Paper
16 October 1984 Remote Sensing Of Vegetation At Regional Scales
Forrest G Hall
Author Affiliations +
Proceedings Volume 0475, Remote Sensing: Critical Review of Technology; (1984) https://doi.org/10.1117/12.966242
Event: 1984 Technical Symposium East, 1984, Arlington, United States
Abstract
The concept of inferring surface cover type and condition from measurements of reflected or emitted radiation has its roots in spectroscopy. In that discipline physicists and physical chemists enjoyed great success in identifying chemical compounds from observing their "spectral signatures." Much of the early research in remote sensing followed this paradigm. Reflectance spectra were obtained for various natural materials: leaf reflectances and transmittances (refs. 1,2,3), and soils reflectance (refs. 4,5) to name a few. Spectral signature catalogs were developed (ref. 6). While these and smaller studies were quite useful and provided the rationale for the band selection of modern aircraft and spacecraft multispectral sensors, it became evident that the paradigm which had provided the basis for the spectroscopic identification of materials, was incomplete when applied to the inference of type and condition of materials in a natural environment. Briefly stated, it was found that one could not collect a remote sensing signature from an unknown ground cover class at a particular time and place and match that signature with an a priori catalog value to infer the properties of the unknown cover class. Thus, for remote sensing, the traditional definition of "spectral signature" found limited utility.
© (1984) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Forrest G Hall "Remote Sensing Of Vegetation At Regional Scales", Proc. SPIE 0475, Remote Sensing: Critical Review of Technology, (16 October 1984); https://doi.org/10.1117/12.966242
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Cited by 6 scholarly publications.
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KEYWORDS
Earth observing sensors

Landsat

Remote sensing

Vegetation

Error analysis

Reflectivity

Data acquisition

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