## 5 Analysis and Discussion  
Vegetation illumination and shadow are relative and opposite phenomena whose proportions refer to the number of solar photons that have been reflected into the imaging spectrometer or satellite sensor. It visually appears to be brighter or darker with different degrees of illumination, and the vegetation reflectance spectra also become higher or lower, but in a similar morphological fashion. These fluctuations have greatly influenced large-scale vegetation diagnoses, such as tree species identification and forest health monitoring. Shadows resulting from the vegetation canopy exhibit significant impact on vegetation growth and can be detected using unique features of vegetation, such as red edge and photosynthetic intensity. Previous studies that focus on vegetation shadows have utilized large amounts of data and parameters, and their proposed models have considered detailed observation conditions. These models are encouraged when applied for research on a single tree or small-area forest with detailed ground measurements. Nevertheless, comprehensive surveys on large-scale areas are also important and practical, where the data sources usually depend on satellite images with several spectral bands and the earth surface is too intricate to conduct ground measurement. Quantitative expressions or measurements of illumination and shadow proportion is difficult to calculate. Models or vegetation indexes with concise forms but precise estimation abilities are necessary for satellite resources in large-scale areas. The NDCSI was proposed using two parameters, vegetation red edge and its NDVI. These two parameters have been studied for decades and have been shown to be closely related to vegetation growth conditions and photosynthesis intensity. Vegetation red edge and NDVI display different reactions to changes in the proportions of vegetation illuminated canopy. Measured brightness values from images have been regarded as true ground samples. They were calculated and accumulated using all reflectance values from bands in the visible light range. Results from UAV data (Figure 4) indicate that red edge slope and reflectance remain positive and display a linear correlation with the change in the proportion of illuminated vegetation. In addition, the linear correlations of two tree species can be merged to obtain the same precision. Vegetation red edge is a sensitive indicator of vegetation shadow and eliminates background noise. The value of NDVI is steady, even with different vegetation illumination proportions (Figure 5), and it can describe the morphological characteristics of vegetation, even in circumstances of high shadow proportions. With these two factors, the use of NDCSI was proposed, based on the classic form of NDVI. The normalized equation is restricted to values ranging from 0 to 1, avoiding overflowed data. Results from Sentinel-2A imaging (Figure 9) have resolved the mixed multispectral data into fractional cover of illuminated vegetation and shaded vegetation using LSMA. The results indicate that NDCSI displays an advantage for further application, especially for its concise formula and easily available parameters. The construction of NDCSI can be applied for many follow-up studies and contribute to further our understanding of shadow impacts on vegetation growth. For example, NDCSI and its theory can be used to have a long-time monitoring to observe and calculate differences of vegetation growth states between the two sides of the hillside using multitemporal satellite data. It can also be used with other vegetation indexes to unmix and exclude shadow interference and influence on satellite data. Images of not only the vegetation canopy but also the soil background can be affected by shadow phenomena.