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Research performed in the accuracy assessment of remotely sensed data is updated and reviewed. The use of discrete multivariate analysis techniques for the assessment of error matrices, the use of computer simulation for assessing various sampling strategies, and an investigation of spatial autocorrelation techniques are examined.
Recent advances in remote sensing led to the development of several techniques to obtain image information. These techniques as effective tools in geological maping are analyzed. A strategy for optimizing the images in basic geological surveying is presented. It embraces as integrated analysis of spatial, spectral, and temporal data through photoptic (color additive viewer) and computer processing at different scales, allowing large areas survey in a fast, precise, and low cost manner.
Scientific requirements for terrestrial remote-sensing satellites in the 1990's and instrument packages to meet them are discussed, summarizing the results of a preliminary study conducted by the NASA System Z Science and Mission Requirements Working Group. Major questions regarding atmospheric chemistry and circulation, global climate, ocean dynamics, biogeochemical cycles, ice-budget and hydrological cycles, continental geology, biomass dynamics, and land cover and use are listed along with the observational frequency and resolution necessary to answer them, and an instrument use matrix is presented. Large essentially permanent man-tended arrays of instruments serving multiple functions and linked to the users via advanced information-processing networks and (possibly including expert systems) are recommended, and a basic payload and two supplemental packages are outlined.
Remote sensing techniques have been used to detect stress in vegetation, but they have not been very successful for identifying which environmental factors cause the vegetation to appear stressed. Controlled comparisons of spectral characteristics of plant communities experiencing known combinations of stresses were designed to examine this problem. Landsat TM, NS-001 TMS, CIR, and AIS imagery was acquired for six stressed areas in the Carolina slate belt. Preliminary results indicated that the areas of stressed vegetation were identifiable, and that the stressed communities appeared to be undergoing early fall leaf senescence. The AIS data seemed to have the greatest potential for identifying differences among plant community spectra, but calibration will be necessary before these differences can be evaluated.
What remote sensing is, how and why it is used, and why more people should be trained in the field is discussed. Suggestions are offered concerning differences in teaching technicians, the general public, and children in schools.
A method for estimating the cloud effect on remote sensing is described, and it is applied to cloudiness in several climatological conditions. The algorithm is based on the Haurwitz (1948) measurements of the cloud layer transmission of solar radiation for an overcast sky and on an empirical interpolation of data for broken cloudiness by Pochop et al. (1968). Radiances for a sunny area observed directly from space and through a cloud, and for a shady area observed from space and through a cloud are computed. Methods for detecting the cloud effect from satellite images are discussed. The relation between cloud reflectance and cloud size is studied. It is observed that the subpixel clouds affect the detected radiance and vegetation index, and the effect depends on the cloud types and the dependence of the cloud transmissivity on cloud fraction. Procedures for decreasing or eliminating cloud effect are examined.
Initial results of a pilot study to link remotely sensed information on tropical forest loss to field-based information on species endangerment are presented. LANDSAT MSS imagery was used to estimate net forest removal, regrowth, and forest edges in Mabira Forest in Uganda during 15-year period. Although four distinct types of closed tropical forest based on structure of dominance could be recognized on the ground, the types could not be distinguished according to differences in spectral reflectance in the MSS bands; yet the closed tropical forest could be readily distinguished from nonforest vegetation types. It is concluded that the utilization of MSS for this purpose is likely to be most successful when combined with larger-scale resolution imagery, such as TM, SPOT, and aerial photography, as well as existing vegetation maps. Of significant promise, however, is the ability to differentiate disturbance regimes, regrowth, and exterior and interior edges of tropical closed forest by the combined use of MSS imagery and ground data.
Timing technique enables recording of images in laser remote sensing of environment. Timings of laser pulse, shutter, and video frame synchronized. Technique also used in industrial or commercial processes requiring clear sighting of laser beams relative to their surroundings.
Field measurements for the Geologic Remote Sensing Field Experiment (GRSFE) were concentrated in the Lunar Lake area of Nevada. The GRSFE data are meant to be used in a variety of investigations, including tests of multispectral radiative transfer models for scattering and emission from planetary surfaces in support of the Earth Observing System (EOS), Mars Observer, and Magellan Missions. Studies will also be pursued to establish the neotectonic and paleoclimatic history of the arid southwestern United States. The data will also be used to support Mars Rover Sample Return (MRSR) simulation studies.
In the application of remote sensing at optical wavelengths to vegetated surfaces from satellite borne high resolution instruments, an understanding of the various physical mechanisms that contribute to the measured signal is important. A numerical method of solving the radiative transfer equation in three dimensions is reported. The reliability of coding and accuracy of the algorithm are evaluated by benchmarching. Parametrization of the methods and results of a simulation are presented. The method is tested with experimental data of canopy bidirectional reflectance factors. The effect of spatial heterogeneity on the relationship between the simple ratio and normalized vrs absorbed Photosynthetically Active Radiation (PAR) is discussed.
Scattering models were developed in support of microwave remote sensing of earth terrains with particular emphasis on model applications to airborne Synthetic Aperture Radar measurements of forest. Practically useful surface scattering models based on a solution of a pair of integral equations including multiple scattering effects were developed. Comparisons of these models with controlled scattering measurements from statistically known random surfaces indicate that they are valid over a wide range of frequencies. Scattering models treating a forest environment as a two and three layered media were also developed. Extensive testing and comparisons were carried out with the two layered model. Further studies with the three layered model are being carried out. A volume scattering model valid for dense media such as a snow layer was also developed that shows the appropriate trend dependence with the volume fraction of scatterers.
