Marine Debris in Central California: Quantifying Type and Abundance of Beach Litter in Monterey Bay, CA

The Monterey Bay on the central coast of California lies within a protected marine sanctuary where recreation, tourism, and marine species coexist. Marine debris washing ashore poses a human health risk as well as contributing to economic losses and ecological harm. Central California’s coastal managers require reliable scientific information on debris abundance, distribution, and type to help ameliorate this threat. To help address potentially harmful beach debris, I created a survey method which allows for trained volunteers to quantify the types and abundance of beach litter. This method was put into effect at twelve beaches within the Monterey Bay in California. Employing trained volunteers increased efficiency and allowed the simultaneous sampling of twelve beaches monthly over one year. We conducted surveys at low tide from July 2009 through June 2010. Surveyed beaches included: Santa Cruz Main, Seabright, Live Oak, Capitola, New Brighton, Sea Cliff, Manresa, Sunset, Zmudowski, Marina, Seaside, and Del Monte. At each survey site volunteers randomly placed quadrats to facilitate data collection along two parallel 50m transects. We found litter on all beaches surveyed. A total of 5966 individual pieces of litter were collected during this study. Styrofoam made up 41% of the total amount of litter, making it the most numerically abundant item found. Unexpected items included plastic pellets (9% of total plastics) and fertilizer capsules (1% of total litter). I analysed spatial and temporal relationships between litter abundance using mixed effect modeling, and best fit was ascertained using Akaike’s Information Criterion (AIC). The results of this study demonstrated that beach location, while influential, had less of an effect on litter abundance than month. The temporal and spatial variance in litter type and abundance suggest a relationship to physical and environmental factors, such as proximity to agricultural fields and surface current movement within the bay. The results of this study can be directly applicable to developing monitoring programs for beach debris and could be adopted by coastal cities to monitor their own environmental and political successes in abating beach litter. In addition, this study has strengthened relationships with agencies, municipalities, educators and community organizations, as these relationships are essential for decision-making, scientific monitoring, and community outreach

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer's accuracy of 93% and a user's accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of >= 95% for cultivated croplands and >= 76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season

Integrating Satellite and Surface Sensor Networks for Irrigation Management Applications in California

Satellite mapping of evapotranspiration (ET) from irrigated agricultural lands can provide agricultural producers and water managers with information that can be used to optimize agricultural water use, especially in regions with limited water supplies. The timely delivery of information on agricultural crop water requirements has the potential to make irrigation scheduling more practical, convenient, and accurate. We present a system for irrigation scheduling and management support in California and describe lessons learned from the development and implementation of the system. The Satellite Irrigation Management Support (SIMS) framework integrates satellite data with information from agricultural weather networks to map crop canopy development, basal crop coefficients (Kcb), and basal crop evapotranspiration (ETcb) at the scale of individual fields. Information is distributed to agricultural producers and water managers via a web-based irrigation management decision support system and web data services. SIMS also provides an application programming interface (API) that facilitates integration with other irrigation decision support tools, estimation of total crop evapotranspiration (ETc) and calculation of on-farm water use efficiency metrics. Accuracy assessments conducted in commercial fields for more than a dozen crop types to date have shown that SIMS seasonal ETcb estimates are within 10 mean absolute error (MAE) for well-watered crops and within 15 across all crop types studied, and closely track daily ETc and running totals of ETc measured in each field. Use of a soil water balance model to correct for soil evaporation and crop water stress reduces this error to less than 8 MAE across all crop types studied to date relative to field measurements of ETc. Results from irrigation trials conducted by the project for four vegetable crops have also demonstrated the potential for use of ET-based irrigation management strategies to reduce total applied water by 20-40 relative to grower standard practices while maintaining crop yields and quality