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Remote Sens. 2016, 8(11), 889; doi:10.3390/rs8110889

Exploiting Differential Vegetation Phenology for Satellite-Based Mapping of Semiarid Grass Vegetation in the Southwestern United States and Northern Mexico

1
Western Geographic Science Center, U.S. Geological Survey, 2255 N. Gemini Drive, Flagstaff, AZ 86001, USA
2
Land Remote Sensing Program, U.S. Geological Survey, 12201 Sunrise Valley Drive, Reston, VA 20192, USA
3
Astrogeology Science Center, U.S. Geological Survey, 2255 N. Gemini Drive, Flagstaff, AZ 86001, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Parth Sarathi Roy, Clement Atzberger and Prasad S. Thenkabail
Received: 11 August 2016 / Revised: 18 October 2016 / Accepted: 20 October 2016 / Published: 28 October 2016
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Abstract

We developed and evaluated a methodology for subpixel discrimination and large-area mapping of the perennial warm-season (C4) 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 (MSAVI2) data from Landsat to develop the NDPI, and MSAVI2 data from MODIS to compare its performance relative to one alternate DVP metric (difference of spring average MSAVI2 and summer maximum MSAVI2), and two simple, conventional VI metrics (summer average MSAVI2, summer maximum MSAVI2). The NDPI in a scaled form (NDPIs) performed best in predicting variation in perennial C4 grass cover as estimated from landscape photographs at 92 sites (R2 = 0.76, p < 0.001), indicating improvement over the alternate DVP metric (R2 = 0.73, p < 0.001) and substantial improvement over the two conventional VI metrics (R2 = 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 C4 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. View Full-Text
Keywords: phenology; differential vegetation phenology; DVP; normalized difference phenometric index; NDPI; vegetation mapping; grassland; rangeland phenology; differential vegetation phenology; DVP; normalized difference phenometric index; NDPI; vegetation mapping; grassland; rangeland
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Dye, D.G.; Middleton, B.R.; Vogel, J.M.; Wu, Z.; Velasco, M. Exploiting Differential Vegetation Phenology for Satellite-Based Mapping of Semiarid Grass Vegetation in the Southwestern United States and Northern Mexico. Remote Sens. 2016, 8, 889.

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