Analysis of scanner data for crop inventories
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Procedures for analyzing the potential of solar photovoltaic collectors to meet energy requirements in a metropolitan region are described and a modeling effort is applied to the San Fernando Valley region of Los Angeles. The procedure involves a series of steps designed to produce maps and tabulations revealing the amount of rooftop area available for establishing solar collectors and the proportion of energy requirement that could be potentially supplied by solar photovoltaics within each of the 533 mainline feeder service areas in the study area. For the sixty five square mile study area, the results showed that, with half the available flat and south facing roofs used and assuming the availability of energy storage, 52.7 percent of the actual kWh energy requirements could have been met in 1978 using photovoltaic collectors. Hourly, daily, weekly, and monthly fluctuations in potential supply and actual loads and recommendations of avenues for further research are discussed. Some further potential applications of the modeling technique are suggested.
There are no author-identified significant results in this report.
There are no author-identified significant results in this report.
There are no author-identified significant results in this report.
There are no author-identified significant results in this report.
The author has identified the following significant results. The effect of grid position was found insignificant for maps but highly significant for isolated mapping units. A modelable relationship between mapping error and cell size was observed for the map segment analyzed. Map data structure was also analyzed with an interboundary distance distribution approach. Map data structure and the impact of cell size on that structure were observed. The existence of a model allowing prediction of mapping error based on map structure was hypothesized and two generations of models were tested under simplifying assumptions.
Procedures for adjustment of brightness values between frames and the digital mosaicking of Landsat frames to standard map projections are developed for providing a continuous data base for multispectral thematic classification. A combination of local terrain variations in the Californian deserts and a global sampling strategy based on transects provided the framework for accurate classification throughout the entire geographic region.
This study was performed to determine if Landsat data could be used to improve the accuracy of the estimation of cotton acreage. A linear classification algorithm and a maximum likelihood algorithm were used for computer classification of the area, and the classification was compared with ground truth. The classification accuracy for some fields was greater than 90 percent; however, the overall accuracy was 71 percent for cotton and 56 percent for soybeans. The results of this research indicate that computer analysis of Landsat data has potential for improving upon the methods presently being used to determine cotton acreage; however, additional experiments and refinements are needed before the method can be used operationally.
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An investigation was conducted into ways to improve the involvement of state and local user personnel in the digital image analysis process by isolating those elements of the analysis process which require extensive involvement by field personnel and providing means for performing those activities apart from a computer facility. In this way, the analysis procedure can be converted from a centralized activity focused on a computer facility to a distributed activity in which users can interact with the data at the field office level or in the field itself. A general image processing software was developed on the University of Minnesota computer system (Control Data Cyber models 172 and 74). The use of color hardcopy image data as a primary medium in supervised training procedures was investigated and digital display equipment and a coordinate digitizer were procured.
Classification and technology development for area estimation of corn, soybeans, wheat, barley, and sunflowers are outlined. Supporting research for corn and soybean foreign commodity production forecasting is highlighted. Graphs profiling the greenness and brightness of the crops are presented.
Six forest cover categories were mapped, along with 10 general land cover classes. To map the state's 100 million acres, 1.6 acre mapping units were utilized. Map products were created. Standing forest acreage for the state was computed to be 26.8 million acres.
The activity concentrated on identifying crop and irrigation data sources for the eight states within the High Plains Aquifer and making contacts concerning the nature of these data. A mail questionnaire was developed to gather specific data not routinely reported through standard data collection channels. Input/output routines were designed for High Plains crop and irrigation data and initial statistical data on crops were input to computer files.
Optimal LANDSAT image dates for 1980 were identified based on the weekly crop-weather reports for Colorado, New Mexico, South Dakota, Texas, Oklahoma, Kansas, Nebraska, and Wyoming. The 1979 agricultural statistics data were entered into computer files and a revised questionnaire was developed and mailed to ASCS county agents. A set of computer programs was developed to allow the preparation of computer-assisted graphic displays of much of the collected data.
A zoom stereoscope was used to interpret aerial color photographs of the Finger Lakes region near Bath, New York, and areas of conifers were delineated on acetate sheets. Scale was determined for each photograph and units were converted to acres. Photographically enlarged positive transparencies of imagery from LANDSAT bands 5,6, and 7 for the southern portion of the study area were placed in a cold additive viewer and registered with each other to provide a composite image. A green filter was used on band 5, blue on band 6, and red on band 7. Conifers appeared at dark, reddish purple. Average was determined using a grid. Results show that the total confer stands within 50 miles of Bath is approximately 176,000 acres of which 60,000 acres are in Pennsylvania. The study was conducted to determine the feasibility of locating a particleboard manufacturing firm in the Southern Tier.
An automated forest stratification procedure based on Landsat and digital terrain data is reviewed. The system uses Landsat multispectral brightness values, a spatial texture channel, and digital terrain data information to divide Klamath National Forest vegetation into timber volume-homogeneous strata. The stratification process involves analyst-supervised class pooling editing, and the modeling of the regional type in a spatial manner using the digital terrain data. It is noted that for a given size and density class, the timber volume will change when the regional type changes, reflecting differences in growth potential.