基于3S草原土壤厚度空间分布与草原退化程度关系的研究
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摘要
近年来日益恶化的草原生态环境对我国北方生态安全构成了极大的威胁,草原退化已成为重大环境问题。本文以西乌珠穆沁旗草原为试验研究区域,利用CBERS卫星数据监测、解译草原退化等级,通过GIS、GPS技术及地统计学方法,采用普通克里格插值方法处理土壤厚度试验数据,制作研究区域土壤厚度的空间分布预测图,将草原退化遥感数据与土壤厚度空间分布状态进行叠加分析,建立了一种土壤厚度空间分布状态与草原退化关系的分析方法,确定研究区域内不同退化程度草原的面积及位置,这将为制定草原科学管理和退化草原自然恢复的保护策略提供基础数据,为国家制定和实施沙源治理工程提供技术支持。论文主要结论如下:
1.对土壤厚度预试验277个数据进行处理及误差分析,结果表明克里格插值法明显优于反距离加权插值法,并确定土壤试验样点合理间距选取原则,即:地势平坦的区域,草原土壤试验样点间距可由理论的19.5m适当调整到200m,局部区域甚至可以放大到500m,对于丘陵区域,则需根据地形地貌复杂程度,草原土壤试验样点间距可在19.5m～60m适当选取,最大不要超过100m。
2.处理500个土壤厚度试验数据,结果表明土壤厚度试验数据接近于正态分布,直方图呈单峰,均值与中值大致相等,且有对称性;正态QQ图再次证实了土壤厚度试验数据是服从正态分布的,数据无须进行转化;土壤厚度试验数据经过趋势分析显示,可以用一个西南——东北向的二阶多项式对其进行拟合最佳;半变异函数/协方差函数云图表明土壤厚度试验数据中存在空间自相关,且知道数据集中没有离群值或错误的采样点,故选用普通克里格插值创建比较精确的土壤表面。
3.应用不同半方差拟合模型进行插值后误差分析,结果表明采用Rational Quadratic model为半方差拟合模型,其预测误差的均值为0.012、均方根为7.384、平均预测标准差为7.426、平均标准差为0.001,均为其各自最小值,均方根标准预测误差为0.987,因此选择Rational Quadratic model为土壤厚度的最佳半方差拟合模型。
4.通过草原退化现状与相应位置的遥感影像对比分析,确定草原退化不同等级的感兴趣区域,利用ERDAS IMAGINE8.6执行草原退化等级监督分类,经过分类模板误差矩阵评价、分类精度评价及实地验证,获得2006年～2008年草原退化等级解译图,其分类精度分别为90.00%、89.74%和87.50%,kappa系数分别为0.88、0.88和0.86。
5.根据2006～2008年三年平均统计数据对比分析,在草原土壤厚度小于10cm的研究区域内,处于中度退化和重度退化程度的草原面积分别占总面积的44.9%和48.2%;在草原土壤厚度为10～20cm的研究区域内,处于中度退化程度的草原面积占总面积的70.3%;在草原土壤厚度为20～30cm的研究区域内,处于轻度退化程度的草原占总面积的64.8%;在草原土壤厚度大于30cm的研究区域内,处于无明显退化和轻度退化程度的草原面积分别占总面积的32.8%、53.3%。
In recent years, the deteriorating ecological environment of grassland poses a great ecological security threats in northern China, and grassland degradated is the major environmenttal problems at home and abroad. The paper selected the Xiwuzhumuqin county grassland as the study area, monitored and classified the extent of degradated grassland by using the CBERS satellite data. Through the GIS, GPS technology and geostatistics methods, the Ordinary Kriging interpolation methods was adopted for processing the soil thickness test data, and the prediction map was produced which is the soil thickness spatial distribution to the study area. The research evaluated the degenerative grassland level by spatial overlay analyzing between the Remote Sensing data of degenerative grassland and soil thickness spatial distribution, and founded the analytical methods of the relation between the degradative grassland and soil thickness, ascertained the location and size of the different degradation levels at the grassland study area.This will offer the basic data for constituting the ecological environment protection strategy on the scientific management of grassland and the natural restore of degradative vegetation, and provides the technical support for the nation to formulate and implement sand sources control project.
Main results and conclusions from the research are as follows:
1.Through processing 277 samples of the soil thickness pre-test data and error analyzing, the results show that the kriging interpolation method is obviously better than the inverse distance weighted interpolation. So the Kriging interpolation method is selected test data interpolation. At the same time, the rearch ascertains the selecting principle of the reasonable distance between sample points in the soil thickness test. namely:To the flat region,the reasonable distance of the grassland soil test samples can be properly adjusted from the theoretic19.5m to 200m, the local area can even zoom into 500m. But hilly region, according to the complexity of the topography, the reasonable distance of the grassland soil test samples can be appropriately selected 19.5m ~ 60m, the largest do not exceed 100m.
2.Dealing with 500 samples data of the soil thickness elicits the results: The data is closer to normal distribution and symmetry. The histogram shows a single peak. The data average is roughly equal with its median in value. QQ figure reconfirms that the soil thickness test data is subject to the normal distribution. So there is no need for data transformation. After extraction of the trend, the soil thickness test data shows a certain trend which can use the second-order polynomial of southwest-northeast to the best fit for its trend. The semivariogram/ covariance cloud figure shows that the soil thickness test data have the spatial autocorrelation. Because there is no data or erroneous outliers sampling points, the Ordinary Kriging interpolation can be used to create more precise soil surface.
3 . Analyzing the different semi-variogram model fitting interpolation error: adopting the rational qadratic model as a semi-variance model, obtains that the mean value of the prediction is 0.012, the root-mean-square value is 7.384, the average standard deviation value is 7.426, the mean standardized value is 0.001. The property are minimum their respective. The root-mean-square standardized value is 0.987. Hence, the rational quadratic model is the best semi-variance fitting model to the soil thickness data.
4.The research, comparatively analyzes the different degenerative vegetation level to grassland and the remote sensing image of the corresponding position, determines the region of interest of the different degenerative vegetation levels implements the supervised classification of the different degenerative vegetation level by ERDAS IMAGINE 8.6. Through the classification error matrix evaluation template, the classification accuracy evaluation and the field verification, receives the classification accuracy which are90.00%, 89.74% and 87.50% , kappa coefficients are 0.88, 0.88 and 0.86 on the degenerative vegetation level to grassland in 2006 ~ 2008.
5.According to comparative analysis of statistical data on the average three years from 2006 to 2008,in the grassland soil thickness less than 10 cm of the study area, the moderate and severe degradation area are respectively 44.9% and 48.2% of the total area. The study area where is soil thickness of 10 ~ 20 cm, the grassland area of moderate degradation accounts for 70.3% of the total area. In the grassland soil thickness of 20 ~ 30cm of the study area, the area of moderate degradation grassland is the total area of 64.8 %. In grassland soil is greater than the thickness of 30 cm within the study area, the area of undegradation and moderate degradation grassland are respectively 32.8% and 53.3% of the total area.
