Monitoring China's Coastal Zones and Adjacent Seas under Global Change by Satellite Data (10470)

Transcription

1 Monitoring China's Coastal Zones and Adjacent Seas under Global Change by Satellite Data (10470) Dr. Tingwei Cui 崔廷伟 First Institute of Oceanography, SOA, Qingdao, China Dr. Juergen Fischer Institut fuer Weltraumwissenschaften, Freie Universitaet Berlin, Germany

2 Outline General introduction Objectives, Research Content, Expected outcome, Team, Data Recent results Global Evaluation of Satellite Ocean Color Products from Europe, America and China: Toward a 15-yr merged global dataset Monitoring coastline change in Guangxi province since 1973 by Landsat and HJ-1 CCD images Coastal wetlands classification by PROBA CHRIS image in Yellow River Delta Coastal shallow water depth inversion model of ZY-3 image Future work

3 Part I: General introduction Motivation: China s coastal zone is experiencing dramatic changes due to significant socio-economical effects and the intense human activities, and the resource and environment condition here is also susceptible to the global change.

4 Objectives To disclose the spatio-temporal variation of land use/cover changes and wetlands along China coast as well as the causes and implications with respect to global change; continued work based on Dragon 2 (ID:5292) To develop new methods to detect submerged macroalgae, river plume by synergistic use of optical, infrared and microwave satellite data; To validate geographical products of satellite data along China coast to assess the uncertainties of these geophysical products and to offer useful suggestions for higher-order applications.

5 Research Content Satellite monitoring and evaluation of land use/cover changes (LUCC) along China coast Satellite monitoring and evaluation of wetland along China coast Macroalgae bloom monitoring by synergistic use of satellite data River plume monitoring by satellite-derived suspended sediment and sea surface salinity Validation of satellite data along China coast

6 Team Chinese Partners- 10 Dr. Tingwei CUI (PI) First Institute of Oceanography (FIO), State Oceanic Administration (SOA) Dr. Yi Ma FIO, SOA, China Dr. Guangbo Ren FIO, SOA, China Dr. Ping Qin Ocean University of China (OUC), China Jianhua Zhu National Ocean Technology Center (NOTC), SOA, China Huping Ye-NOTC, SOA, China Bing Mu-OUC, China Shaoqi Ni FIO, SOA, China Xiaomin Li FIO, SOA, China Xiaoqing-Cai FIO, SOA, China European Partners- 3 Dr. Juergen FISCHER(PI) Institute for Space Sciences, Freie Universitat Berlin, Germany; Dr. David DOXARAN Laboratoire d'océanographie de Villefranche, France; Dr. Lena Kritten Institute for Space Sciences, Freie Universitat Berlin, Germany

7 Expected outcome New method of monitoring macroalgae bloom by synergistic use of optical, infrared and microwave satellite data New method of monitoring river plume by satellite derived suspended sediment as well as synthetic sea surface salinity Annual changes of land use/land cover and wetlands along China coast as well as the causes and implications Macroalgae bloom dynamics in the Yellow Sea and East China Sea and annual variability River plume dynamics in the Yellow River, Yangtze River and Pearl River and the mechanism Uncertainties of MERIS, OLCI products along China coast

8 Part II: Recent results Global Evaluation of Satellite Ocean Color Products from Europe, America and China: Toward a 15-yr merged global dataset Monitoring coastline change in Guangxi province since 1973 by Landsat and HJ-1 CCD images Coastal wetlands classification by PROBA CHRIS image in Yellow River Delta

9 Global Evaluation of Satellite Ocean Color Products from Europe, America and China: Toward a 15-yr merged global dataset

10 Background A 15-yr long global ocean color dataset is to be produced by merging major satellite data from Europe (MERIS), America (SeaWiFS and MODIS) and China (MERSI, COCTS). The first step is to evaluate the accuracy of these satellite data in the global scale and make a detailed comparison.

11 Major global ocean color sensors

12 SeaWiFS/Orbview-2 August 1997~December bands from 412 to 865 nm spatial resolution: 1 km swath width: 1502km Bands Signal-to-Noise Purpose 402~422nm 499 CDOM 433~453nm 674 Chlorophyll 480~500nm 667 Pigment, Kd ~520nm 640 Chlorophyll 545~565nm 596 Pigment, optical property, SPM 660~680nm 442 Atmosphere correction, chlorophyll 745~785nm 455 Atmosphere correction, chlorophyll 845~885nm 467 Atmosphere correction, chlorophyll

13 MODIS/Terra (Aqua) 1999~ (Terra); 2002~(Aqua) totally 36 bands, including 9 ocean color bands from 405 to 877 nm spatial resolution: 1 km swath width: 2330 km Bands Signal-to-Noise 405~420nm ~448nm ~493nm ~536nm ~556nm ~672nm ~683nm ~753nm nm 516

