Mangrove forests in Iran are highly productive and complex ecosystems since they represent the interface between land and sea. They are a unique environment for supporting biodiversity, and they provide direct and indirect benefits to humans. Investigating changes in mangrove forests is essential for ecologists and forest managers to improve the assessment and conservation of natural ecosystems. The goals of the present study include: (I) to evaluate and compare classification algorithms and multi-scale satellite imagery to detect mangrove cover in southern Iran, (II) to detect changes in mangrove cover between 1985, 1998, and 2017; and (III) using remote sensing and landscape metrics to modeling the spatial disturbance index to assess the status of mangrove forest. An accuracy assessment was conducted using k-fold cross-validation and independent validation, and differences between the classification techniques were analyzed. The percentages of increasing and decreasing cover over the entire study area are shown in a highlighted changes image made by subtracting the 1985 classification map from the 2017 map. In order to compare classified maps, and provide “from-to” change information, a post-classification comparison was applied to assess changes in mangrove cover area from the different classified images.The studied area was divided into 70 hexagonal zones with the area of 1500 hectares each. Spatial distribution, connectivity, and composition of patches in each zone were measured using 8 spatial metrics (PD, ED, AREA-MN, SHAPE-MN, ENN-MN, CONTIG_MN, SPLIT, and SHID). After standardization of metrics values, the principal component analysis was also performed in order to determine the effective metrics in disturbance and their associate weight in the spatial disturbance model. Although all algorithms produced high overall accuracy (ranging from 81% to 93%) and Kappa values (from 0.81 to 0.92), visual comparisons of the predictions revealed that Random Forest (RF) performed best. The results of the change analysis showed that mangrove cover areas decreased by approximately 4% from 1985 to 1998 and then increased by approximately 8.9% from 1998 to 2017. A change detection map shows a decrease in mangrove cover in near coastal regions, such as the Tabl and Gavarzin areas, and an increase in mangrove cover at a distance from the Qeshm coastline that involves open spaces between the trees.Rising water levels and human development are important factors in the decline of mangroves. According to the spatial disturbance index, five levels were identified in the study area including 1) without disturbance, 2) low disturbance 3) medium disturbance 4) high disturbance and 5) very high disturbance. The results showed that the mentioned classes cover 24772, 22971, 14891, 9000 and 3000 hectares of the studied area, respectively. With the approach used in this study, the disturbances map of the under management forest areas can be prepared periodically and used as one of its monitoring tools. The results of this study could contribute to our understanding of fragmentation from a seascape ecological perspective. The findings of this research are useful for the management, restoration and conservation planning of mangrove forest in southern Iran.