草原产草量模型方法研究
|
摘要
本文以锡林郭勒草原为研究区,以草原产草量模型方法为研究对象,以植被指数、气象数据为主要变量,运用相关分析、一元回归、多元回归及岭回归等不同数理统计方法,构建了估产模型,对模型进行了精度验证,应用优选模型估测2008年产草量。本研究选择产草量方法热点问题为研究内容,具有一定的理论意义和应用价值。本文取得了如下的进展和创新:
(1)基于MODIS植被指数和地面数据,利用回归分析,通过精度验证和评价,建立了以NDVI与地面样方数据拟合的三次曲线模型: y=-901.287+15548.395x-35026.104x2+34419.533x3
其中,y为锡林郭勒估测地面产量,x为同步时间内的NDVI,该模型的决定系数为0.394,模型精度为69.31%。
通过将遥感模型用于不同草地类型草原产量估测并进行精度验证,发现NDVI遥感模型总估产精度高于EVI遥感模型,在草地生产力水平偏高和偏低的区域,二次、三次曲线拟合模型的精度明显高于线性模型;
(2)通过对不同气象因子、植被指数和地面样方数据进行多元回归分析,将遥感模型加入有效降水、有效干燥度,模型为: y=1064.357+7003.184NDVI-1111.753有效降水+353.123有效干燥度
其中,y为锡林郭勒估测地面产量,该模型的值R2为0.4350,模型精度有所提高,精度为70.66%;
(3)以EVI、有效降水、有效温度、有效干燥度作为变量与地面样方进行拟合的岭回归产草量模型: y=1897.02+10577.47EVI-521.02有效降水-472.021有效温度+141.44有效干燥度
其中,y为锡林郭勒估测地面产量,该模型的值R2为0.4530,模型精度高于常规多元回归产草量模型,达到73.40%;
(4)通过对上述三种模型的比较得出2008年产草量估产优选模型为以EVI、有效降水、有效温度、有效干燥度作为变量与地面样方为变量的岭回归产草量模型。
(5)将气象因子引入了产草量模型的建立中,为从量化上探讨植物物候期对于生长旺季产草量水平提供了一种可行的思路及手段。
This thesis chose Xilinguole grassland, the typical grassland in the semi-arid temperate zone, as a research area. The method of grass yield model was studied by some statistics methods, such as related analysis, single regression, multiple regression and ridge regression. The model’s precision was validated. The yield of grass was estimated by the model which was chosen. The thesis chooses the hotpot problem as study content and has theory meaning and application value. The main development and innovation of the thesis are the followings:
(1)The cubic remote sensing estimation model was found in MODIS-VI and square sample with regression method. y=-901.287+15548.395x-35026.104x2+34419.533x3
y is the estimated grassland production in Xilinguole. x is the synchronous NDVI. The R2 is 0.394 and the precision is 69.31%.
The remote sensing model was used by estimating the different types of grass yeild and validating the precision. The precisions of NDVI models were higher than EVI model. In addition, the precision of cubic and quadratic model was significantly higher than the linear model in the area of higher and lower grassland production.
(2)The synthetic estimation model was found in meteorology data, MODIS-VI and square sample by using multiple regression method. The estimation model was chosen as the model of NDVI, valid precipitation and the valid dryness. y=1064.357+7003.184NDVI-1111.753 valid precipitation +353.123 valid dryness
y is the estimated grassland production in Xilinguole. The R2 is 0.4350 and the precision is 70.66%.
(3)The ridge regression model was built by meteorology data, MODIS-VI and square sample. The estimation model was chosen as the model of EVI, valid precipitation, valid temperature and the valid dryness. y=1897.02+10577.47EVI-521.02 valid precipitation -472.021 valid temperature +141.44 valid dryness
y is the estimated grassland production in Xilinguole. The R2 is 0.4540. The precision is higher than multiple regression. The precision is 73.40%.
(4)The ridge regression model i.e. the model of EVI, valid precipitation, valid temperature and the valid dryness was chosen as the grassland production estimated model in 2008 by contrasting the three models.
(5) The meteorological factors were lead to the grass yield model. An ideal and methods was lead for discussing the effect of Phonology to grass yield.
(1)基于MODIS植被指数和地面数据,利用回归分析,通过精度验证和评价,建立了以NDVI与地面样方数据拟合的三次曲线模型: y=-901.287+15548.395x-35026.104x2+34419.533x3
其中,y为锡林郭勒估测地面产量,x为同步时间内的NDVI,该模型的决定系数为0.394,模型精度为69.31%。
通过将遥感模型用于不同草地类型草原产量估测并进行精度验证,发现NDVI遥感模型总估产精度高于EVI遥感模型,在草地生产力水平偏高和偏低的区域,二次、三次曲线拟合模型的精度明显高于线性模型;
(2)通过对不同气象因子、植被指数和地面样方数据进行多元回归分析,将遥感模型加入有效降水、有效干燥度,模型为: y=1064.357+7003.184NDVI-1111.753有效降水+353.123有效干燥度
其中,y为锡林郭勒估测地面产量,该模型的值R2为0.4350,模型精度有所提高,精度为70.66%;
(3)以EVI、有效降水、有效温度、有效干燥度作为变量与地面样方进行拟合的岭回归产草量模型: y=1897.02+10577.47EVI-521.02有效降水-472.021有效温度+141.44有效干燥度
其中,y为锡林郭勒估测地面产量,该模型的值R2为0.4530,模型精度高于常规多元回归产草量模型,达到73.40%;
(4)通过对上述三种模型的比较得出2008年产草量估产优选模型为以EVI、有效降水、有效温度、有效干燥度作为变量与地面样方为变量的岭回归产草量模型。
(5)将气象因子引入了产草量模型的建立中,为从量化上探讨植物物候期对于生长旺季产草量水平提供了一种可行的思路及手段。
This thesis chose Xilinguole grassland, the typical grassland in the semi-arid temperate zone, as a research area. The method of grass yield model was studied by some statistics methods, such as related analysis, single regression, multiple regression and ridge regression. The model’s precision was validated. The yield of grass was estimated by the model which was chosen. The thesis chooses the hotpot problem as study content and has theory meaning and application value. The main development and innovation of the thesis are the followings:
(1)The cubic remote sensing estimation model was found in MODIS-VI and square sample with regression method. y=-901.287+15548.395x-35026.104x2+34419.533x3
y is the estimated grassland production in Xilinguole. x is the synchronous NDVI. The R2 is 0.394 and the precision is 69.31%.
The remote sensing model was used by estimating the different types of grass yeild and validating the precision. The precisions of NDVI models were higher than EVI model. In addition, the precision of cubic and quadratic model was significantly higher than the linear model in the area of higher and lower grassland production.
(2)The synthetic estimation model was found in meteorology data, MODIS-VI and square sample by using multiple regression method. The estimation model was chosen as the model of NDVI, valid precipitation and the valid dryness. y=1064.357+7003.184NDVI-1111.753 valid precipitation +353.123 valid dryness
y is the estimated grassland production in Xilinguole. The R2 is 0.4350 and the precision is 70.66%.
