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NASA NTRS2020

Synergistic Use of Hyperspectral UV-Visible OMI and Broadband Meteorological Imager MODIS Data for a Merged Aerosol Product

The retrieval of optimal aerosol datasets by the synergistic use of hyperspectral ultraviolet(UV)–visible and broadband meteorological imager (MI) techniques was investigated. The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) was used as a proxy for hyperspectral UV–visible instrument data to which the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol algorithm was applied. Moderate-Resolution Imaging Spectroradiometer (MODIS) L1B and dark target aerosol Level 2 (L2) data were used with a broadband MI to take advantage of the consistent time gap between the MODIS and the OMI. First, the use of cloud mask information from the MI infrared (IR) channel was tested for synergy. High-spatial-resolution and IR channels of the MI helped mask cirrus and sub-pixel cloud contamination of GEMS aerosol, as clearly seen in aerosol optical depth (AOD) validation with Aerosol Robotic Network (AERONET) data. Second, dust aerosols were distinguished in the GEMS aerosol-type classification algorithm by calculating the total dust confidence index (TDCI) from MODIS L1B IR channels. Statistical analysis indicates that the Probability of Correct Detection (POCD) between the forward and inversion aerosol dust models (DS) was increased from 72% to 94% by use of the TDCI for GEMS aerosol-type classification, and updated aerosol types were then applied to the GEMS algorithm. Use of the TDCI for DS type classification in the GEMS retrieval procedure gave improved single-scattering albedo (SSA) values for absorbing fine pollution particles (BC) and DS aerosols. Aerosol layer height (ALH) retrieved from GEMS was compared with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, which provides high-resolution vertical aerosol profile information. The CALIOP ALH was calculated from total attenuated backscatter data at 1064 nm, which is identical to the definition of GEMS ALH. Application of the TDCI value reduced the median bias of GEMS ALH data slightly. The GEMS ALH bias approximates zero, especially for GEMS AOD values of>~0.4 and GEMS SSA values of<~0.95.Finally, the AOD products from the GEMS algorithm and MI were used in aerosol merging with the maximum-likelihood estimation method, based on a weighting factor derived from the standard deviation of the original AOD products. With the advantage of the UV–visible channel in retrieving aerosol properties over bright surfaces, the combined AOD products demonstrated better spatial data availability than the original AOD products, with comparable accuracy. Furthermore, pixel-level error analysis of GEMS AOD data indicates improvement through MI synergy.

aerosol↗

NASA NTRS1980

Seasat SAR identification of dry climate urban land cover

Digitally processed Seasat synthetic aperture radar (SAR) imagery of the Denver, Colorado area was examined to assess its potential for mapping urban land cover and the compatibility of SAR derived classes with those described in the U.S. Geological Survey classification system. The entire scene was interpreted to generate a small-scale land cover map. In addition, six subscene enlargements representative of urban land cover categories extant in the area were used as test sites for detailed analysis of land cover types. Two distinct approaches were employed and compared in examining the imagery - a visual interpretation of black-and-white positive transparencies and an automated-machine/visual interpretation. The latter used the Image 100 interactive image analysis system to generate land cover classes by density level slicing of the image frequency histogram.

Henderson, F. M.↗

NASA NTRS1993

Automatic definition of spectral units in the equatorial regions of Mars

ISM was the first instrument that acquired spectra of small areas (25x25 sq km) on a planetary surface. The data set consists mainly in images made up of about 3000 such pixels, each one corresponding to a near-infrared spectrum (0.76 to 3.15 microns in 128 spectral channels) with high signal-to-noise ratio. These data were used to define spectral units and to constrain the mineralogy of surface materials in the equatorial regions of the planet. Future spaceborne imaging spectrometers (e.g. OMEGA on board the Mars-94 Russian spacecraft) are expected to provide several hundred times as much data as ISM, so there's a strong need for fast and reliable processing methods. The present work is an attempt to define spectral units in the region of Syrtis Major-Isidis Planitia by means of G-mode analysis. The method allows to cluster the spectra according to their similarities; different levels of classification can be achieved by tuning a threshold of confidence.

