A Remote Sensing Approach to Estimate Vertical Profile Classes of Phytoplankton in a Eutrophic Lake
"> Figure 1
<p>Study region and sampling locations in Lake Chaohu, China. <span class="html-italic">In situ</span> measurements of bio-optical parameters made during three cruise surveys in 28 May 2013 (N = 9), 19–24 July 2013 (N = 32) and 10–12 October 2013 (N = 27). The solid circles indicate field measurement sites of wind speed during two cruise surveys on 11 June (N = 13) and 13 June (N = 9) 2013, respectively. The solid black triangle represents the location of meteorological station.</p> "> Figure 2
<p>Selected Chlorophyll-a concentration profiles with original data points and fitted curves for the four vertical Chla profile classes: (<b>a</b>) Class 1, vertically uniform; (<b>b</b>) Class 2, Gaussian distribution; (<b>c</b>) Class 3, exponential distribution; and (<b>d</b>) Class 4, negative power function distribution.</p> "> Figure 3
<p>Spectral curves of <span class="html-italic">Rrs</span> (λ) from 400 nm to 900 nm of the four vertical Chla profile classes ((<b>a</b>) Class 1, vertically uniform; (<b>b</b>) Class 2, Gaussian distribution; (<b>c</b>) Class 3, exponential distribution; and (<b>d</b>) Class 4, negative power function) in Lake Chaohu in May, July and October 2013.</p> "> Figure 4
<p>(<b>a</b>) Outputs of three algal bloom indices; (<b>b</b>) the corresponding wind speed (m/s); and (<b>c</b>) distance of stations to shore (km). NDVI: Normalized Difference Vegetation Index; CSI: Chlorophyll Spectral Index; NDBI: Normalized Difference algal Bloom Index.</p> "> Figure 5
<p>Decision tree (CART) of identifying Chla vertical profile class based on NDBI<span class="html-italic"><sub>Rrs</sub></span> and wind speed, where NDBI<span class="html-italic"><sub>Rrs</sub></span> is the NDBI index derived from <span class="html-italic">in situ</span> measurements, and “w” represents wind speed.</p> "> Figure 6
<p>Sensitivity analysis of threshold value of the CART decision tree: (<b>a</b>) classification accuracy (%) with changing NDBI; and (<b>b</b>) classification accuracy (%) with changing wind speed. The red markers (NDBI = 0.25, w1 = 2.75 m/s, w2 = 1.75 m/s) are the thresholds used in this study with highest classification accuracy.</p> "> Figure 7
<p>Relationships between (<b>a</b>) NDBI<span class="html-italic"><sub>Rrs</sub></span> and NDBI<span class="html-italic"><sub>Rrs</sub></span>,<sub>MODIS</sub>; and (<b>b</b>) NDBI<span class="html-italic"><sub>Rrc</sub></span>,<sub>MODIS</sub> and NDBI<span class="html-italic"><sub>Rrs</sub></span>,<sub>MODIS</sub> at different atmospheric conditions (seven aerosol types and three optical thickness) through model simulations based on SeaDAS Look up table. τ<sub>a</sub>(555) is the aerosol optical thickness at 555 nm. Threshold value of NDBI<span class="html-italic"><sub>Rrs</sub></span>,<sub>MODIS</sub> to detect algal bloom is 0.15, while the corresponding average NDBI<span class="html-italic"><sub>Rrc</sub></span> value is 0.125, which is independent of aerosol type and optical thickness. Note that NDBI<span class="html-italic"><sub>Rrs</sub></span> is NDBI index derived from <span class="html-italic">in situ</span> measurements, NDBI<span class="html-italic"><sub>Rrs</sub></span><sub>,MODIS</sub> is resampled NDBI<span class="html-italic"><sub>Rrs</sub></span> according to spectral response function of MODIS, and NDBI<span class="html-italic"><sub>Rrc</sub></span><sub>,MODIS</sub> is NDBI index derived from MODIS images.</p> "> Figure 8
<p>Chla vertical profile class for Lake Chaohu estimated from MODIS measurements using NDBI<span class="html-italic"><sub>Rrc</sub></span>,<sub>MODIS</sub> (0.125) and measured wind speed on (<b>a</b>,<b>b</b>) 28 May 2013, (<b>c</b>,<b>d</b>) 24 July 2013, (<b>e</b>,<b>f</b>) 10 October 2013, (<b>g</b>) 11 October 2013, and (<b>h</b>) 12 October 2013.</p> "> Figure 8 Cont.
