Time-Series Monitoring of Dust-Proof Nets Covering Urban Construction Waste by Multispectral Images in Zhengzhou, China
<p>Study area. (<b>a</b>) Location of Henan Province in China; (<b>b</b>) Location of the study area in Henan Province; (<b>c</b>) Sentinel-2 remote sensing image of Zhengzhou (22 February 2018) and delineation of the eastern/western suburbs and developing/built-up zones.</p> "> Figure 2
<p>Field investigation pictures of (<b>a</b>) construction waste covered with dust-proof nets, (<b>b</b>) bare soil covered with dust-proof nets captured by drones and (<b>c</b>) the process of dumping bare soil with a shovel.</p> "> Figure 3
<p>The workflow of this study.</p> "> Figure 4
<p>Example data of features from Google Earth. (<b>a</b>) Bare soil; (<b>b</b>) Green and blue dust-proof nets; (<b>c</b>) Regularly arranged temporary buildings with roofs similar in color to blue dust-proof nets, commonly found on construction sites.</p> "> Figure 5
<p>Two subsets of construction waste samples from western Zhengzhou in Sentinel-2 images. (<b>a</b>) The yellow and green parts represent bare soil and dust-proof nets, respectively (22 February 2018); (<b>b</b>) The yellow, blue, green and purple parts represent bare soil, blue dust-proof nets, green dust-proof nets and temporary buildings, respectively (7 July 2019).</p> "> Figure 6
<p>The reflectance plot showing the spectral characteristic of four classes derived from the Sentinel-2 image on 7 July 2019.</p> "> Figure 7
<p>Construction waste and dust-proof nets map of Zhengzhou city from 2015 to 2020. (The green, blue and yellow parts represent green dust-proof nets, blue dust-proof nets and bare soil, respectively. The temporary buildings in the classification results are not displayed after post-processing).</p> "> Figure 8
<p>Comparison of ground truth and classification results. (<b>A</b>–<b>D</b>) Different regions of this study area. In each group, the left is the ground truth, and the right is the prediction result.</p> "> Figure 9
<p>The area of construction waste in western suburbs, eastern suburbs and the whole area from 2015 to 2020.</p> "> Figure 10
<p>The variation of construction waste in the developing zone and built-up zone, which are represented by the yellow and red line, respectively. The green line named “Reference density” is the density of construction waste in the developing zone relative to the built-up zone. A comparison of the red and green lines (with the difference in the area of partitions removed) highlights the true disparity between the two regions.</p> "> Figure 11
<p>The coverage of dust-proof nets in the built-up zone and the whole area from 2015 to 2020.</p> "> Figure 12
<p>The linear regression models of (<b>a</b>) air quality index, (<b>b</b>) concentration of PM2.5, (<b>c</b>) concentration of PM10 and the dust-proof net’s coverage. The dashed lines denote the best-fit lines from linear regression.</p> ">
Abstract
:1. Introduction
- (1)
- A multi-layer identification method based on remote sensing was proposed to extract both dust-proof nets and uncover construction waste.
- (2)
- Combined with urban construction planning, the variations in the area and distribution of construction waste were analyzed in terms of time and space.
- (3)
- Comparing the coverage rate of dust-proof nets and AQI data of the same period, it is concluded that mulching dust-proof nets have a positive impact on urban air quality.
2. Materials
2.1. Study Area
2.2. Data
2.2.1. Remote Sensing Data
2.2.2. Fieldwork Data
2.2.3. Atmospheric Data
3. Method
3.1. Preprocessing
3.2. Sample Data Acquisition
3.3. Multi-Layer Classification
- Parallelepiped Classification is generally used when there is no overlap between point clouds of different categories in the feature space. The decision boundary of each class is limited by a parallelepiped consisting of its mean vector and standard deviation. If an unlabeled pixel vector falls inside the parallelepiped, it is assigned to the category [37].
- The discriminant function of Mahalanobis Distance Classification is the spectral distance; that is, the unlabeled pixel is classified into the class with the smallest spectral distance. Unlike Euclidean distance, which is also spectral distance, Mahalanobis distance is weighted by the covariance matrix between different bands, and so it is a direction-sensitive distance classifier [38,39].