Erard, Stephane↗

NASA NTRS2008

An Automatic Cloud Mask Algorithm Based on Time Series of MODIS Measurements

Quality of aerosol retrievals and atmospheric correction depends strongly on accuracy of the cloud mask (CM) algorithm. The heritage CM algorithms developed for AVHRR and MODIS use the latest sensor measurements of spectral reflectance and brightness temperature and perform processing at the pixel level. The algorithms are threshold-based and empirically tuned. They don't explicitly address the classical problem of cloud search, wherein the baseline clear-skies scene is defined for comparison. Here, we report on a new CM algorithm which explicitly builds and maintains a reference clear-skies image of the surface (refcm) using a time series of MODIS measurements. The new algorithm, developed as part of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm for MODIS, relies on fact that clear-skies images of the same surface area have a common textural pattern, defined by the surface topography, boundaries of rivers and lakes, distribution of soils and vegetation etc. This pattern changes slowly given the daily rate of global Earth observations, whereas clouds introduce high-frequency random disturbances. Under clear skies, consecutive gridded images of the same surface area have a high covariance, whereas in presence of clouds covariance is usually low. This idea is central to initialization of refcm which is used to derive cloud mask in combination with spectral and brightness temperature tests. The refcm is continuously updated with the latest clear-skies MODIS measurements, thus adapting to seasonal and rapid surface changes. The algorithm is enhanced by an internal dynamic land-water-snow classification coupled with a surface change mask. An initial comparison shows that the new algorithm offers the potential to perform better than the MODIS MOD35 cloud mask in situations where the land surface is changing rapidly, and over Earth regions covered by snow and ice.

Lyapustin, Alexei↗

NASA NTRS1974

Interpretation of Pennsylvania agricultural land use from ERTS-1 data

The author has identified the following significant results. To study the complex agricultural patterns in Pennsylvania, a portion of an ERTS scene was selected for detailed analysis. Various photographic products were made and were found to be only of limited value. This necessitated the digital processing of the ERTS data. Using an unsupervised classification procedure, it was possible to delineate the following categories: (1) forest land with a northern aspect, (2) forest land with a southern aspect, (3) valley trees, (4) wheat, (5) corn, (6) alfalfa, grass, pasture, (7) disturbed land, (8) builtup land, (9) strip mines, and (10) water. These land use categories were delineated at a scale of approximately 1:20,000 on the line printer output. Land use delineations were also made using the General Electric IMAGE 100 interactive analysis system.

Mcmurtry, G. J.↗

NASA NTRS1989

The discovery of a highly polarized bipolar nebula

During a search for the optical counterparts of IRAS sources whose flux peaks at 25 microns, a small faint bipolar nebula was discovered in Monoceros at the position of IRAS 07131-0147. The CCD images display the object's considerable structure. The central star seems relatively free of closeby nebulosity: the two lobes have a bow-tie structure with those parts nearest to the star consisting of series of small knots. The outer parts of the lobes seem to be made up of filaments streaming away from knots. On the basis of its optical spectrum, the central star was classified as a M5-6 giant. In the IRAS color classification scheme of Van der Veen and Habing (1988), the central star is VIb which indicates that there are distinct hot and cold components of circumstellar dust and that the mass loss process may have temporarily abated. Therefore, it is proposed that the object is in the post main sequence stage of evolution and is a protoplanetary nebulae. Young protoplanetary nebulae have totally obscured central stars illuminating reflective lobes whereas older ones such as M2-9 have lobes seen in emission from gas ionized by the central hot star which is clearly visible. Since the central object of IRAS07131-0147 is a relatively unobscured late type star and the lobes are seen only by reflection, it is suggested that this nebula is a protoplanetary nebula in an evolutionary stage intermediate between that of CRL2688 and M2-9.

Wolstencroft, Ramon D.↗

NASA NTRS2020

Huntsville Urban Development: Utilizing NASA Earth Observations to Evaluate Urban Tree Canopy and Land Surface Temperature for Green Infrastructure Development and Urban Heat Mitigation in Huntsville, AL

Huntsville, Alabama’s population has grown by approximately 11% since 2010, due in part to the city’s advancing engineering industry. Rapid urban growth negatively impacts the environment by decreasing tree canopy cover and increasing impervious surface cover, which can intensify the urban heat island effect. To examine the impacts of this urban growth on the environment, the team partnered with the City of Huntsville to utilize Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and the International Space Station’s Global Ecosystems Dynamic Investigation (GEDI) and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). The team utilized these Earth observations in combination with ancillary datasets to create a suite of end products to assist in mitigating the effects of extreme heat due to urban expansion and tree canopy loss. Annual land surface temperature (LST) was calculated and land cover classes were derived through supervised and threshold classification methods to distinguish trees, other vegetation types, impervious surfaces, and water. From 2010 to 2019, LST increased approximately 4 °F for all census tracts within the city and the total amount of tree cover increased by approximately 3%. The findings will aid the city in future decision-making processes by indicating areas that would benefit from increased green infrastructure.

