| 83 | 0 | 45 |
| 下载次数 | 被引频次 | 阅读次数 |
全球气候变化情景下干旱事件频率和强度持续增加，识别和理解干旱胁迫下植被光合过程的突变是生态监测与气候适应管理的关键，当前对植被光合变化点的识别方法多样，但缺乏对不同方法性能差异及其干旱响应解析能力的系统评估。该文聚焦中国亚热带地区，基于太阳诱导叶绿素荧光(SIF)数据、标准化降水蒸散指数(SPEI)数据，综合运用BFAST(Breaks For Additive Season and Trend)、DBEST(Detecting Breakpoints and Estimating Segments in Trend)、PELT(Pruned Exact Linear Time)和Pettitt 4种变化点检测方法，从多维度协同解析区域植被光合动态的变化点特性，并刻画SIF与干旱指数SPEI的时空关系。结果表明：(1)2003—2022年中国亚热带地区SIF整体呈上升态势，区域植被光合作用总体增强。(2)不同方法在变化点识别上各具优势：BFAST适用于识别长期趋势性变化(单调递增类占比达82.25%);DBEST能捕捉云贵高原等复杂地形的渐进式变化；PELT对多断点和频繁扰动敏感；Pettitt主要识别突发性变化引起的结构性转折。(3)SIF与SPEI相关关系呈现显著空间异质性，四川盆地、闽粤沿海等水热条件较优区域以正相关为主，滇南及藏东则以负相关为主。(4)多方法协同分析显示，变化点后SIF与SPEI相关性整体增幅达112.07%,而PELT方法增幅仅为62.94%,反映该方法易受噪声干扰；BFAST与PELT的变化点解析具有互补性，二者组合可有效提升变化点识别的空间完整性与结果可靠性。研究结果可为亚热带地区生态系统韧性评估与气候适应性管理提供理论支撑。
Abstract：Under global climate change, the frequency and intensity of drought events continue to increase, making the identification and understanding of abrupt changes in vegetation photosynthetic processes under drought stress crucial for ecological monitoring and climate adaptation management.However, although a variety of methods have been developed to identify change points in vegetation photosynthesis, a systematic evaluation of the performance differences among these methods and their ability to characterize drought-related responses is still lacking.Focusing on subtropical regions of China, this study utilized solar-induced chlorophyll fluorescence(SIF) data and the standardized precipitation evapotranspiration index(SPEI) data, and comprehensively applied four change point detection methods, namely BFAST(Breaks For Additive Season and Trend),DBEST(Detecting Breakpoints and Estimating Segments in Trend),PELT(Pruned Exact Linear Time),and the Pettitt test, to synergistically analyze the characteristics of change points in regional vegetation photosynthetic dynamics from multiple dimensions and to characterize the spatiotemporal relationship between SIF and SPEI.It is found as follows.(1) SIF in subtropical China exhibited a upward trend from 2003 to 2022,reflecting an overall enhancement of regional vegetation photosynthetic activity.(2) Each detection method demonstrated specific advantages in detecting change points: BFAST performed well in capturing long-term trend variations, with monotonic increases accounting for 82.25% of its detected change types; DBEST showed strong capability in identifying gradual changes in complex terrains such as the Yunnan-Guizhou Plateau; PELT was highly sensitive to multiple breakpoints and frequent disturbances; and the Pettitt test primarily identified structural shifts caused by abrupt changes.(3) The correlation between SIF and SPEI displayed significant spatial heterogeneity, with predominantly positive correlations in hydrothermally favorable regions such as the Sichuan Basin and the Fujian-Guangdong coastal areas, and mainly negative correlations in southern Yunnan and eastern Xizang.(4) Integrated multi-method analysis further revealed that the overall correlation between SIF and SPEI increased by 112.07% after detected change points, whereas the increase associated with PELT-derived change points was only 62.94%,indicating its greater susceptibility to noise.The complementary characteristics of BFAST and PELT improved the spatial completeness and robustness of change-point identification when jointly applied.Overall, the study provides theoretical support for assessing ecosystem resilience and climate adaptive management in subtropical regions of China.
[1] WU H W,CUI H L,FU C X,et al.Unveiling the crucial role of soil microorganisms in carbon cycling:a review[J].Science of the Total Environment,2024,909:168627.DOI:10.1016/j.scitotenv.2023.168627.
[2] 郑振灿，庄留文，缪国芳，等.中国亚热带地区2000—2019年森林海拔分布特征及其时空动态[J].中国科学(地球科学),2024,54(8):2604-2624.
