Publications

Dye, DG; Middleton, BR; Vogel, JM; Wu, Z; Velasco, M (2016). Exploiting Differential Vegetation Phenology for Satellite-Based Mapping of Semiarid Grass Vegetation in the Southwestern United States and Northern Mexico. REMOTE SENSING, 8(11), UNSP 889.

Abstract
We developed and evaluated a methodology for subpixel discrimination and large-area mapping of the perennialwarm-season (C-4) grass component of vegetation cover in mixed-composition landscapes of the southwestern United States and northern Mexico. We describe the methodology within a general, conceptual framework that we identify as the differential vegetation phenology (DVP) paradigm. We introduce a DVP index, the Normalized Difference Phenometric Index (NDPI) that provides vegetation type-specific information at the subpixel scale by exploiting differential patterns of vegetation phenology detectable in time-series spectral vegetation index (VI) data from multispectral land imagers. We used modified soil-adjusted vegetation index (MSAVI(2)) data from Landsat to develop the NDPI, and MSAVI(2) data from MODIS to compare its performance relative to one alternate DVP metric (difference of spring average MSAVI(2) and summer maximum MSAVI(2)), and two simple, conventional VI metrics (summer average MSAVI(2), summer maximum MSAVI(2)). The NDPI in a scaled form (NDPIs) performed best in predicting variation in perennial C-4 grass cover as estimated from landscape photographs at 92 sites (R-2 = 0.76, p < 0.001), indicating improvement over the alternate DVP metric (R-2 = 0.73, p < 0.001) and substantial improvement over the two conventional VI metrics (R-2 = 0.62 and 0.56, p < 0.001). The results suggest DVP-based methods, and the NDPI in particular, can be effective for subpixel discrimination and mapping of exposed perennial C-4 grass cover within mixed-composition landscapes of the Southwest, and potentially for monitoring of its response to drought, climate change, grazing and other factors, including land management. With appropriate adjustments, the method could potentially be used for subpixel discrimination and mapping of grass or other vegetation types in other regions where the vegetation components of the landscape exhibit contrasting seasonal patterns of phenology.

DOI:
10.3390/rs8110889

ISSN:
2072-4292