2.3.2. Image Fusion

We selected Gram–Schmidt (GS) [61] and principal component analysis (PCA) [62] among many other available image fusion methods to generate higher quality (spectral and spatial resolution) MS images. These two methods presented better results compared to the modified intensity–hue–saturation (IHS) and Brovey transformation (BT) methods in a study by Quang et al. (2019) [15]. In the GS fusion technique, suitable weights assigned to the high-resolution panchromatic (PAN) layers are simulated from lower spatial multispectral bands [61,63]. Inverse GS image sharpening is then used to form the pan-sharpened spectral bands [64].

PCA is a statistical technique that identifies the key variability among variables within a dataset, reducing it to fewer dimensions or "components" of related variables that are uncorrelated with each other [14]. In this study, we fused a SPOT-7 multispectral band, a panchromatic band and a Sentinel-1 VH or VV layer once for each image fusion method to generate fused images prior to mangrove type classification.

#### 2.3.3. Mangrove Species Categorization

Mangrove species mapping is a common application of hyperspectral remote sensing data [65,66]. Other useful information for mangrove species parcellation that can be derived from SAR RS data includes general structural information in relation to mangrove zonation [67]. Hyperspectral remote sensing data tend to provide finer-detailed information (reflectance and finer spatial resolution). An analysis of SAR backscatters on di fferent mangrove species can help to separate mangrove species

as well as provide a better understanding of the e ffects of di fferent polarizations on the radar scattering for the target geographical features [68]. Hence, we employed the fused SPOT-7 with Sentinel-1 images to classify the mangrove species, applying the SVM classifier as its presentation is most accurate for mangrove age estimations. Additionally, the SPOT-7 image and the S1 VH were used separately for classifying mangrove types for comparisons with the fused images also applying the SVM classifier.

#### 2.3.4. Mangrove Extent Classification

When it is di fficult to obtain a su fficiently comprehensive set of training sites to apply a supervised classification approach, unsupervised classifications could be suitable options [28] to deliver acceptable outputs. We applied the iterative self-organizing data analysis technique (ISODATA) unsupervised classifier for nine Landsat-X datasets from 1975 to 2019 since it generated more reliable results (81.7%) than the K-means method (77.3%) in an examination by El-Rahman (2016) [69]. As typical land use and land cover (LULC) in the study site are agriculture (rice), aquaculture, residence, water bodies, and mangrove forest, we defined 10 to classify and five for maximum iterations, while other parameters were set to default. The result of ISODATA unsupervised classification of the Landsat-8 (2019) was examined for accuracy using the ground-truth data and compared with results from the SPOT-7. The results of all Landsat image classifications were used for mapping mangrove extent changes. The post-classification processes after ISODATA classification were done for all the years, an accuracy assessment for year 2019, and converting classified layers to vector files to enable the subsequent calculation of statistics.
