# Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation
## 3 Materials and Methods
### Overview of the Study Area
#### 3.1.3 Vegetation Cover  
In conjunction with the climatic characteristics of the vast majority of counties (cities) in the study area, the annual summer could usher in the same period of rain and heat. As verified by the Sichuan Statistical Yearbook 2017 (data webpage: [[http://tjj.sc.gov.cn/scstjj/c105855/nj.shtml](http://tjj.sc.gov.cn/scstjj/c105855/nj.shtml)]([http://tjj.sc.gov.cn/scstjj/c105855/nj.shtml](http://tjj.sc.gov.cn/scstjj/c105855/nj.shtml)), accessed on 13 May 2013), the average precipitation in a single month during the summer period will be 180 mm, 2-3 times more than in other months, with an average temperature of about 25 degrees Celsius. To focus on the influence of topography on plant growth, it is important to eliminate the effect of other factors. Hence, the remote sensing data used in this study are Landsat-8 satellite images taken in May 2017. It is mid to late spring in the northern hemisphere, when the snow and ice cover has largely melted, making it easy to use supervised classification methods to delineate vegetation growth areas. In addition, the temperature and rainfall of May do not reach the optimum stage of vegetation growth, and the topographic effect of the site environment will be more prominent in this stage [42; 43], offering a more direct and higher reference value for the field of DTA. Landsat-8 is equipped with an operational land imager (OLI) sensor that uses a pansharpening spectral range to make the contrast between vegetation and non-vegetation on panchromatic images become large. The advanced technical design also eliminates the effect of partial water vapor absorption [44], providing a reliable and stable data source for accurate acquisition of vegetation information. The five images downloaded from the National Geospatial Data Cloud were all shot in 2017 (Data webpage: [[https://www.gscloud.cn/](https://www.gscloud.cn/)]([https://www.gscloud.cn/](https://www.gscloud.cn/)), accessed on 4 May 2023); one was taken on 26 May, two on 8 May, and the remaining two on 1 May. With ENVI 5.3 software as the platform, the visual interpretation of the acquired remote sensing images was enhanced after a series of operations such as radiometric calibration, atmospheric correction, geometric correction, and edge enhancement. By using the Computer ROI (Region of Interest) Separability tool in ENVI 5.3 software, it was possible to calculate the statistical distances between vegetated and non-vegetated areas (including urban and rural residential, transportation land, construction land, and water). This index measures the separability of the training samples based on the Jeffries-Matusita distance and the transformation separation degree. The value of separability ranges from 0 to 2. It is qualified when the value is greater than 1.8 [45], and the larger the separation degree is, the easier it is to distinguish the two geographical features. Upon calculation, the separability of vegetation from other features was 1.947624, 1.966721, 1.994156, and 1.979636, respectively. Given that the separability of vegetation from other features is greater than 1.9, the operability of supervised classification is illustrated at a theoretical level [45]. Based on existing studies [46; 47; 48], it is known that Maximum Likelihood is the nonlinear classification method that minimizes the probability of classification error based on the Bayesian criterion. The classifier's excellent and robust classification has been demonstrated in numerous studies, making it ideal for this study. Meanwhile, a clustering method [49] is applied to smooth the fragmented patches in the classification results, and the smoothed supervised classification results are used as the initial classification data.