基于数码照片的植被物候提取多方法比较研究

doi:10.18306/dlkxjz.

摘要/Abstract

摘要：

植被物候能反映植被生长状况及其对气候变化的响应,在景观或更小尺度上自动化观测分析植被物候的演化是对大尺度遥感分析和单株植物人工观测的有效补充。基于物候相机观测网络(PhenoCam)中3种典型植被类型(森林、草地和农作物)站点数据,首先在群落尺度的感兴趣区(region of interesting, ROI)和像素2个尺度上计算植被指数,然后利用多种曲线拟合植被生长轨迹,提取关键物候参数,最后对相机物候参数进行了不确定性分析和卫星遥感物候的比较验证。结果表明：自定义ROI区域可以精确划定植被聚集区域,减少天空、地面等非植被要素的干扰;多方法的生长曲线拟合实验表明双逻辑斯蒂拟合法比较适用于单生长期植被,样条法较适用于多生长期植被;单生长期植被可直接采用多种物候参数提取方法(Klosterman, Gu, TRS, Derivatives)从生长曲线上提取关键物候参数,而多生长期植被可先用样条法拟合生长轨迹,然后采用变化点方法提取关键物候参数;生长曲线拟合与物候参数提取组合方法的不确定性分析发现,Klosterman方法具有较好的鲁棒性,各组合方法模拟实验的均方根误差均小于0.005;相机物候参数与MODIS EVI提取的遥感物候参数对比验证表明,二者在森林、农作物上的物候参数比较一致;像素级返青期参数的探索性分析发现,在像素尺度上能够识别群落内物种及个体间的物候差异,未来经过更深入的不确定性分析后,可尝试作为自动化分析群落尺度生物多样性的方法。

关键词: 植被物候, 物候相机, 感兴趣区, 生长期开始点, 生长期结束点, 双逻辑斯蒂曲线, 阈值法, 导数法

Abstract:

Vegetation phenology is an effective index that reflects the growth condition of vegetation and their response to climate change. Analyzing vegetation phenology at landscape or smaller scale will be a powerful supplement to remote-sensing based and artificial observed phenological studies. In this study, we applied the time series digital images from typical observation stations in camera-based phenology network (PhenoCam) to fit the seasonal growth curves at region of interest (ROI) and pixel scales. Key vegetation phenological parameters on the growth curve were extracted using multiple methods. First, we realized the custom definition of ROI by arbitrarily drawing polygons on the image and growth curve fitting based on the greenness index in the ROI. The results indicate that double logistic method is adapted for modeling the vegetation growing process at middle-high latitude that have a single growth peak. The spline method shows a good performance for fitting growth curves of vegetation that has multiple growth seasons. Second, based on the fitted curve, we used four models (Klosterman, Gu, TRS, and Derivatives) to derive the key phenological parameters for vegetation having single growth season. MODIS EVI-based phenological parameters were extracted as a comparison with camera based phenological parameters. We found that their capabilities in detecting phenological parameters in forest and cropland were consistent. For the vegetation with more than one growth season, the growth curve was fitted with spline method, then the change point detection method was applied to determine growth seasons on the curve and extract the key phenological parameters. The uncertainty of the fitting methods and phenology methods were estimated, with all of the RMSE less than 0.005. Klosterman method is identified as the most robust. Vegetation greenup date was extracted for each pixel in the ROI that can be useful for distinguishing the phonological diversity between different species. Pixel level vegetation phenology analysis could be used to identify biodiversity at landscape scale in the future.

Key words: vegetation phenology, phenology camera, ROI(region of interesting), SOS(start of growing season), EOS(end of growing season), double Logistic curve, threshold method, derivatives method

周玉科. 基于数码照片的植被物候提取多方法比较研究[J]. 地理科学进展, 2018, 37(8): 1031-1044.

Yuke ZHOU. Comparative study of vegetation phenology extraction methods based on digital images[J]. PROGRESS IN GEOGRAPHY, 2018, 37(8): 1031-1044.

