Читать книгу Urban Remote Sensing - Группа авторов - Страница 40

With a broader variety of data options now available to remote sensing scientists, comprehensive 3D analyses of urban environments are well within our grasp. This chapter outlines the importance of lidar and radar of many types as data sources to support such efforts. Although lidar remains the best option for building height estimation and built‐up volume extraction, a number of limitations persist related to its high cost and sporadic acquisition. Radar provides an innovative alternative to lidar with the potential to conduct similar analysis at a lower cost and higher repeatability (Gamba and Houshmand 2002; Sportouche et al. 2011; Mathews et al. 2019). Looking forward, SAR data offer the best data option due to the high spatial and temporal resolutions (Bagheri et al. 2018; Geiß et al. 2019) offered by several platforms currently and with more SARs planned. Such high spatial resolution enables intra‐urban analyses that are important for many fields beyond remote sensing. Specifically, estimating global population based on built‐up volumes including diurnal dynamics (Dong et al. 2010; Zhao et al. 2017), examining vegetation (i.e. greenspace) in urban areas and its environmental and anthropological benefits (Alonzo et al. 2014; Ellis and Mathews 2019), and making linkages with other data to explore urban pollution – air, water, etc. (Fang et al. 2015; Masetti et al. 2015; Jacobson et al. 2015, 2019). Significantly, previous (e.g. QuikSCAT), existing/current (e.g. CSM, TDX), and future (e.g. LS1, LS2) radar data sources will enable interdecadal analyses, which was emphasized repeatedly in the “Decadal Strategy for Earth Observation from Space” (NASEM 2018), at the intra‐urban scale – e.g. facilitating 3D characterization of urban typologies: analysis and refinement of urban surface material types such as building materials, impervious, and other surface types. Data fusion, cross‐calibration, and integration are and will continue to be major challenges faced within this area of research for both data product generation and validation.

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.