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Abstract

A necessary stage in calculations for prediction purposes is the study of the tsunami recurrence function

which yields mean recurrence of tsunami with maximum wave height not greater than a specified level h. The major problem in using these functions for prediction purposes is the fact that a well‐grounded approximation of empirical data on wave heights is difficult to obtain, because the mathematical model for prediction is an extrapolation of this function for tsunami heights whose recurrence remains uncertain. We shall show that the natural relation of observable tsunamis statistics to extremum statistics leads to the discovery of at least two and possibly three temporal scale intervals with different tsunami modes. It has also been clarified that for the 10 years < T < 103 years range of time periods, which is the most important one for tsunami wave height prediction purposes, the tsunami recurrence is described by two parameters: frequency A of occurrence of large tsunamis and coefficient k of wave ampliflcation near the shore. As an example, a diagram of tsunami hazard zoning of the eastern Honshu coast has been plotted.  相似文献   

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Abstract

Small stands of mangrove trees are difficult to detect and monitor using satellite remote sensing because the width of the narrow strips of vegetation are typically much smaller than the spatial resolution of the imagery. Every mangrove pixel also contains water and bare soil reflectance. Linear spectral unmixing, which estimates the fractional presence of specific land cover types per pixel, was performed on Landsat 8 imagery to detect mangroves on the eastern shoreline of the Bay of La Paz on the Baja California Peninsula of Mexico. Low-altitude aerial imagery collected from a DJI Mavic Pro drone was used as ground-reference data in the accuracy assessment. Continuous fractional presence of mangroves was detected with 80% accuracy and 85% of mangrove area was found. Future work will use linear spectral unmixing to systematically monitor mangrove extent and health in the region relative to expected growth in tourism development.

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