Classification of Riparian Saltcedar in the Desert Southwest Using Landsat Data and the HANTS Algorithm
McCarville, D.C.; Bleiweiss, M.P.; Bawazir, S.
Saltcedar (Tamarix spp.) is one of the most invasive species threatening the ecosystem health in riparian regions across the southwestern United States. This research compared maps of saltcedar growth in the Bosque del Apache National Wildlife Refuge derived using traditional pixel-wise classification methods, to maps derived from a series of normalized difference vegetation index (NDVI) images that were processed using the harmonic analysis of time series (HANTS) algorithm. For 2000/2001 the overall prediction accuracies for saltcedar classification based on traditional methods ranged from 88.0 to 91.0%. Corresponding overall accuracies based on the HANTS algorithm ranged from 81.5 to 90.5%. For 2010/2011 the overall prediction accuracies for saltcedar classification based on traditional methods ranged from 88.0 to 89.0%. Corresponding overall accuracies based on the HANTS algorithm ranged from 77.5 to 85.0%. The traditional classification required more data preparation and expertise than the HANTS based classification; however, the HANTS based classification required a larger dataset. The results show that the HANTS reconstruction of NDVI data can be used directly to classify areas with saltcedar. The phenological changes revealed by the HANTS algorithm reconstruction could also be used to select data used with other classification methods.
HANTS algorithm; NDVI; saltcedar; remote sensing; Landsat
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