Existing spatiotemporal similarity analysis methods for trajectories have the problems of spatiotemporal unsynchronization and low efficiency in processing large‐scale datasets, which cannot satisfy the increasingly urgent requirements of real‐time or quasi‐real‐time applications. To address these problems, this article proposes a grid‐based and synchronized spatiotemporal similarity analysis method based on the spatiotemporal grid model called gsstSIM. First, a low‐dimensional and multi‐scale trajectory coding representation is implemented based on the spatiotemporal grid model. Second, a synchronized spatiotemporal similarity measure is proposed based on trajectory codes. It transforms the similarity analysis from complex geometric calculations to simple algebraic operations of code sets, which reduces the computational complexity. In addition, the trajectory encoding representation with space‐time collinearity enables gsstSIM to measure the synchronized spatiotemporal similarity. Third, the efficient Multi‐scale grid index, called MSGrid, is established to realize fast query of top‐K similar trajectories for large‐scale datasets. Experimental results demonstrate that gsstSIM is more robust to noise positioning points and various sampling rates than the state‐of‐the‐art algorithms STLCSS, TWS and SWS. It can achieve a second‐level response of spatiotemporal similarity query in processing large‐scale datasets, which is much faster than existing algorithms. The proposed method has promising to support the applications with high time‐efficiency requirements such as epidemic tracking and traffic condition calculation.
Overstocked dumping sites associated with open-pit coal mining occupy original vegetation areas and cause damage to the environment. The monitoring of vegetation disturbance and restoration at dumping sites is important for the accurate planning of ecological restoration in mining areas. This paper aimed to monitor and assess vegetation disturbance and restoration in the dumping sites of the Baorixile open-pit mine using the LandTrendr algorithm and remote sensing images. Firstly, based on the temporal datasets of Landsat from 1990 to 2021, the boundaries of the dumping sites in the Baorixile open-pit mine in Hulunbuir city were extracted. Secondly, the LandTrendr algorithm was used to identify the initial time and duration of vegetation disturbance and restoration, while the Normalized Difference Vegetation Index (NDVI) was used as the input parameter for the LandTrendr algorithm. Thirdly, the vegetation restoration effect at the dumping sites was monitored and analyzed from both temporal and spatial perspectives. The results showed that the dumping sites of the Baorixile open-pit mine were disturbed sharply by the mining activities. The North dumping site, the South dumping site, and the East dumping site (hereinafter referred to as the North site, the South site, and the East site) were established in 1999, 2006, and 2010, respectively. The restored areas were mainly concentrated in the South site, the East site, and the northwest of the North site. The average restoration intensity in the North site, South site, and East site was 0.515, 0.489, and 0.451, respectively, and the average disturbance intensity was 0.371, 0.398, and 0.320, respectively. The average restoration intensity in the three dumping sites was greater than the average disturbance intensity. This study demonstrates that the combination of temporal remote sensing images and the LandTrendr algorithm can follow the vegetation restoration process of an open-pit mine clearly and can be used to monitor the progress and quality of ecological restoration projects such as vegetation restoration in mining areas. It provides important data and support for accurate ecological restoration in mining areas.
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