(3)The ridge regression model was built by meteorology data, MODIS-VI and square sample. The estimation model was chosen as the model of EVI, valid precipitation, valid temperature and the valid dryness. y=1897.02+10577.47EVI-521.02 valid precipitation -472.021 valid temperature +141.44 valid dryness
y is the estimated grassland production in Xilinguole. The R2 is 0.4540. The precision is higher than multiple regression. The precision is 73.40%.
(4)The ridge regression model i.e. the model of EVI, valid precipitation, valid temperature and the valid dryness was chosen as the grassland production estimated model in 2008 by contrasting the three models.
(5) The meteorological factors were lead to the grass yield model. An ideal and methods was lead for discussing the effect of Phonology to grass yield.
引文
1. Aman A.,Randriamananantena H.P.,Podaire A,et al..Up scale integration of normalized difference vegetation index: the problem of spatial heterogeneity[J].Geosciences and Remote Sensing. 1992,30(2):326-338.
2. Anderson G L.Hanson J D,Hass R H.Evaluating Langsat Thematic Mapper Derived Vegetation Indics For Estimating Above–Ground Biomass 11 Semiarid Ranglands[J].Remote Sensing Of Environment. 1993,45(2):165-175.
3. Baret F. and Guyot G..Potentials and limits of vegetation indices for LAI and APAR assessment[J].Remote Sensing of Enviroment.1991,35(2-3):161-173.
4. Beck, Louisa R.et al.A comparison of greenness measures in two semi-arid grasslands[J].Climatic change. 1990,17(2-3):287-303.
5. Bella C.DL.,Faibre R.,Ruget F.,et al..Remote sensing capabilities to estimate pasture production in France [J].International Journal of Remote secsing. 2004, 25(23):5359-5372.
6. Benie G.B.,Kabore S.S.,Goita K.,et al..Remote sensing-based spatio-temporal modeling to predict biomass in Sahelian grazing ecosystem [J].Ecological Modelling. 2005, 184(2-4):341-354.
7. C J,Vanpraet C,Sharman M J,et al.Satellite remote sensing of total herba-ceous biomass production in the Senegalese Sahel:1980～1984[J].Remote Sens Environ. 1985,17:233-249.
8. David P.R..Jordan S.B.,Sadashiva D..The MODIS Land product quality assessment approach[J].Remote Sensing of Environment. 2002,83(1-2):62-76.
9. Davidson A. and Csillag F..The influence of vegetation index and spatial resolution on a two-date remote sensing-derived relation to V4 species coverage [J].Remote Sensing of Environment. 2001, 75:138-151.
10. Diallo O, Diouf A., Hanan N P, et al. AVHRR monitoring of savanna primary production in Senegal.West Africa:1987-88[J]. Remote Sensing of Environment. 1991,12:1259～1279.
11. Dorothy K Hall, Senior Member. Sea Ice Surface Temperature Product From MODIS [J]. Geosciences and Remote Sensing. 2004,(10):76-87.
12. Eric F.Lambin and Danniele Ehrilich. Land-cover Change in Sub-Saharan Africa(1982-1991): Application of a Change Index Based on Remotely Sensed Surface Temperature and Vegetation Indices at a Continental Scale[J]. Remote Sensing of Environment. 1997,61:151-200.
13. Farrar T.J. Nicholson S.E., Lare A.R..The influence of soil type on the relationships between NDVI,rainfall,and soil moisture in semiarid Botswana.II.NDVI response to soil moisture[J]. Remote Sensing of Environment.1994, 50(2):121-133.
14. Fisher M J, Rao I M, Lascano CE,et al.Ayarzama Pasture soils as carbon sink[J]. Nature.1995:376 -472, 473.
15. Flanagan, D.C., Foster G.R., Neibling W.H.,et al..Simplified equations for filter strip design [J].Trans. Amer. Soc. Agric. Eng. 1989, 32:2001-2007.
16. Friedl M A,Michaelsen J,Davis F W,et al..Estimating Grassland Biomass And Leaf Area Index Using Ground And Satellite Data[J].International Journal Of Remote Sensing. 1994, 15(7):1401-1420.
17. Guo X., Price K.P., Stiles J.M..Modeling Biophysical Factors for Grasslands Using Landsat TM Data in Eastern Kansas [J].The Transaction of Kansas Academy of Science.2000, 102(3-4):122-138.
18. Gupta HV, Sorooshian S, Yapo PO. Toward improved calibration of hydrologic models: Multiple and no commensurable measures of information [J]. Water Resources. 1998,34(4):751-763
19. H.Ikeda,K. Okamoto and M. Fukuhara. Estimation of aboveground grassland phytomass with a growth model using Landsat TM and climate data [J]. International Journal Of Remote Sensing.1999, 20(11):2283-2294.
20. Hassett R C,Wood H L,Carter J et al. A field method for statewide ground-truthing of a spatial pasture growth model [J].Australian Journal of Experimental Agriculture. 2000, 40:1069-1079.
21. Hirata M, Koga N, Shin J.O.H., et al. Vegetation Classification By Satellite Image Processing In A Dry Area Of North-Eastern Syria[J].International Journal Of Remote Sensing.2000,22(4):507-516.
22. Horler D H N, Dock ray M, Barber J. The Red Edge of Plant Leaf Reflectance [J]. International Journal Of Remote Sensing. 1983, 4:273-288.
23. Huete A, Didan K,M iura T, et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices[J]. Remote Sensing of Environment. 2002, 83(1-2),195-213.
24. Huete,A.R..A soil adjusted vegetation index (SAVI) [J]. Remote Sensing of Environment. 1988,25(2),295-309·
25. Jeffrey T.M., Jeffrey L.P, Christopher O.J..A framework for the validation of MODIS Land products [J].Remote sensing of Environment. 2002, 83(1-2):77-96.
26. Jonathan H.Smith,Stephen V.Stehman.Effects of landscape character-istics on land-cover class accuracy[J].Remote Sensing of Environment. 2003 (84):342-349.
27. Jordan C.F.. Derivation of leaf-area index from quality of light on the forest floor [J].Ecology. 1969, 50:663-666.
28. Junichi Imanishi,Yukihiro Morimoto,Ayumi Imanishi,et al. The independent detection of drought stress and leaf density using hyper spectral resolution data[J].Landscape Ecol Eng. 2007,3:55-65.
29. Justice C.O., Hiernaux P.H.Y. Monitoring the grasslands of the Sahel using NOAA/AVHRR data:Niger 1983[J]. International Journal Of Remote Sensing. 1986,7:1475～1497.
30. Justice,C.O., Holben B.N., Gwynne M D.. Monitoring east African vegetation using AVHRR data [J]. International Journal Of Remote Sensing. 1986,7:1453～1474.
