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 rarely 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.
Highlights
Monitoring of plant phenology and aboveground net primary production (ANPP) is critical for effective management of grassland and rangeland ecosystems, for managers seeking to flexibly match forage demand of livestock with forage availability [1,2]
Accurate measurements of spatial and temporal variability in ANPP at scales relevant to rangeland managers is challenging due the substantial labor costs associated with directly harvesting biomass with sufficient replication across the vast areas of the earth managed for livestock production
Studies using these satellite platforms have demonstrated clear relationships between iNDVI and ANPP based on regression models describing spatial variation among locations that span a broad gradient in ANPP, or models examining temporal variation either within the growing season [8,14] or among years for a given locality, where the size of a locality varies from a single pixel [15] to several square kilometers [16]
Summary
Monitoring of plant phenology and aboveground net primary production (ANPP) is critical for effective management of grassland and rangeland ecosystems, for managers seeking to flexibly match forage demand of livestock with forage availability [1,2]. Approaches for relating NDVI to ANPP typically rely on a season-long integration of NDVI values from frequently repeated measurements of a location over time (iNDVI), such as from the Advanced Very High Resolution Radiometer/National Oceanic and Atmospheric Agency (AVHRR/NOAA) available at a 1-km resolution [6], or the Moderate Resolution Imaging Spectroradiometer (MODIS) available at a 250–1000-m resolutions [4,14] Studies using these satellite platforms have demonstrated clear relationships between iNDVI and ANPP based on regression models describing spatial variation among locations that span a broad gradient in ANPP (e.g., across the rainfall gradients in grasslands of North and South America [6,7,8,9,14]), or models examining temporal variation either within the growing season [8,14] or among years for a given locality, where the size of a locality varies from a single pixel [15] to several square kilometers [16]. RLöydveb).).,Tahneddforimngineadnstafgoerwb oarntd(AsurtbesmhirsuiabfrairgeidSacWarilleltd.g),lorbesepmeacltliovwely(.SAphnaneuraallcegaracoscsceisnea [Ncuotnt.s]isRt yadlmb.o),sat nedntfirrienlygeodf ssaigx-ewweoerkts(Aferstceumeis(iaVufrlipgiaidaocWtofilloldra.),[rWesaplteecrt]ivReylyd.bA.).nTnhuealmgreaasnseasncnounaslist almproesctipenittaitrieolny iosf3s4ixc-mwaenekdsthfeesmcueean(Vaunlnpiuaaolctteomflopreara[Wtuarelteisr]8.R4y°Cdb, .r)a.nTghinegmfreoamn a−n2n.6u°Cal pinreDceipceitmatbioenr is 34 tcom21a.n3d°CthienmJuelayn. annual temperature is 8.4 °C, ranging from −2.6 °C in December to 21.3 °C in July
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