基于环境卫星的松辽平原盐渍土盐分含量研究
|
摘要
土壤是生态系统的重要组成部分，是连接动物和植物的有机载体，随着社会进程的发展，土壤环境问题逐渐加剧。目前，土地退化和荒漠化是制约土壤潜力的最主要问题，而土壤盐渍化则在松辽平原内影响巨大。土壤盐渍化的形成通常在地下水中可溶性盐含量高，气候干燥，土壤水分蒸发强度较大的地区，多数人类活动也是形成盐渍土的主要问题之一。松辽平原西部是我国土壤盐渍化最集中的分布区，该地区土壤盐渍化面积约占总土壤面积的60%，在松辽平原内，土壤盐碱化对土地资源造成了严重的破坏，制约了农业产业的发展，打破了该区域内生态系统的平衡。土壤盐渍化已经成为该地区人类生产生活的巨大阻碍。
对盐渍土盐分含量的研究，能从多角度、全方位掌握研究区内的土壤盐分信息，能有效的区分该区域内盐渍土的盐分含量等级、分布范围、地理位置及发展变化过程，研究结果制成的土壤盐分含量专题图能为土地管理部门、农业生产部门等提供有效的基础辅助信息，为合理的制定管理政策和防治土壤盐渍化进一步扩展提供依据。
早期的土壤盐分含量监测方法主要以实地采集、结果检测和同步观测为主，这不仅增加了监测的难度、耗费大量物质，而且在大面积的定量监测过程中需要的时间更长，工作量更大。因此，面对未来环境的发展，需要建立一种更加快速、经济、有效的监测体系。遥感科学的发展，为土壤盐分含量的监测和制图提供了全新的方法，遥感技术的基础即为非接触、远距离的探测技术，它能从宏观的角度，快速、有效的进行监测，不仅降低了经济成本，而且提高了大面积监测的精度并在在重复监测和动态分析上具有明显的优势。在遥感技术应用于土地监测的初期，对盐渍土的区分主要利用多光谱遥感数据，区分能力单一，高光谱技术的发展是基于物质组成和结构差异获取的物质反射光谱，有效的利用光谱曲线的反射及吸收特征，使得遥感方法对盐渍土进行定量监测研究成为可能。
本论文以研究松辽平原盐渍土的盐分组成、光谱曲线特征为基础，采用光谱分析的方法，提取该区域盐渍土的敏感波段，并利用最小二乘支持向量机回归分析方法建立了土壤盐分含量及其相关判断指标与盐渍土反射光谱间的关系模型。在验证环境减灾卫星高光谱可行性的前提下，将模型应用于土壤盐分含量空间分布信息的提取，结合野外采样点的检测数据对模型在松辽平原内盐渍土盐分含量空间分布信息的估算结果进行评价，揭示了环境减灾卫星在土壤盐渍化中的监测和盐分定量估算的能力。论文主要研究内容和结论如下：
1、松辽平原的土壤盐分组成特征。利用野外采样数据进行检测，结果表明，在松辽平原的盐渍土的表层土壤中，阳离子含量主要以Na~+为主，占阳离子总量71.18%。阴离子则以HCO_3-、CO_3~2-为主，占阴离子总量的74.27%。利用统计学方法对监测数据进行分析可以得出HCO_3-、CO_3~2-离子、Na~+离子含量与土壤盐分的相关性显著，这可能是由于成土母质原因造成离子结合方式主要以钠离子和碳酸根离子及碳酸氢根离子为主。
2、环境减灾卫星高光谱数据应用可行性分析。环境减灾卫星高光谱数据的实际应用在国内的研究中利用的较少，为了验证该卫星获取的高光谱数据对研究区内的盐渍土含量研究是否具有实际意义，本文采用了数据对比的方法，以目前应用较广的Hyperion数据为比较对象，在去除坏线条和条纹的基础上，采用FLAASH模型对图像数据进行了大气校正，利用MNF变换进行大气校正后处理，得到光谱优化的图像数据。最后采用相关分析和差值分析的方法对两组数据进行对比，结果表明环境减灾卫星高光谱数据具有实际应用价值。
3、基于盐渍土的光谱特征，确定了识别盐渍土的敏感波段。在结合前人研究结果，分析松辽平原成土母质的基础上，确定了本研究区内主要粘土矿物成分为高岭石、绿泥石、伊利石和蒙脱石为主，利用USGS光谱库数据，提取粘土矿物的光谱特征并进行分析。并以野外采样点的光谱数据为基础，采取光谱分析的方法，以原始光谱曲线，一阶微分曲线和二阶微分曲线为比较对象，初步确定本区对盐渍土敏感波段在460-508nm、540-571nm、765-827nm范围内。
4、建立了基于最小二乘支持向量机回归分析方法的高光谱数据土壤盐分含量估算模型。最小二乘支持向量机回归（LS-SVM）建模方法是在等式约束的条件下，把数据非线性的映射到高维特征空间上，并构造出最优化的分类超平面。LS-SVM回归模型能够反映光谱对土壤盐分含量的响应，与盐渍土光谱特征研究结果一致，说明LS-SVM回归模型具有一定的物理意义，该建模相比传统的数理统计方法精度较高。
5、环境减灾卫星高光谱遥感土壤盐分含量估算。为实现环境减灾卫星对土壤盐分含量的空间分布制图，本研究利用NDVI对反射率图像进行土壤与植被、水体、城镇的分离，最终提取土壤信息。利用所建的土壤盐分含量估算模型，实现了环境减灾高光谱数据的土壤盐分含量信息提取，对估算结果的分析表明，基于LS-SVM方法所建的模型用于环境减灾卫星高光谱数据的土壤盐分含量估算精度较高，结合土壤采样样本进行检验，由环境卫星估算的土壤盐分含量值与检验样本实验室实测值的决定系数R2=0.784,RMSE=1.037。通过对土壤盐分含量的空间分布进行分析可知，本文研究结果与前期调查基本一致。
Soil is a significant component of ecosystem, and it is also the organic carrier forthe connection of animal and plant. With the development of society, soil environmentproblem is becoming more and more serious. Presently, land degradation anddesertification are the key limitation to potential of soil. Soil salinity has a greatinfluence on Songliao Plain. Salinization often occurred in areas where salinegroundwaters are elevated to where they approach the ground surface and evaporationexceeds precipitation. Human activities may also cause soil salinity. Soliao Plain isthe area with the highest concentration of soil salinity, where salinized soils are nearly60%of the total land area and soil salinity damages soil properties, while at the sametime restricting the