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The current status of remote sensing techniques developed for the screwworm eradication program of the Mexican-American Screwworm Eradication Commission was reported. A review of the type of data and equipment used in the program is presented. Future applications of remote sensing techniques are considered.
The theoretical concepts underlying remote sensing of estuarine parameters using laser excitation are examined. The concepts are extended to include Mie scattering as a measure of the total suspended solids and to develop the water Raman signal as an internal standard. Experimental validation of the theory was performed using backscattered laser light from a laboratory tank to simulate a remote-sensing geometry. Artificially prepared sediments and biological cultures were employed to check specific aspects of the theory under controlled conditions. Natural samples gathered from a variety of water types were also analyzed in the tank to further enhance the simulation. The results indicate that it should be possible to remotely quantify total suspended solids, dissolved organics, attenuation coefficient, chlorophyll a, and phycoerythrin in estuarine water using laser excitation.
The needs of the remote sensing research and application community which will be served by the Earth Observing System (EOS) and space station, including associated polar and co-orbiting platforms are examined. Research conducted was used to extend and expand existing remote sensing research activities in the areas of georeferenced information systems, machine assisted information extraction from image data, artificial intelligence, and vegetation analysis and modeling. Projects are discussed in detail.
Tremendous advances in remote sensing technology and computing power over the last few decades are now providing scientists with the opportunity to investigate, measure, and model environmental patterns and processes with increasing confidence. Such advances are being pursued by the Nebraska Remote Sensing Facility, which consists of approximately 30 faculty members and is very competitive with other institutions in the depth of the work that is accomplished. The development of this facility targeted at applications, commercialization, and education programs in the area of precision agriculture provides a unique opportunity. This critical area is within the scope of NASA goals and objectives of NASA s Applications, Technology Transfer, Commercialization, and Education Division and the Earth Science Enterprise. This innovative integration of Aerospace (Aeronautics) Technology Enterprise applications with other NASA enterprises serves as a model of cross-enterprise transfer of science with specific commercial applications.
The research conducted in the United States for the past 20 years with the objective of developing automated satellite remote sensing for monitoring the earth's major food crops is reviewed. The highlights of this research include a National Academy of Science study on the applicability of remote sensing monitoring given impetus by the introduction in the mid-1960's of the first airborne multispectral scanner (MSS); design simulations for the first earth resource satellite in 1969; and the use of the airborne MSS in the Corn Blight Watch, the first large application of remote sensing in agriculture, in 1970. Other programs discussed include the CITAR research project in 1972 which established the feasibility of automating digital classification to process high volumes of Landsat MSS data; the Large Area Crop Inventory Experiment (LACIE) in 1974-78, which demonstrated automated processing of Landsat MSS data in estimating wheat crop production on a global basis; and AgRISTARS, a program designed to address the technical issues defined by LACIE.
We report recent progress derived from comparison of global CO2 flux and transport models with new remote sensing and other sources of CO2 data including those from satellite. The overall objective of this activity is to improve the process models that represent our understanding of the workings of the atmospheric carbon cycle. Model estimates of CO2 surface flux and atmospheric transport processes are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, to provide the basic framework for carbon data assimilation, and ultimately for future projections of carbon-climate interactions. Models can also be used to test consistency within and between CO2 data sets under varying geophysical states. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 2000 through 2009. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at 1x1 degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-3), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to remote sensing observations from TCCON, GOSAT, and AIRS as well as relevant in situ observations. Examples of the influence of key process representations are shown from both forward and inverse model comparisons. We find that the model can resolve much of the synoptic, seasonal, and interannual variability in the observations, although reasons for persistent discrepancies in northern hemisphere vegetation uptake are examined. At this time, we do not find any serious shortcomings in the model transport representation, but this is still the subject of close scrutiny. In general, the fidelity of these simulations leads us to anticipate incorporation of real-time, highly resolved remote sensing and other observations into quantitative analyses that will reduce uncertainty in CO2 fluxes and revolutionize our understanding of the key processes controlling atmospheric CO2 and its evolution with time.
