## 2 Materials and Methods
### Methods
#### 2.3.1 Data Preprocessing and Extraction of _U. prolifera_  
Similarly to land vegetation, _U. prolifera_ has a reflection valley in the red wavelength, and a peak in the near-infrared. Based on the spectral reflectance differences of ground objects, the ratio operation can highlight the characteristics of vegetation in images, identify the plant category, and extract the green biomass of the plant. A series of indices based on these principles have been used to delineate _U. prolifera_; for example, the NDVI index [9; 14; 20], which is the best indicator of the growth state and coverage area of land vegetation, and which is widely used in vegetation remote sensing [27]. Compared with other parameters, such as FAI and VB-FAH, NDVI is more sensitive to perturbations from sun glint and possibly from aerosols, and the value is easily saturated, and the sensitivity to high vegetation density region is reduced. However, NDVI has a wide adaptability to different remote sensing images, which also can partially eliminate the influence of radiation changes that are related to atmospheric conditions such as solar altitude angle, satellite observation angle, terrain, and cloud shadow.  
The distribution of _U. prolifera_ can usually be expressed in two areal forms, the coverage area and the distribution area. The former is a collection of pixels of _U. prolifera_ that can be directly identified in the remote-sensing images. Clustered pixels create patches with different shapes and sizes, which is also called the scattered fragments of _U. prolifera_. The distribution area, which can also be called the influence area, aggregates scattered fragments of the _U. prolifera_ coverage area into relatively large fragmented areas, including all of the patches and the region among the adjacent patches.  
Figure 2: Research flow chart for this study.  
In this study, the MODIS images were first preprocessed. Based on MOD03 data and the MODIS Swath Tool (MST) software, the geometric correction results of MOD02 QKM data were obtained in batch form. Then, radiometric calibration, atmospheric correction, image splicing, and clipping, and band fusion of the remote-sensing images were carried out by the Environment for Visualizing Images (ENVI) using the Interactive Data Language (IDL). After sea-land separation and cloud masking, the distribution information of _U. prolifera_ was obtained by calculating the NDVI, which was first applied by [PERSON] et al. [28] in the Great Plains study, and which can be calculated as:  
\[NDVI=\frac{R_{NIR}-R_{RED}}{R_{NIR}+R_{RED}} \tag{1}\]  
where \(R_{NIR}\) and \(R_{RED}\) represents the near-infrared and red bands, respectively.  
The value of \(NDVI\) is between \(-1\) to \(1\), negative values indicate cloud, water, snow, and some other objects with highly reflecting to visible light; \(0\) means rock or bare soil, while \(NIR\) and \(R\) are approximately equal; a positive value indicates that there is vegetation coverage, which increases with the increase of coverage. The threshold selection is mainly based on these rules.  
By combining gray segmentation with manual interpretation, the influence and coverage range of the green tide was determined. Then, the layer was transformed into vector data, and the ArcGIS software was used to calculate the area of the vectorial map patches. Finally, the temporal-spatial information describing the coverage area and the distribution margin of _U. prolifera_ were obtained.