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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Rangeland Resources & Systems Research » Research » Publications at this Location » Publication #354726

Research Project: Improved Management to Balance Production and Conservation in Great Plains Rangelands

Location: Rangeland Resources & Systems Research

Title: Using APAR to predict aboveground plant productivity in semi-arid rangelands: Spatial and temporal relationships differ

Author
item Gaffney, Rowan
item Porensky, Lauren
item Gao, Feng
item Irisarri, J. Gonzalo - University Of Buenos Aires
item Durante, Martin - National Agricultural Research Institute(INIA)
item Derner, Justin
item Augustine, David

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/30/2018
Publication Date: 9/14/2018
Citation: Gaffney, R.M., Porensky, L.M., Gao, F.N., Irisarri, J., Durante, M., Derner, J.D., Augustine, D.J. 2018. Using APAR to predict aboveground plant productivity in semi-arid rangelands: Spatial and temporal relationships differ. Remote Sensing. 10, 1474. https://doi.org/10.3390/4s10091474.
DOI: https://doi.org/10.3390/4s10091474

Interpretive Summary: Monitoring of forage production is critical for effective management of rangeland ecosystems but is problematic due to the vast extent of rangelands globally, and the high costs of ground-based measurements. Satellite-based remote sensing shows promise as a tool for monitoring production at fine-scale temporal and spatial resolutions across broad regions. Satellite measurements can be combined with ground-based meteorological data to calculate absorbed photosynthetically active radiation (APAR), which can then be used to predict plant production. The relationship between plant production and APAR has often been quantified based on either spatial variation across a broad region or temporal variation at a location over time, but not both. Here we assess whether the relationship between production and APAR is consistent when evaluated across time and space. We explore potential factors driving differences between temporal versus spatial models, and quantify the magnitude of potential errors relating to space for time transformations. Using two complimentary plant production datasets and advanced remote sensing techniques that include fusion of data from different satellite platforms, we found that slopes of spatial models were generally greater than slopes of temporal models. This indicates that for a given increase in satellite-based APAR, forage production increased more when moving across space than when moving in time. The abundance of plant species with different structural attributes, specifically the abundance of C4 shortgrasses with prostrate canopies versus taller, more productive C3 species with more vertically complex canopies, tended to vary more dramatically in space than over time. This difference in spatial versus temporal variation in these key plant functional groups appears to be the primary driver of differences in spatial vs. temporal models for forage production. While the individual models revealed strong relationships between production and APAR, the use of temporal models to predict variation in space (or vice versa) can increase error in remotely sensed predictions of production.

Technical Abstract: Monitoring of aboveground net primary production (ANPP) is critical for effective management of rangeland ecosystems but is problematic due to the vast extent of rangelands globally, and the high costs of ground-based measurements. Remote sensing of absorbed photosynthetically active radiation (APAR) can be used to predict ANPP, potentially offering an alternative means of quantifying ANPP at both high temporal and spatial resolution across broad spatial extents. The relationship between ANPP and APAR has often been quantified based on either spatial variation across a broad region or temporal variation at a location over time, but not both. Here we assess: (i) if the relationship between ANPP and APAR is consistent when evaluated across time and space; (ii) potential factors driving differences between temporal versus spatial models, and (iii) the magnitude of potential errors relating to space for time transformations in quantifying productivity. Using two complimentary ANPP datasets and remotely sensed data derived from MODIS and a Landsat/MODIS fusion data product, we find that slopes of spatial models are generally greater than slopes of temporal models. The abundance of plant species with different structural attributes, specifically the abundance of C4 shortgrasses with prostrate canopies versus taller, more productive C3 species with more vertically complex canopies, tended to vary more dramatically in space than over time. This difference in spatial versus temporal variation in these key plant functional groups appears to be the primary driver of differences in slopes among regression models. While the individual models revealed strong relationships between ANPP to APAR, the use of temporal models to predict variation in space (or vice versa) can increase error in remotely sensed predictions of ANPP.