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Article Transformative Urban Changes of in the of the

Alessandro Sorichetta 1,* , Son V. Nghiem 2,*, Marco Masetti 3 , Catherine Linard 4 and Andreas Richter 5 1 WorldPop, School of and Environmental , of Southampton, Highfield Campus, Southampton SO17 1BJ, UK 2 Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, MS 300-235, Pasadena, CA 91109, USA 3 Dipartimento di Scienze della Terra “A. ”, Università degli Studi di Milano, Via , 34, 20133 , ; [email protected] 4 Department of Geography, Université de Namur, rue de Bruxelles 61, B-5000 Namur, ; [email protected] 5 Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1, D-28359 Bremen, ; [email protected] * Correspondence: [email protected] (A.S.); son.v.nghiem@jpl..gov (S.V.N.)

 Received: 11 January 2020; Accepted: 11 February 2020; Published: 16 February 2020 

Abstract: The rapid economic growth, the exodus from rural to urban areas, and the associated extreme urban development that occurred in in the decade of have severely impacted the environment in Beijing, its vicinity, and beyond. This article presents an innovative approach for assessing mega-urban changes and their impact on the environment based on the use of decadal QuikSCAT (QSCAT) satellite data, acquired globally by the SeaWinds scatterometer over that period. The Dense Sampling Method (DSM) is applied to QSCAT data to obtain reliable annual infrastructure-based urban observations at a posting of ~1 km. The DSM-QSCAT data, along with different DSM-based change indices, were used to delineate the extent of the Beijing infrastructure-based in each year between 2000 and 2009, and assess its development over time, enabling a physical quantification of its urbanization which reflects the implementation of various development policies during the same time period. Eventually, as a proxy for the impact of Beijing urbanization on the environment, the decadal trend of its infrastructure-based urbanization is compared with that of the corresponding tropospheric nitrogen dioxide (NO2) column densities as observed from the Global Monitoring Experiment (GOME) instrument aboard the second European Remote Sensing satellite (ERS-2) between 2000 and 2002, and from the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY aboard of the ESA’s ENVIronmental SATellite (SCIAMACHY /ENVISAT) between 2003 and 2009. Results reveal a threefold increase of the yearly tropospheric NO2 column density within the Beijing infrastructure-based urban area extent in 2009, which had quadrupled since 2000.

Keywords: dense sampling method; Beijing; urbanization; change indices; tropospheric NO2 columns

1. Introduction The Intergovernmental Panel on Change (IPCC) Working Groups (WG) II and III recognize that a large fraction of greenhouse gases from power generation, industry, transportation, and consumption can be attributed to settlements [1]. Due to the rapid growth of urban populations, and associated infrastructure, are becoming increasingly vulnerable to both , which may exacerbate both natural and man-made hazards [2–4], and pollution [5,6].

Remote Sens. 2020, 12, 652; doi:10.3390/rs12040652 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 652 2 of 24

The economic boom that took place in rapidly developing countries, such as those observed in China and in the 2000s, and the associated great exodus from rural to urban areas, has led to a rapid and extreme urban development that has affected the environment not only in the urban vicinity, but also at regional [7,8] and, perhaps, global scales [9]. Nevertheless, major gaps exist in the assessment of the impact of urbanization on the environment at multiple spatial and temporal scales; mostly because of the difficulty of consistently estimating the extent and typology of urban areas through time and space [10] and the subsequent, and often biased, distribution of environmental monitoring stations [11,12]. Indeed, administrative extents of urban areas are often defined differently and inconsistently both within and among countries, which may not be representative of the underlying physical infrastructure [13]. Furthermore, the use of moderate spatial resolution optical remote sensing data, such as multi-temporal and globally available Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, to consistently estimate the type and extent of urban areas over space and time has proven to be a substantial challenge due to the presence of clouds, a recurrent phenomenon in tropical regions [14], the considerable intraurban and interurban spectral heterogeneity [15], and the spectral similarity among many impervious surfaces and their surrounding natural environments [16,17]. While high spatial resolution optical data can resolve individual components of the urban [18], such data are only available for small areas at irregular points in time [19]. Similarly, the National Oceanic and Atmospheric Administration (NOAA) Defense Meteorological Satellite Program Operational Line Scanner (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) night light data present a number of well-known limitations precluding their use to solely distinguish between different urban typologies and quantitatively assessing the horizontal and vertical growth of urban areas over space and time [20–22]. Synthetic aperture radar (SAR) data, which could potentially be used to consistently map infrastructure-based urban areas [23], are generally not collected globally over regular time intervals—with partial exceptions represented by the Shuttle Radar Topography Mission (SRTM) [24], which collected global SAR data in February 2000, and the TanDEM-X satellite, which collected global SAR data in 2011–2012 [25]. Very high-resolution spotlight SAR [26] and Lidar data [27] have been successfully used to map infrastructure-based urban areas at a meter or even sub-meter resolution, but their coverage is extremely limited in both space and time [27]. In this study, an innovative approach for consistently and quantitatively assessing infrastructure-based urbanization over space (i.e., horizontal and vertical urbanization/urban growth) and time, and its impact on the environment, is demonstrated for Beijing as a case study (Figure1). Such an approach is based on the use of (i) decadal L1B Ku-Band (at the frequency of 13.4 GHz and wavelength of 2.24 cm) radar backscatter data, collected by the SeaWinds scatterometer aboard the QuikSCAT satellite (henceforth referred to as QSCAT data) [28], and (ii) decadal tropospheric nitrogen dioxide (NO2) column densities, retrieved from the Global Ozone Monitoring Experiment (GOME) spectrometer on the European Remote Sensing Satellite 2 (ERS-2) [29,30] and the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) aboard the European Space Agency’s (ESA) ENVIronmental SATellite (ENVISAT) [31]. First, the Dense Sampling Method (DSM), developed at the Jet Propulsion Laboratory of the National Aeronautics and Space Administration [19], is applied to QSCAT slice data to obtain reliable annual physical infrastructure-based urban observations at 1-km posting. The DSM-processed QSCAT data (henceforth referred to as DSM data) are then used to (i) estimate the extent of the Beijing infrastructure-based urban area in each year of the 2000s, (ii) distinguish among different urban typologies, and (iii) define four different DSM-based indices enabling a quantitative assessment of cover and intra-urban changes over time. Ultimately, to evaluate the impact of the Beijing infrastructure-based urbanization on the environment in terms of , its decadal urbanization trend is compared with that of the corresponding tropospheric nitrogen dioxide (NO2) column densities. Remote Sens. 2019, 11, x FOR PEER REVIEW 3 of 25 Remote Sens. 2019, 11, x FOR PEER REVIEW 3 of 25 urbanization trend is compared with that of the corresponding tropospheric nitrogen dioxide (NO2) urbanization trend is compared with that of the corresponding tropospheric nitrogen dioxide (NO2) Remote Sens. 2020, 12, 652 3 of 24 columncolumn densities. densities.

Figure 1. Schematic overview of the approach used for assessing Beijing infrastructure-based Figure 1. SchematicSchematic overview overview of of the the approach approach used used for for assessing Beijing infrastructure-based urbanization over space and time and its impact on the environment. urbanization over space and time an andd its impact on the environment. 2. Study Area 2.2. Study Study Area Area Beijing (Figure2) has experienced rapid economic growth in the past two (the ’s BeijingBeijing (Figure(Figure 2)2) hashas experiencedexperienced rapidrapid economiceconomic growthgrowth inin thethe pastpast twotwo decadesdecades (the(the city’scity’s increasing from 28.49 billion CNY in 1986 to 787 billion CNY at the end of grossgross domesticdomestic productproduct increasingincreasing fromfrom 28.4928.49 billionbillion CNYCNY inin 19861986 toto 787787 billionbillion CNYCNY atat thethe endend ofof 2006 [32]) and represents a prominent example of rapid urbanization in a strongly emerging economy. 20062006 [32])[32]) andand representsrepresents aa prominentprominent exampleexample ofof rapidrapid urbanizationurbanization inin aa stronglystrongly emergingemerging During the same period, Beijing’s economic growth was accompanied by the development of its urban economy.economy. During During the the same same period, period, Beijing’s Beijing’s econ economicomic growth growth was was accompanied accompanied by by the the development development area, which was planned through three urban Master Plans in 1982, 1992, and 2004 [33]. ofof its its urban urban area, area, which which was was planned planned through through three three urban urban Master Master Plans Plans in in 1982, 1982, 1992, 1992, and and 2004 2004 [33]. [33].

