应用遥感技术评价植被生化物质含量的研究进展

摘要/Abstract

摘要： 植物叶片中的各种生化物质(包括叶绿素、叶黄素、类胡萝卜素等色素,N、P、K等营养物质,纤维素、半纤维素、木质素、蛋白质、淀粉、糖和油等)都直接或间接地参与生物地球化学循环。为进一步理解生态系统功能,必须对不同时间、不同空间的这些参数进行调查。遥感技术为获取多时相、大范围数据提供了极大的方便,在地表植被生化特征测定方面发挥着重要的作用。20世纪80年代以来,世界各国相继推出了机载和星载高光谱传感器,高光谱遥感技术为植被生化物质的遥感开辟了新局面。本文从植被光谱特征出发,总结了目前生化参数遥感中的植被指数、导数光谱、红边位移分析和连续统去除等基本光谱处理方法,重点介绍了统计回归模型和光学-几何模型在植被生化物质含量遥测中的应用。对现有利用遥感技术研究植被生化物质含量的研究进行了综合评述,并讨论了其局限性及未来的发展趋势.

关键词: 生态系统服务功能, 莽措湖流域, 经济价值

Abstract: The main biochemicals (such as lignin,protein,cellulouse,sugar,starch,chlorophyll and water) of vegetation are directly or indirectly involved in major ecological processes and function of terrestrial ecosystem (i.e.nutrient cycling processes,primary production,and decomposition).Being able to cover large area several times during the season,remote sensing techniques represented a very convenient way of data acquisition.With these techniques,we could relate remote sensing measurements to the biochemical characteristics of the Earth surfaces in a reliable and operational way.Therefore,it may play an important role in the estimation of the biochemical contents.Since early 1980s,many countries put forward their own airborne or space borne high spectral radiometers.The spectroscopic estimation of vegetation biochemical concentration was welcoming a new dawn with the developments of high spectral remote sensing technologies.Based on the spectral features of the vegetation,the commonly used spectral analysis methods and data management methods (e.g.vegetation indices,derivative spectral,continuum removal,red edge etc.) were summarized in this paper.The applications of multiple linear regression models and illuminate-geometry models in the assessment of vegetation biochemical material content were emphasized.Many radiation transfer models and three dimensional geometry models,such as PROSPECT,SAIL,LIBERTY,DART,IAPI,KUUSK,NADI,LEAFMOD,RSADU and FLIGHT were introduced.By further development,the imaging spectroscopic estimation of vegetation biochemistry concentration over large area may obtain a higher robustness and accuracy for using in global change studies,ecosystem studies,species identification,accurate agriculture,disaster and pollution estimation, and resources investigation,etc.The limitations and further development possibilities of this technology were pointed out at the end of this paper.

Key words: Ecosystem services, Mangcuo Lake drainage basin, Economic value

中图分类号:

TP79

丁圣彦, 李昊民, 钱乐祥. 应用遥感技术评价植被生化物质含量的研究进展[J]. 生态学杂志, 2004, (4): 109-117.

DING Shengyan, LI Haomin, QIAN Lexiang. Research advances in remote sensing techniques in estimation of vegetation biochemical material contents[J]. cje, 2004, (4): 109-117.

