PNNL-13501 - Pacific Northwest National Laboratory

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Figure 1. Ordinary kriging of 2 m data using 100 samples (upper left), 200 samples (upper right), and 400 samples (lower left). The original 2 m imagery is shown in the lower right for comparison. classes. The local mean used for each 2 m airborne pixel was then determined by the corresponding landscape class each pixel was situated in. The results of the Gaussian simulation with locally varying means are shown in Figure 2, as with the ordinary kriging, the simulations were performed using 100, 200, and 400 samples. Summary and Conclusions The most significant result was our evaluation of three methods for interpolating biomass data, and an estimation of the number of samples required. This result is summarized in Figure 3, which shows the root mean square error for linear regression (top), Gaussian simulation with locally varying means (middle), and ordinary kriging (bottom) plotted as a function of the number of 2 m samples used. For the 46-ha farmland site that was evaluated, increasing the number of samples did little to improve the regression error, but greatly improved the simulation and kriging errors. Another result was that the Gaussian simulation with locally varying means did not provide an improved interpolation over the ordinary kriging for this test site. We believe that this is a result of what is referred to as the mixed pixel problem; that is, a 15 m satellite pixel is viewing several 2 m pixels of 222 FY 2000 <strong>Laboratory</strong> Directed Research and Development Annual Report possibly differing targets or compositions. The mixed pixel problem is inherent to almost any application with spatial analysis applied to imagery at different scales. We see this as edge effects in the test site. Figure 4 shows the highest absolute errors for the simulated map occur near field edges and roads. Boundary conditions such as roads and field edges may cause different pixel classification in the 15 m satellite imagery relative to the 2 m airborne data. Thus, a 2 m pixel representing a road might actually be categorized as field and assigned the local mean based on that misclassification. These boundary conditions are a challenge that could be explored in other datasets. References Goovaerts P. 1997. Geostatistics for natural resources evaluation. Oxford University Press, New York. Murray CJ, LL Cadwell, JL Downs, and MA Simmons. 1999. “Fusing vegetation data sets to provide a spatial analysis of Sage Grouse habitat on the Army’s Yakima Training Center.” In: ED McArthur, WK Ostler, and CL Wambolt (eds.) Shrubland Ecotones; Proc. RMRS-P- 00. Ogden, Utah: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.
Figure 2. Gaussian simulation with locally varying means based on 2 m data using 100 samples (upper left), 200 samples (upper right), and 400 samples (lower left) RMSE (NDVI) 25.0 20.0 15.0 10.0 5.0 Farm Data - RMSE 0.0 0 100 200 300 400 500 Number of Samples Reg OK LVM Figure 3. Root mean square errors for linear regression (top line), Gaussian simulation with locally varying means (center) and ordinary kriging (bottom line) plotted against number of samples used Figure 4. Absolute error of interpolated imagery based on Gaussian simulation with locally varying means. Note that the errors are greatest near boundaries (field edges, roads). Earth System Science 223
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Laboratory Directed Research and De
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Laboratory Directed Research and De
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Computational Science and Engineeri
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Policy and Management Sciences Asse
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Introduction Department of Energy O
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5. After reviews by the Technical a
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• The Technical Council peer revi
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methods applicable to combustion, s
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Summary Each national laboratory mu
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Study Control Number: PN0004/1411 A
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Table 1. Positive and negative ions
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m/z Arbitrary Units Figure 1. Mass
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introduce low-volatility compounds
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arrangement, but the charge is reta
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two liquid chromatographic gradient
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Integrated Microfabricated Devices
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Matrix Assisted Laser Desorption/Io
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Molecular Beam Synthesis and Charac
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temperature distribution of the des
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expected to result in significant a
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Study Control Number: PN99069/1397
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Seuss DT and KA Prather. 1999. “M
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Analysis of Receptor-Modulated Ion
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laboratory (Lu and Xie 1997; Lu et
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Automated DNA Fingerprinting Microa
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Comparison of 4 Xanthomonas Isolate
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Approach Hematopoietic (bone marrow
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Anti-Human lgG Human lgG Protein G
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Characterization of Global Gene Reg
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PCR products for immobilization on
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identify novel proteins of importan
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Immunoprecipitaton of tyrosinephosp
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Coupled NMR and Confocal Microscopy
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In the optical microscopy image, th
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Development and Applications of a M
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Figure 3. Hepa-1 cells undergoing a
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cellular processing (such as post-t
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8. Initial demonstration of an off-
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expression systems for several year
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accumulation of protein products, i
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Fungal Conversion of Agricultural W
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Fungal Conversion of Waste Glucose/
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Summary and Conclusions We demonstr
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are shown in Figure 1(a) and 1(b),
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Anchorage-Independent Growth (% con
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selection of seedlings demonstratin
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NMR-Based Structural Genomics Micha
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Solution-State Structure and Fold-C
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Plant Root Exudates and Microbial G
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98 99 100 6 7 10 3 1 12 11 15 16 17
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Summary and Conclusions • The gre
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Jones-Oliveira JB, JS Oliveira, HE
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• securely moves files to a targe
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Plenary invited lecture, “The Ele
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Figure 1. X3DGEN tetrahedral mesh o
