Monitoring California's forage resource using ERTS-1 and supporting aircraft data

NASA's Earth Resource Technology Satellite (ERTS-1) launched July 23, 1972, offers for the first time the unique capabilities for regional monitoring of forage plant conditions. The repetitive coverage every 18 days, the synoptic view and the real-time recovery of the imagery for immediate analysis, combine to make the ERTS satellite a valuable tool for improving the evaluation of our rangeland resources. Studies presently underway at the University of California, Berkeley (sponsored jointly by NASA and the Bureau of Land Management), seek to determine if imagery obtained from high altitude aircraft and spacecraft (ERTS) can provide: (1) a means for monitoring the growth and development of annual and perennial range plants in California, and for determining the time and the rate of initial plant growth (germination) and terminal plant growth (maturation and senescence); (2) a means for determining or predicting the relative amount of forage that is produced; and (3) a means for mapping rangeland areas having different forage producing capabilities

Remote sensing research for agricultural applications

Materials and methods used to characterize selected soil properties and agricultural crops in San Joaquin County, California are described. Results show that: (1) the location and widths of TM bands are suitable for detecting differences in selected soil properties; (2) the number of TM spectral bands allows the quantification of soil spectral curve form and magnitude; and (3) the spatial and geometric quality of TM data allows for the discrimination and quantification of within field variability of soil properties. The design of the LANDSAT based multiple crop acreage estimation experiment for the Idaho Department of Water Resources is described including the use of U.C. Berkeley's Survey Modeling Planning Model. Progress made on Peditor software development on MIDAS, and cooperative computing using local and remote systems is reported as well as development of MIDAS microcomputer systems

A5. Identifying Susceptible Areas for Gully Erosion Using a Geospatial Analysis

Many studies have noted that gully erosion, the severe stage of soil erosion, has become one of the most challenging environmental problems restricting the long term productivity agriculture and water quality in developing countries. Even though several soil and water conservation practices have been implemented, the effects are far below expectations mainly due to lack of information to identify vulnerable areas for gully erosion. In this study, we specifically tested reliability of the topographic wetness index (TWI) to predict areas sensitive to gully erosion where saturation excess overland flow controls the erosion process. We used Debre Mewi watershed 30 km south of Lake Tana in the head waters of the Blue Nile where upland erosion takes place and gullies are actively forming in downhill locations. Wells were installed to measure groundwater table depths in the gully and in surrounding areas to assess the influence of subsurface flow on gully formation. Using geospatial analysis, TWI was correlated with ground water table depths during rainy months and can be used to estimate gully susceptibility in the studied region when data availability is limited

Response of a Watershed Model to Varying Spatial Landscape Characteristics

9 pages, 1 article*Response of a Watershed Model to Varying Spatial Landscape Characteristics* (Swaney, D. P.; Kuo, W. L.; Weinstein, D. A.; Mohler, C.; DeGloria, S.; Pelkie, C.; Tsai, F.; Steenhuis, T. S.; McCulloch, C. E.) 9 page

Engineering: Cornell Quarterly, Vol.28, No.3 (Spring 1994): The Center for the Environment: Marketing Green Engineering

IN THIS ISSUE: The Center for the Environment: Marketing Green Engineering /2 (Practical people seek to fix environmental problems with a mix of teaching, research, and outreach.) ... Training Toxicologists to Be Team Players /4 (To be effective, toxicologists need to know about more than just the effects of poisonous substances.) ... Development and Watershed Protection: Finding the Middle Ground /8 (The rights of watershed residents must be weighted in the balance with New York City's need for water.) ... Mapping Land Use for Local Government /14 (Remote sensing and geographic information systems make it possible to track changing patterns of land use.) ... Recycling Organic Wastes: Research, Engineering, and Outreach /18 (Composting can greatly reduce the volume of municipal solid waste.) ... Reducing Greenhouse Gases: Promoting an International Accord /24 (A program for the voluntary reduction of greenhouse gases can work if it is perceived as flexible and fair.) ... Faculty Publications /3

Visualization of Diffusely-Distributed Pollutants Using Spatially-Explicit Landscape Models (VIDEO FORMAT ONLY)

1 page, 1 article*Visualization of Diffusely-Distributed Pollutants Using Spatially-Explicit Landscape Models (VIDEO FORMAT ONLY)* (DeGloria, S.; Swaney, D.; Pelkie, C.; Tsai, F.; Kuo, W.; McCulloch, C.) 1 pag

Retrospective tillage differentiation using the Landsat‐5 TM archive with discriminant analysis

Accurate and site-specific information on tillage practice is vital to understand the impacts of crop management on water quality, soil conservation, and soil carbon sequestration. Remote sensing is a cost-effective technique for surveillance and rapid assessment of tillage practice over large areas. A new empirical approach for accurately predicting tillage class using discriminant analysis (DA) on historical multitemporal Landsat-TM 5 imagery has been developed. Ground truth data were obtained from the USDA-NRCS at 48 locations (20 conventional till [CT] and 28 conservation tillage or no-till [NT]). Classification accuracies were obtained for the DA models using reflectance values of Landsat-5 TM bands and Normalized Difference Tillage Index (NDTI) values. The performance of the DA models was compared with Logistic Regression (LR) models. On the basis of classification accuracy and kappa (κ) value, our results showed that the DA models performed better in tillage classification than the LR models. However, using NDTI values, both the DA and LR models performed similarly in tillage class discrimination. Model performance improved when a subset of locations rather than years was used. The results indicated broad-scale mapping of tillage practices is feasible using historical Landsat-5 TM imagery and DA-based classification