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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (2) : 185-193     DOI: 10.6046/zrzyyg.2023378
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Analysis of the changes in spring phenology of vegetation in Beijing City from 2000 to 2022
XIE Yijia1(), YANG Beibei2,3, ZHANG Zhen1, CHEN Jia1, WANG Zhe1, MENG Lingkui1()
1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
2. Changjiang Spatial Information Technology Engineering Co., Ltd. (Wuhan), Wuhan 430010, China
3. Hubei Water Conservancy Information Perception and Big Data Engineering Technology Research Center, Wuhan 430010, China
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Abstract  

Investigating spring phenology is critical for understanding the growth and development cycles of vegetation and the response mechanisms to climate and environmental changes. It also provides significant insights for guiding agricultural production and protecting and restoring ecosystems. This study reconstructed the time series of MOD13Q1 data for Beijing City from 2000 to 2022. Based on dynamic thresholding, this study extracted the spring phenology of vegetation in Beijing City over the past 23 years. Furthermore, this study analyzed the spatiotemporal changes in spring phenology in Beijing City using the Mann-Kendall (M-K) trend test. Finally, this study examined the differential responses of spring phenology to climate change through partial correlation analysis. The results of this study indicate that the average spring phenology of vegetation in Beijing City occurred on the 117th day of a year (in late April), advancing at an average rate of approximately 1.14 days per year over the past 23 years. Different duo exhibited distinct hierarchical variations in spring phenology. Forests showed the earliest spring phenology starting from the 107th day, followed by shrubs (the 117th day) and grasslands (the 119th day), with the latest being farmland (the 130th day). The impacts of average annual temperature on spring phenology exhibited significant spatial variations. A positive correlation was observed in water-rich areas such as rivers and reservoirs, whereas a significant negative correlation occurred in eastern Fangshan District. On a monthly scale, temperatures in November, December, January, and February significantly influenced spring phenology. As winter temperatures rose, the spring phenology of vegetation tended to advance. This study explores the response mechanisms of spring phenology of vegetation in Beijing City to temperature and precipitation, providing valuable insights for vegetation management under climate change.
Keywords remote sensing vegetation phenology      NDVI      time series reconstruction     
ZTFLH:  TP79  
Issue Date: 09 May 2025
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Yijia XIE
Beibei YANG
Zhen ZHANG
Jia CHEN
Zhe WANG
Lingkui MENG
Cite this article:   
Yijia XIE,Beibei YANG,Zhen ZHANG, et al. Analysis of the changes in spring phenology of vegetation in Beijing City from 2000 to 2022[J]. Remote Sensing for Natural Resources, 2025, 37(2): 185-193.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023378     OR     https://www.gtzyyg.com/EN/Y2025/V37/I2/185
Fig.1  Technology roadmap
Fig.2  Interannual variation of spring phenology by remote sensing
Fig.3  Interannual variation of spring phenology in four vegetation types
Fig.4  Analysis of interannual variation trend of spring phenology in Beijing
Fig.5  Spatial distribution of perennial mean value of spring phenology in Beijing
Fig.6  Partial correlation analysis between spring phenology and meteorological factors
月份 气温 降水
11月 -0.231 0.047*
12月 -0.664** -0.033
1月 -0.208 0.096
2月 -0.234 -0.265
3月 -0.248 -0.039
4月 -0.351 -0.195
Tab.1  Correlation analysis between temperature, precipitation from November to April and spring phenology
月份 气温 降水
4月 -0.351 -0.195
3—4月 -0.374 -0.135
2—4月 -0.498* -0.226
1—4月 -0.606** -0.140
上一年12月—4月 -0.562** -0.117
上一年11月—4月 -0.512* -0.098
Tab.2  Monthly correlation between spring phenology and cumulative precipitation of temperature and precipitation
[1] 陆佩玲, 于强, 贺庆棠. 植物物候对气候变化的响应[J]. 生态学报, 2006, 26(3):923-929.
[1] Lu P L, Yu Q, He Q T. Responses of plant phenology to climatic change[J]. Acta Ecologica Sinica, 2006, 26(3):923-929.
[2] 付永硕, 张晶, 吴兆飞, 等. 中国植被物候研究进展及展望[J]. 北京师范大学学报(自然科学版), 2022, 58(3):424-433.
[2] Fu Y S, Zhang J, Wu Z F, et al. Vegetation phenology response to climate change in China[J]. Journal of Beijing Normal University (Natural Science), 2022, 58(3):424-433.
[3] Li S, Xu L, Jing Y, et al. High-quality vegetation index product generation:A review of NDVI time series reconstruction techniques[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 105:102640.
[4] Viovy N, Arino O, Belward A S. The best index slope extraction (BISE):A method for reducing noise in NDVI time-series[J]. International Journal of Remote Sensing, 1992, 13(8):1585-1590.
[5] Chen J, Jönsson P, Tamura M, et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J]. Remote Sensing of Environment, 2004, 91(3/4):332-344.
