基于多源遥感数据的矿区植被信息监测方法研究
|
摘要
由于煤炭开采而引起的矿区生态环境污染和破环问题不断涌现,且呈现日益增长的趋势,矿区环境污染灾害的调查和治理已成为矿区可持续发展亟待解决的重要课题。植被信息是指示生态环境的重要参数,矿区植被信息的有效提取与监测是矿区生态环境监测和治理的重要组成部分。本文以肥城矿区为研究区,利用多源遥感数据,根据植被的波谱特征和遥感图像特征,从空间和时间序列上动态分析了矿区植被生长变化状况及其与周围景观因素的关系,从而间接地反映出矿区扩展及生态环境状况,为矿区环境污染监测提供技术支持。
本文主要进行了以下几方面的研究：
1.针对矿区生态环境监测中的实际需求,本文充分利用SPOT-5数据2.5m分辨率的全色波段,运用小波变换、主成分分析等融合方法,分析了其在矿区生态环境要素调查中的应用,并将其作为矿区植被信息提取分析的基础数据。
2.为有效提取与监测矿区植被信息,综合运用缨帽变换、植被与土壤相关性分析、支持向量机分类等方法提取了矿区植被信息,并制作了植被等级分布图,确定了不同污染程度的植被覆盖面积；运用光谱变化向量分析方法对矿区2000年-2009年的生态环境的变化信息进行了监测,确定了植被信息的动态变化。
3.针对矿区植被与生态环境要素间相关分析的研究相对不足,本文反演了矿区地表温度,确定了植被覆盖度,从而分析了植被覆盖度与地表温度、大气污染及不同下垫面类型的关系,为决策部门制定相关生态措施提供了科学依据。
4.以综合指数法为基础,通过计算生物丰度指数、植被覆盖指数、水网密度指数、土地退化指数、环境质量指数综合分析了矿区生态环境状况,给出了各分矿区的生态环境状况指数等级,这不仅丰富和完善了生态环境影响评价的实践应用案例,还为矿区节能减排提供重要的技术支撑。
5.通过野外调查,结合航拍数据对监测的矿区植被信息进行精度评价,根据分析结果及矿区生态环境状况指数提出了相关措施。
实验结果表明,综合利用3S技术,采用数据融合、缨帽变换、支持向量机分类、光谱变化向量分析等方法,结合矿区实际获取的数据,能有效地提取矿区植被信息,提高矿区生态环境监测水平,在同类型的矿区生态环境遥感动态监测方面具有很大的推广应用价值。
The environmental pollution and ecological problems appeared continuously with production of coal mining, and these were becoming more and more serious. It had become an important subject research to investigate and manage the environmental pollution and disaster for sustainable development in mining area. The vegetation information is an important parameter that indicates the state of ecological environment. So the effective extraction and monitor vegetation information of mine is an important component of monitoring and managing the ecological environment. In this paper, taking Feicheng mining as the study area, using multi-source remote sensing data, according to the characteristics of vegetation spectral and remote sensing image, the status of vegetation growth and the relationship with the surrounding landscape were investigated based on dynamic analysis from the space and time-series. The results reflect indirectly the mining expansion and ecological environment, and provide technical support for monitoring pollution of the mining area.
In this paper, the studies had been made as following:
1. In cognizance of the actual demand of monitoring the ecological environment, this paper makes full use of SPOT-5 panchromatic data that resolution at 2.5m, using the fusion method such as wavelet transform, principal component analysis, analyzing the elements of the ecological environment in mining areas and make it as the basic data that extract and analyze the vegetation information of mining.
2. In order to extract and monitor the vegetation information of mining effectively, the vegetation information in mining areas was extracted comprehensive use of tasseled cap transformation, vegetation and soil correlation analysis, support vector machine classification, meanwhile the grade distribution figures of vegetation were made, and the pollution levels of different vegetation cover also were determined. The ecological environment of mine was monitored and the dynamic changes of vegetation information were determined in the period 2000-2009 using spectral change vector analysis.
3. According to lack of quantitative analysis between the vegetation and ecological environment in mining area, in this paper the surface temperature of the mining area was inversed and the vegetation coverage was determined, so the relationship of vegetation coverage, surface temperature, air pollution and different types of underlying surface were quantitative described. The results would provide scientific basis for departments making relevant policy measures.
