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We compared 10 established and 2 new satellite reflectance algorithms 36 for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio 37 using coincident hyperspectral aircraft imagery and dense coincident surface 38 observations collected within one hour of image acquisition to develop simple 39 proxies for algal blooms in water bodies sensitive to algal blooms (especially toxic 40 or harmful algal blooms (HABs)) and to facilitate portability between multispectral 41 satellite imagers for regional algal bloom monitoring. All algorithms were 42 compared with narrow band hyperspectral aircraft images. These images were 43 subsequently upscaled spectrally and spatially to simulate 5 current and near future 44 satellite imaging systems. Established and new Chl-a algorithms were then applied 45 to the synthetic satellite images and compared to coincident surface observations of 46Chl-a collected from 44 sites within one hour of aircraft acquisition of the imagery. 47We found several promising algorithm/satellite imager combinations for routine 48Chl-a estimation in smaller inland water bodies with operational and near-future 49 satellite systems. The CI, MCI, FLH, NDCI, 2BDA and 3 BDA Chl-a algorithms 50 worked well with CASI imagery. The NDCI, 2BDA, and 3BDA Chl-a algorithms 51 worked well with simulated WorldView-2 and 3, Sentinel-2, and MERIS-like 52 imagery. NDCI was the most widely applicable Chl-a algorithm with good 53 performance for CASI, WorldView 2 and 3, Sentinel-2 and MERIS-like imagery 54 and limited performance with MODIS imagery. A new fluorescence line height 55 "greenness" algorithm yielded the best Chl-a estimates with simulated Landsat-8 56 imagery. 57 ARTICLE INFO 58 Article history: 59 Received ….. 60 Submission to Remote Sensing of Environment 3 Keywords: chorophyll-a, algal bloom, harmful algal bloom, algorithm, satellite, 61 hyperspectral, multispectral 62 63 64 65

Supreme Court cases have questioned if jurisdiction under the Clean Water Act extends to water bodies such as streams without year-round flow. Headwater streams are central to this issue because many periodically dry, and because little is known about their influence on navigable waters. An accurate account of the extent and flow permanence of headwater streams is critical to estimating downstream contributions. We compared the extent and permanence of headwater streams from two field surveys with values from databases and maps. The first used data from 29 headwater streams in nine U.S. forests, whereas the second had data from 178 headwater streams in Oregon. Synthetic networks developed from the nine-forest survey indicated that 33 to 93% of the channel lacked year-round flow. Seven of the nine forests were predicted to have >200% more channel length than portrayed in the high-resolution National Hydrography Dataset (NHD). The NHD and topographic map classifications of permanence agreed with~50% of the field determinations across~300 headwater sites. Classification agreement with the field determinations generally increased with increasing resolution. However, the flow classification on soil maps only agreed with~30% of the field determination despite depicting greater channel extent than other maps. Maps that include streams regardless of permanence and size will aid regulatory decisions and are fundamental to improving water quality monitoring and models.

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Abstract:We analyzed 27 established and new simple and therefore perhaps portable satellite phycocyanin pigment reflectance algorithms for estimating cyanobacterial values in a temperate 8.9 km 2 reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense coincident water surface observations collected from 44 sites within 1 h of image acquisition. The algorithms were adapted to real Compact Airborne Spectrographic Imager (CASI), synthetic WorldView-2, Sentinel-2, Landsat-8, MODIS and Sentinel-3/MERIS/OLCI imagery resulting in 184 variants and corresponding image products. Image products were compared to the cyanobacterial coincident surface observation measurements to identify groups of promising algorithms for operational algal bloom monitoring. Several of the algorithms were found useful for estimating phycocyanin values with each sensor type except MODIS in this small lake. In situ phycocyanin measurements correlated strongly (r 2 = 0.757) with cyanobacterial sum of total biovolume (CSTB) allowing us to estimate both phycocyanin values and CSTB for all of the satellites considered except MODIS in this situation.

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Restoration monitoring is generally perceived as costly and time consuming, given the assumptions of successfully restoring ecological functions and services of a particular ecosystem or habitat. Opportunities exist for remote sensing to bolster the restoration science associated with a wide variety of injured resources, including resources affected by fire, hydropower operations, chemical releases, and oil spills, among others. In the last decade, the role of remote sensing to support restoration monitoring has increased, in part due to the advent of high-resolution satellite sensors as well as other sensor technology, such as lidar. Restoration practitioners in federal agencies require monitoring standards to assess restoration performance of injured resources. This review attempts to address a technical need and provides an introductory overview of spatial data and restoration metric considerations, as well as an in-depth review of optical (e.g., spaceborne, airborne, unmanned aerial vehicles) and active (e.g., radar, lidar) sensors and examples of restoration metrics that can be measured with remotely sensed data (e.g., land cover, species or habitat type, change detection, quality, degradation, diversity, and pressures or threats). To that end, the present article helps restoration practitioners assemble information not only about essential restoration metrics but also about the evolving technological approaches that can be used to best assess them. Given the need for monitoring standards to assess restoration success of injured resources, a universal monitoring framework should include a range of remote sensing options with which to measure common restoration metrics. Integr Environ Assess Manag 2017;13:614-630. Published 2016. This article is a US Government work and is in the public domain in the USA.