A Prediction Model for the Outbreak Date of Spring Pollen Allergy in Beijing Based on Satellite-Derived Phenological Characteristics of Vegetation Greenness
<p>Study area. Note the downtown area of Beijing is mainly located within the Outer Ring Road.</p> "> Figure 2
<p>Technical process diagram for the extraction of satellite-derived vegetation greenness phenological characteristics before the pollen allergy outbreak. T in the right plots of (<b>c</b>) is the average DOY of spring pollen allergy outbreak during 2011–2021; VI in the right plots of (<b>c</b>) and (<b>d</b>) is the vegetation index value corresponding to the average DOY of pollen allergy outbreak; <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>1</mn> </msub> </mrow> </semantics></math> in the left plot of (<b>c</b>) is the DOY that 20 days earlier than the earliest breakout date of spring pollen allergy during 2011–2021, i.e., the 50th day; <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>2</mn> </msub> </mrow> </semantics></math> is the average DOY of spring pollen allergy outbreak during 2011–2021, i.e., the 81st day; <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> </mrow> </semantics></math> is the vegetation index value corresponding to <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>1</mn> </msub> </mrow> </semantics></math> on the fitted curve of the smoothed multiyear average daily curve of a vegetation index for a vegetation type; <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> </mrow> </semantics></math> is the vegetation index value corresponding to <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>2</mn> </msub> </mrow> </semantics></math> on the fitted curve of the smoothed multiyear average daily curve of a vegetation index for a vegetation type; <span class="html-italic">t</span><sub>n</sub> in the right plot of (<b>d</b>) is the corresponding DOY of VI in each year during 2011–2021; and <span class="html-italic">T</span><sub>n</sub> is the DOY of spring pollen allergy outbreak in each year during 2011–2021.</p> "> Figure 3
<p>An example shows how the prediction model of pollen allergy works for each year. (<b>a</b>) The preprocess of vegetation index data during 2011–2021; (<b>b</b>) the final cumulative linear fit prediction model; (<b>c</b>) the preprocess of vegetation index data in 2021 from the 1st day to the 50th day; and (<b>d</b>) the process of predicting pollen allergy in 2021. <span class="html-italic">Y</span> is the vegetation index value; <span class="html-italic">W</span> is the predicted number of days to the outbreak date of spring pollen allergy in Beijing (a negative value indicates that the outbreak date has not yet arrived, and a positive value indicates that the outbreak date has passed); <span class="html-italic">y</span> is the smoothed cumulative vegetation index value on the 50th day in 2021; and <span class="html-italic">w</span> is the predicted number of days to the outbreak date of spring pollen allergy in Beijing in 2021. Note the difference between the model building with the multi-year average vegetation index time series in (<b>a</b>) and the model prediction with the daily vegetation index time series in a certain year in (<b>c</b>).</p> "> Figure 4
<p>The establishment of two prediction models based on EVI2 of evergreen forest during 2011–2021. <span class="html-italic">W</span> is the number of days to the outbreak date of spring pollen allergy in Beijing, <span class="html-italic">Y</span><sub>1</sub> is the smoothed daily EVI2 value of evergreen forest for a given forecast year, and <span class="html-italic">Y</span><sub>2</sub> is the cumulative smoothed daily EVI2 value of evergreen forest for a given forecast year.</p> "> Figure 5
<p>The EVI2 value corresponding to the green-up date of evergreen forest. The black dots correspond to the green-up date of the evergreen forest.</p> "> Figure A1
<p>Weibo data with the keyword “pollen allergy” in Beijing during 2011–2021. The sharp increase in Weibo data in 2018 is because the mobile side of Weibo began to be widely popular in that year, with mobile active users accounting for 93% of the total active users, which is sourced from the Weibo Data Center. Weibo 2018 User Development Report [EB/0 L], 2019–03-15.</p> "> Figure A2
<p>A schematic diagram for extracting the outbreak dates of pollen allergies based on valid microblog data.</p> "> Figure A3
<p>Process diagram of vegetation classification.</p> "> Figure A4
<p>Pixel frequency probability distribution of NDVI values of vegetation and non-vegetation training samples.</p> "> Figure A5
<p>Pixel frequency probability distribution of NDVI values of evergreen and deciduous forest training samples.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. The Outbreak Dates of Spring Pollen Allergy
2.2.2. Remote Sensing Data
2.2.3. Vegetation Classification Data
2.3. Methods
2.3.1. Extraction of Satellite-Derived Phenological Characteristics of Vegetation Greenness within the 30 Days before the Spring Average Pollen Allergy Outbreak Date during 2011–2021