1.对土壤厚度预试验277个数据进行处理及误差分析,结果表明克里格插值法明显优于反距离加权插值法,并确定土壤试验样点合理间距选取原则,即:地势平坦的区域,草原土壤试验样点间距可由理论的19.5m适当调整到200m,局部区域甚至可以放大到500m,对于丘陵区域,则需根据地形地貌复杂程度,草原土壤试验样点间距可在19.5m～60m适当选取,最大不要超过100m。
2.处理500个土壤厚度试验数据,结果表明土壤厚度试验数据接近于正态分布,直方图呈单峰,均值与中值大致相等,且有对称性;正态QQ图再次证实了土壤厚度试验数据是服从正态分布的,数据无须进行转化;土壤厚度试验数据经过趋势分析显示,可以用一个西南——东北向的二阶多项式对其进行拟合最佳;半变异函数/协方差函数云图表明土壤厚度试验数据中存在空间自相关,且知道数据集中没有离群值或错误的采样点,故选用普通克里格插值创建比较精确的土壤表面。
3.应用不同半方差拟合模型进行插值后误差分析,结果表明采用Rational Quadratic model为半方差拟合模型,其预测误差的均值为0.012、均方根为7.384、平均预测标准差为7.426、平均标准差为0.001,均为其各自最小值,均方根标准预测误差为0.987,因此选择Rational Quadratic model为土壤厚度的最佳半方差拟合模型。
4.通过草原退化现状与相应位置的遥感影像对比分析,确定草原退化不同等级的感兴趣区域,利用ERDAS IMAGINE8.6执行草原退化等级监督分类,经过分类模板误差矩阵评价、分类精度评价及实地验证,获得2006年～2008年草原退化等级解译图,其分类精度分别为90.00%、89.74%和87.50%,kappa系数分别为0.88、0.88和0.86。
5.根据2006～2008年三年平均统计数据对比分析,在草原土壤厚度小于10cm的研究区域内,处于中度退化和重度退化程度的草原面积分别占总面积的44.9%和48.2%;在草原土壤厚度为10～20cm的研究区域内,处于中度退化程度的草原面积占总面积的70.3%;在草原土壤厚度为20～30cm的研究区域内,处于轻度退化程度的草原占总面积的64.8%;在草原土壤厚度大于30cm的研究区域内,处于无明显退化和轻度退化程度的草原面积分别占总面积的32.8%、53.3%。
In recent years, the deteriorating ecological environment of grassland poses a great ecological security threats in northern China, and grassland degradated is the major environmenttal problems at home and abroad. The paper selected the Xiwuzhumuqin county grassland as the study area, monitored and classified the extent of degradated grassland by using the CBERS satellite data. Through the GIS, GPS technology and geostatistics methods, the Ordinary Kriging interpolation methods was adopted for processing the soil thickness test data, and the prediction map was produced which is the soil thickness spatial distribution to the study area. The research evaluated the degenerative grassland level by spatial overlay analyzing between the Remote Sensing data of degenerative grassland and soil thickness spatial distribution, and founded the analytical methods of the relation between the degradative grassland and soil thickness, ascertained the location and size of the different degradation levels at the grassland study area.This will offer the basic data for constituting the ecological environment protection strategy on the scientific management of grassland and the natural restore of degradative vegetation, and provides the technical support for the nation to formulate and implement sand sources control project.
Main results and conclusions from the research are as follows:
1.Through processing 277 samples of the soil thickness pre-test data and error analyzing, the results show that the kriging interpolation method is obviously better than the inverse distance weighted interpolation. So the Kriging interpolation method is selected test data interpolation. At the same time, the rearch ascertains the selecting principle of the reasonable distance between sample points in the soil thickness test. namely:To the flat region,the reasonable distance of the grassland soil test samples can be properly adjusted from the theoretic19.5m to 200m, the local area can even zoom into 500m. But hilly region, according to the complexity of the topography, the reasonable distance of the grassland soil test samples can be appropriately selected 19.5m ~ 60m, the largest do not exceed 100m.
2.Dealing with 500 samples data of the soil thickness elicits the results: The data is closer to normal distribution and symmetry. The histogram shows a single peak. The data average is roughly equal with its median in value. QQ figure reconfirms that the soil thickness test data is subject to the normal distribution. So there is no need for data transformation. After extraction of the trend, the soil thickness test data shows a certain trend which can use the second-order polynomial of southwest-northeast to the best fit for its trend. The semivariogram/ covariance cloud figure shows that the soil thickness test data have the spatial autocorrelation. Because there is no data or erroneous outliers sampling points, the Ordinary Kriging interpolation can be used to create more precise soil surface.
3 . Analyzing the different semi-variogram model fitting interpolation error: adopting the rational qadratic model as a semi-variance model, obtains that the mean value of the prediction is 0.012, the root-mean-square value is 7.384, the average standard deviation value is 7.426, the mean standardized value is 0.001. The property are minimum their respective. The root-mean-square standardized value is 0.987. Hence, the rational quadratic model is the best semi-variance fitting model to the soil thickness data.
4.The research, comparatively analyzes the different degenerative vegetation level to grassland and the remote sensing image of the corresponding position, determines the region of interest of the different degenerative vegetation levels implements the supervised classification of the different degenerative vegetation level by ERDAS IMAGINE 8.6. Through the classification error matrix evaluation template, the classification accuracy evaluation and the field verification, receives the classification accuracy which are90.00%, 89.74% and 87.50% , kappa coefficients are 0.88, 0.88 and 0.86 on the degenerative vegetation level to grassland in 2006 ~ 2008.
5.According to comparative analysis of statistical data on the average three years from 2006 to 2008,in the grassland soil thickness less than 10 cm of the study area, the moderate and severe degradation area are respectively 44.9% and 48.2% of the total area. The study area where is soil thickness of 10 ~ 20 cm, the grassland area of moderate degradation accounts for 70.3% of the total area. In the grassland soil thickness of 20 ~ 30cm of the study area, the area of moderate degradation grassland is the total area of 64.8 %. In grassland soil is greater than the thickness of 30 cm within the study area, the area of undegradation and moderate degradation grassland are respectively 32.8% and 53.3% of the total area.
引文
1刘起.草地与国民经济的持续发展[J].四川草原,1998,3:1～5
2许志信.草地建设与畜牧业可持续发展[J].中国农村经济,2000,3 :32～34
3张新时.草地的生态经济功能及其范式[J].科技导报,2000, 8 :3～8
4王宗礼.中国草原生态保护战略思考[J].中国草地,2005,27(4):1～2
5熊顺贵.基础土壤学(植物生产类专业用)[M].北京:中国农业科技出版社,1996,9:225～230
6中国资源《科学百科全书》草地资源学卷[M].中国百科全书出版社.中国北京:石油大学出版杜,2000:30～45
7农业部畜牧兽医司、全国畜牧兽医总站,中国草地资源[M].北京:中国科学技术出版社,1996:11
8 Tueller P T. Remote sensing technology for rangeland management applications[J]. Journal of Range management,1989,42(6):442～53
9 Wessman, C.A. Imaging spectrometry for remote sensing of eco-system processing[J] Advances in Space Research, 1992, 12 (7): 361～368
10 Takahata, S.,Osman, A.E. Analysis of rangeland vegetation using remote sensing[J].Research Highlight-Tropical Agriculture Research Center[C].1992: 3033
11 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.