<p>Chla vertical profile class for Lake Chaohu estimated from MODIS measurements using NDBI<span class="html-italic"><sub>Rrc</sub></span>,<sub>MODIS</sub> (0.125) and measured wind speed on (<b>a</b>,<b>b</b>) 28 May 2013, (<b>c</b>,<b>d</b>) 24 July 2013, (<b>e</b>,<b>f</b>) 10 October 2013, (<b>g</b>) 11 October 2013, and (<b>h</b>) 12 October 2013.</p> "> Figure 9
<p>Relationships between structure parameters of Chla vertical Class 2 and surface variables: (<b>a</b>) C<sub>0</sub> <span class="html-italic">versus</span> a<sub>g</sub>(443), (<b>b</b>) C<sub>0</sub> <span class="html-italic">versus</span> Chla<sub>s</sub>, (<b>c</b>) h <span class="html-italic">versus</span> <span class="html-italic">R<sub>rs</sub></span>(709), and (<b>d</b>) σ <span class="html-italic">versus</span> <span class="html-italic">R<sub>rs</sub></span>(709). Note that Chla<sub>s</sub> is average Chla concentration from water surface to 0.4 m.</p> "> Figure 10
<p>Relationships between structure parameters of Chla vertical Class 3 and 4 and surface variables: (<b>a</b>) m<sub>1</sub> of Class 3 <span class="html-italic">versus</span> <span class="html-italic">R<sub>rs</sub></span>(709), (<b>b</b>) m<sub>2</sub> of Class 3 <span class="html-italic">versus</span> Chla<sub>s</sub>, (<b>c</b>) n<sub>1</sub> of Class 4 <span class="html-italic">versus</span> Chla<sub>s</sub>, and (<b>d</b>) n<sub>2</sub> of Class 4 <span class="html-italic">versus</span> Chla<sub>s</sub>. Note that Chla<sub>s</sub> is average Chla concentration from water surface to 0.4 m.</p> "> Figure 10 Cont.
<p>Relationships between structure parameters of Chla vertical Class 3 and 4 and surface variables: (<b>a</b>) m<sub>1</sub> of Class 3 <span class="html-italic">versus</span> <span class="html-italic">R<sub>rs</sub></span>(709), (<b>b</b>) m<sub>2</sub> of Class 3 <span class="html-italic">versus</span> Chla<sub>s</sub>, (<b>c</b>) n<sub>1</sub> of Class 4 <span class="html-italic">versus</span> Chla<sub>s</sub>, and (<b>d</b>) n<sub>2</sub> of Class 4 <span class="html-italic">versus</span> Chla<sub>s</sub>. Note that Chla<sub>s</sub> is average Chla concentration from water surface to 0.4 m.</p> "> Figure 11
<p>Comparison of FAI and NDBI derived by MODIS images on (<b>a</b>–<b>c</b>) 14 October 2011, (<b>d</b>–<b>f</b>) 10 October 2013, (<b>g</b>–<b>i</b>) 03 February 2012, and (<b>j</b>–<b>l</b>) 14 April 2013, respectively. The high NDBI and FAI values (dimensionless) in the middle of the lake in (b) and (c) indicate a cyanobacterial bloom. Areas circled in red with high FAI (k) and low NDBI (l) appear to be caused high turbidity. The dashed lines represent MODIS detector error.</p> ">
Abstract
:1. Introduction
2. Study Region
3. Methods
3.1. Field Measurements
3.2. MODIS Satellite Data
3.3. Parameterization of Vertical Chla Profiles
3.4. Algal Bloom Indexes
3.5. Classification and Regression Tree (CART)
4. Results
4.1. Vertical Characteristics of Optically Active Substances
Water Surface Value | CV of Vertical Profile | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
N | Mean | SD | Min | Max | N | Mean (%) | SD (%) | Min (%) | Max (%) | |
