中国土地覆盖分类与变化监测遥感研究
|
摘要
土地覆盖和土地覆盖的自然变更,在地球系统的气候和生物化学全球尺度模式中扮演着重要角色,对全球生态环境产生着巨大的影响。地表土地覆盖信息是全球气候和陆地水文地理模型的基本输入。遥感为获取全球性和区域性土地覆盖信息提供了有效的技术手段。我国幅员辽阔,地表土地覆盖分布状况复杂多样,因此对我国地表土地覆盖状况进行研究对全球环境变化研究具有重要意义。
本文以MODIS植被指数和陆地表面温度为主要遥感数据源,进行了全国地表土地覆盖信息提取研究。首先提出基于NDVI-Ts特征空间的温度植被角度和距离(TVA&TVD)作为表征地表土地覆盖状况的生物物理参数。选择TVA和TVD作为主要分类特征,并用主成分分析(PCA)提取主要信息。用人工神经元网络算法进行大尺度低分辨率的全国土地覆盖分类研究,提出基于GA优化的BP神经网络遥感数据分类算法,用其对全国土地覆盖进行分类,并将该算法与模糊ARTMAP算法和最大似然法分类结果进行分析比较。本文还利用变化矢量方法对地表植被和土地覆盖状况进行变化监测研究。
本文的方法创新点归纳为:
1) 提出表征地表土地覆盖信息的生物物理参数:温度植被角度和距离(TVA&TVD)。
不同的地表土地覆盖类型在NDVI-Ts特征空间中空间分布差异非常明显,基于NDVI-Ts特征空间的TVA和TVD,能够唯一表达不同地表土地覆盖类别在NDVI-Ts特征空间中的位置。无论从信息含量的角度,还是不同类别的年内点轨迹,TVA和TVD对不同地表土地覆盖类别差异的体现都要优于NDVI和Ts。
2) 选择和提取了一组适合全国土地覆盖分类的特征
选择TVA和TVD时间序列数据、空间结构信息、数字高程作为分类特征,并用主成分变换(PCA)对TVA和TVD时间序列数据进行特征提取,得到既利于区分不同类别又含有主要信息的特征作为分类输入参数。为了比较,选择NDVI和Ts时间序列数据、空间结构信息、数字高程作为分类特征,经过同样的特征提取,得到另外一组输入参数。用相同的样本和分类算法对全国土地覆盖进行分类,结果表明:以TVA和TVD为主要参数提取的分类特征,其全国土地覆盖分类结果要优于以NDVI和TS为主要参数提取的分类特征。
3) 用遗传算法(GA)对反向传播神经网络算法(BP)进行优化,并将其用于大尺度低分辨率土地覆盖分类应用中
Land cover and its natural alteration, which play an important role in climate system and biochemistry pattern in globe scale, have brought tremendous effect to global entironment. The land cover information of the earth's surface is the primary input parameters for globe climate and terrestrial hydrography models. Remote sensing offered an effective method for acquiring surface land cover information. China is a country with a vast territory and diversiform, complicated surface land cover; upon that the research about Chinese surface land cover condition is one of the import parts of globe environment change.The article studied acquiring Chinese surface land cover information which the 1km MODIS NDVI&Ts data product which were used as main remote sensing data. Temperature-Vegetation Angel and Temperature-Vegetation Distance (TVA&TVD) based on NDVI-Ts space were proposed as biophysical parameters which can be thought as a token of surface land cover condition. TVA&TVD were selected as main classification features, and Principal Component Analysis was used to extract the primary information. Artificial Neural Network (ANN) algorithm was adopted to identify the Chinese surface land cover types at coarse spatial scales. One side, BP neural network classifier based on genetic algorithm for remotely-sensed data was proposed; on the other hand, classification of Chinese surface land cover was made with the algorithm, and other algorithms: fuzzy ARTMAP ANN and Maximum likelihood classifier (MLC), subsequently the three algorithms and their classification results were compared and analyzed. The surface vegetation and land cover condition detection using Change-Vector analysis is another important part of the article.Main research results and initiatives in this thesis include as following:1. Two index were proposed as biophysical parameters : TVA&TVDThere is an obvious diversity for distribution of different surface land cover classes in NDVI-Ts space, and TVA&TVD based on NDVI-Ts space can locate the spacial distribution uniquely. TVA&TVD can be in the person of different surface land cover classes much better than NDVI &Ts, which can be demonstrated by the information entropy and the different change trajectory per annum of each class.2. Several suitable features for Chinese surface land cover classification were selected and extracted.After TVA&TVD temporal series data, spacial structure information and Chinese digital elevation model were selected classification features, Primary Components Analysis (PCA) was
本文以MODIS植被指数和陆地表面温度为主要遥感数据源,进行了全国地表土地覆盖信息提取研究。首先提出基于NDVI-Ts特征空间的温度植被角度和距离(TVA&TVD)作为表征地表土地覆盖状况的生物物理参数。选择TVA和TVD作为主要分类特征,并用主成分分析(PCA)提取主要信息。用人工神经元网络算法进行大尺度低分辨率的全国土地覆盖分类研究,提出基于GA优化的BP神经网络遥感数据分类算法,用其对全国土地覆盖进行分类,并将该算法与模糊ARTMAP算法和最大似然法分类结果进行分析比较。本文还利用变化矢量方法对地表植被和土地覆盖状况进行变化监测研究。
本文的方法创新点归纳为:
1) 提出表征地表土地覆盖信息的生物物理参数:温度植被角度和距离(TVA&TVD)。
不同的地表土地覆盖类型在NDVI-Ts特征空间中空间分布差异非常明显,基于NDVI-Ts特征空间的TVA和TVD,能够唯一表达不同地表土地覆盖类别在NDVI-Ts特征空间中的位置。无论从信息含量的角度,还是不同类别的年内点轨迹,TVA和TVD对不同地表土地覆盖类别差异的体现都要优于NDVI和Ts。
2) 选择和提取了一组适合全国土地覆盖分类的特征
选择TVA和TVD时间序列数据、空间结构信息、数字高程作为分类特征,并用主成分变换(PCA)对TVA和TVD时间序列数据进行特征提取,得到既利于区分不同类别又含有主要信息的特征作为分类输入参数。为了比较,选择NDVI和Ts时间序列数据、空间结构信息、数字高程作为分类特征,经过同样的特征提取,得到另外一组输入参数。用相同的样本和分类算法对全国土地覆盖进行分类,结果表明:以TVA和TVD为主要参数提取的分类特征,其全国土地覆盖分类结果要优于以NDVI和TS为主要参数提取的分类特征。
3) 用遗传算法(GA)对反向传播神经网络算法(BP)进行优化,并将其用于大尺度低分辨率土地覆盖分类应用中
Land cover and its natural alteration, which play an important role in climate system and biochemistry pattern in globe scale, have brought tremendous effect to global entironment. The land cover information of the earth's surface is the primary input parameters for globe climate and terrestrial hydrography models. Remote sensing offered an effective method for acquiring surface land cover information. China is a country with a vast territory and diversiform, complicated surface land cover; upon that the research about Chinese surface land cover condition is one of the import parts of globe environment change.The article studied acquiring Chinese surface land cover information which the 1km MODIS NDVI&Ts data product which were used as main remote sensing data. Temperature-Vegetation Angel and Temperature-Vegetation Distance (TVA&TVD) based on NDVI-Ts space were proposed as biophysical parameters which can be thought as a token of surface land cover condition. TVA&TVD were selected as main classification features, and Principal Component Analysis was used to extract the primary information. Artificial Neural Network (ANN) algorithm was adopted to identify the Chinese surface land cover types at coarse spatial scales. One side, BP neural network classifier based on genetic algorithm for remotely-sensed data was proposed; on the other hand, classification of Chinese surface land cover was made with the algorithm, and other algorithms: fuzzy ARTMAP ANN and Maximum likelihood classifier (MLC), subsequently the three algorithms and their classification results were compared and analyzed. The surface vegetation and land cover condition detection using Change-Vector analysis is another important part of the article.Main research results and initiatives in this thesis include as following:1. Two index were proposed as biophysical parameters : TVA&TVDThere is an obvious diversity for distribution of different surface land cover classes in NDVI-Ts space, and TVA&TVD based on NDVI-Ts space can locate the spacial distribution uniquely. TVA&TVD can be in the person of different surface land cover classes much better than NDVI &Ts, which can be demonstrated by the information entropy and the different change trajectory per annum of each class.2. Several suitable features for Chinese surface land cover classification were selected and extracted.After TVA&TVD temporal series data, spacial structure information and Chinese digital elevation model were selected classification features, Primary Components Analysis (PCA) was
引文
Antonio Di Gregorio, Louisa J. M. Jansen, 1997. FAO Land Cover Classification: A Dichotomous, Modular-Hierarchical pproach, http://www.fao.org/waicent/faoinfo/agricult/AGL/AGLS/FGDCFAO.HTM
Anyamba A, Eastman J R., 1996. Interannual Variability of NDVI over Africa and its Relation to EL Nino/Southern Oscillation [J] International Journal of Remote Sensing. 17 (13): 2533-2548.
