Dynamics of the urban extent at fine spatial and temporal
resolutions over large areas are crucial for developing urban growth models
and achieving sustainable development goals. However, there are limited
practices of mapping urban dynamics with these two merits combined. In this
study, we proposed a new method to map urban dynamics from Landsat time
series data using the Google Earth Engine (GEE) platform and developed a
national dataset of annual urban extent (1985–2015) at a fine spatial
resolution (30 m) in the conterminous United States (US). First, we derived
the change information of urbanized years in four periods that were
determined from the National Land Cover Database (NLCD), using a temporal
segmentation approach. Then, we classified urban extents in the beginning (1985) and ending (2015) years at the cluster level through the implementation of a
change vector analysis (CVA)-based approach. We also developed a
hierarchical strategy to apply the CVA-based approach due to the spatially
explicit urban sprawl over large areas. The overall accuracy of mapped
urbanized years is around 90 % with the 1-year tolerance strategy. The
mapped urbanized areas in the beginning and ending years are reliable, with
overall accuracies of 96 % and 88 %, respectively. Our results reveal
that the total urban area increased by about 20 % during the period
of 1985–2015 in the US, and the annual urban area growth is not linear over the years. Overall, the growth pattern of urban extent in most coastal states is
plateaued over the past three decades while the states in the Midwestern US
show an accelerated growth pattern. The derived annual urban extents are of
great use for relevant urban studies such as urban area projection and urban
sprawl modeling over large areas. Moreover, the proposed mapping framework
is transferable for developing annual dynamics of urban extent in other
regions and even globally. The data are available at
The rapid global urbanization causes environmental, ecological, and public
concerns for human beings for sustainable development goals (SDGs)
(Rodriguez et al., 2018). Globally, urban areas, commonly defined as the
space dominated by the built environment (e.g., buildings, roads, and
runways) from remote sensing, only account for a tiny fraction of the
Earth's surface (Schneider et al., 2010); however, they are home
to most of the global economy, population, energy consumption, and greenhouse
gas emissions (Solecki et al., 2013). According to the latest
The datasets of urban extent dynamics at the fine spatial (e.g., 30 m) and temporal (e.g., annual) resolutions are the key to capturing the rate, trend, and stage of urbanization for a better understanding of this process (Zhang et al., 2014). Such datasets can provide fine information about the urban form (e.g., layout, geometry, and distribution), which can be further used for relevant studies such as urban energy consumption (Chen et al., 2011), biodiversity in urban ecosystem (Andersson and Colding, 2014), and air pollutant emissions (Fan et al., 2018). In addition, the relationship between urban dynamics and annual socioeconomic development (e.g., population and gross domestic product) can help to better understand the reasons behind urbanization (Seto et al., 2002; Xie and Weng, 2017). Finally, the long temporal span (e.g., decades) of urban dynamics can capture a relatively complete process of urban sprawl with different stages (Li et al., 2019a; Gong et al., 2019, 2020; Cao et al., 2020). The information of long-term urban dynamics is valuable in developing urban growth models, such as investigating the generation and propagation of errors (or uncertainties) in urban sprawl models (Santé et al., 2010; Li and Gong, 2016a). However, current mapping approaches that focus on multitemporal (e.g., decade and half-decade) urban extent are limited to reflect the process of urban sprawl (e.g., acceleration or deceleration) of cities and explain their differences caused by demographic and socioeconomic drivers (Sexton et al., 2013).
Urban extent mapping at fine spatial and temporal resolutions, especially over large areas, is still lacking, although urban extent maps with a variety of spatial and temporal resolutions have been developed. For example, there are several global urban extent products such as those from the nighttime light (NTL) data (1 km) (Zhou et al., 2015, 2018; Xie and Weng, 2016), the Moderate Resolution Imaging Spectroradiometer (MODIS) data (500 m) (Schneider et al., 2010), and even the fine-resolution Landsat data (30 m) (Chen et al., 2015; Gong et al., 2013; Liu et al., 2018). However, these existing multitemporal national or global urban extent maps were generally produced separately in each period, with limited consideration of the temporal consistency of urban growth (Li et al., 2015; Song et al., 2016; Shi et al., 2017).
There are several challenges in mapping urban extent at fine spatial and temporal resolutions over large areas. First, land use and cover changes in urban domains are complicated, with the interclass conversions and multiphase changes before urbanization or during the posturbanization period (Li and Gong, 2016b; Lu and Weng, 2004). For example, various land cover types such as vegetation, water, and barren can be potentially converted to built-up areas, and such a conversion may experience multiple phases, e.g., from highly vegetated land to lightly vegetated or barren, and then eventually to built-up areas with posturbanization changes. Second, durations of land surface change introduced by urbanization are different across regions; that is, urban sprawl may occur within a short period or last for a couple of years in different regions (Song et al., 2016; Kennedy et al., 2010).
