Efficiency of mangrove indices in mapping some mangrove forests using Landsat 8 imagery in southern Iran
Subject Areas : Agriculture, rangeland, watershed and forestryYousef Erfanifard 1 , Mohsen Lotfi Nasirabad 2
1 - Associate Professor, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
2 - MSc. Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
Keywords: Support vector machine, Govatr Gulf, Nayband Gulf, Avicennia marina, Sirik, Receiver operating characteristic curve,
Abstract :
Background and Objective 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. Materials and Methods 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).Results and Discussion 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.Conclusion 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.
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Shi T, Liu J, Hu Z, Liu H, Wang J, Wu G. 2016. New spectral metrics for mangrove forest identification. Remote Sensing Letters, 7(9): 885–894. doi:https://doi.org/10.1080/2150704X.2016.1195935.
Taghizadeh A, Danehkar A, Kamrani E, Mahmoudi, B. 2010. Mangrove forest communities in Hormozgan province. Journal of Forest, 1: 25-34. (In Persian)
Xia Q, Qin C, Li H, Huang C, Su F. 2018. Mapping mangrove forests based on multi-tidal high-resolution satellite imagery. Remote Sensing, 10: 1343. doi:https://doi.org/10.3390/rs10091343.
Xia Q, Qin C, Li H, Huang C, Su F, Jia M. 2020. Evaluation of submerged mangrove recognition index using multi-tidal remote sensing data. Ecological Indicators, 113: 106196. doi:https://doi.org/10.1016/j.ecolind.2020.106196.
Wang D, Wan B, Qiu P, Zuo Z, Wang R, Wu X. 2018. Evaluating the performance of Sentinel-2, Landsat 8 and Pléiades-1 in mapping mangrove extent and species. Remote Sensing, 10: 1468. doi:https://doi.org/10.3390/rs10091468.
Winarso G, Purwanto AD, Yuwono DM. 2014. New mangrove index as degradation/health indicator using Remote Sensing data: Segara Anakan and Alas Purwo case study. In Proceedings of the 12th Biennial Conference of Pan Ocean Remote Sensing Conference. November 2014, Bali, Indonesia, 309-316.
Zhang X, Treitz PM, Chen D, Quan C, Shi L, Li X. 2017. Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure. International Journal of Applied Earth Observation and Geoinformation, 62: 201–214. doi:https://doi.org/10.1016/j.jag.2017.06.010.
_||_Ali A, Nayyar ZA. 2020. Extraction of mangrove forest through Landsat 8 Mangrove Index (L8MI). Arabian Journal of Geosciences, 13: 1132. doi:https://doi.org/10.1007/s12517-020-06138-4.
Alatorre LC, Sánchez-Andrés R, Cirujano S, Beguería S, Sánchez-Carrillo S. 2011. Identification of mangrove areas by remote sensing: the ROC curve technique applied to the northwestern Mexico coastal zone using Landsat imagery. Remote Sensing, 3(8): 1568-1583. doi:http://doi.org/10.3390/rs3081568.
Amiri N, Sajadi J, Sadough Vanini H. 2011. Application of vegetation indices derived from IRS data for detecting the Avicennia forest area near the south Pars Oil Apparatus. Environmental Sciences, 8(1): 69-84. (In Persian)
Baloloy AB, Blanco AC, Raymund Rhommel RRC, Nadaoka K. 2020. Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 166: 95-117. doi:https://doi.org/10.1016/j.isprsjprs.2020.06.001.
Behera MD, Barnwal S, Paramanik S, Das P, Bhattyacharya BK, Jagadish B, Roy PS, Ghosh SM, Behera SK. 2021. Species-level classification and mapping of a mangrove forest using random forest-Utilisation of AVIRIS-NG and Sentinel data. Remote Sensing, 13(11): 2027. doi:https://doi.org/10.3390/rs13112027.
Bihamta Toosi N, Soffianian A, Fakheran S, Pourmanafi S, Ginzler C, Waser L. 2019. Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran. Global Ecology and Conservation, 19: e00662. doi:https://doi.org/10.1016/j.gecco.2019.e00662.
Danekar A, Mahmoudi B, Sabaei M, Ghadirian T, Asadolahi Z, Sharifi N, Petrosian H. 2012. Iran national plan for sustainable mangrove management. National Forests, Range and Watershed Management Organization, 624 pp.
Diniz C, Cortinhas L, Nerino G, Rodrigues J, Sadeck L, Adami M, Souza-Filho PWM. 2019. Brazilian mangrove status: Three decades of satellite data analysis. Remote Sensing, 11: 808. doi:https://doi.org/10.3390/rs11070808.
Ghandi S, Jones TG. 2019. Identifying mangrove deforestation hotspots in South Asia, Southeast Asia and Asia-Pacific. Remote Sensing, 11: 728. doi:https://doi.org/10.3390/rs11060728.
Gupta K, Mukhopadhyay A, Giri S, Chanda A, Datta Majumdar S, Samanta S, Mitra D, Samal RN, Pattnaik AK, Hazra S. 2018. An index for discrimination of mangroves from non-mangroves using LANDSAT 8 OLI imagery. MethodsX, 5: 1129-1139. doi:https://doi.org/10.1016/j.mex.2018.09.011.