Greta Paris↗

NASA NTRS2020

Huntsville Urban Development: Utilizing NASA Earth Observations to Evaluate Urban Tree Canopy and Land Surface Temperature for Green Infrastructure Development and Urban Heat Mitigation in Huntsville, Alabama

Greta Paris↗

NASA NTRS1985

Analysis of data acquired by Shuttle Imaging Radar SIR-A and Landsat Thematic Mapper over Baldwin County, Alabama

Seasonally compatible data collected by SIR-A and by Landsat 4 TM over the lower coastal plain in Alabama were coregistered, forming a SIR-A/TM multichannel data set with 30 m x 30 m pixel size. Spectral signature plots and histogram analysis of the data were used to observe data characteristics. Radar returns from pine forest classes correlated highly with the tree ages, suggesting the potential utility of microwave remote sensing for forest biomass estimation. As compared with the TM-only data set, the use of SIR-A/TM data set improved classification accuracy of the seven land cover types studied. In addition, the SIR-A/TM classified data support previous finding by Engheta and Elachi (1982) that microwave data appear to be correlated with differing bottomland hardwood forest vegetation as associated with varying water regimens (i.e., wet versus dry).

Wu, S.-T.↗

NASA NTRS2021

Mangrove Mapping with the Freeman-Durden Polarimetric Decomposition and Insar Coherence from ALOS-2

We map mangrove extents in Pongara National Park, Gabon using the Freeman-Durden Decomposition and InSAR Coherence derived from ALOS-2 imagery. Specifically, we obtain a land cover map derived from both this polarimetric decom-position and a 14-day repeat-pass coherence. Our classification model and results are highly interpretable based on a depth 2 decision tree. We further illustrate the correlation between InSAR coherence and height obtaining rough man-grove height estimates from TanDEM-X data. From our results, we observe that repeat-pass interferometric coherence provides invaluable information about mangrove extents and coastal forests. The clear identification of mangrove extents presents a significant opportunity for NISAR, which will pro-vide 12-day repeat pass images over coastal areas globally.

Tien-Hao Liao↗

NASA NTRS1997

A scale space feature based registration technique for fusion of satellite imagery

Feature based registration is one of the most reliable methods to register multi-sensor images (both active and passive imagery) since features are often more reliable than intensity or radiometric values. The only situation where a feature based approach will fail is when the scene is completely homogenous or densely textural in which case a combination of feature and intensity based methods may yield better results. In this paper, we present some preliminary results of testing our scale space feature based registration technique, a modified version of feature based method developed earlier for classification of multi-sensor imagery. The proposed approach removes the sensitivity in parameter selection experienced in the earlier version as explained later.

Raghavan, Srini↗

NASA NTRS1991

Kinematics and ionization of extended gas in active galaxies. VI - The Seyfert 2 galaxy NGC 1386

Results are presented of direct imaging with a CCD detector and long-slit medium- and low-dispersion spectroscopy of the spatially extended narrow-line-emitting gas in the nearby Seyfert 2 galaxy NGC 1386. The distribution, kinematics, and ionization structure of the excited gas were examined in the regions surrounding the nucleus out to a distance of about 1.0. Results present evidence for a spiral structure in this galaxy and for the presence of H II regions in the disk (supporting the classification of NGC 1386 as a Sa galaxy).

Weaver, K. A.↗

NASA NTRS1999

Hyperspectral Remote Sensing and Ecological Modeling Research and Education at Mid America Remote Sensing Center (MARC): Field and Laboratory Enhancement

The purpose of this project was to establish a new hyperspectral remote sensing laboratory at the Mid-America Remote sensing Center (MARC), dedicated to in situ and laboratory measurements of environmental samples and to the manipulation, analysis, and storage of remotely sensed data for environmental monitoring and research in ecological modeling using hyperspectral remote sensing at MARC, one of three research facilities of the Center of Reservoir Research at Murray State University (MSU), a Kentucky Commonwealth Center of Excellence. The equipment purchased, a FieldSpec FR portable spectroradiometer and peripherals, and ENVI hyperspectral data processing software, allowed MARC to provide hands-on experience, education, and training for the students of the Department of Geosciences in quantitative remote sensing using hyperspectral data, Geographic Information System (GIS), digital image processing (DIP), computer, geological and geophysical mapping; to provide field support to the researchers and students collecting in situ and laboratory measurements of environmental data; to create a spectral library of the cover types and to establish a World Wide Web server to provide the spectral library to other academic, state and Federal institutions. Much of the research will soon be published in scientific journals. A World Wide Web page has been created at the web site of MARC. Results of this project are grouped in two categories, education and research accomplishments. The Principal Investigator (PI) modified remote sensing and DIP courses to introduce students to ii situ field spectra and laboratory remote sensing studies for environmental monitoring in the region by using the new equipment in the courses. The PI collected in situ measurements using the spectroradiometer for the ER-2 mission to Puerto Rico project for the Moderate Resolution Imaging Spectrometer (MODIS) Airborne Simulator (MAS). Currently MARC is mapping water quality in Kentucky Lake and vegetation in the Land-Between-the Lakes (LBL) using Landsat-TM data. A Landsat-TM scene of the same day was obtained to relate ground measurements to the satellite data. A spectral library has been created for overstory species in LBL. Some of the methods, such as NPDF and IDFD techniques for spectral unmixing and reduction of effects of shadows in classifications- comparison of hyperspectral classification techniques, and spectral nonlinear and linear unmixing techniques, are being tested using the laboratory.

