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遥感技术与应用  2016, Vol. 31 Issue (5): 966-974    DOI: 10.11873/j.issn.1004-0323.2016.5.0966
遥感应用     
基于数字相机的草地物候模拟及其与气象因子关系的研究
周惠慧1,2,付东杰3,张立福1,王文生1,2,岑奕1,王晋年1
(1.中国科学院遥感与数字地球研究所遥感科学国家重点实验室,北京 100101;
2.中国科学院大学,北京 100049;
3.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101)
Modeling Grassland Phenology and Analyzing Relationship with Corresponding Meteorological Factors based on Digital Camera
Zhou Huihui1,2,Fu Dongjie3,Zhang Lifu1,Wang Wensheng1,2,Cen Yi1,Wang Jinnian1
(1.Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,
Chinese Academy of Sciences,Beijing 100101,China;
2.University of Chinese Academy of Sciences,Beijing 100049,China;
3.State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences
and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China)
 全文: PDF(3954 KB)  
摘要:

研究植被物候及其与气候之间的关系对于理解全球生态环境变化意义重大。近地面数字相机凭借其监测频率高、数据质量好等优势已成为一种有效的监测植被物候的遥感平台。以北美地区瓦瑞(Vaira Ranch)牧场为例,对研究区植被的春季生长情况进行监测,利用近地表数字相机获取的影像计算绿度相对亮度(Greenness Chromaticity Coordinates,Gcc)并构成时间序列,模拟植被春季物候,将所得植被物候信息分别与地面同步实测的总初级生产力(Gross Primary Production,GPP)以及气象数据进行对比分析。结果表明:研究区植被春季生长季开始于第20 d,结束于第145 d,Gcc与GPP的总体相关性为0.88,二者提取的7项物候指标平均相对差异为0.05;降雨、土壤湿度、空气温度、土壤温度、太阳辐射通量对植被生长存在影响:空气温度、土壤温度、太阳辐射通量三者整体对于Gcc变化的解释力为91.3%,其中,气温和土温的单因子解释力分别为30.9%和49.0%,此外,由于水分缺乏,降水成为制约研究区植被生长的重要因素。

关键词: 草地物候数字相机Gcc时间序列气象因子    
Abstract:

Studying vegetation phenology and the relationship between climate and phenology is of vital importance in understanding the change of global ecological environment.Near-surface digital camera has become an effective detection method in vegetation phenology due to its frequent and accurate data\|acquirement capability.Taking Vaira Ranch in North America for example,this study used digital camera based time series of Greenness Chromaticity Coordinates (Gcc) to monitor the growing condition in spring and to model its phonology.The phonological metrics were extracted from Gcc,after which GPP and meteorological data were used to compare and analyze with the extracted metrics.The results showed that vegetation within research area grew from the 20th to 145 th day of year,and the correlation index between Gcc and GPP was 0.88,the relative mean deviation of phonological features was 0.05.Meanwhile,precipitation,soil moisture,soil temperature and solar radiation are factors that can affect the vegetation growth.In details,the combination of atmosphere temperature,soil temperature and solar radiation can explain 91.3% of Gcc change,among which,atmosphere and soil temperature can explain the most of phonology with 30.9% and 49.0% individually.Furthermore,as the lacking of water content,precipitation behaves as an essential factor for inhibiting vegetation growth in our research area.

Key words: Grassland phenology    Digital camera    Gcc    Time series    Meteorological factors
收稿日期: 2015-08-28 出版日期: 2016-11-25
:  TP 79  
基金资助:

国家863计划项目(2012AA12A301),国家自然科学基金项目(41371359),中国博士后基金项目(2014M550871),中国科学院遥感与数字地球研究所所长青年基金项目(Y5SJ2000CX)。

通讯作者: 王晋年(1966-),男,山西运城人,研究员,博导,主要从事高光谱遥感技术研究。Email:wangjn@radi.ac.cn。   
作者简介: 周惠慧(1991-),女,湖南衡阳人,硕士研究生,主要从事高光谱遥感及时谱分析研究。Email:zhouhh@radi.ac.cn。
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引用本文:

周惠慧,付东杰,张立福,王文生,岑奕,王晋年. 基于数字相机的草地物候模拟及其与气象因子关系的研究[J]. 遥感技术与应用, 2016, 31(5): 966-974.