Figure 2. Beijing geographical setting: (a) location of the Beijing Area (dark gray area) Figure 2. Beijing geographical setting: setting: ( (aa)) location location of of the the Beijing Beijing Municipality Municipality Area Area (dark (dark gray gray area) area) and (b) Beijing administrative units [34]. Coordinates refer to Geographic Coordinate System World and ( b)) Beijing administrative units units [34]. [34]. Coordinates Coordinates refer refer to to Geographic Geographic Coordinate Coordinate System System World World Geodetic System (GCS WGS) 1984. Geodetic System (GCS WGS) 1984. The city’s development has been characterized by two primary phases that can be distinguished

based on their urban growth rate and mechanism. In particular, the period 1996–2001 was characterized Remote Sens. 2020, 12, 652 4 of 24 by strong development along roads, followed by new city developments, and limited growth in areas around the city, while the period 2001–2006 experienced the highest development in riverside areas, followed by development in the city, and an increase of roadside built-up areas [35]. Furthermore, the urban development within the Beijing Municipality Area was significantly different from the that detailed in the plan. Indeed, during the two planning periods of 1983–1993 and 1993–2005, more urban development was carried out outside the planned urban growth boundaries than within [10]. Nowadays, Beijing Municipality covers a total area of 16,410 km2 with a population of about 21.5 million [36], which is almost twice that of 1976 [33]–this coincided with the number of vehicles increasing from 0.82 to more than 3.5 million since 1994 [37]. The simultaneous increase of vehicles and industrial activities has significantly increased the sources of emissions of both volatile organic compounds and NOx (NO+NO2). Since 1998, Beijing adopted a series of measures in an effort to both control and decrease air pollution [32]. However, even though some air quality improvements were achieved by the end of the , air pollution worsened again during the 2000s due to the impressive growth rate that characterized the same period [7]. Beijing is also surrounded by three other highly populated, urbanized, and industrialized , with four large cities having between 3.3 and 9.1 million inhabitants [7], which are considered significant contributors in the deterioration of Beijing’s air quality [8,37,38].

3. Materials and Methods

3.1. QSCAT Data and Dense Sampling Method for Urban Observations Level L1B Ku-Band QSCAT data, available through the NASA Jet Propulsion Laboratory Physical Oceanography Distributed Active Archive Center (.DAAC; https://podaac.jpl.nasa.gov/dataset/ QSCAT_LEVEL_1B_V2), were continuously collected between July 19th, 1999 and November 22nd, 2009 by the SeaWinds scatterometer. The data were collected using a scanning pencil-beam antenna across a large swath (as wide as 1800 km) over Earth’s surface, as the QuickSCAT satellite moved along drifting orbits around the Earth [39]. QSCAT data present a number of clear advantages, with respect to other remote sensing-based datasets, for providing consistent physical infrastructure-based urban observations without gaps in time or space [19,21]. Indeed, (i) QSCAT data have a daily coverage of about 90% of Earth’s surface regardless of cloud coverage or level of light, (ii) QSCAT backscatter responses are dependent on the number, size, and construction materials of buildings and other structures (e.g., higher backscatter for more structures, larger and taller buildings, and stronger construction materials such as steel versus wood), and (iii) QSCAT drifting orbits allow the azimuth diversity in the backscatter measurements at each targeted area to detect the presence of buildings and other structures regardless of their directional alignment. Furthermore, QSCAT backscatter measurement is accurate to 0.2 dB (3-σ); [40], which is equivalent to approximately 1.57% in root-mean-square error, enabling QSCAT to detect large and rapid, as well as small and slow, environmental changes [41]. However, an important limitation of the raw QSCAT data is represented by their low spatial resolution—about 25 km in azimuth by 37 km in range [28], which is clearly inadequate to delineate and monitor inter-annual changes in intraurban areas. To overcome this limitation, DSM is used to increase the spatial resolution of the QSCAT data, at the expense of their temporal resolution (by reducing the latter from daily to annual), and thus to enable their use for assessing urbanization and its effect on multiple environmental matrices [13,41–44]. Remote Sens. 2020, 12, 652 5 of 24

Here, DSM is summarized to provide a basic understanding of the DSM data used in this study. At each location, with original QSCAT data densely compiled annually and posted at 30 arc seconds (approximately 1 km at the equator) in latitude and longitude (World Geodetic System 84; WGS84), a mathematical transform, called the Rosette Transform is applied to calculate an ensemble average of measured QSCAT backscatter values, (dB), using Equation (1):

N 1 X σ = σ (φ , t ) = σ M + (1) 0 N 0 i i 0 < i=1 where N is the total number of scatterometer footprints centered at the considered location, and σ0(φi, ti) is the backscatter value measured at time ti and azimuth direction φi of each footprint. In (1), σ0M is the mean part obtained by the Rosette Transform, and represents the residual from the zero-mean < fluctuating part of the radar backscatter. For a target like an urban area, having certain persistent characteristics over time whilst transient perturbations occur (e.g., new/less structures, traffic, rain, snowfall, etc.), σ0M tends to a significant and stable value while becomes small with sufficiently large number of samples [19]. Thus, for each < considered location, by sacrificing the temporal resolution of the QSCAT data over a long time period (over a year, for instance) in order to collect a sufficient number of samples, it is possible to (i) obtain a stable value for the mean part, and (ii) characterize the perturbations represented by the fluctuation term by an Index of Variability, IV, using Equation (2):

IV = 1 + (δ/σ0) (2) where σ0 (dB) is the linearized ensemble average of measured QSCAT backscatter values, and δ (dB) is the linearized standard deviation of σ0 (dB) [19]. Thus, each DSM dataset is produced from a rectangular array of points, uniformly gridded at 30 arc seconds in latitude and in longitude (GCS-WGS84), with each point containing a stable σ0 value and the associated IV. Here, QSCAT data acquired in the 2000s are used to generate annual DSM σ0 and IV gridded datasets of the study area (Figures3 and4, respectively) bounded by latitudes 39.375 ◦S–40.5◦N and longitudes 115.75◦E–117.25◦E. In Figure3 (and in subsequent figures), the “filtered” Beijing 1994–1995 stable night light-based extent [20], obtained using a 10% detection frequency threshold to include only persistent lights from the city while excluding gas flares and ephemeral events such as fires, is used as a and consistent spatial reference. This is to better visualize the spatiotemporal change of the Beijing DSM-based urban area extent (see also Section 4.1). Refer to Figure S1, available in the Supplementary Materials, to visually compare annual DSM-based urban area extents against the corresponding intercalibrated stable night light data [45] for each year in the 2000s. Beyond the capability of delineating boundaries demarcating DSM-based urban and rural/natural areas, σ0 values can also be used to (i) distinguish among different intra-urban typologies, such as city cores (including commercial and industrial centers) with larger building volume and residential areas with lower building volume, and (ii) monitor their changes over time (see Section 4.2). This is because the value of σ0 in urban areas is dependent on the 3D building volume, with a consistent linear relationship over a large dynamic range without a saturation problem [27]. It is noted that the capability of DSM data to capture physical urban patterns has been validated both in two-dimensional (2D) extent [46] and in three-dimensional (3D) building volume [27]. Remote Sens. 2020, 12, 652 6 of 24 Remote Sens. 2019, 11, x FOR PEER REVIEW 6 of 25

FigureFigure 3. Annual 3. Annual DSM DSM 𝜎 griddedσ0 gridded datasets datasets of the of the study study area, area, from from 2000 2000 to to 2010, 2010, with with a aposting posting of of 30 30 arc arc secondsseconds (GCS WGSWGS 1984).1984). TheThe “filtered” “filtered” Beijing Beijing 1994–1995 1994–1995 stable stable night night light light extent extent [20] [20] (black (black contour) contour)is overlaid is overlaid on all on datasets all datasets as a commonas a common and consistentand consistent spatial spatial reference reference area. area.

Remote Sens. 2020, 12, 652 7 of 24 Remote Sens. 2019, 11, x FOR PEER REVIEW 7 of 25

FigureFigure 4. 4.Annual Annual DSM DSMIV IVgridded gridded dataset dataset of of the the study study area, area, from from 2000 2000 to 2010, to 2010, with with a posting a posting of 30 of arc 30 secondsarc seconds (GCS (GCS WGS 1984).WGS 1984). The “filtered” The “filtered” Beijing Beijing 1994–1995 1994–1995 stable night stable light night extent light [20 extent] (black [20] contour) (black iscontour) overlaid is on overlaid all datasets on all as datasets a common as a and common consistent and spatialconsistent reference spatial area. reference area.