31. Ulbricht K.A., Heekendorff W.D.. Satellite images for recognition of landscape and land use changes [J]. Journal of Photogrammetry and Remote Sensing. 1998, 53(4):235-243.
32. Kaufman Y J, Tanre D. Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS [J].IEEE Trans on Geoscience Remote Sensing. 1992, 30: 261-270.
33. Kawamura K, Akiyama T. Monitoring of forage conditions with MODIS imagery in the Xilingol steppe[J].Inner Mongolia International Journal of Remote Sensing. 2005,26(7):1423-1436.
34. Lefsky M A, Cohen W B, Harding D J. Lidar Remote Sensing of Above-ground Biomass in Three Biomes[J].Global Ecology and Biogeography. 2002,(5):393-399.
35. Legates DR, McCabe GJ. Evaluating the use of“goodness-of-fit”measures in hydrologic and hydroclimate model validation. Water Resources. 1999,35(1): 233-241
36. Lei Ji, Albert J.P.. Performance evaluation of spectral vegetation indices using a statistical sensitivity function [J].Remote sensing of Environmnet. 2007, 106(1):59-65.
37. Leprieur.C.Y.H., Kerr J.M.Pichon. Critical assessment of vegetation indices from AVHRR in a semi-arid environment [J].International Journal of Remote Sensing. 1996, 17(13):2549-2563.
38. Lillesand,T.M,Kiefer,R.W.. Remote Sensing and Image Interpretation (fourth edition) [M].John Wiley & Sons, Inc. 2000.
39. Lim K H,Kwoh Llew S C.Development of a MODIS Data Reception System for Regional Environmental Monitoring in Southeast Asia[C]. Singapore: 22nd Asian Conference on Remote Sensing. 2001.
40. Liu H.Q.,Huete A.R. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise [J].IEEE Transactions Geosciences and Remote Sensing. 1995, 33:457-465.
41. Lyon J G,D Yuan,R S Lunetta.A change detection experiment using vegetation indices[J]. Photogrammetric Engineering and Remote Sensing. 1998, 64(2):143-50.
42. Merrill E H,Bramble-Brodahl M K,Marrs R W,et al..Estimation of green herbaceous phytomass from Landsat MSS data in Yellowstone National Park [J]. Journal of Range Management. 1993, 46(2):151-157.
43. Mintor,T.B.,lancaster J.Wade T.G.et al. Evaluating change in rangeland condition using multitemporal AVHRR dam and geographic information system analysis[J].Environment Monitoring and Assessment. 1999,59:211-223.
44. Mokany K, Raison RJ, Prokushkin AS. Critical analysis of root: shoot ratios in terrest rial biomes [J]. Global Change Biology. 2005 ,11:1-13
45. Moleele N,Ringrose S,Arnberg W,et al.Assessment of vegetation indexes useful for browse (forage) prediction in semi-arid rangelands[J].International Journal of Remote Sensing. 2001,22(5):741-756.
46. Moulin S L, Kergoat N Viovy G Dedieu. Global scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements [J].Journal of climate.1991, 10(6):1154-1170.
47. Muldavin E H, Neville P, Happer G.Indices of grassland biodiversity in the Chihuahuan desert ecoregion derived from remote sensing [J].Conservation Biology. 2001, 15(4):844-855.
48. Mutang A, Skidmore A K. Narrow band vegetation indices overcome the saturation problem in biomass estimation [J].International Journal of Remote Sensing. 2004, 25(19):3999-4014.
49. Nash JE, Stucliffe JV. River flow forecasting through conceptual models: A discussion of principles. J Hydrol. 1970,10(3):282-290.
50. New man J E.Climate change impact on the growing season of the North American cornbelt[J].Biometeorology. 1980,7(2):128-142.
51. Ni J. Carbon storage in grasslands of China [J].Journal of Arid Environments.2002, 50(2):205-218.
52. Parry ML. Duinker PN. The potential effects of climate change on agriculture. In: Intergovernmental panel on climatic change [J].The IPCC Impacts Assessment.Geneva.WMO and UNEP. 1990,2-1,2-45.
53. Parton W.J.,Scurlock J M.O.,Ojima D.S.,et al..Impact of climate change on grassland production and soil carbon worldwide [J].Global Change Biology.1995, 1(1):13-22.
54. Parton W J,Scurlock J M O, Ojima D S,et al. Observations and modeling of biomass and soil organic - matter dynamics for the grassland biome worldwide[J]. Global Biogeochemical Cycles.1993, 7(4):785-809.
55. Paruelo J M H E,Epstein W K,Lauenroth I C,Burke.ANPP estimates from NDVI for the central grassland region of the United States[J].Ecology.1997,78(3):953-958.
56. Paruelo J M, Golluscio R A. Range assessment using remote sensing in Northwest Patagonia (Argentina) [J].Journal of Range Management. 1994, 47(6):498-502.
57. Prince S D,Tucker C J. Satellte remote sensing of rangelands in Botswana NOAA AVHRR and herbaceous vegetation[J].International Journal of Remote Sensing. 1986, 7(11):1555-1570.
58. Prince S D. Satellite remote sensing of primary production: comparison of results for Sahelian grasslands: 1981～1988 [J]. International Journal of Remote Sensing. 1991, 12:1301-1312.
59. Purevdor J T,Tateisah I R,Ishiyama T,Honda Y. Relationship between percent vegetation cover and vegetation indices[J].International Journal of Remote sensing. 1998,19(18):3519-3535.
60. Richardson A.J.,Wiegand C.L..Distinguishing vegetation from soil background information [J]. Photogrammetric Engineering and Remote Sensing. 1977, 43(12):541-552.
61. Rick L.L., William J.R. Comparisons among vegetation indices and band wise regression in a highly disturbed, heterogeneous landscape: Mount St.Helens Washington [J].Remote sensing of Eevironment. 1998, 64:91-102.
62. Ritchie J C,Everitt J H,.Escobar D E et al. Airbome measurement of rangeland canopy cover and distribution[J].Journal of Range Management.1992,45(2):189-193.
63. Shunlin Liang,Hongling Fang,Mingzhen Chen. Validating MODIS land surface reflectance and albedo products: methods and preliminary results [J].Remote Sensing of Enviroment. 2002, 83(1-2):149-162.
64. Sooroshian S, Gupta VK. Model calibration. In: Singh VP, eds. Computer Models of Watershed Hydrology. Littleton, Colo.: Water Resources Publications. 1995, 23-68.
65. Stephen H Hallett, Robert J A Jones Compilation of an accumulated temperature database for use in an environmental information system [J].Agricultural and Forest Meteorology. 1993,63(1):21-34.
66. Taylor B F et al. Determination of seasonal and inter annual variation in New Zealand pasture growth from NOAA-7 data [J].Remote Sensing of Environment. 1985, 18(2):177-192.
67. Townshend J R G,Justice C O.Analysis of the dynamics of African vegetation using the Normalized difference Vegetation Index[J]. International Journal of Remote Sensing. 1986,7:1435-1445.