12 Mertin E H,Bramble-Brodahl M K,Marrs R W,etal.Estimation of green herbaceous Phytomass from Landsat MSS data in Yellowstone National Park [J]. Journal of Range management, 1993,46(2):151～157
13 Peterson D.L., Price K.P., Martinko. Discriminating between cool season and warm season grassland cover types in northeastern Kansas[J].International Journal of Remote Sensing, 2002,23(23):5015～5030
14 Saltz D, Selunidt H, Rowen M,etal.Assessing grazing impacts by remote sensing in hyper-arid environments [J]. Journal of Range management,1999,52(5):500～507
15 Ringrose S, Musisi-Nkambwe S,Coleman T,et al. Use of Landsat Thematie MaPPer data to assess seasonal rangeland changes in the Southeast Kalahari,Botswan [J]. Journal of Range management , 1999,23(l):125～138
16 Senav G.B.,Elliott R.L. Capability of AVHRR data in discrimination rangeland cover mixtures [J]. International Journal of Remote Sensing,2002, 23(2):299～312
17 Hassett RC,Wood H L,Carter J O etal. A field methed for Statewide ground-truthing of a spatial pasture growth medel [J].Australian Journal of Experimental Agriculture, 2000,40:1069～1079
18查勇.草原植被变化遥感监测方法研究[D].南京师范大学博士学位论文,2003:2～11
19 Lewis M. Discrimination of dvegetation composition with high resolution CASI imagery [J]. Range Journal,2000,22(l):141～167
20 Schmidt K S, Skidmore A K, Exploring spectral discrimination of grass species in African rangelands [J]. International Journal of remote sensing, 2001, 22 (17):3421～3434
21 Hirata M, Koga N shinjo H, et al. Vegetation classification by sallite image processing in a dry area of north-eastern Syria [J]. International Journal of remote sensing ,2001,22(4):507～516
22 Guo X, Price K P, Stiles J M. Modeling biophysical factors or grasslands in Eastern Kansas using Landsat TM data [J].Transactions of the Kansas Academy 2000,103(3-4):122～138
23 Larss,H. Anaiysis of variations inland cover between 1972 and 1990, Kassala Province, Eastem Sudan, using Landsa tMSS data[J]. International Journal of remote sensing, 2002, 23(2):325～333
24 Han L-C. A method of modifying error for non-synchronicity of grass yield remote sensing estimation and measment[J]. International Journal of remote sensing, 2001,22(17):3363～3372
25 Muldavin E H, Neville P, Happer G. Indces of grassland biodiversity in the Chihuahuan desert ecoregion derived from remote sensing[J]. Conservation Biology, 200l,15 (4): 844～855
26 Furby S L, Campbell N A.Calibrating images from different dates to‘like-value’digital counts[J]. Remote Sensing of Environment,2001,77(2):186～196
27李博,史培军,林小泉.我国温带草地草畜平衡动态监测系统的研究[J].草地学报,1995, 3 (2):95～102
28王承军,胡新博,顾详,等.利用卫星遥感技术改进草地估产方法[J].草食家畜,1998,1:39～41
29陈家琪,周波,张峰,等.基于遥感与GIS技术的定西县土地利用变化研究[J].遥感技术与应用,2002,17 (6): 390～393
30颜长珍,王一谋,冯毓荪,等.全数字方式下对甘肃草地覆盖的遥感宏观研究[J].中国草地,2000,2:1～7
31陈全功,梁人刚,徐宗保.基于3S的甘肃省定西试验区生态环境本底调查及退耕还林还草监测[J].草业学报,2001,10:91～98
32陈全功,卫亚星,梁天刚.使用NOAA/AVHRR资料进行牧草产量及载畜量监测的方法研究[J].草业学报,1994,3 (4):50～60
33梁天刚,陈全功.新疆阜康县草地资源产量动态监测模型的研究[J].遥感技术与应用,1996,11(1):27～32
34黄敬峰,王秀珍,王人潮,等.天然草地牧草产量遥感综合监测预测模型研究[J].遥感学报,2001,5(1):69～74
35李建龙,戴若兰,任继周.遥感技术在新疆阜康县草地估产中的应用研究[J].中国草地,1998,1:11～14
36刘志明,晏明,王贵卿等.基于卫星遥感信息的吉林省西部草地退化分析[J].地理科学,2001,21(5):452～456
37陈全功,卫亚星,梁天刚.青海省达日县退化草地研究:Ⅰ退化草地遥感调查[J].草业学报,1998,7 (2):58～63
38王兮之,杜国祯,梁天刚,等.基于RS和GIS的甘南草地生产力估测模型构建及其降水量空间分布模式的确立[J].草业学报,2001,10(2):95～102
39邹亚荣,赵晓丽,张增祥等.遥感与GIS支持下的中国草地动态变化分析[J].国土资源遥感,2002,l:29～33
40胡娟.CBERS数字图像处理技术及其在喀斯特山区土地利用动态监测中的应用[D].贵州师范大学硕士学位论文,2006:11～32
41付俏燕,王志民,闵祥军,等.CBERS-1林被信息挖掘的GIS多元分析[J].地球信息科学,2005,7(1):53～58.
42曾涌,王文宇,何善铭.CBERS-1卫星CCD图像高效去噪方法[J].航天返回与遥感,2004,25(2):29～33.
43党福星,方洪宾,赵福岳.利用CBERS-1CCD数据进行地质矿产信息提取方法研究[J].航天返回与遥感,2002,4:63～66.