Belward, A. S., et al, 1996. The IGBP-DIS globe 1 km land cover data set (DISCover)-proposal and implementation plans: IGBP-DIS Working Paper No. 13, Toulours, France [R].
Benediktsson, J. A., P. H. Swain and O. K. Ersoy, 1990. Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Transactions on Geoscience and Remote Sensing. 28(4): 540-552.
Benediktsson, J. A., P. H. Swain and O. K. Ersoy, 1993. Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data. International Journal of Remote Sensing. 14: 2883-2903.
Bischof, H., W. Schneider and A. J. Prinz, 1992. Multispectral classification of Landsat-images using neural networks. IEEE Transactions on Geoscience and Remote Sensing. 30: 482-490.
Boegh, E. H. Soegaard, N. Hanan, P. Kabat and L. Lesch, 1998. A Remote Sensing Study of the NDVI-Ts Relationship and the Transpiration from Sparse Vegetation in the Sahel Based on High-Resolution Satellite Data [J]. REMOTE SENS. ENVIRON. 69: 224—240
Brutsaert W, 1982. Evaporation into the atmosphere. Dordrecht: Kluwer Academic Publishers.
Byrne, G. F., P. F. Crapper, and K. K. Mayo, 1980. Monitoring of land-cover change by principal components analysis of multitemporal Landsat data. Remote Sensing of Environment. 10: 175-184.
Cambin E F. Strahler A H., 1994. Indicator of land cover change for change-vector analysis in multitemporal space at coarse spatial scale, INT. J. REMOTE SENSING. 15(10): 2099-2119.
Carpente G A, Grossbery S, Rosen D B, 1991. Fussy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Network. 4: 759～771
Carpenter, G. A., S. Grossberg, 1987a. A massively parallel architecture for a selforganizing neural pattern recognition machine. Computer Vision. Graphics and Image Processing. 37: 54-115.
Carpenter, G. A., S. Grossberg, 1987b. ART 2: Stable self-organization of pattern recognition codes for analog input pattems. Applied Optics. 26: 4919-4930.
Carpenter, G., M. Gjaja, S. Gopal, and C. Woodcock, 1997. ART networks in remote sensing. IEEE Transactions on Geoscience and Remote Sensing. 35(2): 308-325.
Cihlar J., 2000. Land cover mapping of large areas from satellites: status and research priorities. Int. J. Remote Sensing. 21(6 & 7): 1093-1113.
Cihlar, J., 1996. Identification of contaminated pixels in AVHRR composite images for studies of land biosphere. Remote Sensing of Environment. 56: 149-163.
Collins, J. B. and C. E. Woodcock, 1994. Change detection using the GrammSchmidt transformation applied to mapping forest mortality. Remote Sensing of Environment. 50: 267-279.
Colwell, J. E. and F. P. Weber, 1981. Forest change detection, Proceedings 15th International Symposium on Remote Sensing of Environment, Environment Research Institute of Michigan, Ann Arbor, MI. pp. 839-852.
Crutzen, P J., 1991. the changing photochemistry of the atmosphere[J]. Tellus.43:136—151
D.E. Goldberg. , 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley New York.
Defines, R. S. Townshend, J. R. G, 1994.NDVI-derived land cover classification at a global scale. International Journal of Remote Sensing. 15(17), 3567—3586.
DeFries, R., M. Hanson and J. Townshend, 1995. Global discrimination of land cover types from metrics derived from AVHRR pathfinder data. Remote Sensing of Environment. 54:209-222.
DeFries, R., M. Hanson, J. Townshend and R. Sohlberg, 1998. Global land cover classifications at 8 km spatial resolutions: The use of training data derived from landsat imagery in decision tree classifiers. International Journal of Remote Sensing, 19(6):3141 -3168
Duvernoy, J. and J. Leger, 1980. Karhunen-Loeve analysis of multispectral data from landscapes. Optics Communications 32(l):39-44.
Fischer, M. M., S. Gopal, P. Staufer, and K. Steinocher, 1997. Evaluation of neural pattern classifiers for a remote sensing application. Geographical Systems.
Fitzgerald, R. W. and B. G Lees, 1994. Assessing the classification accuracy of multisource remote sensing data. Remote Sensing of Environment 47:362-368.
Friedl, M. A., & Davis, F.W.,1994. Sourse of variation in radiometric surface temperature over a tallgrass prairie [J]. REMOTE SENS. ENVIRON. 48:1—17
Friedl, M. A., C. E. Brodley, and A. H. Strahler, 1999. Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEE Transactions on Geoscience and Remote Sensing 37(2):969-977.
Fung, t., Ledrew, E.,1987. Application of principal components analysis to change detection. Photogrammetry Engineering & Remote Sensing. 53(12): 1649-1658.
Goetz, S.J., 1997.Multisensor analysis of NDVI, surface temperature and biophysicalvariables at a mixed grassland site [J]. International Journal of Remote Sensing. 18 (15): 71-94
Gopal, S. and C. E. Woodcock, 1996. Remote sensing of forest change using artificial neural networks. IEEE Transactions on Geoscience and Remote Sensing. 34:1-7.
Gopal, S., C. Woodcock, and A. H. Strahler, 1996. Fuzzy ARTMAP classification of global land cover from AVHRR data set. Proceedings of the 1996 International Geoscience and Remote Sensing Symposium, Lincoln, Nebraska, May 27-31.1:538-540.
Gopal, S., D. M. Sklarew, and E. Lambin, 1994, Fuzzy-neural networks in multitemporal classification of land cover change in the Sahel. New Tools for Spatial Analysis, Proceedings of the Workshop, Lisbon, 18 to 20 November 1993, Office for Official Publications of the European Communities, Luxembourg, pp. 69-81.
Goward, S. N., Xue, Y., & Czajkowski, K. P., 2002. Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurement: A exploration with the simplified simple biosphere model [J]. REMOTE SENS. ENVIRON.79: 225—242.
Goward, S.N, Hope, A. S., 1989. Evaporation from combined reflected salar and emitted terrestrial radiation: Preliminary FIFE results from AVHRR data [J]. Advances in Space Research. 9:239-249
Goward, S.N., Waring, R.H., Dye, D. G, and Yang. J. 1994.Ecological remote sensing at OTTER: satellite macroscale observations [J]. Ecological applications. 4:322-343.
Goward, S.N.,Cruickshanks, GD., and Hope, A.S. 1985. Observed relation between thermal
emission and reflected radiatiance of a complex vegetated landscape [J]. Remote Sensing of Environment. 18: 137-146.
Grossberg S., 1987.The Adaptive Brain. New York: North-Holland.
Hansen, M., R. Dubayah and R. DeFries, 1996. Classification trees: An alternative to traditional land cover classifiers. International Journal of Remote Sensing. 17:1075-1081.
Hansen, M.C., Reed B., 2000. A comparison of the IGBP DISCover and University of Maryland lkm global land cover products. Int. J. Remote Sensing, vol. 21(6&7): 1365-1373
Hara, Y., R. G Atkins, S. H. Yueh, R. T. Shin, and J. A. Kong, 1994. Application of neural networks to radar image classification. IEEE Transactions on Geoscience and Remote Sensing. 32:100-112.
Hellden, U. and L. Eklundh, 1988. National Drought Impact Monitoring. A NOAA NDVI and Precipitation Data Study of Ethiopia, Lund Studies in Geography, Series C, No. 15, Lund University Press, Lund, Sweden, 55.
