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The Earth Resources Technology Satellites, ERTS-A and -B, are a significant part of the NASA Earth Resources Survey Program. A general description of the requirements and implementation of the ERTS system - sensors, spacecraft, operational control, and data processing developed to acquire, process, and distribute multi-spectral images of the earth's surface is presented.
Use of LANDSAT data and an interaction system is considered for identifying and measuring small scale compact human settlements (villages) for demographic and anthropological studies. Because village components are not uniformly distributed within any one village, they apparently are multimodal, spectrally. Therefore, the functions of location and enumeration are kept separate. Measurement of a known village is compared with CCT response.
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 image dissector sensor for the Earth Resources Program is evaluated using contrast and reflectance data. The ground resolution obtainable for low contrast at the targeted signal to noise ratio of 1.8 was defined. It is concluded that the system is capable of achieving the detection of small, low contrast ground targets from satellites.
NASA-Ames Research Center has investigated the role and performance capabilities of the Landsat Multi-Spectral Scanner (MSS) for forest policy analysis for the past four years in cooperation with the California Department of Forestry. A thorough series of studies, from a statewide land cover map to smaller, highly detailed studies including collateral data, have been conducted with a view to comprehensive forest policy needs. The strengths and limitations of MSS data have been evaluated. Some observations about the information needed from new satellite sensors such as the Thematic Mapper are discussed against this background.
The recording system discussed is required to record and reproduce wideband data from either of the two primary Earth Resources Technology Satellite sensors: Return Beam Vidicon (RBV) camera or Multi-Spectral Scanner (MSS). The camera input is an analog signal with a bandwidth from dc to 3.5 MHz; this signal is accommodated through FM recording techniques which provide a recorder signal-to-noise ratio in excess of 39 db, black-to-white signal/rms noise, over the specified bandwidth. The MSS provides, as initial output, 26 narrowband channels. These channels are multiplexed prior to transmission, or recording, into a single 15 Megabit/second digital data stream. Within the recorder, the 15 Megabit/second NRZL signal is processed through the same FM electronics as the RBV signal, but the basic FM standards are modified to provide an internal, 10.5 MHz baseland response with signal-to-noise ratio of about 25 db. Following FM demodulation, however, the MSS signal is digitally re-shaped and re-clocked so that good bit stability and signal-to-noise exist at the recorder output.
Both LANDSAT 2 and LANDSAT 3 are currently in operational status following recovery from technical problems. The LANDSAT D program continues NASA's R&D with the dual objectives of assessing the capabilities of the thematic mapper sensor and evaluating the requirements for an operational land observing system. LANDSAT D' will not be launched until LANDSAT D ceases operation, to provide data continuity through 1988. Future NASA remote sensing programs will continue to emphasize research, technology development, cooperative projects with end users of data and cooperation with the National Oceanic and Atmospheric Administration (NOAA) on transition to an operational land observing system. Technology development should extend the results of research to practical applications through improved ground data processing and information extraction techniques and development of new sensors such as the solid state multi-spectral linear array.
Images from the Earth Resources Technology Satellite (ERTS-1) contain data useful in studies of hydrogeology, geomorphology, and paleoclimatology. Sixteen Return Beam Vidicon (RBV) images and 15 Multi-Spectral Scanner (MSS) images were studied. These covered deserts and semidesert areas in southwestern Bolivia, northwestern Argentina, northern Chile, and southeastern Peru from July 30 to November 17, 1972. During the first 3 months after launching, high-quality cloud-free imagery was obtained over approximately 90 percent of the region of interior drainage, or an area of 170,000 square miles.
Three tasks related to soil moisture sensing at microwave wavelengths were undertaken: (1) analysis of data at L, X and K sub 21 band wavelengths over bare and vegetated fields from the 1975 NASA sponsored flight experiment over Phoenix, Arizona; (2) modeling of vegetation canopy at microwave wavelengths taking into consideration both absorption and volume scattering effects; and (3) investigation of overall atmospheric effects at microwave wavelengths that can affect soil moisture retrieval. Data for both bare and vegetated fields are found to agree well with theoretical estimates. It is observed that the retrieval of surface and near surface soil moisture information is feasible through multi-spectral and multi-temporal analysis. It is also established that at long wavelengths, which are optimal for surface sensing, atmospheric effects are generally minimal. At shorter wavelengths, which are optimal for atmosheric retrieval, the background surface properties are also established.
Automatic stratification of forested land from ERTS-1 data provides a valuable tool for resource management. The results are useful for wood product yield estimates, recreation and wild life management, forest inventory and forest condition monitoring. Automatic procedures based on both multi-spectral and spatial features are evaluated. With five classes, training and testing on the same samples, classification accuracy of 74% was achieved using the MSS multispectral features. When adding texture computed from 8 x 8 arrays, classification accuracy of 99% was obtained.
Research in the application of remotely sensed data from LANDSAT or other airborne platforms to the efficient management of a large timber based forest industry was divided into three phases: (1) establishment of a photo/ground sample correlation, (2) investigation of techniques for multi-spectral digital analysis, and (3) development of a semi-automated multi-level sampling system. To properly verify results, three distinct test areas were selected: (1) Jacksonville Mill Region, Lower Coastal Plain, Flatwoods, (2) Pensacola Mill Region, Middle Coastal Plain, and (3) Mississippi Mill Region, Middle Coastal Plain. The following conclusions were reached: (1) the probability of establishing an information base suitable for management requirements through a photo/ground double sampling procedure, alleviating the ground sampling effort, is encouraging, (2) known classification techniques must be investigated to ascertain the level of precision possible in separating the many densities involved, and (3) the multi-level approach must be related to an information system that is executable and feasible.
