Spatial thresholds, image-objects, and upscaling: A multiscale evaluation

When examining a remotely sensed signal through various scale changes, what is the most appropriate upsealing technique to represent this signal at different scales? And how can this be validated? Solutions to these questions were approached by examining how the 660 nm signal of six forest stands vary through four different scales of same-.sensor imagery, four traditional resampling techniques, and a new object-specific resampling technique. Analysis of the original and modeled datasets suggests that appropriately upscaled imagery represents a more accurate scene-model than an image obtained at the upscaled resolution. Results further indicate the need for a multiscale approach to feature extraction and upscaling, as no single spatial resolution of imagery appears optimal for detecting or Upscaling the varying sized, shaped, and spatially distributed objects within a scene. By employing the human eye as a model, we describe a novel object-specific approach for addressing this challenge. Upscaling evaluation is based on visual interpretation, an understanding of the applied resampling theories, and the root mean square error results of 6000 samples collected from a 10 in CASI scene, and from 1.5 m, 3 in, and 5 m same site CASI images upscaled to 10 m. Potential application of this object-specific approach in hierarchical ecosystem modeling is also briefly described.