Henderson-Sellers, A. and A. J. Pitman, 1992. Land-surface schemes for future climate models: Specification, aggregation and heterogeneity. Journal of Geophysical Research. 97:2678-2696
Hepner, G F., T. Logan, N. Ritter and N. Bryant, 1990. Artificial neural network classification using a minimal training set: Comparison to conventional supervised classification. Photogrammetric Engineering and Remote Sensing. 56(4):469-473.
Holben, B., 1986. Characteristics of maximum-value composite images from multitemporal AVHRR data. International Journal of Remote Sensing. 7:1417- 1434.
Huete A R, Jackson R D, Post D F. 1985.Spectral response of a plant canopy with different soil backgrounds et al. Remote Sensing of Environment. 17:37-53.
Huete A R., 1988. A soil adjusted vegetation index (SAVI), Remote Sens. Environ.25:295~309.
Hyman, A. H. and M. J. Barnsley, 1997. On the potential for land cover mapping from multiple-view-angle (MVA) remotely sensed images. International Journal of Remote Sensing. 18(11): 2471-2475.
IGBP-DIS, 1995. The IGBP-DIS Global 1 Km Land Cover Data Set: A Validation Strategy, A. Belward, ed., IGBP-DIS, University de Paris, Paris, France.
J.H. Holland,1975. Adaptation in Natural and Artificial Systems. The University of Michigan Press.
John C, Price, 2003. Compare MODIS and ETM+ data for regional and global land classification [J]. Remote Sensing of Environment. 86:491-499.
Kaufman Y J and Tanre D,1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Tans. Geosci. Remote Sens. 30:261 ~270.
Kauth, R. J. and G S. Thomas, 1976. The tasseled cap—A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Symposium Proceedings on Machine Processing of Remotely Sensed Data, June 29-July 1, 1976, West Lafayette, IN, pp. 41-51.
Kelle M, Jacob D J.Wofsy S C, et al, 1991. Effects of tropical deforestation on global and regional atmospheric chemistry[J].Climatic Change 19:145-158.
Kyriakidis P. C. and J. L. Dungan, 2001. A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions. Environmental and Ecological statistics. 8: 311-330.
Lambin, E. F and A. H. Strahler, 1994a. Indicators of land-cover change for changevector analysis in multitemporal space at coarse spatial scales. International Journal Remote Sensing.
15:2099-2119.
Lambin, E. F. and A. H. Strahler, 1994b. Change-vector analysis: A tool to detect and categorize land-cover change processes using high temporal-resolution satellite data. Remote Sensing of Environment. 48:231-244.
Lambin, E. F. and D. Ehrlich, 1996a. The surface temperature-vegetation index space for land cover and land-cover change analysis. International Journal of Remote Sensing. 17(3):463-487.
Lambin, E. F. and D. Ehrlich, 1996b. Broad scale land-cover classification and interannual climatic variability. International Journal of Remote Sensing. 17(5):845-862.
Lambin, E. F. and Strahler, A. H. 1994a. The surface temperature-vegetation index space for land cover and land-cover change analysis. Int. J. remote sens. 17:463-487.
Lambin, E. F., 1996. Change detection at multiple temporal scales: Seasonal and annual variations in landscape variables. Photogrammetric Engineering and Remote Sensing. 62:931-938.
Lean J., 1989.Simulation of the regional climatic impact on Amazon deforrstation[J].Nature. 342:411-413
Lee, J., W. C. Weger, S. K. Sengupta, and R. M. Welch, 1990. A neural network approach to cloud classification. IEEE Transactions on Geoscience and Remote Sensing. 28:846-855.
Lenney, M. P., C. E. Woodcock and H. Hamdi, 1996. The status of agricultural lands in Egypt: The use of multitemporal NDVI features derived from Landsat TM. Remote Sensing of Environment. 56(1):8-17
Li Sh, Zhuang E X.,2003. The Decsion Tree Classification and Its Application in Land Cover [J]. Areal Research and Development. 22(1): 17-21.
Liang,S. ,2001. Land-cover classification methods for multi-year AVHRR data.International Journal of Remote Sensing.22(8):1479-1493.
Liu H Q and Huete A R., 1995.A feedback based modification of the NDVI to minimize canopy back ground and atmospheric noise. IEEE Tans. Geosci. Remote Sens. 33:457-465.
Liu J., and Buheaosier, 2000, Study on spatial-temporal feature of modern land-use change in China: using remote sensing technique. Quaternary Sciences. 3, 229-239
Lloyd D., 1990. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. Int. J. Remote Sensing. 11(12): 2269-2279.
Loveland T R, Reed B C, Brown J F, et al., 2000. Development of a global land cover characteristics database and IGBP discover from 1-km AVHRR data[J]. International Journal of Remote Sensing. 21 (6/7) :1303-1330.
Loveland, T. R., J. W. Merchant, D. O. Ohlen, and J. F. Brown, 1991. Development of a land-cover characteristics database for the conterminous U. S. Photogrammetric Engineering and Remote Sensing .57:1453-1463.
Loveland, T. R., J. W. Merchant, J. F. Brown, D. O. Ohlen, B. C. Reed, P. Olson, and J. Hutchinson, 1995. Seasonal land-cover of the United States. Annals Association of American Geographers. 85:339-355.
M.A Friedl, D.K Mclver, et al, 2002. Global land cover mapping from MODIS: algorithms and early results. Remote Sens, of Environ.. 83: 287-302
Malila, W. A., 1980. Change vector analysis: An approach for detecting forest changes with Landsat, Proceedings 6th Annual Symposium on Machine Processing of Remotely Sensed Data, Laboratory for the Application of Remote Sensing, Purdue University, pp. 326-335.
Malingreau, J.-P., C. J. Tucker and N. Laporte, 1989. AVHRR for monitoring global tropical deforestation. International Journal of Remote Sensing. 10:855-867.
Mandelbrot, B. B., 1967. How long is the coast of Britain? Statistical self-similarity and fractional dimension. Science. 156: 636-638
Mandelbrot, B. B., 1977. Fractals: Form, Chance and Dimension. San Francisco, CA: Freeman.
Mark. A. Friedl et al,2002.Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment. 83: 287-302.
Monteith J L. 1973. Principles of environment physics. London : Edward Arnold Press.
Moran, M. S., Clarke, T. R., Inoue, Y., & Vidal, A., 1994. Estimating crop water deficitusing the relation between surface-air temperature and spectral vegetation index[J]. REMOTE SENS. ENVIRON.49: 246-63
Muchoney, D. M. and B. Haack, 1994. Change detection for monitoring forest defoliation. Photogrammetric Engineering and Remote Sensing 60(10): 1243-1251.
Muchoney. D. M., J. Borak, H. Chi, M. Friedl, J. Hodges, N. Morrow and A. Strahler, 1999. Application of the MODIS Global Supervised Classification Model to Vegetation and Land Cover Mapping of Central America. International Journal of Remote Sensing, 21 (6/7) :1115-1138.
Nelson, R. and B. Holben, 1986. Identifying deforestation in Brazil using multiresolution satellite data. International Journal of Remote Sensing 7:429-448
Nemani, Pierce, Running, Goward, 1993. Developing satellite derived estimates of surface moisture status [J]. Journal of Applied Meteorology. 32: 548-557
Nemani, R. R., &Running, S. W., 1989. Estimation of regional surface resistance to evapotranspiration from NDVI and thirmal IR AVHRR data [J]. Journal of Applied Meteorology. 7: 1-11.
Nemani, R. R., Running, S. W., Pielke, R. A., and Chase, T. N.,1996. Global vegetation cover changes from coarse resolution satellite data. J. Geophs. Res. 101(D3):7157-7162.
Nemani, R., and S. W. Running, 1996, Implementing a hierarchical global vegetation classification in ecosystem function models. Journal of Vegetation Science. 7:337-346.
Nemani, R., and S. W. Running, 1997, Land cover characterization using multitemporal red, near-IR, and thermal-IR data from NOAA/AVHRR. Ecological Applications 7:79-90.