参考文献

[1] 王长耀,牛铮,唐华俊,等.2001.对地观测技术与精细农业[M].北京:科学出版社,55.
[2] 王秀珍,王人潮,李云梅,等.2001.不同氮素营养水平的水稻冠层光谱红边参数及其应用研究[J].浙江大学学报(农业和生命科学版),27(3):301～306.
[3] 苏理宏,黄裕霞,李小文,等.2002.三维结构真实遥感像元场景的生成[J].中国图像图形学报,7(6):570～575.
[4] 陈楚群,施平,毛庆文,等.1996.应用TM数据估算沿岸海水表层叶绿素模型研究[J].环境遥感,11(3):168～176.
[5] 郑兰芬,童庆禧,王晋年.1995.高光谱分辨率遥感研究进展[A].见:中国科学院遥感应用研究所编,遥感科学进展[C].北京:科学出版社,42～50.
[6] 宫鹏,史培军,浦瑞良,等.1996.对地观测技术与地球系统科学[M].北京:科学出版社,1～208.
[7] 赵碧云,贺彬,朱云燕.2001.滇池水体中叶绿素A含量的遥感定量模型[J].云南环境科学,20(3):1～3.
[8] 浦瑞良,宫鹏.2000.高光谱遥感及其使用[M].北京:高等教育出版社,191～192.
[9] 浦瑞良,宫鹏.1997.森林生物化学与CASI高光谱分辨率遥感数据的相关分析[J].遥感学报,1(2):115～123.
[10] 董国权,李正直.1995.绿色植物的激光荧光谱及遥感应用[J].光学仪器,(增刊1):71.
[11] Anatoly AG, Mark NET. 1996. Detection of red position and chlorophyll content by reflectance measurements near 700 nm[J]. J. Plant Physiol., 148: 501 ～508.
[12] Asner GP. 1998. Biophysical and biochemical sources of variability in canopy reflectance[J]. Remote Sens. Environ., 64:234～253.
[13] Bannari A, Morin D, Bonn FA. 1995. Review of vegetation indices [J]. Remote Sens. Reviews, 13: 95～120.
[14] Barry D. 1998. A New within-leaf radiative transfer model [J].Remote Sens. Environ., 63:182～193.
[15] Blackburn GA. 1998. Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves[J]. Int .J. Remote Sens., 19:657～675.
[16] Caetano M, Pereira J MC. 1997. Analysis of the integrated hyperspectral response of pine stands [J]. SPIE, 3(222) :26～37.
[17] Card DH, Peterson DL, Matson PA. 1988. Prediction of leaf chemistry by use of visible and near infrared reflectance spectroscopy[J].Remote Sens. Environ., 26:123～147.
[18] Curran PJ, Kupiec JA. 1995. Imaging spectrometry:a new tool for ecology[A]. In: FM Denson & SE Plummer eds. Advances in Environment Remote sensing[C]. Chichester: Wiley, 71～88.
[19] Danson FM. 1995. Developments in the remote sensing of forest canopy structure [A]. In: FM Danson & SE Plummer eds. Advances in Environmental Remote Sensing[C]. Chichester: Wiley,53～69.
[20] Dawson TP, Curran PJ. 1993. Factors affecting the remotely sensed response of coniferous forest plantations [J]. Remote Sen. Environt.,43:55～65.
[21] Dawson TP. 1997. The potential for understanding the biochemical signal in the spectra of forest canopies using a coupled leaf and canopy model[J]. Physical Measurements and Signatures in Remote Sens., 45: 463 ～470.
[22] Dawson TP, Curran PJ, North PRJ, et al. 1999. The propagation of foliar biochemical absorption features in forest canopy reflectance: a theoretical analysis[J]. Remote Sens. Environ., 67:147～159.
[23] Demares V, Gastellu-Etchegorry JPA. 2000. Modeling approach for studying forest chlorophyll content [J]. Remote Sens. Environ., 71:226 ～238.
[24] Dungan JL, Johnson LF, Billow CR, et al. 1996. High spectral resolution reflectance of Douglas fir grown under different fertilization treatments:experiment design and treatment effects [J].Remote Sens. Environ., 55: 217～228.
[25] Fourty T, Baret F, Jacquemoud S, et al. 1996. Leaf optical properties with explicit description of its biochemical composition: direct and inverse problems[J]. Remote Sens. Environ., 56:104～117.
[26] Fourty T, Baret F. 1998. On spectral estimates of fresh leaf biochemistry[J]. Int. J. Remote Sens., 19:1283～1297.
[27] Gastellu-Etchegorry JP, Bruniquel-Pinel V. 2001. A modeling approsch to assess robustness of spectrometric predictive equations for canopy chemistry [J]. Remote Sens. Environ., 76: 1～15.