68. Tucker C J et al. Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel: 1980-1984[J].Remote Sensing of Environment. 1985, 17(3):233-249.
69. Tucker C.J. Red and photographic infrared linear combinations for monitoring vegetation [J]. Remote Sensing of Environment. 1979, 8(2):127-150.
70. Tucker Purevdor J T,Tateshi R,Ishiyama T,Honda Y.Relationship between percent vegetation cover and vegetation indices[J].International Journal of Remote Sensing. 1998,19(18):3519-3535.
71. Tumer DP,Ritts WD,Cohen WB,et al..Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring [J].Global Change Biology. 2005, 11(4):666-684.
72. W.J.D.van Leeuwen,Alfredo R.Huete and Trevor W.Laing. MODIS vegetation index compositing approach:Aprototype with AVHRR Data[J]. Remote Sensing of Environment. 1999,69(3):164-284
73. Warren B.Cohen and Christopher O.Justice.Validating MODIS Terrestrial Ecology Products:Linking In Situ and Satellite Mewsurement[J]. Remote Sensing of Environment. 1999,70(1):1-3.
74. Weiss E,Marsh E P,Firman E S.Applicationg of NOAA-AVHRR time-series data to assess changes in Saudi Arabia’s Rangelands[J].International Journal of Remote Sensing. 2001,22(6):1005-10027.
75. Wylie B K, Harrington Jr., Prince S D, Denda I, Satellite and ground-based pasture production assessment in niger.1986-1988[J]. International Journal of Remote Sensing. 1991,12(6):1281-1300.
76. Xiao X, Ojima D S, Parton W J, et al. Sensitivity of Inner Mongolia grassland to climate change [J].Journal of Biogeography.1995,22:643-648.
77. Xu B, Yang X C, Tao W G, et al., MODIS-based Remote Sensing Monitoring upon the Grass Production in China.Second Recent Advances in Quantitative[J]. Remote Sensing of Environment, ISBN:84-370-6533X.Prined in Spain by Guada Imoresores. 2006,698-703.
78. Yang F, Ichii K, White M A., et al. Developing a continental-scale measure of gross primary production by combining MODIS and AmerifFux data through Support Vector Machine approach [J]. Remote Sensing of Environment. 2007, 110(1):109-122.
79. Zhang L, Wylie B, Loveland T, et al. Evaluation and comparison of gross primary production estimates for the northern Great Plains grasslands [J].Remote sensing of Environment. 2007, 106(2),173-189.
80. Zhao M, Heinsch F A, Nemani, S. W. Running, et al. Improvements of the MODIS terrestrial gross and net primary production global data set [J]. Remote Sensing of Environment. 2005,95(2):164-175.
81.陈超,赖国毅.SPSS 17中文版统计分析典型实例精粹[M].北京:电子工业出版社.2010.
82.陈全功,卫亚星,梁天刚.NOAA资料在草地资源监测中的应用研究[J].草业科学.1994, 11(1):56-60.
83.陈全功.草原资源遥感监测系统的开发研究[J].资源生态环境网络研究动态杂志.1992, (2):23-25.
84.陈世雄.草原在维护国家生态安全中的地位和作用. http://www.caaa.cn/show/ article/2010/03/175165.html.
85.陈述彭,等.遥感地学分析[M].北京:测绘出版社出版.1990.
86.丁志,童庆禧,郑兰芬,等.应用气象卫星图象资料进行草场生物量测量方法的初步研究[J],干旱区研究.1986,2:8-13.
87.额尔敦布和,恩和,双喜.内蒙古草原荒漠化问题及其防治对策研究[M],内蒙古:内蒙古大学出版社.2002:45-50.
88.樊江文等.草地生态系统碳储量及其影响因素[J].中国草地.2003,6 (25):51-58.
89.顾卫,史培军,刘杨,等.渤海和黄海北部地区负积温资源的时空分布特征[J].自然资源学报,2002,17(2):168-173.
90.国家MODIS数据中心http://satellite.cma.gov.cn/eos/pro_eos.html.
91.韩汉鹏.试验统计引论[M].北京:中国林业出版社.2006.
92.黄海,罗友丰,陈志英,等.SPSS 10.0 for Windows统计分析[M].北京:人民邮电出版社.2001.
93.黄敬峰,李军.草地生产力遥感动力模拟模型[J].草地学报.2005,13:10-14.
94.靳立亚,李静,王新,等.近!"年来中国西北地区干湿状况时空分布[J].地理学报.2004,59(6):847-854.
95.李博.中国北方草地畜牧业动态监测研究[M].北京:中国农业科技出版社.1993,1-128.
96.李海亮,赵军.草地遥感估产的原理与方法[J].草业科学.2009,26(3),34-38.
97.李建龙,蒋平,戴若兰.RS, GPS和GIS集成系统在新疆北部天然草地估产技术中的应用进展[J].生态学报.1998, 18(5):504-510.
98.李建龙,任继周,胡自治,等.草地遥感应用动态与研究进展[J].草原科学. 1996,13(1):55-60.
99.李轶冰,杨改河,王得祥.江河源区四十多年来干湿变化分析[J].西北农林科技大学学报(自然科学版).2006,34(3):73-77.
100.李育中,李博,内蒙古锡林河流域羊草草原生物量动态的研究[J].中国草地.1991,1:5-8.
101.林而达.全球气候变化对中国农业影响的模拟[M].北京:中国农业科技出版社.1997:92-97.
102.刘及东等.基于水热因子波动的呼伦贝尔草原产草量模型[J].环境科学研究.2010(3).
103.刘建梅,裴铁璠,王安志,杨弘.高山峡谷地区森林流域分布式降雨-径流模型的构建与验证[J].应用生态学报.2005,16(9): 1638-1644.
104.刘勤,严昌荣,何文清,等.黄河流域近40a积温动态变化研究[J].自然资源学报.2009,24(1):147-153.
105.刘志刚,王英舜等,内蒙古锡林郭勒盟牧业气候区划.北京:气象出版社. 2006.
106.罗晓玲,张萍,任鹏,等.非参数Spearman秩相关检验在油气识别中的应用[J].沈阳大学学报.2008,20(3):99-102.
107.马文红等.内蒙古温带草地植被的碳储量干旱区资源与环境[J].2006,(20)3:192-195.
108.毛学诗,朱庆斌.天然牧草产量与气象条件的关系[J].中国草原.1983(4),46-50.
109.朴世龙,方精云,贺金生.中国草地植被生物量及其空间分布格局[J].植物生态学报.2004,28(4):491-498.
110.浦瑞良,宫鹏.高光谱遥感及其应用[M].北京:高等教育出版社.2000,83.
111.史培军,李博,李忠厚,等.大面积草地遥感估产技术研究--以内蒙古锡林郭勒草原估产为例[J].草地学报.1994,2(1),9-13.
