Empirical Estimation of Total Nitrogen and Total Phosphorus Concentration of Urban Water Bodies in China Using High Resolution IKONOS Multispectral Imagery
<p>Location of study areas and sampling sites.</p> "> Figure 2
<p>Average surface reflectance of nine samples in Lake Cihu with different pixel array sizes.</p> "> Figure 3
<p>A neural network diagram for this study.</p> "> Figure 4
<p>Relationship between measured and predicted TN (<b>a,b</b>) and TP (<b>c,d</b>) concentrations with both MLR and ANN models using IKONOS images.</p> "> Figure 5
<p>Standardized residual plot (<b>a</b>,<b>c</b>) and Normal P-P plot of regression standardized residual (<b>b</b>,<b>d</b>) of MLR models.</p> "> Figure 6
<p>Relationship between surface reflectance of band and measured data predicted (<b>a</b>) TN and (<b>b</b>) TP concentrations with MLR models.</p> "> Figure 7
<p>Spatial distribution maps of (<b>a</b>) TN and (<b>b</b>) TP over Lake Cihu.</p> "> Figure 8
<p>Spatial distribution maps of (<b>a</b>) TN and (<b>b</b>) TP over lower reaches of WRT River.</p> ">
Abstract
:1. Introduction
Reference | Site | Model | Spectral Regions Used | WQP | R2 |
---|---|---|---|---|---|
Kutser et al. [12] | Lake Peipsi, Estonia | Simple linear model | 415–455,655–685, and 405–605 nm of in situ | TP | 0.87 |
Li et al. [13] | Pearl River estuary, China | Multivariate linear model | Band 1–Band 6 of SeaWiFS | TIN | 31.9% (MRE) |
Lei et al. [14] | Lake Taihu, China | Multivariate linear model | Band 2–Band 4 of CBERS-1/CCD | TN | 12.2% (MRE) |
Wu et al. [15] | Qiantang river, China | Multivariate linear model | Band 1–Band 3 of Landsat/TM | TP | 0.77 |
Pan et al. [16] | Lake Chaohu, China | Improved multivariate linear model | B72, B79, and B97 of HJ1A/HIS | TN | 0.76 |
Song et al. [17] | Lake Chagan, China | Multivariate linear model | Band 1–Band 3 of Landsat/TM | TP | 0.63 |
Artificial Neural Network model | Band 1–Band 4 of Landsat/TM | 0.94 | |||
Chebud et al. [18] | Kissimmee River, USA | Artificial Neural Network model | Band 1–Band 7 of Landsat/TM | TP | 0.95 |
Torbick et al. [19] | Lower Peninsula of Michigan, USA | Multivariate linear model | Band 1–Band 3 of Landsat/TM | TP | 0.65 |
Band 1 and Band 3 of Landsat/TM | TN | 0.75 | |||
Chang et al. [20] | Tampa Bay, FL, USA | Genetic programming model | Band 1, Band 3, and Band 4 of MODIS | TP | 0.58 |
Sun et al. [21] | Lake Taihu, Chaohu, Three Gorges Reservoir, and Dianchi, China | Type-specific SVR model | Rrs (559)–Rrs (769) of HJ1A/HIS | TP | 0.71 |
2. Study Area
3. Study Data
3.1. In Situ Data Collection
Water Regions | Sampling Date | Data Subset | Sites | TN(mg/L) | TP(mg/L) |
---|---|---|---|---|---|
WRT River | 20 December 2008 | Calibration | W1 | 10.55 | 0.87 |
W4 | 18.28 | 1.19 | |||
W5 | 21.25 | 1.31 | |||
W6 | 14.93 | 1.01 | |||
W8 | 9.09 | 0.63 | |||
W7 | 9.09 | 0.73 | |||
W9 | 9.14 | 0.58 | |||
W11 | 7.13 | 0.60 | |||
Validation | W2 | 7.36 | 0.58 | ||
W3 | 7.62 | 0.58 | |||
W10 | 8.75 | 0.66 | |||
W12 | 11.30 | 0.73 | |||
Lake Cihu | 17 November 2011 | Calibration | C1 | 1.80 | 0.13 |
C2 | 1.53 | 0.13 | |||
C3 | 1.54 | 0.13 | |||
C4 | 1.62 | 0.13 | |||
C5 | 1.22 | 0.07 | |||
C6 | 1.05 | 0.08 | |||
Validation | C7 | 0.96 | 0.08 | ||
C8 | 1.03 | 0.10 | |||
C9 | 1.30 | 0.10 |
3.2. Satellite Image Processing
Satellite | Band | Spectral Region (μm) | Bandwidth (μm) | Centre Wavelength (μm) |
---|---|---|---|---|
IKONOS | 1 | 0.45–0.52 | 0.07 | 0.48 |
2 | 0.51–0.60 | 0.09 | 0.55 | |
3 | 0.63–0.70 | 0.07 | 0.66 | |
4 | 0.76–0.85 | 0.09 | 0.81 | |
Landsat/TM | 1 | 0.45–0.52 | 0.07 | 0.49 |
2 | 0.52–0.60 | 0.08 | 0.56 | |
3 | 0.63–0.69 | 0.06 | 0.66 | |
4 | 0.76–0.90 | 0.14 | 0.83 |
Meteorological Station | Date | Wind Speed | Rainfall | Average Temperature | Maximum Temperature | Minimum Temperature |
---|---|---|---|---|---|---|
Huangshi | 14 November 2011 | 0.6 | 0 | 15.7 | 21.0 | 11.3 |
15 November 2011 | 0.7 | 0 | 15.3 | 21.6 | 11.0 | |
16 November 2011 | 1.5 | 0 | 16.4 | 22.6 | 12.2 | |
17 November 2011 | 1.1 | 0 | 18.9 | 25.2 | 13.9 | |
Wenzhou | 20 December 2008 | 0.6 | 0 | 14.6 | 22.7 | 9.1 |