3.2.3.2. Tropospheric Tropospheric NO NO2 2Columns Columns

TroposphericTropospheric NO NO2 2column column densities densities can can be be measured measured from from space space using using the the Di Differentialfferential Optical Optical AbsorptionAbsorption Spectroscopy Spectroscopy (DOAS) (DOAS) Method Method [47]. The[47]. data data in thisused study, in representingthis study, troposphericrepresenting NOtropospheric2 column densities NO2 column averaged densities annually averaged and postedannually at and 7.5 arc posted minutes at 7.5 (approximately arc minutes (approximately 14 km at the equator),14 km at are the derived equator), from are GOMEderived [29 from] for GOME 2000–2002 [29] and for SCIAMACHY2000–2002 and [SCIAMACHY31] for 2003–2009 [31] [48 for]. 2003– 15 2 2009GOME [48]. and SCIAMACHY tropospheric NO2 observations (expressed as 10 molec/cm ) are availableGOME through and theSCIAMACHY University of tropospheric Bremen IUP DOASNO2 observations data archive ((expressedhttp://doas-bremen.de as 1015 molec/cm/no2_from_2) are gome.htmavailable andthrough http://doas-bremen.de the University/no2_tropos_from_scia.htm of Bremen IUP DOAS, respectively) data andarchive can be successfully(http://doas- usedbremen.de/no2_from_gome.htm to observe the temporal ofand tropospheric http://d NO2 columnsoas-bremen.de/no2_tropos_from_scia.htm, worldwide [49–51]. It is important torespectively) note that although and can the be uncertaintysuccessfully ofused absolute to observe values the for temporal individual evolut NOion2 observations of tropospheric may NO be 2 considerable,columns worldwide uncertainty [49–51]. is significantly It is important reduced to note when that averagingalthough the over uncertainty a long period of absolute such as values one yearfor [individual52]. Furthermore, NO2 observations since many sourcesmay be ofcons uncertaintyiderable, areuncertainty systematic, is relativesignificantly values reduced such as thosewhen averaging over a long period such as one year [52]. Furthermore, since many sources of uncertainty are systematic, relative values such as those used in this study, representing the variation of

Remote Sens. 2020, 12, 652 8 of 24

used in this study, representing the variation of tropospheric NO2 column densities over time, have an even lowerRemote uncertainty,Sens. 2019, 11, x FOR which PEER isREVIEW estimated to be around 15% over China [48]. 8 of 25

4. Resultstropospheric and Discussion NO2 column densities over time, have an even lower uncertainty, which is estimated to be around 15% over China [48]. 4.1. Beijing Urban Extent 4. Results and Discussion

Although values of σ0 are expected to be high within urban areas, where structures are located 4.1. Beijing Urban Extent (e.g., houses, factories, shopping malls, , road network, etc.), in some cases it may be difficult to distinguishAlthough values high-backscatter of 𝜎 are expected urban to be areas high fromwithin high-backscatter urban areas, where natural structures areas are suchlocated as forests or snowcapped(e.g., houses, mountain factories, peaksshopping [53 ].malls, Thus, skyscrapers, backscatter road values network, need etc.), to in be some combined cases it withmay be IV values difficult to distinguish high-backscatter urban areas from high-backscatter natural areas such as to delineateforests the or snowcapped actual extent mountain of infrastructure-based peaks [53]. Thus, backscatter urban areas.values need Indeed, to be due combined to the with anisotropy IV of structures,values which to delineate behave asthe radaractual corner extent reflectorsof infrastructure-based aligned in a certainurban areas. azimuth Indeed, direction, due to thethe angular variabilityanisotropy of the of backscatter structures, iswhich typically behave larger as radar in urbancorner areasreflectors than aligned in natural in a certain environments azimuth where scattererdirection, orientations the angular are more variability random of the and backscatter scattering is typically effects arelarger mostly in urban incoherent areas than [ 19in ].natural environments where scatterer orientations are more random and scattering effects are mostly Hence, co-registered DSM σ0 and IV gridded datasets can be combined to correctly identify grid incoherent [19]. cells characterized by the presence of structures. This can be done by identifying (i) an appropriate Hence, co-registered DSM 𝜎 and IV gridded datasets can be combined to correctly identify σ0 thresholdgrid cells value characterized to select by all the grid presence cells potentiallyof structures. representing This can be done urban by areasidentifying (i.e., all(i) an grid cells characterizedappropriate by backscatter 𝜎 threshold values value to higher select all than grid the cells considered potentiallyσ representing0 threshold), urban and areas (ii) an (i.e., IV all threshold value togrid exclude cells characterized cells with by high-backscatter backscatter values natural higher than targets the considered from previously 𝜎 threshold), selected and (ii) all an grid cells IV threshold value to exclude cells with high-backscatter natural targets from previously selected all (i.e., to exclude all grid cells characterized by backscatter values higher than the considered σ0 threshold but lowergrid IVcells values (i.e., to thanexclude the all considered grid cells characterize IV threshold).d by backscatter In this values study, higher to delineate than the considered the extent of the 𝜎 threshold but lower IV values than the considered IV threshold). In this study, to delineate the Beijing urban area in each year from 2000 to 2009, each annual DSM σ0 gridded dataset (Figure3) is extent of the Beijing urban area in each year from 2000 to 2009, each annual DSM 𝜎 gridded dataset used in(Figure combination 3) is used with in combination the decadal with (2000–2009) the decadal (2000–2009) DSM IV gridded DSM IV gridded dataset dataset representing representing how much changehow occurred much change within occurred each grid within cell each during grid thecell entireduring decadethe entire (Figure decade 5(Figure). 5).

FigureFigure 5. Decadal 5. Decadal (2000–2009) (2000–2009) DSM DSMIV IV griddedgridded dataset dataset of ofthe the study study area areawith witha posting a posting of 30 arc of 30 arc secondsseconds (GCS (GCS WGS WGS 1984). 1984). The The black black contour contour representsrepresents the the “filtered” “filtered” Beijing Beijing 1994–1995 1994–1995 stable night stable night light extent [20]. light extent [20].

For each year, the decadal (2000–2009) DSM IV gridded dataset, is used instead of the corresponding annual DSM IV dataset, to better distinguish between high backscatter grid cells representing urban areas and high backscatter grid cells representing natural/rural areas. By using stable decadal IV values, it is possible to identify a set of potential urban (SIV) versus natural/rural grid cells for each Remote Sens. 2020, 12, 652 9 of 24

year, and then use the above-the-threshold σ0 value to determine the actual urban grid cells in each annual SIV. It is noted that a natural/rural grid cell in 2000 not becoming urban will have a low decadal IV value, while a natural/rural grid cell becoming urban at some point will have a higher decadal IV value. On the contrary, the decadal IV value for a grid cell that was urban from the beginning will be high. Thus, the role of IV is to detect grid cells that are already urban or may become urban; for those pixels that can be urban, DSM σ0 is used to monitor annual urban change throughout the 2000s. To identify the optimal threshold values for σ0 and decadal IV, both thresholds were iteratively adjusted by comparing the result from each iteration with high-resolution Landsat images of the city of (China)—used as a test case for calibrating and validating the urban classification algorithm [27,43]. The annual results (Figure6), obtained using an optimal σ0 threshold of –8.0 dB and decadal IV threshold of 2.0, show that the Beijing infrastructure-based urban area (i) is much smaller than the 1994–1995 stable night light extent in the early 2000s (i.e., 2000–2003) due to the well-known blooming effect [22], (ii) becomes comparable to it in the mid-2000s (i.e., 2004–2006), and (iii) finally sprawls well beyond it in the late 2000s (i.e., 2007–2009). Remote Sens. 2019, 11, x FOR PEER REVIEW 10 of 25

FigureFigure 6. Gridded 6. Gridded datasets, datasets, with with a posting posting of 30 of arc 30 seconds arc seconds (GCS WGS (GCS 1984), WGS representing 1984), representing the DSM- the based urban area extent of Beijing in each year of the 2000s (gray; obtained using a 𝜎 threshold of – DSM-based urban area extent of Beijing in each year of the 2000s (gray; obtained using a σ0 threshold 8.0 dB and a decadal IV threshold of 2.0). The “filtered” Beijing 1994–1995 stable night light extent of –8.0 dB and a decadal IV threshold of 2.0). The “filtered” Beijing 1994–1995 stable night light extent ( contours) [20] and the Beijing administrative units ( contour) [34] are overlaid on all datasets (red contours)as a common [20] and consistent the Beijing spatial administrative reference area. units (blue contour) [34] are overlaid on all datasets as a common and consistent spatial reference area.