112.孙力,安刚,高枞亭,等.中国东北地区地表水资源与气候变化关系的研究[J].地理科学.2004,24(1):42-49
113.孙艳玲,延晓东,谢德体.基于布迪科指标的中国植被-气候关系研究[J].资源科学.2005,28(3) :23-29
114.陶伟国.内蒙古草原产草量遥感监测方法研究.中国农业科学院硕士论文.2007.
115.陶伟国,徐斌,刘丽军,等.不同利用状况下草原遥感估产模型[J].生态学杂志. 2007,26(3):332-337.
116.陶伟国,徐斌,缪建明等.草地遥感估产中不同尺度信息源关联方法对比及评价[J].中国草地学报.2006,28(4):68-74.
117.陶伟国,徐斌,杨秀春.草原产草量遥感估算方法发展趋势及影响因素[J].草业学报. 2007,16(2):1-8.
118.童庆禧,丁志,郑兰芬,等.应用NOAA气象卫星图像资料估算草场生物量方法的初步研究[J].自然资源学报.1986,1(2):87-95.
119.王海梅.锡林郭勒地区气候变化规律与植被覆盖变化驱动机制研究.内蒙古农业大学博士学位论文.2009.
120.王秀珍,黄敬峰.利用AVHRR资料监测北疆北部天然草地[J].中国农业气象.1995, (6): 43-47.
121.王玉辉,周广胜.内蒙古地区羊草草原植被对温度变化的动态响应[J].植物生态学报.2004,28(4):507-514.
122.王正兴,刘闯,HUETE Alfredo.植被指数研究进展:从AVHRR_NDVI到MODIS—EVI[J].生态学报.2003,23(5):179-987.
123.王正兴,刘闯,赵冰茹,等.利用MODIS增强型植被指数反演草地地上生物量[J].兰州大学学报(自然科学版).2005,41(2),10-16.
124.武晓波,肖春生,魏成阶,等.用生产效率模型估算阿鲁科尔沁旗南部草场产草量[J].第15届全国遥感学术交流会. 2005.8.
125.徐斌,杨秀春,陶伟国,等.2007.中国草原产草遥感监测[J].生态学报.27(2):405-413.
126.徐希孺,金丽芳,赁常恭,等.利用NOAA-CCT估计内蒙古草场产草量的原理和方法[J].地理学报.1985,40(4):333-346.
127.杨国奇.从数字看草原.内蒙古农牧信息网. http://www.nmagri.gov.cn/Html/ 2007-11-2/2_2110_2007-11-2_130885.html.2007.
128.杨建平,丁永建,陈仁升,等.近50年来中国干湿气候界线的10年际波动[J].地理学报.2002,57(6),655-661.
129.杨秀春,徐斌,朱晓华,等.北方农牧交错带草原产草量遥感监测模型[J].地理研究.2007,26(2),213-221.
130.张存厚,李云鹏,李兴华,等.内蒙古近30年10℃积温变化特征分析[J].干旱区资源与环境.2009,23(5):100-105.
131.张建华.作物估产的遥感技术—数值模拟方法[J].干旱区资源与环境.2000,14(2):82-86.
132.张文彤.SPSS 11统计分析教程[M].北京:北京希望电子出版社.2002.
133.张湘平,邹逢兴,李大琪.线性回归模型岭迹选择主元方法及应用研究[J].航天控制.1998,(1):33-38
134.赵冰茹,刘闯,刘爱军,等.利用MODIS-NDVI进行草地估产研究——以内蒙古锡林郭勒草地为例[J].草业科学.2004,21(8):12-15.
135.赵冰茹,马龙.基于MODIS EVI的内蒙古草地多源信息综合分类研究[J].浙江大学学报(农业与生命科学版).2007,33(3):342-347.
136.赵英时.遥感应用分析原理与方法[M].北京:科学出版社.2003.
137.中国行政区划.http://www.xzqh.org/quhua/15nmg/25xilinguole.htm.
138.中国科学院内蒙古宁夏综合考察队.内蒙古植被[M].北京:科学出版社. 1985.
139.中华人民共和国民政部.中华人民共和国行政区划简册2006[M].中国社会出版社.2006.
2. Anderson G L.Hanson J D,Hass R H.Evaluating Langsat Thematic Mapper Derived Vegetation Indics For Estimating Above–Ground Biomass 11 Semiarid Ranglands[J].Remote Sensing Of Environment. 1993,45(2):165-175.
3. Baret F. and Guyot G..Potentials and limits of vegetation indices for LAI and APAR assessment[J].Remote Sensing of Enviroment.1991,35(2-3):161-173.
4. Beck, Louisa R.et al.A comparison of greenness measures in two semi-arid grasslands[J].Climatic change. 1990,17(2-3):287-303.
5. Bella C.DL.,Faibre R.,Ruget F.,et al..Remote sensing capabilities to estimate pasture production in France [J].International Journal of Remote secsing. 2004, 25(23):5359-5372.
6. Benie G.B.,Kabore S.S.,Goita K.,et al..Remote sensing-based spatio-temporal modeling to predict biomass in Sahelian grazing ecosystem [J].Ecological Modelling. 2005, 184(2-4):341-354.
7. C J,Vanpraet C,Sharman M J,et al.Satellite remote sensing of total herba-ceous biomass production in the Senegalese Sahel:1980～1984[J].Remote Sens Environ. 1985,17:233-249.
8. David P.R..Jordan S.B.,Sadashiva D..The MODIS Land product quality assessment approach[J].Remote Sensing of Environment. 2002,83(1-2):62-76.
9. Davidson A. and Csillag F..The influence of vegetation index and spatial resolution on a two-date remote sensing-derived relation to V4 species coverage [J].Remote Sensing of Environment. 2001, 75:138-151.
10. Diallo O, Diouf A., Hanan N P, et al. AVHRR monitoring of savanna primary production in Senegal.West Africa:1987-88[J]. Remote Sensing of Environment. 1991,12:1259～1279.
11. Dorothy K Hall, Senior Member. Sea Ice Surface Temperature Product From MODIS [J]. Geosciences and Remote Sensing. 2004,(10):76-87.
12. Eric F.Lambin and Danniele Ehrilich. Land-cover Change in Sub-Saharan Africa(1982-1991): Application of a Change Index Based on Remotely Sensed Surface Temperature and Vegetation Indices at a Continental Scale[J]. Remote Sensing of Environment. 1997,61:151-200.
13. Farrar T.J. Nicholson S.E., Lare A.R..The influence of soil type on the relationships between NDVI,rainfall,and soil moisture in semiarid Botswana.II.NDVI response to soil moisture[J]. Remote Sensing of Environment.1994, 50(2):121-133.
14. Fisher M J, Rao I M, Lascano CE,et al.Ayarzama Pasture soils as carbon sink[J]. Nature.1995:376 -472, 473.
15. Flanagan, D.C., Foster G.R., Neibling W.H.,et al..Simplified equations for filter strip design [J].Trans. Amer. Soc. Agric. Eng. 1989, 32:2001-2007.
