The Sign of the Fall, the Scars of Orestes and Odysseus

Classic

Continental land cover classification using meteorological satellite data

The use of the National Oceanic and Atmospheric Administration's advanced very high resolution radiometer satellite data for classifying land cover and monitoring of vegetation dynamics over an extremely large area is demonstrated for the continent of Africa. Data from 17 imaging periods of 21 consecutive days each were composited by a technique sensitive to the in situ green-leaf biomass to provide cloud-free imagery for the whole continent. Virtually cloud-free images were obtainable even for equatorial areas. Seasonal variation in the density and extent of green leaf vegetation corresponded to the patterns of rainfall associated with the inter-tropical convergence zone. Regional variations, such as the 1982 drought in east Africa, were also observed. Integration of the weekly satellite data with respect to time produced a remotely sensed assessment of biological activity based upon density and duration of green-leaf biomass. Two of the 21-day composited data sets were used to produce a general land cover classification. The resultant land cover distributions correspond well to those of existing maps

Swath Mapping on the Continental Shelf and Slope: The Eel River Basin, Northern California

First Paragraph The STRATAFORM program sponsored by the Office of Naval Research (Nittrouer and Kravitz, 1996, this issue) seeks to understand how sedimentary processes lead to the formation of the stratigraphic sequences on continental margins. A central challenge facing this effort is to understand the transport of sediments in shore-parallel as well as shore-perpendicular directions• Multidimensionality is necessary to describe, for example, the accumulation of sediments from river inputs, the distribution of gullies and canyons on the slope, the meandering of channels, and the structure of slumps and slides

Multiscale spectral analysis of bathymetry on the flank of the Mid-Atlantic Ridge : modification of the seafloor by mass wasting and sedimentation

Author Posting. © American Geophysical Union, 1997. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 102, no. B7 (1997): 15447–15462, doi:10.1029/97JB00723.The results of a multiscale spectral analysis of bathymetric data on the flank of the Mid-Atlantic Ridge are described. Data were collected during two cruises using Hydrosweep multibeam (tens of kilometers to ∼0.2 km scale range) and Mesotech scanning pencil-beam sonar attached to remotely operated vehicle Jason (∼1 km to ∼0.5 m scale range). These data are augmented by visual data which enabled us to identify bathymetric profiles which are over unsedimented or thinly sedimented crust. Our analysis, therefore, is focused primarily on statistical characterization of basement morphology. Work is concentrated at two sites: site B on ∼24 Ma crust in an outside-corner setting, and site D on ∼3 Ma crust in an inside-corner setting. At site B we find that an anisotropic, band-limited fractal model (i.e., the “von Kármán” model proposed for abyssal hill morphology by Goff and Jordan [1988]) is not sufficient to describe the full range of scales observed in this study. Our observations differ from this model in two ways: (1) strike and cross-strike (dip) spectral properties converge for wavelengths smaller than ∼300 m, and (2) in both strike and dip directions the fractal dimension changes at ∼10 m wavelength, from ∼1.27 at larger scales to ∼1.0 at smaller scales. The convergence of strike and dip spectral properties appears to be associated with destruction of ridge-parallel fault scarps by mass wasting, which develops canyon-like incisions that cross scarps at high angles. The change in fractal dimension at ∼10 m scale appears to be related to a minimum spacing of significant slope breaks associated with scarps which are created by faulting and mass wasting. At site D, although there is no significant abyssal hill anisotropy, the spectral properties at all scales are consistent with the von Kármán model. The fractal dimension at this site (∼1.15) is less than at site B. This difference may be reflect different morphology related to crustal formation at inside-corner versus outside-corner position or, more likely, differences in the degree of mass wasting. The smoothing of seafloor morphology by sediments is evident in Hydrosweep periodograms where, relative to basement roughness, spectral power decreases progressively with decreasing wavelength.This work was supported under ONR grants N00014-94-1-0197 and N00014-96-1-0462 (J.A.G.) and N00014-90-J-1621 and N00014-94-1-0466 (B.E.T.)

Cross-Modal Health State Estimation

Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geospatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.Comment: Accepted to ACM Multimedia 2018 Conference - Brave New Ideas, Seoul, Korea, ACM ISBN 978-1-4503-5665-7/18/1