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The major factors which influenced the large area crop inventory experiment design, the technical approach being pursued, the detail of the implementation of this approach, and initial results of the experiment are described.
The application of remote sensing technology by the U.S. Department of Agriculture (USDA) is examined. The activities of the USDA Remote-Sensing User Requirement Task Force which include cataloging USDA requirements for earth resources data, determining those requirements that would return maximum benefits by using remote sensing technology and developing a plan for acquiring, processing, analyzing, and distributing data to satisfy those requirements are described. Emphasis is placed on the large area crop inventory experiment and its relationship to the task force.
To determine the feasibility of satellite data for surface-mine inventory, particularly as it applies to coal, a case study was conducted in Maryland. A band-ratio method was developed to measure disturbed surface areas, and it proved to be extendible both temporally and geographically. This method was used to measure area changes in the region over three time periods from September 1972 through July 1974 and to map the entire two-county area for 1973. For mines ranging between 31 and 244 acres (12 to 98 hectares) the measurement accuracy of total affected acreage was determined to be 92%. Mines of 120 acres (50 hectares) and larger were measured with greater accuracy, some within one percent of the actual area. The ability to identify, classify, and measure strip-mine surfaces in a two-county area (1,541 square kilometers - 595 square miles) of western Maryland was demonstrated through the use of computer processing. On the basis of these results the use of LANDSAT satellite data and multilevel sampling of aircraft and field verification inspections, multispectral analysis of digital data is shown to be an effective, rapid, and accurate means of monitoring the surface mining cycle.
A Large Area Crop Inventory Experiment (LACIE) has been undertaken jointly by the U.S. Department of Agriculture, the National Oceanic and Atmospheric Administration (NOAA) of the Department of Commerce and the National Aeronautics and Space Administration (NASA) to prove out an economically important application of remote sensing from space. At the outset LACIE will concentrate on wheat grown in the North American area. The experiment will combine crop area measurements obtained from LANDSAT data and meteorological information from NOAA satellites and from ground stations designed to relate weather conditions to yield assessment and ultimately to production forecasts. The Department of Agriculture will study the utilization of the experimentally derived production estimates in its crop reports. These reports are made public as a routine service to the domestic and international agriculture community. If this activity is successful and the results prove useful the application will be extended to other regions and ultimately to other crops.
The use of ERTS-1 imagery permitted a rapid, economic, and accurate inventory of wetlands in Nebraska that are ten acres or larger in size. Four categories of wetlands - Open Water, Subirrigated Meadows, Marshes, and Seasonally Flooded Basins - were delineated by using two seasons of imagery and an electronic image-enhancing system. Positive print enlargements of bands 5 and 7 at a scale of 1:250,000 (acquired in the spring) as well as band 7 (acquired in late summer) were used to delineate all categories. Electronic enhancement of band 6 (acquired in the fall) was used as an aid to further differentiate marshes. Accuracy estimates based on color infrared aerial photography as ground truth indicated, as an overall average, 85 percent correct identification.
Predictions for the total inventory of outgassed volatiles on Mars can be developed by studying volatiles in meteorites, terrestrial rocks, and the atmospheres of Venus, the moon, and the earth. Two models are presented, following the basic assumption that the devolatilization of Mars has been analogous to that of the earth. The recent discovery of a high abundance of argon in the Martian atmosphere appears to indicate that Mars has outgassed as completely as the earth, but present uncertainties and lacunae in the essential data set permit several other interpretations.
A Large Area Crop Inventory Experiment (LACIE) has been undertaken jointly by the U.S. Department of Agriculture (USDA), the National Oceanic and Atmospheric Administration (NOAA) of the Department of Commerce and the National Aeronautics and Space Administration (NASA) to prove out an economically important application of remote sensing from space. The first phase of the Experiment, which focused upon determinations of wheat area in the U.S. Great Plains and upon the development and testing of yield models, is now nearing completion. The system implemented to handle and analyze the Landsat and meteorological data has generally worked well and met operational goals. A very preliminary assessment of results to date indicates that the accuracy goals of the experiment can be met.
A Large Area Crop Inventory Experiment (LACIE) was undertaken to prove out an economically important application of remote sensing from space. The experiment focused upon determination of wheat acreages in the U.S. Great Plains and upon the development and testing of yield models. The results and conclusions are presented.
It appears that the Large Area Crop Inventory Experiment over the Great Plains, can with a reasonable expectation, be a satisfactory component of a 90/90 production estimator. The area estimator produced more accurate area estimates for the total winter wheat region than for the mixed spring and winter wheat region of the northern Great Plains. The accuracy does appear to degrade somewhat in regions of marginal agriculture where there are small fields and abundant confusion crops. However, it would appear that these regions tend also to be marginal with respect to wheat production and thus increased area estimation errors do not greatly influence the overall production estimation accuracy in the United States. The loss of segments resulting from cloud cover appears to be a random phenomenon that introduces no significant bias into the estimates. This loss does increase the variance of the estimates.
