Use of Remotely Sensed Data for Assessing Forest Stand Conditions in the Eastern United States

Abstract

The results of three interrelated research activities conducted by Goddard scientists in support of the AgRISTARS Renewable Resources Inventory (RRI) project are summarized. The central theme of the research conducted at Goddard was the development of techniques for the detection, classification, and measurement of forest disturbances using digital, remotely sensed data. Three study areas located in Pennsylvania, North Carolina, and Maine were investigated with respect to: a) the delineation and assessment of forest damage associated with two different forest insect defoliators, and b) an assessment of the improved capabilities to be expected from Landsat Thematic Mapper (TM) data relative to Multispectral Scanner (MSS) data for delineating forest stand characteristics. Key results include the development of a statewide MSS digital data base and associated image processing techniques for accurately delineating (approximately 90 precent correct classification accuracy) insect damaged and healthy forest. Comparison of analyses using MSS and TM Simulator (TMS) data indicated that for broad land cover classes which are spectrally homogeneous, the accuracy of the classification results are similar. However, TMS data provided superior results (20 percent overall accuracy increase relative to MSS results) when detailed (Level III) forest classes were mapped. These studies also illustrated the utility of having at least one band in the visible, near infrared, and middle infrared portion of the electromagnetic spectrum for assessing specific (Level III) forest cover types.