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Remote Sensing for Natural Resources    2024, Vol. 36 Issue (2) : 218-228     DOI: 10.6046/zrzyyg.2023040
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Analysis of the spatio-temporal variations in vegetation phenology in Beijing based on MODIS time series data
YAO Jiahui(), DING Haiyong()
School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 211800, China
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Abstract  

Vegetation can indicate the changes in ecological environments. Analyzing the spatio-temporal variations and influencing factors of vegetation phenology holds critical significance for exploring the carbon, water, and energy balance of terrestrial ecosystems. In this study, the MOD13Q1 EVI dataset was employed to extract the start of season (SOS), the growing season length (GSL), and the end of season (EOS) for vegetation in Beijing from 2001 to 2020 using the double logistic (D-L) function fitting method and the dynamic threshold method. The spatio-temporal variations of vegetation phenology in urban and rural areas of Beijing were analyzed by constructing an urban-rural gradient zone. The response of vegetation phenological parameters to climate factors like temperature, precipitation, sunshine, and wind speed, as well as urban heat island intensity and urbanization, was investigated through regression and trend analyses. The results show that from 2001 to 2020, the vegetation phenology of Beijing manifested a trend of earlier SOS, extended GSL, and delayed EOS. Compared to grassland, woodland and shrubs manifested earlier SOS and later EOS, suggesting that the phenology of woody plants started earlier and ended later. As revealed by the relationship between climate factors and phenology, temperature, precipitation, sunshine, and wind speed all displayed certain effects on vegetation phenology in Beijing, with SOS and EOS being the most sensitive to sunshine and wind speed, respectively. The vegetation phenology was characterized by a significant gradient change along the urban-suburban-rural direction. Compared to the rural area, the urban area showed SOS 12.2 d earlier and EOS 18.9 d later on average. The urban nighttime heat island intensity was significantly correlated with the SOS of vegetation in the urban-rural gradient zone (p<0.01). Moreover, the SOS, GSL, and EOS were significantly linearly correlated with population density, urban built-up area, and GDP per square kilometer of land (p<0.01). Therefore, urbanization played a significant role in advancing SOS, extending GSL, and delaying EOS of vegetation phenology in Beijing.
Keywords vegetation phenology      climate change      urbanization      urban heat island effect     
ZTFLH:  TP79  
Issue Date: 14 June 2024
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Jiahui YAO
Haiyong DING
Cite this article:   
Jiahui YAO,Haiyong DING. Analysis of the spatio-temporal variations in vegetation phenology in Beijing based on MODIS time series data[J]. Remote Sensing for Natural Resources, 2024, 36(2): 218-228.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023040     OR     https://www.gtzyyg.com/EN/Y2024/V36/I2/218
Fig.1  Land cover type map of Beijing in 2020
Fig.2  Urban area and buffer zones of Beijing in 2000
数据集 数据来源 数据属性 数据类型 时间范围
MOD13Q1 美国国家航空航天局 时间分辨率为16 d,空间分辨率为250 m EVI数据 2001—2020年
MOD11A2 美国国家航空航天局 时间分辨率为8 d,空间分辨率为1 000 m 白天地表温度和夜晚地表温度 2001—2020年
MCD12Q2 美国国家航空航天局 空间分辨率为500 m 植被生长季开始期和结束期 2001—2019年
气候数据 中国地面气候资料日值数据集V3.0 中国地面699个国家级基准、基本站 气温、降水、日照、风速 2001—2020年
土地利用覆盖数据 中国逐年土地覆盖数据集CLCD 空间分辨率为30 m 耕地、草地、灌木林、林地 2001—2020年
城市建成区数据 全球城市边界(Global Urban Boundary-GUB)数据集 空间分辨率为30 m ———— 2000年
城市统计数据 中国城市建设统计年鉴 ———— 人口密度、城市区建成区面积、地均GDP 2001—2020年
Tab.1  Data sources and descriptions
Fig.3  Phenological parameter validation
Fig.4  Spatial distribution of the mean values of vegetation phenology in Beijing from 2001 to 2020
Fig.5  Interannual variation of vegetation phenology in Beijing from 2001 to 2020
Fig.6  Spatial variation trend and significant area of vegetation phenology in Beijing from 2001 to 2020
Fig.7  Interannual variation of phenology in different vegetation cover types in Beijing from 2001 to 2020
Fig.8  Interannual variation of vegetation phenology in different buffer zones in Beijing from 2001 to 2020
Fig.9  Variation of land surface temperature along the urban-rural gradient
Fig.10  Relationship between vegetation phenology differences and UHII differences in each region on urban-rural gradient zones
UHII 城区 郊区 农村 年均
[0,5) km [5,10) km [10,15) km [15,20) km
SOS -0.508*① -0.389 -0.307 -0.254 -0.228 -0.257 -0.324
GSL 0.427 0.356 0.140 0.021 -0.045 -0.040 0.123
EOS 0.314 0.250 -0.013 -0.159 -0.239 -0.277 -0.053
Tab.2  Correlation between daytime UHII and vegetation phenology in each region
UHII 城区 郊区 农村 年均
[0,5) km [5,10) km [10,15) km [15,20) km
SOS -0.675**① -0.679** -0.626** -0.626** -0.593** -0.573** -0.630**
GSL 0.643** 0.604** 0.499* 0.487* 0.473* 0.403 0.490*
EOS 0.494* 0.417 0.253 0.246 0.229 0.154 0.262
Tab.3  Correlation between night UHII and vegetation phenology in each region
Fig.11  Relationship between vegetation phenology and urbanization factors in Beijing
植被物
候参数		 缓冲区 人口密度 城市建成区面积 地均GDP
SOS 城区 -0.634**① -0.639** -0.624**
郊区 -0.650** -0.587** -0.654**
农村 -0.624** -0.584** -0.627**
GSL 城区 0.696** 0.738** 0.673**
郊区 0.852** 0.786** 0.855**
农村 0.765** 0.728** 0.748**
EOS 城区 0.673** 0.685** 0.665**
郊区 0.784** 0.751** 0.788**
农村 0.765** 0.728** 0.748**
Tab.4  Correlation between urbanization factors and vegetation phenology in urban, suburban and rural areas
气候因子 城区 郊区 农村
[0,
5) km		 [5,
10) km		 [10,
15) km		 [15,
20] km
最高气温 -0.448*① -0.375 -0.367 -0.340 -0.357 -0.377
最低气温 -0.256 -0.137 -0.116 -0.166 -0.150 -0.177
降水 -0.291 -0.374 -0.257 -0.315 -0.361 -0.389
日照 -0.372 -0.451* -0.512* -0.475* -0.456* -0.426
风速 -0.168 -0.261 -0.323 -0.246 -0.161 -0.179
Tab.5  Correlation between climate factors and SOS in different buffer zones
气候因子 城区 郊区 农村
[0,
5) km		 [5,
10) km		 [10,
15) km		 [15,
20] km
最高气温 0.397 0.403 0.418 0.452*① 0.461* 0.459*
最低气温 0.166 0.134 0.171 0.176 0.201 0.220
降水 0.494* 0.641** 0.593** 0.533* 0.516* 0.527*
日照 -0.0019 -0.033 0.037 0.054 0.029 0.070
风速 0.690** 0.833** 0.822** 0.782** 0.754** 0.768**
Tab.6  Correlation between climate factors and EOS in different buffer zones
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