Identifying and forecasting potential biophysical risk areas within a tropical mangrove ecosystem using multi-sensor data |
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| Institution: | 1. Department of Forest Sciences, Faculty of Natural Resources and Earth Sciences, Shaherkord University, Shaherkord, Iran;2. Department of Ecosystem Science and Management, The Pennsylvania State University, Forest Resources Building, University Park, PA 16802, USA;3. Young Researchers and Elite Club, Karaj Branch, Islamic Azad University, Karaj, Iran;4. Centre for Aquatic Environments, School of the Environment and Technology, University of Brighton, Cockcroft Building, Moulsecoomb, Brighton BN2 4GJ, United Kingdom;5. Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51014 Tartu, Estonia;1. School of Resources, Environment and Materials, Guangxi University, 530004 Nanning, China;2. State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, China;1. Department of Biological Sciences, Old Dominion University, Norfolk, VA, USA;2. Department of Geography, National University of Singapore, Singapore;3. Division of Sciences, Yale-NUS College, Singapore;4. Systems Ecology and Resource Management Research Unit, Department of Organism Biology, Université Libre de Bruxelles - ULB, Av. F.D. Roosevelt 50, CPi 264/1, 1050, Brussels, Belgium;5. Ecology & Biodiversity, Laboratory of Plant Biology and Nature Management, Biology Department, Vrije Universiteit Brussel - VUB, Pleinlaan 2, VUB-APNA-WE, 1050, Brussels, Belgium;6. Mangrove Specialist Group (MSG)- Species Survival Commission (SSC), International Union for the Conservation of Nature (IUCN);7. School of Earth, Atmospheric and Life Sciences, University of Wollongong, Australia;8. Leibniz Centre for Tropical Marine Research, Fahrenheitstrasse 6, 28359 Bremen, Germany;9. Swire Institute of Marine Science and Division for Ecology and Biodiversity, The University of Hong Kong, Hong Kong, Hong Kong SAR, China;10. Department of Biology, University of Florence, via Madonna del Piano 6, I-50019 Sesto Fiorentino, Italy;11. Smithsonian Marine Station, Fort Pierce, FL 34949, USA;12. Working Land and Seascapes, Conservation Commons, Smithsonian Institution, DC 20013, USA;13. Ecology and Environment Research Centre, Department of Natural Sciences, Faculty of Science and Engineering, Manchester Metropolitan University, Chester St, Manchester M1 5GD, UK;14. Faculty of Geoscience, University of Bremen, Klagenfurter Strasse, Bremen, Germany.;15. Institute of Environment, Florida International University, Miami, FL, USA;p. Department of Botany, Institute for Coastal and Marine Research, Nelson Mandela University, PO Box 77000, Port Elizabeth, South Africa;q. Coastal and Marine Research Centre, Griffith University, Gold Coast, Queensland, Australia;r. Australian Rivers Institute – Coast & Estuaries, School of Environment and Science, Griffith University, Gold Coast, Queensland, Australia;s. School of Applied Sciences, Edinburgh Napier University, Edinburgh, UK;t. Simon F.S. Li Marine Science Laboratory, School of Life Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China;u. Institute of Ocean and Earth Sciences, Universiti Malaya, Kuala Lumpur, Malaysia;v. Department of Geography, University of California, Los Angeles, CA, USA;w. Department of Earth and Environmental Sciences, Macquarie University, Sydney, NSW, Australia;x. Smithsonian Environmental Research Center, Edgewater, MD, USA;y. School of Environmental and Geographical Sciences, University of Nottingham Malaysia, Semenyih 43500, Malaysia;z. Guangxi Key Laboratory of Forest Ecology and Conservation, College of Forestry, Guangxi University, Nanning, Guangxi, China;11. Centre for Nature-based Climate Solutions, National University of Singapore, Singapore |
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| Abstract: | Mangroves are one of the most productive ecosystems known for provisioning of various ecosystem goods and services. They help in sequestering large amounts of carbon, protecting coastline against erosion, and reducing impacts of natural disasters such as hurricanes. Bhitarkanika Wildlife Sanctuary in Odisha harbors the second largest mangrove ecosystem in India. This study used Terra, Landsat and Sentinel-1 satellite data for spatio-temporal monitoring of mangrove forest within Bhitarkanika Wildlife Sanctuary between 2000 and 2016. Three biophysical parameters were used to assess mangrove ecosystem health: leaf chlorophyll (CHL), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). A long-term analysis of meteorological data such as precipitation and temperature was performed to determine an association between these parameters and mangrove biophysical characteristics. The correlation between meteorological parameters and mangrove biophysical characteristics enabled forecasting of mangrove health and productivity for year 2050 by incorporating IPCC projected climate data. A historical analysis of land cover maps was also performed using Landsat 5 and 8 data to determine changes in mangrove area estimates in years 1995, 2004 and 2017. There was a decrease in dense mangrove extent with an increase in open mangroves and agricultural area. Despite conservation efforts, the current extent of dense mangrove is projected to decrease up to 10% by the year 2050. All three biophysical characteristics including GPP, LAI and CHL, are projected to experience a net decrease of 7.7%, 20.83% and 25.96% respectively by 2050 compared to the mean annual value in 2016. This study will help the Forest Department, Government of Odisha in managing and taking appropriate decisions for conserving and sustaining the remaining mangrove forest under the changing climate and developmental activities. |
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| Keywords: | Bhitarkanika MODIS Landsat Remote Sensing Leaf Area Index Leaf Chlorophyll Gross Primary Productivity TerrSet Land Change Modeler Google Earth Engine NASA Giovanni Climate Image Classification Southeast Asia |
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