4. Based the comprehensive index, the ecological environment of mining area were analyzed by calculating the biological abundance index, vegetation coverage index, water density index, land degradation index, environmental quality comprehensive index. It is not only enrich and improve the practical application cases of the ecological environment impact assessment, but also provides an important technical support energy saving for the mining.
5. Through field surveys, combine the aerial data evaluate the accuracy of vegetation information. According to the results of quantitative analysis and the ecological environment index, the relevant measures were proposed.
Experimental results show that the comprehensive utilization of 3S technology, using the methods of data fusion, tasseled cap transformation, support vector machine classification, spectral change vector analysis, combined with the actual acquisition of data mining, the vegetation information in the mining area could be effectively extracted, and the level of monitoring the ecological environment could be improved. It has great application value in dynamic monitoring of the ecological environment in the same type of mining.
本文主要进行了以下几方面的研究：
1.针对矿区生态环境监测中的实际需求,本文充分利用SPOT-5数据2.5m分辨率的全色波段,运用小波变换、主成分分析等融合方法,分析了其在矿区生态环境要素调查中的应用,并将其作为矿区植被信息提取分析的基础数据。
2.为有效提取与监测矿区植被信息,综合运用缨帽变换、植被与土壤相关性分析、支持向量机分类等方法提取了矿区植被信息,并制作了植被等级分布图,确定了不同污染程度的植被覆盖面积；运用光谱变化向量分析方法对矿区2000年-2009年的生态环境的变化信息进行了监测,确定了植被信息的动态变化。
3.针对矿区植被与生态环境要素间相关分析的研究相对不足,本文反演了矿区地表温度,确定了植被覆盖度,从而分析了植被覆盖度与地表温度、大气污染及不同下垫面类型的关系,为决策部门制定相关生态措施提供了科学依据。
4.以综合指数法为基础,通过计算生物丰度指数、植被覆盖指数、水网密度指数、土地退化指数、环境质量指数综合分析了矿区生态环境状况,给出了各分矿区的生态环境状况指数等级,这不仅丰富和完善了生态环境影响评价的实践应用案例,还为矿区节能减排提供重要的技术支撑。
5.通过野外调查,结合航拍数据对监测的矿区植被信息进行精度评价,根据分析结果及矿区生态环境状况指数提出了相关措施。
实验结果表明,综合利用3S技术,采用数据融合、缨帽变换、支持向量机分类、光谱变化向量分析等方法,结合矿区实际获取的数据,能有效地提取矿区植被信息,提高矿区生态环境监测水平,在同类型的矿区生态环境遥感动态监测方面具有很大的推广应用价值。
The environmental pollution and ecological problems appeared continuously with production of coal mining, and these were becoming more and more serious. It had become an important subject research to investigate and manage the environmental pollution and disaster for sustainable development in mining area. The vegetation information is an important parameter that indicates the state of ecological environment. So the effective extraction and monitor vegetation information of mine is an important component of monitoring and managing the ecological environment. In this paper, taking Feicheng mining as the study area, using multi-source remote sensing data, according to the characteristics of vegetation spectral and remote sensing image, the status of vegetation growth and the relationship with the surrounding landscape were investigated based on dynamic analysis from the space and time-series. The results reflect indirectly the mining expansion and ecological environment, and provide technical support for monitoring pollution of the mining area.
In this paper, the studies had been made as following:
1. In cognizance of the actual demand of monitoring the ecological environment, this paper makes full use of SPOT-5 panchromatic data that resolution at 2.5m, using the fusion method such as wavelet transform, principal component analysis, analyzing the elements of the ecological environment in mining areas and make it as the basic data that extract and analyze the vegetation information of mining.
2. In order to extract and monitor the vegetation information of mining effectively, the vegetation information in mining areas was extracted comprehensive use of tasseled cap transformation, vegetation and soil correlation analysis, support vector machine classification, meanwhile the grade distribution figures of vegetation were made, and the pollution levels of different vegetation cover also were determined. The ecological environment of mine was monitored and the dynamic changes of vegetation information were determined in the period 2000-2009 using spectral change vector analysis.