Cetin, Haluk↗

NASA NTRS2008

New Insights into the Composition and Texture of Lunar Regolith Using Ultrafast Automated Electron-Beam Analysis

Sieved grain mounts of Apollo 16 drive tube samples have been examined using QEMSCAN - an innovative electron beam technology. By combining multiple energy-dispersive X-ray detectors, fully automated control, and off-line image processing, to produce digital mineral maps of particles exposed on polished surfaces, the result is an unprecedented quantity of mineralogical and petrographic data, on a particle-by-particle basis. Experimental analysis of four size fractions (500-250 microns, 150-90 microns, 75-45 microns and < 20 microns), prepared from two samples (64002,374 and 64002,262), has produced a robust and uniform dataset which allows for the quantification of mineralogy; texture; particle shape, size and density; and the digital classification of distinct particle types in each measured sample. These preliminary data show that there is a decrease in plagioclase modal content and an opposing increase in glass modal content, with decreasing particle size. These findings, together with data on trace phases (metals, sulphides, phosphates, and oxides), provide not only new insights into the make-up of lunar regolith at the Apollo 16 landing site, but also key physical parameters which can be used to design lunar simulants, and compute Figures of Merit for each material produced.

Rickman, Doug↗

NASA NTRS1974

Inventory of forest and rangeland and detection of forest stress

The author has identified the following significant results. Controlled visual interpretation of one ERTS-1 scene taken at the peak of the growing season has indicated that classification to the ECOCLASS Series level is not entirely satisfactory. For five forest classes, aspen, Douglas-fir, lodgepole pine, ponderosa pine, and Spruce/fir, correct identification ranged from 60 to 70 percent. With the exception of shortgrass and wet shrubby meadow classes in the nonforest categories (81 and 100 percent correct, respectively), correct identification of the nonforest classes is so far unacceptable. The low accuracies are believed due to: (1) edge effects due to ecotones between plant community classes with apparent similar image characteristics; (2) confounding effects of amount of plant crown cover and ground surface material in the scene; and (3) variable land slope degree and aspect as it affects the image signature.

Heller, R. C.↗

NASA NTRS2017

Nominal 30-M Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, is a starting point to develop high-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based Geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94, with a producers accuracy of 85.9 (or omission error of 14.1), and users accuracy of 68.5 (commission error of 31.5) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015.

Cropland mapping; cropland areas; 30-m; Landsat-8;↗

NASA NTRS1981

Elevation of a cane-growing area of the state of Sao Paulo using LANDSAT data

Images at a scale of 1:250.000 were visually interpreted for identification and area estimates of sugar cane plantations in Sao Paulo. The basic criteria for crop identification were the spectral characteristics of channels 5 and 7 and their temporal variations observed from different LANDSAT passes. Using this technique, it was possible to map the sugar cane areas as well as the sugar cane already harvested. An area of 801,950 hectares was estimated within the study area. The confidence interval of correct classification ranged from 87.11% to 94.71%.

Dejesusparada, N.↗

NASA NTRS1992

Using calibrated SAR data

A number of approaches for calibrating SAR imaging data including data from multipolarization and multifrequency systems developed at the Jet Propulsion Laboratory are discussed. It is shown how calibrated SAR data can be used to improve the understanding of the physical properties of the earth's surface layer and how calibration uncertainties and system noise should be handled by the SAR data user. Examples of calibrated SAR data from the NASA/JPL DC-8 SAR are used to demonstrate use of calibrated data to monitor temporal change and to improve the classification of land cover type.

Freeman, Anthony↗