A study of agricultural applications of remote sensing with a major US agricultural firm was undertaken in mid-1973. The study continued for eighteen months, and covered the areas of crop monitoring and management as well as large scale crop inventories. Pilot programs in the application of aircraft remote sensing and LANDSAT data were conducted. An operational aircraft survey program for ranch management has subsequently been implemented by the agricultural firm. LANDSAT data was successfully used to produce a ninety-seven percent accurate inventory of cotton over 4.8 million acres of California's San Joaquin Valley.
The use of airborne remote sensing techniques to: (1) detect drainage problem areas, (2) delineate the problem in terms of areal extent, depth to the water table, and presence of excessive salinity, and (3) evaluate the effectiveness of existing subsurface drainage facilities, is discussed. Experimental results show that remote sensing, as demonstrated in this study and as presently constituted and priced, does not represent a practical alternative as a management tool to presently used visual and conventional photographic methods in the systematic and repetitive detection and delineation of wetlands.
A comprehensive overview is presented on how remote sensing technology has been applied to tropical forest monitoring over the past 20 years. Research needs for monitoring the condition and extent of tropical forests are suggested. The discussion focuses on nonphotographic sensors, especially those on orbiting satellites. Several remote sensing approaches to tropical forest monitoring are outlined, including NOAA AVHRR, Landsat MSS, the Landsat Thematic Mapper, SPOT-1, and Synthetic Aperture Radar. Suggested research needs are addressed, along with discussions on the use of Geographic Information Systems, and multistage and multisensor approaches in data analysis and acquisition. It is concluded that additional research and technique development is urgently needed to advance the utility of remotely sensed data for tropical forest monitoring. However, there is sufficient information available now to prototype a global tropical forest monitoring system that would utilize current satellite sensors complemented with airborne sensors for detailed measurements on sample locations.
A NASA method is described for forecasting remote sensing needs in the last decade of this century and for planning the necessary space system technology R&D. Five- and ten-year plans for earth observations in various disciplines are extrapolated forward to circa 1995 via a scenario which envisions major advances in remote sensing and information management. Space system studies identify the 'technology drivers', and development programs are initiated to develop the enabling technology. An example is given for a multipurpose large-aperture microwave radiometer spacecraft.
Various technologies for facilitating analysis of large remote sensing and geolocation datasets to identify features of interest are described herein. A search query can be submitted to a computing system that executes searches over a geospatial temporal semantic (GTS) graph to identify features of interest. The GTS graph comprises nodes corresponding to objects described in the remote sensing and geolocation datasets, and edges that indicate geospatial or temporal relationships between pairs of nodes in the nodes. Trajectory information is encoded in the GTS graph by the inclusion of movable nodes to facilitate searches for features of interest in the datasets relative to moving objects such as vehicles.
Existing, planned and future NASA capabilities in the field of remote sensing satellites are reviewed in relation to the use of remote sensing techniques for the identification of irrigated lands. The status of the currently operational Landsat 2 and 3 satellites is indicated, and it is noted that Landsat D is scheduled to be in operation in two years. The orbital configuration and instrumentation of Landsat D are discussed, with particular attention given to the thematic mapper, which is expected to improve capabilities for small field identification and crop discrimination and classification. Future possibilities are then considered, including a multi-spectral resource sampler supplying high spatial and temporal resolution data possibly based on push-broom scanning, Shuttle-maintained Landsat follow-on missions, a satellite to obtain high-resolution stereoscopic data, further satellites providing all-weather radar capability and the Large Format Camera.
The contents of 17 major textbooks and reference guides on remote sensing technology are listed and compared. As a group, these books concentrate more on principles of the electromagnetic spectrum, sensors and data processing, than on applications. Secondly, it appears that basic photointerpretation receives considerable attention, but that photogrammetry does not. Four texts which provide well-rounded treatments of the systematics of remote sensing are suggested.