3. According to lack of quantitative analysis between the vegetation and ecological environment in mining area, in this paper the surface temperature of the mining area was inversed and the vegetation coverage was determined, so the relationship of vegetation coverage, surface temperature, air pollution and different types of underlying surface were quantitative described. The results would provide scientific basis for departments making relevant policy measures.
4. Based the comprehensive index, the ecological environment of mining area were analyzed by calculating the biological abundance index, vegetation coverage index, water density index, land degradation index, environmental quality comprehensive index. It is not only enrich and improve the practical application cases of the ecological environment impact assessment, but also provides an important technical support energy saving for the mining.
5. Through field surveys, combine the aerial data evaluate the accuracy of vegetation information. According to the results of quantitative analysis and the ecological environment index, the relevant measures were proposed.
Experimental results show that the comprehensive utilization of 3S technology, using the methods of data fusion, tasseled cap transformation, support vector machine classification, spectral change vector analysis, combined with the actual acquisition of data mining, the vegetation information in the mining area could be effectively extracted, and the level of monitoring the ecological environment could be improved. It has great application value in dynamic monitoring of the ecological environment in the same type of mining.
引文
1.侯艳辉.矿区生态环境评估及预警实现技术研究[D].山东青岛：山东科技大学,2004.
2.王俊芳.攀枝花钒钦磁铁矿—宝鼎煤矿区矿山环境遥感监测研究[D].四川成都：成都理工大学,2007.
3. Legg C A. Application of remote sensing to environment aspects of surface operations in the United Kingdom [C]. Remote sensing:an operational technology for the mining and petroleum industries, Conference, JMM, London,1999,159～164.
4. Cloutis E A. Hyperspectral geological remote sensing:evaluation of analytical techniques [J]. Remote Sensing,1996,17(12):215-224.
5. Venkataraman Q Kumar S P, Ratha D S. Open cast mine monitoring and environmental impact studies through remote sensing a case study from Goa, India [J], Geocarto International,1997,12(2):39-53.
6. Ferrier G. Application of image spectrometer data in identifying environmental pollution caused by mining at Rodaquilar [M]. Spain:Remote Sensing Environment,1999, (68): 125-137.
7. Christian Fischer, Wolfgang Busch. Monitoring of environmental changes caused by hard coal mining [J]. Remote sensing, GIS Application and Geology Proceeding of SPIE, 2002,4545-4548.
8. 金学林,郭达志,盛业华.地球信息科学技术与矿区环境监测和治理[J].煤炭科学,1997,22(4)：370-374.
9.盛业华,郭达志,张书毕,等.工矿区环境动态监测与分析研究[M].北京：地质出版社,2001,5-22.
10.赵英时等.遥感应用分析原理与方法[M].北京：科学出版社,2003,251-273.
11.杨忠义,白中科,张前进,等.矿区生态破坏阶段的土地利用／覆被变化研究——以平朔安家岭矿为例[J].山西农业大学学报,2003,23(4)：367-370.
12.陈华丽,陈刚,郭金柱.Landsat TM在矿区生态环境动态监测中的应用[J].遥感信息,2004,1：31-34.
13.吴根梅.植被覆盖信息的提取方法研究[D].北京：北京林业大学,2007.
14.雷利卿,李永庆.矸石山附近植物的反射光谱特性研究[J].山东科技大学学报,1998,17(2)：115-118.
15.张云霞,李晓兵,陈云浩.草地植被盖度的多尺度遥感与实地测量方法综述[J].地球科学进展,2003,18(1)：85-93.
16.李苗苗,吴炳方,颜长珍,等.密云水库上游植被覆盖信息的遥感估算[J].资源科学,2004,26(4)：153-159.
17. Duncan J, Stow D, Franklin J, et al. Assessing the relationship between spectral vegetation indices and shrub cover in the Jornada Basin, New Mexico [J]. International Journal Remote Sensing,1993,14(18):3395-3416.
18. Gaston, G. et al. Identification of carbon quantifiable regions in the former Soviet Union using wised classfication of AVHRR global vegetation index images [J]. Remote Sensing. 1994, (15):3199-3221.
19.雷国静.赣州市龙南地区稀土矿矿山开采现状与动态监测遥感研究[D].北京：中国地质大学,2006.
20. Lunetta R S, Knight J F, Ediriwickrema J, et al. Land-cover change detection using multi-temporal MODIS NDVI data [J]. Remote Sensing of Environment,2006,105(2): 142-154.
