Urban Remote Sensing. Группа авторов

Urban Remote Sensing - Группа авторов


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href="#ulink_a77410dd-865c-5453-afd9-e73814273a81">Figure 2.5, the height values are in meters and the spatial resolution is 1 m making the per‐pixel volume the same as the height value (though in m3). The per‐building alternative would sum the values of all pixel centroids falling within individual building footprint extents (e.g. zonal statistics in a GIS) and spatially join the per‐building sum value to the building footprint polygons as an attribute.

Schematic illustration of lidar-derived rasters for Detroit, Michigan, 2004: (a) reference map. (b) Digital Terrain Model (DTM), (c) Digital Surface Model, and (d) Digital Height Model (DHM).

      Source: OpenStreetMap.

      (b) Digital Terrain Model (DTM), (c) Digital Surface Model, and (d) Digital Height Model (DHM).

Photos depict lidar workflow to obtain building-only volume: raw point cloud data (a), lidar-derived DHM raster (b), DHM clipped by red building footprints (c); white represents high buildings, black signifies low/ground. Photos depict an example of built-up change in southeast San Antonio, Texas: 2003 DHM (a), 2012 DHM (b), and difference of DHM (dDHM) (c). For the DHMs (a, b), pixels with higher values are shown with white (i.e. tall buildings) whereas lower values are black. Schematic illustration of citywide built-up change (shown with dDHM) in San Antonio, Texas, 2003–2013 shown with blue indicating increased height values (i.e. new build-up) and red decreased height values.

      2.3.2.1 Case Study: San Antonio, Texas

      Texas cities have undergone tremendous growth over the past several decades. Unlike other US cities that have witnessed population stagnation or decline (e.g. Detroit, Buffalo), cities such as San Antonio have undergone a 34% increase in population in less than two decades; from 1.1 million residents in 2000 to 1.5 million by 2018 (US Census 2019). Subsequently, such changes are reflected on the urban landscape through residential development (e.g. housing, apartment building) and growth of retail/commercial spaces. Multi‐temporal lidar data provide an ideal geospatial dataset with which to identify such changes in 3D space; in this brief case study, we use lidar‐derived 1 m spatial resolution raster data provided by the Army Geospatial Center collected during the summer months (leaf‐on conditions) in 2003 and 2012.

      At the city‐scale (Figure 2.7), the dDHM highlights extreme (large area) changes well. In this case, many large buildings removed between 2003 and 2012 (red) are visible west/southwest of downtown and along the interstate southeast of downtown. New build‐up (blue) is especially prevalent along the highways in the east/northeast and north parts of the city. At this scale though, much of the change, which covers small areas, is indistinguishable. During this period, much of the northern area shown in Figure 2.7 was developed for residential land use, which can only partially be seen at this scale. In total, the dDHM‐based summation of volumetric change resulted in a net increase of around 27 Mm3 over this nine‐year period. Vegetation is included in this example and becomes apparent along the eastern extent of the analysis area stretching from north‐to‐south where tree canopy increased in size (some of this is a data artifact due to the 2012 data containing more vegetation points). In this way, dDHM calculation will reveal the changes in the urban environment but often not without also unveiling underlying data issues. In this example, because we did not have access to the raw lidar data, line‐up issues identified false changes on the landscape


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