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

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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

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

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

Part I: General introduction Motivation:

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 Satellite data ESA MERIS, SAR, AATSR, OLCI (Sentinel-3), SPOT, CHINESE HJ, FY, ZY, HY, TPM ALOS, GOCI, TM,

9 Land use/cover Changes(LUCC) a). Analyze spectral characteristics of LUCC types based on field work; HJ-1 CCD and MERIS images b). Establish LUCC classification system consider LUCC type changes in different geographical regions along China coast c). Extract LUCC types information and generate thematic maps develop semi-automatic information extraction method d). Analyze driving factors of LUCC changes policy, human activity, climate changes, sea level rise. develop a model to make the forecast

10 a). Analyze HJ-1 CCD and MERIS imaging capabilities of the coastal wetland focusing on typical coastal wetland types: reed, spartina alterniflora, mangrove, tamarix, sea grass, b). Extract coastal wetlands information and generate thematic maps Human-Computer Interaction method, semi-automatic method c). Analyze wetland landscape structure distribution and dynamics as well as the driving factors Wetland d). Evaluate wetland ecological quality health and service function assessment

11 Macroalgae bloom a).evaluate the effects of environmental conditions and satellite data processing methods on the detection of free floating macroalgae bloom atmospheric condition, observation geometry, thin cloud, haze, atmospheric correction, NDVI method b).comparison of detection capabilities of free floating macroalgae bloom by different satellite data optical, SAR and infrared images focus on MERIS chlorophyll fluorescence measurements c).develop methods to detect submerged macroalgae by concurrent optical, SAR and infrared data d).analyze the dynamics from just the beginning to the collapse of the macroalgae bloom in the Yellow Sea time series of satellite images, detect the bloom as early as possible

12 River plume a). Develop regional algorithms to retrieve suspended sediment concentration in situ observation; calibration and validation of the regional SPM algorithms for ENVISAT MERIS and HJ-1 CCD b).develop regional algorithms to retrieve sea surface salinity(sss) of the river plume in situ observation of SSS and ocean optical properties calibrate and validate empirical and semi-analytical models to retrieve SSS by spectral remote sensing reflectance (Rrs) c).analyze river plume dynamics by time series of satellite images in terms of river discharge, hydrodynamic condition and wind.

13 Validation a). Inter-comparison and cross-calibration of satellite radiance data OLCI (Sentinel-3), VIIRS(Suomi NPP), MERSI (FY-3) Based on L1b data b). Validation of satellite data product along China coast OLCI (Sentinel-3), MERIS (ENVISAT), MERSI (FY-3), VIIRS(Suomi NPP) The Bohai Sea, Yellow Sea, East China Sea, South China Sea

14 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 Coastal shallow water depth inversion model of ZY-3 image

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

16 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.

17 Major global ocean color sensors

SeaWiFS/Orbview-2 August 1997~December 2010 8 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

18 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

19 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

MERIS/ENVISAT April 2002~ March 2012. 15 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 412.

20 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

21 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.

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

23 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

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

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

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

27 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

28 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. 443nm 490nm 565nm FY-3A R=0.487 R=0.370 R=0.557 FY-3B R=0.806 R=0.580 R=0.547

29 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.

30 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

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

32 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

33 MERIS VS. SeaWiFS

34 MERIS VS. MODIS

35 MODIS VS. SeaWiFS

36 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.

37 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

38 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.

39 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.

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.

40 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

41 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.

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

43 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

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

46 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

47 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

48 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

49 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

50 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

51 Coastline monitoring results since

52 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.

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

54 Area(ha) The changes in the Qinzhou city 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.

55 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.

57 Study area National Nature Reserve of the Yellow River Delta

Data sets Remote sensing data - PROBA CHRIS Acquisition date 2012-06-01 Spatial resolution 17m Spectral bands 400~1050nm Bands Central Band width wavelength (nm) (nm) Band 1 411.3 10.9 Band 2 443.

58 Data sets Remote sensing data - PROBA CHRIS Acquisition date Spatial resolution 17m Spectral bands 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

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

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

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

62 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

63 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.

65 Study area Yongxing island The water around Yongxing island is very clean, which is applicable to water depth inversion research.

66 Data sets Satellite remote sensing image-zy-3( 资源三号 ) Acquisition date Spatial resolution 2.1m Spectral bands 4 bands: 485nm(Blue), 555nm(Green), 660nm(Red), 830nm(NIR) Water depth data-marine chart Measure date 1971 Scale 10000

67 Method Linear regression model (LRM) -Single band -Dual bands Water depth for regression (N=122) Water depth for validation (N=60)

68 Water depth inversion results Single band inversion result-validation table Water Blue band Green band Red band depth region Absolute(m) relative Absolute(m) relative Absolute(m) relative 0 ~ % % % 2 ~ % % % 5 ~ % % % 10 ~ % % % > % % % All % % % Overall, the inversion results of green band was the best.

69 Dual bands inversion result-validation table Water Blue-Green bands Blue-Red band Green-Red band depth Absolute Absolute Absolute relative relative relative region (m) (m) (m) 0 ~ % % % 2 ~ % % % 5 ~ % % % 10 ~ % % % > % % % All % % % Overall, the inversion results of green band was the best.

70 Summary Different coastal shallow water depth inversion model was established and validated. The inversion results of green band was the best.

71 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.

72 Thanks for your attention!

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

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