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Clinical exercise tests are used primarily for diagnosis of coronary insufficiency or appraisal of working capacity. Occasionally hemodynamic responses are observed during diagnostic catherization of the heart. Since interpretation of findings depends upon quantitative differences from normal values, variations due to age, sex, physical status and circumstances of the test need to be appreciated. To be effective, exercise stresses must involve large muscle masses, and the patient or subject must be ambulatory, cooperative and properly motivated. Some of the approaches to testing will be reviewed, and preliminary observations on a new technique will be presented.The simplest test of exercise tolerance is for a physician to accompany a cardiac patient over one or more flights of stairs and to observe the symptoms. If this is not rewarding, increasing the work load by accelerating the pace or taking two steps at a time is more likely to be informative. But under these conditions the work loads are not well standardized, physiological responses are not usually monitored, and the physician is not necessarily of the same sex and comparable age.Master's two step test is familiar to most internists; its value for diagnosis and prognosis of coronary insufficiency has been established. 1,2 An abnormal resting electro-cardiogram is considered a contraindication for performance of this test. Although interpretation of results depends upon electrocardiographic changes, these have not been monitored routinely during exertion, even though a suitable technique was described 10 years ago. 3 The exercise stress varies with age, sex and body weight, but in terms of energy expenditure per unit of weight the underweight individual is stressed much more than the overweight person.

Accurately quantifying surface water extent in wetlands is critical to understanding their role in ecosystem processes. However, current regional-to global-scale surface water products lack the spatial or temporal resolution necessary to characterize heterogeneous or variable wetlands. Here, we proposed a fully automatic classification tree approach to classify surface water extent using Sentinel-1 synthetic aperture radar (SAR) data and training datasets derived from prior class masks. Prior classes of water and non-water were generated from the Shuttle Radar Topography Mission (SRTM) water body dataset (SWBD) or composited dynamic surface water extent (cDSWE) class probabilities. Classification maps of water and non-water were derived over two distinct wetlandscapes: the Delmarva Peninsula and the Prairie Pothole Region. Overall classification accuracy ranged from 79% to 93% when compared to high-resolution images in the Prairie Pothole Region site. Using cDSWE class probabilities reduced omission errors among water bodies by 10% and commission errors among non-water class by 4% when compared with results generated by using the SWBD water mask. These findings indicate that including prior water masks that reflect the dynamics in surface water extent (i.e., cDSWE) is important for the accurate mapping of water bodies using SAR data.

No abstract

In order to produce useful hydrologic and aquatic habitat data from the Landsat system, the U.S. Geological Survey has developed the “Dynamic Surface Water Extent” (DSWE) Landsat Science Product. DSWE will provide long-term, high-temporal resolution data on variations in inundation extent. The model used to generate DSWE is composed of five decision-rule based tests that do not require scene-based training. To allow its general application, required inputs are limited to the Landsat at-surface reflectance product and a digital elevation model. Unlike other Landsat-based water products, DSWE includes pixels that are only partially covered by water to increase inundation dynamics information content. Previously published DSWE model development included one wetland-focused test developed through visual inspection of field-collected Everglades spectra. A comparison of that test’s output against Everglades Depth Estimation Network (EDEN) in situ data confirmed the expectation that omission errors were a prime source of inaccuracy in vegetated environments. Further evaluation exposed a tendency toward commission error in coniferous forests. Improvements to the subpixel level “partial surface water” (PSW) component of DSWE was the focus of this research. Spectral mixture models were created from a variety of laboratory and image-derived endmembers. Based on the mixture modeling, a more “aggressive” PSW rule improved accuracy in herbaceous wetlands and reduced errors of commission elsewhere, while a second “conservative” test provides an alternative when commission errors must be minimized. Replication of the EDEN-based experiments using the revised PSW tests yielded a statistically significant increase in mean overall agreement (4%, p = 0.01, n = 50) and a statistically significant decrease (11%, p = 0.009, n = 50) in mean errors of omission. Because the developed spectral mixture models included image-derived vegetation endmembers and laboratory spectra for soil groups found across the US, simulations suggest where the revised DSWE PSW tests perform as they do in the Everglades and where they may prove problematic. Visual comparison of DSWE outputs with an unusual variety of coincidently collected images for locations spread throughout the US support conclusions drawn from Everglades quantitative analyses and highlight DSWE PSW component strengths and weaknesses.