Zhou Huihui,Fu Dongjie,Zhang Lifu,Wang Wensheng,Cen Yi,Wang Jinnian. Modeling Grassland Phenology and Analyzing Relationship with Corresponding Meteorological Factors based on Digital Camera. Remote Sensing Technology and Application, 2016, 31(5): 966-974.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2016.5.0966        http://www.rsta.ac.cn/CN/Y2016/V31/I5/966

[1]Gordo O,Juan J S.Phenology and Climate Change:A Long-Term Study in a Mediterranean Locality[J].Oecologia,2005,146(3):484-495.

[2]Menzel A,Sparks T H,Estrella N,et al.European Phenological Response to Climate Change Matches the Warming Pattern[J].Global Change Biology,2006,12(10):1969-1976.[3]Parmesan C,Yohe G.A Globally Coherent Fingerprint of Climate Change Impacts Across Natural Systems[J].Nature,2003,421(6918):37-42.

[4]Richardson A D,Keenan T F,Migliavacca M,et al.Climate Change,Phenology,and Phenological Control of Vegetation Feedbacks to the Climate System[J].Agricultural and Forest Meteorology,2013,169:156-173.

[5]Ganguly S,Friedl M A,Tan B,et al.Land Surface Phenology from MODIS:Characterization of the Collection 5 Global Land Cover Dynamics Product[J].Remote Sensing of Environment,2010,114(8):1805-1816.

[6]Justice C O,Townshend J R G,Holben B N,et al.Analysis of the Phenology of Global Vegetation Using Meteorological Satellite Data[J].International Journal of Remote Sensing,1985,6(8):1271-1318.

[7]Melaas E K,Mark A F,Zhe Z.Detecting Interannual Variation in Deciduous Broadleaf Forest Phenology Using Landsat TM/ETM+ Data[J].Remote Sensing of Environment,2013,132:176-185.

[8]Pan Y,Li L,Zhang J,et al.Winter Wheat Area Estimation from MODIS-EVI Time Series Data Using the Crop Proportion Phenology Index[J].Remote Sensing of Environment,2012,119:232-242.

[9]Hmimina G,Dufrêne E,Pontailler J Y,et al.Evaluation of the Potential of MODIS Satellite Data to Predict Vegetation Phenology in Different Biomes:An Investigation Using Ground-based NDVI Measurements[J].Remote Sensing of Environment,2013,132:145-158.

[10]Hufkens K,Friedl M,Sonnentag O,et al.LinkingNear-surface and Satellite Remote Sensing Measurements of Deciduous Broadleaf Forest Phenology[J].Remote Sensing of Environment,2012,117:307-321.

[11]Yang X,Tang J,Mustard J F.BeyondLeaf Color:Comparing Camera-based Phenological Metrics with Leaf Biochemical,Biophysical,and Spectral Properties Throughout the Growing Season of a Temperate Deciduous Forest[J].Journal of Geophysical Research:Biogeosciences,2014,119(3):181-191.

[12]Adamsen F G,Pinter P J,Barnes E M,et al.Measuring Wheat Senescence with a Digital Camera[J].Crop Science,1999,39(3):719-724.

[13]Ryu Y,Verfaillie J,Macfarlane C,et al.Continuous Observation of Tree Leaf Area Index at Ecosystem Scale Using Upward-pointing Digital Cameras[J].Remote Sensing of Environment,2012,126:116-125.

[14]Ahrends H E,Etzold S,Kutsch W L,et al.Tree Phenology and Carbon Dioxide Fluxes:Use of Digital Photography for Process-based Interpretation at the Ecosystem Scale[J].Climate Research,2009,39(3):261-274.

[15]Richardson A D,Jenkins J P,Braswell B H,et al.Use of Digital Webcam Images to Track Spring Green-up in a Deciduous Broadleaf Forest[J].Oecologia,2007,152(2):323-34.[16]Ide R,Hiroyuki O.A Cost-effective Monitoring Method Using Digital Time-lapse Cameras for Detecting Temporal and Spatial Variations of Snowmelt and Vegetation Phenology in Alpine Ecosystems[J].Ecological Informatics,2013,16:25-34.

[17]Zhao J,Zhang Y,Tan Z,et al.Using Digital Cameras for Comparative Phenological Monitoring in an Evergreen Broad-leaved Forest and a Seasonal Rain Forest[J].Ecological Informatics,2012,10:65-72.

