Vegetation Phenology Influenced by Rapid Urbanization of the Yangtze Delta Region

remote sensing

Article Vegetation Phenology Inﬂuenced by Rapid Urbanization of The Yangtze Delta Region

Haiyong Ding 1,*, Luming Xu 2, Andrew J. Elmore 3,4 and Yuli Shi 1

1 School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China; [email protected] 2 School of Geography Science, Nanjing University of Information Science and Technology, Nanjing 210044, China; [email protected] 3 Appalachian Laboratory, University of Maryland Center for Environmental Science, 301 Braddock Road, Frostburg, MD 21532, USA; [email protected] 4 National Socio-Environmental Synthesis Center (SESYNC), 1 Park PI, Suite 300, Annapolis, MD 21401, USA * Correspondence: [email protected]

Received: 6 May 2020; Accepted: 29 May 2020; Published: 1 June 2020

Abstract: Impacts of urbanization and climate change on ecosystems are widely studied, but these drivers of change are often difficult to isolate from each other and interactions are complicated. Ecosystem responses to each of these drivers are perhaps most clearly seen in phenology changes due to global climate change (warming climate) and urbanization (heat island effect). The phenology of vegetation can influence many important ecological processes, including primary production, evapotranspiration, and plant fitness. Therefore, evaluating the interacting effects of urbanization and climate change on vegetation phenology has the potential to provide information about the long-term impact of global change. Using remotely sensed time series of vegetation on the Yangtze River Delta in China, this study evaluated the impacts of rapid urbanization and climate change on vegetation phenology along an urban to rural gradient over time. Phenology markers were extracted annually from an 18-year time series by fitting the asymmetric Gaussian function model. Thermal remote sensing acquired at daytime and nighttime was used to explore the relationship between land surface temperature and vegetation phenology. On average, the spring phenology marker was 9.6 days earlier and the autumn marker was 6.63 days later in urban areas compared with rural areas. The spring phenology of urban areas advanced and the autumn phenology delayed over time. Across space and time, warmer spring daytime and nighttime land surface temperatures were related to earlier spring, while autumn daytime and nighttime land surface temperatures were related to later autumn phenology. These results suggest that urbanization, through surface warming, compounds the effect of climate change on vegetation phenology.

Keywords: vegetation phenology; urbanization; climate change; remote sensing; the Yangtze River Delta

1. Introduction Vegetation phenology refers to annual reoccurring cycles of plant activity, such as leaf onset and oﬀset in spring and autumn, respectively. These cycles are driven by climatic cues [1] and are therefore an important indicator of trends in annual weather conditions [2,3]. For example, in Europe it was found that a spring air temperature increase of 1 ◦C has been associated with an advance in the beginning of the growing season by 7 days [4]. Similar results are found in the eastern United States with spring arriving 4–6 days earlier since the mid-1960s [5–10]. In China, spring has advanced on average 2.88 days per decade in response to spring warming [1]. Therefore, trends in vegetation

Remote Sens. 2020, 12, 1783; doi:10.3390/rs12111783 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 1783 2 of 18 phenology have emerged as one of the most obvious and easily measured vegetation responses to climate change [1,4–6,11–15]. Rapid urbanization observed in developing countries such as China and elsewhere [6,16–20] is accepted as an important factor leading to ecosystem change [6,15,21]. Notably, the increasing area of impervious surfaces (roads, buildings, parking lots, etc.) has changed the energy exchange process between the land surface and the atmosphere. These changes have intensified urban temperatures and contribute to the urban heat island phenomena [17,19,22,23]. Consequently, it has been widely observed that due to the Urban Heat Island (UHI) effect, urban areas typically exhibit an earlier spring phenology and later autumn phenology than their rural counterparts [6,11,16,24–26]. In some cases, urban landscapes have been invoked as examples of how climate change might be expected to impact vegetation phenology. It was found in research of China’s 32 major cities that the growing season started 11.9 days earlier and ended 5.4 days later in urban zones compared to rural counterparts, and the phenology change was closely related to the land surface temperature (LST) [26]. It was also found that UHI reduced the sensitivity of vegetation to climate change [27]. Urban areas stayed warmer in the winter, which reduces the exposure of vegetation to winter chilling and, therefore, spring warming does not have as strong an effect on vegetation phenology [27]. Although urban heat islands are generally associated with the extent of hardened surfaces, urban population was also found to be related to earlier spring budburst (by up to 13 days) and delayed leaf coloring (up to 15 days) in the Seoul Capital Area, South Korea, through a study of the spring budburst dates of six species and the leaf coloring date for two species [11]. However, most of these studies explored the difference in phenology between urban centers and their rural counterparts. Therefore, they do not account for the possibility that vegetation is compositionally different in cities and rural areas. Research into vegetation phenology spatial gradients and temporal trends is facilitated through the analysis of time series of satellite remote sensing data. Remote sensing of phenology is typically accomplished using broad-band, moderate-resolution instruments such as the Moderate Resolution Imaging Spectra-radiometer (MODIS), which can provide annual estimates of spring and autumn vegetation phenology. While there are several different approaches for extracting phenology markers from these timeseries [10], the most reliable methods fit functions to the time series data that down-weight cloud-affected or otherwise unreliable observations [28]. Here we adopted such an approach to analyze the influence of urbanization on vegetation phenology from 2001 to 2018 in a rapidly urbanizing area, the Yangtze River Delta. Our analysis includes the relationship between vegetation phenology and land surface temperature in both urban and rural areas, which experienced different rates of development during the analysis period. Specifically, the aims of this research are: (1) to quantify and compare the influences of urbanization and time on vegetation phenology, and (2) to understand the relationship between LST and vegetation phenology among urban and rural regions.