44刘爱霞,刘正军.基于CBERS图像的干旱半干旱区土地利用分类[J].中国科学院研究生院学报,2003,20(3):334～340
45雷坤,郑丙辉,王桥.基于中巴地球资源1号卫星的太湖表层水体水质遥感[J].环境科学学报,2004,24(3):376～380
46谢忙义,马立鹏.中巴地球资源一号卫星在金塔绿洲荒漠化监测中的应用研究[J].干旱区资源与环境,200l,15(4):44～50
47姚建华.中巴地球资源卫星数据在毛乌素沙漠生态环境监测中的应用[J].卫星应用2003,11(4):48～52
48陈绍辉,高志海.CBERS-1数据处理及在荒漠化监测中的应用评价[J].内蒙古林业科技,2005,4:23～27
49韩爱惠,王庆杰,孙向然.CBERS-02星CCD数据在林业资源监测中的应用评价[J].国土资源遥感,2004,6(2):61～64
50傅俏燕,王志民.CBERS-1林被信息挖掘的GIS多元分析[J].地球信息科学,2005,7(1):53～58
51郭建宁,于晋.CBERS-01/02卫星CCD图像相对辐射校正研究[J].中国科学E辑信息科学2005,35(增刊I):11～25
52陈劲松,朱博勤,邵云.基于小波变换的多波段遥感图像条带噪声的去除[J].遥感信息, 2003,2:6～9
53雷学武,吴君丽,刘俊荣.CBERS-1CCD星上定标数据在辐射校正中的应用[J].国土资源遥感,2003,3:63～66
54沈思源.土壤空间变异研究中的地统计学的应用及其展望[J].土壤学进展,1989,17(3):11～25
55黄绍文,金继运等.乡(镇)级区域土壤养分空间变异与分区管理技术研究.精准农业与土壤养分管理[M].北京:中国大地出版社,2001:248～259
56侯景儒,黄竞先.地质统计学在固体矿产资源/储量分类中的应用[J].地质与勘探,2001,37(6):61～66
57宋建中,于赤灵,彭平安,等.珠江三角洲地区土壤与表层沉积物有机质的性质结构研究[J].土壤学报,2003,40(3):335～343
58王学军,邓宝山,张泽浦.北京东郊污灌区表层土壤微量元素的小尺度空间结构特征[J].环境科学学报,1997,17(4):412～416
59刘瑞民,王学军,郑一,等.地统计学在太湖水质研究中的应用[J].环境科学学报,2002,22(2):209～212
60刘瑞民,王学军,郑一.湖泊水质参数空间分析中异常值的识别与处理[J].环境科学与技术,2003,26(5):17～15
61刘瑞民,王学军.湖泊水质参数空间优化估算的原理与方法[J].中国环境科学,2001,21(2):177～179
62刘瑞民,王学军,王翠红.湖泊水质参数空间最优估计精度分析[J].环境科学,2001,22(5):91～94
63刘多森,曾志远.土壤和环境研究中的数学方法与建模[M].北京:农业出版社,1987:14～24
64周慧珍,龚子同,Lamp.土壤空间变异性研究[J].土壤学报,1996,33(3):23～26
65 Larisa Poszdnyakova.Renduo Zhang.Geostatistical analysis of soil salinity in alarge field[J]. Preeision Aghculture,1999,l(2):153～165
66 MeBratney,A.B.,Webster,R. How many observations are needed for regional estimation of Soil ProPerties? [J]Soil Sci,1983,135:177～183
67张建辉,何敏容.丘陵区土地湿度的空间变异性研究[J].土壤通报,1996,27(2):61～62
68 Davis,J.G,Hossner,L.R.,et al.Variability of soil chemical properties in two sandy, dunal soil of Niger[J]. Soil Seience,1995,159:5,321～330
69 ChungC.K.,Chong,et al. Sampling for fertility on a stony siltloam[J]. Soil communieation of soil seience and plant analysis,1995,26:741～763
70 Lee.C.K.,Kaho.T., et al. field information maps using geostatiatics in the paddyfield[J]. 2000-ASAE-Annual-International-Meeting, Milwaukee, Wisconsin, USA, 2000, 1～17
71李艳.地统计学在土壤科学中的应用及展望[J].水土保持学报,2003, 3 :178～182
72李海滨,林忠辉,刘苏峡.Kriging方法在区域土壤水分估值中的应用[J].地理研究,2001,20(4):446～452
73张征,鞠硕华,刘淑春.地下水环境模拟中多元信息空间最优估计原理与方法[J].工程勘察,2000,3:12～15
74李子忠,龚元石.农田土壤水分和电导率空间变异性及确定其采样数的方法[J].中国农业大学学报,2000,5(5):59～66
75胡克林,李保国,陈德立,R.E.White.农田土壤水分和盐分的空间变异性及其同克里格估值[J].水科学进展,2001,12(4):460～466
76李保国,胡克林,陈德立,R.E.White.农田土壤表层饱和导水率的条件模拟[J].水利学报,2002,2:36～40,46
77吕军,俞劲炎.水稻土物理性质空间变异性研究[J].土壤学报,1990,27(l):8～15
78雷志栋.土壤特性空间变异性初步研究[J].水利学报,1985(9):10～21
79梁春祥,姚贤良.华中丘陵红坡物理性质空间变异性的研究[J].土壤学报,1993,30(1):69～77
80邱扬,傅伯杰,王军,等.黄土丘陵小流域土壤物理性质的空间变异[J].地理学报,2002,57(5):587～594
81 Vanclin M.et al., The use of cokriging with limited field soil observations [J].Soil Soc.Am.J, 1983,47:175～184
82 Vangham P.I., Esch S.R,et al., Water content effect on soil salinity predietion:Geostatistieal study using cokriging Soil Sci.Soc.Am.J. 1995,59:1146～1156
83秦耀东,李宝国.应用析取克里格方法估计区域地下水埋深分布[J].水利学报,1998,8:28～32
84龚元石,廖超子等.土壤含水量和容重的空间变异及其分形特征[J].土壤学报,1993,5(l):10～15
85杨敏华.基于多维空间变异分析的精准农业作业单元自适应决策[J].农业工程学报,2002,3:149～152
86郭旭东,傅伯杰.基于GIS和地统计学的土壤养分空间变异特征研究[J].应用生态学报,2000,11(4):555～563
87高祥照,胡克林.土壤养分与作物产量的空间变异特征与精准施肥[J].中国农业科学,2002,35(6):660～666
88王苛.精确农业田间土壤空间变异与采样方式研究[J].农业工程学报,2001,3:33～36
89李敏,帅艳民,刘素红,等.中巴资源一号02星土地覆被多阶段信息提取[J].遥感信息应用技术,2006:34～40
90朱震达,刘恕,邸醒民.我国沙漠化研究的历史回顾与若干问题[J].中国沙漠,1984,4(2):2～8
91 Bo Wu. Long J. Ci. Landscape change and desertification development in the Mu Us Sandland, Northern China[J]. Journal of Arid Environments .2002,50: 429～444
92李慧卿.荒漠化研究动态[J].世界林业研究,2004 ,17(1):11～14
93陈怀满.环境土壤学[M].北京:科学出版社,2005:490～492
94汤国安,杨昕.ArcGIS地理信息系统空间分析实验教程[M].北京:科学出版社, 2006,4:300～350
95北京超图地理信息技术有限公司.SuperMap DeskPro用户手册[M].北京:北京超图地理信息技术有限公司,2001,11:168～186
96王政权.地质统计学及在生态学中的应用[M].北京:科学出版社,1999:45～75
97裘正军.基于GPS、GIS及虚拟仪器的精细农业信息采集与处理技术的研究[D].浙江大学博士学位论文,2003:94～100
98吕安民,李成名,林宗坚.面积内插算法初探[J].测绘通报,2002,(1):44～46
99张治国.生态学空间分析原理与技术[M].北京:科学出版社,2007:119～139
