## 3 Results and Discussion  
### Potential of the NDVI in Analyzing Mangrove Condition  
The distribution of the average value of the Normalized Difference Vegetation Index (NDVI) in the space and the area of each NDVI category from 2015 to 2019 is presented in Table 3 and Figure 2, respectively. The solidity of vegetation (NDVI \(>0.3\)) mainly concentrated on the core zone and part of the buffer zone (accounting for 6321.22 ha and 28,375.48 ha, respectively), while the rest of the classifications (NDVI \(<0.3\)) were mostly distributed in the transition zone of the study area.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & **Ecological Zones** & **Transition** & **Buffer** & **Core** \\ \hline Lower than 0.2 & 12,685.91 & 13,364.16 & 1682.58 \\ From 0.2 to 0.3 & 2229.38 & 995.73 & 92.89 \\ From 0.3 to 0.4 & 2198.65 & 1341.00 & 131.08 \\ Higher than 0.4 & 2502.80 & 27,034.48 & 6190.14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The statistical area (ha) of NDVI categories through three ecological zones during the 2015–2019 period.  
Figure 2: Spatial distribution of the mean Normalized Difference Vegetation Index (NDVI) classifications on three ecological zones during 2015–2019 period.  
The relationship between the training variable (NDVI \(>\) 0.3) and testing variable (recorded mangrove) presented in Table 3 contributed a considerable validation of the NDVI to distinguish mangrove forests from non- mangrove. A high value of O-index above 0.85 showed the precise accuracy between the two mentioned variables (Table 4).  
Based on the overlap similarity algorithm between the NDVI and the mangrove status in 2015, the value of the mangrove forests derived from the NDVI was observed at above 0.3, as presented in Figure 3. A similarity in shape and distribution was illustrated in Figure 3a,b between mangrove populations derived from NDVI indices and the referenced mangrove map. Previous studies implemented the mangrove classifications in the world using the NDVI, obtained from high-resolution Quick Bird multispectral satellite imagery with a 2.4 m spatial resolution and a ground truth measurement [10; 45]. These studies also observed that the mangrove species obtained from the NDVI value was recorded as above 0.3. This enables us to find an appropriate signal of the spectral index for the characterization of mangrove coverage in the study area.  
Additionally, this study concentrated on characterizing mangrove patterns through three primarily ecological zones of a biosphere reserve (i.e., transition, buffer, and core zones), while most of the previous studies mainly focused on recording mangrove population via its association with non-mangrove areas, or analysis based on the administrative boundary. Moreover, a validation of the NDVI showing mangrove population is presented in Table 5.  
A total of 300 randomly distributed ground truth training datasets of the mangrove area was collected using portable GPS. The substantial agreement was obtained with a Kappa statistic agreement of 0.7 and an overall classification accuracy of 83%. The accuracy of the NDVI categories in this study reveals the applicability of the vegetation index derived from Sentinel-2 for mangrove patterns across space and time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Transition** & **Buffer** & **Core** \\ \hline \(O\) index & 0.86 & 0.89 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The overlap index between mangrove of NDVI and referenced mangrove in 2015.  
Figure 3: A comparison between (**a**) mangrove cover derived from NDVI value and (**b**) reference map of mangrove forests in 2015.