- Maximum Likelihood Classification assumes that the statistics for each class in each band are normally distributed. The main idea is to predict the category label y that maximizes the likelihood of our observed data; that is, pixels are classified into the most probable type after calculating the attribution probability of pixels belonging to various types of features. If the highest probability is less than the threshold you specify, the pixel remains unclassified [39,40].
- Support Vector Machine is a machine learning method based on statistical learning theory. It can automatically find those support vectors that have a greater ability to distinguish between classifications and establish classification rules based on them to maximize the interval between classes. When the sample data cannot be divided linearly, the method of increasing dimension mapping is adopted to map the vectors to a higher dimensional space for division. However, this process requires a lot of computing resources [41].
3.4. Model Optimization
3.4.1. Feature Selection
- (1)
- Spectral Features Method
- (2)
- Band Statistic Features
3.4.2. Thresholds Selection
3.5. Verification Method
3.6. Atmospheric Data Integration and Correlation Analysis
4. Results and Analysis
4.1. Time-Series Urban Construction Waste Map in Zhengzhou from 2015 to 2020
4.2. Accuracy Assessment
4.3. Spatial Patterns of Urban Construction Waste
4.3.1. Eastern and Western Distribution
4.3.2. Developing Area and Built-Up Area Distribution
4.4. Temporal Changes of Dust-Proof Nets Coverage
4.5. Correlation of Dust-Proof Net Coverage and Atmospheric Quality
5. Discussion
5.1. Analysis of Construction Waste Changes in the Context of Urbanization
5.2. Effectiveness of Dust-Proof Nets on Ambient Air Quality
5.3. Limitation of the Study
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Satellite | Landsat-8 | Sentinel-2 |
---|---|---|
Spatial Resolution (m) | 30 | 10/20 |
Available Bands | Band 2 (Blue: 0.49–0.51 m) Band 3 (Green: 0.53–0.59 m) Band 4 (Red: 0.64–0.67 m) Band 5 (NIR: 0.85–0.88 m) Band 6 (SWIR: 1.57–1.65 m) Band 7 (SWIR: 2.11–2.29 m) | Band 2 (Blue: 0.46–0.52 m) |
Band 3 (Green: 0.54–0.58 m) | ||
Band 4 (Red: 0.65–0.69 m) | ||
Band 5 (Red Edge: 0.70–0.71 m) | ||
Band 6 (Red Edge: 0.73–0.75 m) | ||
Band 7 (Red Edge: 0.77–0.79 m) | ||
Band 8 (NIR: 0.79–0.90 m) | ||
Band 8A (Red Edge: 0.86–0.88 m) | ||
Band 11 (SWIR: 1.57–1.66 m) | ||
Band 12 (SWIR: 2.10–2.29 m) | ||
Image Acquisition Time | 14 September 2015 2 October 2016 | 27 February 2017 |
22 February 2018 | ||
7 July 2019 | ||
6 July 2020 |
Image Acquisition Time | Bare Soil | Green Dust-Proof Nets | Blue Dust-Proof Nets | Temporary Buildings |
---|---|---|---|---|
September 2015 | 12,762 | 6570 | — | — |
October 2016 | 6921 | 14,238 | — | — |
February 2017 | 6827 | 8728 | — | — |
February 2018 | 6193 | 14,173 | — | — |
July 2019 | 6784 | 15,490 | 4545 | 11,558 |
July 2020 | 3143 | 1715 | 4130 | 7852 |
Classifier | Overall Accuracy (%) | Kappa Coefficient | Time Consumption (s) |
---|---|---|---|
Parallelepiped Classification | 86.83 | 0.81 | 8 |
Mahalanobis Distance Classification | 95.77 | 0.94 | 26 |
Maximum Likelihood Classification | 97.15 | 0.96 | 35 |
Support Vector Classification | 97.73 | 0.97 | 558 |
Rank | Sentinel-2 | Landsat-8 |
---|---|---|