100 http://www.cresda.com/cn/products_service.htm[EB/OL].2009.3.5
101 http://www.gissky.net/Article/arcgis[EB/OL].200606/18.htm:1～5
102 http://www.cresda.com/cn/products.htm[EB/OL]. 2008.3.11
103黄妙芬,徐曼.中巴地球资源02星数据特性分析[J].干旱区地理2004,27(4):485～491
104胡如忠.中巴地球资源卫星与西部大开发[J].中国航天,2001,2:16～20
105吴美蓉.中巴地球资源卫星应用及其发展[J].测绘科学2000,25(2):25～29
106陈冬花,咎梅.中巴地球资源卫星的应用与研究[J].新疆师范大学学报(自然科学版) 2006,25(3):173～177
107内蒙古遥感中心,陕西遥感应用中心.毛乌素沙地治理遥感动态监测与预警研究[M].呼和浩特,内蒙古计算机应用研究院,2008,9:21～30
108戴昌达,雷莉萍.TM图像的光谱信息特征与最佳波段组合[J] .环境遥感,1989,4(4):282～292
109年波,杨士剑,王金亮.植被遥感信息提取的最佳波段选择—以云南中部山区为例[J] .云南地理环境研究,2004,16(2):18～21
110卢玉东,尹黎明,何丙辉等.利用TM影像在土地利用灌盖遥感解译中波段选取研究[J] .西南农业大学学报(自然科学版)2005,27(4):479～482
111刘建平,赵英时.高光谱遥感数据解译的最佳波段选择方法研究[J] .中国科学院研究生院学报,1999,16(2):153～161
112孙华生,黄敬峰,王杰,等.CBERS-02 CCD图像中居民点用地信息提取方法研究[J].科技通报,2008,24(4):504～509
113李玉霞,杨武年,郑泽忠,中巴资源卫星(CBERS-02)遥感图像在生态环境动态监测中的应用研究[J].水土保持研究2006,1(6):199～120
114 http://www.rsgs.ac.cn/1_landsat5.html[EB/OL] .2008.3.11
115党福星,高志海.CBERS-01CCD数据进行地质矿产信息提取方法研究[J].国土资源遥感2002.4:63～67
116国家海域使用动态监视监测管理系统总体技术方案,国家海洋局2006,7:9～13
117彭望禄.遥感数据的计算机处理与地理信息系统[M].北京师范大学出版社,北京,1991:29～96
118党安荣,王晓栋,陈晓峰,张建宝.ERDASIMAGINE遥感图像处理方法[M].北京:清华大学出版杜,2003:186～237
119卫建军.延安示范区土地利用变化及驱动力分析[D].西北农林科技大学硕士学位论文,2007:31～50
120张霞,张兵,赵永超等.中巴地球资源一号卫星多光谱扫描图像质量评价[J].中国图象图形学报,2002,6(6):581～586
121李锦,王让会,薛英. CBERS-2/CCD影像数据在干旱区绿洲景观信息图谱中的应用与研究[J].干旱区资源与环境,2008,22(3):116～122
122陈述彭,胡如忠,刘高焕.CBERS-01卫星图像在黄河二角洲可持续发展中的应用研究[J].航天返回与遥感,2001,22(3):34～39
123刘云,赵群,寇明军,等.基于TM影像的城郊景观土地利用初步研究——以北京昌平沙河区为例[J].遥感技术与应用,2005,20(6):563～568
124吴健平.区域土地利用/土地覆盖遥感调查[M].上海:华东师范大学出版社,1999:34～50
125李玉霞,李丽,郑泽忠.CBERS-02图像在土地利用/覆盖动态监测中的应用[J].四川师范大学学报(自然科学版),2006,29(5):627～631
126王娟敏.多源遥感影像沙化土地信息提取研究[D].西北大学,2007:3～14
127周旭.3S支持下喀斯特退化景观生态安全评价研究——以贵阳市为例[D].贵州师范大学硕士学位论文,2006:81～86
128梅安新,彭望碌等.遥感导论[M].北京:高等教育出版社,2001:135～141
129王铁成,刘兴文.利用TM图像提取土地荒漠化信息的方法与效果一一以阜康地区为例[J].遥感技术与应用,1994,9(l):34～41
130廉毅,高极亭.吉林省西部荒漠化发展的陆地卫星遥感监测分析[J].气象学报,1999,57(6):662～667
131 Jaekson,R.D. Huete,A.R. Interpreting vegetation indices, J.reventative Vet.Med., 1991 (11): 185～200
132李金桐,镨拉提.基于GIS的MODIS环境荒漠化监测中的应用方法研究[J].新疆气象,2003,26(2):21～23
133李宝林,周成虎.东北平原西部沙地近10年的沙质荒漠化[J].地理学报,2001,6(3):307～315
134张洪玲,李国春.科尔沁地区荒漠化状况的遥感监测[J].农业网络信息,2006,2:42～44
135赵国忱,杜明义.基于形态学的土地荒漠化遥感图象纹理分析[J].辽宁工程技术大学学报自然科学版,2002,21(2):148～150
136乔平林.基于神经网络的土地荒漠化信息提取方法研究[J].测绘学报,2004,33(l):58～62
137李晓松,吴波.基于光谱混合分析的荒漠化信息提取一一以毛乌素沙地为例[J].林业科学研究,2006,19(2):192～198
138赵维兵.中巴地球资源卫星图像中伪“同谱异物”区分方法研究[J].遥测遥控,2008,29(2)19～25
139国家制定的天然草原退化标准《天然草地退化、沙化、盐渍化的分级指标》(GB 19377-2003):1～8
140陕西师范大学旅游与环境学院地理信息系统实验室.遥感图像处理练习[M].西安:陕西师范大学,2003,9:36～67
141李四海.浅谈提高遥感数据分类精度的方法[J].遥感信息,1995,4(3):17～19
142贾勤学,张玮,李建林,等.基于遥感技术的耕地面积动态监测研究[J].农业信息科学,2006,7(22):530～534
143 ERDAS IMAGING Field Guides[M].Fifth Edition.ERDAS Inc, Atlanta, Georgia, 1999: 160～161
144 LIU Yong-mei,TANG Guo-an,LI Tian-wen,et al.An applied research on remote sensing classification in the Loess Plateau[J].Journal of Geographical Sciences,2003.13(4):395～399
145 Lucas I F J,Frans J M.Accuracy Assessment of Satelite Derived Land cover Data:A Review[J].Photogrammetric Engineering&Remote Sensing,1994.60(4):410～432
146 eTrex Venture——奇遇手持式GPS使用手册.2003,3:63～81
2许志信.草地建设与畜牧业可持续发展[J].中国农村经济,2000,3 :32～34
3张新时.草地的生态经济功能及其范式[J].科技导报,2000, 8 :3～8
4王宗礼.中国草原生态保护战略思考[J].中国草地,2005,27(4):1～2
5熊顺贵.基础土壤学(植物生产类专业用)[M].北京:中国农业科技出版社,1996,9:225～230
6中国资源《科学百科全书》草地资源学卷[M].中国百科全书出版社.中国北京:石油大学出版杜,2000:30～45
7农业部畜牧兽医司、全国畜牧兽医总站,中国草地资源[M].北京:中国科学技术出版社,1996:11
8 Tueller P T. Remote sensing technology for rangeland management applications[J]. Journal of Range management,1989,42(6):442～53
9 Wessman, C.A. Imaging spectrometry for remote sensing of eco-system processing[J] Advances in Space Research, 1992, 12 (7): 361～368
10 Takahata, S.,Osman, A.E. Analysis of rangeland vegetation using remote sensing[J].Research Highlight-Tropical Agriculture Research Center[C].1992: 3033
11 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.
