Remote sensing of coastal food resources

Since tidal marshes and estuaries cover large areas of the world's coasts and exhibit a very high net primary productivity, they offer a most important food source for an ever increasing world population. The food web of numerous estuaries and coastal waters is based on the primary productivity of coastal marshes that constitute centers of solar energy fixation and an important link in the mineral cycles. The fixed carbon and minerals enterthe water primarily as detritus where a complex food web makes them accessible to commercially important fish and benthic communities. With the launch of LANDSAT, NOAA-2, and Skylab, relatively high resolution spacecraft data became available for mapping and inventorying tidal marshes and their productivity on a global scale. Upwelling regions that attract large fish populations as well as other coastal water properties relating to the presence of finfish, Crustacea, and shellfish could be identified and observed. Using multispectral analysis techniques, classification accuracies greater than 80 percent have been obtained for most marsh plant species, and greater than 90 percent for key types such asSpartina alterniflora, which is the primary producer in large tide marshes of the coastal eastern USA. The capacity of remote sensors on spacecraft such as NOAA-2, LANDSAT, and Skylab to assess coastal food resources on a global scale is discussed from the point of view of resolution, classification accuracy, and cost effectiveness.     

USING LANDSAT DATA TO CLASSIFY LAND USE FOR ASSESSING THE BASINWIDE RUNOFF INDEX1

Remote sensing data in the form of Landsat computer compatible tapes (CCT) was used to determine land use and land cover as an aid in hydrologic studies that were used to estimated a basinwide runoff index. With the use of the General Electric Image 100 multispectral image processing system in conjunction with the Earth Resources Laboratory Application Software (ELAS), CCT's on February 9, 1976, were analyzed by spectral differences to determine unique land use conditions within the Econlockhatchee (Econ) River Basin, Florida. The result showed that the Landsat data can be successfully used to monitor the USGS land use Level 1. An advantage of using the Landsat data for land use classification is that new data are periodically available for updating the land use information. The Soil Conservation Service curve number was used to establish a basinwide runoff index which includes a prime variable of land use changes with the time. The basinwide runoff index in 1972 (with USGS 1972 Land Use maps) was similar to the one in 1976 (with Landsat data dated February 9, 1976). This implies that the runoff from the entire Econ Basin was not noticeably changed during the period of 1972 and 1976.     

Evaluation of a fire behavior information integration system for southern California chaparral wildlands

Close contact between arid, fire-vulnerable chaparral wildlands and urban development in southern California results in conflagrations that have burned 200,000 ha, destroyed 700 structures, and claimed 16 lives in a single year. In 1972, the U.S. Congress established FIRESCOPE to assist southern California fire and emergency agencies and to develop computer methods for the simulation of wildland fire behavior.     

Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed

Riparian zones are deemed significant due to their interception capability of non-point source impacts and the maintenance of ecosystem integrity region wide. To improve classification and change detection of riparian buffers, this paper developed an evolutionary computational, supervised classification method--the RIparian Classification Algorithm (RICAL)--to conduct the seasonal change detection of riparian zones in a vast semi-arid watershed, South Texas. RICAL uniquely demonstrates an integrative effort to incorporate both vegetation indices and soil moisture images derived from LANDSAT 5 TM and RADARSAT-1 satellite images, respectively. First, an estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) images was conducted via the first-stage genetic programming (GP) practice. Second, for the statistical analyses and image classification, eight vegetation indices were prepared based on reflectance factors that were calculated as the response of the instrument on LANDSAT. These spectral vegetation indices were then independently used for discriminate analysis along with soil moisture images to classify the riparian zones via the second-stage GP practice. The practical implementation was assessed by a case study in the Choke Canyon Reservoir Watershed (CCRW), South Texas, which is mostly agricultural and range land in a semi-arid coastal environment. To enhance the application potential, a combination of Iterative Self-Organizing Data Analysis Techniques (ISODATA) and maximum likelihood supervised classification was also performed for spectral discrimination and classification of riparian varieties comparatively. Research findings show that the RICAL algorithm may yield around 90% accuracy based on the unseen ground data. But using different vegetation indices would not significantly improve the final quality of the spectral discrimination and classification. Such practices may lead to the formulation of more effective management strategies for the handling of non-point source pollution, bird habitat monitoring, and grazing and live stock management in the future.