基于遥感技术的雅鲁藏布江源区植被类型及覆盖度研究
|
摘要
遥感科学技术的形成与发展,以及与全球定位系统等地理信息系统科学的融合、渗透和统一,形成了新型的对地观测系统,遥感信息在科学研究和国民经济中的应用越来越受到各行各业的重视。
本文研究对象是环保公益专项“青藏高原生态退化及环境管理研究”中一个重要研究区雅鲁藏布江源区,根据野外实地勘察数据,结合可用的遥感影像数据,对源区的草地类型进行遥感识别,并对植被盖度进行遥感的定量反演,为源区生态退化研究服务。
本文选取成像质量较好的2009年8月1日左右的Landsat5 TM影像,根据不同草地类型的波段组合特征,结合源区1：100万植被类型图、DEM和NDVI数据,构建草地识别的判别规则,利用决策树分类的方法对雅鲁藏布江源区草地类型进行遥感识别研究；以TM派生数据NDVI、RVI、VI3、PVI、DVI、MSAVI、SAVI、TM4/TM5为主要分析因子,结合野外植被样方调查数据,选取相关性最高的因子与实测植被盖度建立回归模型,利用该模型生成植被盖度分布图。研究结果表明：
(1)不同的草地由于其生境不同,受背景土壤类型和土壤水分的干扰和影响,一定程度上加大了不同草种之间的光谱可分性,利用不同波段组合特征进行草地类型识别能够达到较好的效果；总体上,利用地物光谱信息的面向对象分类在分类效果上有了很大的提高,弥补了传统的基于像素统计特征分类方法的不足。同时光谱信息参与面向对象分类,在信息提取之前,通过野外采样数据,找出草地之间的区分规律,较光谱未参与面向对象分类的目标性强,提高了分类精度。和传统的监督分类法相比,基于波段组合特征的决策树分类法具有较高的识别精度：采样决策树分类的总体精度可以达到62.5%,采用监督分类的总体精度47.1%；决策树分类的Kappa系数0.493,监督分类的Kappa系数为0.268。和监督分类相比,决策树分类的总分类精度提高15.4%,Kappa系数提高0.225。本文采用的决策树分类法仅是基于影像的光谱特征、波段间的相互运算以及高程等信息,并没有加入其它分类特征,其分类精度不是特别高；若在以后的分类决策树模型中加入纹理等信息,则决策树分类法的优势会更明显,分类精度会更高。
(2)草地盖度实测值与对应的TM4/TM5值变化趋势基本相同,且TM4/TM5增强了不同退化程度草地植被的光谱反射值的差异,以TM4/TM5为因子构建的草地盖度估测模型能够准确反映出源区草地盖度的基本分布趋势,模型的整体预测精度较高：RMSE为0.074,相对误差为19.6%,草地植被盖度模型验证精度达到0.91,达到模型验证精度要求。
A new earth observation system, which is formed by remote sensing system, global positioning system and geographic information system, has been paid attention to by all industries in the scientific research and application in the national economy.
The study area of this paper is the source region of Yarlung Zangbo river, which is the important study area in the environmental protection special public-"Research on Ecological Degradation and Envirmental Management".According to quadrat investigating, we recognize the types of grassland and inversion the vegetation coverage, which server the research on ecological degradation in the source region of Yarlung Zangbo River.
We selected the Landsat5 TM images of high quality in August 1st,2009. According to different features of spectral combination, we build the recognition rules of grass identification with the data of 1:400million vegetation map, DEM, and NDVI. We did the research on grass recognition in the source region of Yarlung Zangbo river based on decision tree classification. This paper analyzed the NDVI、RVI、VI3、PVI、DVI、MSAVI、SAVI and TM4/TM5, combining quadrat investigating, and we select the TM4/TM5 as the main factor to construct model with vegetation coverage. We calculate the vegetation coverage of the image with this model and create the image of the distribution of grassland, which is of great important for grasping the desertification of grassland. It was shown that:
(1)As a result of different habitat,it is increased the separability in some extent, inflected by soil type and moisture. We can achieve good results of remote sensing recognition of grass on spectral combination features. Using the spectral information,the classification effect of object-oriented classification has been improved greatly overall. It has made up the deficiency of traditional classification based on statistical characteristics of pixels.Compared with traditional supervised classification, the decision tree classification based on spectral combination has high precision of identification, overall classification accuracy has improved by 15.4% and Kappa coefficient has increased by 0.225. Decision tree classification used in this paper only consider the spectral characteristics of images, the inter-band operations, elevation and so on. We did not join the other categories, and theclassification accuracy is not particularly high.If we can consider texture information, the advantage of decision tree classification will be more obvious and its classification accuracy will be higher.
