## 4 Discussion
munconata_ on the Island of Pemba is 9549 seedlings/ha, compared to the 1592 seedlings/ha on Unguja Island [33; 35]. The poor regeneration of _R. munconata_ on Unguja may be caused by the cutting and removal of the mangrove root system [67; 68]. The mangrove forest cover in places such as Kisakasaka, Pete, Muungoni, and Muwanda in Unguja as well as Gawani, Mkoani, Kisiwa Panza, Makoongwe, and Micheweni in Pemba has significantly increased--this could be attributed to increased community participation in the conservation and management of mangroves [69]. Since the mid-1990s, the Department of Forestry in association with the local communities joined hands to rehabilitate the mangroves of Zanzibar [27]. Landsat imagery is highly useful for tracking the spatial changes in mangroves, although it is limited in terms of spatial resolution and was unavailable before the 1970s. What the extent of mangroves was before the use of satellites is unknown; however, satellite images revealed broad variations in mangrove extent over a short period of time, i.e., 47 years. This study found that mangrove forests in Zanzibar could be distinguished from other LCLUs using Landsat Earth observation data. This could be attributable to Landsat's higher spectral resolution, compared to other satellites such as SPOT XS. A crucial step in the processing of remote sensing data is the assessment or validation of accuracy. Previously, studies on satellite images' categorization did not place a high importance on accuracy evaluation [70]. Yet, accuracy assessment has become a crucial step due to the increased potential for inaccuracy that digital imaging presents. An important step in evaluating the results of the classification process is the accuracy assessment [70]. In order to properly use the data, the user of land-cover results must be aware of how accurate the result is [70]. The minimum degree of interpretation accuracy in the identification of land use and land cover classes (LULC) using remote sensing data should be at least 85% [49; 50; 70]. Furthermore, this study's overall accuracies (OA) in both study areas were above 85%, except the 1973 image for Chwaka Bay which recorded an overall accuracy of 82.5%. Overall accuracies in both study bays seem to improve from 1976 to 2020. For instance, the overall accuracies improved from 82.5% to 92.7% in Chwaka Bay and from 85.5% to 94.5% in Menai Bay from 1976 to 2020, respectively. Furthermore, the kappa coefficients improved in Chwaka Bay from 0.72 to 0.89 and from 0.77 to 0.90 in Menai Bay from 1976 to 2020, respectively. According to [70], there are variations between image resolutions in the relative disparities in overall accuracy and kappa coefficients. More precision will be achieved with higher quality images than with lower resolution ones [70]. The commission error is higher in the case of forest-related classes (magnroves, other forests, and agricultural land), which resulted in the classification of a greater number of points that do not fall into a specific category because of the confusion during classification [71]. Equally, the number of points that are incorrectly excluded from a category despite really being a part of that category is also reflected by the omission mistake. In comparison with water, the commission error was less because the nature of water bodies is quite different from other land covers. The average overall kappa coefficient for this study was 0.805 for Chwaka Bay and 0.828 for Menai Bay, which is considered to be significant. Therefore, the ability to accurately discriminate between mangroves, agricultural land, and other vegetation was one of the main hurdles we faced while classifying the Landsat images. This study encountered several obstacles, including the need to collect ground truth data in muddy and watery mangrove forests, and due to frequent cloud cover in Zanzibar, this study was not able to acquire equal study periods between the years, which are appropriate for comparison purposes.