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GF-1 And Landsat Observed A 40-year Wetland Spatiotemporal Variation And Its Coupled Environmental Factors In Yangtze River Estuary

Posted on:2019-05-25Degree:MasterType:Thesis
Country:ChinaCandidate:N SunFull Text:PDF
GTID:2370330548479548Subject:Physical oceanography
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Studying time-series wetland changes is important for protecting and managing wetland and evaluating coastal resources.In this paper,13 Landsat images in 1979,1988,1995,2000,2009,2015 and 2016 as well as 13 images from GF-1 PMS in 2015 were used to study wetland in estuarine and coastal regions of the Yangtze River.Comparing different classification methods of GF PMS and Landsat OLI in two typical regions(Chongming Dongtan Wetland and Qidong Coastal Wetland),of which Object-Oriented was applied to classify GF PMS and five remote sensing image classification methods,ISODATA,Maximum Likelihood,Support Vector Machine,Decision Tree,and Object-Oriented,were applied to Landsat OLI.Finally,we used the decision tree method which gives the best classification accuaracy with total accuaracy 87%and kappa coefficients 0.84.The classification results show that in Yangtze River estuarine and coastal regions,wetland area is 4,725 km~2,in which the natural wetland area accounts for 63.5%(3,000 km~2),the human-made wetland area accounts for 21.2%(1,002 km~2)in 2015.From 1979 to 2015,the total wetland area has increased 662 km~2-the growth of human-made wetlands positively contributed to 766 km~2 due to the development and constructions of large reservoirs,aquaculture ponds,and ports.The natural wetlands area shrunk to 920 km~2 at 2000.After that,the natural wetland coverage keeps relatively stable because of the enhancement of wetland protection and management.The general variation trend is the gradually deposition in estuarine zones and transformations from natural wetland to constructed wetland,and further from constructed wetland to the construction land.Based on estimation model of ecosystem service value by Costanza,this paper refined and revised the unit ecological service value of the seven wetland types and the three non-wetlands.We obtained ESV in 1988,1995,2000,2009 and 2015 was 658.7,551.4,681.7,1244.1,and 137.9 billion yuan per year,respectively.The results show that the unit value of natural wetland is significantly larger than that of human-made wetland,and ESV of natural wetland accounts for more than 80%.The ESV of natural wetland increased from 57.3 to 111.3 billion yuan per year,and the human-made wetland also increased from 8.4 to 24 billion yuan per year over the past 30 years.The ESV of marshes show a great increase,while ESV of tidal flat gradually decreased from 2000 due to excess reclamation for economic development.In the three regions of the Yangtze River Estuary,there were a relatively stable ESV of natural wetland and an increased ESV of human-made wetland.The ESV of central zone is the highest,of which 84.7%comes from natural wetlands,but the ESV of constructed wetland increases slightly.We also analyzed and compared different environment driving factors related to wetland variations in the study site.Wetlands in Shanghai and Qidong urban regions were mainly affected by human activities,while wetland variations in Chongming Island were mainly controlled by natural factors such as the upstream discharge,precipitation,diurnal variation of tidal level and long-term sea level rising.The same trend is also coupled between GDP and the total areas of the major human-made wetlands in Qidong coasts and Nanhui Dongtan,but the opposite trend were seen between GDP and the area of tidal flat due to the rapid economic development and hydraulic projects.During the flood season(June to September),the natural wetland in central zone usually experiences significant shrinking.The growth rate of Dongtan wetland was significantly slowed down since 2000 due to some hydraulic engineering projects in upper Yangtze River and some climate change,such as sea-level rising.At the same time,seawater intrusion,soil erosion and other environment factors may also influence the wetland area of central branch in Yangtze River estuary.
Keywords/Search Tags:Yangtze estuarine wetland, TM/OLI/PMS data, long-time series, spatiotemporal analysis, ecological Service Value, driving factors
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