(2)It has the same trend with the measured data of grassland coverage and TM4/TM5, which increase the separability of spectral inflection value of different degraded grassland.The grassland coverage estimation model built by TM4/TM5 as the main factor can reflect the distribution of of grassland accurately. The prediction accuracy of model is high:RMSE reaches to 0.074 and relative error is 19.6%. The verification accuracy of model reaches to 0.91 and it can meet the requairement of model validation.
本文研究对象是环保公益专项“青藏高原生态退化及环境管理研究”中一个重要研究区雅鲁藏布江源区,根据野外实地勘察数据,结合可用的遥感影像数据,对源区的草地类型进行遥感识别,并对植被盖度进行遥感的定量反演,为源区生态退化研究服务。
本文选取成像质量较好的2009年8月1日左右的Landsat5 TM影像,根据不同草地类型的波段组合特征,结合源区1：100万植被类型图、DEM和NDVI数据,构建草地识别的判别规则,利用决策树分类的方法对雅鲁藏布江源区草地类型进行遥感识别研究；以TM派生数据NDVI、RVI、VI3、PVI、DVI、MSAVI、SAVI、TM4/TM5为主要分析因子,结合野外植被样方调查数据,选取相关性最高的因子与实测植被盖度建立回归模型,利用该模型生成植被盖度分布图。研究结果表明：
(1)不同的草地由于其生境不同,受背景土壤类型和土壤水分的干扰和影响,一定程度上加大了不同草种之间的光谱可分性,利用不同波段组合特征进行草地类型识别能够达到较好的效果；总体上,利用地物光谱信息的面向对象分类在分类效果上有了很大的提高,弥补了传统的基于像素统计特征分类方法的不足。同时光谱信息参与面向对象分类,在信息提取之前,通过野外采样数据,找出草地之间的区分规律,较光谱未参与面向对象分类的目标性强,提高了分类精度。和传统的监督分类法相比,基于波段组合特征的决策树分类法具有较高的识别精度：采样决策树分类的总体精度可以达到62.5%,采用监督分类的总体精度47.1%；决策树分类的Kappa系数0.493,监督分类的Kappa系数为0.268。和监督分类相比,决策树分类的总分类精度提高15.4%,Kappa系数提高0.225。本文采用的决策树分类法仅是基于影像的光谱特征、波段间的相互运算以及高程等信息,并没有加入其它分类特征,其分类精度不是特别高；若在以后的分类决策树模型中加入纹理等信息,则决策树分类法的优势会更明显,分类精度会更高。
(2)草地盖度实测值与对应的TM4/TM5值变化趋势基本相同,且TM4/TM5增强了不同退化程度草地植被的光谱反射值的差异,以TM4/TM5为因子构建的草地盖度估测模型能够准确反映出源区草地盖度的基本分布趋势,模型的整体预测精度较高：RMSE为0.074,相对误差为19.6%,草地植被盖度模型验证精度达到0.91,达到模型验证精度要求。
A new earth observation system, which is formed by remote sensing system, global positioning system and geographic information system, has been paid attention to by all industries in the scientific research and application in the national economy.
The study area of this paper is the source region of Yarlung Zangbo river, which is the important study area in the environmental protection special public-"Research on Ecological Degradation and Envirmental Management".According to quadrat investigating, we recognize the types of grassland and inversion the vegetation coverage, which server the research on ecological degradation in the source region of Yarlung Zangbo River.
We selected the Landsat5 TM images of high quality in August 1st,2009. According to different features of spectral combination, we build the recognition rules of grass identification with the data of 1:400million vegetation map, DEM, and NDVI. We did the research on grass recognition in the source region of Yarlung Zangbo river based on decision tree classification. This paper analyzed the NDVI、RVI、VI3、PVI、DVI、MSAVI、SAVI and TM4/TM5, combining quadrat investigating, and we select the TM4/TM5 as the main factor to construct model with vegetation coverage. We calculate the vegetation coverage of the image with this model and create the image of the distribution of grassland, which is of great important for grasping the desertification of grassland. It was shown that:
(1)As a result of different habitat,it is increased the separability in some extent, inflected by soil type and moisture. We can achieve good results of remote sensing recognition of grass on spectral combination features. Using the spectral information,the classification effect of object-oriented classification has been improved greatly overall. It has made up the deficiency of traditional classification based on statistical characteristics of pixels.Compared with traditional supervised classification, the decision tree classification based on spectral combination has high precision of identification, overall classification accuracy has improved by 15.4% and Kappa coefficient has increased by 0.225. Decision tree classification used in this paper only consider the spectral characteristics of images, the inter-band operations, elevation and so on. We did not join the other categories, and theclassification accuracy is not particularly high.If we can consider texture information, the advantage of decision tree classification will be more obvious and its classification accuracy will be higher.
