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    Use of membranes and detailed Hysplit analyses to understand particulate, gaseous oxidized, and reactive mercury chemistry
    (2020) Gustin, Mae S.; Dunham-Cheatham, Sarrah M.; Zhang, Lei; Lyman, Seth; Choma, Nicole; Castro, Mark
    The atmosphere is the primary pathway by which Hg enters ecosystems. Despite the importance of atmospheric deposition, concentrations and chemistry of gaseous oxidized (GOM) and particulate (PBM) Hg are poorly characterized. Here the use of 3 membranes in tandem (cation exchange (CEM), nylon, and polytetrafluoroethylene (PTFE) membranes) was used as means for quantification of concentrations and identification of the chemistry of GOM and PBM. Detailed Hysplit analyses was done to determine sources of oxidants forming RM. Despite the coarse resolution of sampling (1-to-2 weeks) a gradient in chemistry was observed with halogenated compounds dominating over the Pacific Ocean and continued influence from the marine boundary layer in Nevada and Utah, and a periodic occurrence in Maryland. Oxide based RM compounds arrived at continental locations via long range transport. Nitrogen, sulfur, and organic RM compounds were due to regional and local air masses. Concentrations were highest over the ocean and decreased moving from west to east across the United States. Comparing concentrations on membranes with and without PTFE in front demonstrated CEM provide a quantitative measure of RM concentrations. This method is viable for understanding the chemistry of GOM and PBM compounds.
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    Quantification of uncertainty in aboveground biomass estimates derived from small-footprint airborne LiDAR. 
    (2018) Xu, Qing; Man, Albert; Fredrickson, Mark; Hou, Zhengyang; Pitkänen, Juho; Wing, Brian; Ramirez, Carlos; Li, Bo; Greenberg, Jonathan A.
    To address uncertainty in biomass estimates across spatial scales, we determined aboveground biomass (AGB) in Californian forests through the use of individual tree detection methods applied to small-footprint airborne LiDAR. We propagated errors originating from a generalized allometric equation, LiDAR measurements, and individual tree detection algorithms to AGB estimates at the tree and plot levels. Larger uncertainties than previously reported at both tree and plot levels were found when AGB was derived from remote sensing. On average, per-tree AGB error was 135% of the estimated AGB, and per-plot error was 214% of the estimated AGB. We found that from tree to plot level, the allometric equation constituted the largest proportion of the total AGB uncertainty. The proportion of the uncertainty associated with remote sensing errors was larger in lower AGB forests, and it decreased as AGB increased. The framework in which we performed the error propagation analysis can be used to address AGB uncertainties in other ecosystems and can be integrated with other analytical techniques.
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     How much can natural resource inventory benefit from finer resolution auxiliary data?
    (2018) Hou, Zhengyang; McRoberts, Ronald E.; Ståhl, Göran; Packalen, Petteri; Greenberg, Jonathan A.; Xu, Qing
    For remote sensing-assisted natural resource inventories, the effects of spatial resolution in the form of pixel size and the effects of subpixel information on estimates of population parameters were evaluated by comparing results obtained using Landsat 8 and RapidEye auxiliary imagery. The study area was in Burkina Faso, and the response variable of interest was firewood volume (m3/ha). A sample consisting of 160 field plots was selected from the population following a two-stage sampling design. Models were fit using weighted least squares; the population mean, ?, and the variance of the estimator of the population mean, , were estimated using two inferential frameworks, model-based and model-assisted, and compared. For each framework, was estimated both analytically and empirically. Empirical variances were estimated using bootstrapping that accounted for the two-stage sampling. The primary results were twofold. First, for the effects of spatial resolution and subpixel information, four conclusions are relevant: (1) finer spatial resolution imagery indeed contributed to greater precision for estimators of population parameter, but despite the finer spatial resolution of RapidEye, the increase was only marginal, on the order of 10% for model-based variance estimators and 36% for model-assisted variance estimators; (2) subpixel information on texture was marginally beneficial for inference of large area population parameters; (3) RapidEye did not offer enough of an improvement to justify its cost relative to the free Landsat 8 imagery; and (4) for a given plot size, candidate remote sensing auxiliary datasets are more cost-effective when their spatial resolutions are similar to the plot size than with much finer alternatives. Second, for the comparison between estimators, three conclusions are relevant: (1) sampling distribution for the model-based was more concentrated and smaller on the order of 42% to 59% than that for the model-assisted , suggesting superior consistency and efficiency of model-based inference to model-assisted inference; (2) bootstrapping is an effective alternative to analytical variance estimators; and (3) prediction accuracy expressed by RMSE is useful for screening candidate models to be used for population inferences.
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    A spatialized classification approach for land cover mapping using hyperspatial imagery
    (2019) Zou, Yi; Greenberg, Jonathan A.
    Maps of classified surface features are a key output from remote sensing. Conventional methods of pixel-based classification label each pixel independently by considering only a pixel's spectral properties. While these purely spectral-based techniques may be applicable to many medium and coarse-scale remote sensing analyses, they may become less appropriate when applied to high spatial resolution imagery in which the pixels are smaller than the objects to be classified. At this scale, there is often higher intra-class spectral heterogeneity than inter-class spectral heterogeneity, leading to difficulties in using purely spectral-based classifications. A solution to these issues is to use not only a pixel's spectral characteristics but also its spatial characteristics. In this study, we develop a generalizable "spatialized" classification approach for high spatial resolution image classification. We apply the proposed approach to map vegetation growth forms such as trees, shrubs, and herbs in a forested ecosystem in the Sierra Nevada Mountains. Our results found that the spatialized classification approach outperformed spectral-only approaches for all cover classes examined, with the largest improvements being in discriminating vegetation classes.
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    Effect of spatial filtering on characterizing soil properties from imaging spectrometer data.
    (2017) Dutta, Debsunder; Kumar, Praveen; Greenberg, Jonathan A.
    Airborne imaging spectroscopy covering wavelength range of 0.35-2.5 ?m can be used to quantify soil textural properties and chemical constituents. In this paper, we evaluate the effects of spatial resolution on the quantification of soil constituents using a lasso algorithm-based ensemble bootstrapping framework. Airborne visible infrared imaging spectrometer data collected at 7.6 m resolution over Bird's Point New Madrid (BPNM) floodway in Missouri, USA, is upscaled using a spatial filter to simulate a satellite-based sensor and generate multiple coarser resolution datasets, including the originally proposed 60.8 m hyperspectral infrared imager like data. The simulated data at multiple spatial resolutions are used in an ensemble lasso algorithm-based modeling framework for developing quantitative prediction models and spatial mapping of the soil constituents. We outline an evaluation framework with a set of metrics that considers the point-scale model performance as well as the consistency of cross-scale spatial predictions. The model results demonstrate that the ensemble quantification method is scalable, and further the model structure indicates the persistence of important spectral features across spatial resolutions. The probability density functions of the constituents over the BPNM landscape show that it is similar for multiple spatial resolutions. Finally, a comparison of the model predictions with statistical central values together with the within pixel variance across fine to coarse resolutions indicate that the model accurately captures the median values of the fine subgrid that the coarse-resolution data is composed of. This study establishes the feasibility for quantifying soil constituents from space-borne hyperspectral sensors.