development of agriculture industry and breaking the ecologicalbalance. Soil salinity has become a giant limitation to human production activity.Through the study of soil salinity, its information can be gathered from multi-angles.Besides, the degree of salinization, the distribution range, the geographic location andthe development process can also be effectively distinguished. The thematic map ofsoil salt content will provide assistant information for land administration departmentand agriculture production department. It will also offer basis for policy making andthe prevention of soil salinization.
Previous techniques for identifying and monitoring salinized land are mainlybased on sampling, result testing and field survey, which not only makes thetechnique difficult and time-consuming, but also brings great difficulty to broad-scalequantitative evaluation. Therefore, efforts are being made to develop morecost-effective monitoring system. The development of remote sensing provides abrand-new method for the monitoring and mapping of soil salt content. This techniqueis non-contact and remote, which is able to monitor fast and effectively from amicroscopic perspective. It not only is economic, but also improves accuracy. At thebeginning, when remote sensing was applied to land monitoring, salt-affected soil isdistinguished mainly by multispectral remote sensing. The development ofhyperspectral remote sensing is based on analysis of component and structure.
This paper is to study the salt composition of Songliao plain saline soil, and takespectrum curve characteristics as the foundation, by the method of the spectralanalysis, drawing the sensitive range of this area, and using the least square to supportvector machine regression analysis method to establish the relative model of the soilsalt content and its correlation judgment index and saline soil spectral reflectance.Oncondition of the hyperspectral satellites feasibility testing environment, apply themodel to the content of soil salt space distribution for information extraction, combined with the field test data sample to estimate the result evaluation of the salinesoil salt content space distribution in Songliao plain to reveal the monitoring ability inenvironmental disaster reduction satellite in soil salinization and the salt quantitativeestimate ability.
The primary results of this dissertation are listed below:
1. Soil content feature of Songliao Plain. Chemical analysis of samples showsthat in the top layer of soils, the predominant cation is Na+, which is86.30%of thetotal cation and that the predominant anion are HCO3-and CO32-, which is85.28%ofthe total anion. Through statistics analysis of the data, it can be shown that there isobvious correlation between soil salinity and HCO3-, CO32-and Na+. This may be dueto the soil forming materials causing ion combining ways mainly by sodium ions andcarbonated root ion and bicarbonate.