The surface of Beijing’s DSM-based urban area extent quadrupled in the 2000s, with an increase of 3035 km2 between 2000 and 2009, corresponding to an average annual increase of 337 km2. DSM- based estimates for 2000, 2007, and 2009 were compared to other independent estimates referring to these years. In particular, the 2000 DSM-based estimate is 6.3% bigger than the corresponding Landsat-based estimate of 1035 km2 [55], the 2007 estimate is just 0.2% smaller than the Constructive Land area estimate of 3325.57 km2 from the Beijing Municipal Bureau of Statistics [56], and the 2009 estimate is 7.6% smaller than the 2009 official urban area extent of 4480 km2 that includes inner and outer —with the remaining 12,239.9 km2 of the Beijing Municipality consisting of rural areas that include nature reserves and lightly populated mountains [57]. While the cumulative growth of the surface of the Beijing urban area extent has an overall linear trend in the 2000s (Figure 7), the year-to-year growth rate shows a large variability (Table 1), with an

Remote Sens. 2020, 12, 652 10 of 24

The urban expansion was extensive in the city’s southeastern sector and to a lesser degree in the northern districts of Huairou and Miyun. The presence of mountainous areas in the western and northwestern parts of the study area restricted the urban expansion in these directions. The gridded DSM-based urban area extent datasets in Figure6 and the ASPHAA algorithm [ 54] were then used to calculate the surface of the Beijing urban area extent in each year of the 2000s and its corresponding annual growth (Table1).

Table 1. Surface of the Beijing DSM-based urban area extent in each year of the 2000s and its corresponding annual growth.

Year Surface of Urban Area Extent Annual Growth (with Respect to the Previous Year) No. of Grid Cells km2 km2 % 2000 1676 1104.83 2001 2002 1319.74 214.9 19.5 2002 2423 1597.23 277.5 21.0 2003 2906 1915.55 318.3 19.9 2004 3477 2291.79 376.2 19.6 2005 3759 2477.53 185.7 8.1 2006 4432 2920.76 443.2 17.9 2007 5035 3317.93 397.2 13.6 2008 5999 3952.85 634.9 19.1 2009 6282 4139.33 186.5 4.7

The surface of Beijing’s DSM-based urban area extent quadrupled in the 2000s, with an increase of 3035 km2 between 2000 and 2009, corresponding to an average annual increase of 337 km2. DSM-based estimates for 2000, 2007, and 2009 were compared to other independent estimates referring to these years. In particular, the 2000 DSM-based estimate is 6.3% bigger than the corresponding Landsat-based estimate of 1035 km2 [55], the 2007 estimate is just 0.2% smaller than the Constructive Land area estimate of 3325.57 km2 from the Beijing Municipal Bureau of Statistics [56], and the 2009 estimate is 7.6% smaller than the 2009 official urban area extent of 4480 km2 that includes inner and outer suburbs—with the remaining 12,239.9 km2 of the Beijing Municipality consisting of rural areas that include nature reserves and lightly populated mountains [57]. While the cumulative growth of the surface of the Beijing urban area extent has an overall linear trendRemote in the Sens. 2000s 2019, 11 (Figure, x FOR 7PEER), the REVIEW year-to-year growth rate shows a large variability (Table1),11 withof 25 an averageaverage value value of aboutof about 16% 16% and and three three significant significant dr dropsops (Figure (Figure 8)8 )reflecting reflecting the the implementation implementation of of differentdifferent development development policies policies during during that that period. period.

Figure 7. Cumulative growth of the Beijing urban area extent in the 2000s (with respect to year 2000). Figure 7. Cumulative growth of the Beijing urban area extent in the 2000s (with respect to year 2000).

Figure 8. Annual growth of the Beijing urban area extent in each year of the 2000s (with respect to the corresponding previous year).

In particular, the first major drop observed in 2005 (of about −12% with respect to that in the previous year) can be related to the growth reduction policy implemented in 2004 to mitigate the increasing social pressure and discontent associated with the mega rapid urbanization that was taking place in China [58]. The drop observed in 2007 (of about −4.5% with respect to that in the previous year) can be associated with the decision of the Chinese government to reassess the preparation of the 2008 , which led to the postponement of construction deadlines by a year [58]. Finally, the third major drop observed in 2009 (of about −14% with respect to that observed in the previous year), in turn, can be interpreted as the inherent consequence of the Olympic-related construction boom that occurred earlier in 2008.

Remote Sens. 2019, 11, x FOR PEER REVIEW 11 of 25 average value of about 16% and three significant drops (Figure 8) reflecting the implementation of different development policies during that period.

Remote Sens. 2020, 12, 652 11 of 24 Figure 7. Cumulative growth of the Beijing urban area extent in the 2000s (with respect to year 2000).

Figure 8. Annual growth of the Beijing urban area extent in each year of the 2000s (with respect to the Figurecorresponding 8. Annual previous growth year).of the Beijing urban area extent in each year of the 2000s (with respect to the corresponding previous year). In particular, the first major drop observed in 2005 (of about 12% with respect to that in the − previousIn particular, year) can the be first related major to thedrop growth observed reduction in 2005 policy (of about implemented −12% with in respect 2004 to to mitigate that in the previousincreasing year) social can pressure be related and discontentto the growth associated reduction with policy the mega implemented rapid urbanization in 2004 to that mitigate was taking the increasingplace in China social [58 pressure]. The drop and observed discontent in 2007associ (ofated about with4.5% the mega with respectrapid urbanization to that in the that previous was − takingyear) can place be associatedin China [58]. with The the drop decision observed of the in Chinese 2007 (of government about −4.5% to reassesswith respect the preparationto that in the of previousthe 2008 Olympicyear) can Games, be associated which led with to thethe postponementdecision of the of Chinese construction government deadlines to by reassess a year [the58]. preparationFinally, the of third the 2008 major Olympic drop observed Games, which in 2009 led (of to aboutthe postponement14% with of respect construction to that deadlines observed by in − athe year previous [58]. Finally, year), the in third turn, major can be drop interpreted observed as in the 2009 inherent (of about consequence −14% with respect of the Olympic-relatedto that observed inconstruction the previous boom year), that in occurredturn, canearlier be interpreted in 2008. as the inherent consequence of the Olympic-related construction boom that occurred earlier in 2008. 4.2. DSM-Based Change Indices

As mentioned in Section 3.1 and seen in Figure3, for each year, the corresponding DSM σ0 gridded dataset can be used to identify the inner core of Beijing (light-gray to red/light-orange), representing a well-established urban area with dense housing and tall buildings, its urban/suburban area (yellow to ) transitioning into its greater outskirt area with sparse houses (cyan), and the surrounding natural environment (blue). One can clearly observe the significant expansion and build-up characterizing the Beijing core with high backscatter [21] and the distinct concentric sprawl representing Beijing’s urban development during the 2000s [10,59]. Thus, annual DSM σ0 gridded datasets can also be used to monitor changes over time, both within urban areas and in their rural/natural surroundings. To this end, to quantitatively investigate these changes, four different DSM-based indices, described in the following subsections, were developed.

4.2.1. Surface Change Indices Two different Surface Change Indices were developed to identify grid cells changing or remaining stable in both urban and rural/natural areas over time. The most simple and intuitive index, computed at the grid cell level and expressed in decibel (dB), is represented by the difference between the 2009 and 2000 DSM σ0 grid cell values. This index is calculated using Equation (3)

∆σ = σ (2009) σ (2000) (3) 0 0 − 0 where σ0(2009) and σ0(2000) represent the DSM σ0 value in each grid cell in 2009 and 2000, respectively. Remote Sens. 2019, 11, x FOR PEER REVIEW 12 of 25

4.2. DSM-Based Change Indices

As mentioned in Section 3.1 and seen in Figure 3, for each year, the corresponding DSM 𝜎 gridded dataset can be used to identify the inner core of Beijing (light-gray to red/light-orange), representing a well-established urban area with dense housing and tall buildings, its urban/suburban area (yellow to green) transitioning into its greater outskirt area with sparse houses (cyan), and the surrounding natural environment (blue). One can clearly observe the significant expansion and build-up characterizing the Beijing core with high backscatter [21] and the distinct concentric sprawl representing Beijing’s urban development during the 2000s [10,59].