21. Galford G L, Mustard J F, Melillo J, et al. Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil [J]. Remote Sensing of Environment,2008,112(2):576-587.
22.雷利卿,岳燕珍,孙九林,等.遥感技术在矿区环境污染监测中的应用研究[J].环境监测,2002,2：33-36.
23.王坚,张继贤,刘正军,等.基于NDVI序列影像精化结果的植被覆盖变化研究[J].测绘科学,2005,30(6)：43-44.
24.李月臣,陈晋,宫鹏,等.墓于NDVI时间序列数据的土地覆盖变化检测指标设计[J].应用基础与工程科学学报,2005,13(3)：261-275.
25.刘旭升,张晓丽.基于BP神经网络的森林植被遥感分类研究[J].林业资源管理,2005,2(1)：51-54.
26.卢霞,刘少峰,郑礼全.矿区植被重金属胁迫高光谱分辨率数据分析[J].测绘科学, 2007,32(2)：111-113.
27.袁金国.遥感图像数字处理[M].北京：中国环境科学出版社,2006,224-226.
28.池宏康,周广胜,许振柱,等.表观反射率及其在植被遥感中的应用[J].植物生态学报,2005,29(1)：74-80.
29.孙蓉桦,郭德方.SPOT-5全色与多光谱数据融合方法的比较研究[J].遥感技术与应用,2005,20(3)：366-370.
30.熊育久,林辉,孙华,等.多源遥感数据融合及其对植被识别的影响[J].林业资源管理,2005,(5)：71-76.
31.张炳智,张继贤,张丽.土地利用动态遥感监测中多源遥感影像融合方法比较研究[J].测绘科学,2000,25(3)：46-50.
32.陈木生.基于小波变换的全色图像与多光谱图像融合算法研究[D].广东广州：暨南大学,2005.
33.张宁玉,吴泉源.Brovey融合与小波融合对QuickBird图像的信息量影响[J].遥感技术与应用,2006,21(1)：67-70.
34.刘丽娟,范文义.基于MATLAB的IHS变换与小波变换影像融合与应用[J].地球信息科学,2008,10(5)：670-677.
35.黄卉.基于小波变换的图像融合方法研究[D].安徽合肥：合肥工业大学,2005.
36.胡良梅,高隽,何柯峰.图像融合质量评价方法的研究[J].电子学报,2004,32(12)：218-221.
37.张永生,戴晨光,张云彬,等.天基多源遥感信息融合—理论、算法与应用系统[M].北京：科学出版社,2005.
38.周丽雅.遥感影像融合及质量评价研究[D].河南郑州：中国人民解放军信息工程大学,2006.
39.阳方林,郭红阳,杨风暴.像素级图像融合效果的评价方法研究[J].测试技术学报,2006,16(4)：276-279.
40.史榕,许惠平,陈华根.遥感影像数据融合技术及融合质量评价研究[J].鲁东大学学报(自然科学版),2008,24(2)：130-134.
41. LI Xin-zhi, WANG Ping, ZANG Yan-bin. Application of SPOT 5 Data Fusion on Investigating the Ecological Environment of Mining Area [C]. Shanghai, China,2009 Urban Remote Sensing Joint Event,2009.
42.梅安新,彭望碌,秦其明,等.遥感导论[M].北京：高等教育出版社,2001,134-162.
43.胡振琪,陈涛.基于ERDAS的矿区植被覆盖度遥感信息提取研究—以陕西省榆林市神府煤矿区为例[J].西北林学院学报,2008,23(2)：164-167.
44.党安荣,王晓栋,陈晓峰,等.ERDAS IMAGING遥感图像处理方法[M].北京：清华大学出版社,2003,186-220.
45.(美)John R. Jensen著,陈晓玲,龚威,李平湘,等译.遥感数字影像处理导论[M].北京：机械工业出版社,2007,294-369.
46.雷国静,刘少峰,程三友.遥感在稀土矿区植被污染信息提取中的应用[J].江西有色金属,2006,20(2)：1-5.
47.程博,刘少峰,梅雪峰.利用多光谱遥感数据提取植被污染信息的新方法[J].现代地质,2005,19(3)：458-464.