2. Materials

2.1. Study Area Our study focused on vegetation phenology during the period of 2001–2018 on the Yangtze River Delta (Figure1), which is within the range of 29 ◦020–33◦250 N and 118◦200–122◦570 E, and was chosen because it is one of the most developed regions in China. In the past 20 years, the Yangtze River Delta, together with two other regions, the Beijing–Tianjin–Hebei region and the Pearl River Delta, have rapidly urbanized. Many metropolitan cities, such as Shanghai City, Nanjing City, Hangzhou City, and Suzhou City, are located on the Yangtze River Delta. These cities have experienced rapid urban expansion and increasing population, currently reaching 76 million people [24]. Remote Sens. 2020, 12, 1783 3 of 18 Remote Sens. 2020, 12, x FOR PEER REVIEW 3 of 18

Figure 1. Figure 1. The location of the Yangtze River DeltaDelta inin China.China. 2.2. Data 2.2. Data The data used in this research includes information on the spatial and temporal extent of urban landThe cover, data spring used and in this autumn research vegetation includes phenology, information and on land the surface spatial temperature.and temporal The extent urban of urban areas wereland cover, identified spring from and the autumn Suomi vegetation National Polar-orbiting phenology, and Partnership land surface (SNPP) temperature. Visible Infrared The urban Imaging areas Radiometerwere identified Suite from (VIIRS) the Suomi nighttime National light Polar-orbi data (Figureting2a) Partnership by applying (SNPP) an optimal Visible thresholds Infrared methodImaging basedRadiometer on the Suite urban (VIIRS) land-use nighttime product light generated data (Figure by the 2a) Resource by applying and an Environment optimal thresholds Data Center method of Chinesebased on Academy the urban of land-use Sciences product [18,29,30 generated]. Strong spatialby the heterogeneityResource and inEnvironment the spectral Data characteristics Center of ofChinese urban Academy areas, including of Sciences many [18,29,3 mixed0]. Strong pixels atspatial moderate heterogeneity resolution, in the increase spectral uncertainty characteristics in the of urban areas, including many mixed pixels at moderate resolution, increase uncertainty in the location location of the urban boundary. Nighttime light data, however, produce reliable delineation of urban of the urban boundary. Nighttime light data, however, produce reliable delineation of urban clusters, clusters, and clearly distinguish urban areas from the surrounding suburban and rural areas that lack and clearly distinguish urban areas from the surrounding suburban and rural areas that lack nighttime lighting [6]. In this study, the urban areas derived from nightlight data from 2000, 2005, 2010, nighttime lighting [6]. In this study, the urban areas derived from nightlight data from 2000, 2005, and 2015 were used to represent the urban area in 2001–2002, 2003–2007, 2008–2012, and 2013–2018, 2010, and 2015 were used to represent the urban area in 2001–2002, 2003–2007, 2008–2012, and 2013– respectively [25]. 2018, respectively [25]. The vegetation types data (30 m resolution; Figure2b were acquired from the work of Gong et al. The vegetation types data (30 m resolution; Figure 2b were acquired from the work of Gong et 2012, and included five main categories: mixed forest (MF), evergreen forest (EF), shrubland (SB), al. 2012, and included five main categories: mixed forest (MF), evergreen forest (EF), shrubland (SB), grassland (GS), and cropland (CP) [31,32]. Land cover was resampled to 250 m to match the MODIS grassland (GS), and cropland (CP) [31,32]. Land cover was resampled to 250 m to match the MODIS enhanced vegetation index (EVI) data. The daytime LST at 10:30 a.m. and nighttime LST at 22:30 p.m. enhanced vegetation index (EVI) data. The daytime LST at 10:30 AM and nighttime LST at 22:30 PM local time were acquired from the MODIS LST product (MOD11A2, 8-day composite) with a spatial local time were acquired from the MODIS LST product (MOD11A2, 8-day composite) with a spatial resolution of 1 1 km. It has been confirmed that the accuracy of MODIS LST reaches 1K [33]. resolution of 1 ×× 1 km. It has been confirmed that the accuracy of MODIS LST reaches 1K [33]. Vegetation phenology information was extracted from Moderate Resolution Imaging Spectrometer (MODIS) enhanced vegetation index (EVI) product (MOD13Q1 EVI, 16-day composite, 250 m spatial resolution) for the period 2001–2018. These data reduce the impact of variation in atmospheric scattering compared with the Normalized Difference Vegetation Index (NDVI) [3,34], and have been demonstrated to be suitable for monitoring phenology information in urban areas [2,14,19,20,35,36]

Remote Sens. 2020, 12, 1783 4 of 18 Remote Sens. 2020, 12, x FOR PEER REVIEW 4 of 18

FigureFigure 2. Urban 2. Urban clusters clusters derived derived from from SNPP SNPP/ VIIRS/ VIIRS nighttime nighttime light data ( (aa) )and and land land cover cover data data (b) ( b) in the yearin the 2015. year 2015. 3. Methods Vegetation phenology information was extracted from Moderate Resolution Imaging TheSpectrometer methods (MODIS) used toanalyze enhanced the vegetation impacts ofindex urbanization (EVI) product on the(MOD13Q1 vegetation EVI, phenology 16-day composite, are given in Figure250m3 to givespatial a clearresolution) representation. for the period First, 2001–2018. vegetation These phenology data reduce markers the were impact derived of variation from MODIS in atmospheric scattering compared with the Normalized Difference Vegetation Index (NDVI) [3,34], EVI time series data, and urban areas as well as buffer zones were extracted from the nighttime light and have been demonstrated to be suitable for monitoring phenology information in urban areas data. Then, we analyzed the spatial-temporal variation of vegetation phenology in urban, suburban, [2,14,19,20,35,36] and rural areas from 2001 to 2018. Finally, the relationship between the urban–rural phenology difference3. Methods and LST difference was constructed using linear regression analysis, and the vegetation phenologyRemote Sens. responses 2020, 12, xto FOR urbanization PEER REVIEW across vegetation types were analyzed. 5 of 18 The methods used to analyze the impacts of urbanization on the vegetation phenology are given in Figure 3 to give a clear representation. First, vegetation phenology markers were derived from MODIS EVI time series data, and urban areas as well as buffer zones were extracted from the nighttime light data. Then, we analyzed the spatial-temporal variation of vegetation phenology in urban, suburban, and rural areas from 2001 to 2018. Finally, the relationship between the urban–rural phenology difference and LST difference was constructed using linear regression analysis, and the vegetation phenology responses to urbanization across vegetation types were analyzed.

Figure 3. The ﬂow chart of the method used in this study. Figure 3. The flow chart of the method used in this study.

3.1. Asymmetric Gaussians Smoothing An asymmetric Gaussian function (A–G) was fitted to the MODIS EVI time series data to retrieve phenology markers for spring and autumn (Equation (1)) [10,37]. To fit EVI data accurately, MODIS pixel quality information was used to assess pixel reliability of EVI data. Pixels with higher reliability (higher quality) were given greater weight in the fitting algorithm (following Table 1), which reduced the impact of data noise [38,39]. The asymmetric Gaussian function applied was:

− −( ) > (; ,,...,)= (1) − −( ) <

where x1 is the Day of Year (DOY) of the maximum or minimum over time, t. The fitted parameters x2 and x3 determine the width and flatness of curve when t > x1. Similarly, x4 and x5 determine the width and flatness of the curve when t < x1.

