Over the past several years, research has been conducted at the Goddard Space Flight Center to develop techniques to facilitate the use of remotely sensed data for monitoring forest disturbances. A majority of this work has involved the digital analysis of Landsat multispectral scanner (MSS) data to detect insect defoliation of hardwood forests. The diverse terrain and topographic conditions typically associated with forest lands are known to cause variations in remotely sensed spectral data, leading to problems in accurately classifying forest cover conditions in mountainous terrain. This study assesses the utility of incorporating high spatial resolution digital terrain data with Landsat MSS data to reduce confusion between spectrally similar forest canopy conditions such as healthy vegetation and moderate defoliation.
The aspect category of each pixel in the central Pennsylvania study area was computed from the terrain data, and integrated with the Landsat MSS data and digitized ground reference data. Mean spectral response values were then extracted from the Landsat MSS data for each defoliation class within each aspect category. Results indicate that heavy defoliation is separable from either moderate defoliation or healthy forest, but these latter two forest canopy conditions cannot be consistently separated from one another even when accounting for any confounding effects on sensor response due to slope orientation (i.e., aspect).
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