Olsen, E.R., R.D. Ramsey, and D.S.. Winn. 1993. A Modified Fractal Dimension as a Measure of Landscape Diversity. Photogrammetric Engineering & Remote Sensing. 59(10): 1517-1520
Ormsby J P, Choudary B J, Owe M. 1987. Vegetation spatial variability and its effecton vegetation indices. International Journal of Remote Sensing. 8:1301-1306.
Paola,J.D. and R.A.Schowengerdt, 1995a. A review and analysis of backpropagation neural networks for classification of remote-sensed multi-spectral imagery. International Journal of Remote Sensing. 16(16):3033-3058.
Paola,J.D. and R.A.Schowengerdt, 1997. The effect of neural network structure on a multispectral land-use/land-cover classification. Photogrammetric Engineering and Remote Sensing.5:535~544.
Price J C. 1990. Using spatial context in satellite data to infer regional scale evapotranspiration. I.E.E.E. Transaction in Geosciences & Remote Sensing. 28:940-948.
Prihodko, L., Goward, S. N., 1997. Estimation of air temperature from remotely sensedsurface observations[J]. REMOTE SENS. ENVIRON. 60(3): 335-346
Quinlan, J. R., 1993, C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.
R.C.G, Choudhury, B.J., 1991. Analysis of nomalized difference and surface temperature observations over southeastern Australia [J]. International Journal of Remote Sensing. 12(10): 2021-2044
Reed, B.D., J.F. Brown, D. VanderZee, T.R. Loveland, J.W. Merchant and D.O. Ohlen. 1994. Measuring phenological variability from satellite imagery. J. Vegetation Science. 5: 703-714.
Rozanov B G, Targulian V,Orlov D S, et al, 1990.the earth as transformed by human Action[C].Cambridge: Cambridge University Press.pp: 203-214
Ruimy,A.,Saugier,B. ,1994. Methodology for the estimation of terrestrial net primary production from remotely sensed data.Jounal of Geophisical Research.99(D3):5263—5283.
Rumelhart D, Hinton G, Williams R., 1986. Learning Representation by Back-Propagating Errores. Nature. 323:533-536
Running S. W., et al, 1995. A Remote Sensing Based Vegetation Classification Logic for Global Land Cover Analysis[J]. Remote Sens, of Environ. 51: 39-48
Safavian, S. R. and D. Landgrebe, 1991, A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics. 21:660-674.
Saunders,R,W. ,1990. The determination of broad band surface albedo from AVHRR visible and near-infrared radiances.International Journal of Remote Sensing. 11(1):49—67.
Sellers, P. J., 1985. Canopy reflectance photosynthesis and transpiration. International Journal of Remote Sensing. 6:1335.
Sellers, P. J., Dickinson, R. E., Randall, D. A., Betts, A. K., Hall, F. G, Mooney, H. A., Nobre, C. A., Sato, N., Field, C. B., and Henderson-Sellers, A., 1997. Modeling the exchanges of energy, water, and carbon between continents and atmosphere. Science. 275: 502-509
Sellers, P. J.,Tucker, C. J.,Collatz.GJ., et al, 1994. A global 1 o by lo NDVI data set for climate studies part 2:the generation of global fields of terrestrial biophysical parameters from the NDVI. International Journal of Remote Sensing. 15: 3519-3545.
Special Issue: Global land cover data set validation, Photogramatric Engineering & Remote Sensing, Vol. 65, No.9, 1999.
Stowe, D. A., R. L. Tinney, and J. E. Estes, 1980. Deriving land use/land cover change statistics from Landsat: A study of prime agricultural land. Proceedings 14th International Symposium on Remote Sensing of Environment, Environment Research Institute of Michigan, Ann Arbor, MI, pp. 1227-1237.
Strahler A. Global Land Cover: Approaches to Validation. GLC2000 Meeting JRC Ispra 3,2002
Swain, P. H., and H. Hauska, 1977. The decision tree classifier: Design and potential. IEEE Transactions on Geoscience and Remote Sensing, GE-15:142-147
Thomlinson John R., Paul V. Bolstad, and Warren B. Cohen, 1999. Coordinating Methodologies for Scaling Landcover Classifications from Site-Specific to Global: Steps toward Validating Global Map Products. REMOTE SENS. ENVIRON. 70:16-28.
Townshend, J. R. G, C. Justice, W. Li, C. Gurney, and J. McManus, 1991. Global land cover classification by remote sensing: Present capabilities and future possibilities. Remote Sensing of Environment 35:243-255.
Townshend, J. R. G, Justice, C. O., Kalb, V., 1987.Characterization and classification of South American land cover types using satellite data. International Journal of Remote Sensing. 7:1395-1416.
Tso, B. and Mather, P. M., 2001. Classification methods for remotely sensed data. Taylor-Francis Inc. New York, NY, USA.
Tucker C J, Sellera P J. 1986. Satellite remote sensing of primary production. International Journal of Remote Sensing.7:1395-1416
Tucker, C. J., B. N. Holben and T. E. Goff, 1984. Intensive forest clearing in Rondonia, Brazil, as detected by satellite remote sensing. Remote Sensing of Environment. 15:255-261.
Tucker, C. J., C. O. Justice and S. D. Prince, 1986. Monitoring the grasslands of the Sahel 1984-1985. International Journal of Remote Sensing. 7: 1571-1581.
Tucker, C. J., H. E. Dregne and W. W. Newcomb, 1991. Expansion and contraction of the Sahara Desert from 1980 to 1990. Science. 253: 299-301.
Tucker, C. J., Townshend, J. R. G. and Goff, T. E. 1985. Continental land cover classification using meteorological satellite data. Science, vol. 227: 369-375.
Turner II BL, Moss RH, Skole DL. Relating land use and global land cover change, IGBP Report No. 24 and HDP report No. 5. Stockholm: IGBP, 1993.
Turner II BL, Skole D, Sanderson S et al. Land-use and land-cover change science/researchplan, IGBP Report No. 35 and HDP Report No. 7. Stochkholm: GIBP, 1995.
Tzeng, Y. C., K. S. Chen, W.-L. Kao and A. K. Fung, 1994. A dynamic learning neural network for remote sensing applications. IEEE Transactions on Geoscience and Remote Sensing. 32: 1096-1103.
Wang J, Dong G R, et al, 2000. Primary Study on the Multi-Layer remote Sensing Information Extraction of Desertion of Desertification Land Types by Using Decision Tree Technology [J]. Jouranl of Desert Research. 20(3): 243-247
Woodcock, C. E. and S. Ryherd, 1989. Generation of texture images using adaptive windows. Technical Papers, 55th Annual Meeting, American Society of Photogrammetry and Remote Sensing, Baltimore, 1989, pp. 11-22.
Woodwell, G. M., R. A. Houghton, T. A. Stone, R. F. Nelson and W. Kovalick, 1987. Deforestation in the tropics: New measurements in the Amazon Basin using Landsat and NOAA Advanced Very High Resolution Radiometer imagery. Journal of Geophysical Research 92(D2): 2157-2163.
X. Zhan, R. A. Sohlberg, et al, 2002. Detection of land cover changes using MODIS 250 m data. Remote Sens. of Environ., 83: 336-350
Yoshida, T. and S. Omatu, 1994. Neural network approach to land cover mapping. IEEE Transactions on Geoscience and Remote Sensing. 32: 1103-1110.
Zhang F, Xiong Zh, Kou N., 2002. Research on Rice Fine Classification using Hyper-spectral Remote Sensing Imagines [J]. Journal of Wuhan University of Technology. 24(10): 36-39.
Zhao P, Feng X Zh, Lin G F., 2003. The Decision Tree Algorithm of Automatically Extracting Residential Information from SPOT Images[J]. Journal of Remote Sensing. 7(4): 309-315
Zhu, Z-L., and L. Yang, 1996., Characteristics of the 1-km AVHRR data set for North America. International Journal of Remote Sensing. 17: 1915-1924.
陈百明,1997.试论中国土地利用和土地覆被变化及其人类驱动力研究.自然资源.2:31-35。
陈晋,何春阳,卓莉,2001.基于变化向量分析(CVA)的土地利用,覆盖变化动态监测(Ⅱ)———变化类型的确定方法.遥感学报.5(5):346-353
陈佑启,Peter H.Verburg,2000.中国土地利用/土地覆盖的多尺度空间分布特征分析,地理科学.20(3):197-202.