12 Mertin E H,Bramble-Brodahl M K,Marrs R W,etal.Estimation of green herbaceous Phytomass from Landsat MSS data in Yellowstone National Park [J]. Journal of Range management, 1993,46(2):151～157
13 Peterson D.L., Price K.P., Martinko. Discriminating between cool season and warm season grassland cover types in northeastern Kansas[J].International Journal of Remote Sensing, 2002,23(23):5015～5030
14 Saltz D, Selunidt H, Rowen M,etal.Assessing grazing impacts by remote sensing in hyper-arid environments [J]. Journal of Range management,1999,52(5):500～507
15 Ringrose S, Musisi-Nkambwe S,Coleman T,et al. Use of Landsat Thematie MaPPer data to assess seasonal rangeland changes in the Southeast Kalahari,Botswan [J]. Journal of Range management , 1999,23(l):125～138
16 Senav G.B.,Elliott R.L. Capability of AVHRR data in discrimination rangeland cover mixtures [J]. International Journal of Remote Sensing,2002, 23(2):299～312
17 Hassett RC,Wood H L,Carter J O etal. A field methed for Statewide ground-truthing of a spatial pasture growth medel [J].Australian Journal of Experimental Agriculture, 2000,40:1069～1079
18查勇.草原植被变化遥感监测方法研究[D].南京师范大学博士学位论文,2003:2～11
19 Lewis M. Discrimination of dvegetation composition with high resolution CASI imagery [J]. Range Journal,2000,22(l):141～167
20 Schmidt K S, Skidmore A K, Exploring spectral discrimination of grass species in African rangelands [J]. International Journal of remote sensing, 2001, 22 (17):3421～3434
21 Hirata M, Koga N shinjo H, et al. Vegetation classification by sallite image processing in a dry area of north-eastern Syria [J]. International Journal of remote sensing ,2001,22(4):507～516
22 Guo X, Price K P, Stiles J M. Modeling biophysical factors or grasslands in Eastern Kansas using Landsat TM data [J].Transactions of the Kansas Academy 2000,103(3-4):122～138
23 Larss,H. Anaiysis of variations inland cover between 1972 and 1990, Kassala Province, Eastem Sudan, using Landsa tMSS data[J]. International Journal of remote sensing, 2002, 23(2):325～333
24 Han L-C. A method of modifying error for non-synchronicity of grass yield remote sensing estimation and measment[J]. International Journal of remote sensing, 2001,22(17):3363～3372
25 Muldavin E H, Neville P, Happer G. Indces of grassland biodiversity in the Chihuahuan desert ecoregion derived from remote sensing[J]. Conservation Biology, 200l,15 (4): 844～855
26 Furby S L, Campbell N A.Calibrating images from different dates to‘like-value’digital counts[J]. Remote Sensing of Environment,2001,77(2):186～196
27李博,史培军,林小泉.我国温带草地草畜平衡动态监测系统的研究[J].草地学报,1995, 3 (2):95～102
28王承军,胡新博,顾详,等.利用卫星遥感技术改进草地估产方法[J].草食家畜,1998,1:39～41
29陈家琪,周波,张峰,等.基于遥感与GIS技术的定西县土地利用变化研究[J].遥感技术与应用,2002,17 (6): 390～393
30颜长珍,王一谋,冯毓荪,等.全数字方式下对甘肃草地覆盖的遥感宏观研究[J].中国草地,2000,2:1～7
31陈全功,梁人刚,徐宗保.基于3S的甘肃省定西试验区生态环境本底调查及退耕还林还草监测[J].草业学报,2001,10:91～98
32陈全功,卫亚星,梁天刚.使用NOAA/AVHRR资料进行牧草产量及载畜量监测的方法研究[J].草业学报,1994,3 (4):50～60
33梁天刚,陈全功.新疆阜康县草地资源产量动态监测模型的研究[J].遥感技术与应用,1996,11(1):27～32
34黄敬峰,王秀珍,王人潮,等.天然草地牧草产量遥感综合监测预测模型研究[J].遥感学报,2001,5(1):69～74
35李建龙,戴若兰,任继周.遥感技术在新疆阜康县草地估产中的应用研究[J].中国草地,1998,1:11～14
36刘志明,晏明,王贵卿等.基于卫星遥感信息的吉林省西部草地退化分析[J].地理科学,2001,21(5):452～456
37陈全功,卫亚星,梁天刚.青海省达日县退化草地研究:Ⅰ退化草地遥感调查[J].草业学报,1998,7 (2):58～63
38王兮之,杜国祯,梁天刚,等.基于RS和GIS的甘南草地生产力估测模型构建及其降水量空间分布模式的确立[J].草业学报,2001,10(2):95～102
39邹亚荣,赵晓丽,张增祥等.遥感与GIS支持下的中国草地动态变化分析[J].国土资源遥感,2002,l:29～33
40胡娟.CBERS数字图像处理技术及其在喀斯特山区土地利用动态监测中的应用[D].贵州师范大学硕士学位论文,2006:11～32
41付俏燕,王志民,闵祥军,等.CBERS-1林被信息挖掘的GIS多元分析[J].地球信息科学,2005,7(1):53～58.
42曾涌,王文宇,何善铭.CBERS-1卫星CCD图像高效去噪方法[J].航天返回与遥感,2004,25(2):29～33.
43党福星,方洪宾,赵福岳.利用CBERS-1CCD数据进行地质矿产信息提取方法研究[J].航天返回与遥感,2002,4:63～66.