Table 1. Weights given when fitting vegetation curve. Quality Values of MODIS quality Assurance Description Weights data (QA) 0 Good Data Use with confidence 1 Useful, but look at other QA 1 Marginal data 0.8 information 2 Snow/Ice Target covered with snow/ice 0.2 3 Cloudy Target not visible, covered with cloud 0.2

3.2. Extraction of Phenology Metrics Three phenology markers, start of season (SOS), end of season (EOS), and growth season length (GSL), were used to analyze the influence of urbanization on vegetation phenology from MODIS EVI data. SOS is the day of year (DOY) when the fitted curve rises beyond a threshold, defined relative

Remote Sens. 2020, 12, 1783 5 of 18

3.1. Asymmetric Gaussians Smoothing An asymmetric Gaussian function (A–G) was fitted to the MODIS EVI time series data to retrieve phenology markers for spring and autumn (Equation (1)) [10,37]. To fit EVI data accurately, MODIS pixel quality information was used to assess pixel reliability of EVI data. Pixels with higher reliability (higher quality) were given greater weight in the fitting algorithm (following Table1), which reduced the impact of data noise [38,39]. The asymmetric Gaussian function applied was:

 h x i t x1 3 i f t > x1  exp ( − )  x2 g(t; x , x2, ... , x5) = h− x i (1) 1  x1 t 5  exp ( x− ) − 4 i f t < x1 where x1 is the Day of Year (DOY) of the maximum or minimum over time, t. The fitted parameters x2 and x3 determine the width and flatness of curve when t > x1. Similarly, x4 and x5 determine the width and flatness of the curve when t < x1.

Table 1. Weights given when ﬁtting vegetation curve.

Values of Quality MODIS Description Weights Assurance (QA) Quality Data 0 Good Data Use with conﬁdence 1 1 Marginal data Useful, but look at other QA information 0.8 2 Snow/Ice Target covered with snow/ice 0.2 3 Cloudy Target not visible, covered with cloud 0.2

3.2. Extraction of Phenology Metrics Three phenology markers, start of season (SOS), end of season (EOS), and growth season length

(GSL),Remote were Sens. used 2020 to, 12 analyze, x FOR PEER the REVIEW influence of urbanization on vegetation phenology from MODIS6 of 18 EVI data. SOS is the day of year (DOY) when the fitted curve rises beyond a threshold, defined relative to the seasonto the amplitude. season amplitude. EOS is theEOS DOY is the when DOY thewhen declining the declining side of side the of fitted the curvefitted curve falls below falls below a threshold, a also definedthreshold, relative also defined to the seasonalrelative to amplitude. the seasonal Compared amplitude. with Compared rural areas, with ru theral mean areas, EVI the ofmean urban EVI areas is low,of so urban a fixed areas threshold, is low, so that a fixed is not threshold, sensitive that to seasonal is not sensitive amplitude, to seasonal cannot beamplitude, applied cannot to both be urban and surroundingapplied to both areas. urban We and adopted surrounding a threshold areas. equalWe adopted to 0.2 of a thethreshold seasonal equal amplitude, to 0.2 of the which seasonal has been amplitude, which has been shown to be suitable for extracting urban phenology [10,16,26,40] (Figure shown to be suitable for extracting urban phenology [10,16,26,40] (Figure4). Pixels with minimal mean 4). Pixels with minimal mean vegetation cover (EVI), such as water and entirely hardened surfaces, vegetation cover (EVI), such as water and entirely hardened surfaces, were removed. These unrealistic were removed. These unrealistic extreme values outside of the range of 50 to 180 for SOS and the extremerange values of 240 outside to 330 of for the EOS range were of removed 50 to 180 from forSOS the an andalysis the [26,41]. range of The 240 GSL to 330 is defined for EOS as were duration removed from thebetween analysis SOS [ 26and,41 EOS]. The in days. GSL is defined as duration between SOS and EOS in days.

Figure 4. Example of asymmetric Gaussian function curve ﬁtting to raw enhanced vegetation index (EVI) observationsFigure 4. Example plotted of asymmetric against Day Gaussian of Year function (DOY). curve fitting to raw enhanced vegetation index (EVI) observations plotted against Day of Year (DOY).

3.3. Urban Buffer Zones and Phenology Change We used a range of distances from urban areas to define zones of urbanization (i.e., urban, suburban, and rural), and then analyzed these areas to understand the difference in phenology response across urbanization levels [23]. It was previously reported [15] that the mean distance from urban land cover affecting vegetation phenology is less than 20 km from the urban boundary. Therefore, we adopted an approach that defined distance from the urban edge within 20 km as the suburban area. Phenology in the rural area (20–25 km from urban edge) was adopted as a reference. To explore the changes of vegetation phenology along the urbanization gradient, suburban areas were further divided into six buffer areas (0–1, 1–2, 2–5, 5–10, 10–15, 15–20 km) (Figure 5) extending from the urban edge [42]. Response of phenology to urbanization was defined as the phenology differences between urban zones (i.e. urban, suburban) and rural areas. We assumed that the farther away from the urban edge, the lower the level of urbanization. Phenology differences were therefore defined as follows:

∆P = − (2)

where ∆P is the difference between phenology markers extracted from (sub)urban buffer zones (Pub) and the rural reference (Pr). A negative value of ∆P for SOS represents an earlier SOS in the urban areas compared with rural, while a positive value of ∆P for EOS represents a later EOS in the urban areas. The earlier SOS and later EOS correspond to a longer GSL.

3.4. Urbanization Effect on Phenology The relationships between LST and phenology were estimated by calculating Spearman’s correlations between the phenology differences (∆P) and LST differences (∆LST) across the urban and six buffer zones toward rural zones. For those higher correlation coefficients, the linear regression model between phenology difference and LST difference was constructed according to Equation (3).