1 | Band 12—SWIR-2 | B7—SWIR-2 |
2 | Band 11—SWIR-1 | B6—SWIR-1 |
3 | Band 8—NIR | B5—NIR |
4 | Band 8A—Red Edge | B4—Red |
Threshold of Minimum Similarity | Overall Accuracy ( %) | Kappa Coefficient |
---|---|---|
0.02 | 91.32 | 0.88 |
0.01 | 92.63 | 0.90 |
0.005 | 94.10 | 0.91 |
0.004 | 95.06 | 0.93 |
0.001 | 95.13 | 0.93 |
Image Acquisition Time | PM10 (g/m) | PM2.5 (g/m) | AQI |
---|---|---|---|
14 September 2015 | 79.66 | 42.36 | 6.84 |
2 October 2016 | 97.15 | 53.10 | 5.49 |
27 February 2017 | 148.96 | 89.28 | 8.79 |
22 February 2018 | 135.88 | 80.21 | 6.84 |
7 July 2019 | 61.45 | 26.95 | 4.48 |
6 July 2020 | 66.44 | 32.53 | 3.80 |
Image Acquisition Time | Class | Prod. Acc. | User Acc. | Commission | Omission | Overall Accuracy | Kappa Coefficient |
---|---|---|---|---|---|---|---|
Bare soil | 99.23% | 100.00% | 0.00 | 0.77 | |||
14 September 2015 | Green dust-proof nets | 94.16% | 100.00% | 0.25 | 5.84 | 97.35% | 0.94 |
Blue dust-proof nets | — | — | — | — | |||
Temporary buildings | — | — | — | — | |||
Bare soil | 97.67% | 100.00% | 0.00 | 2.33 | |||
2 October 2016 | Green dust-proof nets | 95.40% | 100.00% | 0.00 | 4.60 | 96.04% | 0.91 |
Blue dust-proof nets | — | — | — | — | |||
Temporary buildings | — | — | — | — | |||
Bare soil | 98.68% | 99.72% | 0.28 | 1.32 | |||
27 February 2017 | Green dust-proof nets | 98.75% | 100.00% | 0.00 | 1.25 | 98.72% | 0.97 |
Blue dust-proof nets | — | — | — | — | |||
Temporary buildings | — | — | — | — | |||
Bare soil | 99.51% | 100.00% | 0.00 | 0.49 | |||
22 February 2018 | Green dust-proof nets | 98.09% | 99.97% | 0.03 | 1.91 | 98.50% | 0.96 |
Blue dust-proof nets | — | — | — | — | |||
Temporary buildings | — | — | — | — | |||
Bare soil | 90.70% | 100.00% | 0.00 | 9.30 | |||
7 July 2019 | Green dust-proof nets | 93.42% | 99.04% | 0.96 | 6.58 | 95.24% | 0.93 |
Blue dust-proof nets | 98.64% | 85.20% | 14.80 | 1.36 | |||
Temporary buildings | 98.13% | 99.84% | 0.16 | 1.87 | |||
Bare soil | 91.78% | 100.00% | 0.00 | 8.22 | |||
6 July 2020 | Green dust-proof nets | 92.08% | 91.37% | 8.63 | 7.92 | 91.82% | 0.89 |
Blue dust-proof nets | 88.71% | 99.69% | 0.31 | 11.29 | |||
Temporary buildings | 94.96% | 98.34% | 1.66 | 5.04 |
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Li, Z.; Guo, H.; Zhang, L.; Liang, D.; Zhu, Q.; Liu, X.; Zhou, H. Time-Series Monitoring of Dust-Proof Nets Covering Urban Construction Waste by Multispectral Images in Zhengzhou, China. Remote Sens. 2022, 14, 3805. https://doi.org/10.3390/rs14153805
Li Z, Guo H, Zhang L, Liang D, Zhu Q, Liu X, Zhou H. Time-Series Monitoring of Dust-Proof Nets Covering Urban Construction Waste by Multispectral Images in Zhengzhou, China. Remote Sensing. 2022; 14(15):3805. https://doi.org/10.3390/rs14153805
Chicago/Turabian StyleLi, Zilu, Huadong Guo, Lu Zhang, Dong Liang, Qi Zhu, Xvting Liu, and Heng Zhou. 2022. "Time-Series Monitoring of Dust-Proof Nets Covering Urban Construction Waste by Multispectral Images in Zhengzhou, China" Remote Sensing 14, no. 15: 3805. https://doi.org/10.3390/rs14153805
APA StyleLi, Z., Guo, H., Zhang, L., Liang, D., Zhu, Q., Liu, X., & Zhou, H. (2022). Time-Series Monitoring of Dust-Proof Nets Covering Urban Construction Waste by Multispectral Images in Zhengzhou, China. Remote Sensing, 14(15), 3805. https://doi.org/10.3390/rs14153805