2. Analysis of the feasibility of environmental mitigation satellite hyperspectraldata application. The application of environmental mitigation satellite hyperspectraldata is not widespread. In order to test whether the hyperspectral obtained from thesatellite has practical value for the study of soil salt content. This paper adopts theapproach of comparative data, and uses the presently wide Hyperion data ascompared object, removing the bad line and stripe, and applying the FLAASH modelto the image data by the atmosphere correction, using the MNF transform atmosphereto correct the post-processing, get the optimization of the spectrum image data At last,through correlation analysis and differential analysis, after the comparison of the twogroups of data, it shows that environmental mitigation satellite hyperspectral data haspractical application value.
3. Based on the spectral features of salt-affected soil, sensitive wave bands ofsalt-affected soil are confirmed. Combined with previous findings, it is assured thatthe main clay mineral contents of Songliao Plain are kaolinite, chlorite, illite andmontmorillonite. Use USGS spectrum library data, and draw the clay mineralextraction of spectral features and do the analysis. And with the foundation of thesample point spectral data, and take the method of spectral analysis, with the originalspectrum curve, first order differential curve and second order differential curve. Anddetermine primarily the sensitive bands of this area’s saline soil are in the range of460-508nm,540-571nm, and765-827nm.
4. Environmental mitigation satellite hyperspectral remote sensing soil saltcontent estimation. In order to make soil salt content distribution map, NDVI wasused in this study to make the separation between soil and vegetation, water andtowns. The models that are previously established based on LS-SVM were applied toEnvironmental mitigation satellite hyperspectral data for mapping of soil salt content.R2=0.784, RMSE=1.037are obtained for environmental mitigation satellite method.Through the analysis of soil salt content distribution map, it is obvious that the resultof this thesis is in accordance with previous findings.
5. Environmental mitigation satellite hyperspectral remote sensing soil saltcontent estimation. In order to make soil salt content distribution map, NDVI wasused in this study to make the separation between soil and vegetation, water andtowns. The models that are previously established based on LS-SVM were applied to Environmental mitigation satellite hyperspectral data for mapping of soil salt content.R2=0.784, RMSE=1.037are obtained for environmental mitigation satellite method.Through the analysis of soil salt content distribution map, it is obvious that the resultof this thesis is in accordance with previous findings.
对盐渍土盐分含量的研究，能从多角度、全方位掌握研究区内的土壤盐分信息，能有效的区分该区域内盐渍土的盐分含量等级、分布范围、地理位置及发展变化过程，研究结果制成的土壤盐分含量专题图能为土地管理部门、农业生产部门等提供有效的基础辅助信息，为合理的制定管理政策和防治土壤盐渍化进一步扩展提供依据。