Remote Sens. 2020, 12, 1783 6 of 18

3.3. Urban Buﬀer Zones and Phenology Change

RemoteWe Sens. used 2020, a12, range x FOR PEER of distances REVIEW from urban areas to define zones of urbanization (i.e., urban,7 of 18 suburban, and rural), and then analyzed these areas to understand the difference in phenology response across urbanization levels [23]. It was previously reported [15] that the mean distance from urban ΔP = m ⋅ ΔLST + n (3) land cover affecting vegetation phenology is less than 20 km from the urban boundary. Therefore, wherewe adopted ΔP is an the approach difference that among defined phenology distance markers from the be urbantween edge urban within or buffer 20 km zones as the and suburban rural areas, area. andPhenology ΔLST is in the the LST rural difference area (20–25 between km from urban urban or edge)buffer was zones adopted and rural as a reference.areas. Parameter m is the slopeTo of explorethe model. the changesWhere m of >0, vegetation later SOS phenology or EOS (whichever along the urbanizationis used to calculate gradient, ∆P) suburban are related areas to higherwere further LST; if divided m <0, earlier into six SOS bu orffer EOS areas is related (0–1, 1–2, to higher 2–5, 5–10, temperatures. 10–15, 15–20 Parameter km) (Figure n represents5) extending the intercept,from the urbansignifying edge the [42 value]. Response of ∆P when of phenologythere is no temperature to urbanization difference was defined between as urban the phenology and rural areas.differences between urban zones (i.e., urban, suburban) and rural areas. We assumed that the farther awayTo from provide the urban greater edge, insight the lower into the the nature level of of urbanization. phenology change Phenology over ditimefferences and between were therefore urban anddefined rural as areas, follows: we performed the previously described analyses on individual land cover types and on all land covers combined. ∆P = P Pr (2) ub − Figure 5. Location of the eight most developed urban areas in the Yangtze River Delta, an example where ∆P is the difference between phenology markers extracted from (sub)urban buffer zones (Pub) from 2015. The eight most developed urban areas are Changzhou (CZ), Hefei (HF), Hangzhou (HZ), and the rural reference (Pr). A negative value of ∆P for SOS represents an earlier SOS in the urban areasNanjing compared (NJ), withNantong rural, (NT), while Ningbo a positive (NB), Shanghai value of-Suzhou-Wuxi∆P for EOS (S-S-W) represents and aYangzhou-Zhenjiang later EOS in the urban areas.(Y-Z). The earlier SOS and later EOS correspond to a longer GSL.

Figure 5. Location of the eight most developed urban areas in the Yangtze River Delta, an example from 4. Results2015. The eight most developed urban areas are Changzhou (CZ), Hefei (HF), Hangzhou (HZ), Nanjing (NJ), Nantong (NT), Ningbo (NB), Shanghai-Suzhou-Wuxi (S-S-W) and Yangzhou-Zhenjiang (Y-Z). 4.1. Phenology Changes with Time 3.4. Urbanization Effect on Phenology Spatial patterns in the phenology markers were complex, but even in map form earlier SOS and later TheEOS relationshipswere evident in between urban areas LST relative and phenology to rural (Figure were 6). estimated These spatial by calculating differences Spearman’s were more noticeablecorrelations earlier between in the the timeseries, phenology motivating differences (an∆P) analysis and LST ofdi urbanfferences and (ru∆LST)ral phenology across the over urban time. and Thesix buYangtzeffer zones River toward Delta has rural experienced zones. For significan those highert phenology correlation changes coeffi duringcients, 2001–2018, the linear regressionespecially inmodel suburban between and phenology rural areas. di Urbanfference areas and exhibited LST difference earlier was SOS constructed and later EOS according and, as a to result, Equation the GSL (3). in urban areas was the longest of any area, in any given year (Figure 7). Conversely, both the latest ∆P = m ∆LST + n (3) SOS and earliest EOS were found in rural areas, resu· lting in the shortest GSL. The SOS in urban areas advanced (became earlier in the year) from 2001–2018 (slope = –0.43 days/year, p < 0.05; Figure 7a). A similar pattern was observed in suburban and rural areas at the rate of −0.85 days/year (p < 0.05) and –0.72 days/year (p < 0.05), respectively. The EOS delayed significantly in suburban (0.52 days/year, p < 0.01) and rural areas (0.64 days/year, p < 0.01). However, there was no trend in the EOS of urban areas, which were more stable and generally later in the year than observed in suburban and rural areas (Figure 7b). The GSL increased in all areas from 2001 to 2018 (Figure 7c). The GSL for suburban

Remote Sens. 2020, 12, 1783 7 of 18 where ∆P is the difference among phenology markers between urban or buffer zones and rural areas, and ∆LST is the LST difference between urban or buffer zones and rural areas. Parameter m is the slope of the model. Where m > 0, later SOS or EOS (whichever is used to calculate ∆P) are related to higher LST; if m < 0, earlier SOS or EOS is related to higher temperatures. Parameter n represents the intercept, signifying the value of ∆P when there is no temperature difference between urban and rural areas. To provide greater insight into the nature of phenology change over time and between urban and rural areas, we performed the previously described analyses on individual land cover types and on all land covers combined.

4. Results

4.1. Phenology Changes with Time Spatial patterns in the phenology markers were complex, but even in map form earlier SOS and later EOS were evident in urban areas relative to rural (Figure6). These spatial di fferences were more noticeable earlier in the timeseries, motivating an analysis of urban and rural phenology over time. The Yangtze River Delta has experienced significant phenology changes during 2001–2018, especially in suburban and rural areas. Urban areas exhibited earlier SOS and later EOS and, as a result, the GSL in urban areas was the longest of any area, in any given year (Figure7). Conversely, both the latest SOS and earliest EOS were found in rural areas, resulting in the shortest GSL. The SOS in urban areas advanced (became earlier in the year) from 2001–2018 (slope = 0.43 days/year, p < 0.05; Figure7a). − A similar pattern was observed in suburban and rural areas at the rate of 0.85 days/year (p < 0.05) − and 0.72 days/year (p < 0.05), respectively. The EOS delayed significantly in suburban (0.52 days/year, − p < 0.01) and rural areas (0.64 days/year, p < 0.01). However, there was no trend in the EOS of urban areas, which were more stable and generally later in the year than observed in suburban and rural areas (FigureRemote Sens.7b). 2020 The, 12 GSL, x FOR increased PEER REVIEW in all areas from 2001 to 2018 (Figure7c). The GSL for suburban and8 of 18 rural areas increased at the rate of 1.37 days/year (p < 0.01) and 1.36 days/year (p < 0.01), respectively, whichand rural was areas a faster increased rate than at that the observed rate of in1.37 urban days/year areas (0.4 (p days< 0.01)/year, andp <1.360.05). days/year (p < 0.01), respectively, which was a faster rate than that observed in urban areas (0.4 days/year, p < 0.05).