In general, two approaches have been used to derive spatiotemporally consistent urban extent maps from high spatial and temporal satellite observations. One is improving the classified urban time series using postprocessing techniques (Liu and Cai, 2012; Li et al., 2015); the other one is identifying the change information using the continuous time series data of relevant indicators such as the vegetation index (Huang et al., 2010; Kennedy et al., 2010). The first method requires intensive labor for collecting training samples for classification and specific postprocessing techniques (Gong et al., 2013, 2019; Chen et al., 2015; Liu and Cai, 2012), which is challenging and time-consuming for regional and global mapping over a long temporal span. The second one poses a new challenge for managing, manipulating, and analyzing the massive amount of time series data over large areas.
Due to these challenges in mapping urban dynamics at fine spatial and temporal resolutions over large areas, the development of a generalized and efficient mapping approach is in high demand. In this study, we mapped the annual dynamics (1985–2015) of urban extent in the conterminous United States (US) by developing a generalized and efficient mapping approach on the state-of-the-art Google Earth Engine (GEE) platform. The remainder of this paper describes the study area and data (Sect. 2), the proposed national-mapping approach (Sect. 3), the results with discussion (Sect. 4), the data availability (Sect. 5), and concluding remarks (Sect. 6).
The proposed framework for mapping annual urban extent dynamics in
the conterminous US through the detection of the urbanized year at the pixel level
using the temporal segmentation approach
Landsat time series data on the GEE platform, spanning from 1985–2015, are
the primary data source for mapping annual urban extent in this study. The
advent of GEE is designed for planetary-scale studies using different
sources of satellite images (Gorelick et al., 2017; Li et al., 2019b), and
it is a good choice for mapping projects over large areas. In this study, we
used multiple L1T-level Landsat surface reflectance products, including the
Thematic Mapper (TM), the Enhanced Thematic Mapper Plus (ETM
The National Land Cover Database (NLCD) and nighttime light data are ancillary datasets in this study. The NLCD provides multitemporal urban maps in 1992, 2001, 2006, and 2011 (Homer et al., 2015; Xian et al., 2009), which were used as the reference urban areas in these years. The NLCD has been widely used for its reliable performance at the national scale (Wickham et al., 2010, 2017). In this study, we derived the urban extent map in 1992 using the NLCD 1992/2001 retrofit land cover change product, so that all urban extents derived from the NLCD in different years (i.e., 1992, 2001, 2006, and 2011) are comparable (Fry et al., 2009). In addition, nighttime light images of the Visible Infrared Imaging Radiometer Suite (VIIRS) were used to delineate the potential urban cluster after 2011 (Li and Zhou, 2017).
In this study, we developed a new framework with a unique hierarchical
strategy for mapping annual urban extents in large areas on the GEE platform
using long-term Landsat observations (Fig. 1). First, we grouped the study
period (1985–2015) into four periods, namely B1 (1985–1992), B2 (1992–2001),
B3 (2001–2011), and F1 (2011–2015), based on the available NLCD. For Landsat
time series data in each period, we detected the urbanized years at the
pixel level by implementing a temporal segmentation approach (Li
et al., 2018) (Fig. 1a). Second, given that NLCD only provides urban extent
from B2 to B3, we classified urbanized areas at the cluster level in the
periods B1 and F1 using a change vector analysis (CVA)-based approach. We
developed a hierarchical strategy to implement the CVA-based approach due to
the spatially explicit urban sprawl over large areas. That is, the CVA-based
approach was applied in potential urban clusters (derived from VIIRS data)
in each grid (around 250 km
We preprocessed the raw Landsat time series data before implementing the
temporal segmentation approach. We systematically corrected the OLI surface
reflectance data to make them consistent with other sensors (i.e., TM and
ETM
We implemented the temporal segmentation approach for each urbanized pixel in four periods (i.e., B1, B2, B3, and F1). These urbanized pixels during each period were identified using urban extent maps derived from NLCD and classified results in B1 and F1 using the CVA-based approach. Within each period, we identified the starting (P1) and ending (P2) years of change using the temporal segmentation approach, according to the overall trend of the indicators (i.e., NDVI, MNDWI, and SWIR). For urbanization from vegetation, the indicator of NDVI shows a decreasing trend (Fig. 2a), while curves of MNDWI and SWIR show increasing trends (Fig. 2b). In this temporal segmentation method, we first applied a linear regression to the annual time series data of three indicators (i.e., NDVI, MNDWI, and SWIR) and then determined these two turning points (i.e., P1 and P2) according to their annual residuals to the regression-based trend line. If the overall trend of NDVI is decreasing, the years with the largest residuals above and below the regression-based trend line were identified as P1 and P2, respectively, and vice versa (Fig. 2). The change year derived from the indicator with the largest change magnitude (i.e., change between P1 and P2) was identified as the final result. In addition, the duration of change is the difference of years between P1 and P2. More details about the temporal segmentation can be found in Li et al. (2018).
