Transforming landscape ecological evaluations using sub-pixel remote sensing classifications: A study of invasive saltcedar ( Tamarix spp.)
Frazier, Amy E.
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Invasive species disrupt landscape patterns and compromise the functionality of ecosystem processes. Non-native saltcedar ( Tamarix spp.) poses significant threats to native vegetation and groundwater resources in the southwestern U.S. and Mexico, and quantifying spatial and temporal distribution patterns is essential for monitoring its spread. Advanced remote sensing classification techniques such as sub-pixel classifications are able to detect and discriminate saltcedar from native vegetation with high accuracy, but these types of classifications are not compatible with landscape metrics, which are the primary tool available for statistically assessing distribution patterns, because they do not have discrete class boundaries. The objective of this research is to develop new methods that allow sub-pixel classifications to be analyzed using landscape metrics. The research will be carried out through three specific aims: (1) develop and test a method to transform continuous sub-pixel classifications into categorical representations that are compatible with widely used landscape metric tools, (2) establish a gradient-based concept of landscape using sub-pixel classifications and the technique developed in the first objective to explore the relationships between pattern and process, and (3) generate a new super-resolution mapping technique method to predict the spatial locations of fractional land covers within a pixel. Results show that the threshold gradient method is appropriate for discretizing sub-pixel data, and can be used to generate increased information about the landscape compared to traditional single-value metrics. Additionally, the super-resolution classification technique was also able to provide detailed sub-pixel mapping information, but additional work will be needed to develop rigorous validation and accuracy assessment techniques.