陈云浩,李晓兵等,2002.1983—1992年中国陆地植被NDVI演变特征的变化矢量分析.遥感学报.6(1):12-18.
陈云浩,李晓兵,史培军,2003.整合陆地表面温度与植被指数信息进行地表覆盖变化研究.第四纪研究.23(3)
冯春,陈建平.2003.土地利用/土地覆盖研究中遥感图像分类精度的提高方法.地理与地理信息科学.19(3):26-28
江东,王乃斌,杨小唤,刘红辉,2002..植被指数一地面温度特征空间的生态学内涵及其应用.地理科学进展.2(2):18-24.
李敏强,寇纪松等编著,2002.遗传算法的基本理论有应用[M].北京:科学出版社.
李晓兵,陈云浩,李霞,2003.基于多尺度遥感测量的区域土地覆盖格局研究.植物生态学报.27(5):577-586.
李晓兵,陈云浩,张云霞.2001.多空间分辨率遥感数据监测土地覆盖特征的比较研究.第四纪研究.21(6)
李晓兵,1999.国际土地利用/土地覆盖变化的环境影响研究.地理科学进展.14(4).395-400.
李秀彬,1996.全球环境变化研究核心领域—土地利用/土地覆盖变化的国际研究动向[J].地理学报.51(67):553-557
刘纪远,张增祥等,2003.20世纪90年代中国土地利用变化特征及成因分析.地理研究.22(1):1-12
刘纪远主编,1996.中国资源环境遥感宏观调查与动态研究.北京:中国科学技术出版社.Pp:1-43
刘硕,2002.国际土地利用与土地覆盖变化对生态环境影响的研究.世界林业研究.15(6):38-45
刘正军.2003.高维遥感数据土地覆盖特征提取与分类研究.博士学位论文.北京:中国科学院遥感应用研究所.
罗迪.1999.基于AVHRR和地理空间数据的中国土地覆盖分类研究.硕士学位论文.北京:中国科学院遥感应用研究所.
潘耀忠,李晓兵,何春阳.2000.中国土地覆盖综合分类研究——基于NOAA/AVHRR和Holdridge PE,第四纪研究.20(3):270-281
史培军,宫鹏等,2000,土地利用/覆盖变化的研究方法与实践.北京:科学出版社.
唐俊梅,张树文.2002.基于MODIS数据的宏观土地利用/土地覆盖监测研究.遥感技术与应用.17(2):104-107
田国良,徐兴奎,柳钦火,2000.用于地表能量交换的动态地表特征模式.遥感学.4(增刊):121-128.
王鹏新,龚健雅,李小文,2001.条件植被温度指数及其在干旱监测中的应用.武汉大学学报信息科学版.26(5):412-418
王正兴、刘闯、HUETE Alfredo,2003.植被指数研究进展:从AVHRR-NDVI到MODIS-EVI.生态学报.23(5):979-987
吴传钧,郭焕成,1994.中国土地利用.北京:科学出版社.
香宝,刘纪远,2003.东亚土地覆盖对ENSO事件的响应特征.遥感学报.7(4):316-320
香宝,刘纪远,2002.基于遥感和GIS的东亚土地覆盖年际变化研究.中国图象图形学报.A7(10):1017-1020
肖乾广,陈维英等,1995.气象卫星监测干旱灾害的研究,卫星气象中心技术报告,第9509期.
辛景峰,2003.中国科学院遥感应用研究所博士后出站报告
延昊,2002.中国土地覆盖变化与环境影响遥感研究.博士学位论文.北京:中国科学院遥感应用研究所.
杨建刚,2001.人工神经网络实用教程[M].杭州:浙江大学出版社.
杨立民,朱智良,1999.全球及区域尺度土地覆盖土地利用遥感研究的现状和展望.自然资源学报.14(1):340-344.
叶笃正,符淙斌,1994.全球变化的主要科学问题[J].大气科学.18(4):498-512.
张炳智,张丽等,2002.利用遥感技术进行全球测图项目中的中国土地利用覆盖和植被分类图的制作.测绘科学.27(1):43-46.
张华国,黄韦艮,周长宝等,2003.利用IKONOS卫星数据和分形方法研究南麂岛土地覆盖状况.生态学报.23(8):1539-1547.
张继贤,2003.论土地利用与覆盖变化遥感信息提取技术框架.中国土地科学.17(4):31-36
张树誉,颜胜安,2002.NOAA/AVHRR资料在陕西省土地覆盖分类中的应用.中国农业资源与区划.23(3):29-31.
Anyamba A, Eastman J R., 1996. Interannual Variability of NDVI over Africa and its Relation to EL Nino/Southern Oscillation [J] International Journal of Remote Sensing. 17 (13): 2533-2548.
Belward, A. S., et al, 1996. The IGBP-DIS globe 1 km land cover data set (DISCover)-proposal and implementation plans: IGBP-DIS Working Paper No. 13, Toulours, France [R].
Benediktsson, J. A., P. H. Swain and O. K. Ersoy, 1990. Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Transactions on Geoscience and Remote Sensing. 28(4): 540-552.
Benediktsson, J. A., P. H. Swain and O. K. Ersoy, 1993. Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data. International Journal of Remote Sensing. 14: 2883-2903.
Bischof, H., W. Schneider and A. J. Prinz, 1992. Multispectral classification of Landsat-images using neural networks. IEEE Transactions on Geoscience and Remote Sensing. 30: 482-490.
Boegh, E. H. Soegaard, N. Hanan, P. Kabat and L. Lesch, 1998. A Remote Sensing Study of the NDVI-Ts Relationship and the Transpiration from Sparse Vegetation in the Sahel Based on High-Resolution Satellite Data [J]. REMOTE SENS. ENVIRON. 69: 224—240
Brutsaert W, 1982. Evaporation into the atmosphere. Dordrecht: Kluwer Academic Publishers.
Byrne, G. F., P. F. Crapper, and K. K. Mayo, 1980. Monitoring of land-cover change by principal components analysis of multitemporal Landsat data. Remote Sensing of Environment. 10: 175-184.
Cambin E F. Strahler A H., 1994. Indicator of land cover change for change-vector analysis in multitemporal space at coarse spatial scale, INT. J. REMOTE SENSING. 15(10): 2099-2119.
Carpente G A, Grossbery S, Rosen D B, 1991. Fussy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Network. 4: 759～771
Carpenter, G. A., S. Grossberg, 1987a. A massively parallel architecture for a selforganizing neural pattern recognition machine. Computer Vision. Graphics and Image Processing. 37: 54-115.
Carpenter, G. A., S. Grossberg, 1987b. ART 2: Stable self-organization of pattern recognition codes for analog input pattems. Applied Optics. 26: 4919-4930.
Carpenter, G., M. Gjaja, S. Gopal, and C. Woodcock, 1997. ART networks in remote sensing. IEEE Transactions on Geoscience and Remote Sensing. 35(2): 308-325.
Cihlar J., 2000. Land cover mapping of large areas from satellites: status and research priorities. Int. J. Remote Sensing. 21(6 & 7): 1093-1113.
Cihlar, J., 1996. Identification of contaminated pixels in AVHRR composite images for studies of land biosphere. Remote Sensing of Environment. 56: 149-163.
Collins, J. B. and C. E. Woodcock, 1994. Change detection using the GrammSchmidt transformation applied to mapping forest mortality. Remote Sensing of Environment. 50: 267-279.
Colwell, J. E. and F. P. Weber, 1981. Forest change detection, Proceedings 15th International Symposium on Remote Sensing of Environment, Environment Research Institute of Michigan, Ann Arbor, MI. pp. 839-852.
Crutzen, P J., 1991. the changing photochemistry of the atmosphere[J]. Tellus.43:136—151
D.E. Goldberg. , 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley New York.
Defines, R. S. Townshend, J. R. G, 1994.NDVI-derived land cover classification at a global scale. International Journal of Remote Sensing. 15(17), 3567—3586.
DeFries, R., M. Hanson and J. Townshend, 1995. Global discrimination of land cover types from metrics derived from AVHRR pathfinder data. Remote Sensing of Environment. 54:209-222.