48.漆小英,晏明星.多时相遥感数据在矿山扩展动态监测中的应用[J].国土资源遥感,2007,(3)：85-88.
49. Zhang Xicheng, Li Xinzhi, Wang Ping, et al. Monitoring and Analysis on Vegetation Information of Mining Area Based on the Multi-source Remote Sensing Data [M]. Journal of Information and Computational Science,2009,6(5):2097-2104.
50. Congalton R G A Review of Assessing the Accuracy of Classification of Remotely Sensed Data [M]. Spain:Remote Sensing Environment,1991, (37):35-46.
51. http://see.xidian.edu.cn/faculty/chzheng/bishe/index.htm.
52.夏建涛,何明一.基于SVM的高维多光谱图像分类算法及其特性的研究[J].计算机工程,2003,29(13)：27-29.
53.张锦水,何春阳,潘耀忠,等.基于SVM的多源信息复合的高空间分辨率遥感数据分类研究[J].遥感学报,2006,10(1)：49-57.
54.贾萍,李海涛,林卉,等.基于SVM的多源遥感影像分类研究[J].2008,33(4)：21-22.
55.何春阳,陈晋,陈云浩,等.土地利用／覆盖变化混合动态监测方法研究[J].自然资源学报,2010,16(3)：255-262.
56.陈晋,何春阳,史培军,等.基于变化向量分析的土地利用／覆盖变化动态监(Ⅰ)——变化阈值的确定方法[J].遥感学报,2001,5(4)：259-266.
57.陈晋,何春阳,卓莉.基于变化向量分析的土地利用／覆盖变化动态监测(Ⅱ)——变化类型的确定方法[J].遥感学报,2001,5(5)：346-352.
58. Mas J F. Monitoring land-cover changes, a comparison of change techniques [J]. International Journal of Remote Sensing,1999,24(1):139-152.
59. Fang T, Ledrew E. The determination of optimal threshold levels for change detection using various accuracy indices [J]. Photogrammetric Engineering and Remote Sensing, 1988, (54):1449-1454.
60. Lunetta R S, Elvidge C D. Remote Sensing Change Detection Environment Monitoring Methods and Application [M]. London:Taylor&Francis Ltd.,1999.
61.郑小炼.基于变化矢量分析的土地利用变化检测方法研究[D].北京：中国科学院研究生院,2006.
62. Fang T, Ledrew E. The determination of optimal threshold levels for change detection using various accuracy indices [J]. Photogrammetric Engineering and Remote Sensing, 1988,54:1449-1454.
63.徐霞,塔西甫拉提·特依拜,哈学萍,等.基于ETM+数据的新疆于田地区地表温度反演研究[J].2008,45(3)：547-552.
64. Wan Z, Li Zhaoliang. A physics-based algorithm for retrieving land surface emissivity and temperature from EOS/MODIS data [J]. IEEE Transaction on Geosciences and Remote Sensmg,1997,35(4):980-996.
65. Li Zhaoliang, Becker F, Stoll M p. Evahiation of six methods for extracting relative emissivity spectra from thermal infrared images [J]. Remote Sensing of Environment, 1999,69:197-214.
66. Shun lin Liang. An optimization algorithm for separating land surface Temperature and emissivity from multispectral thermal infrared imager [J]. IEEE Transaction on Geosciences and Remote Sensmg,2001,39(2):264-274.
67.覃志豪,Zhang Minghua, Arnon Karnieli,等.用陆地卫星TM6数据演算地表温度的单窗算法[J],地理学报,2001,56(4)：456-466.
68.马超飞,马建文,布和敖斯尔.USLE模型中植被覆盖因子的遥感数据定量估算[J].水土保持学报,2001,21(4)：629.
69.韩兰英,王宝鉴,张正偲,等.基于RS的石羊河流域植被覆盖度动态监测[J].草业科学,2008,25(2)：11-15.
70.张小飞,王仰麟,吴健生,等.城市地域地表温度—植被覆盖定量关系分析——以 深圳市为例[J].地理研究,2006,25(3)：369-378.
71.颜丽英.利用遥感法研究广州市区大气污染与植被生态变化[J].生态科学,1990,(2)：95-104.
72.孙岩,刘雪华,杜桂森,等.类比法在圆明园防渗工程环评中的应用[J].环境科学与技术,2009,32(10)：194-199.
