21 research outputs found

    IDENTIFYING SPECTRA IMPORTANT FOR PREDICTION OF SENESCENT GRASSLAND CANOPY STRUCTURE

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    Managers of the nearly 0.5 million ha of public lands in North and South Dakota, USA rely heavily on manual measurements of vegetation properties to ensure conservation of grassland structure for wildlife and forage for livestock. Spectral imaging data may be useful in assessment of large (\u3e100,000 ha) landscapes, as in the Grand River National Grassland (GRNG), South Dakota. Here, we examined the predictive potential for the Advanced High Resolution Spectrometer (AVIRIS) to estimate mixed-grass prairie canopy structural attributes (photosynthetically active vegetation (kg PV ha-1), non-photosynthetically active vegetation (kg NPV ha-1), total standing crop (kg PV+NPV ha-1), nitrogen content (kg N ha-1), and visual estimates of bare ground (%) in October 2010. We conducted the study on a 36,000-ha herbaceous area using 24 randomly selected plots divided into summit, midslope and toeslope positions. Field data were collected during the AVIRIS flyover, and three approaches for building a prediction model of canopy attributes based on spectra were evaluated based on R2 values. These approaches included Partial Least Squares Regression (PLS), a variable selection method with predictor variables based on functions of the AVIRIS spectra, and a variable selection method using individual bands or combinations of individual bands of spectra as predictors. All variable selection methods involved randomly partitioning the data into training and validations sets and choosing a final prediction model based on model selection frequency. PLS regression out-performed regression models (based on the variable selection methods) with R2 values of 0.73, 0.56, 0.62, 0.67, and 0.58, for PV, NPV, total standing crop, nitrogen content, and bare ground, respectively

    Upscaling fluxes from towers to regions, continents and global scales using datadriven approaches

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    Quantifying the current carbon cycle of terrestrial ecosystems requires that we translate spatially sparse measurements into consistent, gridded flux estimates at the regional scale. This is particularly challenging in heterogeneous regions such as the northern forests of the United States. We use a network of 17 eddy covariance flux towers deployed across the Upper Midwest region of northern Wisconsin and Michigan and upscale flux observations from towers to the regional scale. This region is densely instrumented and provides a unique test bed for regional upscaling. We develop a simple Diagnostic Carbon Flux Model (DCFM) and use flux observations and a data assimilation approach to estimate the model parameters. We then use the optimized model to produce gridded flux estimates across the region. We find that model parameters vary not only across plant functional types (PFT) but also within a given PFT. Our results show that the parameter estimates from a single site are not representative of the parameter values of a given PFT; cross-site (or joint) optimization using observations from multiple sites encompassing a range of site and climate conditions considerably improves the representativeness and robustness of parameter estimates. Parameter variability within a PFT can result in substantial variability in regional flux estimates. We also find that land cover representation including land cover heterogeneity and the spatial resolution and accuracy of land cover maps can lead to considerable uncertainty in regional flux estimates. In heterogeneous, complex regions, detailed and accurate land cover maps are essential for accurate estimation of regional fluxes

    Upscaling carbon fluxes from towers to the regional scale: Influence of parameter variability and land cover representation on regional flux estimates

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    Quantifying the current carbon cycle of terrestrial ecosystems requires that we translate spatially sparse measurements into consistent, gridded flux estimates at the regional scale. This is particularly challenging in heterogeneous regions such as the northern forests of the United States. We use a network of 17 eddy covariance flux towers deployed across the Upper Midwest region of northern Wisconsin and Michigan and upscale flux observations from towers to the regional scale. This region is densely instrumented and provides a unique test bed for regional upscaling. We develop a simple Diagnostic Carbon Flux Model (DCFM) and use flux observations and a data assimilation approach to estimate the model parameters. We then use the optimized model to produce gridded flux estimates across the region. We find that model parameters vary not only across plant functional types (PFT) but also within a given PFT. Our results show that the parameter estimates from a single site are not representative of the parameter values of a given PFT; cross-site (or joint) optimization using observations from multiple sites encompassing a range of site and climate conditions considerably improves the representativeness and robustness of parameter estimates. Parameter variability within a PFT can result in substantial variability in regional flux estimates. We also find that land cover representation including land cover heterogeneity and the spatial resolution and accuracy of land cover maps can lead to considerable uncertainty in regional flux estimates. In heterogeneous, complex regions, detailed and accurate land cover maps are essential for accurate estimation of regional fluxes

    Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush–steppe ecosystem

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    The net ecosystem exchange (NEE) of carbon flux can be partitioned into gross primary productivity (GPP) and respiration (R). The contribution of remote sensing and modeling holds the potential to predict these components and map them spatially and temporally. This has obvious utility to quantify carbon sink and source relationships and to identify improved land management strategies for optimizing carbon sequestration. The objective of our study was to evaluate prediction of 14-day average daytime CO2 fluxes ( Fday) and nighttime CO2 fluxes (Rn) using remote sensing and other data. Fday and Rn were measured with a Bowen ratio–energy balance (BREB) technique in a sagebrush (Artemisia spp.)–steppe ecosystem in northeast Idaho, USA, during 1996–1999. Micrometeorological variables aggregated across 14-day periods and time-integrated Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (iNDVI) were determined during four growing seasons (1996–1999) and used to predict Fday and Rn. We found that iNDVI was a strong predictor of Fday (R2= 0.79, n = 66, P \u3c 0.0001). Inclusion of evapotranspiration in the predictive equation led to improved predictions of Fday (R2= 0.82, n = 66, P \u3c 0.0001). Cross-validation indicated that regression tree predictions of Fday were prone to overfitting and that linear regression models were more robust. Multiple regression and regression tree models predicted Rn quite well (R2 = 0.75–0.77, n = 66) with the regression tree model being slightly more robust in cross-validation. Temporal mapping of Fday and Rn is possible with these techniques and would allow the assessment of NEE in sagebrush–steppe ecosystems. Simulations of periodic Fday measurements, as might be provided by a mobile flux tower, indicated that such measurements could be used in combination with iNDVI to accurately predict Fday. These periodic measurements could maximize the utility of expensive flux towers for evaluating various carbon management strategies, carbon certification, and validation and calibration of carbon flux models

    Root-shoot interactions in the response of sugarcane to drought

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    Thesis (Ph. D.)--University of Hawaii at Manoa, 1991.Includes bibliographical references (leaves 112-124)Microfiche.xiv, 124 leaves, bound ill. 29 cmStudies using greenhouse-grown plants of sugarcane cultivars known to have varying levels of resistance to drought were conducted to identify distinctive physiological features that may confer drought resistance in sugarcane. When leaf water relations, stomatal conductance (g), and shoot growth rate (SGR) were measured concurrently during a soil drying cycle, it was found that both osmotic and elastic adjustment occurred in the leaves of all cultivars in response to soil drying and diurnal water deficit. These adjustments led to almost complete maintenance of symplast volume, but only partial maintenance of turgor. During the early stages of drought, reductions in g and SGR were not accompanied by significant reductions in bulk leaf water status, suggesting that signals originating within the roots may have regulated shoot behavior. When the hydraulic properties of entire root systems and isolated roots were characterized by the transpiration gradient and pressure-flux techniques, cultivar differences in both root- and leaf-specific root hydraulic conductance (Groot) were discerned. At high soil moisture, transpiration and Groot differed considerably among cultivars and were positively correlated, whereas leaf water potential (ψL) was similar among cultivars. Within a narrow range of soil water suction (0 to 0.1 MPa), over which Groot and g fell to nearly zero, ψL remained nearly constant because the vapor phase conductance of the leaves and the liquid phase conductance of the roots declined in parallel. These patterns reinforced the suggestion that control of g in sugarcane plants exposed to drying soil was exerted primarily at the root rather than at the leaf level. Cultivar variation in water relations characteristics, especially bulk tissue elasticity, was more distinct in the roots than in the leaves, suggesting that the previously reported cultivar differences in drought resistance were likely to be root-based. It was hypothesized that coordination of g with declining Groot during soil drying was accomplished by a chemical signal moving from the roots to the leaves via the transpiration stream. Decreased root osmotic potential may have stimulated export of this putative substance from the roots
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