Hauser LT, An Binh N, Viet Hoa P, Hong Quan N, Timmermans J. 2020. Gap-free monitoring of annual mangrove forest dynamics in Ca Mau Province, Vietnamese Mekong Delta, using the Landsat-7-8 archives and post-classification temporal optimization. Remote Sensing, 12: 3729. doi:https://doi.org/10.3390/rs12223729.
Heumann BW. 2011. An object-based classification of mangroves using a hybrid decision tree - support vector machine approach. Remote Sensing, 3: 2440-2460. doi:https://doi.org/10.3390/rs3112440.
Jia M, Wang Z, Wang C, Mao D, Zhang Y. 2019. A new vegetation index to detect periodically submerged mangrove forest using single-tide Sentinel-2 imagery. Remote Sensing, 11: 2043. doi: https://doi.org/10.3390/rs11172043.
Jiang Y, Zhang L, Yan M, Qi J, Fu T, Fan S, Chen B. 2021. High-resolution mangrove forests classification with machine learning using Worldview and UAV hyperspectral data. Remote Sensing, 13: 1529. doi:https://doi.org/10.3390/rs13081529.
Kumar T, Mandal A, Dutta D, Nagaraja R, Dadhwal V. 2019. Discrimination and classification of mangrove forests using EO-1 Hyperion data: a case study of Indian Sundarbans. Geocarto International, 34(4): 415-442. doi:http://doi.org/10.1080/10106049.2017.1408699.
Li W, El-Askary H, Qurban M, Li J, ManiKandan K, Piechota T. 2019. Using multi-indices approach to quantify mangrove changes over the Western Arabian Gulf along Saudi Arabia coast. Ecological Indicators, 102: 734-745. doi:https://doi.org/10.1016/j.ecolind.2019.03.047.
Liu K, Li X, Shi X, Wang S. 2008. Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning. Wetlands, 28: 336. doi:https://doi.org/10.1672/06-91.1.
Long JB, Giri C. 2011. Mapping the Philippines’ mangroves forests using Landsat Imagery. Sensors, 11: 2972-2981. doi:https://doi.org/10.3390/s110302972.
Mafi-Gholami D, Baharlouii M, Mahmoudi B. 2017. Mapping area chnages of mangroves using RS and GIS (Case study: mangroves of Hormozgan province). Environmental Sciences, 15(2): 75-91. (In Persian)
Mafi-Gholami D, Zenner E, Jaafari A, Bui D. 2020. Spatially explicit predictions of changes in the extent of mangroves of Iran at the end of the 21st century. Estuarine Coastal and Shelf Science, 237: 106644. doi: https://doi.org/10.1016/j.ecss.2020.106644.
Makowski C, Finkl C. 2018. Threats to mangrove forests. Springer USA, 724 pp.
Maurya K, Mahajan S, Chaube N. 2021. Remote sensing techniques: mapping and monitoring of mangrove ecosystem-a review. Complex & Intelligent Systems. doi:https://doi.org/10.1007/s40747-021-00457-z.
Razali SM, Nuruddin AA, Lion M. 2019. Mangrove vegetation health assessment based on remote sensing indices for Tanjung Piai, Malay Peninsular. Journal of Landscape Ecology, 12: 26-40. doi:https://doi.org/10.2478/jlecol-2019-0008.
Shi T, Liu J, Hu Z, Liu H, Wang J, Wu G. 2016. New spectral metrics for mangrove forest identification. Remote Sensing Letters, 7(9): 885–894. doi:https://doi.org/10.1080/2150704X.2016.1195935.
Taghizadeh A, Danehkar A, Kamrani E, Mahmoudi, B. 2010. Mangrove forest communities in Hormozgan province. Journal of Forest, 1: 25-34. (In Persian)
Xia Q, Qin C, Li H, Huang C, Su F. 2018. Mapping mangrove forests based on multi-tidal high-resolution satellite imagery. Remote Sensing, 10: 1343. doi:https://doi.org/10.3390/rs10091343.
Xia Q, Qin C, Li H, Huang C, Su F, Jia M. 2020. Evaluation of submerged mangrove recognition index using multi-tidal remote sensing data. Ecological Indicators, 113: 106196. doi:https://doi.org/10.1016/j.ecolind.2020.106196.
Wang D, Wan B, Qiu P, Zuo Z, Wang R, Wu X. 2018. Evaluating the performance of Sentinel-2, Landsat 8 and Pléiades-1 in mapping mangrove extent and species. Remote Sensing, 10: 1468. doi:https://doi.org/10.3390/rs10091468.
Winarso G, Purwanto AD, Yuwono DM. 2014. New mangrove index as degradation/health indicator using Remote Sensing data: Segara Anakan and Alas Purwo case study. In Proceedings of the 12th Biennial Conference of Pan Ocean Remote Sensing Conference. November 2014, Bali, Indonesia, 309-316.
Zhang X, Treitz PM, Chen D, Quan C, Shi L, Li X. 2017. Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure. International Journal of Applied Earth Observation and Geoinformation, 62: 201–214. doi:https://doi.org/10.1016/j.jag.2017.06.010.