DeFries, R., M. Hanson, J. Townshend and R. Sohlberg, 1998. Global land cover classifications at 8 km spatial resolutions: The use of training data derived from landsat imagery in decision tree classifiers. International Journal of Remote Sensing, 19(6):3141 -3168
Duvernoy, J. and J. Leger, 1980. Karhunen-Loeve analysis of multispectral data from landscapes. Optics Communications 32(l):39-44.
Fischer, M. M., S. Gopal, P. Staufer, and K. Steinocher, 1997. Evaluation of neural pattern classifiers for a remote sensing application. Geographical Systems.
Fitzgerald, R. W. and B. G Lees, 1994. Assessing the classification accuracy of multisource remote sensing data. Remote Sensing of Environment 47:362-368.
Friedl, M. A., & Davis, F.W.,1994. Sourse of variation in radiometric surface temperature over a tallgrass prairie [J]. REMOTE SENS. ENVIRON. 48:1—17
Friedl, M. A., C. E. Brodley, and A. H. Strahler, 1999. Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEE Transactions on Geoscience and Remote Sensing 37(2):969-977.
Fung, t., Ledrew, E.,1987. Application of principal components analysis to change detection. Photogrammetry Engineering & Remote Sensing. 53(12): 1649-1658.
Goetz, S.J., 1997.Multisensor analysis of NDVI, surface temperature and biophysicalvariables at a mixed grassland site [J]. International Journal of Remote Sensing. 18 (15): 71-94
Gopal, S. and C. E. Woodcock, 1996. Remote sensing of forest change using artificial neural networks. IEEE Transactions on Geoscience and Remote Sensing. 34:1-7.
Gopal, S., C. Woodcock, and A. H. Strahler, 1996. Fuzzy ARTMAP classification of global land cover from AVHRR data set. Proceedings of the 1996 International Geoscience and Remote Sensing Symposium, Lincoln, Nebraska, May 27-31.1:538-540.
Gopal, S., D. M. Sklarew, and E. Lambin, 1994, Fuzzy-neural networks in multitemporal classification of land cover change in the Sahel. New Tools for Spatial Analysis, Proceedings of the Workshop, Lisbon, 18 to 20 November 1993, Office for Official Publications of the European Communities, Luxembourg, pp. 69-81.
Goward, S. N., Xue, Y., & Czajkowski, K. P., 2002. Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurement: A exploration with the simplified simple biosphere model [J]. REMOTE SENS. ENVIRON.79: 225—242.
Goward, S.N, Hope, A. S., 1989. Evaporation from combined reflected salar and emitted terrestrial radiation: Preliminary FIFE results from AVHRR data [J]. Advances in Space Research. 9:239-249
Goward, S.N., Waring, R.H., Dye, D. G, and Yang. J. 1994.Ecological remote sensing at OTTER: satellite macroscale observations [J]. Ecological applications. 4:322-343.
Goward, S.N.,Cruickshanks, GD., and Hope, A.S. 1985. Observed relation between thermal
emission and reflected radiatiance of a complex vegetated landscape [J]. Remote Sensing of Environment. 18: 137-146.
Grossberg S., 1987.The Adaptive Brain. New York: North-Holland.
Hansen, M., R. Dubayah and R. DeFries, 1996. Classification trees: An alternative to traditional land cover classifiers. International Journal of Remote Sensing. 17:1075-1081.
Hansen, M.C., Reed B., 2000. A comparison of the IGBP DISCover and University of Maryland lkm global land cover products. Int. J. Remote Sensing, vol. 21(6&7): 1365-1373
Hara, Y., R. G Atkins, S. H. Yueh, R. T. Shin, and J. A. Kong, 1994. Application of neural networks to radar image classification. IEEE Transactions on Geoscience and Remote Sensing. 32:100-112.
Hellden, U. and L. Eklundh, 1988. National Drought Impact Monitoring. A NOAA NDVI and Precipitation Data Study of Ethiopia, Lund Studies in Geography, Series C, No. 15, Lund University Press, Lund, Sweden, 55.
Henderson-Sellers, A. and A. J. Pitman, 1992. Land-surface schemes for future climate models: Specification, aggregation and heterogeneity. Journal of Geophysical Research. 97:2678-2696
Hepner, G F., T. Logan, N. Ritter and N. Bryant, 1990. Artificial neural network classification using a minimal training set: Comparison to conventional supervised classification. Photogrammetric Engineering and Remote Sensing. 56(4):469-473.
Holben, B., 1986. Characteristics of maximum-value composite images from multitemporal AVHRR data. International Journal of Remote Sensing. 7:1417- 1434.
Huete A R, Jackson R D, Post D F. 1985.Spectral response of a plant canopy with different soil backgrounds et al. Remote Sensing of Environment. 17:37-53.
Huete A R., 1988. A soil adjusted vegetation index (SAVI), Remote Sens. Environ.25:295~309.
Hyman, A. H. and M. J. Barnsley, 1997. On the potential for land cover mapping from multiple-view-angle (MVA) remotely sensed images. International Journal of Remote Sensing. 18(11): 2471-2475.
IGBP-DIS, 1995. The IGBP-DIS Global 1 Km Land Cover Data Set: A Validation Strategy, A. Belward, ed., IGBP-DIS, University de Paris, Paris, France.
J.H. Holland,1975. Adaptation in Natural and Artificial Systems. The University of Michigan Press.
John C, Price, 2003. Compare MODIS and ETM+ data for regional and global land classification [J]. Remote Sensing of Environment. 86:491-499.
Kaufman Y J and Tanre D,1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Tans. Geosci. Remote Sens. 30:261 ~270.
Kauth, R. J. and G S. Thomas, 1976. The tasseled cap—A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Symposium Proceedings on Machine Processing of Remotely Sensed Data, June 29-July 1, 1976, West Lafayette, IN, pp. 41-51.
Kelle M, Jacob D J.Wofsy S C, et al, 1991. Effects of tropical deforestation on global and regional atmospheric chemistry[J].Climatic Change 19:145-158.
Kyriakidis P. C. and J. L. Dungan, 2001. A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions. Environmental and Ecological statistics. 8: 311-330.
Lambin, E. F and A. H. Strahler, 1994a. Indicators of land-cover change for changevector analysis in multitemporal space at coarse spatial scales. International Journal Remote Sensing.
15:2099-2119.
Lambin, E. F. and A. H. Strahler, 1994b. Change-vector analysis: A tool to detect and categorize land-cover change processes using high temporal-resolution satellite data. Remote Sensing of Environment. 48:231-244.
Lambin, E. F. and D. Ehrlich, 1996a. The surface temperature-vegetation index space for land cover and land-cover change analysis. International Journal of Remote Sensing. 17(3):463-487.
Lambin, E. F. and D. Ehrlich, 1996b. Broad scale land-cover classification and interannual climatic variability. International Journal of Remote Sensing. 17(5):845-862.
Lambin, E. F. and Strahler, A. H. 1994a. The surface temperature-vegetation index space for land cover and land-cover change analysis. Int. J. remote sens. 17:463-487.
Lambin, E. F., 1996. Change detection at multiple temporal scales: Seasonal and annual variations in landscape variables. Photogrammetric Engineering and Remote Sensing. 62:931-938.
Lean J., 1989.Simulation of the regional climatic impact on Amazon deforrstation[J].Nature. 342:411-413
Lee, J., W. C. Weger, S. K. Sengupta, and R. M. Welch, 1990. A neural network approach to cloud classification. IEEE Transactions on Geoscience and Remote Sensing. 28:846-855.
Lenney, M. P., C. E. Woodcock and H. Hamdi, 1996. The status of agricultural lands in Egypt: The use of multitemporal NDVI features derived from Landsat TM. Remote Sensing of Environment. 56(1):8-17
Li Sh, Zhuang E X.,2003. The Decsion Tree Classification and Its Application in Land Cover [J]. Areal Research and Development. 22(1): 17-21.
Liang,S. ,2001. Land-cover classification methods for multi-year AVHRR data.International Journal of Remote Sensing.22(8):1479-1493.
Liu H Q and Huete A R., 1995.A feedback based modification of the NDVI to minimize canopy back ground and atmospheric noise. IEEE Tans. Geosci. Remote Sens. 33:457-465.
Liu J., and Buheaosier, 2000, Study on spatial-temporal feature of modern land-use change in China: using remote sensing technique. Quaternary Sciences. 3, 229-239
Lloyd D., 1990. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. Int. J. Remote Sensing. 11(12): 2269-2279.