Landsat digital data are presently available by frames whose size and location are determined by satellite orbit. The utility of Landsat data was increased by the image processing support for JPL's planetary program which provided the basic software and procedures necessary for image mosaicking. The computer software has been extended to perform this task on Landsat Multi-spectral scanner imagery and a ten frame digital mosaic of the Southern California desert has been completed. Major processing steps include location of geographic points in the digital frame and of common geographic points in adjacent frames, conversion to a map projection, lateral 'rubber sheet' correction of the digital frames, brightness correction of adjacent frames, and mosaicking of the frames to eliminate overlap and produce a single large frame.
To provide guidance for conducting future water monitoring missions over U.S. coasts, aircraft and spacecraft approaches were defined and quantitatively compared. Sensors, aircraft and spacecraft were selected from current or developmental types for the hardware concepts and monitoring was assumed to begin in 1981-1983. Comparative data are presented on capabilities and costs to monitor both recognized pollution sites and broad shelf areas. For these mission requirements, a large fleet of light aircraft provided better coverage and at lower costs generally than one spacecraft, assuming a single, multi-spectral sensor on each platform. This result could change, however, should additional useful sensors with low cost penalties be found for the spacecraft.
Measurements of the Total Mass Loss (TML) and the Collected Volatile Condensable Materials (CVCM) are used as a guide for selecting those materials being low in outgassing. A contamination modelling method utilizing the data and also the rate at which a material outgasses in a system with a known conductance has been developed and has been applied to the problem encountered with the Very High Resolution Radiometer (VHRR). Recently, the contamination model has been applied to the problem with the 5th band of the Multi-Spectral Scanner on the Landsat-C satellite. In the MSS design, the volumes involved and their respective conductance in a molecular flow field demonstrated that the water vapor adsorbed and absorbed by an epoxy paint was being pumped off and re-ad(b)sorbed, with a resultant very slow loss of water during each degassing temperature cycle of the instrument's cooler. Recommendations for designing improved coolers are presented.
The Visible Infrared Spin Scan Radiometer Atmospheric Sounder (VAS) is a radiometer possessing eight visible channel detectors and six thermal detectors that sense infrared radiation in 12 spectral bands. Housed in the GOES satellite, VAS spins in a west to east direction at 100 rpm and achieves spatial coverage at resolutions of 1 km in the visible and 7 or 14 km in the infrared by stepping a scan mirror in a north to south direction. Designed for multipurpose applications, the VAS can be operated in two different modes: (1) a multi-spectral imaging (MSI) mode, and (2) a dwell sounding (DS) mode. The MSI mode of operation is used for sea surface temperature (SST) determination. Currently, a full-disk MSI image for SST determination is received every hour, 18 hours a day during weekdays. This MSI mode of operation for SST consists of data obtained from wavelengths centered at 3.9 microns (channel 12), 11.6 microns (channel 8), and 12.6 microns (channel 7) as well as visible data.
Imagery available with the Goes satellite visible-IR spin-scan radiometer (VISSR) atmospheric sounders (VAS) are examined in terms of mid- and low-tropospheric moisture sensing and mesoscale soundings. The VAS can be operated in a Dwell Sounding mode (DS) involving preprogrammed scanning of a specific area using any number of combinations of 12 channels between 4-15 microns. A second, multi-spectral imaging mode (MSI) comprises operation of two IR channels simultaneously when time constraints are in effect. Case studies are presented to demonstrate the effectiveness of VAS imagery for characterizing mesoscale moisture conditions when identifying severe storms. The moisture patterns of the upper and lower troposphere are made visible, and a 6.7 micron channel image can be overlaid on a low level moisture field to delineate fields of potential instability.
The Cluster Compression Algorithm (CCA), which was developed to reduce costs associated with transmitting, storing, distributing, and interpreting LANDSAT multispectral image data is described. The CCA is a preprocessing algorithm that uses feature extraction and data compression to more efficiently represent the information in the image data. The format of the preprocessed data enables simply a look-up table decoding and direct use of the extracted features to reduce user computation for either image reconstruction, or computer interpretation of the image data. Basically, the CCA uses spatially local clustering to extract features from the image data to describe spectral characteristics of the data set. In addition, the features may be used to form a sequence of scalar numbers that define each picture element in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. Various forms of the CCA are defined and experimental results are presented to show trade-offs and characteristics of the various implementations. Examples are provided that demonstrate the application of the cluster compression concept to multi-spectral images from LANDSAT and other sources.
Photographic data received from the ERTS-1 satellite over the Lesser Antilles Islands show distinct ocean features on the leeward side of each island. Attempts to relate these features to ocean eddy formations with the aid of ground truth data proved unsuccessful. However, surface and upper air wind data indicate a good correlation with the size, shape, and downwind extent of the ocean features. Studies to date indicate strongly that these features result from horizontal differences in a sea surface roughness due to the wind-shadow effect of the islands. The results suggest that horizontal variations in the reflectance of the sea surface will make remote sensing of the ocean mixed layer more difficult than previously anticipated. The surface reflection seems to be large enough to mask the smaller variations in backscattered energy from the mixed layer. Efforts to limit the effect of surface reflectance by photographic differencing of two multi-spectral scanner bands were unsuccessful.