气候变化领域集成服务门户(CCPortal): Identifying and forecasting potential biophysical risk areas within a tropical mangrove ecosystem using multi-sensor data

CCPortal

DOI	10.1016/j.jag.2018.09.017
	Identifying and forecasting potential biophysical risk areas within a tropical mangrove ecosystem using multi-sensor data
	Shrestha S.; Miranda I.; Kumar A.; Pardo M.L.E.; Dahal S.; Rashid T.; Remillard C.; Mishra D.R.
发表日期	2019
ISSN	15698432
起始页码	281
结束页码	294
卷号	74
英文摘要	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. © 2018 Elsevier B.V.
英文关键词	Bhitarkanika; Climate; 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
语种	英语
scopus关键词	chlorophyll; climate; ecosystem health; image classification; land cover; Landsat; leaf area index; mangrove; MODIS; primary production; remote sensing; satellite data; Terra (satellite); Bhitarkanika National Park; India; Odisha; Rhizophoraceae
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156557
作者单位	Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, United States; Department of Geography, University of Georgia, Athens, GA 30602, United States; Department of Geography, Clark University, Worcester, MA 01610, United States; College of Engineering, University of Georgia, Athens, GA 30602, United States; Department of Crop and Soil Sciences, University of Georgia, Athens, GA 30602, United States; NASA DEVELOP Program, University of Georgia, Athens, GA 30602, United States
推荐引用方式 GB/T 7714	Shrestha S.,Miranda I.,Kumar A.,et al. Identifying and forecasting potential biophysical risk areas within a tropical mangrove ecosystem using multi-sensor data[J],2019,74.
APA	Shrestha S..,Miranda I..,Kumar A..,Pardo M.L.E..,Dahal S..,...&Mishra D.R..(2019).Identifying and forecasting potential biophysical risk areas within a tropical mangrove ecosystem using multi-sensor data.International Journal of Applied Earth Observation and Geoinformation,74.
MLA	Shrestha S.,et al."Identifying and forecasting potential biophysical risk areas within a tropical mangrove ecosystem using multi-sensor data".International Journal of Applied Earth Observation and Geoinformation 74(2019).