[3] MUKARRAM M,CHOUDHARY S,KURJAK D,et al.Drought:sensing,signalling,effects and tolerance in higher plants[J].Physiologia Plantarum,2021,172(2):1291-1300.
[4] 牛继强，王子羽，林昊，等.淮河生态经济带NPP时空演变特征及驱动因素分析[J].地理与地理信息科学，2024,40(3):37-43.
[5] DONG X J,ZHOU Y K,LIANG J Z,et al.Assessment of spatiotemporal patterns and the effect of the relationship between meteorological drought and vegetation dynamics in the Yangtze River Basin based on remotely sensed data[J].Remote Sensing,2023,15(14):3641.DOI:10.3390/rs15143641.
[6] ZHOU Z Q,DING Y B,FU Q,et al.Comprehensive evaluation of vegetation responses to meteorological drought from both linear and nonlinear perspectives[J].Frontiers in Earth Science,2022,10:953805.DOI:10.3389/feart.2022.953805.
[7] MAGNEY T S,BARNES M L,YANG X.On the covariation of chlorophyll fluorescence and photosynthesis across scales[J].Geophysical Research Letters,2020,47(23):e2020GL091098.DOI:10.1029/2020GL091098.
[8] MA J N,ZHANG C,GUO H,et al.Analyzing ecological vulnerability and vegetation phenology response using NDVI time series data and the BFAST algorithm[J].Remote Sensing,2020,12(20):3371.DOI:10.3390/rs12203371.
[9] JAMALI S,JÖNSSON P,EKLUNDH L,et al.Detecting changes in vegetation trends using time series segmentation[J].Remote Sensing of Environment,2015,156:182-195.
[10] XULU S,PHUNGULA P T,MBATHA N,et al.Multi-year mapping of disturbance and reclamation patterns over Tronox′s Hillendale Mine,South Africa with DBEST and Google Earth Engine[J].Land,2021,10(7):760.DOI:10.3390/land10070760.
[11] PETTITT A N.A non-parametric approach to the change-point problem[J].Journal of the Royal Statistical Society.Series C (Applied Statistics),1979,28(2):126-135.
[12] MILITINO A F,MORADI M,UGARTE M D.On the performances of trend and change-point detection methods for remote sensing data[J].Remote Sensing,2020,12(6):1008.DOI:10.3390/rs12061008.
[13] HOW P,MESSERLI A,MÄTZLER E,et al.Greenland-wide inventory of ice marginal lakes using a multi-method approach[J].Scientific Reports,2021,11(1):4481.DOI:10.1038/s41598-021-83509-1.
[14] XIE P,GU H T,SANG Y F,et al.Comparison of different methods for detecting change points in hydroclimatic time series[J].Journal of Hydrology,2019,577:123973.DOI:10.1016/j.jhydrol.2019.123973.
[15] 张治梅，雷馨雨，吴立新.基于优化温度植被干旱指数的亚热带干旱遥感分析：以鄂渝地区为例[J].地理与地理信息科学，2025,41(3):27-34.
[16] SEO M,KIM H C.Arctic greening trends:change points in satellite-derived normalized difference vegetation indexes and their correlation with climate variables over the last two decades[J].Remote Sensing,2024,16(7):1160.DOI:10.3390/rs16071160.
[17] CHEN S Y,QIU R N,CHEN Y M,et al.Impacts of drought and heatwave on the vegetation and ecosystem in the Yangtze River Basin in 2022[J].Remote Sensing,2024,16(16):2889.DOI:10.3390/rs16162889.
[18] LIANG J Z,HAN X Y,ZHOU Y K,et al.Investigating the temporal lag and accumulation effect of climatic factors on vegetation photosynthetic activity over subtropical China[J].Ecological Indicators,2024,166:112406.DOI:10.1016/j.ecolind.2024.112406.
[19] LIU H,LI X J,MAO F J,et al.Spatiotemporal evolution of fractional vegetation cover and its response to climate change based on MODIS data in the subtropical region of China[J].Remote Sensing,2021,13(5):913.DOI:10.3390/rs13050913.
[20] 罗爽，刘会玉，龚海波.1982-2018年中国植被覆盖变化非线性趋势及其格局分析[J].生态学报，2022,42(20):8331-8342.
[21] LI X,XIAO J F.A global,0.05-degree product of solar-induced chlorophyll fluorescence derived from OCO-2,MODIS,and reanalysis data[J].Remote Sensing,2019,11(5):517.DOI:10.3390/rs11050517.