早期的土壤盐分含量监测方法主要以实地采集、结果检测和同步观测为主，这不仅增加了监测的难度、耗费大量物质，而且在大面积的定量监测过程中需要的时间更长，工作量更大。因此，面对未来环境的发展，需要建立一种更加快速、经济、有效的监测体系。遥感科学的发展，为土壤盐分含量的监测和制图提供了全新的方法，遥感技术的基础即为非接触、远距离的探测技术，它能从宏观的角度，快速、有效的进行监测，不仅降低了经济成本，而且提高了大面积监测的精度并在在重复监测和动态分析上具有明显的优势。在遥感技术应用于土地监测的初期，对盐渍土的区分主要利用多光谱遥感数据，区分能力单一，高光谱技术的发展是基于物质组成和结构差异获取的物质反射光谱，有效的利用光谱曲线的反射及吸收特征，使得遥感方法对盐渍土进行定量监测研究成为可能。
本论文以研究松辽平原盐渍土的盐分组成、光谱曲线特征为基础，采用光谱分析的方法，提取该区域盐渍土的敏感波段，并利用最小二乘支持向量机回归分析方法建立了土壤盐分含量及其相关判断指标与盐渍土反射光谱间的关系模型。在验证环境减灾卫星高光谱可行性的前提下，将模型应用于土壤盐分含量空间分布信息的提取，结合野外采样点的检测数据对模型在松辽平原内盐渍土盐分含量空间分布信息的估算结果进行评价，揭示了环境减灾卫星在土壤盐渍化中的监测和盐分定量估算的能力。论文主要研究内容和结论如下：
1、松辽平原的土壤盐分组成特征。利用野外采样数据进行检测，结果表明，在松辽平原的盐渍土的表层土壤中，阳离子含量主要以Na~+为主，占阳离子总量71.18%。阴离子则以HCO_3-、CO_3~2-为主，占阴离子总量的74.27%。利用统计学方法对监测数据进行分析可以得出HCO_3-、CO_3~2-离子、Na~+离子含量与土壤盐分的相关性显著，这可能是由于成土母质原因造成离子结合方式主要以钠离子和碳酸根离子及碳酸氢根离子为主。
2、环境减灾卫星高光谱数据应用可行性分析。环境减灾卫星高光谱数据的实际应用在国内的研究中利用的较少，为了验证该卫星获取的高光谱数据对研究区内的盐渍土含量研究是否具有实际意义，本文采用了数据对比的方法，以目前应用较广的Hyperion数据为比较对象，在去除坏线条和条纹的基础上，采用FLAASH模型对图像数据进行了大气校正，利用MNF变换进行大气校正后处理，得到光谱优化的图像数据。最后采用相关分析和差值分析的方法对两组数据进行对比，结果表明环境减灾卫星高光谱数据具有实际应用价值。
3、基于盐渍土的光谱特征，确定了识别盐渍土的敏感波段。在结合前人研究结果，分析松辽平原成土母质的基础上，确定了本研究区内主要粘土矿物成分为高岭石、绿泥石、伊利石和蒙脱石为主，利用USGS光谱库数据，提取粘土矿物的光谱特征并进行分析。并以野外采样点的光谱数据为基础，采取光谱分析的方法，以原始光谱曲线，一阶微分曲线和二阶微分曲线为比较对象，初步确定本区对盐渍土敏感波段在460-508nm、540-571nm、765-827nm范围内。
4、建立了基于最小二乘支持向量机回归分析方法的高光谱数据土壤盐分含量估算模型。最小二乘支持向量机回归（LS-SVM）建模方法是在等式约束的条件下，把数据非线性的映射到高维特征空间上，并构造出最优化的分类超平面。LS-SVM回归模型能够反映光谱对土壤盐分含量的响应，与盐渍土光谱特征研究结果一致，说明LS-SVM回归模型具有一定的物理意义，该建模相比传统的数理统计方法精度较高。
5、环境减灾卫星高光谱遥感土壤盐分含量估算。为实现环境减灾卫星对土壤盐分含量的空间分布制图，本研究利用NDVI对反射率图像进行土壤与植被、水体、城镇的分离，最终提取土壤信息。利用所建的土壤盐分含量估算模型，实现了环境减灾高光谱数据的土壤盐分含量信息提取，对估算结果的分析表明，基于LS-SVM方法所建的模型用于环境减灾卫星高光谱数据的土壤盐分含量估算精度较高，结合土壤采样样本进行检验，由环境卫星估算的土壤盐分含量值与检验样本实验室实测值的决定系数R2=0.784,RMSE=1.037。通过对土壤盐分含量的空间分布进行分析可知，本文研究结果与前期调查基本一致。
Soil is a significant component of ecosystem, and it is also the organic carrier forthe connection of animal and plant. With the development of society, soil environmentproblem is becoming more and more serious. Presently, land degradation anddesertification are the key limitation to potential of soil. Soil salinity has a greatinfluence on Songliao Plain. Salinization often occurred in areas where salinegroundwaters are elevated to where they approach the ground surface and evaporationexceeds precipitation. Human activities may also cause soil salinity. Soliao Plain isthe area with the highest concentration of soil salinity, where salinized soils are nearly60%of the total land area and soil salinity damages soil properties, while at the sametime restricting the development of agriculture industry and breaking the ecologicalbalance. Soil salinity has become a giant limitation to human production activity.Through the study of soil salinity, its information can be gathered from multi-angles.Besides, the degree of salinization, the distribution range, the geographic location andthe development process can also be effectively distinguished. The thematic map ofsoil salt content will provide assistant information for land administration departmentand agriculture production department. It will also offer basis for policy making andthe prevention of soil salinization.