16. Friedl M A,Michaelsen J,Davis F W,et al..Estimating Grassland Biomass And Leaf Area Index Using Ground And Satellite Data[J].International Journal Of Remote Sensing. 1994, 15(7):1401-1420.
17. Guo X., Price K.P., Stiles J.M..Modeling Biophysical Factors for Grasslands Using Landsat TM Data in Eastern Kansas [J].The Transaction of Kansas Academy of Science.2000, 102(3-4):122-138.
18. Gupta HV, Sorooshian S, Yapo PO. Toward improved calibration of hydrologic models: Multiple and no commensurable measures of information [J]. Water Resources. 1998,34(4):751-763
19. H.Ikeda,K. Okamoto and M. Fukuhara. Estimation of aboveground grassland phytomass with a growth model using Landsat TM and climate data [J]. International Journal Of Remote Sensing.1999, 20(11):2283-2294.
20. Hassett R C,Wood H L,Carter J et al. A field method for statewide ground-truthing of a spatial pasture growth model [J].Australian Journal of Experimental Agriculture. 2000, 40:1069-1079.
21. Hirata M, Koga N, Shin J.O.H., et al. Vegetation Classification By Satellite Image Processing In A Dry Area Of North-Eastern Syria[J].International Journal Of Remote Sensing.2000,22(4):507-516.
22. Horler D H N, Dock ray M, Barber J. The Red Edge of Plant Leaf Reflectance [J]. International Journal Of Remote Sensing. 1983, 4:273-288.
23. Huete A, Didan K,M iura T, et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices[J]. Remote Sensing of Environment. 2002, 83(1-2),195-213.
24. Huete,A.R..A soil adjusted vegetation index (SAVI) [J]. Remote Sensing of Environment. 1988,25(2),295-309·
25. Jeffrey T.M., Jeffrey L.P, Christopher O.J..A framework for the validation of MODIS Land products [J].Remote sensing of Environment. 2002, 83(1-2):77-96.
26. Jonathan H.Smith,Stephen V.Stehman.Effects of landscape character-istics on land-cover class accuracy[J].Remote Sensing of Environment. 2003 (84):342-349.
27. Jordan C.F.. Derivation of leaf-area index from quality of light on the forest floor [J].Ecology. 1969, 50:663-666.
28. Junichi Imanishi,Yukihiro Morimoto,Ayumi Imanishi,et al. The independent detection of drought stress and leaf density using hyper spectral resolution data[J].Landscape Ecol Eng. 2007,3:55-65.
29. Justice C.O., Hiernaux P.H.Y. Monitoring the grasslands of the Sahel using NOAA/AVHRR data:Niger 1983[J]. International Journal Of Remote Sensing. 1986,7:1475～1497.
30. Justice,C.O., Holben B.N., Gwynne M D.. Monitoring east African vegetation using AVHRR data [J]. International Journal Of Remote Sensing. 1986,7:1453～1474.
31. Ulbricht K.A., Heekendorff W.D.. Satellite images for recognition of landscape and land use changes [J]. Journal of Photogrammetry and Remote Sensing. 1998, 53(4):235-243.
32. Kaufman Y J, Tanre D. Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS [J].IEEE Trans on Geoscience Remote Sensing. 1992, 30: 261-270.
33. Kawamura K, Akiyama T. Monitoring of forage conditions with MODIS imagery in the Xilingol steppe[J].Inner Mongolia International Journal of Remote Sensing. 2005,26(7):1423-1436.
34. Lefsky M A, Cohen W B, Harding D J. Lidar Remote Sensing of Above-ground Biomass in Three Biomes[J].Global Ecology and Biogeography. 2002,(5):393-399.
35. Legates DR, McCabe GJ. Evaluating the use of“goodness-of-fit”measures in hydrologic and hydroclimate model validation. Water Resources. 1999,35(1): 233-241
36. Lei Ji, Albert J.P.. Performance evaluation of spectral vegetation indices using a statistical sensitivity function [J].Remote sensing of Environmnet. 2007, 106(1):59-65.
37. Leprieur.C.Y.H., Kerr J.M.Pichon. Critical assessment of vegetation indices from AVHRR in a semi-arid environment [J].International Journal of Remote Sensing. 1996, 17(13):2549-2563.
38. Lillesand,T.M,Kiefer,R.W.. Remote Sensing and Image Interpretation (fourth edition) [M].John Wiley & Sons, Inc. 2000.
39. Lim K H,Kwoh Llew S C.Development of a MODIS Data Reception System for Regional Environmental Monitoring in Southeast Asia[C]. Singapore: 22nd Asian Conference on Remote Sensing. 2001.
40. Liu H.Q.,Huete A.R. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise [J].IEEE Transactions Geosciences and Remote Sensing. 1995, 33:457-465.
41. Lyon J G,D Yuan,R S Lunetta.A change detection experiment using vegetation indices[J]. Photogrammetric Engineering and Remote Sensing. 1998, 64(2):143-50.
42. Merrill E H,Bramble-Brodahl M K,Marrs R W,et al..Estimation of green herbaceous phytomass from Landsat MSS data in Yellowstone National Park [J]. Journal of Range Management. 1993, 46(2):151-157.
43. Mintor,T.B.,lancaster J.Wade T.G.et al. Evaluating change in rangeland condition using multitemporal AVHRR dam and geographic information system analysis[J].Environment Monitoring and Assessment. 1999,59:211-223.
44. Mokany K, Raison RJ, Prokushkin AS. Critical analysis of root: shoot ratios in terrest rial biomes [J]. Global Change Biology. 2005 ,11:1-13
45. Moleele N,Ringrose S,Arnberg W,et al.Assessment of vegetation indexes useful for browse (forage) prediction in semi-arid rangelands[J].International Journal of Remote Sensing. 2001,22(5):741-756.
46. Moulin S L, Kergoat N Viovy G Dedieu. Global scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements [J].Journal of climate.1991, 10(6):1154-1170.
47. Muldavin E H, Neville P, Happer G.Indices of grassland biodiversity in the Chihuahuan desert ecoregion derived from remote sensing [J].Conservation Biology. 2001, 15(4):844-855.
48. Mutang A, Skidmore A K. Narrow band vegetation indices overcome the saturation problem in biomass estimation [J].International Journal of Remote Sensing. 2004, 25(19):3999-4014.
49. Nash JE, Stucliffe JV. River flow forecasting through conceptual models: A discussion of principles. J Hydrol. 1970,10(3):282-290.
50. New man J E.Climate change impact on the growing season of the North American cornbelt[J].Biometeorology. 1980,7(2):128-142.
51. Ni J. Carbon storage in grasslands of China [J].Journal of Arid Environments.2002, 50(2):205-218.
52. Parry ML. Duinker PN. The potential effects of climate change on agriculture. In: Intergovernmental panel on climatic change [J].The IPCC Impacts Assessment.Geneva.WMO and UNEP. 1990,2-1,2-45.