The paper examines software techniques for classifying remotely sensed data such that the number of computational steps and the amount of resources are bounded. The combination of both neural network and expert system methodology for classifying these data based on land use/land cover categories is examined. The method involves pipelining images through a neural net for initial classification and then through the expert system which resolves the ambiguous classifications. As with any pipeline, every component must have approximately equivalent run-times or otherwise a bottleneck will occur. If real-time is a requirement, each of the components must execute within a bounded number of steps. Attention is focused on the real-time system technique, which is argued to prevent a bottleneck for this data classification application.
A remote sensing, medical satellite (MEDSAT) aids in the control of carrier (vector) borne disease. The prototype design is a light satellite to test for control of malaria. The design features a 340-kg satellite with visual/IR and SAR sensors in a low inclination orbit observing a number of worldwide test sites. The approach is to use four-band visual/IR and dual-polarized L-band SAR images obtained from MEDSAT in concert with in-situ data to estimate the temporal and spatial variations of malaria risk. This allows public health resources to focus on the most vulnerable areas at the appropriate time. It is concluded that a light-satellite design for a MEDSAT satellite with a Pegasus launch is feasible.
A remote sensing research agenda designed to expand the knowledge of the spatial distribution of species richness and its ecological determinants and to predict its response to global change is proposed. Emphasis is placed on current methods of mapping species richness of both plants and animals, hypotheses concerning the biophysical factors believed to determine patterns of species richness, and anthropogenic processes causing the accelerating rate of extinctions. It is concluded that biodiversity should be incorporated more prominently into the global change and earth system science paradigms.
This viewgraph presentation profiles the US remote sensing industry based on responses to a survey by 1450 industry professionals. The presentation divides the industry into three sectors: academic, commercial, and government; the survey results from each are covered in a section of the presentation. The presentation also divides survey results on user needs into the following sectors: spatial resolution, geolocation accuracy; elevation accuracy, area coverage, imagery types, and timeliness. Data, information, and software characteristics are also covered in the presentation.
Spatial resolution of panchromatic imagery from commercial remote sensing satellites was characterized based on edge response measurements using edge targets and the tilted-edge technique. Relative Edge Response (RER) was estimated as a geometric mean of normalized edge response differences measured in two directions of image pixels at points distanced from the edge by -0.5 and 0.5 of ground sample distance. RER is one of the engineering parameters used in the General Image Quality Equation to provide predictions of imaging system performance expressed in terms of the National Imagery Interpretability Rating Scale (NIIRS). By assuming a plausible range of signal-to-noise ratio and assessing the effects of Modulation Transfer Function compensation, the NIIRS estimates were made and then compared with vendor-provided values and evaluations conducted by the National Geospatial-Intelligence Agency.
NASA has been collecting massive amounts of remote sensing data about Earth's systems for more than a decade. Missions are selected to be complementary in quantities measured, retrieval techniques, and sampling characteristics, so these datasets are highly synergistic. To fully exploit this, a rigorous methodology for combining data with heterogeneous sampling characteristics is required. For scientific purposes, the methodology must also provide quantitative measures of uncertainty that propagate input-data uncertainty appropriately. We view this as a statistical inference problem. The true but notdirectly- observed quantities form a vector-valued field continuous in space and time. Our goal is to infer those true values or some function of them, and provide to uncertainty quantification for those inferences. We use a spatiotemporal statistical model that relates the unobserved quantities of interest at point-level to the spatially aggregated, observed data. We describe and illustrate our method using CO2 data from two NASA data sets.
This work relates to the generic problem of remote active imaging; that is, a source illuminates a target of interest and a receiver collects the scattered light off the target to obtain an image. Conventional imaging systems consist of an imaging lens and a high-resolution detector array [e.g., a CCD (charge coupled device) array] to register the image. However, conventional imaging systems for remote sensing require high-quality optics and need to support large detector arrays and associated electronics. This results in suboptimal size, weight, and power consumption. Computational ghost imaging (CGI) is a computational alternative to this traditional imaging concept that has a very simple receiver structure. In CGI, the transmitter illuminates the target with a modulated light source. A single-pixel (bucket) detector collects the scattered light. Then, via computation (i.e., postprocessing), the receiver can reconstruct the image using the knowledge of the modulation that was projected onto the target by the transmitter. This way, one can construct a very simple receiver that, in principle, requires no lens to image a target. Ghost imaging is a transverse imaging modality that has been receiving much attention owing to a rich interconnection of novel physical characteristics and novel signal processing algorithms suitable for active computational imaging. The original ghost imaging experiments consisted of two correlated optical beams traversing distinct paths and impinging on two spatially-separated photodetectors: one beam interacts with the target and then illuminates on a single-pixel (bucket) detector that provides no spatial resolution, whereas the other beam traverses an independent path and impinges on a high-resolution camera without any interaction with the target. The term ghost imaging was coined soon after the initial experiments were reported, to emphasize the fact that by cross-correlating two photocurrents, one generates an image of the target. In CGI, the measurement obtained from the reference arm (with the high-resolution detector) is replaced by a computational derivation of the measurement-plane intensity profile of the reference-arm beam. The algorithms applied to computational ghost imaging have diversified beyond simple correlation measurements, and now include modern reconstruction algorithms based on compressive sensing.