Illustration of the temporal segmentation approach using indicators
with decreasing (NDVI)
We classified urbanized areas in periods B1 (1985–1992) and F1
(2011–2015) using a CVA-based approach at the national level (Fig. 1b).
Urbanized areas of two middle periods (i.e., B2 and B3) were directly
obtained from NLCD. Results from the temporal segmentation approach (i.e.,
change magnitude within each period) were used to identify urbanized areas
in the CVA-based approach in the beginning (B1) and ending (F1) periods.
Full time series data were used in our CVA-based approach, which is
different from the commonly used approach based on a pair of images in two
periods (Xian et al., 2009; Yu et al., 2016). The change magnitude
(
We implemented the CVA-based approach within urban masks in the first (B1) and last (F1) periods. For B1, the urban extent of NLCD 1992 was used as a potential urban mask before 1992. For the period F1, an approximate urban extent derived from VIIRS data in 2015 (Li et al., 2018) was used as a potential urban boundary for classification. Within the derived urban boundary, we classified urban areas in 2015 using urban pixels sampled from NLCD 2011. Finally, we derived urbanized areas in the period F1, using the potential change areas from the CVA approach, the urban boundary from NTL data, and the urban extent from NLCD 2011. Pseudo-changes that are not relevant to the urban sprawl were removed during this process. More details about the CVA-based approach can be found in Li et al. (2018).
We developed a hierarchical strategy to implement the CVA-based approach at
the national level. This strategy enables us to detect urbanized areas over
large areas with spatially explicit patterns of urban sprawl. In this
strategy, we grouped all potential urban clusters into two categories using
a size filter of 100 km
An illustration of the CVA-based approach. An example grid with
potential urban clusters including Dallas, Texas, for implementing the CVA-based approach
The annual growth of urban areas varies across years in the conterminous US,
which cannot be revealed by the NLCD (Fig. 4). Overall, the average growth
rate at the national scale is around 1000 km
Annual growth of urban areas in the conterminous US (1985–2015)
The annual growth of urban areas is different across states. There is an
overall increasing trend in the early years and a decreasing trend in the
latter years in period of 2001–2011 (Fig. 5). The mean growth rate in all
states is 25 km
State-based relative change of annual growth of urban areas compared to the mean growth rate of NLCD during the period of 2001–2011 (Basemap data© 2019 Esri).
A distinctive urban area growth was observed for cities with a rapid population growth. We chose the top 10 cities in the US based on the population growth rate during 2010–2017 (Fig. 6). Most of them are in the southern and the eastern US, such as TX, FL, and North Carolina (NC). Overall, the growth of urban areas in these top 10 cities is significant. The rank-based urban area growth agrees well with the result from population growth (Fig. S4). For example, there is a remarkable urban sprawl around 2006–2015 in the Village city (FL), which is also the city with the fastest population growth among the top 10.
An illustration of urban area growth in the top 10 fast-growing cities in the US according to the population growth during 2010–2017. (1) Village (Florida), (2) Myrtle Beach (South Carolina/North Carolina), (3) Round Rock (Texas), (4) Midland (Texas), (5) Greeley (Colorado), (6) St. George (Utah), (7) Fort Myers (Florida), (8) Redmond (Oregon), (9) Raleigh (North Carolina), and (10) Orlando (Florida) (Basemap data© 2019 Google).
Our annual urban extent data reveal different long-term patterns of urban growth across states in the US during the past three decades. We calculated the percentage of urban area growth relative to the base year 1985 for each state from 1986 to 2015. States show different patterns (i.e., convex or concave hull) according to the time series of relative change (Fig. 7a). As such, we defined two urban area growth patterns by comparing the derived time series curve to the reference line (Fig. 7b). If the curve of urban area growth was overall above the reference line (e.g., Missouri, MO), then we regarded this pattern as a plateaued growth; on the contrary, it belongs to an accelerated growth pattern (e.g., Arizona, AZ). In general, urban area growth patterns in most coastal states are plateaued, and states in the south and the midwestern US show an accelerated growth pattern in general (Fig. 7c). In particular, the relatively accelerated growth of urban areas over the past three decades in agricultural states such as Iowa (IA), North Dakota (ND), and South Dakota (SD) challenges the sustainable development of agriculture system. Also, the annual urban areas over a long term indicate that the urban area growth is not linear over the years, although the linear growth of urban areas was widely used in urban sprawl modeling, if only the coarse-temporal-resolution urban extent data are available (Li et al., 2014; Sexton et al., 2013).
