Interannual Variability in Dry Mixed-Grass Prairie Yield: A Comparison of MODIS, SPOT, and Field Measurements

Abstract

Remote sensing is often used to assess rangeland condition and biophysical parameters across large areas. In particular, the relationship between the Normalized Difference Vegetation Index (NDVI) and above-ground biomass can be used to assess rangeland primary productivity (seasonal carbon gain or above-ground biomass "yield"). We evaluated the NDVI-yield relationship for a southern Alberta prairie rangeland, using seasonal trends in NDVI and biomass during the 2009 and 2010 growing seasons, two years with contrasting rainfall regimes. The study compared harvested biomass and NDVI from field spectrometry to NDVI from three satellite platforms: the Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Système Pour l'Observation de la Terre (SPOT 4 and 5). Correlations between ground spectrometry and harvested biomass were also examined for each growing season. The contrasting precipitation patterns were easily captured with satellite NDVI, field NDVI and green biomass measurements. NDVI provided a proxy measure for green plant biomass, and was linearly related to the log of standing green biomass. NDVI phenology clearly detected the green biomass increase at the beginning of each growing season and the subsequent decrease in green biomass at the end of each growing season due to senescence. NDVI-biomass regressions evolved over each growing season due to end-of-season senescence and carryover of dead biomass to the following year. Consequently, mid-summer measurements yielded the strongest correlation (R 2 = 0.97) between NDVI and green biomass, particularly when the data were spatially aggregated to better match the satellite sampling scale. Of the three satellite platforms (MODIS Aqua, MODIS Terra, and SPOT), Terra yielded the best agreement with ground-measured NDVI, and SPOT yielded the weakest relationship. When used properly, NDVI from satellite remote sensing can accurately estimate peak-season productivity and detect interannual variation in standing green biomass, and field spectrometry can provide useful validation for satellite data in a biomass monitoring program in this prairie ecosystem. Together, these methods can be used to identify the effects of year-to-year precipitation variability on above-ground biomass in a dry mixed-grass prairie. These findings have clear applications in monitoring yield and productivity, and could be used to support a rangeland carbon monitoring program.

Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data

“…Our results indicated that all four RSVIs estimated GPP well when weekly and monthly data were used (Figure 3, Table 1). These findings are consistent with previous studies that have investigated the relationship between productivity and RSVIs [9,10,23,24]. We also found, however, a sharp decrease in the performances of the RSVIs when the seasonal component was removed from the time series: the correlations between the temporal anomalies of the RSVIs and GPP were very weak ( Table 1).…”

“…Remotely sensed vegetation indices (RSVIs) are frequently used to broadly and efficiently monitor spatial and temporal variations in terrestrial primary productivity (e.g., ecosystem photosynthesis). The accuracies of the RSVIs, however, have only been evaluated using few ecosystems using one or two indices [9][10][11][12]. Broad-scale and global studies often cannot accurately assess the relationships between productivity and vegetation indices [1,[13][14][15], so how well these relationships between vegetation indices and productivity can be transferred to different ecosystems is still not clear.…”

“…Terrestrial primary productivity (in a broad sense) has many facets and sources of variability, and RSVIs may not be equally good at monitoring all of them. Several studies have reported good relationships between the spatial variability of biomass stocks or the production of aboveground biomass and RSVIs such as the normalised difference vegetation index (NDVI) and the enhanced vegetation index (EVI) for boreal forests [16], shrublands [17], grasslands [10,18,19], and croplands [20]. Interannual variability of tree growth and fruit production have also been satisfactorily monitored by various RSVIs in Mediterranean evergreen broadleaved forests [2,12], but few studies have compared the performances of RSVIs across multiple ecosystems.…”

Comparing stability in random forest models to map Northern Great Plains plant communities in pastures occupied by prairie dogs using Pleiades imagery

“…2). Yearly rainfall patterns can result in large differences in NDVI and biomass measurements across years (Wehlage et al, 2016). While overall total rainfall between years was similar, differences in timing of precipitation that occurred likely affected timing of green-up and dormancy for many of the cool-and warm-season species on the site.…”

Comparing Stability in Random Forest Models to Map Northern Great Plains Plant Communities Using 2015 and 2016 Pleiades Imagery

“…This, then, would create different NDVI patterns between years (Figure 4). Wehlage et al (2016) for example, found that yearly rainfall differences resulted in large differences in NDVI and biomass measurements across two years in a dry mixed-grass prairie. Goward and Prince (1995) suggested that the relationship between NDVI and annual rainfall in any given year also depends on the previous year history of rainfall at the site, and Oesterheld et al (2001) showed that annual above ground primary production of shortgrass communities is related to current as well as previous two years precipitation.…”