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Figure 5a. OSO volume rendering of
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Development of Models of Cell-Signa
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SOS p 23 EGFR Professor Rodland at
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Oliveira JS, JB Jones-Oliveira, and
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Figure 2. Associating a dataset mar
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to 1) incorporate three-dimensional
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shown in Figure 1. Simulated result
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HostBuilder: A Tool for the Combina
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Invariant Discretization Methods fo
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Graphics, Inc., workstation. The co
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Mixed Hamiltonian Methods for Geoch
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guyanaite, and grimaldiite (see Fig
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in the Environmental Molecular Scie
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Simulation and Modeling of Electroc
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Summary and Conclusions We implemen
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Since the implementation of the gen
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asis of previous performance, perce
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Bioinformatics for High-Throughput
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Summary and Conclusions In previous
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User Cat - Supervisor (client) Anal
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The second goal was to research way
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Figure 1. A visualization technique
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and interactions. The flexibility o
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Page 182 and 183: Study Control Number: PN99078/1406
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[Pb-212]/[Pb-212] final 100 10 1 0.
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Plasma Particle Dielectric Fiber El
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(species, sub-species, and sex), sp
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Figure 3. Relative risk of bone and
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Development of a Preliminary Physio
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Development of a Virtual Respirator
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Figure 3. Use of the chest cavity a
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Examination of the Use of Diazolumi
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Indoor Air Pathways to Nuclear, Che
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Source Building Release Observed re
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To illustrate the potential impact
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Non-Invasive Biological Monitoring
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Materials Science and Technology
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Figure 1. (a) - (c) Successive magn
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Component Fabrication and Assembly
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Development of a Low-Cost Photoelec
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elaxations, indicating the adequacy
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Promising chemical durability, as m
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Study Control Number : PN98028/1274
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High Power Density Solid Oxide Fuel
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Hybrid Plasma Process for Vacuum De
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Matson DW, PM Martin, WD Bennett, J
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We showed the proof-of-principle ap
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Bandgap Energy (eV) 2.9 2.85 2.8 2.
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Ultrathin Electrolyte Test Results
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Detailed Investigations on Hydrogen
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identified low energy step structur
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Development of Novel Photo-Catalyst
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-2 -1 Energy (eV) 0 1 2 Cu2O SrTiO3
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Here, we perform adsorption and des
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exceed those in the all-metal devic
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Intensity (a.u.) 0 100 200 300 400
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integrated voltage (V) 0.10 0.08 0.
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The first step was to implement thi
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Spontaneous Formation of Cu2O Nano-
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Actinide Chemistry at the Aqueous-M
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Figure 5. Close-up atomic force mic
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to 300°C and 400°C. Unfortunately
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Summary and Conclusions Transmutati
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Advanced Calibration/Registration T
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Figure 1a. Violet filter Figure 1b.
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Autonomous Ultrasonic Probe for Pro
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containing nitrous oxide, an optica
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Chemical Detection Using Cavity Enh
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Figure 3 is a color rendered simula
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appropriate study has been performe
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Exploitation of Conventional Chemic
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Figures 3 and 4 illustrate the impa
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enefits of using a modified spread-
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Single Channel Signal 0.0025 0.0020
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Therefore, in order to extract the
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A next-generation technology is nee
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Wind Dir. Figure 2. Negative ion co
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Volumetric Imaging of Materials by
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amplitude and peak frequency change
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Figure 3. 13 C NMR spectrum of corn
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Approach Two flow loops, one that o
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Modification of Ion Exchange Resins
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Permanganate Treatment for the Deco
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minimized by medium exchange. After
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Figure 1. Thermogravimetric analysi
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Validation and Scale-Up of Monosacc
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Concentration (g/100g solution) Con
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Analysis of High-Volume, Hyper-Dime
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ecent figure of merit values. Shoul
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Probabilistic Methods Development f
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a) Traditional PCA-based process co
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Acronyms and Abbreviations 2-D two-
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