Tidal creeks are the main channels of land-sea ecosystem interactions, and their high dynamics are an important factor affecting the hydrological connectivity of tidal flats. Taking the Yellow River Delta as the research area, we selected remote sensing images obtained during five periods from 1998 to 2018 as the data sources. Based on the spatial analysis function in GIS, the typical morphological characteristics of tidal creeks, such as the level, length, density, curvature, bifurcation ratio, and overmarsh path length (OPL), were extracted to characterize the degree of development of the tidal creeks in the Yellow River Delta wetlands. The spatio-temporal evolution of the tidal creeks was studied, and the development process and the characteristics of the tidal creeks during the different stages of development were investigated. The results revealed that (1) The number, density, and bifurcation ratio of tidal creeks exhibit an increasing trend, but the growth of the trend is slowing. The number of tidal creeks increased by 44.9% from the initial stage of the Yellow River diversion to the late stage of the wetland restoration, but it only increased by 26.2% from the late stage of the wetland restoration to the slow expansion of the Spartina alterniflora. (2) The curvature of the tidal creeks on the landward side is greater than that on the seaward side. (3) The development degree of tidal creek has spatial heterogenetiy, which is Area III > Area II > Area I. (4) The drainage efficiency is significantly correlated with the tidal creak density and bifurcation ratio. Based on the analysis of the various morphological parameters and the drainage efficiency, it was found that after the rapid change in the tidal creek system in the early stage, the tidal creeks entered a state of slow change, and the development state of the tidal creeks tends to be in dynamic balance. The results of this study are expected to provide scientific support for the sustainable development and utilization of coastal tidal flats.
Sea ice is an important part of the global cryosphere and an important variable in the global climate system. Sea ice also presents one of the major natural disasters in the world. The automatic and accurate extraction of sea ice extent is of great significance for the study of climate change and disaster prevention. The accuracy of sea ice extraction in the Yellow River Estuary is low due to the large dynamic changes in the suspended particulate matter (SPM). In this study, a set of sea ice automatic extraction method systems combining image spectral information and textural information is developed. First, a sea ice spectral information index that can adapt to sea areas with different turbidity levels is developed to mine the spectral information of different types of sea ice. In addition, the image’s textural feature parameters and edge point density map are extracted to mine the spatial information concerning the sea ice. Then, multi-scale segmentation is performed on the image. Finally, the OTSU algorithm is used to determine the threshold to achieve automatic sea ice extraction. The method was successfully applied to Gaofen-1 (GF1), Sentinel-2, and Landsat 8 images, where the extraction accuracy of sea ice was over 93%, which was more than 5% higher than that of SVM and K-Means. At the same time, the method was applied to the Liaodong Bay area, and the extraction accuracy reached 99%. These findings reveal that the method exhibits good reliability and robustness.
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