publication titleThe National Extension Web-mapping Tool: From Data Exploration and Discovery to Decision Making
publication dateSep 2018 publication descriptionJournal of Extension
publication descriptionThe University of New Hampshire (UNH), Virginia Tech (VT), and Texas A&M University collaborated to envision and plan (all) and then create (UNH and VT) the National Extension Web-mapping Tool (NEWT) to increase the use of spatial data in planning and programming decision making throughout Extension. With NEWT, Extension professionals can access and use national Extension-relevant spatial data sets available at varied scales (county, Extension district, state) and in varied formats (maps, tables), without needing mapping experience or associated technical skills. NEWT encourages users to look past state borders and traditional administrative boundaries to discover opportunities for collaboration.
See publication The National Extension Web-mapping Tool: From Data Exploration and Discovery to Decision MakingSee publication
publication titleOptical types of inland and coastal waters
publication dateMar 1, 2018 publication descriptionLimnology and Oceanography
publication descriptionInland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However , progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in-water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n 5 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions.
See publication Optical types of inland and coastal watersSee publication
publication titleCyanobacteria Monitoring Collaborative
publication dateJul 1, 2017 publication descriptionLakeline
publication descriptionAn approach to harmful cyanobacteria education, monitoring, and managing.
See publication Cyanobacteria Monitoring CollaborativeSee publication
publication titleAn optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters
publication dateMar 5, 2014 publication descriptionRemote Sensing of Environment
publication descriptionBio-optical models are based on relationships between the spectral remote sensing reflectance and optical properties of in-water constituents. The wavelength range where this information can be exploited changes depending on the water characteristics. In low chlorophyll-a waters, the blue/green region of the spectrum is more sensitive to changes in chlorophyll-a concentration, whereas the red/NIR region becomes more important in turbid and/or eutrophic waters. In this work we present an approach to manage the shift from blue/green ratios to red/NIR-based chlorophyll-a algorithms for optically complex waters. Based on a combined in situ data set of coastal and inland waters, measures of overall algorithm uncertainty were roughly equal for two chlorophyll-a algorithms—the standard NASA OC4 algorithm based on blue/green bands and a MERIS 3-band algorithm based on red/NIR bands—with RMS error of 0.416 and 0.437 for each in log chlorophyll-a units, respectively. However, it is clear that each algorithm performs better at different chlorophyll-a ranges. When a blending approach is used based on an optical water type classification, the overall RMS error was reduced to 0.320. Bias and relative error were also reduced when evaluating the blended chlorophyll-a product compared to either of the single algorithm products. As a demonstration for ocean color applications, the algorithm blending approach was applied to MERIS imagery over Lake Erie. We also examined the use of this approach in several coastal marine environments, and examined the long-term frequency of the OWTs to MODIS-Aqua imagery over Lake Erie.
Tim Moore Mark Dowell Antonio Ruiz Verdu
See publication An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal watersSee publication
publication titleRemote Sensing of African Lakes: A Review
publication date2014 publication descriptionRemote Sensing of the African Seas - Springer
publication descriptionThe optical complexity and small size of inland waters make the application of remote sensing more challenging than for the open ocean. However, in Africa, where in situ monitoring of important water bodies is financially, institutionally and spatially constrained, there is strong demand for remote sensing to fill the critical information gap. Here we review a wide range of applications of both passive and active remote sensing to African lakes. The applications fall into five main categories: (1) visible, NIR, thermal and microwave sensing of lake area; (2) altimetric and grav-imetric sensing of lake level; (3) thermal sensing of lake surface tempera-ture; (4) visible, NIR and microwave sensing of macrophytes; and (5) opti-cal sensing of trophic conditions including chlorophyll-a and euphotic depth. Sensors used include Landsat MSS, TM and ETM+, MERIS, MODIS, SeaWiFS, AVHRR, Meteosat, TOPEX/Poseidon, Jason-1, OSTM/Jason-2, ERS-1, ERS-2, Envisat, GFO, ICESat, ALOS/PALSAR and GRACE. The majority of studies have been applied to the “great” lakes such as Chad, Malawi, Tanganyika and Victoria; however, there is a growing body of literature on smaller lakes. We examine the possibilities that remote sensing offers to monitoring and management of African lakes as well as the potential limitations of the technology using Lake Victoria as an illustrative case.
See publication Remote Sensing of African Lakes: A ReviewSee publication
publication title"The aerial flyover: a real-world remote sensing test of lake condition" in Gauging the Health of New England Lakes & Ponds
publication date2010 publication descriptionUS Environmental Protection Agency
publication description(article found on pages 39-40)
The New England Lakes and Pond (NELP) project provided an opportunity to develop a fundamental understanding of how remote sensing can best be used to measure lake water quality in New England. The project also provided a tremendous opportunity to test a real-world application of remote sensing for New England lakes with “on-the-ground” sampling in conjunction with aerial flyovers. With coordination through the NELP project, the logistical hurdles (typical of collaborative efforts) involved with testing an aerial remote-sensing approach in New England were overcome in late summer 2009. The NELP project coordinated lake sampling efforts timed with the flyover, and was responsible for both analyzing water chemistry and compiling the lake sampling data. The Remote Sensing of Phytoplankton Research Program at the EPA Atlantic Ecology Division (AED) arranged and executed the flyover component through a cooperative effort with the NASA Langley Aerospace Research Center (LARC).
See publication "The aerial flyover: a real-world remote sensing test of lake condition" in Gauging the Health of New England Lakes & Ponds