73.余艳红.景观格局指数在生态环境影响评价中的应用—以丽江至香格里拉铁路生态影响评价为例[J].环境科学导刊,2010,29(2)：82-85.
74.董文福,管东生.浅论非污染生态影响评价[J].环境科学动态,2003,(1)：17-19.
75.王迎春,李永志,王冲.综合指数法在生态环境影响评价中的应用[J].民营科技,2009,2：101.
76.HJ/T192-2006,国家环境保护总局：生态环境状况评价技术规范(试行)[S].
2.王俊芳.攀枝花钒钦磁铁矿—宝鼎煤矿区矿山环境遥感监测研究[D].四川成都：成都理工大学,2007.
3. Legg C A. Application of remote sensing to environment aspects of surface operations in the United Kingdom [C]. Remote sensing:an operational technology for the mining and petroleum industries, Conference, JMM, London,1999,159～164.
4. Cloutis E A. Hyperspectral geological remote sensing:evaluation of analytical techniques [J]. Remote Sensing,1996,17(12):215-224.
5. Venkataraman Q Kumar S P, Ratha D S. Open cast mine monitoring and environmental impact studies through remote sensing a case study from Goa, India [J], Geocarto International,1997,12(2):39-53.
6. Ferrier G. Application of image spectrometer data in identifying environmental pollution caused by mining at Rodaquilar [M]. Spain:Remote Sensing Environment,1999, (68): 125-137.
7. Christian Fischer, Wolfgang Busch. Monitoring of environmental changes caused by hard coal mining [J]. Remote sensing, GIS Application and Geology Proceeding of SPIE, 2002,4545-4548.
8. 金学林,郭达志,盛业华.地球信息科学技术与矿区环境监测和治理[J].煤炭科学,1997,22(4)：370-374.
9.盛业华,郭达志,张书毕,等.工矿区环境动态监测与分析研究[M].北京：地质出版社,2001,5-22.
10.赵英时等.遥感应用分析原理与方法[M].北京：科学出版社,2003,251-273.
11.杨忠义,白中科,张前进,等.矿区生态破坏阶段的土地利用／覆被变化研究——以平朔安家岭矿为例[J].山西农业大学学报,2003,23(4)：367-370.
12.陈华丽,陈刚,郭金柱.Landsat TM在矿区生态环境动态监测中的应用[J].遥感信息,2004,1：31-34.
13.吴根梅.植被覆盖信息的提取方法研究[D].北京：北京林业大学,2007.
14.雷利卿,李永庆.矸石山附近植物的反射光谱特性研究[J].山东科技大学学报,1998,17(2)：115-118.
15.张云霞,李晓兵,陈云浩.草地植被盖度的多尺度遥感与实地测量方法综述[J].地球科学进展,2003,18(1)：85-93.
16.李苗苗,吴炳方,颜长珍,等.密云水库上游植被覆盖信息的遥感估算[J].资源科学,2004,26(4)：153-159.
17. Duncan J, Stow D, Franklin J, et al. Assessing the relationship between spectral vegetation indices and shrub cover in the Jornada Basin, New Mexico [J]. International Journal Remote Sensing,1993,14(18):3395-3416.
18. Gaston, G. et al. Identification of carbon quantifiable regions in the former Soviet Union using wised classfication of AVHRR global vegetation index images [J]. Remote Sensing. 1994, (15):3199-3221.
19.雷国静.赣州市龙南地区稀土矿矿山开采现状与动态监测遥感研究[D].北京：中国地质大学,2006.
20. Lunetta R S, Knight J F, Ediriwickrema J, et al. Land-cover change detection using multi-temporal MODIS NDVI data [J]. Remote Sensing of Environment,2006,105(2): 142-154.
21. Galford G L, Mustard J F, Melillo J, et al. Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil [J]. Remote Sensing of Environment,2008,112(2):576-587.
22.雷利卿,岳燕珍,孙九林,等.遥感技术在矿区环境污染监测中的应用研究[J].环境监测,2002,2：33-36.
23.王坚,张继贤,刘正军,等.基于NDVI序列影像精化结果的植被覆盖变化研究[J].测绘科学,2005,30(6)：43-44.