[28] Gitelson A, Merzlyak MN. 1994. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L., and Acer platanoides L., leaves spectral features and relation to chlorophyll estimation[J]. J. Plant Physiol., 143: 286～292.
[29] Gitelson A, Merzlyak MN. 1994. Quantitative estimation of chlorophyll-a using reflectance spectrum: Experiments with autumn chestnut and maple leaves [J]. J. Photochem. Photobiol. (Biology), 22: 247～252.
[30] Gitelson A, Kaufman YJ, Merzlyak MN. 1996. Use of a green channel in remote sensing, global vegetation. EOS-MODIS [J].Remote Sens. Environ. 58: 289 ～298.
[31] Gitelson A, Merzlyak MN. 1997. Remote sensing of chlorophyll content in higher plant leaves[J]. Int. J. Remote Sens., 18:2691 ～2697.
[32] Goetz SJ, Prince SD. 1996. Remote sensing of net primary production in boreal forest stands[J]. Agri. For. Meteorol., 78:149～179.
[33] Grant L. 1987. Diffuse, specular characteristics of leaf reflectance [J]. Remote Sens. Environ., 22:309 ～322.
[34] Huguenin RL, Jones JL. 1986. Intelligent Information extraction from reflectance spectra: absorption band position [J]. J. Geophysical Res., 91:9585～9598.
[35] J acquemoud S, Baret F. 1990. PROSPECT: a model of leaf optical properties spectra[J]. Remote Sens. Environ., 34: 75～91.
[36] Jacquemoud S, Ustin SL. 1996. Estimating leaf biochemistry using the PROSPECT leaf optical properties model[J]. Remote Sens. Environ., 56:194 ～202.
[37] Jago RA, Cutler ME, Curran PJ. 1999. Estimating caropy chlorophyll concentration from field and airborne spectra[J]. Remote Sens. Environ.,68:206～216.
[38] Johnson LF, Billow CR. 1996. Spectrometric estimation of total nitrogen concentration in Douglas-fir foliage[J]. Int. J. Remote Sens., 17:489～500.
[39] Johnson LF, Hlavka CA, Peterson DL. 1994. Multivariate analysis of AVIRIS data for canopy biochemical estimation along the Oregon transect [J]. Remote Sens. Environ., 47: 216 ～230.
[40] Kokaly RF. 2001. Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration [J]. Remote Sens. Environ., 75:153 ～163.
[41] Kokaly RF, Clark RN. 1999. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression [J]. Remote Sens. Environ.,67: 267～287.
[42] Kupiec JA, Curran PJ. 1995. Decoupling the effect of the canopy and foliar biochemical concentration in AVIRIS spectra [J]. Int.J. Remote Sens., 16:1731 ～1739.
[43] Lichtenhaler HK. 1987. Chlorophylls and carotenoids: pigments of photosynthetic biomembranes[J]. Methods Enzymol, 148:350～382.
[44] Lichtenthaler HK, Gitelson A, Lang M. 1996. Non-destructive determination of chlorophyll concentration of leaves of a green and an aurea mutant of tobacco by reflectance measurements [J]. J. Plant Physiol., 148: 483 ～493.
[45] Lillesan TM, Kiefer RW. 1994. Remote sensing and Image Interpretation[M]. Third Edition. New York: John Wiley & Sons.,1～750.
[46] Lucas NS, Curran PJ. 1999. Forest ecosystem simulation modelling: the role of remote sensing [J]. Prog. Phys. Geog., 23:391～423.
[47] Martin ME, Newman SD, Aber JD, et al. 1998. Determining forest species composition using high spectral resolution remote sensing data [J]. Remote Sens. Environ.,65:249～254.
[48] Martin ME, Aber JD. 1997. High spectral resolution remote sensing of forest canopy lignin, nitrogen and ecosystem process[J]. Ecol.Appl., 7:431 ～443.