53. Parton W.J.,Scurlock J M.O.,Ojima D.S.,et al..Impact of climate change on grassland production and soil carbon worldwide [J].Global Change Biology.1995, 1(1):13-22.
54. Parton W J,Scurlock J M O, Ojima D S,et al. Observations and modeling of biomass and soil organic - matter dynamics for the grassland biome worldwide[J]. Global Biogeochemical Cycles.1993, 7(4):785-809.
55. Paruelo J M H E,Epstein W K,Lauenroth I C,Burke.ANPP estimates from NDVI for the central grassland region of the United States[J].Ecology.1997,78(3):953-958.
56. Paruelo J M, Golluscio R A. Range assessment using remote sensing in Northwest Patagonia (Argentina) [J].Journal of Range Management. 1994, 47(6):498-502.
57. Prince S D,Tucker C J. Satellte remote sensing of rangelands in Botswana NOAA AVHRR and herbaceous vegetation[J].International Journal of Remote Sensing. 1986, 7(11):1555-1570.
58. Prince S D. Satellite remote sensing of primary production: comparison of results for Sahelian grasslands: 1981～1988 [J]. International Journal of Remote Sensing. 1991, 12:1301-1312.
59. Purevdor J T,Tateisah I R,Ishiyama T,Honda Y. Relationship between percent vegetation cover and vegetation indices[J].International Journal of Remote sensing. 1998,19(18):3519-3535.
60. Richardson A.J.,Wiegand C.L..Distinguishing vegetation from soil background information [J]. Photogrammetric Engineering and Remote Sensing. 1977, 43(12):541-552.
61. Rick L.L., William J.R. Comparisons among vegetation indices and band wise regression in a highly disturbed, heterogeneous landscape: Mount St.Helens Washington [J].Remote sensing of Eevironment. 1998, 64:91-102.
62. Ritchie J C,Everitt J H,.Escobar D E et al. Airbome measurement of rangeland canopy cover and distribution[J].Journal of Range Management.1992,45(2):189-193.
63. Shunlin Liang,Hongling Fang,Mingzhen Chen. Validating MODIS land surface reflectance and albedo products: methods and preliminary results [J].Remote Sensing of Enviroment. 2002, 83(1-2):149-162.
64. Sooroshian S, Gupta VK. Model calibration. In: Singh VP, eds. Computer Models of Watershed Hydrology. Littleton, Colo.: Water Resources Publications. 1995, 23-68.
65. Stephen H Hallett, Robert J A Jones Compilation of an accumulated temperature database for use in an environmental information system [J].Agricultural and Forest Meteorology. 1993,63(1):21-34.
66. Taylor B F et al. Determination of seasonal and inter annual variation in New Zealand pasture growth from NOAA-7 data [J].Remote Sensing of Environment. 1985, 18(2):177-192.
67. Townshend J R G,Justice C O.Analysis of the dynamics of African vegetation using the Normalized difference Vegetation Index[J]. International Journal of Remote Sensing. 1986,7:1435-1445.
68. Tucker C J et al. Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel: 1980-1984[J].Remote Sensing of Environment. 1985, 17(3):233-249.
69. Tucker C.J. Red and photographic infrared linear combinations for monitoring vegetation [J]. Remote Sensing of Environment. 1979, 8(2):127-150.
70. Tucker Purevdor J T,Tateshi R,Ishiyama T,Honda Y.Relationship between percent vegetation cover and vegetation indices[J].International Journal of Remote Sensing. 1998,19(18):3519-3535.
71. Tumer DP,Ritts WD,Cohen WB,et al..Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring [J].Global Change Biology. 2005, 11(4):666-684.
72. W.J.D.van Leeuwen,Alfredo R.Huete and Trevor W.Laing. MODIS vegetation index compositing approach:Aprototype with AVHRR Data[J]. Remote Sensing of Environment. 1999,69(3):164-284
73. Warren B.Cohen and Christopher O.Justice.Validating MODIS Terrestrial Ecology Products:Linking In Situ and Satellite Mewsurement[J]. Remote Sensing of Environment. 1999,70(1):1-3.
74. Weiss E,Marsh E P,Firman E S.Applicationg of NOAA-AVHRR time-series data to assess changes in Saudi Arabia’s Rangelands[J].International Journal of Remote Sensing. 2001,22(6):1005-10027.
75. Wylie B K, Harrington Jr., Prince S D, Denda I, Satellite and ground-based pasture production assessment in niger.1986-1988[J]. International Journal of Remote Sensing. 1991,12(6):1281-1300.
76. Xiao X, Ojima D S, Parton W J, et al. Sensitivity of Inner Mongolia grassland to climate change [J].Journal of Biogeography.1995,22:643-648.
77. Xu B, Yang X C, Tao W G, et al., MODIS-based Remote Sensing Monitoring upon the Grass Production in China.Second Recent Advances in Quantitative[J]. Remote Sensing of Environment, ISBN:84-370-6533X.Prined in Spain by Guada Imoresores. 2006,698-703.
78. Yang F, Ichii K, White M A., et al. Developing a continental-scale measure of gross primary production by combining MODIS and AmerifFux data through Support Vector Machine approach [J]. Remote Sensing of Environment. 2007, 110(1):109-122.
79. Zhang L, Wylie B, Loveland T, et al. Evaluation and comparison of gross primary production estimates for the northern Great Plains grasslands [J].Remote sensing of Environment. 2007, 106(2),173-189.
80. Zhao M, Heinsch F A, Nemani, S. W. Running, et al. Improvements of the MODIS terrestrial gross and net primary production global data set [J]. Remote Sensing of Environment. 2005,95(2):164-175.
81.陈超,赖国毅.SPSS 17中文版统计分析典型实例精粹[M].北京:电子工业出版社.2010.
82.陈全功,卫亚星,梁天刚.NOAA资料在草地资源监测中的应用研究[J].草业科学.1994, 11(1):56-60.
83.陈全功.草原资源遥感监测系统的开发研究[J].资源生态环境网络研究动态杂志.1992, (2):23-25.
84.陈世雄.草原在维护国家生态安全中的地位和作用. http://www.caaa.cn/show/ article/2010/03/175165.html.
85.陈述彭,等.遥感地学分析[M].北京:测绘出版社出版.1990.
86.丁志,童庆禧,郑兰芬,等.应用气象卫星图象资料进行草场生物量测量方法的初步研究[J],干旱区研究.1986,2:8-13.
87.额尔敦布和,恩和,双喜.内蒙古草原荒漠化问题及其防治对策研究[M],内蒙古:内蒙古大学出版社.2002:45-50.
88.樊江文等.草地生态系统碳储量及其影响因素[J].中国草地.2003,6 (25):51-58.
89.顾卫,史培军,刘杨,等.渤海和黄海北部地区负积温资源的时空分布特征[J].自然资源学报,2002,17(2):168-173.
90.国家MODIS数据中心http://satellite.cma.gov.cn/eos/pro_eos.html.
