The application of airborne imaging radars (L and X-band) to earth resources problems

For abstract, see N75-24064

Observations of internal waves generated by an anticyclonic eddy: a case study in the ice edge region of the Greenland Sea

Internal waves in the ocean play an important role in turbulence generation due to wave-breaking processes and mixing of the ocean. Airborne radar images of internal waves and ocean eddies north of Svalbard suggested that ocean eddies could generate internal waves. Here, we test this hypothesis using data from a dedicated internal wave experiment in the Greenland Sea. Internal waves with dominant frequencies of 1–3 cycles per hour and amplitudes up to 15 m were observed using three thermistor chains suspended from a drifting array conveniently placed on the ice in a triangle with sides of several km. Analysis shows that internal waves propagated westwards with a speed of about 0.2 m/s and wavelength of 0.4–1.0 km, away from an anticyclonic ocean eddy located just east of the array. This was consistent with the remote-sensing observations of internal waves whose surface signature was imaged by an airborne radar in the western part of this eddy, and with theories that eddies and vortexes can directly generate internal waves. This case study supports our hypothesis that ocean eddies can be the direct sources of internal waves reported here for the first time and not only enhancing the local internal wave field by draining energy from the eddies, as studied previously. The present challenge is to explore the role of eddies as a new source in generating internal waves in the global ocean

Theory of synthetic aperture radar ocean imaging: A MARSEN view

This paper reviews basic synthetic aperture radar (SAR) theory of ocean wave imaging mechanisms, using both known work and recent experimental and theoretical results from the Marine Remote Sensing (MARSEN) Experiment. Several viewpoints that have contributed to the field are drawn together in a general analysis of the backscatter statistics of a moving sea surface. A common focus for different scattering models is provided by the mean image impulse response function, which is shown to be identical to the (spatially varying) frequency variance spectrum of the local complex reflectivity coefficient. From the analysis has emerged a more complete view of the SAR imaging phenomenon than has been previously available. A new, generalized imaging model is proposed

Coastal ecosystem investigations with LiDAR (light detection and ranging) and bottom reflectance: Lake Superior reef threatened by migrating tailings

Where light penetration is excellent, the combination of LiDAR (Light Detection And Ranging) and passive bottom reflectance (multispectral, hyperspectral) greatly aids environmental studies. Over a century ago, two stamp mills (Mohawk and Wolverine) released 22.7 million metric tons of copper-rich tailings into Grand Traverse Bay (Lake Superior). The tailings are crushed basalt, with low albedo and spectral signatures different from natural bedrock (Jacobsville Sandstone) and bedrock-derived quartz sands. Multiple Lidar (CHARTS and CZMIL) over-flights between 2008–2016—complemented by ground-truth (Ponar sediment sampling, ROV photography) and passive bottom reflectance studies (3-band NAIP; 13-band Sentinal-2 orbital satellite; 48 and 288-band CASI)—clarified shoreline and underwater details of tailings migrations. Underwater, the tailings are moving onto Buffalo Reef, a major breeding site important for commercial and recreational lake trout and lake whitefish production (32% of the commercial catch in Keweenaw Bay, 22% in southern Lake Superior). If nothing is done, LiDAR-assisted hydrodynamic modeling predicts 60% tailings cover of Buffalo Reef within 10 years. Bottom reflectance studies confirmed stamp sand encroachment into cobble beds in shallow (0-5m) water but had difficulties in deeper waters (\u3e8 m). Two substrate end-members (sand particles) showed extensive mixing but were handled by CASI hyperspectral imaging. Bottom reflectance studies suggested 25-35% tailings cover of Buffalo Reef, comparable to estimates from independent counts of mixed sand particles (ca. 35% cover of Buffalo Reef by \u3e20% stamp sand mixtures)

Spatial-temporal variability of in situ cyanobacteria vertical structure in Western Lake Erie: Implications for remote sensing observations

Remote sensing has provided expanded temporal and spatial range to the study of harmful algal blooms (cyanoHABs) in western Lake Erie, allowing for a greater understanding of bloom dynamics than is possible through in situ sampling. However, satellites are limited in their ability to specifically target cyanobacteria and can only observe the water within the first optical depth. This limits the ability of remote sensing to make conclusions about full water column cyanoHAB biomass if cyanobacteria are vertically stratified. FluoroProbe data were collected at nine stations across western Lake Erie in 2015 and 2016 and analyzed to characterize spatio-temporal variability in cyanobacteria vertical structure. Cyanobacteria were generally homogenously distributed during the growing season except under certain conditions. As water depth increased and high surface layer concentrations were observed, cyanobacteria were found to be more vertically stratified and the assumption of homogeneity was less supported. Cyanobacteria vertical distribution was related to wind speed and wave height, with increased stratification at low wind speeds (bathymetry and environmental conditions could lead to improved biomass estimates. Additionally, cyanobacteria contributions to total chlorophyll-a were shown to change throughout the season and across depth, suggesting the need for remote sensing algorithms to specifically identify cyanobacteria

Evaluating visible derivative spectroscopy by varimax-rotated, principal component analysis of aerial hyperspectral images from the western basin of Lake Erie

The Kent State University (KSU) spectral decomposition method provides information about the spectral signals present in multispectral and hyperspectral images. Pre-processing steps that enhance signal to noise ratio (SNR) by 7.37–19.04 times, enables extraction of the environmental signals captured by the National Aeronautics and Space Administration (NASA) Glenn Research Center\u27s, second generation, Hyperspectral imager (HSI2) into multiple, independent components. We have accomplished this by pre-processing of Level 1 HSI2 data to remove stripes from the scene, followed by a combination of spectral and spatial smoothing to further increase the SNR and remove non-Lambertian features, such as waves. On average, the residual stochastic noise removed from the HSI2 images by this method is 5.43 ± 1.42%. The method also enables removal of a spectrally coherent residual atmospheric bias of 4.28 ± 0.48%, ascribed to incomplete atmospheric correction. The total noise isolated from signal by the method is thu

COVID-19: time to rethink palliative care strategy in resource-poor settings

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Assessing the influence of watershed characteristics on chlorophyll a in water bodies at global and regional scales

Prediction of primary production of lentic water bodies (i.e., lakes and reservoirs) is valuable to researchers and resource managers alike, but is very rarely done at the global scale.  With the development of remote sensing technologies, it is now feasible to gather large amounts of data across the world, including understudied and remote regions. To determine which factors were most important in explaining the variation of chlorophyll a (Chl-a), an indicator of primary production in water bodies, at global and regional scales, we first developed a geospatial database of 227 water bodies and watersheds with corresponding Chl-a, nutrient, hydrogeomorphic, and climate data. Then we used a generalized additive modeling approach and developed model selection criteria to select models that most parsimoniously related Chl-a to predictor variables for all 227 water bodies and for 51 lakes in the Laurentian Great Lakes region in the data set. Our best global model contained two hydrogeomorphic variables (water body surface area and the ratio of watershed to water body surface area) and a climate variable (average temperature in the warmest model selection criteria to select models that most parsimoniously related Chl-a to predictor variables quarter) and explained ~ 30% of variation in Chl-a. Our regional model contained one hydrogeomorphic variable (flow accumulation) and the same climate variable, but explained substantially more variation (58%). Our results indicate that a regional approach to watershed modeling may be more informative to predicting Chl-a, and that nearly a third of global variability in Chl-a may be explained using hydrogeomorphic and climate variables