specific mortality. As competing demands for conservation, recreational use, and economic extraction increase, GIS databases, using GPS linked site data, and remote sensing monitoring, offer the hope that site-specific methods can be used to optimize management decisions.
specific area of the field can be matched with the expected yield, and supplied at a more economically optimal level (Hergert et al., 1997). Additional on-farm research is necessary to determine the economic returns from different approaches to soil fertility management in precision agriculture.
The evaluation of soil nutrient levels across a field is typically performed by taking soil samples, analyzing them for nutrient content, and interpolating values between the sampling points (Wollenhaupt et al., 1997). Figure 2-3 shows a field with the sites where soil samples were taken, and a resulting interpolated phosphorus map. The actual values for soil phosphorus concentration are known only at the sampled points; all other values are estimated. Both the method used for