Abstract
BackgroundA simulation model that relies on satellite observations of vegetation cover from the Landsat 7 sensor and from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate net primary productivity (NPP) of forest stands at the Bartlett Experiment Forest (BEF) in the White Mountains of New Hampshire.ResultsNet primary production (NPP) predicted from the NASA-CASA model using 30-meter resolution Landsat inputs showed variations related to both vegetation cover type and elevational effects on mean air temperatures. Overall, the highest predicted NPP from the NASA-CASA model was for deciduous forest cover at low to mid-elevation locations over the landscape. Comparison of the model-predicted annual NPP to the plot-estimated values showed a significant correlation of R2 = 0.5. Stepwise addition of 30-meter resolution elevation data values explained no more than 20% of the residual variation in measured NPP patterns at BEF. Both the Landsat 7 and the 250-meter resolution MODIS derived mean annual NPP predictions for the BEF plot locations were within ± 2.5% of the mean of plot estimates for annual NPP.ConclusionAlthough MODIS imagery cannot capture the spatial details of NPP across the network of closely spaced plot locations as well as Landsat, the MODIS satellite data as inputs to the NASA-CASA model does accurately predict the average annual productivity of a site like the BEF.
Highlights
A simulation model that relies on satellite observations of vegetation cover from the Landsat 7 sensor and from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate net primary productivity (NPP) of forest stands at the Bartlett Experiment Forest (BEF) in the White Mountains of New Hampshire
The Enhanced Vegetation Index (EVI) has been found useful in estimating absorbed photosynthetically-active radiation (PAR) related to chlorophyll contents in vegetated canopies [15], and has been shown to be highly correlated with processes that depend on absorbed light, such as gross primary productivity (GPP) [16,17]
We present the results of the NASA-CASA (Carnegie-Ames-Stanford Approach) model to predict net primary productivity (NPP) fluxes using both Landsat 7 and MODIS imagery as a means to infer variability in temperate forests of the eastern United States
Summary
A comparison of different modeling methods is presented . Each of these methods use remote sensing data inputs to characterize forest cover attributes that can influence NPP. CASA Model NPP Comparison using MODIS EVI MODIS EVI composite images for all months of 2001 were used in place of the single Landsat 7 EVI imagery to predict annual NPP at the BEF These continuous gridded observations of canopy greenness cover from the MODIS instrument have been collected to improve the tracking of canopy green-up and green-down over a growing season, for Deciduous and Mixed forest stand types. Estimated plot NPP values showed no significant correlation result (at p < 0.1), regardless of whether the entire collection of plot estimates were considered together, or were separated into predominantly Deciduous, Coniferous, and Mixed forest classes (data not shown) This implies that the relatively coarse spatial resolution of the MODIS 250-m EVI imagery cannot capture landscape-level variability in tree production in the same way that 30-m Landsat image data can for this eastern hardwood forest location. This finding suggests that, MODIS 250-m EVI imagery cannot capture the spatial details of NPP across the network of closely spaced plot locations, it does accurately predict the average annual productivity of a site like the BEF
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