21 December 2008 | 1.1 | 0.1 | 13.5 | 22.5 | 10.2 |
4. Methodology
4.1. Multiple Linear Regression
4.2. Artificial Neural Network
5. Results
5.1. Regression Analysis
WQP | B1 | B2 | B3 | B4 | B1/B4 | B2/B4 | B3/B4 | B1/B3 | B2/B3 |
---|---|---|---|---|---|---|---|---|---|
TN | −0.81 ** | −0.93 ** | −0.92 ** | −0.89 ** | 0.75 ** | 0.65 ** | 0.47 * | 0.88 ** | 0.82 ** |
TP | −0.77 ** | −0.91 ** | −0.93 ** | −0.91 ** | 0.80 ** | 0.71 ** | 0.54 * | 0.90 ** | 0.85 ** |
WQP | B1/B2 | B1+B2 | |||||||
TN | 0.89 ** | 0.89 ** | 0.88 ** | −0.90 ** | 0.89 ** | 0.88 ** | 0.87 ** | 0.87 ** | 0.88 ** |
TP | 0.91 ** | 0.90 ** | 0.91 ** | −0.88 ** | 0.91 ** | 0.89 ** | 0.89 ** | 0.89 ** | 0.89 ** |
WQP | MLR | ANN | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
95% Confidence Interval | Calibration | Validation | Calibration | Validation | |||||||
Term | Lower Bound | Upper bound | R2 | RMSE (mg/L) | R2 | RMSE (mg/L) | R2 | RMSE (mg/L) | R2 | RMSE (mg/L) | |
TN | Constant | 42.25 | 66.86 | 0.85 | 2.50 | 0.88 | 1.47 | 0.99 | 0.63 | 0.96 | 0.89 |
Band2 | −347.94 | −204.09 | |||||||||
TP | Constant | 2.196 | 3.431 | 0.84 | 0.17 | 0.87 | 0.09 | 0.98 | 0.06 | 0.85 | 0.14 |
Band3 | −19.669 | −11.356 |
5.2. Artificial Neural Network
5.3. Retrieval Accuracy Comparison
WQP | Data Subset | Measured Data | MLR | ANN | |||
---|---|---|---|---|---|---|---|
Mean (mg/L) | Variance | Mean (mg/L) | Variance | Mean (mg/L) | Variance | ||
TN | Calibration | 7.73 | 45.86 | 7.76 | 38.58 | 7.71 | 43.84 |
Validation | 5.47 | 18.40 | 5.54 | 21.54 | 5.80 | 20.61 | |
TP | Calibration | 0.54 | 0.19 | 0.54 | 0.16 | 0.54 | 0.19 |
Validation | 0.40 | 0.09 | 0.41 | 0.08 | 0.43 | 0.14 |
5.4. Spatial Distribution of Water Quality Parameters
6. Discussion
- (1)
- The commercial satellite has limited image archives across the study area because of the lack of research using the IKONOS images in the area. As a result, little IKONOS image data are available to correlate with the in situ TN and TP concentrations.
- (2)
- The spatial detail of high-resolution IKONOS images is impressive. However, the problem of spectral-radiometric similarity between certain classes is compounded. Mixed pixels are still present, and the variability within classes may be greater than in lower-resolution images [29]. The resulting high-interclass and low-interclass variability may lead to a reduction in the statistical separability of the different classes in the spectral domain. This characteristic of IKONOS images may be one factor that differentiates it from the images of middle and low spatial resolution satellites, such as MODIS and Landsat/TM. To overcome this inadequacy and complement the spectral feature space, textural, structural, scale, and object-based features should be effectively exploited in future studies.
- (3)
- Several studies [48,49,50,51] revealed that lake or river water quality is highly dependent on the landscape characteristics with respect to watershed and geographical scales. Therefore, the difference in water quality configurations between urban water in this study and the large lakes and rivers examined in previous studies may lead to the differences in the relationships of surface reflectance and TN and TP concentrations.
7. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Liu, J.; Zhang, Y.; Yuan, D.; Song, X. Empirical Estimation of Total Nitrogen and Total Phosphorus Concentration of Urban Water Bodies in China Using High Resolution IKONOS Multispectral Imagery. Water 2015, 7, 6551-6573. https://doi.org/10.3390/w7116551
Liu J, Zhang Y, Yuan D, Song X. Empirical Estimation of Total Nitrogen and Total Phosphorus Concentration of Urban Water Bodies in China Using High Resolution IKONOS Multispectral Imagery. Water. 2015; 7(11):6551-6573. https://doi.org/10.3390/w7116551
Chicago/Turabian StyleLiu, Jiaming, Yanjun Zhang, Di Yuan, and Xingyuan Song. 2015. "Empirical Estimation of Total Nitrogen and Total Phosphorus Concentration of Urban Water Bodies in China Using High Resolution IKONOS Multispectral Imagery" Water 7, no. 11: 6551-6573. https://doi.org/10.3390/w7116551