Remote sensing water depth inversion has the advantages of large-scale comprehensive coverage, based on pixel scale, and uniform spatial distribution. It overcomes the shortcomings of traditional depth measurement methods and compensates for the disadvantages of traditional measurements in terms of uncontinuous, multi-point, and real-time measurement of vast water areas. This paper proposes a technique for geographically adaptive multispectral remote sensing water depth inversion based on BP neural networks, and tests it using data from Resource 1 satellite (02E) to Gaoya Reservoir in Shandong Province. The results show that the error in elevation is ±1.04m, and the error distribution shows randomness.
Owing to the interplay of natural forces and human-driven developmental endeavors, the existing continental coastline could no longer precisely depict the foundational state of Shandong Province’s coastline. Shandong Province embarked on a fresh round of continental coastline surveys. Based on remote sensing and field investigation, this survey obtained the location of the coastline and related data more efficiently and accurately. Based on the results of this survey, this paper studied the overall changes of the coastline in the past ten years, delves into the examination of typical regions like the Yellow River Delta, Laizhou Bay, employing them as case studies to dissect the principal changes and underlying causes behind the shifts in the province’s continental coastline.
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