24.李月臣,陈晋,宫鹏,等.墓于NDVI时间序列数据的土地覆盖变化检测指标设计[J].应用基础与工程科学学报,2005,13(3)：261-275.
25.刘旭升,张晓丽.基于BP神经网络的森林植被遥感分类研究[J].林业资源管理,2005,2(1)：51-54.
26.卢霞,刘少峰,郑礼全.矿区植被重金属胁迫高光谱分辨率数据分析[J].测绘科学, 2007,32(2)：111-113.
27.袁金国.遥感图像数字处理[M].北京：中国环境科学出版社,2006,224-226.
28.池宏康,周广胜,许振柱,等.表观反射率及其在植被遥感中的应用[J].植物生态学报,2005,29(1)：74-80.
29.孙蓉桦,郭德方.SPOT-5全色与多光谱数据融合方法的比较研究[J].遥感技术与应用,2005,20(3)：366-370.
30.熊育久,林辉,孙华,等.多源遥感数据融合及其对植被识别的影响[J].林业资源管理,2005,(5)：71-76.
31.张炳智,张继贤,张丽.土地利用动态遥感监测中多源遥感影像融合方法比较研究[J].测绘科学,2000,25(3)：46-50.
32.陈木生.基于小波变换的全色图像与多光谱图像融合算法研究[D].广东广州：暨南大学,2005.
33.张宁玉,吴泉源.Brovey融合与小波融合对QuickBird图像的信息量影响[J].遥感技术与应用,2006,21(1)：67-70.
34.刘丽娟,范文义.基于MATLAB的IHS变换与小波变换影像融合与应用[J].地球信息科学,2008,10(5)：670-677.
35.黄卉.基于小波变换的图像融合方法研究[D].安徽合肥：合肥工业大学,2005.
36.胡良梅,高隽,何柯峰.图像融合质量评价方法的研究[J].电子学报,2004,32(12)：218-221.
37.张永生,戴晨光,张云彬,等.天基多源遥感信息融合—理论、算法与应用系统[M].北京：科学出版社,2005.
38.周丽雅.遥感影像融合及质量评价研究[D].河南郑州：中国人民解放军信息工程大学,2006.
39.阳方林,郭红阳,杨风暴.像素级图像融合效果的评价方法研究[J].测试技术学报,2006,16(4)：276-279.
40.史榕,许惠平,陈华根.遥感影像数据融合技术及融合质量评价研究[J].鲁东大学学报(自然科学版),2008,24(2)：130-134.
41. LI Xin-zhi, WANG Ping, ZANG Yan-bin. Application of SPOT 5 Data Fusion on Investigating the Ecological Environment of Mining Area [C]. Shanghai, China,2009 Urban Remote Sensing Joint Event,2009.
42.梅安新,彭望碌,秦其明,等.遥感导论[M].北京：高等教育出版社,2001,134-162.
43.胡振琪,陈涛.基于ERDAS的矿区植被覆盖度遥感信息提取研究—以陕西省榆林市神府煤矿区为例[J].西北林学院学报,2008,23(2)：164-167.
44.党安荣,王晓栋,陈晓峰,等.ERDAS IMAGING遥感图像处理方法[M].北京：清华大学出版社,2003,186-220.
45.(美)John R. Jensen著,陈晓玲,龚威,李平湘,等译.遥感数字影像处理导论[M].北京：机械工业出版社,2007,294-369.
46.雷国静,刘少峰,程三友.遥感在稀土矿区植被污染信息提取中的应用[J].江西有色金属,2006,20(2)：1-5.
47.程博,刘少峰,梅雪峰.利用多光谱遥感数据提取植被污染信息的新方法[J].现代地质,2005,19(3)：458-464.
48.漆小英,晏明星.多时相遥感数据在矿山扩展动态监测中的应用[J].国土资源遥感,2007,(3)：85-88.
49. Zhang Xicheng, Li Xinzhi, Wang Ping, et al. Monitoring and Analysis on Vegetation Information of Mining Area Based on the Multi-source Remote Sensing Data [M]. Journal of Information and Computational Science,2009,6(5):2097-2104.
50. Congalton R G A Review of Assessing the Accuracy of Classification of Remotely Sensed Data [M]. Spain:Remote Sensing Environment,1991, (37):35-46.