[22] ZHOU Z Q,LIU S N,DING Y B,et al.Assessing the responses of vegetation to meteorological drought and its influencing factors with partial wavelet coherence analysis[J].Journal of Environmental Management,2022,311:114879.DOI:10.1016/j.jenvman.2022.114879.
[23] VICENTE-SERRANO S M,BEGUERÍA S,LÓPEZ-MORENO J I.A multiscalar drought index sensitive to global warming:the standardized precipitation evapotranspiration index[J].Journal of Climate,2010,23(7):1696-1718.
[24] YEVJEVICH V.An objective approach to definitions and investigations of continental hydrologic droughts[R].Fort Collins:Colorado State University,1967:1-89.
[25] LIU H,JIANG L L,LIU B,et al.Characteristics of drought in China and its effect on vegetation change in recent 40 years[J].Acta Ecologica Sinica,2023,43(19):7936-7949.
[26] LIU X B,YU S Y,YANG Z W,et al.The first global multi-timescale daily SPEI dataset from 1982 to 2021[J].Scientific Data,2024,11(1):223.DOI:10.1038/s41597-024-03047-z.
[27] VERBESSELT J,HYNDMAN R,NEWNHAM G,et al.Detecting trend and seasonal changes in satellite image time series[J].Remote Sensing of Environment,2010,114(1):106-115.
[28] YAN J N,HE H X,WANG L Z,et al.Inter-comparison of four models for detecting forest fire disturbance from MOD13A2 time series[J].Remote Sensing,2022,14(6):1446.DOI:10.3390/rs14061446.
[29] ERSI C,BAYAER T,BAO Y H,et al.Temporal and spatial changes in evapotranspiration and its potential driving factors in Mongolia over the past 20 years[J].Remote Sensing,2022,14(8):1856.DOI:10.3390/rs14081856.
[30] LI J,LI Z L,WU H,et al.Trend,seasonality,and abrupt change detection method for land surface temperature time-series analysis:evaluation and improvement[J].Remote Sensing of Environment,2022,280:113222.DOI:10.1016/j.rse.2022.113222.
[31] KILLICK R,ECKLEY I A.Changepoint:an R package for changepoint analysis[J].Journal of Statistical Software,2014,58(3):1-19.
[32] HINKLEY D V.Inference about the change-point in a sequence of random variables[J].Biometrika,1970,57(1):1-17.
[33] 王若茹，李小马，甘德欣，等.湖南省2002—2020年植被动态演变特征及影响因子[J].应用生态学报，2024,35(5):1312-1320.
[34] XU Y L,YANG G Z,ZHANG Y C,et al.Mapping and interpreting spatio-temporal trends in vegetation restoration following mining disturbances in large-scale surface coal mining areas[J].Frontiers in Environmental Science,2025,13:1531424.DOI:10.3389/fenvs.2025.1531424.
[35] ROCHA R V,DE SOUZA FILHO F A.Mapping abrupt streamflow shift in an abrupt climate shift through multiple change point methodologies:Brazil case study[J].Hydrological Sciences Journal,2020,65(16):2783-2796.
[36] LI J,XI M F,PAN Z W,et al.Response of NDVI and SIF to meteorological drought in the Yellow River Basin from 2001 to 2020[J].Water,2022,14(19):2978.DOI:10.3390/w14192978.
[37] WU H,ZHOU P P,SONG X Y,et al.Dynamics of solar-induced chlorophyll fluorescence (SIF) and its response to meteorological drought in the Yellow River Basin[J].Journal of Environmental Management,2024,360:121023.DOI:10.1016/j.jenvman.2024.121023.
[38] 陆梦恬.长江流域干旱和热浪对植被绿度和生产力的影响研究[D].杭州：浙江大学，2022.
[39] 王荣，王亚琴，闫浩文.六盘山山地植被物候地带性分异及其对气候变化的响应[J].地理与地理信息科学，2021,37(4):90-98.
[40] 王媛媛，罗维均，张小琴，等.西南喀斯特地区典型农田生态系统碳通量特征：以普定陈旗小流域为例[J].生态学报，2025,45(19):9529-9541.
[41] MARTINI D,SAKOWSKA K,WOHLFAHRT G,et al.Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production[J].New Phytologist,2022,233(6):2415-2428.
基本信息:
中图分类号:Q948;P208;P426.616
引用信息:
[1]蒋佳敏,梁娟珠,周玉科.中国亚热带植被光合变化点多方法检测及其与干旱的时空关联[J].地理与地理信息科学,2026,42(01):25-35.
基金信息:
国家重点研发计划项目(2021xjkk0303)