(2)It has the same trend with the measured data of grassland coverage and TM4/TM5, which increase the separability of spectral inflection value of different degraded grassland.The grassland coverage estimation model built by TM4/TM5 as the main factor can reflect the distribution of of grassland accurately. The prediction accuracy of model is high:RMSE reaches to 0.074 and relative error is 19.6%. The verification accuracy of model reaches to 0.91 and it can meet the requairement of model validation.
引文
[1]何萍,郭柯,高吉喜等；雅鲁藏布江源头区的植被及其地理分布特征；山地学报[J],2005,5,23(3)：267-273.
[2]毛飞,候英雨,唐世浩.基于近20年遥感数据的藏北草地分类及其动态变化.应用生态学报[J],2007,18(8)：1745-1750.
[3]赵连春,刘荣堂,杨予海.基于地形因子的草地遥感分类方法研究.草业科学[J],2006,12,23(12)：26-30.
[4]李扬,江南,吕恒等.基于水稻特征波段的决策树分类研究.地理与地理信息科学[J],2010,3,26(2).
[5]乐通潮,陈杰,罗彩莲等.决策树分类在红树林自然保护区SPOT影像解译中的应用.福建林业科技[J],2008,35(4)：115-118.
[6]潘琛,杜培军,罗艳等.一种基于植被指数的遥感影像决策树分类方法.计算机应用[J],2009,3,29(3)：777-797.
[7]刘礼,于强.分层分类与监督分类相结合的遥感分类法研究.林业调查与规划[J],2007,8,32(4)：37-44.
[8]罗永明,钟仕全,莫伟华等.基于TM数据的南宁市水体和建筑用地变化研究.气象研究与应用[J],2008,3,29(1)：37-40.
[9]闫利,孙颖超.基于影像多种特征的决策树分类方法.地理空间信息[J],2009,12,6(7).
[10]章文波,刘宝元等.小区植被覆盖度动态快速测量方法研究[J].水土保持通报,2001,21(6)：60-63.
[11]拉尔R.土壤侵蚀研究方法[M].北京：科学出版社,1991：157-170.
[12]Adams J E,Arkin G F.Alightin terception method for measuring rowcrop ground cover [J].Soil Science Society of America Journal,1977,41(4):789-792.
[13]张光辉,梁一民.黄土丘陵人工草地径流起始时间研究[J].水土保持学报,1995,9(3)：78-83.
[14]卜兆宏,赵宏夫,刘绍清等.用于土壤流失量遥感监测的植被因子算式的初步研究[J].遥感技术与应用,1993,8(4)：16-22.
[15]罗伟祥,自立强,宋西等.不同覆盖度林地和草地的径流量与冲刷量[J].水土保持报,1990,4(1)：30-34.
[16]王宏,李晓兵,龙慧灵.整合1982—1999年NDVI与降雨量时间序列模拟中国北方温带草原植被盖度.应用基础与工程科学学报[J],2008,8,16(4)：525-536.
[17]刘丹丹,范文义.植被盖度的定量反演中植被指数的应用研究.林业勘察设计[J],2004,4：50-51.
[18]李爽,丁圣彦.决策树分类方法及其在土地覆盖分类中的应用[J].遥感技术与应用,2002,17(1)：6-11.
[19]罗来平,宫辉力.遥感图像决策树分类器研究与实现阴.遥感信息,2006,(3)：13-16.
[20]李飞雪,李满春.基于人工神经网络与决策树结合模型的遥感图像自动分类研究[J].遥感信息,2003,(3)：23-25
[21]姜青香,刘惠平.利用纹理分析方法提取TM图像信息；遥感学报[J],2004,8(5)：458-464.
