ABSTRACT
Cyanobacterial harmful algal blooms (cyanoHAB) cause human and ecological health problems in lakes worldwide. The timely distribution of satellite-derived cyanoHAB data is necessary for adaptive water quality management and for targeted deployment of water quality monitoring resources. Software platforms that permit timely, useful, and cost-effective delivery of information from satellites are required to help managers respond to cyanoHABs. The Cyanobacteria Assessment Network (CyAN) mobile device application (app) uses data from the European Space Agency Copernicus Sentinel-3 satellite Ocean and Land Colour Instrument (OLCI) in near realtime to make initial water quality assessments and quickly alert managers to potential problems and emerging threats related to cyanobacteria. App functionality and satellite data were validated with 25 state health advisories issued in 2017. The CyAN app provides water quality managers with a user-friendly platform that reduces the complexities associated with accessing satellite data to allow fast, efficient, initial assessments across lakes.
ABSTRACT
This study details the development of a U.S. Commonwealth of Puerto Rico above-ground forest biomass (agb) product (baseline 2000) developed by the United States Environmental, Protection Agency (epa) that was compared to another AGB product developed by the U.S. Forest Service (usfs) for the same area. The USEPA product tended to over-predict in areas of low biomass and under-predict in high biomass areas when compared to observed plot data, but compared favorably to a Forest Inventory Analysis (fia) assessment of structure and condition of Puerto Rico forests (72.6 Mg/ha versus 80.0 Mg/ ha, respectively). AGB estimates were highly correlated with reference FIA biomass for both maps at their native spatial resolutions (USEPA: r =0.93, USFS: r = 0.92). AGB mean difference between both products was 33.5 Mg/ha (USFS mean = 106.1 Mg/ha; USEPA mean = 72.6 Mg/ha), a difference not out-of- scope when compared to other biomass comparative studies.
ABSTRACT
In the Laurentian Great Lakes Basin (GLB), corn acreage has been expanding since 2005 in response to high demand for corn as an ethanol feedstock. This study integrated remote sensing-derived products and the Soil and Water Assessment Tool (SWAT) within a geographic information system (GIS) modeling environment to assess the impacts of cropland change on sediment yield within four selected watersheds in the GLB. The SWAT models were calibrated during a 6 year period (2000-2005), and predicted stream flows were validated. The R(2) values were 0.76, 0.80, 0.72, and 0.81 for the St. Joseph River, the St. Mary River, the Peshtigo River, and the Cattaraugus Creek watersheds, respectively. The corresponding E (Nash and Sutcliffe model efficiency coefficient) values ranged from 0.24 to 0.79. The average annual sediment yields (tons/ha/year) ranged from 0.12 to 4.44 for the baseline (2000 to 2008) condition. Sediment yields were predicted to increase for possible future cropland change scenarios. The first scenario was to convert all "other" agricultural row crop types (i.e., sorghum) to corn fields and switch the current/baseline crop rotation into continuous corn. The average annual sediment yields increased 7-42 % for different watersheds. The second scenario was to further expand the corn planting to hay/pasture fields. The average annual sediment yields increased 33-127 % compared with baseline conditions.
