# NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
## 2 Materials and Methods
### Methodology and Implementation
#### 2.2.2 Vegetation Cover  
Regarding the vegetation cover classification, three vegetation classes have been extracted from the High-Resolution Layers of the CLMS: deciduous (broadleaved) and coniferous trees and grasslands for Valais. The limits of the canton have been obtained from the swissBOUNDARIES3D product ([[https://www.swissstopo.admin.ch/en/landscape-model-swissboundaries3d](https://www.swissstopo.admin.ch/en/landscape-model-swissboundaries3d)]([https://www.swissstopo.admin.ch/en/landscape-model-swissboundaries3d](https://www.swissstopo.admin.ch/en/landscape-model-swissboundaries3d)) accessed on 03 August 2023).  
To retrieve the shrulands layer, we followed the methodology proposed by [49,50]. All available Sentinel-2 scenes over Valais for the years 2017 to 2021 from 1 September to 1 November have been extracted in GEE. Then, the Sentinel-2 cloud probability product has been applied on every scene with a threshold of 0.65 to remove the clouds and clouds shadows. Third, the NARI and NCRI were computed on all scenes, and a median composite per year was generated. Finally, we computed a median of the 5 years.  
The NARI is sensitive to the plant canopy anthocyanin content. Using this index, ref. [50] developed a methodology to improve the mapping of mountain shrulands. As they noted, shrulands are dominated in the European Alps by Ericaceae (i.e., _Vaccinium_ spp. and _Rhododend ferriteum_). These species have the particularity to accumulate red anthocyanin pigments in the late autumn. This characteristic offers an opportunity to differentiate Ericaceae shrulands from other vegetation types. Indeed, as demonstrated by [50], their presence is often underestimated and confounded with grasslands. Therefore, Sentinel-2 data could be useful to enhance the discrimination of shrulands using the NARI index that is computed as follows [50]:  
\[\text{NARI}=\frac{\frac{1}{\rho\text{ Green}}-\frac{1}{\rho\text{ Red}-\text{edge}}}{\frac{1}{\rho\text{ Green}}+\frac{1}{\rho\text{ Red}-\text{edge}}} \tag{1}\]  
where \(\rho\) Green is the reflectance in the Green band, \(\rho\) Red-edge is the reflectance of the Red-edge band.  
The NCRI is a normalized adjustment of the canopy chlorophyll content proposed by [49]. It allows us, in complement to NARI, to discriminate forest from Ericaceae shrublands and grasslands. NCRI is calculated using Sentinel-2 band 5 (Red-edge) and band 7 (Red-edge 2) [49]:  
\[\text{NCRI}=\frac{\frac{1}{\frac{\rho\text{ Red-edge}}{\rho\text{ Red-edge}}}-\frac{1}{\frac{1}{\rho\text{ Red-edge}}}}{\frac{1}{\rho\text{ Red-edge}}+\frac{1}{\rho\text{ Red-edge}^{2}}} \tag{2}\]  
where \(\rho\) is the reflectance in the respective Red-edge or Red-edge 2 band.  
Following the results of [49], we applied a threshold of \(>\)0.325 for the NARI and a threshold of \(<\)0.42 for the NCRI to discriminate among the Ericaceae shrulands.  
The resulting shrulands layer has a resolution of 20 \(\times\) 20 m. This raster layer has been resampled and reprojected using the nearest neighbor method to correspond to the \(10\times 10\) m resolution of the Copernicus High Resolution layers. It is important to note that this methodology does not allow for the identification of non-Ericaceae ever-green shrulands like Juniperus and, consequently, results in an incomplete mapping of shrulands [49; 50].  
The final vegetation cover map was obtained by attributing the corresponding classes to each pixel of Valais in a single raster layer.