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参考文献 28

[1]	范德芹, 赵学胜, 朱文泉, 等. 2016. 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, 35(3): 304-319. doi: 10.18306/dlkxjz.2016.03.005
	[Fan D Q, Zhao X S, Zhu W Q, et al.2016. Review of influencing factors of accuracy of plant phenology monitoring based on remote sensing data. Progress in Geography, 35(3): 304-319.] doi: 10.18306/dlkxjz.2016.03.005
[2]	宫攀, 陈仲新. 2009. 基于MODIS数据的东北地区植被物候参数提取[J]. 土壤通报, 40(2): 213-217.
	[Gong P, Chen Z X.2009. Regional vegetation phenology monitoring based on MODIS[J]. Chinese Journal of Soil Science, 40(2): 213-217.]
[3]	康峻, 侯学会, 牛铮, 等. 2014. 基于拟合物候参数的植被遥感决策树分类[J]. 农业工程学报, 30(9): 148-156.
	[Kang J, Hou X H, Niu Z, et al.2014. Decision tree classification based on fitted phenology parameters from remotely sensed vegetation data[J]. Transactions of the Chinese Society of Agricultural Engineering, 30(9): 148-156.]
[4]	刘玲玲, 刘良云, 胡勇. 2012. 基于AVHRR和MODIS数据的全球植被物候比较分析[J]. 遥感技术与应用, 27(5): 754-762.
	[Liu L L, Liu L Y, Hu Y.2012. Comparative analysis of global vegetation phenology based on AVHRR and MODIS[J]. Remote Sensing Technology & Application, 27(5): 754-762.]
[5]	鹿琳琳, 郭华东. 2009. 基于SPOT/VEGETATION时间序列的冬小麦物候提取方法[J]. 农业工程学报, 25(6):174-179. doi: 10.3969/j.issn.1002-6819.2009.06.033
	[Lu L L, Guo H D.2009. Extraction method of winter wheat phenology from time series of SPOT/VEGETATION data[J]. Transactions of the Chinese Society of Agricultural Engineering, 25(6):174-179.] doi: 10.3969/j.issn.1002-6819.2009.06.033
[6]	宛敏渭. 1986. 中国自然历选编[M]. 北京: 科学出版社.
	[Wan M W.1986. Chinese natural history collection[M]. Beijing, China: Science Press.]
[7]	宛敏渭, 刘秀珍. 1987. 中国物候观测方法[M]. 北京: 科学出版社.
	[Wan M W, Liu X Z.1987. Phenological observation methods in China[M]. Beijing, China: Science Press.]
[8]	王宏, 李晓兵, 莺歌, 等. 2006. 基于NOAA NDVI的植被生长季模拟方法研究[J]. 地理科学进展, 25(6): 21-30. doi: 10.11820/dlkxjz.2006.06.003
	[Wang H, Li X B, Ying G, et al.2006. The methods of simulating vegetation growing season based on NOAA NDVI[J]. Progress in Geography, 25(6): 21-32.] doi: 10.11820/dlkxjz.2006.06.003
[9]	武永峰, 李茂松, 宋吉青. 2008. 植物物候遥感监测研究进展[J]. 气象与环境学报, 24(3): 51-58. doi: 10.3969/j.issn.1673-503X.2008.03.011
	[Wu Y F, Li M S, Song J Q.2008. Advance in vegetation phenology monitoring based on remote sensing[J]. Journal of Meteorology & Environment, 24(3): 51-58.] doi: 10.3969/j.issn.1673-503X.2008.03.011
[10]	于信芳. 2005. 基于MODIS数据的森林物候期遥感监测及森林类型识别研究[D]. 北京: 中国科学院地理科学与资源研究所: 10-12.
	[Yu X F.2005. Forest phenological monitoring and forest type identifying using multi-temporal MODIS data[D]. Beijing, China: Institute of Geographic Sciences and Natural Resources Research, CAS: 10-12.]
[11]	周惠慧, 付东杰, 张立福, 等. 2016. 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, 31(5): 966-974. doi: 10.11873/j.issn.1004-0323.2016.5.0966
	[Zhou H H, Fu D J, Zhang L F, et al.2016. Modeling grassland phenology and analyzing relationship with corresponding meteorological factors based on digital cameral[J]. Remote Sensing Technology and Application, 31(5): 966-974.] doi: 10.11873/j.issn.1004-0323.2016.5.0966
[12]	周磊, 何洪林, 孙晓敏, 等. 2012. 基于数字相机的冬小麦物候和碳交换监测[J]. 生态学报, 32(16): 5146-5153. doi: 10.5846/stxb201110271606
	[Zhou L, He H L, Sun X M, et al.2012. Using digital repeat photography to model winter wheat phenology and photosynthetic CO2 uptake[J]. Acta Ecologica Sinica, 32(16): 5146-5153.] doi: 10.5846/stxb201110271606
[13]	周磊, 何洪林, 张黎, 等. 2012. 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, 36(11): 1125-1135. doi: 10.3724/SP.J.1258.2012.01125