[22]Friedl M A, Brodley C E,Strahler A H. Maximizing Land Cover Classification Accuracies Produced by Decision Trees at Continental to Global Scales[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(2):969-977.
[23]邸凯昌,李德仁,李德毅.基于空间数据挖掘的遥感图像分类研究[J].武汉测绘科技大学学报,2000,125(1)：42-48.
[24]Mclver D K,Friedl M A.Estimating Pixel-scale Land Cover Classification Confidence Using Non-parametric Machine Learning Methods[J].IEEE Transaction on Geoscience and Remote Sensing 2001,39:1959-1968.
[25]Mclver D K,Friedl M A.Using Prior Probabilities in Decision-tree Remotely Sensed Data [J].Remote Sensing of Enviroment.2002,81:253～261.
[26]Zhan X,Sohlberg R A,Townshend J R G,et al.Detection of Land Cover Changes Using MODIS 250 m Data[J].Remote Sensing of Enviroment,2002,83:336-350.
[27]Rogan J,Franklin J,Roberts D A.A Comparison of Methods of Monitoring Multi-temporal Vegetation Change Using Thematic Mapper Imagery[J].Remote Sensing of Enviroment,2002,80(1):143-156.
[28]李爽,张二勋.基于决策树的遥感影像分类方法研究[J].地域研究与发,2003,22(1)：17-21.
[29]汤国安,张友顺等.遥感数字图像处理[M].北京：科学出版.
[30]许定成,游先祥,韩熙春.中国遥感进展[M].北京：万国学术出版社,1992.205-210.
[31]FasART模糊神经网络用于遥感图象监督分类的研究[J].中国图象图形学,2002,17(12)：1263-1268.
[32]陈华,陈书海,张平,严卫东K-means算法在遥感分类中的应用[J].红外与激光工 程,2000,29(2)：26-30.
[33]常庆瑞,蒋平安,周勇等.遥感技术导论[M].北京：科学出版社,2004.
[34]李爽,张二勋.基于决策树的遥感影像分类方法研究[J].地域研究与开发,2003,22(1)：17-21.
[35]杨长保,丁继红.面向对象的遥感图像分类方法研究[J].吉林大学学报(地球科学版),2006,36(4)：642-646.
[36]Running,Steven W,Loveland,TomasR.A remote sensing based vegetation classification logic for global land cover annlysis[J].Remote sensing of Environment,1995,51(1):39-48.
[37]Hansen,M.Dubayah,R. and DeFries.R. Classification Trees:An Alternative to Traditional Land Cover Classifiers[J],International Journal of Remote Sensing,1996,17(5):1075-1082.
[38]Carpenter,Gaila.ART neural networks for remote sensing vegetation classification from landsat TM and terrain data[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(2):308-325.
[39]孙明,沈渭寿,李海东,等.雅鲁藏布江源区风沙化土地演变趋势[J].自然资源学报,2010,25(7)：1163-1171.
[40]杨逸畴.雅鲁藏布江河谷风沙地貌的初步观察[J].中国沙漠,1984,4(3)：12-16.
[41]陈杰,龚子同,高尚玉.干旱地区草场荒漠化及其评价[J].地理科学,2000(2)：176-181.
[42]丁国栋,赵廷宁,范建友,等.荒漠化评价指标体系研究现状述评[J].北京林业大学学报,2004,(01)：92-96.
[43]Eastwood JA, Yates M G, Thomson A G, et al. There liability of vegetation in dices for monitoring salt marsh vegetation cover[J]. Int J Remote Sensing,1997(18):3901-3907.
[44]高尚武,王葆芳,朱灵益,等.中国沙质荒漠化土地监测评价指标体系[J].林业科学,1998,(02)：1-9.
[45]Gutman G, Ignalov A.The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weatherprediction models[J]. Int J Remote Sensing, 1998,(8):1533-1543.
[46]海春兴,刘宝元,赵烨.土壤湿度和植被盖度对土壤风蚀的影响[J].应用生态学报,2002,13(8)：1057-1058.
[47]王葆芳.国内外沙漠化监测评价指标体系概述[J].林业科技通讯,1997(1)：4-8.
[48]刘同海,吴新宏,董永平,等.基于TM影像的草原沙漠化植被盖度分析研究[J].干旱区资源与环境,2010,2,24(2)：141-144.
[49]倪忠云,何政伟,赵银兵等.汶川地震前后都江堰植被盖度变化的遥感研究[J].水土保持研究,2009,8,16(4)：45-48.