FigureFigure 6.6. Three phenology indicators of start of season (SOS) (a),), endend ofof seasonseason (EOS)(EOS) ((b)) andand growthgrowth seasonseason lengthlength (GSL)(GSL) ((cc),), anan exampleexample fromfrom 2015.2015.

Figure 7. Inter-annual variations of (a) SOS, (b) EOS, (c) GSL in urban, suburban, and rural areas from 2001 to 2018. k values represent the slope of linear trends. In this analysis, the area defined as suburban is the combined area of all buffer areas between 0 and 20 km from the urban edge.

4.2. Phenology Changes along the Urbanization Gradient Phenology differences (∆P) exhibited heterogeneities along an urban–rural gradient across years in the Yangtze River Delta (Figure 8). The SOS was earlier in urban compared with rural areas in 14 out of 18 years, ranging from −22.3 days in 2006 to 0.3 days in 2003. Conversely, the EOS was later in urban zones (16 out of 18 years) with the differences ranging from 15 days to –0.9 days. On average, SOS advanced 9.6 days and EOS delayed 6.63 days across the urban–rural gradient. This is consistent with previous reports that the differences for SOS and EOS in urban centers relative to rural zones

Remote Sens. 2020, 12, x FOR PEER REVIEW 8 of 18

and rural areas increased at the rate of 1.37 days/year (p < 0.01) and 1.36 days/year (p < 0.01), respectively, which was a faster rate than that observed in urban areas (0.4 days/year, p < 0.05).

Figure 6. Three phenology indicators of start of season (SOS) (a), end of season (EOS) (b) and growth Remote Sens.season2020 length, 12, 1783 (GSL) (c), an example from 2015. 8 of 18

Figure 7. Inter-annual variations of (a) SOS, (b) EOS, (c) GSL in urban, suburban, and rural areas from Figure 7. Inter-annual variations of (a) SOS, (b) EOS, (c) GSL in urban, suburban, and rural areas from 2001 to 2018. k values represent the slope of linear trends. In this analysis, the area defined as suburban 2001 to 2018. k values represent the slope of linear trends. In this analysis, the area defined as is the combined area of all buffer areas between 0 and 20 km from the urban edge. suburban is the combined area of all buffer areas between 0 and 20 km from the urban edge. 4.2. Phenology Changes along the Urbanization Gradient 4.2. Phenology Changes along the Urbanization Gradient Phenology differences (∆P) exhibited heterogeneities along an urban–rural gradient across years in the YangtzePhenology River differences Delta (Figure (∆P) exhibited8). The SOS heterogeneities was earlier in along urban an compared urban–rural with gradient rural areas across in years 14 outin ofthe 18 Yangtze years, ranging River Delta from (Figure22.3 days 8). The in 2006 SOS to was 0.3 earlier days in in 2003. urban Conversely, compared thewith EOS rural was areas later in in 14 − urbanout of zones 18 years, (16 out ranging of 18 years)from − with22.3 days the di inff erences2006 to 0.3 ranging days in from 200 153. Conversely, days to –0.9 the days. EOS On was average, later in SOSurban advanced zones 9.6(16 daysout of and 18 years) EOS delayed with the 6.63 differences days across ranging the urban–rural from 15 days gradient. to –0.9 This days. is On consistent average, withSOS previous advanced reports 9.6 days that and the EOS diff erencesdelayed for6.63 SOS days and across EOS the in urban–rural urban centers gradient. relative This to rural is consistent zones werewith 11.9 previous and 5.4 reports days, respectivelythat the differences [26]. Thus, for SOS SOS and is more EOS sensitive in urban tocenters the urbanization relative to rural gradient, zones compared to EOS [11]. In addition, we also found that the maximum value of EOS tended to occur in the buffered zones adjacent to urban areas, rather than within urban areas. Remote Sens. 2020, 12, x FOR PEER REVIEW 9 of 18 were 11.9 and 5.4 days, respectively [26]. Thus, SOS is more sensitive to the urbanization gradient, Remotecompared Sens. 2020to EOS, 12, 1783[11]. In addition, we also found that the maximum value of EOS tended to occur9 of in 18 the buffered zones adjacent to urban areas, rather than within urban areas.

Figure 8. Phenology diﬀerences across the urban–rural gradient. The green and orange dots represent

the SOS and EOS diﬀerences, respectively, calculated as urban minus rural. Remote Sens. 2020, 12, 1783 10 of 18

4.3. Phenology Response to Urbanization Across Vegetation Types To explore the variations in phenology response to urbanization by vegetation types, phenology metrics were compared between urban zones (i.e. urban, suburban) and rural areas for each vegetation type over the period 2013 to 2018. Phenology differences of different vegetation types from 2013 to 2018 are given in Figure9. ∆SOS1, ∆EOS1, and ∆GSL1 refer to the differences in SOS, EOS, and GSL, respectively, between urban and rural areas. ∆SOS2, ∆EOS2, and ∆GSL2 are defined as the differences in SOS, EOS, and GSL, respectively, between suburban and rural areas. The SOS for different vegetation types was found to have distinct differences between urban, suburban, and rural zones (p < 0.05), and was earlier in urban areas by more than 10 days compared with rural areas (Figure9). Croplands in suburban areas exhibited the earliest SOS di fference relative to rural areas ( 6.8 days, p < 0.01), followed by the evergreen forests ( 4.3 days, p < 0.01) and shrublands − − ( 4.0 days, p < 0.01). On average, across all land cover types, the EOS in suburban areas was later than − the EOS observed in urban areas. This is consistent with the previous finding (Figure8). Only croplands showed significant differences in EOS between urban and rural areas (3.6 days, p < 0.05). However, the EOS for all vegetation types in suburban areas was greater than that in rural areas (p < 0.05), ranging from 1.1 to 4.8 days. Urbanization had the greatest impact on the GSL of croplands, with mean differences of 13.9 days (urban) and 10.4 days (suburban) (p < 0.01; Table2). This is consistent with a previous report, which found the GSL difference in croplands between urban and rural areas was 15.2 days [6]. Comparison of GSL in urban and rural areas, it was found that grassland had lowest GSL increase (8.8 days), followed by mixed forest, evergreen forest and shrubland types (11.4, 11.6, 12.1 days). However, between suburban and rural areas, the GSL difference of grassland, mixed forest, evergreen forest and shrubland types were 4.1, 6.8, 7.2, 8.8 days, respectively.

Table 2. Mean phenology differences (2013–2018) of urban, suburban, and rural areas for different vegetation types in units of days. Variable definitions are as in Figure9.