Loveland T R, Reed B C, Brown J F, et al., 2000. Development of a global land cover characteristics database and IGBP discover from 1-km AVHRR data[J]. International Journal of Remote Sensing. 21 (6/7) :1303-1330.
Loveland, T. R., J. W. Merchant, D. O. Ohlen, and J. F. Brown, 1991. Development of a land-cover characteristics database for the conterminous U. S. Photogrammetric Engineering and Remote Sensing .57:1453-1463.
Loveland, T. R., J. W. Merchant, J. F. Brown, D. O. Ohlen, B. C. Reed, P. Olson, and J. Hutchinson, 1995. Seasonal land-cover of the United States. Annals Association of American Geographers. 85:339-355.
M.A Friedl, D.K Mclver, et al, 2002. Global land cover mapping from MODIS: algorithms and early results. Remote Sens, of Environ.. 83: 287-302
Malila, W. A., 1980. Change vector analysis: An approach for detecting forest changes with Landsat, Proceedings 6th Annual Symposium on Machine Processing of Remotely Sensed Data, Laboratory for the Application of Remote Sensing, Purdue University, pp. 326-335.
Malingreau, J.-P., C. J. Tucker and N. Laporte, 1989. AVHRR for monitoring global tropical deforestation. International Journal of Remote Sensing. 10:855-867.
Mandelbrot, B. B., 1967. How long is the coast of Britain? Statistical self-similarity and fractional dimension. Science. 156: 636-638
Mandelbrot, B. B., 1977. Fractals: Form, Chance and Dimension. San Francisco, CA: Freeman.
Mark. A. Friedl et al,2002.Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment. 83: 287-302.
Monteith J L. 1973. Principles of environment physics. London : Edward Arnold Press.
Moran, M. S., Clarke, T. R., Inoue, Y., & Vidal, A., 1994. Estimating crop water deficitusing the relation between surface-air temperature and spectral vegetation index[J]. REMOTE SENS. ENVIRON.49: 246-63
Muchoney, D. M. and B. Haack, 1994. Change detection for monitoring forest defoliation. Photogrammetric Engineering and Remote Sensing 60(10): 1243-1251.
Muchoney. D. M., J. Borak, H. Chi, M. Friedl, J. Hodges, N. Morrow and A. Strahler, 1999. Application of the MODIS Global Supervised Classification Model to Vegetation and Land Cover Mapping of Central America. International Journal of Remote Sensing, 21 (6/7) :1115-1138.
Nelson, R. and B. Holben, 1986. Identifying deforestation in Brazil using multiresolution satellite data. International Journal of Remote Sensing 7:429-448
Nemani, Pierce, Running, Goward, 1993. Developing satellite derived estimates of surface moisture status [J]. Journal of Applied Meteorology. 32: 548-557
Nemani, R. R., &Running, S. W., 1989. Estimation of regional surface resistance to evapotranspiration from NDVI and thirmal IR AVHRR data [J]. Journal of Applied Meteorology. 7: 1-11.
Nemani, R. R., Running, S. W., Pielke, R. A., and Chase, T. N.,1996. Global vegetation cover changes from coarse resolution satellite data. J. Geophs. Res. 101(D3):7157-7162.
Nemani, R., and S. W. Running, 1996, Implementing a hierarchical global vegetation classification in ecosystem function models. Journal of Vegetation Science. 7:337-346.
Nemani, R., and S. W. Running, 1997, Land cover characterization using multitemporal red, near-IR, and thermal-IR data from NOAA/AVHRR. Ecological Applications 7:79-90.
Olsen, E.R., R.D. Ramsey, and D.S.. Winn. 1993. A Modified Fractal Dimension as a Measure of Landscape Diversity. Photogrammetric Engineering & Remote Sensing. 59(10): 1517-1520
Ormsby J P, Choudary B J, Owe M. 1987. Vegetation spatial variability and its effecton vegetation indices. International Journal of Remote Sensing. 8:1301-1306.
Paola,J.D. and R.A.Schowengerdt, 1995a. A review and analysis of backpropagation neural networks for classification of remote-sensed multi-spectral imagery. International Journal of Remote Sensing. 16(16):3033-3058.
Paola,J.D. and R.A.Schowengerdt, 1997. The effect of neural network structure on a multispectral land-use/land-cover classification. Photogrammetric Engineering and Remote Sensing.5:535~544.
Price J C. 1990. Using spatial context in satellite data to infer regional scale evapotranspiration. I.E.E.E. Transaction in Geosciences & Remote Sensing. 28:940-948.
Prihodko, L., Goward, S. N., 1997. Estimation of air temperature from remotely sensedsurface observations[J]. REMOTE SENS. ENVIRON. 60(3): 335-346
Quinlan, J. R., 1993, C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.
R.C.G, Choudhury, B.J., 1991. Analysis of nomalized difference and surface temperature observations over southeastern Australia [J]. International Journal of Remote Sensing. 12(10): 2021-2044
Reed, B.D., J.F. Brown, D. VanderZee, T.R. Loveland, J.W. Merchant and D.O. Ohlen. 1994. Measuring phenological variability from satellite imagery. J. Vegetation Science. 5: 703-714.
Rozanov B G, Targulian V,Orlov D S, et al, 1990.the earth as transformed by human Action[C].Cambridge: Cambridge University Press.pp: 203-214
Ruimy,A.,Saugier,B. ,1994. Methodology for the estimation of terrestrial net primary production from remotely sensed data.Jounal of Geophisical Research.99(D3):5263—5283.
Rumelhart D, Hinton G, Williams R., 1986. Learning Representation by Back-Propagating Errores. Nature. 323:533-536
Running S. W., et al, 1995. A Remote Sensing Based Vegetation Classification Logic for Global Land Cover Analysis[J]. Remote Sens, of Environ. 51: 39-48
Safavian, S. R. and D. Landgrebe, 1991, A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics. 21:660-674.
Saunders,R,W. ,1990. The determination of broad band surface albedo from AVHRR visible and near-infrared radiances.International Journal of Remote Sensing. 11(1):49—67.
Sellers, P. J., 1985. Canopy reflectance photosynthesis and transpiration. International Journal of Remote Sensing. 6:1335.
Sellers, P. J., Dickinson, R. E., Randall, D. A., Betts, A. K., Hall, F. G, Mooney, H. A., Nobre, C. A., Sato, N., Field, C. B., and Henderson-Sellers, A., 1997. Modeling the exchanges of energy, water, and carbon between continents and atmosphere. Science. 275: 502-509
Sellers, P. J.,Tucker, C. J.,Collatz.GJ., et al, 1994. A global 1 o by lo NDVI data set for climate studies part 2:the generation of global fields of terrestrial biophysical parameters from the NDVI. International Journal of Remote Sensing. 15: 3519-3545.
Special Issue: Global land cover data set validation, Photogramatric Engineering & Remote Sensing, Vol. 65, No.9, 1999.
Stowe, D. A., R. L. Tinney, and J. E. Estes, 1980. Deriving land use/land cover change statistics from Landsat: A study of prime agricultural land. Proceedings 14th International Symposium on Remote Sensing of Environment, Environment Research Institute of Michigan, Ann Arbor, MI, pp. 1227-1237.
Strahler A. Global Land Cover: Approaches to Validation. GLC2000 Meeting JRC Ispra 3,2002
Swain, P. H., and H. Hauska, 1977. The decision tree classifier: Design and potential. IEEE Transactions on Geoscience and Remote Sensing, GE-15:142-147
Thomlinson John R., Paul V. Bolstad, and Warren B. Cohen, 1999. Coordinating Methodologies for Scaling Landcover Classifications from Site-Specific to Global: Steps toward Validating Global Map Products. REMOTE SENS. ENVIRON. 70:16-28.
Townshend, J. R. G, C. Justice, W. Li, C. Gurney, and J. McManus, 1991. Global land cover classification by remote sensing: Present capabilities and future possibilities. Remote Sensing of Environment 35:243-255.
Townshend, J. R. G, Justice, C. O., Kalb, V., 1987.Characterization and classification of South American land cover types using satellite data. International Journal of Remote Sensing. 7:1395-1416.