[50]李辉霞,鄢燕,刘淑珍,等.西藏高原草地退化遥感分析-以藏北高原典型区为例[M].北京：科学出版社,2008.75-99.
[51]周兴民.组成嵩草草甸植物的生态特征和生活型.中国嵩草草甸[M],科学出版社,北京,2001：39～50.
[52]周兴民,青藏高原嵩草草甸基车特征和主要类型.高原生物学集(1)[M].科学出版牡,1982：151-164.
[53]杨福囤,王启基,何海菊.青藏高原植物热值含量与畜牧业生产,自然资源(2)[J],1986：24-30.
[2]毛飞,候英雨,唐世浩.基于近20年遥感数据的藏北草地分类及其动态变化.应用生态学报[J],2007,18(8)：1745-1750.
[3]赵连春,刘荣堂,杨予海.基于地形因子的草地遥感分类方法研究.草业科学[J],2006,12,23(12)：26-30.
[4]李扬,江南,吕恒等.基于水稻特征波段的决策树分类研究.地理与地理信息科学[J],2010,3,26(2).
[5]乐通潮,陈杰,罗彩莲等.决策树分类在红树林自然保护区SPOT影像解译中的应用.福建林业科技[J],2008,35(4)：115-118.
[6]潘琛,杜培军,罗艳等.一种基于植被指数的遥感影像决策树分类方法.计算机应用[J],2009,3,29(3)：777-797.
[7]刘礼,于强.分层分类与监督分类相结合的遥感分类法研究.林业调查与规划[J],2007,8,32(4)：37-44.
[8]罗永明,钟仕全,莫伟华等.基于TM数据的南宁市水体和建筑用地变化研究.气象研究与应用[J],2008,3,29(1)：37-40.
[9]闫利,孙颖超.基于影像多种特征的决策树分类方法.地理空间信息[J],2009,12,6(7).
[10]章文波,刘宝元等.小区植被覆盖度动态快速测量方法研究[J].水土保持通报,2001,21(6)：60-63.
[11]拉尔R.土壤侵蚀研究方法[M].北京：科学出版社,1991：157-170.
[12]Adams J E,Arkin G F.Alightin terception method for measuring rowcrop ground cover [J].Soil Science Society of America Journal,1977,41(4):789-792.
[13]张光辉,梁一民.黄土丘陵人工草地径流起始时间研究[J].水土保持学报,1995,9(3)：78-83.
[14]卜兆宏,赵宏夫,刘绍清等.用于土壤流失量遥感监测的植被因子算式的初步研究[J].遥感技术与应用,1993,8(4)：16-22.
[15]罗伟祥,自立强,宋西等.不同覆盖度林地和草地的径流量与冲刷量[J].水土保持报,1990,4(1)：30-34.
[16]王宏,李晓兵,龙慧灵.整合1982—1999年NDVI与降雨量时间序列模拟中国北方温带草原植被盖度.应用基础与工程科学学报[J],2008,8,16(4)：525-536.
[17]刘丹丹,范文义.植被盖度的定量反演中植被指数的应用研究.林业勘察设计[J],2004,4：50-51.
[18]李爽,丁圣彦.决策树分类方法及其在土地覆盖分类中的应用[J].遥感技术与应用,2002,17(1)：6-11.
[19]罗来平,宫辉力.遥感图像决策树分类器研究与实现阴.遥感信息,2006,(3)：13-16.
[20]李飞雪,李满春.基于人工神经网络与决策树结合模型的遥感图像自动分类研究[J].遥感信息,2003,(3)：23-25
[21]姜青香,刘惠平.利用纹理分析方法提取TM图像信息；遥感学报[J],2004,8(5)：458-464.
[22]Friedl M A, Brodley C E,Strahler A H. Maximizing Land Cover Classification Accuracies Produced by Decision Trees at Continental to Global Scales[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(2):969-977.
[23]邸凯昌,李德仁,李德毅.基于空间数据挖掘的遥感图像分类研究[J].武汉测绘科技大学学报,2000,125(1)：42-48.
[24]Mclver D K,Friedl M A.Estimating Pixel-scale Land Cover Classification Confidence Using Non-parametric Machine Learning Methods[J].IEEE Transaction on Geoscience and Remote Sensing 2001,39:1959-1968.
[25]Mclver D K,Friedl M A.Using Prior Probabilities in Decision-tree Remotely Sensed Data [J].Remote Sensing of Enviroment.2002,81:253～261.