Urban area growth patterns over the past three decades of each state in
the US
The primary conversion sources of urbanized areas are different across states and change over time (Fig. 8). Most urbanized areas were converted from cropland and forest within a relatively short duration (i.e., 1–3 years) (Fig. S5–S6). Overall, vegetation (i.e., cropland, forest, grass, and shrub) is the dominant source of urbanized areas over all the states and years. In particular, the cropland is the most predominant source of urbanized areas, accounting for 46 % of the total urbanized areas during 1992–2015. Besides, there is a certain percentage of urbanized areas converted from water or wetland in some states in the eastern and southern coastal areas, e.g., FL, Louisiana (LA), and South Carolina (SC). Additionally, percentages of land cover encroached by urban vary over the years. For example, the percentage of encroached cropland decreases, while the encroached grass increases in North Dakota (ND).
Conversion sources of urbanized areas during 1992–2015.
The identified urbanized years using the temporal segmentation approach agree well with the manually interpreted result using samples from NLCD, with an overall accuracy of around 90 % using the 1-year tolerance strategy (Fig. 9). We visually interpreted more than 500 samples randomly collected from urbanized regions from NLCD during the periods B1, B2, and B3 (Fig. S7), aided by multitemporal Landsat images, Google Earth high-resolution images, and time series data of relevant indicators (i.e., NDVI, MNDWI, and SWIR) (Li et al., 2018). Period F1 is not included due to its short term (2011–2015). Given that there are uncertainties in the manual interpretation, we validated our results using the identified absolute year and the 1-year tolerance strategy (Song et al., 2016). The overall accuracies of B1, B2, and B3 without the 1-year tolerance strategy are 58 %, 48 %, and 57 %, respectively. When the 1-year tolerance strategy was used, their agreements were considerably improved to 89 %, 83 %, and 88 %, respectively (Fig. 9). The adoption of the 1-year strategy is reasonable because the urban sprawl may occur in the beginning or ending phases of a given year, which may cause confusion among neighboring years (Song et al., 2016; Huang et al., 2010).
Accuracy assessment of urbanized years over different periods B1
(1985–1992)
The spatial pattern of detected urbanized years is reliable through the visual inspection in eight selected representative cities, with different urban sprawl rates during 1985–2015 and population sizes ranging from 200 000 to 900 000 (Fig. 10). In general, urban areas in these cities expanded from the center to the fringe areas, whereas the pathways of urban sprawl are notably different among these cities. For example, the direction of urban sprawl is opposite between Des Moines (IA) and Memphis (TN). The snapshots in Fig. 11 suggest a good agreement of urbanized years between our results and Landsat observations. For example, most urbanized areas in Las Vegas (Region B in Fig. 11) occurred after 2000, which is consistent with our mapped urbanized years (i.e., pixels colored from yellow to red). Similar cases can also be found in other regions such as Des Moines (A) and Kansas (C) (Fig. 11).
Annual dynamics of urban extent in eight selected US cities over the past three decades. The black frames are regions in Fig. 11 for further comparison with Landsat images.
Comparison of annual urban dynamics with Landsat images. The
geographic location of each region (A–H) corresponds to the black frames in
Fig. 10. The spatial extent of each region is 25 km
The CVA-based approach performs well for classifying urbanized areas, according to the accuracy assessment using samples randomly generated on both nonurban and urbanized areas during the periods B1 (1985–1992) and F1 (2011–2015) (Table 1). Validation samples for period B1 were randomly collected from persistent urban areas since 1985 and urbanized areas during 1985–1992. For the period F1, samples were generated based on nonurban areas and urbanized areas during 2011–2015, within the VIIRS-derived potential urban boundary (Fig. S8). The manual interpretation is based on the time series of Landsat and high-resolution Google Earth images in the two periods. The overall accuracies of classified urbanized areas for periods B1 and F1 are 96 % and 88 %, respectively (Table 1). The higher accuracy in the period B1 compared with the period F1 is because the validation samples in this period are within the possible urban extent of 1992 from the NLCD. Also, the misclassified urbanized areas in the period F1 are mainly caused by the confusion between bared land (e.g., rocks, or dry soil) and an urban area with similar spectral features (Mertes et al., 2015).
Accuracy assessment of classified urbanized areas for periods B1 and F1.
There are several sources of uncertainties in our annual urban extent data. The first is the classification error in the NLCD, despite this being the most reliable database in the US with a fine resolution and multiple periods (Homer et al., 2015). On the one hand, the detected change information is incorrect in the misclassified urbanized pixels from NLCD. On the other hand, for those urbanized pixels, but not identified in NLCD, their change information is not captured in our result. However, the overall accuracy of land cover classification in NLCD is about 85 %–90 % (Wickham et al., 2017), and the accuracy of urban land cover is even higher (i.e., larger than 95 % in selected examples of US) (Li et al., 2018). Moreover, the CVA-based approach can be implemented to improve the urban extent maps of NLCD as change magnitudes of those pseudo-urban pixels in the NLCD are notably lower than changes caused by urban sprawl. In addition, the omitted urbanized pixels in the NLCD can be potentially captured using the CVA-based approach. The second is the classification error in mapped urbanized areas in the beginning and ending years. Uncertainties caused by spectral similarities between urban and bared lands could still exist in our results (Table 1), although we have used different constraints (e.g., change vector, classification results, and NTL) to mitigate such uncertainties. More advanced classification algorithms and additional information such as thermal features could be helpful for improving our algorithm in monitoring urban dynamics.
The generated data of annual urban dynamics are available at
In this study, we mapped annual urban extents in the conterminous US by developing an efficient framework on the GEE platform using long-term Landsat observations. First, aided by the NLCD, we temporally grouped the entire temporal span into four periods (i.e., B1: 1985–1992; B2: 1992–2001, B3: 2001–2011; and F1: 2011–2015). Then, we derived the urbanized years and change magnitudes measured by indicators of NDVI, MNDWI, and SWIR at the pixel level, using a temporal segmentation approach in each period. After that, we classified urbanized areas at the cluster level in the beginning (1985) and the ending (2015) years through the implementation of a CVA-based approach. Considering the spatially explicit urban sprawl over large areas, we developed a unique hierarchical strategy to apply the CVA-based approach at the national level. Finally, the mapped urban dynamics in these four periods were combined as a complete dataset of 30-year dynamics of urban extent in the conterminous US.
The proposed mapping framework with the unique hierarchical strategy achieves a good performance in mapping annual dynamics of urban extent at a fine spatial resolution at the national level. The overall accuracies of detected urbanized years for the periods B1, B2, and B3 are 89 %, 83 %, and 88 %, respectively, with a 1-year tolerance strategy. Meanwhile, the CVA-based approach on the output from temporal segmentation can classify urbanized areas well, with overall accuracies of 96 % and 88 % for periods B1 and F1, respectively. Also, the implementation of the CVA-based approach using the proposed hierarchical strategy can capture the heterogeneity of urban growth over different regions, periods, and urban sizes, which helps to build a reliable dataset of urban dynamics.
There is a notable difference in the growth rates and patterns of annual urban areas across states in the US over the past three decades. The total
increment of urban areas is about 31 000 km
This study provides a successful application of mapping annual urban extent at the national scale through the combination of existing good-quality NLCD urban extent maps, long-term Landsat time series data, and the GEE cloud-based platform. The proposed approach can be transferred to other regions with similar multitemporal land cover datasets to the NLCD, for updating existing land cover datasets with a higher temporal resolution. This study opens a new avenue to use all available Landsat observations for mapping annual urban extent at the national level compared with previous studies using the supervised classification or postprocessing (Schneider et al., 2010; Li et al., 2015; Liu and Cai, 2012). Moreover, the derived change information from the temporal segmentation using annual observations is more reliable compared with the research using a pair of Landsat images in 2 years (Yu et al., 2016). However, this approach may introduce uncertainties if the composited annual time series Landsat observations fluctuate too much, especially when this fluctuation is larger than the change induced by urbanization.
The supplement related to this article is available online at:
YZ and XL designed the research. XL and YZ implemented the research and wrote the paper. ZZ and WC revised the paper.
The authors declare that they have no conflict of interest.
This research was funded in part by the US Department of Agriculture (USDA) and Iowa State University. We would like to thank the organizations that shared their datasets for use in this study.
This research has been supported by the USDA Natural Resources Conservation Service (grant no. 68-7482-17-009).
This paper was edited by Kirsten Elger and reviewed by two anonymous referees.