Remote sensing and GIS in forestry

Remote sensing and GIS are complementary technologies that, when combined, enable improved monitoring, mapping, and management of forest resources (Franklin 2001). The information that supports forest management is stored primarily in the form of forest inventory databases within a GIS environment. A forest inventory is a survey of the location, composition, and distribution of forest resources. As one of the principal sources of forest management information, these databases support a wide range of management decisions from harvest plans to the development of longterm strategies. Historically, forest management inventories were primarily for timber management and focused on capturing area and volume by species. In the past decade, forest management responsibilities have broadened. As a result, inventory data requirements have expanded to include measures of nonharvest related characteristics such as forest structure, wildlife habitat, biodiversity, and forest hydrology. The entire forest inventory production cycle, from planning to map generation, can take several years. Except for the photo interpretation component, forest inventory production is largely a digital process. Operational level inventories, based on both aerial photo interpretation and fi eld-sampled measurements, provide location-specifi c information required for harvest planning. Forest management level inventories meet longer-term forest management planning objectives. Though these levels differ in detail, they both require information fundamentally based on forest inventory data. A forest management inventory generalizes complex forest resource attributes into mapping units useful for forest management. The types of attributes attached to individual mapping units, or polygons, might include stand species composition, density, height, age, and, more recently, new attributes such as leaf area index (Waring and Running 1998). Much of the information collected for forest inventory is generated by interpretation of aerial photographs at photo scales of 1:10,000 to 1:20,000, depending on the level of detail required. Other remote sensing sources such as airborne and satellite digital imagery have been valuable in updating forest attributes such as disturbance, habitat, and biodiversity. In providing more frequent information updates, remotely sensed data can improve the quality of forest inventory databases, thereby improving the resource management activities they support. The quality of photointerpreted data depends on the experience of the interpreters and the use of quality assurance procedures such as interpreter calibration and fi eld verifi cation. Other factors can introduce inconsistencies that compromise the quality of forest inventory data. For example, there may be source data inconsistencies when aerial photography is acquired on different dates or in different weather conditions or inconsistencies in analysis when multiple contractors are used. The quality of the resulting data may vary signifi cantly within a map area. For example, information about disturbances related to fi re and insects may be inconsistent within a map area because the aerial photography from which it was interpreted was acquired in different years. Similarly, inconsistencies may occur at the edge of neighboring map sheets because data was collected in different years or was produced by different contractors.