FIGURE 2.1 Current, former, and future 3D data sources for Earth observation. Airborne lidar provide very high spatial resolution but with sporadic temporal frequency. SARs such as COSMO‐SkyMed and TanDEM‐X provide the best options with both high temporal and spatial resolution data. Other SARs are planned to launch in future years, which will provide a multidecadal 3D record from QuikSCAT in 2000 to COSMO‐SkyMed and TanDEM‐X and eventually NISAR and LOTUSats 1 and 2 in the 2020s.

      Airborne discrete‐return lidar data provide high spatial resolution data (see Figure 2.1) but is collected on a nonregular basis over small areas due to high cost. Lidar data acquisition is expensive and, regarding multi‐temporal data capture of particular locations for change analyses, is often not repeated for several years unless a specific funding source or project is in place. Multi‐temporal lidar analyses, therefore, commonly need to integrate lidar data from a variety of sources with varying point densities (Gamba et al. 2005). In the United States, government agency‐collected lidar data are often publicly available and free to download, but this is not always the case with other data collectors and holders. Some of these lidar data sources include the following:

      Unfortunately, lidar data are not collected regularly in many countries worldwide or not accessible due to potential classified military operations; hence, the desire for spaceborne lidar and/or other data collection avenues for 3D data such as radar platforms. Spaceborne lidar platforms such as ICESat‐2 and GEDI offer open, free lidar data with increased repeatability though not to the level of spatial detail of the airborne options previously discussed and with extended repeat acquisition time (see Figure 2.1).

      Remote sensing analyses increasingly require higher/finer spatial and temporal resolutions along with large swaths that realistically can only all be provided by spaceborne radar platforms, which attests to the significance of integration of radar data including Synthetic Aperture Radar (SAR) into analyses. To date and for the foreseeable future, radar platforms/sensors provide ample spatial and temporal resolution data for urban built‐up analysis compared to lidar alternatives (e.g. airborne lidar, GEDI, ICESat‐2). QuikSCAT SeaWinds scatterometer data have proven effective at estimating 3D build‐up within urban environments though at relatively coarse spatial and temporal resolutions (Nghiem et al. 2017; Mathews et al. 2019). Unfortunately, QuikSCAT is no longer operating while fully calibrated backscatter data at Level‐1B have not yet become completely and freely available from international satellite scatterometers (e.g. Oceansat‐2 in 2009–2014, HY‐2A in 2011–present, and SCATSAT‐1 in 2016–present). Subsequently, SAR platforms, of which several are now operating (or have operated over the past several decades) and with many future missions are planned such as NISAR and LOTUSat 1 and 2, provide the most promising 3D data option for future urban remote sensing analyses. SAR data options (e.g. COSMO‐SkyMed [CSK], TanDEM‐X [TDX], Sentinel‐1) too, as shown in Figure 2.1, provide both high spatial and temporal resolution data for highly detailed multi‐temporal assessments.

2.3 LIGHT DETECTION AND RANGING (LIDAR) APPROACHES

      2.3.1 BACKGROUND

      Airborne discrete‐return lidar is the de facto data standard for urban built‐up height estimation and volume calculation in research and in practice because it rapidly captures highly detailed 3D data for relatively large areas with minimal vertical error (Dong and Chen 2018). In fact, lidar data commonly are more accurate in the vertical dimension than the horizontal (Cheuk and Yuan 2009). Significantly, discrete‐return lidar data collection records multiple returns for each laser pulse sent toward the Earth's surface and laser pulses can penetrate vegetative canopy gaps (unlike passive, optical data). Lidar, therefore, captures terrain data beneath canopy as well as structural information about the canopy itself (Shan and Toth 2018). However, for urban applications emphasizing built‐up infrastructure, this is not as important as acquiring rooftop heights and structures – although in residential areas tree canopies often overhang rooftops of homes where multiple returns are important for vertically separating the two.

From a broader urban remote sensing perspective, lidar data provide a wealth of information on urban infrastructure applicable to many research topics such as small area population estimation (Dong et al. 2010; Zhao et al. 2017), urban feature extraction (Priestnall et al. 2000; e.g. roads: Boyko and Funkhouser 2011, building footprints: Rottensteiner and Briese 2002), visualization (Palmer and Shan 2002; Habib et al. 2008), and through a variety of change detection applications (e.g. pre‐/post‐disaster: Dong and Guo 2012, buildings/building types: Dong et al. 2018, etc.). In addition, given its importance in improving air quality and human health outcomes among other benefits, tree canopy delineation and quantification is also a common application for lidar data in the urban environment (Alonzo et al. 2014; Ellis and Mathews 2019). Regardless of application within urban research, lidar data handling is similar (see next section for data processing and analysis details).

      Specific to this chapter on built‐up volume, research focusing on buildings is of special interest. For example, Dong et al. (2010) and Zhao et al. (2017) use lidar data to calculate building volume for portions of several Texas cities (Dong et al.: Denton; Zhao et al.: Austin, Dallas, Houston, and San Antonio) to estimate population with the assumption that higher building volumes signify increased population. Both studies extracted buildings from lidarderived raster data to explore how built‐up volume relates to several US Census population datasets. Results of regression analyses indicate that built‐up volume can serve as a proxy for population with moderate to high success. Although from remotely sensed data alone, complexities of land use are difficult to resolve – i.e. large industrial buildings might indicate many people work at these locations but does not mean they reside there (Zhao et al. 2017). Other urban remote sensing studies implement rasterized lidar data to demarcate building footprints by way of height thresholding and masking (Rottensteiner and Briese 2002) or through image classification techniques (Priestnall et al. 2000). Evaluation of built‐up change requires differencing of multi‐temporal 3D datasets along with additional efforts to extract and characterize types of change (Vu et al. 2004; Stal et al. 2013; Teo and Shih 2013; Dong et al. 2018).

      2.3.2 DATA PROCESSING AND ANALYSIS

      Raw lidar data are stored as point clouds, simple vector points with XYZ locations (with intensity, return number, etc. attributes), typically in LAS format (ASPRS 2013; LAZ files are compressed versions of LAS files) although other formats exist (e.g. XYZ, PLY, OBJ, PCD). Though a relatively simple file type, lidar point clouds are large datasets