107 results on '"Dungan, Jennifer L."'
Search Results
2. The Harmonized Landsat and Sentinel-2 surface reflectance data set
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Claverie, Martin, Ju, Junchang, Masek, Jeffrey G., Dungan, Jennifer L., Vermote, Eric F., Roger, Jean-Claude, Skakun, Sergii V., and Justice, Christopher
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- 2018
- Full Text
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3. Development of Landsat-based annual US forest disturbance history maps (1986–2010) in support of the North American Carbon Program (NACP)
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Zhao, Feng, Huang, Chengquan, Goward, Samuel N., Schleeweis, Karen, Rishmawi, Khaldoun, Lindsey, Mary A., Denning, Elaine, Keddell, Louis, Cohen, Warren B., Yang, Zhiqiang, Dungan, Jennifer L., and Michaelis, Andrew
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- 2018
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4. Application of the metabolic scaling theory and water-energy balance equation to model large-scale patterns of maximum forest canopy height
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Choi, Sungho, Kempes, Christopher P., Park, Taejin, Ganguly, Sangram, Wang, Weile, Xu, Liang, Basu, Saikat, Dungan, Jennifer L., Simard, Marc, Saatchi, Sassan S., Piao, Shilong, Ni, Xiliang, Shi, Yuli, Cao, Chunxiang, Nemani, Ramakrishna R., Knyazikhin, Yuri, and Myneni, Ranga B.
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- 2016
5. Generating Accurate and Consistent Top-Of-Atmosphere Reflectance Products from the New Generation Geostationary Satellite Sensors
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Wang, Weile, Li, Shuang, Hashimoto, Hirofumi, Takenaka, Hideaki, Dungan, Jennifer L, Kalluri, Satya, and Nemani, Ramakrishna R
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Earth Resources And Remote Sensing - Abstract
GeoNEX is a collaborative project by scientists from NASA, NOAA, JAXA, and other organizations around the world with the purpose of generating a suite of Earth-monitoring products using data streams from the latest geostationary (GEO) sensors including the GOES-16/17 ABI and the Himawari-8/9 AHI. An accurate and consistent top-of-atmosphere (TOA) reflectance product, in particular the bidirectional reflectance factor (BRF), is the starting point in the scientific processing chain. We describe the main considerations and corresponding algorithms in generating the GeoNEX TOA BRF product. First, a special advantage of geostationary data streams is their high temporal resolution (~10 minutes per full-disk scan), providing a key source of information for many downstream products. To fully utilize this high temporal frequency demands a high georegistration accuracy for every acquired image. Our analysis shows that there can be substantial georegistration uncertainties in both GOES and Himawari L1b data which we addressed by implementing a phase-based correction algorithm to remove residual errors. Second, geostationary sensors have distinct illumination-view geometry features in that the solar angle changes for every pixel. Therefore, to accurately derive a BRF requires a solar position algorithm and the estimation of the pixel-wise acquisition time within an uncertainty of 10 seconds. Third, we discuss the measures we adopted to check and correct residual radiometric calibration issues of individual sensors to enable time-series analysis as well as the cross calibration between different satellite sensors (including those from low-Earth orbit). Finally, we also explain the rationale for the choice of the global grid/tile system of the GeoNEX TOA BRF product.
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- 2019
6. GeoNEX: A Cloud Gateway for Near Real-time Processing of Geostationary Satellite Products
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Xiong, Jun, Jiang, Yunfeng, Michaelis, Andrew R, Malek, Sami Andrew, Wang, Weile, Kalluri, Satyanarayana, Zhang, Jia, Dungan, Jennifer L, and Nemani, Ramakrishna R
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Earth Resources And Remote Sensing - Abstract
The emergence of a new generation of geostationary satellite sensors provides land andatmosphere monitoring capabilities similar to MODIS and VIIRS with far greater temporal resolution (5-15 minutes). However, processing such large volume, highly dynamic datasets requires computing capabilities that (1) better support data access and knowledge discovery for scientists; (2) provide resources to enable real-time processing for emergency response (wildfire, smoke, dust, etc.); and (3) provide reliable and scalable services for the broader user community. This paper presents an implementation of GeoNEX (Geostationary NASA-NOAA Earth Exchange) services that integrate scientific algorithms with Amazon Web Services (AWS) to provide near realtime monitoring (~5 minute latency) capability in a hybrid cloud-computing environment. It offers a user-friendly, manageable and extendable interface and benefits from the scalability provided by Amazon Web Services. Four use cases are presented to illustrate how to (1) search and access geostationary data; (2) configure computing infrastructure to enable near real-time processing; (3) disseminate and utilize research results, visualizations, and animations to concurrent users; and (4) use a Jupyter Notebook-like interface for data exploration and rapid prototyping. As an example of (3), the Wildfire Automated Biomass Burning Algorithm (WF_ABBA) was implemented on GOES-16 and -17 data to produce an active fire map every 5 minutes over the conterminous US. Details of the implementation strategies, architectures, and challenges of the use cases are discussed.
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- 2019
7. GEONEX Data Products: Geostationary Satellite Derived Land Surface and Atmospheric Public Data Products
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Wang, Weile, Michaelis, Andrew R, Nemani, Ramakrishna R, and Dungan, Jennifer L
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Earth Resources And Remote Sensing - Abstract
The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GEONEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GEONEX pipeline will begin to produce provisional data products to be consumed by external collaborators and the academic community. In order to better inform and introduce the GEONEX products to the science community, the provisional products shall be distributed from the NAS data portal, located at data.nas.nasa.gov, and simple webpage at www.nasa.gov/geonex has been deployed, which describes any algorithms used in deriving the products, user manuals and data file information. We will also update the status of the data processing, on the website and provide links to the latest datasets, and use a geonex mailing list, using lists.nasa.gov.