Thus, annual DSM 𝜎 gridded datasets can also be used to monitor changes over time, both within urban areas and in their rural/natural surroundings. To this end, to quantitatively investigate these changes, four different DSM-based indices, described in the following subsections, were developed.

4.2.1. Surface Change Indices Two different Surface Change Indices were developed to identify grid cells changing or remaining stable in both urban and rural/natural areas over time. The most simple and intuitive index, computed at the grid cell level and expressed in decibel (dB), is represented by the difference between the 2009 and 2000 DSM 𝜎 grid cell values. This index is calculated using Equation (3)

∆𝜎 =𝜎(2009) −𝜎(2000) (3) Remote Sens. 2020, 12, 652 12 of 24 where 𝜎(2009) and 𝜎(2000) represent the DSM 𝜎 value in each grid cell in 2009 and 2000, respectively. TheThe Surface ChangeChange IndexIndex∆ ∆𝜎σ0describes describes the the total total change change which which occurred occurred between between 2000 2000 and 2009and 2009(Figure (Figure9). High 9). High positive positive values values primarily primarily indicate indicate highly highly urbanized urbanized areas areas while while negative negative valuesvalues areare likely likely associated associated with with significant significant urban urban decay decay (e.g., (e.g., demolition demolition and/or and/or abandonment) abandonment) and/or and/or environmentalenvironmental degradation degradation (e.g., (e.g., deforest deforestationation leading leading to to denuded denuded land). land). The The ∆𝜎∆σ0 griddedgridded dataset dataset (Figure(Figure 99)) shows larger changes changes both both in in the the inner inner core core of of Beijing Beijing and and in in the the urbanized urbanized area area around around it (i.e.,it (i.e., the the area area corresponding corresponding to tothe the grid grid cells cells classified classified as as nonurban nonurban in in 2000 2000 and and as as urban urban in in 2009). 2009). LowLow values values of of ∆𝜎∆σ0 indicateindicate that that the the rural/natural rural/natural surroundings surroundings remained remained relatively relatively stable stable in in the the 2000–20092000–2009 time time period. period.

Remote Sens. 2019, 11, x FOR PEER REVIEW 13 of 25

Figure 9. Gridded dataset, with a posting of 30 arc seconds (GCS WGS 1984), representing the Surface

Change Index ∆𝜎 for the 2000s defined as the difference calculated subtracting the DSM ∆𝜎 values for 2000 from the DSM ∆𝜎 values for 2009. The black contour represents the “filtered” Beijing 1994– 1995 stable night light extent [20]. The + sign marks the “hotspot” location showed in Figure 10.

As an important capability, the ∆𝜎 gridded dataset can be used to identify urbanization ∆𝜎 “hotspots”,Figure 9.characterizedGridded dataset, by withhigh avalues posting of of 30 arc , where seconds the (GCS largest WGS changes 1984), representing between the2000 Surface and 2009 occurred due to significant build-up. One of these urbanization “hotspots”, where many large Change Index ∆σ0 for the 2000s defined as the difference calculated subtracting the DSM ∆σ0 values for buildings were constructed between 2003 and 2009, is detected in the westernmost part of the 2000 from the DSM ∆σ0 values for 2009. The black contour represents the “filtered” Beijing 1994–1995 provincestable of night Tongzhou light extent (plus [20 sign]. The in+ Figuresign marks 9) and the verified “hotspot” with location high-resolution showed in Figure images 10. (Figure 10).

Figure 10. High-resolution imagesimages ofof urbanization urbanization “hotspot” “hotspot” located located in in the the westernmost westernmost part part of theof theTongzhou Tongzhou province of Beijing of Beijing (Map data:(Map , data: Google, DigitalGlobe). DigitalGlobe). The center The of thiscenter area of is this represented area is representedby the plus signby the in Figureplus sign9. Coordinates in Figure 9. referCoordinates to GCS WGSrefer to 1984. GCS WGS 1984.

The second index, computed at the grid cell level and expressed in decibel per year (dB/year), is

calculated as the slope of the linear regression line obtained by plotting annual DSM ∆𝜎 grid cell values against time over all years in the 2000s. This index is calculated using Equation (4):

𝑆(𝜎)=𝑑𝐹(𝜎,𝑡)⁄𝑑𝑡 (4)

where 𝑑𝐹(𝜎,𝑡)⁄𝑑𝑡 represents the time derivative of the linear regression function 𝐹(𝜎,𝑡) of σ in each grid cell.

The Surface Change Index 𝑆(𝜎) describes the rate of change of the land cover throughout the 2000s. Large positive slope values primarily indicate urban development within the corresponding grid cells, with greater slope values associated with significantly higher urban development. In contrast, null or negative slope values indicate grid cells typically associated with the steadier rural and natural areas surrounding Beijing.

Since 𝑆(𝜎) employs all annual data in the 2000s, rather than just the difference between two years as in (3), it has the advantage of being more stable compared to the noisier results of ∆𝜎 . This is evident when looking at the urban “hotspot” located in the Tongzhou province of Beijing, where

the 𝑆(𝜎) gridded dataset contains a more consistent set of very high value grid cells (i.e., red cells in Figure 11) compared to that observed in the ∆𝜎 gridded dataset, which shows an inconsistent number of very high value grid cells (i.e., red cells) mixed with others having lower values (Figure 9 vs. Figure 11). This result illustrates the advantage of DSM data providing observations without gaps in time and space for rural/urban monitoring.

Remote Sens. 2020, 12, 652 13 of 24

As an important capability, the ∆σ0 gridded dataset can be used to identify urbanization “hotspots”, characterized by high values of ∆σ0, where the largest changes between 2000 and 2009 occurred due to significant build-up. One of these urbanization “hotspots”, where many large buildings were constructed between 2003 and 2009, is detected in the westernmost part of the province of Tongzhou (plus sign in Figure9) and verified with high-resolution images (Figure 10). The second index, computed at the grid cell level and expressed in decibel per year (dB/year), is calculated as the slope of the linear regression line obtained by plotting annual DSM ∆σ0 grid cell values against time over all years in the 2000s. This index is calculated using Equation (4):

S(σ0) = d[F(σ0, t)]/dt (4) where d[F(σ0, t)]/dt represents the time derivative of the linear regression function F(σ0, t) of σ ¯0 in each grid cell. The Surface Change Index S(σ0) describes the rate of change of the land cover throughout the 2000s. Large positive slope values primarily indicate urban development within the corresponding grid cells, with greater slope values associated with significantly higher urban development. In contrast, null or negative slope values indicate grid cells typically associated with the steadier rural and natural areas surrounding Beijing. Since S(σ0) employs all annual data in the 2000s, rather than just the difference between two years as in (3), it has the advantage of being more stable compared to the noisier results of ∆σ0. This is evident when looking at the urban “hotspot” located in the Tongzhou province of Beijing, where the S(σ0) gridded dataset contains a more consistent set of very high value grid cells (i.e., red cells in Figure 11) compared to that observed in the ∆σ0 gridded dataset, which shows an inconsistent number of very high value grid cells (i.e., red cells) mixed with others having lower values (Figure9 vs. Figure 11). This result illustrates the advantage of DSM data providing observations without gaps in timeRemote and spaceSens. 2019 for, 11 rural, x FOR/urban PEER REVIEW monitoring. 14 of 25

FigureFigure 11. Gridded11. Gridded datasets, datasets, with with a posting a posting of 30of arc30 secondsarc seconds (GCS (GCS WGS WGS 1984), 1984), representing representing the the Surface ChangeSurface Index ChangeS(σ0) Indexin the 𝑆(𝜎 2000s) in as the the 2000s slope as calculated the slope calculat from theed linearfrom the regression linear regression of ∆σ0 values of ∆𝜎 using all yearsvalues between using all 2000 years and between 2009. The2000 black and 2009. contour The representsblack contour the represents “filtered” the Beijing “filtered” 1994–1995 Beijing stable night1994–1995 light extent stable [20 night]. light extent [20].

The coefficient of determination R2 associated with the regression line is calculated as an 2 additional data product from the 𝑆(𝜎) regression. Since R represents how well the response variable variation can be explained by a linear , high values of R2 indicate a consistent linear trend that can be associated with planned urban development. Conversely, low values of R2 indicate more random natural characteristics, with transient or intermittent variations, in areas remaining undeveloped. The R2 gridded dataset (Figure 12) seems to confirm that the urban development in both the inner core of Beijing and its surroundings are the results of structural build-up.