14 MERIS/ENVISAT April 2002~ March bands from 390 to 1040 nm spatial resolution: 1.2 km/0.3 km (RR/FR) swath width: 1150km Central wavelength/nm Band width/nm Design objective Yellow substance and detrital pigments Chlorophyll absorption maximum Chlorophyll other pigments Suspended sediment, red tides Chlorophyll absorption minmum Suspended sediment Chlorophyll absorption & fluo. reference Chlorophyll fluorescence peak Fluo. Reference, atmosphere corrections Vegetation, cloud O2-branch absorption band Atmosphere corrections Vegetation, water vapor reference Atmosphere corrections Water vapor, land

15 MERSI/FY-3 series The second generation of China s polar-orbit meteorological satellites. FY-3A, FY-3B, FY-3C was launched in May 2008, Nov and Sep. 2013, respectively. Totally 20 bands, including 9 ocean color bands Swath width: 2900 km.

16 MODIS 8.43% MERIS 9.68% SeaWiFS 6.25% MERSI 11.75%

17 Evaluation of Rrs products Satellite v.s. In-situ (SeaBASS) Match-ups: ±3 hours, 3 3pixels 50 N 0 N -50 N MODIS MERIS SeaWiFS -150 E -100 E -50 E 0 E 50 E 100 E 150 E

18 SeaWiFS 412nm 443nm 490nm 530nm 555nm 670nm

19 MODIS 412nm 443nm 488nm 531nm 547nm 667nm

20 MERIS 413nm 443nm 490nm 510nm 560nm 665nm

21 MODIS MERIS SeaWiFS N R 2 RMSE(sr -1 ) APD( %) Rrs Rrs Rrs Rrs Rrs Rrs Rrs Rrs Rrs Rrs Rrs Rrs Rrs Rrs Rrs Rrs Rrs Rrs

22 Satellite VS. Satellite MERSI/FY-3 v.s. MODIS/AQUA MERSI daily products in 2011 from FY-3A and FY-3B were compared with concurrent MODIS. Close correlation and significant bias (>52%) were found. FY-3A 443nm 490nm 565nm R=0.487 R=0.370 R=0.557 FY-3B R=0.806 R=0.580 R=0.547

23 In summary, Satellite Rrs from SeaWiFS, MODIS and MERIS show comparable accuracy. Rrs uncertainties have the same spectral feature for, e.g. higher (28%~45%) in blue and red bands, and lower in green bands (13%~23%). Performance of MODIS and SeaWiFS is slightly better than MERIS, especially for the blue (412nm) and red band (670nm). MERSI/FY-3 Rrs data needs to be improved before being merged.

24 Evaluation of CHL products Satellite v. s. In-situ In situ data: from Ocean Biology Processing Group (OBPG) period: 2000~ observation data (8:00-16:00) were selected

25 MERIS Mach-ups R 2* APD-median(%) RMSE * MERIS MODIS SeaWiFS FY-3A CHL FY-3B CHL * in log10 scale MODIS SeaWiFS MERSI FY-3A MERSI FY-3B

26 Satellite v. s. Satellite Based on the daily satellite chlorophyll in 2010, the consistency of MERIS, MODIS and SeaWiFS products were analyzed with the variation of coefficient CV. CV i, j = t STD mean i, j, m m= 1 i, j

27 MERIS VS. SeaWiFS

28 MERIS VS. MODIS

29 MODIS VS. SeaWiFS

30 For most regions, CV of MERIS, MODIS and SeaWiFS chlorophyll a content products is less than 0.2. For the clear water of sub-tropic where the chlorophyll content is low all the year round, CV is higher.

31 MERSI/FY-3 VS. MODIS/AQUA FY-3A FY-3B R* RMSE* Bias* APD-median FY-3A vs. MODIS % FY-3B vs. MODIS % * in log10 scale

32 In summary, Satellite Chl from SeaWiFS, MODIS and MERIS show comparable accuracy (35%~40%). MERSI/FY-3 Chl data needs to be improved before being merged.

33 Improvement of MERSI/FY-3 CHL products Step 1: Evaluate the accuracy of MERSI-derived ocean color indices (e.g. R rs (443)/R rs (565), R rs (490)/R rs (565), CI) based on the MODIS counterparts. Step 2: Modify the MERSI-derived indices based on their regression relationships with MODIS counterparts. Step 3: Retrieve chlorophyll concentration using the modified MERSI-derived.

