## 1 Introduction  
Since the large-scale outbreak in 2008 [1; 2; 3; 4; 5; 6; 7], the green tide in the Yellow Sea has occurred annually. Through years of monitoring, the process of green tides is usually divided into five stages: occurrence, development, outbreak, decline, and extinction [4; 5; 6; 7; 8]. Each year, the green tide originates from the radial sandbanks in northern Jiangsu in early May and drifts northward under the influence of wind and ocean currents. The green tide reaches its maximum scale in the waters near the southern coast of Shandong around late June or early July. From early to mid-July, the green tide begins to decline and gradually disappears in mid-to-late August. As the green tide drifts from south to north and east, it causes varying degrees of harm to aquaculture, water sports, tourism and marine ecology [4; 5; 6; 7; 8; 9; 10; 11]. Therefore, the Marine Management Department of the area affected by the disaster expends a lot of workforce and material resources in preventing and eliminating the green tide disaster [3; 4; 5]. Satellite remote sensing, with its wide range and advantage of instantaneous data acquisition, has become the primary data resource for monitoring green tides in the YellowSea and the only way to obtain complete green tide information [2; 3; 4; 5; 6; 12; 13]. Satellite remote sensing typically employs vegetation indices to extract green tide information. Vegetation indices effectively enhance vegetation information while mitigating interferences from imaging angles, solar glare, the atmosphere, and other factors. Commonly used indices include the Normalized Difference Vegetation Index (NDVI) [2; 8; 12], Normalized Difference Algae Index (NDAI) [14], Floating Algae Index (FAI) [15], Scaled Algae Index (SAI), Index of floating Green Algae for GOCI (IGAG) [16], Virtual-Baseline Floating macroAlgae Height (VB-FAH) [17], and Multispectral Green Tide Index (MGT) [18]. Among these, the NDVI only requires two bands, red and near-infrared, making it applicable to all satellite imagery and the most widely used in operational applications and green tide research. During green tide satellite remote sensing emergency monitoring, firstly, satellite imagery is used to detect the green tide patches, and then, patches are used to produce the distribution envelope [2; 5; 12]. Finally, the distribution envelope vertices are used as the initial field for predicting the drift of the green tide [19; 20; 21; 22]. Based on the monitoring and drift prediction results, disaster management plans are determined [22; 23]. The main disaster management methods include ship-based removal, fence-based blocking, and coastal cleanup. At present, the distribution envelope of green tide can be delineated by manual interpretation [2; 24] or computer analysis based on the green tide patches [25]. The manual interpretation option has the following advantages: the quantity of distribution envelope vertices is moderate, they can be directly used as the initial field, and predicting drift requires only a short calculation. The weaknesses of this method include intense subjectivity, a lack of uniform standards, and the fact that different scientists analyzing the same green tide patches will draw different distribution envelopes. The second option (computer analysis) uses the buffer analysis method. The buffer analysis tool of ArcGIS is usually applied to form a green tide distribution envelope by buffering the green tide patches [25]. The distribution envelope formed by this method is automatic and standard. However, the number of distribution envelope vertices often reaches tens or hundreds of thousands. Due to the high computational pressure, they cannot be used directly as initial fields for drift prediction and must be simplified. A simplification method for polygons created by buffer analysis has not yet been reported. Existing polygon simplification models include the equidistance method, the ray-barrier method, the vertical distance tolerance method, and the Douglas-Peucker method [26; 27; 28; 29].