Remote Sens. 2020, 12, 652 14 of 24

The coefficient of determination R2 associated with the regression line is calculated as an additional 2 data product from the S(σ0) regression. Since R represents how well the response variable variation can be explained by a linear model, high values of R2 indicate a consistent linear trend that can be associated with planned urban development. Conversely, low values of R2 indicate more random natural characteristics, with transient or intermittent variations, in areas remaining undeveloped. The R2 gridded dataset (Figure 12) seems to confirm that the urban development in both the inner core Remoteof Beijing Sens. 2019 and, 11 its, x surroundings FOR PEER REVIEW are the results of structural build-up. 15 of 25

FigureFigure 12. 12. GriddedGridded dataset, dataset, with with a aposting posting of of 30 30 ar arcc seconds seconds (GCS (GCS WGS WGS 1984), 1984), representing representing the the coefficientcoefficient of of determination determination R2R for2 for thethe 2000s. 2000s. The black The black contour contour represents represents the “filtered” the “filtered” Beijing Beijing1994– 19951994–1995 stable stablenight light night extent light extent[20]. [20].

4.2.2. Development Indices 4.2.2. Development Indices To quantitatively assess the horizontal and vertical growth of Beijing urban areas over time, To quantitatively assess the horizontal and vertical growth of Beijing urban areas over time, three different DSM-based aerial indices, partitioned on the basis of different urban area extents, three different DSM-based aerial indices, partitioned on the basis of different urban area extents, were were defined. First, the Urban Development Index (UDI), calculated for each year in the 2000s with defined. First, the Urban Development Index (UDI), calculated for each year in the 2000s with respect respect to 2009, represents a quantitative urbanization measure accounting for both horizontal and to 2009, represents a quantitative urbanization measure accounting for both horizontal and vertical vertical growth of areas identified as urban in 2009. The yearly UDI is calculated by averaging all DSM growth of areas identified as urban in 2009. The yearly UDI is calculated by averaging all DSM 𝜎 gridσ0 grid cell cellvalues values within within the the 2009 2009 Beijing Beijing urban urban area area extent. extent. This This index index is iscalculated calculated using using Equation Equation (5): (5): () N X2009 UDI(y) = σ ((y))/N((2009)) (5)(5) 𝑈𝐷𝐼(𝑦)0 =𝜎 i 𝑦 𝑁 2009 i=1 where 𝜎(𝑦) is the DSM backscatter value (dB) of a grid cell i in the year y classified as urban in where σ0i (y) is the DSM backscatter value (dB) of a grid cell i in the year y classified as urban in 2009, 2009,and N and(2009 N()2009 is the) is total the total number number of grid of cellsgrid classifiedcells classified as urban as urban in 2009. in 2009. AsAs defined, defined, UDI(y)) accountsaccounts forfor grid grid cells cells both both within within and and outside outside the the urban urban area area extent extent in a givenin a givenyear yyear classified y classified as urban as urban in 2009. in 2009. Thus, Thus, since since backscatter backscatter correlates correlates to the to presencethe presence of structures of structures [19], [19],UDI (UDIy) represents(y) represents the total the total urban urban growth growth/development/development integrated integrated over theover existing the existing urban urban area extent, area extent,with more with and more larger and buildings,larger buildings, as well as as well over as the over nonurban the nonurban areas that areas would that becomewould become urban by urban 2009. by 2009. The persistently increasing UDI(y) for Beijing from 2000 to 2009, that closely follows a linear trend (Figure 13), indicates that in this period Beijing experienced a continuous constant urban growth, in terms of both number and size of its physical structures. The drop observed in 2005 (Figure 8) represents an exceptional case, as a consequence of a temporary policy change in the overall decadal urban build-up and expansion of Beijing [58].

Remote Sens. 2020, 12, 652 15 of 24

The persistently increasing UDI(y) for Beijing from 2000 to 2009, that closely follows a linear trend (Figure 13), indicates that in this period Beijing experienced a continuous constant urban growth, in terms of both number and size of its physical structures. The drop observed in 2005 (Figure8) represents an exceptional case, as a consequence of a temporary policy change in the overall decadal Remoteurban Sens. build-up 2019, 11 and, x FOR expansion PEER REVIEW of Beijing [58]. 16 of 25

Figure 13. Yearly Urban Development Index (UDI) calculated with respect to the 2009 Beijing urban Figurearea extent. 13. Yearly The regressionUrban Development fit function Index (black ( line)UDI) iscalculatedUDI = 0.3628 with respecty 9.1267 to the where 2009y Beijing= year urban1999, × − − areaand extent.R2 = 0.998 The thatregression explains fit 99.8% function of the (blackUDI line)with is respect UDI = to0.3628 the linear × y − 9.1267 trend. where y = year − 1999, and R2 = 0.998 that explains 99.8% of the UDI with respect to the linear trend. Second, the Building Development Index (BDI), for each year in the 2000s, represents a quantitative indicatorSecond, of havingthe Building more and Development/or larger structures, Index ( beingBDI), builtfor withineach year the DSM-basedin the 2000s, urban represents area extent a quantitativedelineated in indicator each year of (see having Section more 4.1 and/or). The yearlylarger BDIstructures,is calculated being bybuilt averaging, within the for aDSM-based given year, urbanthe values area extent of all DSMdelineatedσ0 grid in cellseach locatedyear (see within Section the 4.1). urban The areayearly extent BDI is referring calculated to by the averaging, same year. forThis a given index year, is calculated the values using of all Equation DSM 𝜎 (6): grid cells located within the urban area extent referring to the same year. This index is calculated using Equation (6): N(y) X() BDI(y) = σ0 (y)/N(y) (6) 𝐵𝐷𝐼(𝑦) =𝜎 i (𝑦)𝑁(𝑦) (6) i=1 where σ0i (y) is the DSM backscatter value (dB) of a grid cell i classified as urban in the year y and N(y) where 𝜎(𝑦) is the DSM backscatter value (dB) of a grid cell i classified as urban in the year y and Nis(y the) is totalthe total number number of grid of grid cells cells classified classified as urban as urban in the in year the yeary. y. AsAs defined, defined, BDIBDI((yy)) accounts accounts only only for for what what occurs occurs within within the urban the urban area extent area occupied extent occupied by buildings by buildingsand other and structures other structures in each year, in each while year, excluding while excluding areas classified areas classified as nonurban. as nonurban. Thus, as expected,Thus, as expected,in each year in each the year value the of valueBDI (Figureof BDI (Figure 14) is higher14) is higher than thethan corresponding the correspondingUDI UDIvalue value (Figure (Figure 13), 13),since since the the latter latter accounts accounts for for both both urban urban and and nonurban nonurban areas. areas. Therefore,Therefore, BDI(y) tracks the the vertical vertical built-upbuilt-up areas areas more more closely closely than than UDIUDI(y),( ywith), with the latter the latter being being more more representative representative of the ofgeneral the general long- termlong-term urban urbandevelopment. development. This is Thisevident is evident in that inthe that BDI the valuesBDI invalues 2005 inand 2005 2006 and (see 2006 ellipse (see in ellipse Figure in 14)Figure are similar14) are as similar a direct as aconsequence direct consequence of the curb of the on curb urban on development urban development in 2005 in[58]. 2005 [58]. The results from the combination of UDI and BDI highlight two simultaneous processes characterizing Beijing’s urban development in the 2000s including (i) the urban expansion/sprawl due to the transformation of nonurban to urban areas and (ii) the densification/upward height of buildings due to increasing construction activities in already existing urban areas.