The airborne microwave remote sending measurements obtained by the Langley Research Center in support of the 1979 Sea-Ice Radar Experiment (SIRE) in the Beaufort and Bering Seas are discussed. The remote sensing objective of SIRE was to define correlations between both active and passive microwave signatures and ice phenomena assocated with practical applications in the Arctic. The instruments used by Langley during SIRE include the stepped frequency microwave radiometer (SFMR), the airborne microwave scatterometer (AMSCAT), the precision radiation thermometer (PRT-5), and metric aerial photography. Remote sensing data are inventoried and cataloged in a user-friendly format. The data catalog is presented as time-history plots when and where data were obtained as well as the sensor configuration.
The use of expert systems for the interpretation of remote-sensing data is discussed. Expert-system architectures are described; the systems in use, under development, or proposed in various scientific and industrial fields are listed in a table; and three image-interpretation systems are briefly characterized. It is found that expert systems have the potential to perform repetitive interpretation of image data compiled from various remote-sensing satellites, especially to detect changes over time as in the case of deforestation. A system comprising an image interpreter, change interpreter, a cause interpreter, and a global data base is outlined and illustrated with a diagram.
Remote sensing techniques may be used to assist in regional mapping.
A cooperative commercial development project designed to focus on cost-effective and practical applications of satellite remote sensing in forest management is discussed. The project, initiated in September, 1988 is being executed in three phases: (1) development of a forest resource inventory and geographic information system (GIS) updating systems; (2) testing and evaluation of remote-sensing products against forest industry specifications; and (3) integration of remote-sensing services and products in an operational setting. An advisory group represented by eleven major forest-product companies will provide direct involvement of the target market. The advisory group will focus on the following questions: Does the technology work for them? How can it be packaged to provide the needed forest-management information? Can the products and information be provided in a cost-effective manner?
The quantitative assessment of cloud cover and atmospheric constituents improves our ability to exploit the climate feedback into the Amazon basin. In the 21st century, three droughts have already occurred in the Amazonia (e.g. 2005, 2010, 2015), inducing regional changes in the seasonal patterns of atmospheric constituents. In addition to climate, the atmospheric dynamic and attenuation properties are long-term challenges for satellite-based remote sensing of this ecosystem: high cloudiness, abundant water vapor content and biomass burning season. Therefore, while climatology analysis supports the understanding of atmospheric variability and trends, it also offers valuable insights for remote sensing applications. In this study, we evaluate the seasonal and interannual variability of cloud cover and atmospheric constituents (aerosol loading, water vapor and ozone content) over the Amazon basin, with focus on both climate analysis and remote sensing implications. We take the advantage of new atmosphere daily products at 1 km resolution derived from Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data. An intercomparison of Aerosol Robotic Network (AERONET) and MAIAC aerosol optical depth (AOD) and columnar water vapor (CWV) showed quantitative information with a correlation coefficient higher than 0.81. Our results show distinct regional patterns of cloud cover across the Amazon basin: northwestern region presets a persistent cloud cover (> 80%) throughout the year, while low cloud cover (0-20%) occurs in the southern Amazon during the dry season. The cloud-free period in the southern Amazon is followed by an increase in the atmospheric burden due to fire emissions. Our results reveal that AOD records are changing in terms of area and intensity. During the 2005 and 2010 droughts, the positive AOD anomalies (δ > 0.1) occurred over 39.03% (240.3 million ha) and 27.14% (165.99 million ha) of total basin in the SON season, respectively. In contrast, the recent 2015 drought occurred towards the end of year (October through December) and these anomalies were observed over 23.72% (145 million ha) affecting areas in the central and eastern Amazon unlike previous droughts. The water vapor presents high concentration values (4.0-5.0 g/sq.cm) in the wet season (DJF), while we observed a strong spatial gradient from northwestern to southeastern of the basin during the dry season. In addition, we also found a positive trend of water vapor content ( ∼0.3 g/sq.cm) between 2000 and 2015. The total ozone typically varies between 220 and 270 DU, and it has a seasonal change of ∼25-35 DU from wet season to dry season caused by large emissions of ozone precursors and long-range transport. Finally, while this study contributes to climatological analysis of atmospheric constituents, the remote sensing users can also understand the regional constraints caused by atmospheric attenuation, such as high aerosol loading and cloud obstacles for surface observations.