Identifying and forecasting potential biophysical risk areas within a tropical mangrove ecosystem using multi-sensor data

• Published in: ITC journal Vol. 74; pp. 281 - 294
• Main Authors: Shrestha, Shanti, Miranda, Isabel, Kumar, Abhishek, Pardo, Maria Luisa Escobar, Dahal, Subash, Rashid, Taufiq, Remillard, Caren, Mishra, Deepak R.
• Format: Journal Article
• Language: English
• Published: Langley Research Center Elsevier B.V 01.02.2019; Elsevier
• Subjects: Bhitarkanika; Climate; Earth Resources And Remote Sensing; Google Earth Engine; Gross Primary Productivity; Image Classification; Land Change Modeler; Landsat; Leaf Area Index; Leaf Chlorophyll; MODIS; NASA Giovanni; Remote Sensing; Southeast Asia; TerrSet; Gross Primary Productivity; Climate; Landsat; NASA Giovanni; Remote Sensing; Leaf Area Index; MODIS; Image Classification; TerrSet; Southeast Asia; Land Change Modeler; Leaf Chlorophyll; Google Earth Engine; Bhitarkanika
• ISSN: 1569-8432; 0303-2434; 1872-826X
• DOI: 10.1016/j.jag.2018.09.017

•Link between mangrove biophysical parameters and climatic variables was examined.•Mangrove biophysical parameters were forecasted to 2050 using multi-sensor data.•GPP and LAI are negatively correlated with surface temperature at the study site.•GPP and LAI are positively correlated with runoff and precipitation at the study site.•Trend suggested decrease in the current extent of dense mangrove up to 10% by 2050. 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.