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Figure 14. Yearly Building Development Index (BDI) within the Beijing urban area extent of each year Figurein the 2000s.14. Yearly The Building regression Development fit function (blackIndex ( line)BDI) is withinBDI = the0.0763 Beijingy urban6.261 area where extenty = ofyear each year1999, × − − inand theR 2000s.2 = 0.972. The The regression ellipse marksfit function the BDI (blackvalues line) for is 2005 BDI and= 0.0763 2006 × that y − are6.261 similar. where y = year − 1999, and R2 = 0.972. The ellipse marks the BDI values for 2005 and 2006 that are similar. The results from the combination of UDI and BDI highlight two simultaneous processes characterizingThird, the Beijing’sVertical Development urban development Index in(VDI the), 2000s calculated including for each (i) the year urban in the expansion 2000s with/sprawl respect due toto year the transformation 2000, represents of nonurban a quantitative to urban urbanizat areas andion (ii) measure the densification accounting/upward for the height vertical of buildings growth withindue to increasingthe inner core construction of Beijing activities identified in already as urban existing in 2000. urban The areas. yearly VDI(y) is calculated by averagingThird, all the DSM Vertical 𝜎 grid Development cell values Index within (VDI the), 2000 calculated Beijing for urban each area year extent in the using 2000s withEquation respect (7): to year 2000, represents a quantitative urbanization( measure) accounting for the vertical growth within the inner core of Beijing identified as urban in 2000. The( ) yearly( VDI)(y) is calculated by averaging(7) all 𝑉𝐷𝐼(𝑦) =𝜎 𝑦 𝑁 2000 DSM σ0 grid cell values within the 2000 Beijing urban area extent using Equation (7): 𝜎 (𝑦) where is the DSM backscatter value (dB)( of )a grid cell i in the year y classified as urban in 2000 N X2000 and N(2000) is the total number of grid cells classified as urban in 2000. VDI(y) = σ0i (y)/N(2000) (7) Confined within the urban extent of 2000,i=1 here referred to as the inner core of Beijing, VDI characterizes its physical growth, between 2000 and 2009, due to more and larger structures. As such, ( ) VDIwhere(y) specificallyσ0i y is the tracks DSM backscatterthe mostly vertical value (dB) build-up, of a grid represented cell i in the by year an increasey classified in the as total urban building in 2000 volume,and N(2000 within) is thethe totalinner number core of ofBeijing grid cells(Figure classified 15). as urban in 2000. OfConfined all of the within development the urban indices extent described of 2000, above, here VDI referred(y) has tothat as largest the inner value corein each of year Beijing, of theVDI 2000s,characterizes confirming its physicalthat the internal growth, core between of Beijing 2000 andcontains 2009, the due most to more and andthe largest larger structures.build-up. Moreover,As such, VDI VDI(y()y specifically) has the largest tracks rate the of mostly change vertical (i.e., slop build-up,e of the represented regression by fit an function), increase compared in the total tobuilding those of volume, UDI and within BDI the, revealing inner core the of continuous Beijing (Figure and 15 strong). development of the inner core of Beijing.Of Together, all of the developmentall of the development indices described indices contribute above, VDI to( ya) more has that complete largest characterization value in each year of Beijingof the 2000s,urban confirmingchange in the that 2000s, the internalfrom the core Beijing of Beijing inner core contains to its theoutward most andexpansion. the largest build-up. Moreover, VDI(y) has the largest rate of change (i.e., slope of the regression fit function), compared to those of UDI and BDI, revealing the continuous and strong development of the inner core of Beijing. Together, all of the development indices contribute to a more complete characterization of Beijing urban change in the 2000s, from the Beijing inner core to its outward expansion.

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Figure 15. Yearly Vertical Development Index (VDI) calculated with respect to the Beijing urban area Figureextent in15. 2000. Yearly The Vertical regression Development fit function Index (black (VDI line)) calculated is VDI = 0.3995 with respecty 6.646 to the where Beijingy = yearurban 1999,area × − − extentand R 2in= 2000.0.995. The regression fit function (black line) is VDI = 0.3995 × y − 6.646 where y = year − 1999, and R2 = 0.995. 4.3. Beijing NO2 Pollution 4.3. Beijing NO2 Pollution Rapid urbanization and economic growth, along with the associated NOx emissions, have a key role inRapid the increaseurbanization of tropospheric and economic NO2 growth,[47,49,50 along]. High with concentrations the associated of NOx NO2 emissions,in the troposphere have a key are roleharmful in the to increase human healthof tropospheric by both aggravating NO2 [47,49,50]. respiratory High concentrations diseases in the ofshort NO2 in term theand troposphere contributing are harmfulto onset to of human asthma in the longby both term aggravating [60]. respiratory diseases in the short term and contributing to onsetFor of each asthma year in in the the long 2000s, term Figure [60]. 16 presents satellite observations of yearly tropospheric NO2 columnFor densitieseach year (seein the Section 2000s, 3.2Figure) over 16 thepresents study satellite area, overlapped observations by of the yearly corresponding tropospheric Beijing NO2 columnDMS-based densities urban (see area Section extent 3.2) (see over Section the stud 4.1).y area, overlapped by the corresponding Beijing DMS- basedTropospheric urban area extent NO2 column (see Section4.1). density values were low in the early 2000s while increasing markedly betweenTropospheric 2003 and NO 20092 column (Figure density16), along values with were Beijing’s low in urban the early development 2000s while during increasing the same markedly period. betweenNevertheless, 2003 anand obvious 2009 (Figure decrease 16), can along be observedwith Beij ining’s 2008 urban (Figure development 16), corresponding during the to the same year period. of the Nevertheless,Olympic Games. an obvious Indeed, becausedecrease of can the be high observed levels ofin air2008 pollution (Figure in16), Beijing corresponding prior to 2008, to the a series year of of thecontrol Olympic measures Games. were Indeed, implemented because toof substantiallythe high levels reduce of air airpollution pollution in Beijing during prior the pre-Olympic to 2008, a series and ofthe control Olympic measures period [were61,62 implemented]. In 2009, however, to substant rightially after reduce the Olympic air pollution year, tropospheric during the pre-Olympic NO2 column anddensity the valuesOlympic starkly period increased [61,62]. againIn 2009, to ahowever, more serious right level after compared the Olympic to 2007 year, (Figure tropospheric 16). NO2 15 2 columnAs density a quantitative values assessmentstarkly increased of tropospheric again to a NOmore2 pollution, serious level column compared densities to 2007 (10 (Figuremolec/ cm16). ) are spatiallyAs a quantitative averaged assessment over the Beijing of tropospheric DSM-based NO urban2 pollution, area extent column in densities 2009 for each(1015 molec/cm year in the2) are2000s. spatially Results averaged clearly suggest over the an Beijing increasing DSM-based trend of troposphericurban area extent NO2 columnin 2009 densitiesfor each inyear the in 2000s, the 2000s.although Results with clearly significant suggest variations an increasing from year trend to yearof tropospheric (Figure 17). NO2 column densities in the 2000s, althoughNotable with aresignificant (i) the variations 17.7% decrease from year between to year 2007 (Figure and 17). 2008 (from 27.9 1015 in 2007 to × 23.7 1015 molec/cm2 in 2008), and (ii) the significant 29.5% increase after the Olympic year (from 23.7 1015 × × in 2008 to 30.7 1015 molec/cm2 in 2009) which effectively negated the benefit from the pre-2008 air × cleanup effort.

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FigureFigure 16. 16.Yearly Yearly tropospherictropospheric NO2NO2 columns,columns, withwith aa postingposting ofof 7.57.5 arcarc minutes minutes (GCS(GCS WGSWGS 1984),1984), overlappedoverlapped by by the the corresponding corresponding Beijing Beijing DSM-based DSM-based urban urban area area (dashed (dashed gray gray areas) areas) for for each each year year in in thethe 2000s. 2000s. The The “filtered” “filtered” Beijing Beijing 1994–1995 1994–1995 stable stable night night light light extent extent [ 20[20]] (black (black contour) contour) is is overlaid overlaid on on allall datasets datasets as as a a common common and and consistent consistent spatial spatial reference reference area. area.

ANotable regression are (i) analysis the 17.7% between decrease yearly betweenUDI 2007values and and2008 yearly(from 27.9 tropospheric × 1015 in 2007 NO to2 columns,23.7 × 1015 bothmolec/cm calculated2 in 2008), withrespect and (ii) to the the significant Beijing DSM-based 29.5% increase urban after extent the in Olympic 2009, is carried year (from out to 23.7 investigate × 1015 in the2008 relation to 30.7 between × 1015 molec/cm Beijing urban2 in 2009) development which effectively and tropospheric negated NO the2 pollutionbenefit from (Figure the 18pre-2008). air cleanupOver effort. the 2000s, regression results indicate an increasing trend in tropospheric NO2 pollution 2 alongsideA regression the urbanization analysis between of Beijing, yearly with UDI a high values correlation and yearly coe ffitroposphericcient of 0.9063. NO2 Thecolumns, R value both ofcalculated 0.821 explains with respect 82.1% ofto the Beijing tropospheric DSM-based NO2 columnurban extent trend in with 2009, respect is carried to the out Beijing to investigate urban developmentthe relation between trend. Beijing urban development and tropospheric NO2 pollution (Figure 18).