2.3.2. Establishment and Accuracy Assessment of the Prediction Models
3. Results
3.1. Satellite-Derived Phenological Characteristics of Vegetation Greenness within 30 Days before the Spring Average Pollen Allergy Outbreak Date during 2011–2021
3.2. The Prediction Models and Their Accuracies
4. Discussion
4.1. Phenological Characteristics of Remote Sensing Vegetation Greenness at the Beginning and Early Stages of the Spring Pollen Allergy Outbreak in Beijing
4.2. Advantages of the Prediction Models
4.3. The Importance of Data Preprocessing for the Daily Vegetation Index Time-Series Data and Limitations for the Application of the Prediction Models
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Extraction of the Start Dates of Pollen Allergy Outbreaks in Beijing Based on Sina Weibo Data
Appendix A.1. Data
Appendix A.2. Methods
Appendix A.3. Results
2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | |
---|---|---|---|---|---|---|---|---|---|---|---|
Day of year | 86 | 87 | 91 | 81 | 82 | 76 | 81 | 81 | 70 | 72 | 88 |
Date | 17 March | 28 March | 01 April | 22 March | 23 March | 17 March | 22 March | 22 March | 11 March | 13 March | 29 March |
Month | Allergic Rhinitis | Bronchial Asthma | Total |
---|---|---|---|
Jan. | 1429 | 1216 | 2645 |
Feb. | 1285 | 963 | 2248 |
Mar. | 2554 | 1263 | 3817 |
Apr. | 2021 | 1279 | 3300 |
May | 1728 | 1161 | 2889 |
June | 1785 | 1232 | 3017 |
July | 1451 | 1165 | 2616 |
Aug. | 3343 | 1471 | 4814 |
Sept. | 2744 | 1465 | 4209 |
Oct. | 2005 | 1238 | 3243 |
Nov. | 1849 | 1288 | 3137 |
Dec. | 2300 | 1513 | 3813 |
Appendix B. Description of Vegetation Classification
Appendix B.1. Data
Appendix B.2. Methods
Appendix B.3. Results
Vegetation Type | User Accuracy/% | Producer Accuracy/% | Number of Pixels | Area/km2 |
---|---|---|---|---|
Non-vegetation area | 99.83 | 92.47 | 13408 | 3352.0 |
Grassland | 95.86 | 84.79 | 2790 | 697.5 |
Evergreen forest | 97.53 | 94.87 | 367 | 91.8 |
Deciduous forest | 91.34 | 82.93 | 607 | 151.8 |
Cropland | 90.76 | 85.65 | 9644 | 2411.0 |
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Vegetation Type | NDVI | EVI | EVI2 | NIR + R + B | G/R |
---|---|---|---|---|---|
Evergreen Forest | 0.257–0.309 | 0.125–0.131 | 0.113–0.138 | 2.074–2.217 | 0.967–0.974 |
Deciduous Forest | 0.196–0.227 | 0.107–0.123 | 0.095–0.126 | 1.800–2.061 | 0.971–0.976 |
Evergreen + Deciduous Forest | 0.233–0.260 | 0.117–0.135 | 0.113–0.124 | 2.008–2.139 | 0.969–0.975 |
Vegetation Type | NDVI | EVI | EVI2 | NIR + R + B | G/R |
---|---|---|---|---|---|
Evergreen Forest | 0.253 | 0.060 | 0.270 | 0.086 | 0.009 |
Deciduous Forest | 0.198 | 0.187 | 0.116 | 0.181 | 0.006 |
Evergreen+ Deciduous Forest | 0.145 | 0.192 | 0.122 | 0.082 | 0.008 |
Vegetation Type | NDVI | EVI | EVI2 | NIR + R + B | G/R |
---|---|---|---|---|---|
Evergreen Forest | 0.724 * | 0.289 | 0.693 * | 0.159 | 0.612 * |
Deciduous Forest | 0.762 * | 0.429 | 0.633 * | 0.485 | 0.538 |
Evergreen+ Deciduous Forest | 0.717 * | 0.675 | 0.705 * | 0.280 | 0.519 |
Years for Model Building | Years for Model Test | RMSE for the Linear Fit Prediction Model | RMSE for the Cumulative Linear Fit Prediction Model | ||||
---|---|---|---|---|---|---|---|
EVI2 of Evergreen Forest | NDVI of Evergreen Forest | EVI2 of Deciduous Forest | EVI2 of Evergreen Forest | NDVI of Evergreen Forest | EVI2 of Deciduous Forest | ||
2011–2017 | 2018–2021 | 52.991 | 61.538 | 42.497 | 3.369 | 18.177 | 14.123 |
2012–2018 | 2011, 2019–2021 | 9.832 | 242.953 | 55.808 | 2.434 | 19.811 | 14.123 |
2013–2019 | 2011–2012, 2020–2021 | 48.862 | 78.272 | 56.464 | 1.766 | 15.813 | 11.989 |
2014–2020 | 2011–2013, 2021 | 11.399 | 53.608 | 18.232 | 2.853 | 11.285 | 10.225 |
2015–2021 | 2011–2014 | 40.172 | 88.491 | 14.385 | 3.073 | 31.525 | 6.865 |
… | … | … | … | … | … | ||
Average of RMSEs (mean ± sd) | / | 95.549 ± 197.572 | 92.636 ± 134.075 | 26.673 ± 21.725 | 3.589 ± 1.101 | 22.519 ± 10.184 | 10.450 ± 2.689 |
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Yang, X.; Zhu, W.; Zhao, C. A Prediction Model for the Outbreak Date of Spring Pollen Allergy in Beijing Based on Satellite-Derived Phenological Characteristics of Vegetation Greenness. Remote Sens. 2022, 14, 5891. https://doi.org/10.3390/rs14225891
Yang X, Zhu W, Zhao C. A Prediction Model for the Outbreak Date of Spring Pollen Allergy in Beijing Based on Satellite-Derived Phenological Characteristics of Vegetation Greenness. Remote Sensing. 2022; 14(22):5891. https://doi.org/10.3390/rs14225891
Chicago/Turabian StyleYang, Xinyi, Wenquan Zhu, and Cenliang Zhao. 2022. "A Prediction Model for the Outbreak Date of Spring Pollen Allergy in Beijing Based on Satellite-Derived Phenological Characteristics of Vegetation Greenness" Remote Sensing 14, no. 22: 5891. https://doi.org/10.3390/rs14225891
APA StyleYang, X., Zhu, W., & Zhao, C. (2022). A Prediction Model for the Outbreak Date of Spring Pollen Allergy in Beijing Based on Satellite-Derived Phenological Characteristics of Vegetation Greenness. Remote Sensing, 14(22), 5891. https://doi.org/10.3390/rs14225891