	[Zhou L, He H L, Zhang L, et al.2012. Simulations of phenology in alpine grassland communities in Damxung, Xizang, based on digital camera images[J]. Chinese Journal of Plant Ecology, 36(11): 1125-1135.] doi: 10.3724/SP.J.1258.2012.01125
[14]	Beck P S, Atzberger T C, Høgda K A, et al.2006. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI[J]. Remote Sensing of Environment, 99(3): 321-334. doi: 10.1016/j.rse.2005.10.021
[15]	Chuine I, Cambon G, Comtois P.2010. Scaling phenology from the local to the regional level: Advances from species-specific phenological models[J]. Global Change Biology, 6(8): 943-952.
[16]	Elmore A J, Guinn S M, Minsley B J.2012. Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests[J]. Global Change Biology, 18(2): 656-674.
[17]	Filippa G, Cremonese E, Galvagno M, et al.2015. Five years of phenological monitoring in a mountain grassland: Inter-annual patterns and evaluation of the sampling protocol[J]. International Journal of Biometeorology, 59(12): 1927-1937.
[18]	Gu L, Post W M, Baldocchi D D, et al.2009. Characterizing the seasonal dynamics of plant community photosynthesis across a range of vegetation types[M]//Noormets A. Phenology of Ecosystem Processes. New York: Springer: 35-58.
[19]	Ide R, Oguma H.2010. Use of digital cameras for phenological observations[J]. Ecological Informatics, 5: 339-347. doi: 10.1016/j.ecoinf.2010.07.002
[20]	Jonsson P, Eklundh L.2002. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. IEEE Transactions on Geoscience & Remote Sensing, 40: 1824-1832.
[21]	Julitta T, Cremonese E, Migliavacca M, et al.2014. Using digital camera images to analyses snowmelt and phenology of a subalpine grassland[J]. Agricultural & Forest Meteorology, 198-199: 116-125. doi: 10.1016/j.agrformet.2014.08.007
[22]	Klosterman S, Hufkens K, Gray J, et al.2014. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery[J]. Biogeosciences, 11(16): 4305-4320.
[23]	Migliavacca M, Galvagnob M, Cremonesec E, et al.2011. Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic CO2 uptake[J]. Agricultural and Forest Meteorology, 151(10): 1325-1337. doi: 10.1016/j.agrformet.2011.05.012
[24]	Rodrigues A, Marcal A R S, Cunha M.2011. PhenoSat: A tool for vegetation temporal analysis from satellite image data[C]//6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images. Trento, Italy: IEEE: 45-48.
[25]	Sonnentag O, Hufkens K, Teshera-Sterne C, et al.2012. Digital repeat photography for phenological research in forest ecosystems[J]. Agricultural & Forest Meteorology, 152(1): 159-177. doi: 10.1016/j.agrformet.2011.09.009
[26]	White M A, Thomton P E, Running S W.1997. A continental phenology model for monitoring vegetation responses to interannual climatic variability[J]. Global Biogeochemical Cycles, 11(2): 217-234.
[27]	Zhang X Y, Friedl M A, Schaaf C B, et al.2003. Monitoring vegetation phenology using MODIS[J]. Remote Sensing of Environment, 84(3): 471-475. doi: 10.1016/S0034-4257(02)00135-9
[28]	Zhou L, He H, Sun X, et al.2013. Species and community-scale simulation of the phenology of a temperate forest in Changbai Mountain based on digital camera images[J]. Journal of Resources and Ecology, 4(4): 317-326. doi: 10.5814/j.issn.1674-764x.2013.04.004

植被类型	站点名	纬度(N)	经度(W)	高程/m	站点描述
草地	Freemangrass	29.9300	98.0100	243	德克萨斯州,圣马可斯弗里曼农场(德克萨斯州立大学草原站点)
森林	Freemanwood	29.9400	97.9900	254	德克萨斯州,圣马可斯弗里曼农场(德克萨斯州立大学林地站点)
	Harvard	42.5378	72.1715	340	马萨诸塞州,彼得舍姆哈佛森林的EMS塔
	Harvardbarn	42.5353	72.1899	350	马萨诸塞州,彼得舍姆哈佛森林的Barn塔相机1
	Harvardbarn2	42.5353	72.1899	350	马萨诸塞州,彼得舍姆哈佛森林的Barn塔相机2
	Morganmonroe	39.3231	86.4131	275	印第安纳州摩根门罗州森林
农作物	Kelloggcorn	42.4375	85.3225	288	密歇根州Kellogg生物站
农作物	Southerngreatplains	36.6970	97.4870	314	俄克拉荷马州比林斯ARM南部大平原中央设施