34 Evaluation and modification of MERSI-derived ocean color indices FY-3A R=0.869 RMSE=2.54 Bias=-2.03 APD=45% R=0.809 RMSE=1.16 Bias=-0.9 APD=28% R=0.933 RMSE=4.33*10-3 Bias=1.71*10-3 APD=144% Y=2.6311*X Y=1.7953*X Y=0.3109*X FY-3B R=0.628 RMSE=4.78 Bias=-4.43 APD=63% R=0.411 RMSE=1.94 Bias=-1.79 APD=42% R=0.795 RMSE=5.62*10-3 Bias=3.44*0-3 APD=112% Y=2.4814*X Y=1.5584*X Y=0.2101X

35 Chlorophyll retrieval based on MERSI-derived indices and the new algorithm (Hu et al., 2012) R* RMSE* Bias* APD-median(%) FY-3A v. s. MODIS (CHL) FY-3B v. s. MODIS (CHL) * In log10 scale.

36 Comparison before and after modification before before MERSI standard FY-3A FY-3B after after After Modification FY-3A FY-3B MODIS

37 Benefit from merging FY-3 MERSI Merged by MODIS and MERIS (2011/01/01), with 13.6% valid coverage Merged by MODIS, MERIS and FY-3 (2011/01/01), with 24.6% valid coverage

38

39 Study area - Important coastal zone for mangrove protection in China - Neighboring Vietnam - Guangxi Beibu gulf economic zone

40 Satellite images Acquisition date Imge type Number Spatial resolution (m) Landsat-1 MSS / Landsat-3 MSS / Landsat-5 TM Landsat-7 ETM HJ-1A 1 30

41 Coastline interpretation Bedrock coastline and its location - The shape of coastline is irregular - The color of the rock tidal flat is brighter and usually without vegetation

42 Sandy coastline and its location - The tidal flat is narrow and long - The shape of coastline is smooth - The color of the tidal flat is brighter and usually without vegetation

43 Muddy coastline and its location - The tidal flat is wide - The shape of coastline is smooth and irregular - The color of the tidal flat is darker - The coastline is located at the seaside of the vegetation

44 Artificial coastline and its location - The shape of coastline is straight - The color of the land-use types landside is brighter - The coastline is located at the seaside of the artificial constructions

45 Coastline monitoring results since

46 The length and ratio of different coastline types in different times since 1973 (km) Bedrock Artificial Sandy Muddy Overall % 16.32% 12.18% 55.03% % 16.31% 12.40% 57.26% % 42.44% 10.66% 34.99% % 45.06% 10.02% 28.44% % 66.72% 7.04% 6.20% The length and ratio of Artificial coastline increased from 1997 to The other three kind of coastline decreased.

47 The changes in the Fangchenggang city Area(ha) to to to to2013 In each period, the coastline of Fangchenggang city moved seaward, generating a changing area from hm 2 to hm 2.

48 The changes in the Qinzhou city Area(ha) to to to to2013 In each period, the coastline of Qinzhou city moved seaward, generating a changing area from 167.5hm 2 to hm 2.

49 Summary The coastline changes in Guangxi province since 1973 was extracted by Landsat and HJ-1 CCD images. The length and ratio of Artificial coastline increased while the other three kind of coastline decreased. The coastline of Fangchenggang and Qinzhou city increased regularly.

50

51 Study area National Nature Reserve of the Yellow River Delta

52 Data sets Remote sensing data - PROBA CHRIS Acquisition date Spatial resolution Spectral bands 17m 400~1050nm Bands Central Band width wavelength (nm) (nm) Band Band Band Band Band Band Band Band Band Band Band Band Band Band Band Band Band Band

53 Field spectral data ASD handheld 2: - Spectral wavelength range: nm - Spectral resolution:1nm - Viewing angle:25

54 Method Hyperspectral remote sensing image Hyperspectral feature extraction based on association mining field spectrum data sets field spectrum feature analysis Hyperspectral feature sets of the typical vegetation Classification method (decision tree) Typical wetlands classification on hyperspectral remote sensing image

55 Classification results Overall accuracy: 82.26% Kappa coefficient: 0.79

56 Comparison with SVM Class Producer accuracy (Percent) Decision tree User accuracy (Percent) Producer accuracy (Percent) SVM User accuracy (Percent) Reeds Nude beach Clear water Muddy water Suaeda salsa Tmarix chinensis Spartina anglica Bare land Overall acuuracy 82.26% 70.90% Kappa coefficient

57 Summary Wetlands in Yellow River Delta were classified by PROBA CHRIS image based on decision tree classification method. The classification results were validated by in-situ data, which showed that Overall accuracy was 82.26% and the kappa coefficient was The decision tree method was better than SVM.

58 Future work Analyze driving factors of LUCC and wetlands changes. Develop methods to detect submerged macroalgae by multisource satellite data. Analyze river plume dynamics by time series of satellite images.

59 Thanks for your attention!