[49] Matson P, Johnson LF, Billow C, et al. 1994. Seasonal patterns and remote spectral estimation of canopy chemistry across the Oregon transect[J]. Ecol. Appl., 4: 280～298.
[50] Melamed MT. 1963. Optical properties of powder. Part Ⅰ. Optical absorption coefficent and absolute value of the diffuse reflectance [J]. Applied Physics, 34: 560 ～570.
[51] Mooney HA, Vitousek PM, Matson PA. 1987. Exchange of materials between terrestrial ecosystems and the atmosphere [J].Science, 238: 926 ～932.
[52] Niemann KO. 1995. Remote sensing of forest stand age using airborne spectrometer data[J]. Photogr. Eng. Rem. Sens., 61 (9):119～1127.
[53] North PRJ. 1996. Tree dimensional forest light interaction model using a Monte Carlo method [J]. IEEE Trans. Geosci. Remote Sens., 34: 946 ～956.
[54] Pablo J, John R. 2000. Chlorophyll fluorescence effects on vegetation apparent reflectance: Leaf-level measurement and model simulation[J]. Remote Sens. Environ., 74: 582 ～595.
[55] Pablo J, John R. 2000. Chlorophyll fluorescence effects on vegetation apparent reflectance: Laboratory and airborne osnopy-level measurement with hyperspectral data[J]. Remote Sens. Environ., 74: 596～608.
[56] Peterson DL, Aber JD, Matson PA, et al. 1988. Remote sensing of forest canopy leaf biochemical contents [J]. Remote Sens. Environ., 24:85～108.
[57] Peterson DL, Hubbard GS. 1991. Scientific issues and potential remote sensing requirements for plant biochemical content [J].J . Imaging Sci. Techn ., 36,446～456.
[58] Rencber AC, Pun FC. 1980. Inflation of R in best Subset Regression [J]. Technol Metrics, 22:49～53.
[59] Running SW. 1990. Estimating terrestrial primary productivity combining remote sensing and ecosystem simulation [A]. In:Hobbs RJ, Mooney HA eds., Remote sensing of biosphere functioning[ C]. New York: Springer, 65 ～86.
[60] Skoog DA, Holler EJ, Nieman TA. 1998. Principle of Instrumental Analysis[M]. 5th Ed. Philadelphia: Saunders College Publishers, 849.
[61] Smith GM, Curran PJ. 1995. The estimation of foliar biochemical content of a slash pine canopy from AVIRIS imagery [J].Can. J. Remote Sens ., 21: 234 ～244.
[62] Steudler PA, Bowden RD, Melillo JM. 1989. Influence of nitrogen fertilization on methane uptake in temperate forest soils [J].Nature,341:314～316.
[63] Terence PD, Paul JC, Stephen EP. 1998. LIBERTY-Modeling the effects of leaf biochemical concentration on reflectance spectra [J]. Remote Sens. Environ.. 65: 50 ～60.
[64] Treitz PM, Howarth PJ. 1999. Hyper spectral remote sensing for estimating biophysical parameters of forest ecosystems[J].Prog. Phys. Geog., 23: 359～390.
[65] Vane G, Goetz FH. 1988. Terrestrial imaging spectroscopy [J]. Remote Sens. Environ.,24:1～29.
[66] Wessman CA. 1994. Remote sensing and the estimation of ecosystem parameters and functions [A]. In:Hill J, Megier eds., Imaging Spectrometry-A tool for Environment Observation[C]. Dordrecht: Kluwer, 39 ～56.
[67] Wessman CA. 1988. Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystem [J]. Nature, 335:154～156.
[68] Wulder M. 1998. Optical remote sensing techniques for the assessment of forest inventory and biophysical parameters[J].Prog . Phys. Geog., 22:449～476.
[69] Yoder BJ, Pettigrew-Crosby RE. 1995. Predicting nitrogen and chlorophyll content and concentration from reflectance spectra(400～2 500 nm) at leaf and canopy scales [J]. Remote Sens.Environ., 53:199～211.