51. http://see.xidian.edu.cn/faculty/chzheng/bishe/index.htm.
52.夏建涛,何明一.基于SVM的高维多光谱图像分类算法及其特性的研究[J].计算机工程,2003,29(13)：27-29.
53.张锦水,何春阳,潘耀忠,等.基于SVM的多源信息复合的高空间分辨率遥感数据分类研究[J].遥感学报,2006,10(1)：49-57.
54.贾萍,李海涛,林卉,等.基于SVM的多源遥感影像分类研究[J].2008,33(4)：21-22.
55.何春阳,陈晋,陈云浩,等.土地利用／覆盖变化混合动态监测方法研究[J].自然资源学报,2010,16(3)：255-262.
56.陈晋,何春阳,史培军,等.基于变化向量分析的土地利用／覆盖变化动态监(Ⅰ)——变化阈值的确定方法[J].遥感学报,2001,5(4)：259-266.
57.陈晋,何春阳,卓莉.基于变化向量分析的土地利用／覆盖变化动态监测(Ⅱ)——变化类型的确定方法[J].遥感学报,2001,5(5)：346-352.
58. Mas J F. Monitoring land-cover changes, a comparison of change techniques [J]. International Journal of Remote Sensing,1999,24(1):139-152.
59. Fang T, Ledrew E. The determination of optimal threshold levels for change detection using various accuracy indices [J]. Photogrammetric Engineering and Remote Sensing, 1988, (54):1449-1454.
60. Lunetta R S, Elvidge C D. Remote Sensing Change Detection Environment Monitoring Methods and Application [M]. London:Taylor&Francis Ltd.,1999.
61.郑小炼.基于变化矢量分析的土地利用变化检测方法研究[D].北京：中国科学院研究生院,2006.
62. Fang T, Ledrew E. The determination of optimal threshold levels for change detection using various accuracy indices [J]. Photogrammetric Engineering and Remote Sensing, 1988,54:1449-1454.
63.徐霞,塔西甫拉提·特依拜,哈学萍,等.基于ETM+数据的新疆于田地区地表温度反演研究[J].2008,45(3)：547-552.
64. Wan Z, Li Zhaoliang. A physics-based algorithm for retrieving land surface emissivity and temperature from EOS/MODIS data [J]. IEEE Transaction on Geosciences and Remote Sensmg,1997,35(4):980-996.
65. Li Zhaoliang, Becker F, Stoll M p. Evahiation of six methods for extracting relative emissivity spectra from thermal infrared images [J]. Remote Sensing of Environment, 1999,69:197-214.
66. Shun lin Liang. An optimization algorithm for separating land surface Temperature and emissivity from multispectral thermal infrared imager [J]. IEEE Transaction on Geosciences and Remote Sensmg,2001,39(2):264-274.
67.覃志豪,Zhang Minghua, Arnon Karnieli,等.用陆地卫星TM6数据演算地表温度的单窗算法[J],地理学报,2001,56(4)：456-466.
68.马超飞,马建文,布和敖斯尔.USLE模型中植被覆盖因子的遥感数据定量估算[J].水土保持学报,2001,21(4)：629.
69.韩兰英,王宝鉴,张正偲,等.基于RS的石羊河流域植被覆盖度动态监测[J].草业科学,2008,25(2)：11-15.
70.张小飞,王仰麟,吴健生,等.城市地域地表温度—植被覆盖定量关系分析——以 深圳市为例[J].地理研究,2006,25(3)：369-378.
71.颜丽英.利用遥感法研究广州市区大气污染与植被生态变化[J].生态科学,1990,(2)：95-104.
72.孙岩,刘雪华,杜桂森,等.类比法在圆明园防渗工程环评中的应用[J].环境科学与技术,2009,32(10)：194-199.
73.余艳红.景观格局指数在生态环境影响评价中的应用—以丽江至香格里拉铁路生态影响评价为例[J].环境科学导刊,2010,29(2)：82-85.
74.董文福,管东生.浅论非污染生态影响评价[J].环境科学动态,2003,(1)：17-19.
75.王迎春,李永志,王冲.综合指数法在生态环境影响评价中的应用[J].民营科技,2009,2：101.
76.HJ/T192-2006,国家环境保护总局：生态环境状况评价技术规范(试行)[S].