Tso, B. and Mather, P. M., 2001. Classification methods for remotely sensed data. Taylor-Francis Inc. New York, NY, USA.
Tucker C J, Sellera P J. 1986. Satellite remote sensing of primary production. International Journal of Remote Sensing.7:1395-1416
Tucker, C. J., B. N. Holben and T. E. Goff, 1984. Intensive forest clearing in Rondonia, Brazil, as detected by satellite remote sensing. Remote Sensing of Environment. 15:255-261.
Tucker, C. J., C. O. Justice and S. D. Prince, 1986. Monitoring the grasslands of the Sahel 1984-1985. International Journal of Remote Sensing. 7: 1571-1581.
Tucker, C. J., H. E. Dregne and W. W. Newcomb, 1991. Expansion and contraction of the Sahara Desert from 1980 to 1990. Science. 253: 299-301.
Tucker, C. J., Townshend, J. R. G. and Goff, T. E. 1985. Continental land cover classification using meteorological satellite data. Science, vol. 227: 369-375.
Turner II BL, Moss RH, Skole DL. Relating land use and global land cover change, IGBP Report No. 24 and HDP report No. 5. Stockholm: IGBP, 1993.
Turner II BL, Skole D, Sanderson S et al. Land-use and land-cover change science/researchplan, IGBP Report No. 35 and HDP Report No. 7. Stochkholm: GIBP, 1995.
Tzeng, Y. C., K. S. Chen, W.-L. Kao and A. K. Fung, 1994. A dynamic learning neural network for remote sensing applications. IEEE Transactions on Geoscience and Remote Sensing. 32: 1096-1103.
Wang J, Dong G R, et al, 2000. Primary Study on the Multi-Layer remote Sensing Information Extraction of Desertion of Desertification Land Types by Using Decision Tree Technology [J]. Jouranl of Desert Research. 20(3): 243-247
Woodcock, C. E. and S. Ryherd, 1989. Generation of texture images using adaptive windows. Technical Papers, 55th Annual Meeting, American Society of Photogrammetry and Remote Sensing, Baltimore, 1989, pp. 11-22.
Woodwell, G. M., R. A. Houghton, T. A. Stone, R. F. Nelson and W. Kovalick, 1987. Deforestation in the tropics: New measurements in the Amazon Basin using Landsat and NOAA Advanced Very High Resolution Radiometer imagery. Journal of Geophysical Research 92(D2): 2157-2163.
X. Zhan, R. A. Sohlberg, et al, 2002. Detection of land cover changes using MODIS 250 m data. Remote Sens. of Environ., 83: 336-350
Yoshida, T. and S. Omatu, 1994. Neural network approach to land cover mapping. IEEE Transactions on Geoscience and Remote Sensing. 32: 1103-1110.
Zhang F, Xiong Zh, Kou N., 2002. Research on Rice Fine Classification using Hyper-spectral Remote Sensing Imagines [J]. Journal of Wuhan University of Technology. 24(10): 36-39.
Zhao P, Feng X Zh, Lin G F., 2003. The Decision Tree Algorithm of Automatically Extracting Residential Information from SPOT Images[J]. Journal of Remote Sensing. 7(4): 309-315
Zhu, Z-L., and L. Yang, 1996., Characteristics of the 1-km AVHRR data set for North America. International Journal of Remote Sensing. 17: 1915-1924.
陈百明,1997.试论中国土地利用和土地覆被变化及其人类驱动力研究.自然资源.2:31-35。
陈晋,何春阳,卓莉,2001.基于变化向量分析(CVA)的土地利用,覆盖变化动态监测(Ⅱ)———变化类型的确定方法.遥感学报.5(5):346-353
陈佑启,Peter H.Verburg,2000.中国土地利用/土地覆盖的多尺度空间分布特征分析,地理科学.20(3):197-202.
陈云浩,李晓兵等,2002.1983—1992年中国陆地植被NDVI演变特征的变化矢量分析.遥感学报.6(1):12-18.
陈云浩,李晓兵,史培军,2003.整合陆地表面温度与植被指数信息进行地表覆盖变化研究.第四纪研究.23(3)
冯春,陈建平.2003.土地利用/土地覆盖研究中遥感图像分类精度的提高方法.地理与地理信息科学.19(3):26-28
江东,王乃斌,杨小唤,刘红辉,2002..植被指数一地面温度特征空间的生态学内涵及其应用.地理科学进展.2(2):18-24.
李敏强,寇纪松等编著,2002.遗传算法的基本理论有应用[M].北京:科学出版社.
李晓兵,陈云浩,李霞,2003.基于多尺度遥感测量的区域土地覆盖格局研究.植物生态学报.27(5):577-586.
李晓兵,陈云浩,张云霞.2001.多空间分辨率遥感数据监测土地覆盖特征的比较研究.第四纪研究.21(6)
李晓兵,1999.国际土地利用/土地覆盖变化的环境影响研究.地理科学进展.14(4).395-400.
李秀彬,1996.全球环境变化研究核心领域—土地利用/土地覆盖变化的国际研究动向[J].地理学报.51(67):553-557
刘纪远,张增祥等,2003.20世纪90年代中国土地利用变化特征及成因分析.地理研究.22(1):1-12
刘纪远主编,1996.中国资源环境遥感宏观调查与动态研究.北京:中国科学技术出版社.Pp:1-43
刘硕,2002.国际土地利用与土地覆盖变化对生态环境影响的研究.世界林业研究.15(6):38-45
刘正军.2003.高维遥感数据土地覆盖特征提取与分类研究.博士学位论文.北京:中国科学院遥感应用研究所.
罗迪.1999.基于AVHRR和地理空间数据的中国土地覆盖分类研究.硕士学位论文.北京:中国科学院遥感应用研究所.
潘耀忠,李晓兵,何春阳.2000.中国土地覆盖综合分类研究——基于NOAA/AVHRR和Holdridge PE,第四纪研究.20(3):270-281
史培军,宫鹏等,2000,土地利用/覆盖变化的研究方法与实践.北京:科学出版社.
唐俊梅,张树文.2002.基于MODIS数据的宏观土地利用/土地覆盖监测研究.遥感技术与应用.17(2):104-107
田国良,徐兴奎,柳钦火,2000.用于地表能量交换的动态地表特征模式.遥感学.4(增刊):121-128.
王鹏新,龚健雅,李小文,2001.条件植被温度指数及其在干旱监测中的应用.武汉大学学报信息科学版.26(5):412-418
王正兴、刘闯、HUETE Alfredo,2003.植被指数研究进展:从AVHRR-NDVI到MODIS-EVI.生态学报.23(5):979-987
吴传钧,郭焕成,1994.中国土地利用.北京:科学出版社.
香宝,刘纪远,2003.东亚土地覆盖对ENSO事件的响应特征.遥感学报.7(4):316-320
香宝,刘纪远,2002.基于遥感和GIS的东亚土地覆盖年际变化研究.中国图象图形学报.A7(10):1017-1020
肖乾广,陈维英等,1995.气象卫星监测干旱灾害的研究,卫星气象中心技术报告,第9509期.
辛景峰,2003.中国科学院遥感应用研究所博士后出站报告
延昊,2002.中国土地覆盖变化与环境影响遥感研究.博士学位论文.北京:中国科学院遥感应用研究所.
杨建刚,2001.人工神经网络实用教程[M].杭州:浙江大学出版社.
杨立民,朱智良,1999.全球及区域尺度土地覆盖土地利用遥感研究的现状和展望.自然资源学报.14(1):340-344.
叶笃正,符淙斌,1994.全球变化的主要科学问题[J].大气科学.18(4):498-512.
张炳智,张丽等,2002.利用遥感技术进行全球测图项目中的中国土地利用覆盖和植被分类图的制作.测绘科学.27(1):43-46.
张华国,黄韦艮,周长宝等,2003.利用IKONOS卫星数据和分形方法研究南麂岛土地覆盖状况.生态学报.23(8):1539-1547.
张继贤,2003.论土地利用与覆盖变化遥感信息提取技术框架.中国土地科学.17(4):31-36
张树誉,颜胜安,2002.NOAA/AVHRR资料在陕西省土地覆盖分类中的应用.中国农业资源与区划.23(3):29-31.