محسن لطفی نصیرآباد

Mangrove forests are one of the important plant ecosystems established across the intertidal zones and consist of evergreen species. According to Food and Agriculture Organization (FAO) reports, the area of world mangrove forests is almost 14.6 million ha and more than 40% of them are located in Asia. Indonesia has the largest mangrove forests with 2.3 million ha with the highest richness. Moreover, Iran with approximately 10,000 ha of mangrove forests in northern parts of the Persian Gulf and Oman Sea is one of the countries with mangrove ecosystems. The ecological and socio-economic importance of mangrove forests is evident to researchers and managers, however, an annual quantitative and qualitative decrease in these forests happens due to natural (e.g., storm) and anthropogenic (e.g., overexploitation) factors. Therefore, it seems essential to develop a practical approach in order to protect the present sites and improve the management, monitoring, and assessment of mangrove forests. The first step in every management and conservation plan in mangrove forests is mapping their spatial distribution and monitoring the spatial changes. It is important to find efficient methods for mensuration and assessment of temporal and spatial changes of mangrove forests for their efficient management and conservation. Field measurement difficulties in these ecosystems result in the rapid development of remote sensing data in mangrove mapping. However, previous studies have shown that common vegetation indices are not efficient in mangrove classification because of the high greenness and moisture content of leaves. Assessing the spectral signature of mangrove forests, researchers have designed specific indices for mangrove classification on satellite imagery. Since the mangrove indices have been recently developed, their efficiency in similar conditions has not been investigated, while they have been compared to some vegetation indices or individually investigated in case studies. Additionally, the mangrove indices have not been applied in mapping mangrove forests of southern Iran. Therefore, the aim of this study was a comparison of eight mangrove indices in mapping mangrove forests of Nayband Gulf (Bushehr province), Sirik (Hormozgan province), and Govatr Gulf (Sistan-Baluchestan province) on Landsat 8 imagery.

 Previous studies have shown that mangrove forests in Iran are distributed in 21 sites in 10 cities in Bushehr, Hormozgand, and Sistan-Baluchestan provinces. In order to assess the mangrove indices, a region was selected in each province. Mangroves in Nayband Gulf are concentrated in Bidkhun and Basatin Creeks. In Sirik, mangroves are located in the Azini wetland, and in Govatr Gulf, they are established in Baho and Govatr Creeks. Low- and high-tide Landsat imagery of each study area related to 2020 was downloaded. After pre-processing, the images were used to compute MI (Mangrove Index), NDMI (Normalized Difference Mangrove Index), CMRI (Combined Mangrove Recognition Index), MDI (Mangrove Discrimination Index), MMRI (Modular Mangrove Recognition Index), L8MI (Landsat 8 Mangrove Index), and MVI (Mangrove Vegetation Index). Moreover, low- and high-tide images were implemented in making SMRI (Submerged Mangrove Recognition Index). The classification of soil, water, and mangrove was performed by a support vector machine (SVM) algorithm. In addition to common accuracy criteria (i.e., overall accuracy, Kappa coefficient, mangrove producer's and user's accuracies), the results were evaluated by area under the curve (AUC) of receiver operating characteristic (ROC).

 The efficiency of 10 mangrove indices was evaluated in similar conditions. The number of selected indices was eight; however, two of them (i.e., L8MI, MDI) were calculated two times, once with SWIR1 and once with SWIR2, and in total, 10 mangrove indices were used in three regions to classify mangrove forests. Between the indices, SMRI was selected as the most efficient mangrove index. One of the likely reasons for the efficiency of the index can be the application of low- and high-tide imagery to detect mangroves. In addition to PAmangrove and UAmangrove, the overall accuracy and kappa coefficient of soil, water, and mangrove of SMRI were more than other indices. The results of MDI and L8MI showed that they were more efficient with SWIR2 in Nayband Gulf. One of the reasons that likely caused the result can be urban areas and non-mangrove vegetation cover in Nayband Gulf. However, both indices were more accurate in mangrove discrimination when calculated with SWIR1 in Govatr Gulf. Investigation of AUC values proved that SMRI was the most efficient index between all studied indices in mangrove mapping within three study areas. The AUC of mangroves in Nayband Gulf, Sirik, and Govatr Gulf were 0.94, 0.92, and 0.93, respectively. The area of mangrove forests was estimated in Nayband Gulf (260.1 ha), Sirik (1049.2 ha), and Govatr Gulf (649.5 ha) using SMRI.

 In general, the results showed that all mangrove indices were reliable in mangrove discrimination in three study areas and no weak results were achieved. The AUC values of mangroves using SMRI were more than 0.9 in three regions and the index was known as the most reliable index in all regions. The outcome in the study areas revealed that the efficiency of mangrove indices was less in Nayband Gulf compared to two other regions (The AUC of 0.6 for NDMI and L8MI-1). The area of mangrove forests in Nayband Gulf, Sirik, and Govatr Gulf was estimated on Landsat 8 imagery of 2020. The results indicated that between the study sites Sirik (1049.2 ha) and Basatin Creek (43.3 ha) had the highest and the lowest area covered by mangroves. It is suggested to use SMRI in other mangrove forests in southern Iran to approve the achievements of the present study.