Rangeland Productivity Partitioned to Sub-Pixel Plant Functional Types
W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA
Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT 59812, USA
Image Processing Laboratory, Universitat de València, 46980 Paterna, Spain
Google, Inc., Mountain View, CA 94043, USA
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(12), 1427; https://doi.org/10.3390/rs11121427
Received: 16 May 2019 / Revised: 4 June 2019 / Accepted: 13 June 2019 / Published: 15 June 2019
(This article belongs to the Special Issue Applications of Remote Sensing in Rangelands Research)
Understanding and monitoring the dynamics of rangeland heterogeneity through time and across space is critical for the effective management and conservation of rangeland systems and the sustained supply of the ecosystem goods and services they provide. Conventional approaches (both field-based and remote sensing) to monitoring rangeland productivity fail to effectively capture important aspects of this heterogeneity. While field methods can effectively capture high levels of detail at fine spatial and temporal resolutions, they are limited in their applicability and scalability to larger spatial extents and longer time periods. Alternatively, remote sensing based approaches that scale broad spatiotemporal extents simplify important heterogeneity occurring at fine scales. We address these limitations to monitoring rangeland productivity by combining a continuous plant functional type (PFT) fractional cover dataset with a Landsat derived gross primary production (GPP) and net primary production (NPP) model. Integrating the annual PFT dataset with a 16-day Landsat normalized difference vegetation (NDVI) composite dataset enabled us to disaggregate the pixel level NDVI values to the sub-pixel PFTs. These values were incorporated into the productivity algorithm, enabling refined estimations of 16-day GPP and annual NPP for the PFTs that composed each pixel. We demonstrated the results of these methods on a set of representative rangeland sites across the western United States. Partitioning rangeland productivity to sub-pixel PFTs revealed new dynamics and insights to aid the sustainable management of rangelands.