A Land Use Change Monitoring System Based on LANDSAT

A procedure for economically determining statistics on acreages of change in the use of land between two dates has been developed to support a Department of Housing and Urban Development program on neighborhood change modeling. The application of image processing techniques to LANDSAT imagery in four stages (registration, differencing, classification, and tabulation) provides one of the basic data sets needed to model future land use in one of six typical urban areas. After appropriate LANDSAT imagery for two desired dates is obtained, date-to-date registration of the study area is performed. Once the two images are adequately registered, the procedures of determining the geographic areas of change are initiated. The ratio of two raw bands for the early date is computed and then subtracted from the same ratio for the late date. This difference is allowed to conform to a gaussian distribution, and those pixels whose values lie beyond two standard deviations from the mean are designated as areas of change. The first step in the creation of a late date classification map is to extract and classify only those areas that show change. Then the early date classified data for unchanged areas is digitally summed with the late date classified data in changed areas. Using polygon overlay routines individually for both the early and late date classifications and then combining the results, a tabulation revealing general land use changes (e.g., the number of acres of residential in the early date versus the number in the late date) can be generated. To determine the manner of the change (e.g., the number of acres changed from rural to urban), the land use classes are first aggregated into rural/urban dicotomies and then a routine which permits comparison of individual pixel values is executed. Finally, a tabulation can display the manner of the land use change aggregated by the administrative district desired (e.g., census tracts)

Data management for support of the Oregon Transect Ecosystem Research (OTTER) project

Management of data collected during projects that involve large numbers of scientists is an often overlooked aspect of the experimental plan. Ecosystem science projects like the Oregon Transect Ecosystem Research (OTTER) Project that involve many investigators from many institutions and that run for multiple years, collect and archive large amounts of data. These data range in size from a few kilobytes of information for such measurements as canopy chemistry and meteorological variables, to hundreds of megabytes of information for such items as views from multi-band spectrometers flown on aircraft and scenes from imaging radiometers aboard satellites. Organizing and storing data from the OTTER Project, certifying those data, correcting errors in data sets, validating the data, and distributing those data to other OTTER investigators is a major undertaking. Using the National Aeronautics and Space Administration's (NASA) Pilot Land Data System (PLDS), a Support mechanism was established for the OTTER Project which accomplished all of the above. At the onset of the interaction between PLDS and OTTER, it was not certain that PLDS could accomplish these tasks in a manner that would aid researchers in the OTTER Project. This paper documents the data types that were collected under the auspices of the OTTER Project and the procedures implemented to store, catalog, validate, and certify those data. The issues of the compliance of investigators with data-management requirements, data use and certification, and the ease of retrieving data are discussed. We advance the hypothesis that formal data management is necessary in ecological investigations involving multiple investigators using many data gathering instruments and experimental procedures. The issues and experience gained in this exercise give an indication of the needs for data management systems that must be addressed in the coming decades when other large data-gathering endeavors are undertaken by the ecological science community

Computer-aided boundary delineation of agricultural lands

The National Agricultural Statistics Service of the United States Department of Agriculture (USDA) presently uses labor-intensive aerial photographic interpretation techniques to divide large geographical areas into manageable-sized units for estimating domestic crop and livestock production. Prototype software, the computer-aided stratification (CAS) system, was developed to automate the procedure, and currently runs on a Sun-based image processing system. With a background display of LANDSAT Thematic Mapper and United States Geological Survey Digital Line Graph data, the operator uses a cursor to delineate agricultural areas, called sampling units, which are assigned to strata of land-use and land-cover types. The resultant stratified sampling units are used as input into subsequent USDA sampling procedures. As a test, three counties in Missouri were chosen for application of the CAS procedures. Subsequent analysis indicates that CAS was five times faster in creating sampling units than the manual techniques were