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Figure 17. Yearly tropospheric NO2 column densities spatially averaged over the Beijing DSM-based Figure 17. Yearly tropospheric NO2 column densities spatially averaged over the Beijing DSM-based Figureurban extent 17. Yearly in 2009. tropospheric The regression NO2 column fit function densities (black spatially line) is NO averaged2 column over = 2.3982 the Beijing × y + 7.8544 DSM-based where urban extent in 2009. The regression fit function (black line) is NO2 column = 2.3982 y + 7.8544 where 2 × urbany = yearyear extent − 1999,1999, in and2009. and RR The 2== 0.823. 0.823.regression fit function (black line) is NO2 column = 2.3982 × y + 7.8544 where y = year −− 1999, and R2 = 0.823.

Figure 18. Yearly tropospheric NO2 column densities and UDI values, both calculated with respect to Figure 18. Yearly tropospheric NO2 column densities and UDI values, both calculated with respect to the Beijing DSM-based urban extent in 2009. The regression fit function (black line) is NO2 column = Figurethe6.5959 Beijing 18.UDI Yearly DSM-based+ 68.0828 tropospheric urban with R extent2NO= 0.821.2 column in 2009. densities The regression and UDI fit values, function both (black calculated line) is with NO 2respect column to = 2 the6.5959 Beijing × UDI DSM-based + 68.0828 withurban R extent = 0.821. in 2009. The regression fit function (black line) is NO2 column = 5. Conclusions6.5959 × UDI + 68.0828 with R2 = 0.821. Over the 2000s, regression results indicate an increasing trend in tropospheric NO2 pollution Mega-urban changes in Beijing and their impacts on the environment are assessed alongsideOver the urbanization2000s, regression of Beijing, results with indicate a high an correlationincreasing coefficienttrend in tropospheric of 0.9063. The NO R2 2pollution value of consistently and quantitatively through an innovative approach using decadal QuickSCAT data alongside0.821 explains the urbanization 82.1% of the of troposphericBeijing, with NOa high2 column correlation trend coefficient with respect of 0.9063. to the The Beijing R2 value urban of and GOME/SCIAMACHY-derived tropospheric nitrogen dioxide (NO2) columns. Annual observations 0.821development explains trend. 82.1% of the tropospheric NO2 column trend with respect to the Beijing urban of Beijing urban area extent are based on the Dense Sampling Method (DSM) that allows a major development trend. increase in the QSCAT data sampling, from about 25 km2 to a posting of about 1 km2, which is 5. Conclusions appropriate to map urban extent based on the presence of buildings and other structures rather than 5. Conclusions on theMega-urban administratively changes defined in Beijing city boundaries.and their impacts on the environment are assessed consistently and Mega-urbanquantitatively changes through in Beijing an innovative and their impact approachs on theusing environment decadal areQuickSCAT assessed consistently data and andGOME/SCIAMACHY-derived quantitatively through antropospheric innovative nitrogen approach dioxide using (NO decadal2) columns. QuickSCAT Annual observations data and GOME/SCIAMACHY-derived tropospheric nitrogen dioxide (NO2) columns. Annual observations

Remote Sens. 2020, 12, 652 20 of 24

Compared to independent sources in multiple years, including results obtained using a Landsat-based method and government statistics, DSM applied to QuickSCAT data from the 2000s enables us to: (i) Monitor the annual growth of Beijing’s urban area extent, which quadrupled in the 2000s with an increase of 3035 km2 between 2000 and 2009, corresponding to an average annual increase of 337 km2. DSM-based results reflect the Chinese political and policy changes affecting Beijing’s urban development over the decade. These include the growth reduction policy implemented in 2005 and the pollution-curbing policy adopted before the Games of the XXIX in 2008. (ii) Observe the significant urban expansion and built-up in Beijing, including its distinct concentric sprawl during the 2000s. DSM backscatter values, combined with the associated Index of Variability values, enable us to distinguish between the inner core of Beijing (representing a well-established urban area with dense housing and tall buildings), the urban and suburban areas transitioning into Beijing’s greater outskirt area (characterized by the presence of sparse houses), and the surrounding natural environment. (iii) Infer processes of urban development (e.g., expansion, densification, and vertical growth) spanning both the inner core of Beijing and its surrounding areas using DSM-based development indices (i.e., Urban Development Index, Building Development Index, and Vertical Development Index). Results show large changes both in the inner urban core and urbanized areas of Beijing, while suggesting that the rural/natural areas around Beijing remained relatively stable in the 2000–2009 time period. Given that DSM can be used to process the whole global QuikSCAT data collected in the 2000s, similar analyses could provide more details and insights on the urbanization rate and type of urban development for the whole of China and other around the world. Furthermore, the DSM-based urban analysis allows a consistent comparison with the year-by-year changes of tropospheric NO2 column densities measured from space. This study highlights a clear relationship between the Beijing urban growth/development and its tropospheric NO2 pollution trend. The comparison reveals a triple increase in the yearly tropospheric NO2 column densities spatially averaged over the 2009 Beijing DSM-based urban extent, which quadrupled since 2000. Furthermore, the results capture the effects of the pollution-curbing policy implemented before the Olympic year (i.e., 2008) as well as the sharp increase in tropospheric NO2 pollution in 2009, with the latter clearly indicating that the series of measures developed to control and decrease Beijing air pollution in preparation for and during the Games of the XXIX Olympiad were temporary. This analysis on the relation between tropospheric NO2 pollution and urban growth/development highlights the capability of multi-source imaging from space to quantitatively assess urbanization impacts on the environment. Future will be necessary to address several limitations in current methods to (i) capture the urban-rural transition as a gradient rather that an abrupt binary boundary while using the Index of Variability to explicitly quantify the larger deviation from the mean value for more urbanized areas compared to that of more rural and natural areas, (ii) delineate fine features and their change in urban areas with the availability of high-resolution products such as the Global Urban Footprint [25] and time-series remote sensing data such as Sentinel [63], and (iii) extent long-term urban monitoring with future satellite missions such as the NASA-Indian Space Research Organisation (ISRO) Synthetic Aperture Radar (SAR) Mission [64]. Nevertheless, the use of DSM-processed QSCAT data offers a significant step toward a consistent and quantitative assessment of both horizontal and vertical urbanization that can be used to complement other currently available remote sensing-based urban and settlement datasets [21] obtained using different approaches and input data [25,65–67]. Multiple current and future satellite X-band SAR missions, such as the TerraSAR-X (launched), TanDEM-X, COnstellation of small Satellites for Mediterranean basin Observation (COSMO-SkyMed), Advanced Satellite with New system for Observation-2 (ASNARO-2), and LOTUSat-1 and LOTUSat-2, will provide radar backscatter global data useful for 3D urban observations at a resolution of 10–100 meters [27]. Remote Sens. 2020, 12, 652 21 of 24

Supplementary Materials: The following are available online at http://www.mdpi.com/2072-4292/12/4/652/s1, Input Data S1: DSM_QSCAT_data.zip, Input Data S2: GOME_SCI_NO2_data.zip; Figure S1: Intercalibrated annual (2000—2009) “Average Visible, Stable Lights, and Cloud Free Coverages” data. Author Contributions: Conceptualization, A.S., S.V.N., and M.M.; methodology, A.S., S.V.N., and M.M.; validation, A.S., S.V.N., and M.M.; formal analysis, A.S.; investigation, A.S., S.V.N., and M.M.; input data resources, S.V.N., and A.R.; data curation, A.S.; writing—original draft preparation, A.S.; writing—review and editing, A.S., S.V.N., M.M., C.L., A.R.; visualization, A.S.; funding acquisition, A.S., S.V.N., M.M., and A.R. All authors have read and agreed to the published version of the manuscript. Funding: The research carried out at the Jet Propulsion Laboratory, California Institute of Technology, was funded by National Aeronautics and Space Administration (NASA) Land-Cover and Land-Use Change (LCLUC) Program, and in part by the NASA Earth Science R&A Program. The APC was funded by National Aeronautics and Space Administration (NASA) Land-Cover and Land-Use Change (LCLUC) Program. Acknowledgments: The research carried out at the School of Geography and Environmental Science, University of Southampton (UK) was done in the framework of the WorldPop Project (www.worldpop.org). The research carried out at the Jet Propulsion Laboratory, California Institute of Technology, was supported by National Aeronautics and Space Administration (NASA) Land-Cover and Land-Use Change (LCLUC) Program, and in part by the NASA Earth Science R&A Program. The authors want to thank to Gregory Neumann of the Jet Propulsion Laboratory for the DSM data processing. Particularly for proofreading this paper, we thank Mr. Edward H. Sewall, who is a JPL technical editor and a native speaker of English with a bachelor degree major in English from the University of California at (UCLA), and Mr. David Kerr, a native speaker of English working at WorldPop as a GIS Specialist Technician. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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