Using Satellite Images to Classify and Analyze the Health of Hemlock Forests Infested by the Hemlock Woolly Adelgid

Abstract

A method is described to classify stands of eastern hemlock by health condition, at the landscape level, using remote sensing. The hemlock woolly adelgid has been a major cause of hemlock decline in Connecticut since 1985, resulting in varying degrees of defoliation in the region. A 1985 Landsat Thematic Mapper (TM) image was classified to develop a base line of once healthy hemlock stands. Radiance normalization and non-hemlock masking techniques were used to pre-process a 1995 TM image. Several techniques were used to transform the 1995 TM image; each was followed by cluster analysis to separate hemlocks into four levels of tree vigor. We evaluated 600 trees at 150 sites across the study area using the USFS Crown Condition Rating Guide. These field data were used to measure the accuracy of various health classification techniques. The Modified Soil Adjusted Vegetation Index-2 (MSAVI2) transform provided the best overall accuracy, 82.1%, for classifying hemlock according to tree vigor. Non-parametric statistics were used to determine if there were any significant variations in distribution of hemlock pixels by health class in association with features in the landscape. Several features were found to be statistically significant at a confidence level of 0.001. These were aspect of slope, hydrology group (infiltration rate), depth to bedrock, soil order, drainage class (hydraulic conductivity), and surface texture.