∆SOS1 ∆SOS2 ∆EOS1 ∆EOS2 ∆GSL1 ∆GSL2 Vegetation Type SOSurban SOSsuburban EOSurban EOSsuburban GSLurban GSLsuburban SOS SOS EOS EOS GSL GSL − rural − rural − rural − rural − rural − rural Mixed forest 10.4 ** 3.7 ** 1.0 3.1 * 11.4 ** 6.8 ** − − Evergreen forest 10.3 ** 4.3 ** 1.4 2.9 ** 11.6 ** 7.2 ** − − Shrubland 10.7 ** 4.0 ** 1.4 4.8 * 12.1 ** 8.8 ** − − Grassland 10.2 ** 3.0 * 1.3 1.1 ** 8.8 ** 4.1 ** − − − Cropland 10.3 ** 6.8 * 3.6 * 3.6 ** 13.9 ** 10.4 ** − − * Signiﬁcant at the 0.05 level. ** Signiﬁcant at the 0.01 level. Remote Sens. 2020, 12, x FOR PEER REVIEW 10 of 18

Figure 8. Phenology differences across the urban–rural gradient. The green and orange dots represent the SOS and EOS differences, respectively, calculated as urban minus rural.

4.3. Phenology Response to Urbanization Across Vegetation Types To explore the variations in phenology response to urbanization by vegetation types, phenology metrics were compared between urban zones (i.e. urban, suburban) and rural areas for each vegetation type over the period 2013 to 2018. Phenology differences of different vegetation types from 2013 to 2018 are given in Figure 9. ∆SOS1, ∆EOS1, and ∆GSL1 refer to the differences in SOS, EOS, and GSL, respectively, between urban and rural areas. ∆SOS2, ∆EOS2, and ∆GSL2 are defined as the differences in SOS, EOS, and GSL, respectively, between suburban and rural areas. The SOS for different vegetation types was found to have distinct differences between urban, suburban, and rural zones (p < 0.05), and was earlier in urban areas by more than 10 days compared with rural areas (Figure 9). Croplands in suburban areas exhibited the earliest SOS difference relative to rural areas (–6.8 days, p < 0.01), followed by the evergreen forests (–4.3 days, p < 0.01) and shrublands (–4.0 days, p < 0.01). On average, across all land cover types, the EOS in suburban areas was later than the EOS observed in urban areas. This is consistent with the previous finding (Figure 8). Only croplands showed significant differences in EOS between urban and rural areas (3.6 days, p < 0.05). However, the EOS for all vegetation types in suburban areas was greater than that in rural areas (p < 0.05), ranging from 1.1 to 4.8 days. Urbanization had the greatest impact on the GSL of croplands, with mean differences of 13.9 days (urban) and 10.4 days (suburban) (p < 0.01; Table 2). This is consistent with a previous report, which found the GSL difference in croplands between urban and rural areas was 15.2 days [6]. Comparison of GSL in urban and rural areas, it was found that grassland had lowest GSL increase Remote Sens.(8.82020 days),, 12 ,followed 1783 by mixed forest, evergreen forest and shrubland types (11.4、11.6、12.1 days). 11 of 18 However, between suburban and rural areas, the GSL difference of grassland, mixed forest, evergreen forest and shrubland types were 4.1, 6.8、7.2、8.8 days, respectively.

Remote Sens. 2020, 12, x FOR PEER REVIEW 11 of 18

Figure 9. Mean phenology differences (2013–2018) of urban, suburban, and rural areas for different Figure 9. Mean phenology differences (2013–2018) of urban, suburban, and rural areas for different vegetation types. ∆SOS1, ∆EOS1, and ∆GSL1 refer to the differences in SOS, EOS, and GSL, respectively, vegetation types. ∆SOS1, ∆EOS1, and ∆GSL1 refer to the differences in SOS, EOS, and GSL, between urban and rural areas. ∆SOS2, ∆EOS2, and ∆GSL2 are defined as the differences in SOS, respectively, between urban and rural areas. ∆SOS2, ∆EOS2, and ∆GSL2 are defined as the differences EOS, and GSL, respectively, between suburban and rural areas. in SOS, EOS, and GSL, respectively, between suburban and rural areas.

Table 2. Mean phenology differences (2013–2018) of urban, suburban, and rural areas for different vegetation types in units of days. Variable definitions are as in Figure 9.

∆SOS1 ∆SOS2 ∆EOS1 ∆EOS2 ∆GSL1 ∆GSL2 Vegetation Type − − − − − − Mixed –10.4** –3.7** 1.0 3.1* 11.4** 6.8** forest Evergreen –10.3** –4.3** 1.4 2.9** 11.6** 7.2** forest Shrubland –10.7** –4.0** 1.4 4.8* 12.1** 8.8** Grassland –10.2** –3.0* –1.3 1.1** 8.8** 4.1** Cropland –10.3** –6.8* 3.6* 3.6** 13.9** 10.4**

4.4. Correlations between the Urban-Rural Phenology Difference and LST Difference Our study reveals the change and trend of phenology in the Yangtze River Delta from the perspective of time and space, which can be attributed to the influence of urbanization. According to Spearman’s correlations (Table 3), along urban to rural gradients, phenology markers are correlated with LST (Figure 10). Positive LST anomalies were observed between urban and suburban buffer areas, and rural areas, and these anomalies were closely related to earlier SOS and later EOS. Via linear regression, we observed that ∆SOS and ∆LST in spring was negatively correlated (p < 0.01) and

Remote Sens. 2020, 12, x FOR PEER REVIEW 12 of 18

∆EOS and ∆LST in autumn were positively correlated (p < 0.05). A 1 ℃ increase in daytime LST led to a 4.3 day advance in SOS, and a 1 ℃ increase in nighttime LST led to a 12.1 day advance in SOS. In autumn, a positive correlation between ∆EOS and ∆LST was observed, which means the increase of LST leads to the delay of EOS. A 1 ℃ increase in daytime LST led to a 3.6 day delay of EOS, and a 1 ℃ increase in nighttime LST led to a 5.7 day delay of SOS.

Table 3. Spearman’s correlations between the phenology and land surface temperature (LST) differences across urban and buffer areas towards rural areas.

∆SOS ∆EOS Year ∆LSTday ∆LSTnight ∆LSTday ∆LSTnight 2001 –0.79* –0.83* 0.93** 0.99** 2002 –0.71* –0.68 0.98** 1.00** 2003 0.62 0.67 0.99** 0.99** 2004 –0.83* –1** 0.83* 1.00** Remote Sens. 20202005, 12 , 1783 –0.64 –0.55 0.98** 1.00** 12 of 18 2006 –0.98** –1.00** 0.85** 0.93** 2007 –0.93** –0.98** 1.00** 1.00** 4.4. Correlations between the Urban-Rural Phenology Difference and LST Difference 2008 0.54 0.52 0.79* 0.98** Our study2009 reveals the–0.98 change** and trend–0.92 of phenology** in the0.86 Yangtze** River Delta0.93** from the perspective2010 of time and space,–0.79 which* can be attributed–0.27 to the influence0.86 of** urbanization.0.86 According** to Spearman’s2011 correlations (Table–0.563), along urban to–0.80 rural* gradients, phenology0.83* markers0.85 are** correlated with LST (Figure2012 10). Positive–0.93 LST** anomalies–0.98 were** observed between0.79* urban and suburban0.81* buffer areas, and2013 rural areas, and these–0.91 anomalies** were–0.98 closely** related to earlier0.93** SOS and later0.93 EOS.** Via linear regression,2014 we observed that –0.99∆SOS** and ∆LST in–1.00 spring ** was negatively0.68 correlated (p < 0.01)0.69 and ∆EOS and ∆LST2015 in autumn were positively–0.99** correlated–1.00 (p **< 0.05). A 1 °C0.48increase in daytime0.48 LST led to a 4.3 day advance2016 in SOS, and–0.93 a** 1 °C increase–0.93 in nighttime** LST led0.1 to a 12.1 day advance0.1 in SOS. In autumn,2017 a positive correlation-0.98 between** ∆EOS–1.00 and ∆** LST was observed,0.67 which means0.69 the increase of LST leads to2018 the delay of EOS.–0.78 A 1 *°C increase in–0.50 daytime LST led to a0.67 3.6 day delay of EOS,0.67 and a 1 °C increaseNote: * Significant in nighttime at the LST 0.05 ledlevel. to ** a Significant 5.7 day delay at the of 0.01 SOS. level.

Figure 10. Linear regression relationships between the difference of phenology and the difference of LST at different time. (a) spring daytime LST difference; (b) spring nighttime LST difference; (c) autumn

daytime LST difference; (d) autumn nighttime LST difference). The difference in spring and autumn vegetation phenology calculated at different buffer distances from urban areas (y-axis) is linearly related to the difference of LST across the same urban to rural gradient (x-axis). Each point represents a different buffer distance; the point at the origin is the rural endpoint, the point furthest from the origin is the urban endpoint. Remote Sens. 2020, 12, 1783 13 of 18

Table 3. Spearman’s correlations between the phenology and land surface temperature (LST) diﬀerences across urban and buﬀer areas towards rural areas.

∆SOS ∆EOS Year ∆LSTday ∆LSTnight ∆LSTday ∆LSTnight 2001 0.79 * 0.83 * 0.93 ** 0.99 ** − − 2002 0.71 * 0.68 0.98 ** 1.00 ** − − 2003 0.62 0.67 0.99 ** 0.99 ** 2004 0.83 * 1 ** 0.83 * 1.00 ** − − 2005 0.64 0.55 0.98 ** 1.00 ** − − 2006 0.98 ** 1.00 ** 0.85 ** 0.93 ** − − 2007 0.93 ** 0.98 ** 1.00 ** 1.00 ** − − 2008 0.54 0.52 0.79 * 0.98 ** 2009 0.98 ** 0.92 ** 0.86 ** 0.93 ** − − 2010 0.79 * 0.27 0.86 ** 0.86 ** − − 2011 0.56 0.80 * 0.83 * 0.85 ** − − 2012 0.93 ** 0.98 ** 0.79 * 0.81 * − − 2013 0.91 ** 0.98 ** 0.93 ** 0.93 ** − − 2014 0.99 ** 1.00 ** 0.68 0.69 − − 2015 0.99 ** 1.00 ** 0.48 0.48 − − 2016 0.93 ** 0.93 ** 0.1 0.1 − − 2017 -0.98 ** 1.00 ** 0.67 0.69 − 2018 0.78 * 0.50 0.67 0.67 − − Note: * Signiﬁcant at the 0.05 level. ** Signiﬁcant at the 0.01 level.

5. Discussion

5.1. Temporal Profile of Vegetation Phenology The impacts of global warming and urbanization on vegetation phenology have been gradually recognized and accepted as a consensus. In our study area, advanced SOS, delayed EOS, and prolonged GSL were observed in urban, suburban, and rural areas, which was expected based on previous research [11,16,24,26,43,44]. However, our work uncovered some specific details about the sensitivity of phenology to climate change, urbanization, and the specific role of LST in each of the land cover types studied. Compared with suburban and rural areas, urban areas exhibited the weakest SOS trends over time, suggesting climate change has the least impact in areas that are already fully developed. The suburban areas have the greatest rate of change ( 0.85 days/year), reflecting the combined effects − of climate warming and increased urban development. In rural areas, where the effect of urbanization is minimized, we also found a strong effect of time on spring phenology ( 0.72 days/year) and − growing season length (1.36 days/year). Autumn phenology (EOS) changed most rapidly in suburban (0.52 days/year) and rural (0.64 days/year) areas, and least rapidly in urban areas ( 0.03 days/year). − Indeed, autumn phenology of urban areas appears to have been relatively stable over this period and the temporal trends in growing season length are driven entirely by trends in SOS in urban areas. Over time, there was a strong convergence in spring and autumn timing across the three land cover types. This could be due to either continued urbanization that makes land cover in suburban and rural landscapes function more like urban land cover over time, or a gradual weakening of the effect of climate change on phenology as summer and winter temperatures pass plant physiological thresholds [45].

5.2. Impacts of Urbanization on Vegetation Phenology While it is difficult to definitively separate the effects of urbanization and climate change on phenology in this rapidly developing region, temporal changes in the spatial pattern of phenology along the urban–rural gradient show that phenology changed the most in suburban areas closest Remote Sens. 2020, 12, 1783 14 of 18 to the urban core where the pressure of development is greatest (Figures6 and7). Our conceptual model is that the effect of urbanization will be weakest in urban and rural areas where the rate of development is slowest, and strongest in suburban areas that developed most rapidly. Conversely, the dominate driver of temporal change in urban and rural areas might be climate change, because these areas were either already built out (urban) or remained relatively distant from urban land cover (rural). The pattern of delayed SOS, earlier EOS, and shorter GSL with increasing distance from urban areas was sometimes close to linear, but there was generally some evidence of a curve linear response, the shape of which changed over time (Figure8). Early in the time series (2001), the SOS and EOS converged to the rural value 15 km from the urban edge. This resulted in a concave downward (SOS) or upward (EOS) pattern (Figure8, year 2001). However, over time, the curve-linear pattern changed, with SOS becoming concave downward and EOS becoming concave upward. The best explanation of this pattern is continued urbanization in the mid-range distances from the urban edge driving more rapid changes in phenology in these suburban areas. These spatio-temporal patterns are a window into the interacting effects of urbanization and climate change, and highlight the rapid changes experienced by vegetation in suburban areas over this period. There has been considerable work that uses remote sensing to describe the effect of the urban heat island on phenology, with most studies finding that vegetation phenology varies significantly along the urbanization gradient [11,16,19,24,26,28,44,46,47]. One study quantified the effect of urbanization as occurring within 6 km of the urban edge [24]. In another study, the effect of urbanization was measured from the urban center and detected out to 32 km [28]. However, remote sensing time series are made longer each year and many previous studies did not attempt to examine patterns over time (i.e., were limited to an urban–rural comparison). Leveraging the temporal dimension, we observed that in 2001 the effect of urbanization extended nearly 15 km from the urban edge. However, by 2018 the effect extended to 25 km from the urban edge, and these effects were seen most strongly in the EOS timing (Figures6 and7).

5.3. Phenology of Different Vegetation Types We found that vegetation type affects the impact of urban and suburban land cover on phenology, which is consistent with the previous findings. Similar to the effect of land cover on temporal trends (Figure7), the e ffect of vegetation type was strongest for EOS. While SOS timing exhibited the expected trend towards earlier springs with increasing urbanization for all vegetation types (Figure8), EOS was similar for urban and suburban areas in mixed forest, evergreen forest, shrubland, and grassland. Only cropland showed the expected earlier EOS in suburban areas relative to urban. This is consistent with the previous finding of White et al [13]. Croplands occupy a large proportion in many cities, and have been subject to urban expansion over the past several decades [17,48]. From this work, it is clear that remaining croplands in urban and suburban landscapes enjoy a longer growing season. These trends might be expected to lead to multiple cropping cycles, and associated changes in fertilization rates, crop types, and other practices. Responses of different types of vegetation to climate warming and urbanization have been noted in previous work. A study of the phenology of leaves and male cones of Chinese pine found that the rural–urban gradient drove spatial variation in phenology in the range of 0.36–1.92 (leaves) and 0.41–0.66 days/km (cones) [46]. Similar results were found in plant species in the Seoul Capital Area, specifically, that spring budburst sensitivity is greatest in Prumus mume, while the most dominant autumn leaf coloring sensitivity was observed in Acer palmatum [11]. Limited by the spatial resolution of MODIS data used here, it is difficult to separate the effect of warming and urbanization on different plant species in this study. However, the differences we did detect among vegetation types are consistent with past findings. Remote Sens. 2020, 12, 1783 15 of 18

5.4. Relations between LST and LSP Urbanization is expected to influence LST through the removal of vegetation and replacement with hardened surfaces with lower evaporative cooling and higher heat capacity. It has been found that the urban heat island intensity contributed greatly to the increase of urbanization effects on SOS [25] and UHI effects exist in many cities [24]. Therefore, we considered LST a critical variable to analyze in the context of phenology, similar to past work [24,49,50]. We focused our analysis on the SOS, EOS, and LST differences between urban, suburban, and rural areas (Figure 10). In the spring, we found a negative correlation between ∆SOS and ∆LST, and in the autumn, a positive correlation between ∆EOS and ∆LST. However, both ∆SOS and ∆EOS were least sensitive to ∆LSTday (compared with ∆LSTnight ), and the effect of ∆LSTday clearly was reduced close to urban areas. Only at a distance of greater than 5 km did ∆SOS become correlated with ∆LSTday, largely due to a lack of any substantial gradient in ∆LSTday over this distance from urban areas. In contrast, ∆SOS and ∆EOS were more sensitive to ∆LSTnight, and this sensitivity continued to the urban area. The dominance of the effect of ∆LSTnight on phenology is therefore pervasive in our results. This suggests that vegetation is more sensitive to nighttime temperature, which would be expected if hardened surfaces were keeping air temperatures warmer through the night. These findings were comparable to past results [11], in which it was found that in spring a 1 °C warming at night resulted in an earlier spring budburst of 2.76 days, and in the autumn, a 1 °C warming at night was related to a later autumn leaf coloring of 2.55 days. Prolonged GSL may lead to cooler surfaces on average due to the fact that vegetated surfaces are substantially cooler than non-vegetated surfaces, which would represent a negative feedback process on phenological changes related to climate warming. It was found that the quality and character of vegetation patches in Boston USA affect local heat island intensity [51]. However, at the scale of the MODIS pixel, the extent and temporal variation of the impact of prolonged GSL on the LST is difficult to characterize.

6. Conclusions The impacts of urbanization on vegetation phenology were observed over space and time in the rapidly developing region of the Yangtze delta. As urbanization progressed across the region, land surface temperatures increased. This change was greater in more rural areas, and changes in land surface temperatures (primarily nighttime temperatures) drove a change in spring and autumn phenology. The continued urbanization is enlarging the urban heat island, and making once rural areas more similar to urban areas in terms of their vegetation phenology. An earlier SOS, a later EOS, and a longer GSL are associated with the increase in LST along the urban–rural gradient direction. Different vegetation types demonstrated different phenology characteristics, but the differences between vegetation types was not as stark as the differences between urban, suburban, and rural areas. By comparing the urban–rural gradient over time, we attempted to separate the effects of urbanization from climate change. While this challenge proved difficult, we observed interesting patterns representing the spread of the UHI from urban areas. This increasing UHI effect drove greater changes in suburban and rural landscapes than in urban areas. There is still work to be done to understand how these observations might inform the management of urban landscapes, possibly through planting different types of vegetation, or adapting agricultural practices to the longer growing seasons experienced in urban areas. In general, higher spatial resolution data will be necessary for most of these applications.

Author Contributions: H.D., L.X., A.J.E. and Y.S. designed the research. L.X. performed the experiments and analyzed the data; H.D. and L.X. wrote the original manuscript; H.D., A.J.E. and L.X. revised the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research was financially supported by the National Natural Science Foundation of China (Grant No. 41571350, 41971298). Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2020, 12, 1783 16 of 18

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