Estimation Of Chlorophyll-A Concentrations Using Field Spectral Measurement And Multi-source Satellite Data In Lake Qiaodao, China (Project ID :10668)

Transcription

1 Estimation Of Chlorophyll-A Concentrations Using Field Spectral Measurement And Multi-source Satellite Data In Lake Qiaodao, China (Project ID :10668) Prof. Gong Jianhua, P.I. (China) Dr. Apostolos Sarris, P.I. (Greece) Prof. Qigen Liu, Co-P.I. (China) Dr Tao CHENG, Co-P.I. (UK) Dr. Ibrahim Abdoul Nasser, Co. I. (China) Dr. Andrew Gill, Co. I. (UK) Dr. Li Yi, Co. I. (China)

2 Presentation Contents 1. Introduction of study area 2. Measured reflectance spectral of Lake Qiaodao 3. Four-band model to estimate Chl-a concentration 4. SVM and random forests to estimate Chl-a concentration 5. Other work in progress

3 Introduction of Lake Qiaodao Lake Qiaodao, located in Zhejiang Province, China, which is the most important drinking water source and ecological buffer for Hangzhou Bay and Yangtze River Delta regions. Lake Qiandao has an area of 573 km 2 with more than 1078 islands, storage capacity of 17.8 billion m 3, an average depth of 34 m. In recent years the eutrophication of Lake Qiandao has been exacerbated

4 A joint field survey on Oct , 2014, Lake Qiaodao. Field spectral + Chlorophyll concentration measurement

5 Measured reflectance spectra of Lake Qiandao In this study, 50 sampling points were set covering the five Lake sections (northeast, northwest, southeast, southwest, central lakes). The Northeast Lake is sampled with denser points because of an obvious concentration gradient of chlorophyll-a.

6 Measured reflectance spectra of Lake Qiandao SVC HR-1024 (measure scope of nm) ρ=0.028 (sunny, breeze wind) Rp=0.3 Algae Field Analyzer (Fluoroprobe) is adopted to measure the concentration of chlorophyll-a

7 Absorption coefficients of each component measured in lab, including Chl-a, CDOM and suspended matter. Absorption coefficient(m -1 ) Yellow material and non-algal particles have a strong cover effect on absorption characteristics of chlorophyll, which will affect the accuracy of chlorophyll concentration inversion model

8 Band ratio model Band ratio model built(using 35 out of 50 points) Correlation between spectral reflectance and chlorophyll a concentration (Pearson analysis). Highest positive correlation coefficient(686.4 nm) Highest negative correlation coefficient(493.1 nm) Validation (using rest 15 points)

9 First order differential model Model built(using 35 out of 50 points) Band (493 nm) whose first derivative result has the highest correlation coefficients with chlorophyll-a concentration. Validation (using the rest 15 points)

10 Three-band model Chla [ R ( λ ) R ( λ )] R( λ ) λ1- Absorption valley in red band (Initial value: 663 nm) λ2- Fluorescence peak λ3- Near infrared (Initial value: 740 nm) 优化次数 λ 1 λ 2 λ 3 最优波段 Rmax 优化结果

11 Four-band model to estimate Chl-a concentration of Lake Qiandao Chla [ R ( λ ) R ( λ )] [ R ( λ ) R ( λ )] λ4 is introduced to eliminate scattering and absorption effect in NIR band caused by suspended matter Iterative circle λ 1 λ 2 λ 3 λ 4 R max Optimum band The four-band model is to apply the principle of biological optical model. The optimized bands combination λ1 - λ4 is achieved by loop iterations calculating method to obtain the largest correlation with the concentration of chlorophyll-a.

12 Brief summary Chla = R R R R [ (661.6) (706.7)][ (714.8) (683.2)] rs rs rs rs Model R 2 RMSE MAE Band ratio First order differential Three bands Four bands Compared with the band ratio model and the first derivative model, the precision of three band and four band model are higher. The four-band model has a higher precision than three band model.

13 Remote sensing Images gathered HJ-CCD Pseudo-color image (NIR-R-G) Month Satellites HJ1 GF1 GF-WFV Pseudo-color image(nir-r-g) Sensors HJ1A-CCD1 GF1-WFV2 Day Track L L1A

14 SVM to estimate Chl-a concentration in Lake Qiandao Work flow chart

15 SVM to estimate Chl-a concentration in Lake Qiandao r XY = ( X X)( X Y) n n 2 2 ( Xi X) ( Yi Y) i= 1 i= 1 Pearson correlation calculation method Band component Correlation coefficient Band component Correlation coefficient B4/B B4/(B1+B2) B4/B B4/(B2+B3) B4/B B4/(B1+B2+B3) (B4-B1)/(B4+B1) B4/(B1*B2) (B4-B2)/(B4+B2) B4/(B2*B3) (B4-B3)/(B4+B3) (B4*B3)/(B1*B2) Correlation coefficient of chl-a concentration and HJ1A-CCD1 band combination ( ).