44刘爱霞,刘正军.基于CBERS图像的干旱半干旱区土地利用分类[J].中国科学院研究生院学报,2003,20(3):334～340
45雷坤,郑丙辉,王桥.基于中巴地球资源1号卫星的太湖表层水体水质遥感[J].环境科学学报,2004,24(3):376～380
46谢忙义,马立鹏.中巴地球资源一号卫星在金塔绿洲荒漠化监测中的应用研究[J].干旱区资源与环境,200l,15(4):44～50
47姚建华.中巴地球资源卫星数据在毛乌素沙漠生态环境监测中的应用[J].卫星应用2003,11(4):48～52
48陈绍辉,高志海.CBERS-1数据处理及在荒漠化监测中的应用评价[J].内蒙古林业科技,2005,4:23～27
49韩爱惠,王庆杰,孙向然.CBERS-02星CCD数据在林业资源监测中的应用评价[J].国土资源遥感,2004,6(2):61～64
50傅俏燕,王志民.CBERS-1林被信息挖掘的GIS多元分析[J].地球信息科学,2005,7(1):53～58
51郭建宁,于晋.CBERS-01/02卫星CCD图像相对辐射校正研究[J].中国科学E辑信息科学2005,35(增刊I):11～25
52陈劲松,朱博勤,邵云.基于小波变换的多波段遥感图像条带噪声的去除[J].遥感信息, 2003,2:6～9
53雷学武,吴君丽,刘俊荣.CBERS-1CCD星上定标数据在辐射校正中的应用[J].国土资源遥感,2003,3:63～66
54沈思源.土壤空间变异研究中的地统计学的应用及其展望[J].土壤学进展,1989,17(3):11～25
55黄绍文,金继运等.乡(镇)级区域土壤养分空间变异与分区管理技术研究.精准农业与土壤养分管理[M].北京:中国大地出版社,2001:248～259
56侯景儒,黄竞先.地质统计学在固体矿产资源/储量分类中的应用[J].地质与勘探,2001,37(6):61～66
57宋建中,于赤灵,彭平安,等.珠江三角洲地区土壤与表层沉积物有机质的性质结构研究[J].土壤学报,2003,40(3):335～343
58王学军,邓宝山,张泽浦.北京东郊污灌区表层土壤微量元素的小尺度空间结构特征[J].环境科学学报,1997,17(4):412～416
59刘瑞民,王学军,郑一,等.地统计学在太湖水质研究中的应用[J].环境科学学报,2002,22(2):209～212
60刘瑞民,王学军,郑一.湖泊水质参数空间分析中异常值的识别与处理[J].环境科学与技术,2003,26(5):17～15
61刘瑞民,王学军.湖泊水质参数空间优化估算的原理与方法[J].中国环境科学,2001,21(2):177～179
62刘瑞民,王学军,王翠红.湖泊水质参数空间最优估计精度分析[J].环境科学,2001,22(5):91～94
63刘多森,曾志远.土壤和环境研究中的数学方法与建模[M].北京:农业出版社,1987:14～24
64周慧珍,龚子同,Lamp.土壤空间变异性研究[J].土壤学报,1996,33(3):23～26
65 Larisa Poszdnyakova.Renduo Zhang.Geostatistical analysis of soil salinity in alarge field[J]. Preeision Aghculture,1999,l(2):153～165
66 MeBratney,A.B.,Webster,R. How many observations are needed for regional estimation of Soil ProPerties? [J]Soil Sci,1983,135:177～183
67张建辉,何敏容.丘陵区土地湿度的空间变异性研究[J].土壤通报,1996,27(2):61～62
68 Davis,J.G,Hossner,L.R.,et al.Variability of soil chemical properties in two sandy, dunal soil of Niger[J]. Soil Seience,1995,159:5,321～330
69 ChungC.K.,Chong,et al. Sampling for fertility on a stony siltloam[J]. Soil communieation of soil seience and plant analysis,1995,26:741～763
70 Lee.C.K.,Kaho.T., et al. field information maps using geostatiatics in the paddyfield[J]. 2000-ASAE-Annual-International-Meeting, Milwaukee, Wisconsin, USA, 2000, 1～17
71李艳.地统计学在土壤科学中的应用及展望[J].水土保持学报,2003, 3 :178～182
72李海滨,林忠辉,刘苏峡.Kriging方法在区域土壤水分估值中的应用[J].地理研究,2001,20(4):446～452
73张征,鞠硕华,刘淑春.地下水环境模拟中多元信息空间最优估计原理与方法[J].工程勘察,2000,3:12～15
74李子忠,龚元石.农田土壤水分和电导率空间变异性及确定其采样数的方法[J].中国农业大学学报,2000,5(5):59～66
75胡克林,李保国,陈德立,R.E.White.农田土壤水分和盐分的空间变异性及其同克里格估值[J].水科学进展,2001,12(4):460～466
76李保国,胡克林,陈德立,R.E.White.农田土壤表层饱和导水率的条件模拟[J].水利学报,2002,2:36～40,46
77吕军,俞劲炎.水稻土物理性质空间变异性研究[J].土壤学报,1990,27(l):8～15
78雷志栋.土壤特性空间变异性初步研究[J].水利学报,1985(9):10～21
79梁春祥,姚贤良.华中丘陵红坡物理性质空间变异性的研究[J].土壤学报,1993,30(1):69～77
80邱扬,傅伯杰,王军,等.黄土丘陵小流域土壤物理性质的空间变异[J].地理学报,2002,57(5):587～594
81 Vanclin M.et al., The use of cokriging with limited field soil observations [J].Soil Soc.Am.J, 1983,47:175～184
82 Vangham P.I., Esch S.R,et al., Water content effect on soil salinity predietion:Geostatistieal study using cokriging Soil Sci.Soc.Am.J. 1995,59:1146～1156
83秦耀东,李宝国.应用析取克里格方法估计区域地下水埋深分布[J].水利学报,1998,8:28～32
84龚元石,廖超子等.土壤含水量和容重的空间变异及其分形特征[J].土壤学报,1993,5(l):10～15
85杨敏华.基于多维空间变异分析的精准农业作业单元自适应决策[J].农业工程学报,2002,3:149～152
86郭旭东,傅伯杰.基于GIS和地统计学的土壤养分空间变异特征研究[J].应用生态学报,2000,11(4):555～563
87高祥照,胡克林.土壤养分与作物产量的空间变异特征与精准施肥[J].中国农业科学,2002,35(6):660～666
88王苛.精确农业田间土壤空间变异与采样方式研究[J].农业工程学报,2001,3:33～36
89李敏,帅艳民,刘素红,等.中巴资源一号02星土地覆被多阶段信息提取[J].遥感信息应用技术,2006:34～40
90朱震达,刘恕,邸醒民.我国沙漠化研究的历史回顾与若干问题[J].中国沙漠,1984,4(2):2～8
91 Bo Wu. Long J. Ci. Landscape change and desertification development in the Mu Us Sandland, Northern China[J]. Journal of Arid Environments .2002,50: 429～444
