Urban Remote Sensing. Группа авторов
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Название: Urban Remote Sensing
Автор: Группа авторов
Издательство: John Wiley & Sons Limited
Жанр: География
isbn: 9781119625858
isbn:
2.6 CONCLUSION
Lidar and radar data provide opportunities for remote sensing scientists to model our world, specifically our urban environments, comprehensively in 3D. As this chapter has illustrated, lidar data enables quantification of urban built‐up volume as well as examination of its change over time. Likewise, radar data also facilitates 3D observation of urban environments with the potential to do so over larger areas and with increased temporal frequency. However, more work on advancing our 3D analysis methodologies and incorporation of various 3D data sources is needed moving forward. In sum, for a better understanding of urban form and its morphology (i.e. intensity and configuration of built‐up material), the vertical dimension is incredibly important (Wentz et al. 2018; Dong et al. 2019; Taubenböck et al. 2012, 2019). To improve upon our understanding of urban processes, including how urban built‐up volume influences environmental processes such as air circulation and urban heat island effect, we must think three‐dimensionally.
ACKNOWLEDGMENTS
This work partially funded by Michigan Space Grant Consortium, NASA grant #NNX15AJ20H (subgrant awarded to A.J. Mathews). The Lucia Harrison Endowment Fund (Department of Geography, Environment, and Tourism, Western Michigan University) provided additional support to A.J. Mathews. The research carried out at the JPL (S.V. Nghiem), California Institute of Technology, was supported by the NASA Land Cover and Land Use Change (LCLUC) Program and in part by the NASA Earth Science R&A Program. The authors thank Claire Gilbert, Greg Neumann, Amy Frazier, Yun Zhao, Gustavo Ovando‐Montejo, K. Colton Flynn, and Emily Ellis for help with data processing and analysis. The authors also thank the Army Geospatial Center for providing some of the lidar data highlighted in this chapter.
REFERENCES
1 Alonzo, M., Bookhagen, B., and Roberts, D.A. (2014). Urban tree species mapping using hyperspectral and LiDAR data fusion. Remote Sensing of Environment 148: 70–83. doi:10.1016/j.rse.2014.03.018.
2 ASPRS (American Society for Photogrammetry and Remote Sensing) (2013). LAS Specification Version 1.4 ‐ R13, 15 July 2013. https://www.asprs.org/wp‐content/uploads/2010/12/LAS_1_4_r13.pdf (accessed 11 November 2019).
3 Axelsson, P. (1999). Processing of laser scanner data: algorithms and applications. ISPRS Journal of Photogrammetry and Remote Sensing 54: 138–147. doi:10.1016/S0924‐2716(99)00008‐8.
4 Babahajiani, P., Fan, L., and Gabbouj, M. (2015). Object recognition in 3D point cloud of urban street scene. Asian Conference on Computer Vision, ACCV 2014 Workshops, 177–190. doi:10.1007/978‐3‐319‐16628‐5_13.
5 Bagheri, H., Schmitt, M., d'Angelo, P., and Zhu, X.X. (2018). A framework for SAR‐optical stereogrammetry over urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, 146: 389–408. doi:10.1016/j.isprsjprs.2018.10.003.
6 Balk, D.L., Nghiem, S. V., Jones, B., Liu, Z., and Dunn, G. (2019). Up and out: a multifaceted approach to characterizing urbanization in Greater Saigon, 2000‐2009. Landscape and Urban Planning, 187: 199–209. doi:10.1016/j.landurbplan.2018.07.009.
7 Boyko, A., and Funkhouser, T. (2011). Extracting roads from dense point clouds in large scale urban environment. ISPRS Journal of Photogrammetry and Remote Sensing 66: S2–S12.
8 Brenner, A.R., and Roessing, L. (2008). Radar imaging of urban areas by means of very high‐resolution SAR and interferometric SAR. IEEE Transactions on Geoscience and Remote Sensing 46(10): 2971–2982. doi:10.1109/TGRS.2008.920911.
9 Brunner, D., Lemoine, G., Bruzzone, L., and Greidanus, H. (2010). Building height retrieval from VHR SAR imagery based on an iterative simulation and matching technique. IEEE Transactions on Geoscience and Remote Sensing, 48(3): 1487–1504. doi:10.1109/TGRS.2009.2031910.
10 Buckreuss, S., Schättler, B., Fritz, T., Mittermayer, J., Kahle, R., Maurer, E., Böer, J., Bachmann, M., Mrowka, F., Schwarz, E., Breit, H., and Steinbrecher, U. (2018). Ten years of TerraSAR‐X operations. Remote Sensing 10(6): 873. doi:10.3390/rs10060873.
11 Cheuk, M.L., and Yuan, M. (2009). Assessing spatial uncertainty of lidar‐derived building model. Photogrammetric Engineering & Remote Sensing 75(3): 257–269. doi:10.14358/PERS.75.3.257.
12 Dell'Acqua, F., and Gamba, P. (2006). Discriminating urban environments using multiscale texture and multiple SAR images. International Journal of Remote Sensing 27(18): 3797–3812. doi:10.1080/01431160600557572.
13 Dong, P., Ramesh, S., and Nepali, A. (2010). Evaluation of small‐area population estimation using LiDAR, Landsat TM and parcel data. International Journal of Remote Sensing 31(21): 5571–5586. doi:10.1080/01431161.2010.496804.
14 Dong, P., and Guo, H.D. (2012). A framework for automated assessment of post‐earthquake building damage using geospatial data. International Journal of Remote Sensing 33: 81–100. doi:10.1080/01431161.2011.582188.
15 Dong, P., and Chen, Q. (2018). LiDAR Remote Sensing and Applications. New York: CRC Press.
16 Dong, P., Zhong, R., and Yigit, A. (2018). Automated parcel‐based building change detection using multitemporal airborne LiDAR data. Surveying and Land Information Science 77(1): 5–13.
17 Dong, T., Jiao, L., Xu, G., Yang, L., and Liu, J. (2019). Toward sustainability? Analyzing changing urban form patterns in the United States, Europe, and China. Science of the Total Environment 671: 632–643. doi:10.1016/j.scitotenv.2019.03.269.
18 Dorninger, P., and Pfeifer, N. (2008). A comprehensive automated 3D approach for building extraction, reconstruction, and regularization from airborne laser scanning point clouds. Sensors, 8, 7323–7343. doi:10.3390/s8117323.
19 Ellis, E.A., and Mathews, A.J. (2019). Object‐based delineation of urban tree canopy: assessing change in Oklahoma City, 2006–2013. Computers, Environment and Urban Systems 73: 85–94. doi:10.1016/j.compenvurbsys.2018.08.006.
20 ESA (European Space Agency) (2019). ESA Sentinel Online – Missions – Sentinel‐3. https://sentinel.esa.int/web/sentinel/missions/sentinel‐3 (accessed 10 December 2019).
21 Esch, T., Thiel, M., Schenk, A., Roth, A., Muller, A., and Dech, S. (2009). Delineation of urban footprints from TerraSAR‐X data by analyzing speckle characteristics and intensity СКАЧАТЬ