Urban Remote Sensing. Группа авторов

Urban Remote Sensing - Группа авторов


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conductivity such as steel), together with contribution from re‐radiation of currents induced by incidence electromagnetic fields, multipath effects, diffraction effects, and multiple reflections similar to the Fabry–Perot reflection interactions between the front side of a building and the backside of an adjacent building constructed on two sides of a road. Thus, radar backscatter contains information to represent 3D building volume at the Ku‐band frequency of 13.4 GHz as validated by Mathews et al. (2019). Since X‐band (~10 GHz) is close to Ku‐band, we hypothesize that X‐band SAR backscatter, such as measured by the current CSK, TSX, and TDX and by the future LS1 and LS2, can also capture 3D building volume in cities.

      2.4.2.2.1 Initial SAR Findings

      Complex SAR data (including both real and imaginary parts in complex‐variable numbers, or magnitude and phase in the phasor form) can be used for urban building detection using time‐paring complex coherence method (Paloscia et al. 2019) together with geometricmean multi‐temporal coherence method from multiple CSK SAR scenes over urban areas (Nghiem and Science Team 2019) showing building density on the land surface not only within the city but also over small villages and minor settlements along roads. The Tân Sơn Nhất International Airport with a large area of runways was also clearly identified. Nghiem and Science Team (2019) detected houses built on the other side of the Yên Phụ levee directly adjacent to the waterway of the Red River in Hanoi, Vietnam. While multiple SAR datasets can be used synergistically to have more extensive and frequent observations of urban areas, cross‐validation of SAR data will be necessary to obtain consistent results for long‐term monitoring of urban built‐up change.

      2.4.3 RADAR FOR BUILT‐UP VOLUME: IMPLICATIONS

      Excitingly, spaceborne radar‐based approaches introduce a potential paradigm shift within the urban remote sensing community because of the ability to liberate remote sensing scientists and their analyses from the constraints of 2D, and do so with use of readily available, free data (unlike most airborne lidar data). Balk et al. (2019) even demonstrated that DSM data can actually capture socioeconomic characteristics, patterns, and status along the decadal urbanization trend of the Great Saigon (including the mega Hồ Chí Minh City), while 2D methods from satellite images may be insensitive to or even misrepresent socioeconomic change due to urbanization.

      Realistically, the SeaWinds DSM approach provides moderate‐resolution data products (1 km pixels) only for the years 2000–2009 when QuikSCAT was operating. The scatterometer record can be extended with international satellite scatterometer data; however, a major issue is whether the data can be fully calibrated and made freely available. On the other hand, spaceborne radar data with spatial resolutions several orders of magnitude higher than that of a scatterometer, likely provided only by SAR systems, have potential to revolutionize urban built‐up analyses. Even with the availability of spaceborne lidar data from ICESat‐2 (Moussavi et al. 2014), GEDI (Qi and Dubayah 2016), and Sentinel 3 (ESA 2019), scientists may not have long‐term data records or high enough spatial resolution for intra‐urban analyses, which can be provided by the many SAR platforms now in operation, previously operated, or planned for the future.

      Other data sources for 3D urban assessment such as Unoccupied Aircraft Systems (UAS), or drones, offer exciting opportunities for low‐cost, very high spatial resolution (imagery and/or lidar), and highly flexible temporal data acquisition (Mathews and Frazier 2017). Civil aviation authorities such as the US Federal Aviation Administration (FAA), though, do not permit UAS flight over highly populated urban areas (although regulations are not static and could change). Avenues do exist, however, to obtain permission to fly in urban areas but not without a number of obstacles.

      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.


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