[6] Roerink G J, Menenti M, Verhoef W. Reconstructing cloudfree NDVI composites using Fourier analysis of time series[J]. International Journal of Remote Sensing, 2000, 21(9):1911-1917.
[7] 李军, 朱慧. 重庆地区MODIS/NDVI时间序列数据重建研究[J]. 地理科学, 2017, 37(3):437-444.
doi: 10.13249/j.cnki.sgs.2017.03.014
[7] Li J, Zhu H. The reconstruction of MODIS/NDVI time series data in Chongqing[J]. Scientia Geographica Sinica, 2017, 37(3):437-444.
doi: 10.13249/j.cnki.sgs.2017.03.014
[8] 王敏钰, 罗毅, 张正阳, 等. 植被物候参数遥感提取与验证方法研究进展[J]. 遥感学报, 2022, 26(3):431-455.
[8] Wang M Y, Luo Y, Zhang Z Y, et al. Recent advances in remote sensing of vegetation phenology:Retrieval algorithm and validation strategy[J]. National Remote Sensing Bulletin, 2022, 26(3):431-455.
[9] Zeng L, Wardlow B D, Xiang D, et al. A review of vegetation phenological metrics extraction using time-series,multispectral satellite data[J]. Remote Sensing of Environment, 2020, 237:111511.
[10] 范德芹, 赵学胜, 朱文泉, 等. 植物物候遥感监测精度影响因素研究综述[J]. 地理科学进展, 2016, 35(3):304-319.
doi: 10.18306/dlkxjz.2016.03.005
[10] Fan D Q, Zhao X S, Zhu W Q, et al. Review of influencing factors of accuracy of plant phenology monitoring based on remote sensing data[J]. Progress in Geography, 2016, 35(3):304-319.
doi: 10.18306/dlkxjz.2016.03.005
[11] Berra E F, Gaulton R. Remote sensing of temperate and boreal fo-rest phenology:A review of progress,challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics[J]. Forest Ecology and Management, 2021, 480:118663.
[12] Han H, Bai J, Ma G, et al. Vegetation phenological changes in multiple landforms and responses to climate change[J]. ISPRS International Journal of Geo-Information, 2020, 9(2):111.
[13] 谭静, 陈正洪, 肖玫. 武汉大学樱花花期长度特征及预报方法[J]. 生态学报, 2021, 41(1):38-47.
[13] Tan J, Chen Z H, Xiao M. Characteristics and forecast of flowering duration of cherry blossoms in Wuhan University[J]. Acta Ecologica Sinica, 2021, 41(1):38-47.
[14] Zhang R, Qi J, Leng S, et al. Long-term vegetation phenology changes and responses to preseason temperature and precipitation in northern China[J]. Remote Sensing, 2022, 14(6):1396.
[15] 赵心睿, 刘冀, 杨少康, 等. 北方地区典型林草地物候时空变化特征及其对气象因子的响应[J]. 生态学报, 2023, 43(9):3744-3755.
[15] Zhao X R, Liu J, Yang S K, et al. Spatio-temporal variations of typical woodland and grassland phenology and its response to meteorological factors in Northern China[J]. Acta Ecologica Sinica, 2023, 43(9):3744-3755.
[16] 张港栋, 包刚, 元志辉, 等. 2001—2020年蒙古高原昼夜非对称变暖对植被返青期的影响[J]. 干旱区地理, 2023, 46(5):700-710.
doi: 10.12118/j.issn.1000-6060.2022.395
[16] Zhang G D, Bao G, Yuan Z H, et al. Effects of asymmetric warming of daytime and nighttime on the start of growing season on the Mongolian Plateau from 2001 to 2020[J]. Arid Land Geography, 2023, 46(5):700-710.
doi: 10.12118/j.issn.1000-6060.2022.395
[17] Piao S, Liu Q, Chen A, et al. Plant phenology and global climate change:Current progresses and challenges[J]. Global Change Bio-logy, 2019, 25(6):1922-1940.
[18] 梁晨, 安菁, 范雅倩, 等. 北京松山国家级自然保护区典型植被类型表层土壤碳密度及周转速率特征[J]. 地球与环境, 2020, 48(6):672-679.
[18] Liang C, An J, Fan Y Q, et al. Surface soil carbon density and turnover rate of typical vegetation types in Beijing Songshan national nature reserve[J]. Earth and Environment, 2020, 48(6):672-679.
[19] Yang J, Huang X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019[J]. Earth System Science Data, 2021, 13(8):3907-3925.
doi: 10.5194/essd-13-3907-2021
[20] Vorobiova N, Chernov A. Curve fitting of MODIS NDVI time series in the task of early crops identification by satellite images[J]. Procedia Engineering, 2017, 201:184-195.
[21] Jonsson P, Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(8):1824-1832.
[22] 李儒, 张霞, 刘波, 等. 遥感时间序列数据滤波重建算法发展综述[J]. 遥感学报, 2009, 13(2):335-341.