[18]Zhou L,He H L,Sun X M,et al.Using Digital Repeat Photography to Model Winter Wheat Phenology and Photosynthetic CO2 Uptake[J].Acta Ecologica Sinica,2012,32(16):5146-5153.[周磊,何洪林,孙晓敏,等.基于数字相机的冬小麦物候和碳交换监测[J].生态学报,2012,16:5146-5153.]

[19]Julitta T,Cremonese E,Migliavacca M,et al.Using Digital Camera Images to Analyse Snowmelt and Phenology of a Subalpine Grassland[J].Agricultural And Forest Meteorology,2014,198:116-125.

[20]Zhou Lei,He Honglin,Zhang Li,et al.Simulations of Phenology Inalpine Grassland Communities in Damxung,Xizang,based on Digital Camera Images[J].Chinese Journal of Pland Ecology,2012,36(11):1125-1135.[周磊,何洪林,张黎,等.基于数字相机图像的西藏当雄高寒草地群落物候模拟[J].植物生态学报,2012,11:1125-1135.]

[21]Ryu Y,Baldocchi D D,Ma S,et al.Interannual Variability of Evapotranspiration and Energy Exchange over an Annual Grassland in California[J].Journal of Geophysical Research:Atmospheres,2008,113(D9):1984-2012.

[22]Gillespie A R,Kahle A B,Walker R E.Color Enhancement of Highly Correlated Images.II.Channel Ratio “Chromaticity” Transformation Techniques[J].Remote Sensing of Environment,1987,22(3):343-365.

[23]Zhang X,Friedl M A,Schaaf C B,et al.Monitoring Vegetation Phenology Using MODIS[J].Remote Sensing of Environment,2003,84(3):471-475.

[24]Li Xiaoyu,Li Fei,Bao Yuhai,et al.Validation and Analysis of MODIS/FPAR Product based on HJ-ICCD Image in Hulunber Grassland[J].Remote Sensing Technology and Application,2015,30(6):1129-1137.[李晓宇,李飞,包玉海,等.基于HJ-1CCD影像的呼伦贝尔草原区 MODIS/FPAR 产品验证与分析[J].遥感技术与应用,2015,30(6):1129-1137.]

[25]Chang Bo,Liu Xiande,Wang Shunli,et al.Study on Grassland Evapotranspiration at Different Slope Orientation and Its Impact Factors in Qilian Mountains[J].Journal of Central South University of Forestry & Technology,2014,34(4):90-95.[常博,刘贤德,王顺利,等.祁连山不同坡向草地蒸散量及其影响因子的分析[J].中南林业科技大学学报,2014,34(4):90-95.]

[26]Zhang Yaosheng,Zhao Xinquan,Zhao Shuangxi,et al.Correlation between Evapotranspiration and Climate Factors in Warm Steppe in Source Region of Yangtze,Yellow and Yalu Tsangpo Rivers[J].Journal of Desert Research,2010,30(2):363-368.[张耀生,赵新全,赵双喜,等.三江源区温性草原蒸散量与主要影响因子的相关分析[J].中国沙漠,2010,30(2):363-368.]

[27]Guo Yaqi,Ali Musi,Gao Qingzhu,et al.Photosynthetic Characteristics of Stipa Purpurea under Irrigation in Northern Tibet and Its Short-term Response to Temperature and CO2 Convertration[J].Chinese Journal of Plant Ecology,2011,35(3):311-321.[郭亚奇,阿里穆斯,高清竹,等.灌溉条件下藏北紫花针茅光合特性及其对温度和CO2浓度的短期响应[J].植物生态学报,2011,35(3):311-321.]

[28]Gordo O,SANZ J J.Impact of Climate Change on Plant Phenology in Mediterranean Ecosystems[J].Global Change Biology,2010,16(3):1082-1106.

[29]Wang Lianxi,Chen Huailiang,Li Qi,et al.Research Advances in Plant Phenology and Climate[J].Acta Ecologica Sinica,2010,30(2):447-454.[王连喜,陈怀亮,李琪,等.植物物候与气候研究进展[J].生态学报,2010,30(2):447-454.]

[30]Chen Xiaoqiu,Li Jing.Relationships between Leymus Chinensis Phenology and Meteorological Factors in Inner Mongolia Grasslands[J].Acta Ecologica Sinica,2009,29(10):5280-5290.[陈效逑,李倞.内蒙古草原羊草物候与气象因子的关系[J].生态学报,2009,29(10):5280-5290.]


 

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