16 Correlation coefficient of chl-a concentration and GF1-WFV2 band combination. Band component Correlation Band component Correlation coefficient coefficient B4/B B4/(B1+B2) B4/B B4/(B2+B3) B4/B B4/(B1+B2+B3) (B4-B1)/(B4+B1) B4/(B1*B2) (B4-B2)/(B4+B2) B4/(B2*B3) (B4-B3)/(B4+B3) (B4*B3)/(B1*B2) Totally 12 bands compositions are selected (6 from HJ1A-CCD1 band compositions and 6 from GF1-WFV2 band compositions), and they are taken as input of SVM model.

17 SVM model inversion results based on HJ-CCD single data source (6 bands ) (a) Model built (using 35 out of 50 points ) (b) Validation (using the rest 15 points )

18 SVM model inversion results based on GF-WFV single data source (6 bands ) (a) Model built (using 35 out of 50 points ) (b) Validation (using the rest 15 points )

19 12 bands are selected from HJ-CCD and GF-WFV as independent variables, Chl-a concentration measured in situ is dependent variable. (a) Model built (using 35 out of 50 points ) (b) Validation (using the rest 15 points )

20 SVM to estimate Chl-a concentration in Lake Qiandao( ) SVM-based collaborative multi-source image retrieval model obtains a high accuracy (83.3% of the variance of chlorophyll a concentration), and the RMSE is μg / L, much lower than the average of the chlorophyll a concentration μg / L.

21 Evaluation of modeling precision Data source model N R 2 RMSE μg/l MRE % MAE μg/l Nr HJ-GF SVM HJ SVM GF SVM HJ-GF Multiple regression Evaluation of validation precision Data source model N R 2 RMSE μg/l MRE % MAE μg/l Nr HJ-GF SVM HJ SVM GF SVM HJ-GF Multiple regression

22 Random forests to estimate Chl-a concentration Random Forest is a non-integrated learning algorithm parameters, specifically a collection of tree classifier: { hx (, θ ), k= 1, 2... i...} k Random Forest algorithm requires less sample with high precision advantages

23 Random Forest model inversion results and accuracy assessment RF model inversion results based on HJ-CCD single data source (a) Model built (using 35 out of 50 points ) (b) Validation (using the rest 15 points )

24 RF model inversion results based on GF-WFV single data source (a) Model built (using 35 out of 50 points ) (b) Validation (using the rest 15 points )

25 12 bands are selected from HJ-CCD and GF-WFV as independent variables, Chl-a concentration measured in situ is dependent variable. (a) Model built (using 35 out of 50 points ) (b) Validation (using the rest 15 points )

26 RF model shows a good prediction of the chlorophyll distribution. The inversion results are spatially accord with the results of SVM. But the phenomenon that high values underestimation exists, especially in the northwest edge of the Lake District.

27 Evaluation of modeling precision Data source model N R 2 RMSE μg/l MRE % MAE μg/l Nr HJ-GF RF HJ RF GF RF HJ-GF Multiple regression Evaluation of validation precision Data source model N R 2 RMSE μg/l MRE % MAE μg/l Nr HJ-GF RF HJ RF GF RF HJ-GF Multiple regression

28 The contribution of GF-WFV accuracy of the model is generally higher than HJ- CCD combinations.

29 Summary Based on multi-source remote sensing data (HJ-CCD, GF-WFV), the support vector machine (SVM) and Random Forest (RF) models were constructed to estimate chlorophyll-a concentrations, and the model performance is verified and assessed. The main conclusions of this chapter are as follows: 1) compared to a single data source, the introduction of multi-source data can reduce errors inversion models of chlorophyll-a. 2) SVM retrieval higher accuracy than random forest, but only to enhance the precision of 0.06% -0.32%, while SVM parameter estimation process is more complex, RF model is more simple and practical; 3) Support vector machine and the random forest accuracy are higher than the regression model. Mainly due to the former two have strong nonlinear fitting capabilities, so they can better describe the complex nonlinear relationship between parameters and water spectral characteristics.

30 Work in progress 1 Relationship analysis between LULCC of Xinanjiang watershed and the water quality of Lake Qiaodao. 2 Chl-a concentration remote sensing inversion method coupled with hydrodynamic and water quality model. Work in plan 1 Chl-a concentration remote sensing inversion using UAV equipped with multispectral camera.

31 The cropland and residential area mainly distributes in the northwest of the Lake Qiaodao, which is upstream of the Lake. So the agricultural non-point source pollution and domestic wastewater contributed greatly to make the Northwest lake chlorophyll-a concentrations significantly higher than the other lake sections. Forest occupies most of Xin'an River basin, which is the main reason of high water quality.

32 Paper published supported by dragon 3 project: [1] Feng Quanlong, Liu Jiantao, Gong Jianhua. UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis [J]. Remote Sensing, 2015, 7(1): [2] Feng Quanlong, Liu Jiantao, Gong Jianhua. Urban Flood Mapping based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier A Case of Yuyao, China [J]. Water, 2015, 7(4): [3] Feng Quanlong, Gong Jianhua, Wang Ying, et al. Estimating Chlorophyll-a Concentration based on a Four-band Model Using Field Spectral Measurement and HJ-1A Hyperspectral Data in Qiandao Lake, China [J]. Remote Sensing Letters, (accepted) Young scientists cultivated: 4 persons of Ph.D. level, and 2 persons of M.Sc. level

33 Thank you!