The Inventory and Monitoring (I&M) Program of the National Park Service (NPS) sought to provide reliable and consistent land cover information for Katmai National Park and Preserve (KATM) that may be used to assess the status and trends of national park ecosystems. GRS was selected based on our unique land cover mapping techniques, which enabled the estimation and mapping of detailed land cover characteristics.
GRS performed landcover mapping on the 4-million acre project area with the goal of providing detailed vegetation information that is useful for making resource management decisions on a park-wide basis. The project was undertaken by GRS, in association with the Alaska Natural Heritage Program (AKNHP), and with support from the Alaska Regional Office (ARO). GRS directed training site selection, navigation, and verification components of field data collection efforts, as well as all image data acquisition, preparation, training, evaluation, and image classification. The AKNHP provided expertise regarding vegetation systems, description, and species identification during field data collection efforts. Simultaneously, NPS personnel carried out ground vegetation transects concurrent with aerial surveys and managed field data collection logistics.
Detailed field data were collected over the entire project area to provide the land cover descriptions that both guided the classification efforts and described the resulting land cover features in the final classification map
. These training data sets encompassed the complete range of significant land cover characteristics present in the project area
GRS used both supervised and unsupervised classification combined in a unique technique that preserves the integrity of the field data and allows for the detailed landcover characteristics to be maintained through the entire classification and mapping process. The resulting landcover maps consist of pixels or polygons that are linked to the detailed field data. Since these maps retain the complete range of characteristics measured in the field, the data can be easily modeled or generalized into specific categorical maps such as bear habitat or wildfire fuels.
