The application of landsat imagery for stand imputation on three northern arizona national forests

摘要

Forest resource specialists need to analyze landscapes to determine resource trends and evaluate alternative management actions. For most landscapes, detailed stand information is often incomplete, which necessitates the need to impute variables onstands without stand information. Most-similar neighbor analysis (MSN) is a tool developed by research scientists at RMRS-Moscow, Idaho to impute stand inventory data. The MSN technique utilizes common variables measured on each stand to determine whichinventoried stand has the most similar characteristics to the non-inventoried stand. Data from Landsat imagery and terrain data can be utilized as the global variables measured for each stand polygon. MSN was applied on the Kaibab, Coconino, and Apache-Sitgreaves National Forests. Zonal means for Landsat bands 3, 4, 5, and 7 along with slope, aspect, aspect strength, and elevation were calculated for each stand polygon. A 5/3 band ratio and a 7X7 variance texture mean were also tested. A number of trials were run to determine the sensitivity of the application to various stand-level variables. The resulting inventory was incorporated into the Forest's stand database and geographic information system (GIS) database. Applications of the results have beenapplied to Mexican spotted owl habitat assessment, woodland inventory, range forage productivity assessment, aspen decline assessment, forest health risk assessment, and for input to prescribed natural fire model (FARSITE).