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- 2019
8. Harmonized Landsat/Sentinel-2 Reflectance Products for Land Monitoring (Invited)
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Masek, Jeffrey G, Dungan, Jennifer L, Ju, Junchang, Roger, Jean-Claude, Claverie, Martin P, Skakun, Sergii, Vermote, Eric, and Justice, Christopher Owen
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Meteorology And Climatology ,Earth Resources And Remote Sensing - Abstract
Many land applications require more frequent observations than can be obtained from a single 'Landsat class' sensor. Agricultural monitoring, inland water quality assessment, stand-scale phenology, and numerous other applications all require near-daily imagery at better than 1ha resolution. Thus the land science community has begun expressing a desire for a '30-meter MODIS' global monitoring capability. One cost-effective way to achieve this goal is via merging data from multiple, international observatories into a single virtual constellation. The Harmonized Landsat/Sentinel-2 (HLS) project has been working to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2. Harmonization in this context requires a series of radiometric and geometric transforms to create a single surface reflectance time series agnostic to sensor origin. Radiometric corrections include a common atmospheric correction using the Landsat-8 LaSRC/6S approach, a simple BRDF adjustment to constant solar and nadir view angle, and spectral bandpass adjustments to fit the Landsat-8 OLI reference. Data are then resampled to a consistent 30m UTM grid, using the Sentinel-2 global tile system. Cloud and shadow masking are also implemented. Quality assurance (QA) involves comparison of the output 30m HLS products with near-simultaneous MODIS nadir-adjusted observations. Prototoype HLS products have been processed for approximately 7% of the global land area using the NASA Earth Exchange (NEX) compute environment at NASA Ames, and can be downloaded from the HLS web site (https://hls.gsfc.nasa.gov). A wall-to-wall North America data set is being prepared for 2018. This talk will review the objectives and status of the HLS project, and illustrate applications of high-density optical time series data for agriculture and ecology. We also discuss lessons learned from HLS in the general context of implementing virtual constellations.
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- 2017
9. Application of Geostatistical Simulation to Enhance Satellite Image Products
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Hlavka, Christine A., Dungan, Jennifer L., Leuangthong, Oy, editor, and Deutsch, Clayton V., editor
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- 2005
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10. Conditional Simulation: An alternative to estimation for achieving mapping objectives
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Dungan, Jennifer L., van der Meer, Freek, editor, Stein, Alfred, editor, Van der Meer, Freek, editor, and Gorte, Ben, editor
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- 2002
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11. OpenNEX Perspective
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Brosnan, Ian G. and Dungan, Jennifer L.
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Presentation given to the Open Source Science for ESO Mission Processing Study
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- 2022
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12. Generating global Leaf Area Index from Landsat: Algorithm formulation and demonstration
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Ganguly, Sangram, Nemani, Ramakrishna R., Zhang, Gong, Hashimoto, Hirofumi, Milesi, Cristina, Michaelis, Andrew, Wang, Weile, Votava, Petr, Samanta, Arindam, Melton, Forrest, Dungan, Jennifer L., Vermote, Eric, Gao, Feng, Knyazikhin, Yuri, and Myneni, Ranga B.
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- 2012
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13. The Sentinel-2 MSI Can Increase the Temporal Resolution of 30m Satellite-Derived LAI Estimates
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Dungan, Jennifer L, Li, Shuang, Ganguly, Sangram, Wang, Weile, Nemani, Ramakrishna R, Ju, Junchang, Claverie, Martin, and Masek, Jeffrey G
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Earth Resources And Remote Sensing - Abstract
The successful launch of the European Space Agency (ESA) Sentinel-2A (S2-A) on 23 June 2015 with its MultiSpectral Instrument (MSI) provides an important means to augment Earth-observation capabilities following the legacy of Landsat. After the three-month satellite commissioning campaign, the MSI onboard S-2A is performing very well (ESA, 2015). By 3 December 2015, the sensor data records have achieved provisional maturity status and have been accessed in level-1C Top-Of-Atmosphere (TOA) reflectance by the remote sensing community worldwide. Near-nadir observations by the MSI onboard S-2A and the Operational Land Imager (OLI) onboard Landsat 8 were collected during Simultaneous Nadir Overpasses as well as nearly coincident overpasses. This paper presents a processing chain using harmonized S-2A MSI and Landsat 8 OLI sensors to obtain increased temporal resolution in Leaf Area Index (LAI) estimates using the red-edge band B8A of MSI to replace the NIR band B08. Results demonstrate that LAI estimates from the MSI and OLI are comparable, and, given sufficient preprocessing for atmospheric correction and geometric rectification, can be used interchangeably to improve the frequency with which low LAI canopies can be monitored.
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- 2016
14. Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset
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Wang, Weile, Nemani, Ramakrishna R, Michaelis, Andrew, Hashimoto, Hirofumi, Dungan, Jennifer L, and Thrasher, Bridget L
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Earth Resources And Remote Sensing ,Meteorology And Climatology - Abstract
The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km by 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future.
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- 2016
15. Combining Remote Sensing and Forest Ecosystem Modeling: An Example Using the Regional HydroEcological Simulation System (RHESSys)
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Coughlan, Joseph C., Dungan, Jennifer L., Shimoda, Haruhisa, editor, Gholz, Henry L., editor, and Nakane, Kaneyuki, editor
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- 1997
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16. On the Limits and Extensions of the Definition of Scale
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Csillag, Ferenc, Fortin, Marie-Josée, and Dungan, Jennifer L.
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- 2000
17. Mapping Urban Expansion Across North America Using Multi-Temporal Landsat and Nighttime Lights Data
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Milesi, Cristina, Small, Christopher, Kumar, Uttam, Raja, Kumar, Michaelis, Andrew, Zhang, Gong, Ganguly, Sangram, Votava, Petr, Wang, Weile, Melton, Forrest S, Dungan, Jennifer L, and Nemani, Ramakrishna R
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Earth Resources And Remote Sensing - Abstract
Urban expansion and the associated changes in land cover have important climatic, hydrologic, biophysical and ecologic and socio-economic impacts on the environment. Yet, despite todays abundance of remote sensing data, an automated characterization of large-scale historical changes in urban spatial extent remains a challenge due to the inherent complexity and variability of the urban environment, the lack of a spectral signature unique to urban land cover, and the absence of an unambiguous definition of what is urban versus non-urban.Here we present a consistent, robust, scalable, physically- based methodology for characterization of urban expansion using Landsat observations. We use atmospherically corrected Landsat Global Land Survey time series, Web-enabled Landsat data time series, DMSP-OLS and NPP-VIIRS nighttime lights, for mapping the built-up and vegetated components of urban settlements at 30m resolution through multi- temporal standardized spectral mixture analysis. The methodology is tested and validated over the North American continent where it provides a first quantification of urban expansion and vegetation abundance changes from 1990 to 2010.
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- 2014
18. Focusing on feature-based differences in map comparison
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Dungan, Jennifer L.
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- 2006
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19. A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions
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Kyriakidis, Phaedon C. and Dungan, Jennifer L.
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- 2001
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20. Forest variable estimation from fusion of sar and multispectral optical data
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Moghaddam, Mahta, Dungan, Jennifer L., and Acker, Steven
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Remote sensing -- Research ,Forests and forestry ,Topographical drawing -- Measurement ,Business ,Earth sciences ,Electronics and electrical industries - Abstract
Radar and optical remote sensing data are used in a unified algorithm to estimate forest variables. The study site is the H. J. Andrews experimental forest in Oregon, which has significant topography and several mature and old-growth conifer stands with biomass values sometimes exceeding 1000 tons/ha. Polarimetric multifrequency Airborne Synthetic Aperture Radar (AIRSAR) backscatter, interferometric C-band Topographic Synthetic Aperture Radar (TOPSAR) coherence, and multispectral Landsat Thematic Mapper (TM) digital numbers are used in a regression analysis that relates them to forest variable measurements on the ground. Parametric expressions are derived and used to estimate the same variables(s) at other locations from the combination of AIRSAR and TM data. It is shown that the estimation accuracy is significantly improved when the radar and optical data are used in combination compared to estimating the same variable from a single data type alone. Index Terms--Forestry, geophysical inverse problems, microwave imaging, nonlinear optimization, optical imaging, scattering.
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- 2002
21. US National Maps Attributing Forest Change: 1986–2010
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Schleeweis, Karen G., primary, Moisen, Gretchen G., additional, Schroeder, Todd A., additional, Toney, Chris, additional, Freeman, Elizabeth A., additional, Goward, Samuel N., additional, Huang, Chengquan, additional, and Dungan, Jennifer L., additional
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- 2020
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22. Application of Geostatistical Simulation to Enhance Satellite Image Products
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Hlavka, Christine A., primary and Dungan, Jennifer L., additional
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- 2005
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23. Sources of Uncertainty in Predicting Land Surface Fluxes Using Diverse Data and Models
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Dungan, Jennifer L, Wang, Weile, Michaelis, Andrew, Votava, Petr, and Nemani, Ramakrishma
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Earth Resources And Remote Sensing - Abstract
In the domain of predicting land surface fluxes, models are used to bring data from large observation networks and satellite remote sensing together to make predictions about present and future states of the Earth. Characterizing the uncertainty about such predictions is a complex process and one that is not yet fully understood. Uncertainty exists about initialization, measurement and interpolation of input variables; model parameters; model structure; and mixed spatial and temporal supports. Multiple models or structures often exist to describe the same processes. Uncertainty about structure is currently addressed by running an ensemble of different models and examining the distribution of model outputs. To illustrate structural uncertainty, a multi-model ensemble experiment we have been conducting using the Terrestrial Observation and Prediction System (TOPS) will be discussed. TOPS uses public versions of process-based ecosystem models that use satellite-derived inputs along with surface climate data and land surface characterization to produce predictions of ecosystem fluxes including gross and net primary production and net ecosystem exchange. Using the TOPS framework, we have explored the uncertainty arising from the application of models with different assumptions, structures, parameters, and variable definitions. With a small number of models, this only begins to capture the range of possible spatial fields of ecosystem fluxes. Few attempts have been made to systematically address the components of uncertainty in such a framework. We discuss the characterization of uncertainty for this approach including both quantifiable and poorly known aspects.
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- 2010
24. Residential Firewood Use in the United States
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Lipfert, Frederick W. and Dungan, Jennifer L.
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- 1983
25. Conditional Simulation: An alternative to estimation for achieving mapping objectives
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Dungan, Jennifer L., primary
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- 1999
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26. Picturing Data With Uncertainty
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Kao, David, Love, Alison, Dungan, Jennifer L, and Pang, Alex
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Earth Resources And Remote Sensing - Abstract
NASA is in the business of creating maps for scientific purposes to represent important biophysical or geophysical quantities over space and time. For example, maps of surface temperature over the globe tell scientists where and when the Earth is heating up; regional maps of the greenness of vegetation tell scientists where and when plants are photosynthesizing. There is always uncertainty associated with each value in any such map due to various factors. When uncertainty is fully modeled, instead of a single value at each map location, there is a distribution expressing a set of possible outcomes at each location. We consider such distribution data as multi-valued data since it consists of a collection of values about a single variable. Thus, a multi-valued data represents both the map and its uncertainty. We have been working on ways to visualize spatial multi-valued data sets effectively for fields with regularly spaced units or grid cells such as those in NASA's Earth science applications. A new way to display distributions at multiple grid locations is to project the distributions from an individual row, column or other user-selectable straight transect from the 2D domain. First at each grid cell in a given slice (row, column or transect), we compute a smooth density estimate from the underlying data. Such a density estimate for the probability density function (PDF) is generally more useful than a histogram, which is a classic density estimate. Then, the collection of PDFs along a given slice are presented vertically above the slice and form a wall. To minimize occlusion of intersecting slices, the corresponding walls are positioned at the far edges of the boundary. The PDF wall depicts the shapes of the distributions very dearly since peaks represent the modes (or bumps) in the PDFs. We've defined roughness as the number of peaks in the distribution. Roughness is another useful summary information for multimodal distributions. The uncertainty of the multi-valued data can also be interpreted by the number of peaks and the widths of the peaks as shown by the PDF walls.
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- 2004
27. Application of Geostatistical Simulation to Enhance Satellite Image Products
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Hlavka, Christine A, Dungan, Jennifer L, Thirulanambi, Rajkumar, and Roy, David
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Earth Resources And Remote Sensing - Abstract
With the deployment of Earth Observing System (EOS) satellites that provide daily, global imagery, there is increasing interest in defining the limitations of the data and derived products due to its coarse spatial resolution. Much of the detail, i.e. small fragments and notches in boundaries, is lost with coarse resolution imagery such as the EOS MODerate-Resolution Imaging Spectroradiometer (MODIS) data. Higher spatial resolution data such as the EOS Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER), Landsat and airborne sensor imagery provide more detailed information but are less frequently available. There are, however, both theoretical and analytical evidence that burn scars and other fragmented types of land covers form self-similar or self-affine patterns, that is, patterns that look similar when viewed at widely differing spatial scales. Therefore small features of the patterns should be predictable, at least in a statistical sense, with knowledge about the large features. Recent developments in fractal modeling for characterizing the spatial distribution of undiscovered petroleum deposits are thus applicable to generating simulations of finer resolution satellite image products. We will present example EOS products, analysis to investigate self-similarity, and simulation results.
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- 2004
28. Modeling and Visualizing Uncertainty in Continuous Variables Predicted using Remotely Sensed Data
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Dungan, Jennifer L, Kao, David L, and Pang, Alex
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Earth Resources And Remote Sensing - Abstract
The use of remotely sensed images to map continuous biophysical variables, such as those related to terrestrial vegetation amount, sea surface temperature, and many other targets of NASA s Earth Observing System (EOS), includes variable, parametric, positional, spatial support and structural sources of uncertainty. A complete description of uncertainty will lead to a probability distribution at each location, allowing the exploration of the spatial dimension of uncertainty, that is, where the field is not well quantified. To achieve this purpose, convenient visualization tools are required. We have produced such a tool, called PDFVis, that facilitates the display of probability density functions (pdfs) on a per-grid-cell basis. The density estimate from Monte-Carlo generated realizations is interactively displayed as well as parametric and non-parametric summaries of the pdf field (such as mean, median, quartiles, standard deviation, number of modes, and locations of modes). Shaded surface renderings of pdfs along a transect can also be projected onto a plane. This tool will become more useful as richer descriptions of spatial uncertainty become available.
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- 2003
29. Combining Remote Sensing and Forest Ecosystem Modeling: An Example Using the Regional HydroEcological Simulation System (RHESSys)
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Coughlan, Joseph C., primary and Dungan, Jennifer L., additional
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- 1997
- Full Text
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30. Sources of Uncertainty in the Prediction of LAI / fPAR from MODIS
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Dungan, Jennifer L, Ganapol, Barry D, and Brass, James A
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Earth Resources And Remote Sensing - Abstract
To explicate the sources of uncertainty in the prediction of biophysical variables over space, consider the general equation: where z is a variable with values on some nominal, ordinal, interval or ratio scale; y is a vector of input variables; u is the spatial support of y and z ; x and u are the spatial locations of y and z , respectively; f is a model and B is the vector of the parameters of this model. Any y or z has a value and a spatial extent which is called its support. Viewed in this way, categories of uncertainty are from variable (e.g. measurement), parameter, positional. support and model (e.g. structural) sources. The prediction of Leaf Area Index (LAI) and the fraction of absorbed photosynthetically active radiation (fPAR) are examples of z variables predicted using model(s) as a function of y variables and spatially constant parameters. The MOD15 algorithm is an example of f, called f(sub 1), with parameters including those defined by one of six biome types and solar and view angles. The Leaf Canopy Model (LCM)2, a nested model that combines leaf radiative transfer with a full canopy reflectance model through the phase function, is a simpler though similar radiative transfer approach to f(sub 1). In a previous study, MOD15 and LCM2 gave similar results for the broadleaf forest biome. Differences between these two models can be used to consider the structural uncertainty in prediction results. In an effort to quantify each of the five sources of uncertainty and rank their relative importance for the LAI/fPAR prediction problem, we used recent data for an EOS Core Validation Site in the broadleaf biome with coincident surface reflectance, vegetation index, fPAR and LAI products from the Moderate Resolution Imaging Spectrometer (MODIS). Uncertainty due to support on the input reflectance variable was characterized using Landsat ETM+ data. Input uncertainties were propagated through the LCM2 model and compared with published uncertainties from the MOD15 algorithm.
- Published
- 2002
31. Visualizing Spatially Varying Distribution Data
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Kao, David, Luo, Alison, Dungan, Jennifer L, Pang, Alex, and Biegel, Bryan A
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Numerical Analysis - Abstract
Box plot is a compact representation that encodes the minimum, maximum, mean, median, and quarters information of a distribution. In practice, a single box plot is drawn for each variable of interest. With the advent of more accessible computing power, we are now facing the problem of visual icing data where there is a distribution at each 2D spatial location. Simply extending the box plot technique to distributions over 2D domain is not straightforward. One challenge is reducing the visual clutter if a box plot is drawn over each grid location in the 2D domain. This paper presents and discusses two general approaches, using parametric statistics and shape descriptors, to present 2D distribution data sets. Both approaches provide additional insights compared to the traditional box plot technique
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- 2002
32. Investigating Uncertainty in Predicting Carbon Dynamics in North American Biomes: Putting Support-Effect Bias in Perspective
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Dungan, Jennifer L and Brass, Jim
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Environment Pollution - Abstract
A fundamental strategy in NASA's Earth Observing System's (EOS) monitoring of vegetation and its contribution to the global carbon cycle is to rely on deterministic, process-based ecosystem models to make predictions of carbon flux over large regions. These models are parameterized (that is, the input variables are derived) using remotely sensed images such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS), ground measurements and interpolated maps. Since early applications of these models, investigators have noted that results depend partly on the spatial support of the input variables. In general, the larger the support of the input data, the greater the chance that the effects of important components of the ecosystem will be averaged out. A review of previous work shows that using large supports can cause either positive or negative bias in carbon flux predictions. To put the magnitude and direction of these biases in perspective, we must quantify the range of uncertainty on our best measurements of carbon-related variables made on equivalent areas. In other words, support-effect bias should be placed in the context of prediction uncertainty from other sources. If the range of uncertainty at the smallest support is less than the support-effect bias, more research emphasis should probably be placed on support sizes that are intermediate between those of field measurements and MODIS. If the uncertainty range at the smallest support is larger than the support-effect bias, the accuracy of MODIS-based predictions will be difficult to quantify and more emphasis should be placed on field-scale characterization and sampling. This talk will describe methods to address these issues using a field measurement campaign in North America and "upscaling" using geostatistical estimation and simulation.
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- 2001
33. Canopy Cover Predictions using Ground Observations and Remotely Sensed Data
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Dungan, Jennifer L
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Geosciences (General) - Abstract
Maps of vegetation status are needed at many scales, from the field level to monitor ecosystem condition to the global level to understand the carbon cycle. Status is quantified by such variables as leaf area index, biomass, and fraction of canopy cover. Current methods of predicting vegetation variables use remote sensing data to provide a spatially exhaustive data source. In a study in western Montana, several hundred ground observations made by the US Forest Service on tenth-acre conifer plots were used to develop aspatial regression and geostatistical prediction models. Normalized Difference Vegetation Index (NDVI) values from Landsat Thematic Mapper images were used as ancillary data. These models were then used to predict canopy cover at unsampled locations in a 97 square kilometer region on the boundary of the Flathead National Forest and the Bob Marshall Wilderness. Independent data from two dates six years apart were used for validation. Given the assumption that actual canopy cover remained relatively unchanged within this time period, partial validation can be achieved by measuring the correspondence of the two maps. This criterion results in ranking the aspatial regression maps as less accurate than the geostatistically generated maps. The geostatistical approach emphasizes ground measurements more heavily than does aspatid regression. Geostatistical simulations of canopy cover also provide a means of describing uncertainty about the patterns of canopy cover.
- Published
- 1999
34. Meta-Modeling: A Knowledge-Based Approach to Facilitating Model Construction and Reuse
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Keller, Richard M and Dungan, Jennifer L
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Numerical Analysis - Abstract
In this paper, we introduce a new modeling approach called meta-modeling and illustrate its practical applicability to the construction of physically-based ecosystem process models. As a critical adjunct to modeling codes meta-modeling requires explicit specification of certain background information related to the construction and conceptual underpinnings of a model. This information formalizes the heretofore tacit relationship between the mathematical modeling code and the underlying real-world phenomena being investigated, and gives insight into the process by which the model was constructed. We show how the explicit availability of such information can make models more understandable and reusable and less subject to misinterpretation. In particular, background information enables potential users to better interpret an implemented ecosystem model without direct assistance from the model author. Additionally, we show how the discipline involved in specifying background information leads to improved management of model complexity and fewer implementation errors. We illustrate the meta-modeling approach in the context of the Scientists' Intelligent Graphical Modeling Assistant (SIGMA) a new model construction environment. As the user constructs a model using SIGMA the system adds appropriate background information that ties the executable model to the underlying physical phenomena under investigation. Not only does this information improve the understandability of the final model it also serves to reduce the overall time and programming expertise necessary to initially build and subsequently modify models. Furthermore, SIGMA's use of background knowledge helps eliminate coding errors resulting from scientific and dimensional inconsistencies that are otherwise difficult to avoid when building complex models. As a. demonstration of SIGMA's utility, the system was used to reimplement and extend a well-known forest ecosystem dynamics model: Forest-BGC.
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- 1997
35. Geostatistical Approaches for Spatial Estimation of Vegetation Quantities Using Ground and Image Data
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Dungan, Jennifer L and Peterson, David L
- Subjects
Earth Resources And Remote Sensing - Abstract
A major challenge in the study of the earth system is the mapping of vegetation quantities over large regions. Geostatistical methods, such as cokriging and stochastic simulation, have the potential to exploit more fully both remotely sensed data and ground information and improve the spatial estimation of vegetation variables over traditional regression methods. A synthetic example constructed from imaging spectrometer data allows a useful comparison among regression, cokriging and a simple probability-field method. A range of linear relationships between direct (sampled) and ancillary images is used. The lowest root-mean-square-error is achieved with cokriging until the correlation between direct and ancillary data exceeds .89, at which point regression is the superior estimator. Probability-field simulation gives a range of possible realizations, most more precise than those from regression. The relationship between ground measurements and image data is thus demonstrated to be one of the critical factors in the choice of a spatial estimation method.
- Published
- 1995
36. A Knowledge-Based Representation Scheme for Environmental Science Models
- Author
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Keller, Richard M, Dungan, Jennifer L, and Lum, Henry, Jr
- Subjects
Environment Pollution - Abstract
One of the primary methods available for studying environmental phenomena is the construction and analysis of computational models. We have been studying how artificial intelligence techniques can be applied to assist in the development and use of environmental science models within the context of NASA-sponsored activities. We have identified several high-utility areas as potential targets for research and development: model development; data visualization, analysis, and interpretation; model publishing and reuse, training and education; and framing, posing, and answering questions. Central to progress on any of the above areas is a representation for environmental models that contains a great deal more information than is present in a traditional software implementation. In particular, a traditional software implementation is devoid of any semantic information that connects the code with the environmental context that forms the background for the modeling activity. Before we can build AI systems to assist in model development and usage, we must develop a representation for environmental models that adequately describes a model's semantics and explicitly represents the relationship between the code and the modeling task at hand. We have developed one such representation in conjunction with our work on the SIGMA (Scientists' Intelligent Graphical Modeling Assistant) environment. The key feature of the representation is that it provides a semantic grounding for the symbols in a set of modeling equations by linking those symbols to an explicit representation of the underlying environmental scenario.
- Published
- 1994
37. Kriging in the Shadows: Geostatistical Interpolation for Remote Sensing
- Author
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Rossi, Richard E, Dungan, Jennifer L, and Beck, Louisa R
- Subjects
Earth Resources And Remote Sensing - Abstract
It is often useful to estimate obscured or missing remotely sensed data. Traditional interpolation methods, such as nearest-neighbor or bilinear resampling, do not take full advantage of the spatial information in the image. An alternative method, a geostatistical technique known as indicator kriging, is described and demonstrated using a Landsat Thematic Mapper image in southern Chiapas, Mexico. The image was first classified into pasture and nonpasture land cover. For each pixel that was obscured by cloud or cloud shadow, the probability that it was pasture was assigned by the algorithm. An exponential omnidirectional variogram model was used to characterize the spatial continuity of the image for use in the kriging algorithm. Assuming a cutoff probability level of 50%, the error was shown to be 17% with no obvious spatial bias but with some tendency to categorize nonpasture as pasture (overestimation). While this is a promising result, the method's practical application in other missing data problems for remotely sensed images will depend on the amount and spatial pattern of the unobscured pixels and missing pixels and the success of the spatial continuity model used.
- Published
- 1994
- Full Text
- View/download PDF
38. SIGMA: A Knowledge-Based Simulation Tool Applied to Ecosystem Modeling
- Author
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Dungan, Jennifer L, Keller, Richard, and Lawless, James G
- Subjects
Environment Pollution - Abstract
The need for better technology to facilitate building, sharing and reusing models is generally recognized within the ecosystem modeling community. The Scientists' Intelligent Graphical Modelling Assistant (SIGMA) creates an environment for model building, sharing and reuse which provides an alternative to more conventional approaches which too often yield poorly documented, awkwardly structured model code. The SIGMA interface presents the user a list of model quantities which can be selected for computation. Equations to calculate the model quantities may be chosen from an existing library of ecosystem modeling equations, or built using a specialized equation editor. Inputs for dim equations may be supplied by data or by calculation from other equations. Each variable and equation is expressed using ecological terminology and scientific units, and is documented with explanatory descriptions and optional literature citations. Automatic scientific unit conversion is supported and only physically-consistent equations are accepted by the system. The system uses knowledge-based semantic conditions to decide which equations in its library make sense to apply in a given situation, and supplies these to the user for selection. "Me equations and variables are graphically represented as a flow diagram which provides a complete summary of the model. Forest-BGC, a stand-level model that simulates photosynthesis and evapo-transpiration for conifer canopies, was originally implemented in Fortran and subsequenty re-implemented using SIGMA. The SIGMA version reproduces daily results and also provides a knowledge base which greatly facilitates inspection, modification and extension of Forest-BGC.
- Published
- 1994
39. Leaf Areas And Spectral Properties Of Slash Pine
- Author
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Curran, Paul J, Dungan, Jennifer L, and Gholz, Henry L
- Subjects
Life Sciences - Abstract
NASA technical memorandum describes experiments to test feasibility of estimating seasonal leaf areas of forest canopies from spectral radiances measured by remote instruments. Accurate estimates of seasonal leaf areas of forests necessary for studies of seasonal exchanges of energy between forest canopies and atmosphere. Potential use of remote sensing in estimating seasonal changes in leaf area index (LAI).
- Published
- 1993
40. Reflectance spectroscopy of fresh whole leaves for the estimation of chemical concentration
- Author
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Curran, Paul J, Dungan, Jennifer L, Macler, Bruce A, Plummer, Stephen E, and Peterson, David L
- Subjects
Earth Resources And Remote Sensing - Abstract
Remotely sensed plant-canopy data in the visible and near-IR ranges are used to establish relations between the canopy reflectance and the chemical content of the leaves. The mathematical relation is generated by means of stepwise regression based on the derivative reflectance at certain wavelengths. Fourier filtering and sample control are used to minimize instrument noise and spectral overlap respectively, and absorption features are noted that correspond to sugar and protein. The coefficients of determination between estimated and measured concentrations are at least 0.82 for such substances as starch and chlorophyll. It is recommended in the analysis of remotly sensed canopy data that the chemicals with strong spectral overlaps with the chemical of interest be accounted for in order to estimate foliar chemical concentrations accurately.
- Published
- 1992
- Full Text
- View/download PDF
41. Seasonal LAI in slash pine estimated with Landsat TM
- Author
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Curran, Paul J, Dungan, Jennifer L, and Gholz, Henry L
- Subjects
Earth Resources And Remote Sensing - Abstract
The leaf area index (LAI, total area of leaves per unit area of ground) of most forest canopies varies throughout the year, yet for logistical reasons it is difficult to estimate anything more detailed than a seasonal maximum LAI. To determine if remotely sensed data can be used to estimate LAI seasonally, field measurements of LAI were compared to normalized difference vegetation index (NDVI) values derived using Lnandsat Thematic Mapper (TM) data, for 16 fertilized and control slash pine plots on 3 dates. Linear relationships existed between NDVI and LAI with R (sup 2) values of 0.35, 0.75, and 0.86 for February 1988, September 1988, and March, 1989, respectively. This is the first reported study in which NDVI is related to forest LAI recorded during the month of sensor overpass. Predictive relationships based on data from eight of the plots were used to estimate the LAI of the other eight plots with a root-mean-square error of 0.74 LAI, which is 15.6 percent of the mean LAI. This demonstrates the potential use of Landsat TM data for studying seasonal dynamics in forest canopies.
- Published
- 1992
- Full Text
- View/download PDF
42. Estimating The SNR Of AVIRIS Data
- Author
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Curran, Paul J and Dungan, Jennifer L
- Subjects
Mathematics And Information Sciences - Abstract
Combination of established statistical and filtering techniques yields improved estimates of signal-to-noise ratio (SNR) of multispectral imagery produced by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Method yields more accurate estimate of random variability in AVIRIS imagery. Combination of techniques called "geostatistical" method. In first step of method, periodic noise removed by notch filtering in frequency domain. In subsequent steps, sensor noise and variability within picture elements isolated by use of semi-variance of picture elements.
- Published
- 1991
43. The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration
- Author
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Curran, Paul J, Dungan, Jennifer L, Macler, Bruce A, and Plummer, Stephen E
- Subjects
Earth Resources And Remote Sensing - Abstract
The effect of a leaf pigment - red amaranthin - on red edge and chlorophyll concentration is investigated in amaranth leaves by means of treatments with nitrate and salts. A near-linear relationship between red edge and chlorophyll concentration is observed for leaves with low amaranthin concentration, and no relationship is noted at high concentrations. The study demonstrates the limitation inherent in estimating chlorophyll concentration by using remotely sensed red edge.
- Published
- 1991
- Full Text
- View/download PDF
44. An image recorded by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)
- Author
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Curran, Paul J and Dungan, Jennifer L
- Subjects
Earth Resources And Remote Sensing - Abstract
The airborne visible/infrared imaging spectrometer (AVIRIS) is described, and an example of a false-color image recorded by this device is provided. The AVIRIS is capable of sensing in 209 visible and near-infrared wavebands with an 11km swath and a 20m spatial resolution. Evaluation flights for AVIRIS were made at an altitude of approximately 20km x 10.2km of low-lying and relatively flat irrigated land near Yuba City and Sacramento, California. Raw data were converted from digital numbers to radiance and radiometrically corrected at the NASA Jet Propulsion Laboratory. Notch filtering in the frequency domain of the image was used to remove periodic noise. The illustration of both spatial and spectral properties on the false-color image are explained. AVIRIS is designed to be flown in an ER-2 aircraft and will serve as a test-bed sensor for the High-Resolution Imaging Spectrometer (HIRIS) planned for the Earth Observing System.
- Published
- 1990
45. Seasonal LAI in slash pine estimated with LANDSAT TM
- Author
-
Curran, Paul J, Dungan, Jennifer L, and Gholz, Henry L
- Subjects
Earth Resources And Remote Sensing - Abstract
The leaf area index (LAI, total area of leaves per unit area of ground) of most forest canopies varies throughout the year, yet for logistical reasons it is difficult to estimate anything more detailed than a seasonal maximum LAI. To determine if remotely sensed data can be used to estimate LAI seasonally, field measurements of LAI were compared to normalized difference vegetation index (NDVI) values derived using LANDSAT Thematic Mapper (TM) data, for 16 fertilized and control slash pine plots on 3 dates. Linear relationships existed between NDVI and LAI with R(sup 2) values of 0.35, 0.75, and 0.86 for February 1988, September 1988, and March, 1989, respectively. This is the first reported study in which NDVI is related to forest LAI recorded during the month of sensor overpass. Predictive relationships based on data from eight of the plots were used to estimate the LAI of the other eight plots with a root-mean-square error of 0.74 LAI, which is 15.6 percent of the mean LAI. This demonstrates the potential use of LANDSAT TM data for studying seasonal dynamics in forest canopies.
- Published
- 1990
46. Exploring the Relationship Between Reflectance Red Edge and Chlorophyll Content in Slash Pine
- Author
-
Curran, Paul J, Dungan, Jennifer L, and Gholz, Henry L
- Subjects
Life Sciences (General) - Abstract
Chlorophyll is a key indicator of the physiological status of a forest canopy. However, its distribution may vary greatly in time and space, so that the estimation of chlorophyll content of canopies or branches by extrapolation from leaf values obtained by destructive sampling is labor intensive and potentially inaccurate. Chlorophy11 content is related positively to the point of maximum slope in vegetation reflectance spectra which occurs at wavelengths between 690-740 nm and is known as the "red edge." The red edge of needles on individual slash pine (Piniis elliottii Engelm.) branches and in whole forest canopies was measured with a spectroradiometer. Branches were measured on the ground against a spectrally flat reflectance target and canopies were measured from observation towers against a spectrally variable understory and forest floor. There was a linear relationship between red edge and chlorophyll content of branches (R(exp 2) = 0.91). Measurements of the red edge and this relationship were used to estimate the chlorophyll content of other branches with an error that was lower than that associated with the colorimetric (laboratory) method. There was no relationship between the red edge and the chlorophyll content of whole canopies. This can be explained by the overriding influence of the understory and forest floor, an influence that was illustrated by spectral mixture modeling. The results suggest that the red edge could be used to estimate the chlorophyll content in branches but it is unlikely to be of value for the estimation of chlorophyll content in canopies unless the canopy cover is high.
- Published
- 1990
47. Sentinel-2 MSI Radiometric Characterization and Cross-Calibration with Landsat-8 OLI
- Author
-
Li, Shuang, primary, Ganguly, Sangram, additional, Dungan, Jennifer L., additional, Wang, Weile, additional, and Nemani, Ramakrishna R., additional
- Published
- 2017
- Full Text
- View/download PDF
48. Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US
- Author
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Schleeweis, Karen, primary, Goward, Samuel N., additional, Huang, Chengquan, additional, Dwyer, John L., additional, Dungan, Jennifer L., additional, Lindsey, Mary A., additional, Michaelis, Andrew, additional, Rishmawi, Khaldoun, additional, and Masek, Jeffery G., additional
- Published
- 2016
- Full Text
- View/download PDF
49. Residential Firewood Use
- Author
-
Bailey, Mark R., Lipfert, Frederick W., and Dungan, Jennifer L.
- Published
- 1984
50. A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions
- Author
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Kyriakidis, Phaedon, Dungan, Jennifer L., and Κυριακίδης, Φαίδων
- Subjects
Classification uncertainty ,Net primary production ,Land cover map quality ,Engineering and Technology ,Stochastic simulation ,Geographic information systems ,Indicator kriging ,Remote sensing ,Biogeochemical cycles ,Civil Engineering - Abstract
Spatial information in the form of geographical information system coverages and remotely sensed imagery is increasingly used in ecological modeling. Examples include maps of land cover type from which ecologically relevant properties, such as biomass or leaf area index, are derived. Spatial information, however, is not error-free: acquisition and processing errors, as well as the complexity of the physical processes involved, make remotely sensed data imperfect measurements of ecological attributes. It is therefore important to first assess the accuracy of the spatial information being used and then evaluate the impact of such inaccurate information on ecological model predictions. In this paper, the role of geostatistics for mapping thematic classification accuracy through integration of abundant image-derived (soft) and sparse higher accuracy (hard) class labels is presented. Such assessment leads to local indices of map quality, which can be used for guiding additional ground surveys. Stochastic simulation is proposed for generating multiple alternative realizations (maps) of the spatial distribution of the higher accuracy class labels over the study area. All simulated realizations are consistent with the available pieces of information (hard and soft labels) up to their validated level of accuracy. The simulated alternative class label representations can be used for assessing joint spatial accuracy, i.e., classification accuracy regarding entire spatial features read from the thematic map. Such realizations can also serve as input parameters to spatially explicit ecological models; the resulting distribution of ecological responses provides a model of uncertainty regarding the ecological model prediction. A case study illustrates the generation of alternative land cover maps for a Landsat Thematic Mapper (TM) subscene, and the subsequent construction of local map quality indices. Simulated land cover maps are then input into a biogeochemical model for assessing uncertainty regarding net primary production (NPP).
- Published
- 2001
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