[26]Zhan X,Sohlberg R A,Townshend J R G,et al.Detection of Land Cover Changes Using MODIS 250 m Data[J].Remote Sensing of Enviroment,2002,83:336-350.
[27]Rogan J,Franklin J,Roberts D A.A Comparison of Methods of Monitoring Multi-temporal Vegetation Change Using Thematic Mapper Imagery[J].Remote Sensing of Enviroment,2002,80(1):143-156.
[28]李爽,张二勋.基于决策树的遥感影像分类方法研究[J].地域研究与发,2003,22(1)：17-21.
[29]汤国安,张友顺等.遥感数字图像处理[M].北京：科学出版.
[30]许定成,游先祥,韩熙春.中国遥感进展[M].北京：万国学术出版社,1992.205-210.
[31]FasART模糊神经网络用于遥感图象监督分类的研究[J].中国图象图形学,2002,17(12)：1263-1268.
[32]陈华,陈书海,张平,严卫东K-means算法在遥感分类中的应用[J].红外与激光工 程,2000,29(2)：26-30.
[33]常庆瑞,蒋平安,周勇等.遥感技术导论[M].北京：科学出版社,2004.
[34]李爽,张二勋.基于决策树的遥感影像分类方法研究[J].地域研究与开发,2003,22(1)：17-21.
[35]杨长保,丁继红.面向对象的遥感图像分类方法研究[J].吉林大学学报(地球科学版),2006,36(4)：642-646.
[36]Running,Steven W,Loveland,TomasR.A remote sensing based vegetation classification logic for global land cover annlysis[J].Remote sensing of Environment,1995,51(1):39-48.
[37]Hansen,M.Dubayah,R. and DeFries.R. Classification Trees:An Alternative to Traditional Land Cover Classifiers[J],International Journal of Remote Sensing,1996,17(5):1075-1082.
[38]Carpenter,Gaila.ART neural networks for remote sensing vegetation classification from landsat TM and terrain data[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(2):308-325.
[39]孙明,沈渭寿,李海东,等.雅鲁藏布江源区风沙化土地演变趋势[J].自然资源学报,2010,25(7)：1163-1171.
[40]杨逸畴.雅鲁藏布江河谷风沙地貌的初步观察[J].中国沙漠,1984,4(3)：12-16.
[41]陈杰,龚子同,高尚玉.干旱地区草场荒漠化及其评价[J].地理科学,2000(2)：176-181.
[42]丁国栋,赵廷宁,范建友,等.荒漠化评价指标体系研究现状述评[J].北京林业大学学报,2004,(01)：92-96.
[43]Eastwood JA, Yates M G, Thomson A G, et al. There liability of vegetation in dices for monitoring salt marsh vegetation cover[J]. Int J Remote Sensing,1997(18):3901-3907.
[44]高尚武,王葆芳,朱灵益,等.中国沙质荒漠化土地监测评价指标体系[J].林业科学,1998,(02)：1-9.
[45]Gutman G, Ignalov A.The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weatherprediction models[J]. Int J Remote Sensing, 1998,(8):1533-1543.
[46]海春兴,刘宝元,赵烨.土壤湿度和植被盖度对土壤风蚀的影响[J].应用生态学报,2002,13(8)：1057-1058.
[47]王葆芳.国内外沙漠化监测评价指标体系概述[J].林业科技通讯,1997(1)：4-8.
[48]刘同海,吴新宏,董永平,等.基于TM影像的草原沙漠化植被盖度分析研究[J].干旱区资源与环境,2010,2,24(2)：141-144.
[49]倪忠云,何政伟,赵银兵等.汶川地震前后都江堰植被盖度变化的遥感研究[J].水土保持研究,2009,8,16(4)：45-48.
[50]李辉霞,鄢燕,刘淑珍,等.西藏高原草地退化遥感分析-以藏北高原典型区为例[M].北京：科学出版社,2008.75-99.
[51]周兴民.组成嵩草草甸植物的生态特征和生活型.中国嵩草草甸[M],科学出版社,北京,2001：39～50.
[52]周兴民,青藏高原嵩草草甸基车特征和主要类型.高原生物学集(1)[M].科学出版牡,1982：151-164.
[53]杨福囤,王启基,何海菊.青藏高原植物热值含量与畜牧业生产,自然资源(2)[J],1986：24-30.
© 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 |