[22] Li R, Zhang X, Liu B, et al. Review on methods of remote sensing time-series data reconstruction[J]. National Remote Sensing Bulletin, 2009, 13(2):335-341.
[23] 王刚, 严登华, 黄站峰, 等. 近52年来滦河流域气候变化趋势分析[J]. 干旱区资源与环境, 2011, 25(7):134-139.
[23] Wang G, Yan D H, Huang Z F, et al. Climatic change characteristics in the latest 52 years in Luan River Basin[J]. Journal of Arid Land Resources and Environment, 2011, 25(7):134-139.
[24] 姜萍, 潘新民, 曾雪莹. 中国不同农业区气温和降水时空演变格局分析[J]. 水土保持研究, 2020, 27(4):270-278.
[24] Jiang P, Pan X M, Zeng X Y. Temporal and spatial variation of temperature and precipitation in each agricultural sub-region of China[J]. Research of Soil and Water Conservation, 2020, 27(4):270-278.
[25] 高弋斌, 路春燕, 钟连秀, 等. 1951—2016年中国沿海地区气温与降水量的时空特征[J]. 森林与环境学报, 2019, 39(5):530-539.
[25] Gao Y B, Lu C Y, Zhong L X, et al. Temporal and spatial characteristics of temperature and precipitation in China’s coastal areas from 1951 to 2016[J]. Journal of Forest and Environment, 2019, 39(5):530-539.
[26] 陈洪滨, 范学花. 2006年极端天气和气候事件及其他相关事件的概要回顾[J]. 气候与环境研究, 2007, 12(1):100-112.
[26] Chen H B, Fan X H. Some extreme events of weather,climate and related phenomena in 2006[J]. Climatic and Environmental Research, 2007, 12(1):100-112.
[27] Xu J, Tang Y, Xu J, et al. Evaluation of vegetation indexes and green-up date extraction methods on the Tibetan Plateau[J]. Remote Sensing, 2022, 14(13):3160.
[28] 徐韵佳, 葛全胜, 戴君虎, 等. 近50年中国典型木本植物展叶始期温度敏感度变化及原因[J]. 生态学报, 2019, 39(21):8135-8143.
[28] Xu Y J, Ge Q S, Dai J H, et al. Variations in temperature sensitivity of leaf unfolding date and their influencing factors for typical woody plants in China over the past 50 years[J]. Acta Ecologica Sinica, 2019, 39(21):8135-8143.
[29] Yuan M, Wang L, Lin A, et al. Vegetation green up under the influence of daily minimum temperature and urbanization in the Yellow River Basin,China[J]. Ecological Indicators, 2020, 108:105760.
[30] Jiang N, Shen M, Chen J, et al. Continuous advance in the onset of vegetation green-up in the Northern Hemisphere,during hiatuses in spring warming[J]. NPJ Climate and Atmospheric Science, 2023, 6(1):7.
[31] 卜亚勤, 丁海勇. 北京植被物候时空变化及其对城市化的响应[J]. 遥感信息, 2022, 37(2):112-118.
[31] Bu Y Q, Ding H Y. Spatiotemporal variation of vegetation phenology and its response to urbanization in Beijing[J]. Remote Sensing Information, 2022, 37(2):112-118.
[32] 孟丹, 刘芯蕊, 张聪聪. 北京市植物物候对热岛效应的响应[J]. 生态学杂志, 2021, 40(3):844-854.
[32] Meng D, Liu X R, Zhang C C. Responses of plant phenology to urban heat island effects in Beijing[J]. Chinese Journal of Ecology, 2021, 40(3):844-854.
doi: 10.13292/j.1000-4890.202103.029
[33] Kloos S, Yuan Y, Castelli M, et al. Agricultural drought detection with MODIS based vegetation health indices in southeast Germany[J]. Remote Sensing, 2021, 13(19):3907.
[34] 张港栋, 包刚, 黄晓君, 等. 蒙古国冬春季气候非对称变暖及其对植被返青期和春季NDVI的影响[J]. 干旱区地理, 2023, 46(8):1238-1249.
doi: 10.12118/j.issn.1000-6060.2022.686
[34] Zhang G D, Bao G, Huang X J, et al. Asymmetrical warming in winter and spring and its effect on start of growing season and spring NDVI in Mongolia[J]. Arid Land Geography, 2023, 46(8):1238-1249.
doi: 10.12118/j.issn.1000-6060.2022.686
[35] Shen X, Jiang M, Lu X. Diverse impacts of day and night temperature on spring phenology in freshwater marshes of the Tibetan Pla-teau[J]. Limnology and Oceanography Letters, 2023, 8(2):323-329.
[36] Wang H, Wu C, Ciais P, et al. Overestimation of the effect of climatic warming on spring phenology due to misrepresentation of chilling[J]. Nature Communications, 2020, 11(1):4945.
doi: 10.1038/s41467-020-18743-8 pmid: 33009378
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