How should vegetation data be derived?
A wide variety of methods exist to derive measures of vegetation cover from remotely sensed data. These methods range widely in complexity, sophistication, and accuracy. The discrepancies are important for researchers to consider when incorporating or generating vegetation data for larger studies. Since the accuracy disparity between techniques is well documented, it should concern researchers who rely on simple linear vegetation indexes for accurate vegetation cover estimates. It is important to determine precisely how different the vegetation measurements will be between a simple, easy method, as compared to a more sophisticated, yet perhaps less accessible one. I have chosen to test two commonly used methods on either side of this divide to determine when and how researchers should use certain types of methodologies in deriving vegetation estimates from remote sensing data. Arguably the most widely used of these techniques is the Normalized Difference Vegetation Index (NDVI). It is