91.韩汉鹏.试验统计引论[M].北京:中国林业出版社.2006.
92.黄海,罗友丰,陈志英,等.SPSS 10.0 for Windows统计分析[M].北京:人民邮电出版社.2001.
93.黄敬峰,李军.草地生产力遥感动力模拟模型[J].草地学报.2005,13:10-14.
94.靳立亚,李静,王新,等.近!"年来中国西北地区干湿状况时空分布[J].地理学报.2004,59(6):847-854.
95.李博.中国北方草地畜牧业动态监测研究[M].北京:中国农业科技出版社.1993,1-128.
96.李海亮,赵军.草地遥感估产的原理与方法[J].草业科学.2009,26(3),34-38.
97.李建龙,蒋平,戴若兰.RS, GPS和GIS集成系统在新疆北部天然草地估产技术中的应用进展[J].生态学报.1998, 18(5):504-510.
98.李建龙,任继周,胡自治,等.草地遥感应用动态与研究进展[J].草原科学. 1996,13(1):55-60.
99.李轶冰,杨改河,王得祥.江河源区四十多年来干湿变化分析[J].西北农林科技大学学报(自然科学版).2006,34(3):73-77.
100.李育中,李博,内蒙古锡林河流域羊草草原生物量动态的研究[J].中国草地.1991,1:5-8.
101.林而达.全球气候变化对中国农业影响的模拟[M].北京:中国农业科技出版社.1997:92-97.
102.刘及东等.基于水热因子波动的呼伦贝尔草原产草量模型[J].环境科学研究.2010(3).
103.刘建梅,裴铁璠,王安志,杨弘.高山峡谷地区森林流域分布式降雨-径流模型的构建与验证[J].应用生态学报.2005,16(9): 1638-1644.
104.刘勤,严昌荣,何文清,等.黄河流域近40a积温动态变化研究[J].自然资源学报.2009,24(1):147-153.
105.刘志刚,王英舜等,内蒙古锡林郭勒盟牧业气候区划.北京:气象出版社. 2006.
106.罗晓玲,张萍,任鹏,等.非参数Spearman秩相关检验在油气识别中的应用[J].沈阳大学学报.2008,20(3):99-102.
107.马文红等.内蒙古温带草地植被的碳储量干旱区资源与环境[J].2006,(20)3:192-195.
108.毛学诗,朱庆斌.天然牧草产量与气象条件的关系[J].中国草原.1983(4),46-50.
109.朴世龙,方精云,贺金生.中国草地植被生物量及其空间分布格局[J].植物生态学报.2004,28(4):491-498.
110.浦瑞良,宫鹏.高光谱遥感及其应用[M].北京:高等教育出版社.2000,83.
111.史培军,李博,李忠厚,等.大面积草地遥感估产技术研究--以内蒙古锡林郭勒草原估产为例[J].草地学报.1994,2(1),9-13.
112.孙力,安刚,高枞亭,等.中国东北地区地表水资源与气候变化关系的研究[J].地理科学.2004,24(1):42-49
113.孙艳玲,延晓东,谢德体.基于布迪科指标的中国植被-气候关系研究[J].资源科学.2005,28(3) :23-29
114.陶伟国.内蒙古草原产草量遥感监测方法研究.中国农业科学院硕士论文.2007.
115.陶伟国,徐斌,刘丽军,等.不同利用状况下草原遥感估产模型[J].生态学杂志. 2007,26(3):332-337.
116.陶伟国,徐斌,缪建明等.草地遥感估产中不同尺度信息源关联方法对比及评价[J].中国草地学报.2006,28(4):68-74.
117.陶伟国,徐斌,杨秀春.草原产草量遥感估算方法发展趋势及影响因素[J].草业学报. 2007,16(2):1-8.
118.童庆禧,丁志,郑兰芬,等.应用NOAA气象卫星图像资料估算草场生物量方法的初步研究[J].自然资源学报.1986,1(2):87-95.
119.王海梅.锡林郭勒地区气候变化规律与植被覆盖变化驱动机制研究.内蒙古农业大学博士学位论文.2009.
120.王秀珍,黄敬峰.利用AVHRR资料监测北疆北部天然草地[J].中国农业气象.1995, (6): 43-47.
121.王玉辉,周广胜.内蒙古地区羊草草原植被对温度变化的动态响应[J].植物生态学报.2004,28(4):507-514.
122.王正兴,刘闯,HUETE Alfredo.植被指数研究进展:从AVHRR_NDVI到MODIS—EVI[J].生态学报.2003,23(5):179-987.
123.王正兴,刘闯,赵冰茹,等.利用MODIS增强型植被指数反演草地地上生物量[J].兰州大学学报(自然科学版).2005,41(2),10-16.
124.武晓波,肖春生,魏成阶,等.用生产效率模型估算阿鲁科尔沁旗南部草场产草量[J].第15届全国遥感学术交流会. 2005.8.
125.徐斌,杨秀春,陶伟国,等.2007.中国草原产草遥感监测[J].生态学报.27(2):405-413.
126.徐希孺,金丽芳,赁常恭,等.利用NOAA-CCT估计内蒙古草场产草量的原理和方法[J].地理学报.1985,40(4):333-346.
127.杨国奇.从数字看草原.内蒙古农牧信息网. http://www.nmagri.gov.cn/Html/ 2007-11-2/2_2110_2007-11-2_130885.html.2007.
128.杨建平,丁永建,陈仁升,等.近50年来中国干湿气候界线的10年际波动[J].地理学报.2002,57(6),655-661.
129.杨秀春,徐斌,朱晓华,等.北方农牧交错带草原产草量遥感监测模型[J].地理研究.2007,26(2),213-221.
130.张存厚,李云鹏,李兴华,等.内蒙古近30年10℃积温变化特征分析[J].干旱区资源与环境.2009,23(5):100-105.
131.张建华.作物估产的遥感技术—数值模拟方法[J].干旱区资源与环境.2000,14(2):82-86.
132.张文彤.SPSS 11统计分析教程[M].北京:北京希望电子出版社.2002.
133.张湘平,邹逢兴,李大琪.线性回归模型岭迹选择主元方法及应用研究[J].航天控制.1998,(1):33-38
134.赵冰茹,刘闯,刘爱军,等.利用MODIS-NDVI进行草地估产研究——以内蒙古锡林郭勒草地为例[J].草业科学.2004,21(8):12-15.
135.赵冰茹,马龙.基于MODIS EVI的内蒙古草地多源信息综合分类研究[J].浙江大学学报(农业与生命科学版).2007,33(3):342-347.
136.赵英时.遥感应用分析原理与方法[M].北京:科学出版社.2003.
137.中国行政区划.http://www.xzqh.org/quhua/15nmg/25xilinguole.htm.
138.中国科学院内蒙古宁夏综合考察队.内蒙古植被[M].北京:科学出版社. 1985.
139.中华人民共和国民政部.中华人民共和国行政区划简册2006[M].中国社会出版社.2006.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |