Engineering topics

Roy, David

Publications and source records attributed to Roy, David.

3 records

NASA NTRS2013

Daily MODIS 500 m Reflectance Anisotropy Direct Broadcast (DB) Products for Monitoring Vegetation Phenology Dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest.

Vegetation↗

NASA NTRS2007

Generating a Long-Term Land Data Record from the AVHRR and MODIS Instruments

The goal of NASA's Land Long Term Iiata Record (LTDR) project is to produce a consistent long term data set from the AVHRR and MODIS instruments for land climate studies. The project will create daily surface reflectance and normalized difference vegetation index (NDVI) products at a resolution of 0.05 deg., which is identical to the Climate Modeling Grid (CMG) used for MODIS products from EOS Terra and Aqua. Higher order products such as burned area, land surface temperature, albedo, bidirectional reflectance distribution function (BRDF) correction, leaf area index (LAI), and fraction of photosyntheticalIy active radiation absorbed by vegetation (fPAR), will be created. The LTDR project will reprocess Global Area Coverage (GAC) data from AVHRR sensors onboard NOAA satellites by applying the preprocessing improvements identified in the AVHRR Pathfinder Il project and atmospheric and BRDF corrections used in MODIS processing. The preprocessing improvements include radiometric in-flight vicarious calibration for the visible and near infrared channels and inverse navigation to relate an Earth location to each sensor instantaneous field of view (IFOV). Atmospheric corrections for Rayleigh scattering, ozone, and water vapor are undertaken, with aerosol correction being implemented. The LTDR also produces a surface reflectance product for channel 3 (3.75 micrometers). Quality assessment (QA) is an integral part of the LTDR production system, which is monitoring temporal trands in the AVHRR products using time-series approaches developed for MODIS land product quality assessment. The land surface reflectance products have been evaluated at AERONET sites. The AVHRR data record from LTDR is also being compared to products from the PAL (Pathfinder AVHRR Land) and GIMMS (Global Inventory Modeling and Mapping Studies) systems to assess the relative merits of this reprocessing vis-a-vis these existing data products. The LTDR products and associated information can be found at http://ltdr.nascom.nasa.gov/ltdr/ltdr.html.

Pedelty, Jeffrey↗

NASA NTRS2004

Application of Geostatistical Simulation to Enhance Satellite Image Products

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.

Hlavka, Christine A.↗