Chla | 64 | 352.68 | 922.32 | 26.00 | 6988.29 | 64 | 67 | 68 | 4 | 239 |
SPIM | 64 | 31.33 | 17.47 | 10.00 | 88.00 | 41 | 28 | 14 | 8 | 64 |
DOC | 64 | 27.27 | 23.21 | 3.23 | 126.32 | 9 | 14 | 9 | 6 | 34 |
Class | N | Average CV | Chla Vertical Profile Class | Fitted Function | R2 | RMSE |
---|---|---|---|---|---|---|
Class 1 | 27 | 19.53% | uniform | – | 9.57 | |
Class 2 | 9 | 29.25% | Gaussian | 0.85 | 3.36 | |
Class 3 | 12 | 97.73% | exponential | 0.91 | 23.29 | |
Class 4 | 16 | 163.60% | hyperbolic | 0.86 | 20.15 |
Class | Parameters | Min | Max | Mean | Sd |
---|---|---|---|---|---|
Class 2 | C0 | 7.76 | 39.81 | 22.42 | 10.85 |
σ | 0.02 | 0.41 | 0.20 | 0.15 | |
h | 1.48 | 75.59 | 34.08 | 26.94 | |
Class 3 | m1 | 129.4 | 613.30 | 280.98 | 146.54 |
m2 | −9.67 | −0.64 | −3.15 | 2.79 | |
Class 4 | n1 | 12.46 | 80.63 | 29.01 | 19.82 |
n2 | −1.10 | −0.28 | −0.71 | 0.26 |
4.2. Identification of Chla Vertical Profile Classes Using in situ Rrs
4.2.1. Rrs Response to Algae Vertical Profile Classes
4.2.2. Wind Speed of Different Chla Vertical Profile Classes
4.2.3. Decision Tree
Actual | User’s Accuracy | ||||||
---|---|---|---|---|---|---|---|
Class 1 | Class 2 | Class 3 | Class 4 | Totals | |||
Predicted | Class 1 | 19 | 3 | 0 | 0 | 22 | 86% |
Class 2 | 2 | 4 | 1 | 2 | 9 | 44% | |
Class 3 | 0 | 0 | 6 | 1 | 7 | 86% | |
Class 4 | 0 | 1 | 1 | 13 | 15 | 87% | |
Totals | 21 | 8 | 8 | 16 | 53 | ||
Producer’s Accuracy | 90% | 50% | 75% | 81% | Overall Acc. | 79% | |
F1-measure | 88% | 47% | 80% | 84% | Kappa | 0.71 |
4.3. Identification of Chla Vertical Profile Classes Using MODIS Rrc Data
Day | GMT Time | Wind Speed (m/s) | NDBIRRC,MODIS | Chla Vertical Class | |
---|---|---|---|---|---|
in situ Class | Estimated Class | ||||
28 May 2013 | 1:00 | 2.25 | 0.118 | 2 | 2 |
28 May 2013 | 2:25 | 2.25 | 0.159 | 4 | 4 |
28 May 2013 | 3:25 | 2.25 | 0.160 | 4 | 4 |
24 July 2013 | 1:55 | 5.00 | 0.101 | 2 | 1 |
24 July 2013 | 2:25 | 5.00 | 0.101 | 2 | 1 |
24 July 2013 | 2:55 | 5.00 | 0.101 | 1 | 1 |
11 October 2013 | 2:30 | 0.43 | 0.135 | 4 | 4 |
11 October 2013 | 3:10 | 0.43 | 0.135 | 4 | 4 |
11 October 2013 | 3:50 | 0.43 | 0.135 | 4 | 4 |
12 October 2013 | 1:30 | 2.20 | 0.101 | 2 | 2 |
12 October 2013 | 2:05 | 2.20 | 0.101 | 2 | 2 |
12 October 2013 | 2:40 | 2.20 | 0.101 | 2 | 2 |
4.4. Relationships of Structure Parameters to Remotely Sensed Data
5. Discussion
6. Conclusion
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Xue, K.; Zhang, Y.; Duan, H.; Ma, R.; Loiselle, S.; Zhang, M. A Remote Sensing Approach to Estimate Vertical Profile Classes of Phytoplankton in a Eutrophic Lake. Remote Sens. 2015, 7, 14403-14427. https://doi.org/10.3390/rs71114403
Xue K, Zhang Y, Duan H, Ma R, Loiselle S, Zhang M. A Remote Sensing Approach to Estimate Vertical Profile Classes of Phytoplankton in a Eutrophic Lake. Remote Sensing. 2015; 7(11):14403-14427. https://doi.org/10.3390/rs71114403
Chicago/Turabian StyleXue, Kun, Yuchao Zhang, Hongtao Duan, Ronghua Ma, Steven Loiselle, and Minwei Zhang. 2015. "A Remote Sensing Approach to Estimate Vertical Profile Classes of Phytoplankton in a Eutrophic Lake" Remote Sensing 7, no. 11: 14403-14427. https://doi.org/10.3390/rs71114403