92李慧卿.荒漠化研究动态[J].世界林业研究,2004 ,17(1):11～14
93陈怀满.环境土壤学[M].北京:科学出版社,2005:490～492
94汤国安,杨昕.ArcGIS地理信息系统空间分析实验教程[M].北京:科学出版社, 2006,4:300～350
95北京超图地理信息技术有限公司.SuperMap DeskPro用户手册[M].北京:北京超图地理信息技术有限公司,2001,11:168～186
96王政权.地质统计学及在生态学中的应用[M].北京:科学出版社,1999:45～75
97裘正军.基于GPS、GIS及虚拟仪器的精细农业信息采集与处理技术的研究[D].浙江大学博士学位论文,2003:94～100
98吕安民,李成名,林宗坚.面积内插算法初探[J].测绘通报,2002,(1):44～46
99张治国.生态学空间分析原理与技术[M].北京:科学出版社,2007:119～139
100 http://www.cresda.com/cn/products_service.htm[EB/OL].2009.3.5
101 http://www.gissky.net/Article/arcgis[EB/OL].200606/18.htm:1～5
102 http://www.cresda.com/cn/products.htm[EB/OL]. 2008.3.11
103黄妙芬,徐曼.中巴地球资源02星数据特性分析[J].干旱区地理2004,27(4):485～491
104胡如忠.中巴地球资源卫星与西部大开发[J].中国航天,2001,2:16～20
105吴美蓉.中巴地球资源卫星应用及其发展[J].测绘科学2000,25(2):25～29
106陈冬花,咎梅.中巴地球资源卫星的应用与研究[J].新疆师范大学学报(自然科学版) 2006,25(3):173～177
107内蒙古遥感中心,陕西遥感应用中心.毛乌素沙地治理遥感动态监测与预警研究[M].呼和浩特,内蒙古计算机应用研究院,2008,9:21～30
108戴昌达,雷莉萍.TM图像的光谱信息特征与最佳波段组合[J] .环境遥感,1989,4(4):282～292
109年波,杨士剑,王金亮.植被遥感信息提取的最佳波段选择—以云南中部山区为例[J] .云南地理环境研究,2004,16(2):18～21
110卢玉东,尹黎明,何丙辉等.利用TM影像在土地利用灌盖遥感解译中波段选取研究[J] .西南农业大学学报(自然科学版)2005,27(4):479～482
111刘建平,赵英时.高光谱遥感数据解译的最佳波段选择方法研究[J] .中国科学院研究生院学报,1999,16(2):153～161
112孙华生,黄敬峰,王杰,等.CBERS-02 CCD图像中居民点用地信息提取方法研究[J].科技通报,2008,24(4):504～509
113李玉霞,杨武年,郑泽忠,中巴资源卫星(CBERS-02)遥感图像在生态环境动态监测中的应用研究[J].水土保持研究2006,1(6):199～120
114 http://www.rsgs.ac.cn/1_landsat5.html[EB/OL] .2008.3.11
115党福星,高志海.CBERS-01CCD数据进行地质矿产信息提取方法研究[J].国土资源遥感2002.4:63～67
116国家海域使用动态监视监测管理系统总体技术方案,国家海洋局2006,7:9～13
117彭望禄.遥感数据的计算机处理与地理信息系统[M].北京师范大学出版社,北京,1991:29～96
118党安荣,王晓栋,陈晓峰,张建宝.ERDASIMAGINE遥感图像处理方法[M].北京:清华大学出版杜,2003:186～237
119卫建军.延安示范区土地利用变化及驱动力分析[D].西北农林科技大学硕士学位论文,2007:31～50
120张霞,张兵,赵永超等.中巴地球资源一号卫星多光谱扫描图像质量评价[J].中国图象图形学报,2002,6(6):581～586
121李锦,王让会,薛英. CBERS-2/CCD影像数据在干旱区绿洲景观信息图谱中的应用与研究[J].干旱区资源与环境,2008,22(3):116～122
122陈述彭,胡如忠,刘高焕.CBERS-01卫星图像在黄河二角洲可持续发展中的应用研究[J].航天返回与遥感,2001,22(3):34～39
123刘云,赵群,寇明军,等.基于TM影像的城郊景观土地利用初步研究——以北京昌平沙河区为例[J].遥感技术与应用,2005,20(6):563～568
124吴健平.区域土地利用/土地覆盖遥感调查[M].上海:华东师范大学出版社,1999:34～50
125李玉霞,李丽,郑泽忠.CBERS-02图像在土地利用/覆盖动态监测中的应用[J].四川师范大学学报(自然科学版),2006,29(5):627～631
126王娟敏.多源遥感影像沙化土地信息提取研究[D].西北大学,2007:3～14
127周旭.3S支持下喀斯特退化景观生态安全评价研究——以贵阳市为例[D].贵州师范大学硕士学位论文,2006:81～86
128梅安新,彭望碌等.遥感导论[M].北京:高等教育出版社,2001:135～141
129王铁成,刘兴文.利用TM图像提取土地荒漠化信息的方法与效果一一以阜康地区为例[J].遥感技术与应用,1994,9(l):34～41
130廉毅,高极亭.吉林省西部荒漠化发展的陆地卫星遥感监测分析[J].气象学报,1999,57(6):662～667
131 Jaekson,R.D. Huete,A.R. Interpreting vegetation indices, J.reventative Vet.Med., 1991 (11): 185～200
132李金桐,镨拉提.基于GIS的MODIS环境荒漠化监测中的应用方法研究[J].新疆气象,2003,26(2):21～23
133李宝林,周成虎.东北平原西部沙地近10年的沙质荒漠化[J].地理学报,2001,6(3):307～315
134张洪玲,李国春.科尔沁地区荒漠化状况的遥感监测[J].农业网络信息,2006,2:42～44
135赵国忱,杜明义.基于形态学的土地荒漠化遥感图象纹理分析[J].辽宁工程技术大学学报自然科学版,2002,21(2):148～150
136乔平林.基于神经网络的土地荒漠化信息提取方法研究[J].测绘学报,2004,33(l):58～62
137李晓松,吴波.基于光谱混合分析的荒漠化信息提取一一以毛乌素沙地为例[J].林业科学研究,2006,19(2):192～198
138赵维兵.中巴地球资源卫星图像中伪“同谱异物”区分方法研究[J].遥测遥控,2008,29(2)19～25
139国家制定的天然草原退化标准《天然草地退化、沙化、盐渍化的分级指标》(GB 19377-2003):1～8
140陕西师范大学旅游与环境学院地理信息系统实验室.遥感图像处理练习[M].西安:陕西师范大学,2003,9:36～67
141李四海.浅谈提高遥感数据分类精度的方法[J].遥感信息,1995,4(3):17～19
142贾勤学,张玮,李建林,等.基于遥感技术的耕地面积动态监测研究[J].农业信息科学,2006,7(22):530～534
143 ERDAS IMAGING Field Guides[M].Fifth Edition.ERDAS Inc, Atlanta, Georgia, 1999: 160～161
144 LIU Yong-mei,TANG Guo-an,LI Tian-wen,et al.An applied research on remote sensing classification in the Loess Plateau[J].Journal of Geographical Sciences,2003.13(4):395～399
145 Lucas I F J,Frans J M.Accuracy Assessment of Satelite Derived Land cover Data:A Review[J].Photogrammetric Engineering&Remote Sensing,1994.60(4):410～432
146 eTrex Venture——奇遇手持式GPS使用手册.2003,3:63～81