## 2 Materials and Methods
### Data and Methodology
#### 2.2.4. Mangrove LAI and GPP  
LAI and GPP are both useful for studying the coverage, health, and potential of carbon assimilation and storage [47]. Mangrove forests have been proven as the highest carbon stock medium; nevertheless, the role of mangrove forest in carbon assimilation and its processes from local to global scale is less understood. This is due to the difficulties of data collection in the mangrove forests. Plant leaves through the photosynthesis process convert the atmospheric CO\({}_{2}\) into nutrients and biomass to support plant growth. The use of LAI to estimate the ecosystem carbon uptake by linking it with GPP is common [48; 49]. Remote sensing products were applied in this study to infer the leaf density and productivity of mangrove areas, aimed at detecting the impact of mangrove changes to the products (LAI and GPP) relevant to carbon stock.  
To identify the impact of mangrove loss and fragmentation over years, the MODIS LAI and GPP of the permanent wetlands (PW) (based on MODIS IGBP land cover classification) were extracted. LAI can be used to describe the leaf density and therefore the vegetation condition/health/ photosynthesis rate and light use efficiency of the mangrove trees [33; 34]. We chose LAI as the vegetation index because it is often widely applied to high leaf and tree density zones, including mangrove forests [50], and LAI has been proven to have a strong relationship with Normalized Difference Vegetation Index (NDVI) at different spatial resolutions [51; 52]. Meanwhile, MODIS GPP is an indication of the amount of CO\({}_{2}\) assimilated by the ecosystem; thus, it can be used to identify carbon gain/loss due to any changes occurring in the ecosystem. Here, we used GPP rather than NPP because (1) GPP has more direct relations to the photosynthesis rate, leaves, and biomass growth, (2) NPP requires the input of respiration (data is very hard to acquire and not available in the study site), and (3) MODIS only distribute the NPP product at an annual timespan, while we aim to compare GPP and LAI at finer temporal scales (8-days). The values of LAI and GPP were extracted and their statistical values were computed to identify if any loss  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Fragmentation Index** & **Definition** \\ \hline \multirow{2}{*}{Area} & Class Area (CA) & Sum of areas of all patches of a given class \\  & Total Landscape Area (TLA) & Sum of areas of all patches in the landscape \\ \hline \multirow{2}{*}{Patch size} & Number of Patches (NP) & Total number of patches in the class/landscape \\  & Mean Patch Size (MPS) & Average patch size \\ \hline \multirow{2}{*}{Edge} & Total Edge (TE) & Total perimeter of patches \\  & Edge Density (ED) & Amount of edge relative to the landscape area \\ \hline \multirow{4}{*}{Shape} & Mean Shape Index (MSI) & Average perimeter-to-area ratio \\  & Average Weighted MSI (AWMSI) & MSI divided by weighted patches area \\ \cline{1-1}  & Mean Perimeter Area Ratio (MPAR) & Average of patch perimeter to patch area ratio \\ \cline{1-1}  & Mean Patch Fractal Dimension (MPFD) & Average of log-transformed patch perimeter to \\ \cline{1-1}  & log-transformed patch area ratio \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of metrics in the Patch Analyst extension used for fragmentation analysis [46].  
or gain had occurred in LAI or carbon intake by mangroves (to the selected permanent wetlands pixels).