Building Structure Mapping on Level Terrains and Sea Surfaces in Vietnam
Abstract
:1. Introduction
2. Methods
2.1. Image Processing
2.2. Mapping
2.3. Ground Truth Data Collection and Accuracy Assessment
2.4. Building Structures on Land
2.5. Building Structures on Sea Surface
3. Results
3.1. Results for Building Structures on Land
3.2. Results for Building Structures on the Sea Surface
4. Discussion
5. Conclusions
- The novel use of satellite Sentinel-1 SAR data in the two-dimensional polarization domain enables the method to be robust against confounding factors such as variations due to different incidence and azimuth angles, due to water-tree radar signal interactions (with synergistic Sentinel-2 MSI data), and due to different landforms on complex typography (with the geomorphon concept), without having to rely on more complicated methods such as neural networks that may introduce non-linearity, non-uniqueness, or extraneous outcomes.
- A demonstration of the ability of radar backscatter signatures to detect building structures is founded on radar responses to true physical structures of buildings [5,6], rather than optical colors or spectral appearances of land cover types. As our method is based on radar signatures of physical building structures, it can successfully capture the characteristics of urban building patterns corresponding to different urban development classes and socioeconomic status (see Table 1 and Table 2), and in different rural-urban landscapes in both inland and coastal regions with wet and arid environmental conditions, or over sea surfaces under different wind and wave effects.
- Founded on time-series satellite SAR data records consistently tracked at each pixel location, the method successfully detects and maps persistent (rather than temporary) building structures, which truly represent sustained human settlements in order to circumvent the shortfalls of the proxy indicator derived from NTL data [4], as illustrated in the case of Phan Thiết city versus the dragon fruit plantations in Bình Thuận (Figure 8). Such spatial data products of physical building structures are crucial for urban mapping applications, in particular for accurate estimations of FFCO2 emission required for the successful implementation of the UNFCCC Paris Agreement. In fact, the U.S. National Academies of Sciences, Engineering, and Medicine recognizes that the improvement in greenhouse gas (GHG, including FFCO2) measurement and monitoring is foundational to the control of global GHG emissions [39].
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
References
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No. | Standard | Unit | Urban Class | ||||||
---|---|---|---|---|---|---|---|---|---|
Special | I | II | III | IV | V | ||||
State | Province | ||||||||
1 | Population in urban and suburban | 1000 people | ≥6000 | ≥5000 | ≥1000 | ≥500 | ≥200 | ≥100 | 4–50 |
5000 | 1000 | 500 | 200 | 100 | 50 | ||||
2 | Population in urban | 1000 people | ≥4000 | ≥3000 | ≥500 | ≥200 | ≥100 | ≥50 | |
3000 | 500 | 200 | 100 | 50 | 20 |
No. | Standard | Unit | Urban Class | |||||
---|---|---|---|---|---|---|---|---|
Special | I | II | III | IV | V | |||
I | Infrastructure standard | |||||||
I.1 | Housing | |||||||
1 | Average floor area | m2 floor/person | ≥29 | ≥29 | ≥29 | ≥29 | ≥29 | ≥29 |
26.5 | 26.5 | 26.5 | 26.5 | 26.5 | 26.5 | |||
2 | House rate | % | 100 | ≥95 | ≥95 | ≥95 | ≥90 | ≥90 |
90 | 90 | 90 | 90 | 85 | 85 | |||
I.2 | Public infrastructure | |||||||
1 | Settlement land, open-green land, parks, traffic land | m2/person | 61 | 61 | 61 | 78 | 78 | 78 |
54 | 54 | 54 | 61 | 61 | 61 | |||
2 | Education | buildings | ≥40 | ≥30 | ≥20 | ≥10 | ≥4 | ≥2 |
30 | 20 | 10 | 4 | 2 | 1 | |||
3 | Culture | buildings | ≥20 | ≥14 | ≥10 | ≥6 | ≥4 | ≥2 |
14 | 10 | 6 | 4 | 2 | 1 | |||
4 | Sports | buildings | ≥15 | ≥10 | ≥7 | ≥5 | ≥3 | ≥2 |
10 | 7 | 5 | 3 | 2 | 1 |
Cities | N_Building (pixels) | N_others (pixels) | False Negative Rate (FNR) (%) | False Positive Rate (FPR) (%) |
---|---|---|---|---|
Bạc Liêu | 320 | 330 | 8.6 | 5.2 |
Cà Mau | 306 | 344 | 9.5 | 5.5 |
Sóc Trăng | 310 | 340 | 11.4 | 6.1 |
Tân An | 318 | 332 | 11.9 | 7.0 |
Phan Thiết | 338 | 312 | 13.3 | 8.2 |
All cities | 1592 | 1658 | Average FNR = 10.9% | Average FPR = 6.4% |
Oil Field | Structure ID | Longitude (o) | Latitude (o) |
---|---|---|---|
Sư Tử Đen | 709 | 108.4377899 | 10.46973133 |
710 | 108.3818665 | 10.44124126 | |
714 | 108.3938675 | 10.42325497 | |
716 | 108.3894806 | 10.39616489 | |
721 | 108.3605118 | 10.38012123 | |
Bạch Hổ | 731 | 107.9596634 | 9.984884262 |
734 | 107.9711685 | 9.972677231 | |
738 | 107.947319 | 9.923978806 | |
739 | 108.0084839 | 9.901568413 | |
742 | 107.9201202 | 9.878500938 | |
743 | 107.9855728 | 9.877530098 | |
745 | 107.9864731 | 9.86863327 |
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Ngo, K.D.; Nghiem, S.V.; Lechner, A.M.; Vu, T.T. Building Structure Mapping on Level Terrains and Sea Surfaces in Vietnam. Remote Sens. 2021, 13, 2439. https://doi.org/10.3390/rs13132439
Ngo KD, Nghiem SV, Lechner AM, Vu TT. Building Structure Mapping on Level Terrains and Sea Surfaces in Vietnam. Remote Sensing. 2021; 13(13):2439. https://doi.org/10.3390/rs13132439
Chicago/Turabian StyleNgo, Khanh D., Son V. Nghiem, Alex M. Lechner, and Tuong T. Vu. 2021. "Building Structure Mapping on Level Terrains and Sea Surfaces in Vietnam" Remote Sensing 13, no. 13: 2439. https://doi.org/10.3390/rs13132439
APA StyleNgo, K. D., Nghiem, S. V., Lechner, A. M., & Vu, T. T. (2021). Building Structure Mapping on Level Terrains and Sea Surfaces in Vietnam. Remote Sensing, 13(13), 2439. https://doi.org/10.3390/rs13132439