Abstract
Coastal tidal flats are important ecological resources. As the dividing line between marine and terrestrial ecosystems, tidal flats provide a large number of ecosystem services. However, with the excessive development of coastal areas, tidal flat resources have been drastically reduced, leading to the deterioration of coastal ecosystems. There is an urgent need to acquire accurate information on the changes in tidal flat resources. This research proposes a tidal flat extraction model (RF-W model) that combines the random forest (RF) method and waterline method, which aims to improve the accuracy of tidal flat extraction. This method can effectively eliminate the shortcomings of the RF method in determining the lower boundary of tidal flats and those of the waterline method in distinguishing river channels and tidal flats. The tidal flat extraction of Qingdao Jiaozhou Bay in 2020 is performed as an example of the model. The results show that the user’s and producer’s accuracies of the RF-W model were both the highest, indicating that the improved model can accurately extract tidal flat information. Then, we used the RF-W model to extract tidal flat information for Jiaozhou Bay in seven periods (1990, 1995, 2000, 2005, 2010, 2015, and 2020) and to study the spatiotemporal changes in the tidal flats and influencing factors from 1990 to 2020. The tidal flat area of Jiaozhou Bay showed an overall downward trend before 2015, and the area decreased by 21.9 km2, with a reduction in the rate of approximately 1.1%/year. After 2015, the tidal flat area rebounded slightly. The overall change in Jiaozhou Bay showed reclamation and expansion toward the sea. The reduction in the sand content of the rivers entering the sea, reclamation and cultivation, and land development were the main factors contributing to the reduction in the tidal flat area in Jiaozhou Bay. In addition, sea level rise due to climate warming is a long-term potential factor.
Highlights
Tidal flats refer to the tidal flooding zone between the high tide line and the low tide line in the coastal zone, and they are mostly distributed in estuaries [1]
Compared with the waterline method, extracted by the random forest (RF) method were more detailed, allowing the effective dist rivers, tidal flats, and creeks and the accurate extraction of tidal flats (Figu waterline method used multiple images to extract low tide point sets to d in the editor toolbar to select a point in the extraction result of the RF method, select the trace element tool to identify the lower boundary extracted by the waterline method, and return to the starting point to form a closed curve to obtain the final tidal flats extraction result (Figure 9b)
This study proposes an improved model (RF-W model) for tidal flat extraction, which combines the RF method and the waterline method, and carries out model verification through the extraction of tidal flats in Jiaozhou Bay in 2020
Summary
Tidal flats refer to the tidal flooding zone between the high tide line and the low tide line in the coastal zone, and they are mostly distributed in estuaries [1]. Many studies have used edge detection or threshold segmentation to extract water lines and obtain high tide lines and low tide lines to determine the tidal flat range and study its temporal and spatial changes [13,14,15]. Multisource remote sensing images are used to extract tidal flat information in the study area through the RF classification method. The RF-W m proposed in this paper is used to extract the tidal flats in the Qingdao Jiaozhou Bay co zone at 5-year intervals (1990–2020) and to study their temporal and spatial changes point set to determine the tidal flat range. FLAASH atmospheric correction model in ENVI [28], and the image to be classified atmospheric including the radiation, irradiance, and photochemical in the Jiaozhou conditions, Bay area was obtained by cropping.
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