The author has identified the following significant results. Center pivot irrigation systems can be inventoried from LANDSAT data in a timely and cost effective manner.
The procedures and costs associated with mapping land cover/use and forest resources from high altitude color infrared (CIR) imagery are documented through an evaluation of several inventory efforts. CIR photos (1:36,000) were used to classify the forests of Mason County, Michigan into six species groups, three stocking levels, and three maturity classes at a cost of $4.58/sq. km. The forest data allow the pinpointing of marketable concentrations of selected timber types, and facilitate the establishment of new forest management cooperatives. Land cover/use maps and area tabulations were prepared from small scale CIR photography at a cost of $4.28/sq. km. and $3.03/sq. km. to support regional planning programs of two Michigan agencies. procedures were also developed to facilitate analysis of this data with other natural resource information. Eleven thematic maps were generated from Windsor Township, Michigan at a cost of $1,500 by integrating grid-geocoded land cover/use, soils, topographic, and well log data using an analytical computer program.
The large area crop inventory experiment is being developed to predict crop production through satellite photographs. This experiment demonstrates how space age technology can contribute to solving practical problems of agriculture management.
An inventory of wetlands for the area included in the proposed Oahe irrigation project was conducted to provide supplemental data for the wildlife mitigation plan. Interpretation techniques for inventoring small wetlands in the low relief terrain of the Lake Dakota Plain were documented and data summaries included. The data were stored and tabulated in a computerized spatial data analysis system.
Documentation of the activities of the Large Area Crop Inventory Experiment during the 1976 Northern Hemisphere crop year is presented. A brief overview of the experiment is included as well as phase two area, yield, and production estimates for the United States Great Plains, Canada, and the Union of Soviet Socialist Republics spring winter wheat regions. The accuracies of these estimates are compared with independent government estimates. Accuracy assessment of the United States Great Plains yardstick region based on a through blind sight analysis is given, and reasons for variations in estimating performance are discussed. Other phase two technical activities including operations, exploratory analysis, reporting, methods of assessment, phase three and advanced system design, technical issues, and developmental activities are also included.
The author has identified the following significant results. Digital analysis of LANDSAT CCT's indicated that two discrete spectral background zones occurred among the five soil zone. K-CLASS classification of corn revealed that accuracy increased when two background zones were used, compared to the classification of corn stratified by five soil zones. Selectively varying film type developer and development time produces higher contract in reprocessed imagery. Interpretation of rangeland and cropped land data from 1968 aerial photography and 1976 LANDSAT imagery indicated losses in rangeland habitat. Thermal imagery was useful in locating potential sources of sub-surface water and geothermal energy, estimating evapotranspiration, and inventorying the land.
The NASA-U.S. Department of Agriculture Large Area Crop Inventory Experiment (LACIE), aimed at using multispectral remote sensing data from Landsat 1 and 2 to generate accurate annual global crop production forecasts, is discussed. The forecasts take into account meteorological conditions as well as yield and acreage, and may be used to increase the discrimination of U.S. harvest estimates down to regional levels and to provide more accurate early-season predictions. Sample problems involving the determination of wheat harvests and the monitoring of drought conditions are described. Difficulties related to misidentification of abnormally-developing plantations, the automatic classification of homogeneous spectral groups, the computerized generation of colored maps, and the estimation of yields during years when exceptional meteorological conditions prevail are also considered. Samples of Landsat-generated classification maps for Western U.S. and for the Saratov, U.S.S.R. crop regions are given.
Conventional approaches to inventories from satellites rely heavily on the judgment of image analysts, who 'train' the computer classification program. The fully automated approach to this problem, described in the present paper, eliminates the need for human judgment in training the computer, by using fully automated techniques for selecting spectral characteristics and classification parameters to optimize agreement with ground truth in specified test areas.
The author has identified the following significant results. Analysis of LANDSAT temporal data, specifically the digitally merged winter and summer scenes, provided the best overall classification results. Comparison of temporal classification results with available ground truth reveal a 94% agreement in the delineation of hardwood categories, a 96% agreement for the combined pine category, and a greater than 50% agreement for each individual pine subcategory. For nearly 1000 acres, compared clearcut acreage estimated with LANDSAT digital data differed from company inventory records by only 3%. Through analysis of summer data, pine stands were successfully classified into subcategories based upon the extent of crown closure. Maximum spectral separability of hardwood and pine stands was obtained from the analysis of winter data.