Previous techniques for identifying and monitoring salinized land are mainlybased on sampling, result testing and field survey, which not only makes thetechnique difficult and time-consuming, but also brings great difficulty to broad-scalequantitative evaluation. Therefore, efforts are being made to develop morecost-effective monitoring system. The development of remote sensing provides abrand-new method for the monitoring and mapping of soil salt content. This techniqueis non-contact and remote, which is able to monitor fast and effectively from amicroscopic perspective. It not only is economic, but also improves accuracy. At thebeginning, when remote sensing was applied to land monitoring, salt-affected soil isdistinguished mainly by multispectral remote sensing. The development ofhyperspectral remote sensing is based on analysis of component and structure.
This paper is to study the salt composition of Songliao plain saline soil, and takespectrum curve characteristics as the foundation, by the method of the spectralanalysis, drawing the sensitive range of this area, and using the least square to supportvector machine regression analysis method to establish the relative model of the soilsalt content and its correlation judgment index and saline soil spectral reflectance.Oncondition of the hyperspectral satellites feasibility testing environment, apply themodel to the content of soil salt space distribution for information extraction, combined with the field test data sample to estimate the result evaluation of the salinesoil salt content space distribution in Songliao plain to reveal the monitoring ability inenvironmental disaster reduction satellite in soil salinization and the salt quantitativeestimate ability.
The primary results of this dissertation are listed below:
1. Soil content feature of Songliao Plain. Chemical analysis of samples showsthat in the top layer of soils, the predominant cation is Na+, which is86.30%of thetotal cation and that the predominant anion are HCO3-and CO32-, which is85.28%ofthe total anion. Through statistics analysis of the data, it can be shown that there isobvious correlation between soil salinity and HCO3-, CO32-and Na+. This may be dueto the soil forming materials causing ion combining ways mainly by sodium ions andcarbonated root ion and bicarbonate.
2. Analysis of the feasibility of environmental mitigation satellite hyperspectraldata application. The application of environmental mitigation satellite hyperspectraldata is not widespread. In order to test whether the hyperspectral obtained from thesatellite has practical value for the study of soil salt content. This paper adopts theapproach of comparative data, and uses the presently wide Hyperion data ascompared object, removing the bad line and stripe, and applying the FLAASH modelto the image data by the atmosphere correction, using the MNF transform atmosphereto correct the post-processing, get the optimization of the spectrum image data At last,through correlation analysis and differential analysis, after the comparison of the twogroups of data, it shows that environmental mitigation satellite hyperspectral data haspractical application value.
3. Based on the spectral features of salt-affected soil, sensitive wave bands ofsalt-affected soil are confirmed. Combined with previous findings, it is assured thatthe main clay mineral contents of Songliao Plain are kaolinite, chlorite, illite andmontmorillonite. Use USGS spectrum library data, and draw the clay mineralextraction of spectral features and do the analysis. And with the foundation of thesample point spectral data, and take the method of spectral analysis, with the originalspectrum curve, first order differential curve and second order differential curve. Anddetermine primarily the sensitive bands of this area’s saline soil are in the range of460-508nm,540-571nm, and765-827nm.
4. Environmental mitigation satellite hyperspectral remote sensing soil saltcontent estimation. In order to make soil salt content distribution map, NDVI wasused in this study to make the separation between soil and vegetation, water andtowns. The models that are previously established based on LS-SVM were applied toEnvironmental mitigation satellite hyperspectral data for mapping of soil salt content.R2=0.784, RMSE=1.037are obtained for environmental mitigation satellite method.Through the analysis of soil salt content distribution map, it is obvious that the resultof this thesis is in accordance with previous findings.
5. Environmental mitigation satellite hyperspectral remote sensing soil saltcontent estimation. In order to make soil salt content distribution map, NDVI wasused in this study to make the separation between soil and vegetation, water andtowns. The models that are previously established based on LS-SVM were applied to Environmental mitigation satellite hyperspectral data for mapping of soil salt content.R2=0.784, RMSE=1.037are obtained for environmental mitigation satellite method.Through the analysis of soil salt content distribution map, it is obvious that the resultof this thesis is in accordance with previous findings.
引文
[1] FAO. Salt-affected soils and their management[R]. FAO, Soils Bulletin,39.Rome: Food and Agriculture Organization of the United Nations,1998.
[2]丁翠华.基于GIS的LUCC在县域环境研究中的应用[D].武汉:华中师范大学,2005.
[3]张子峰,宫伟光.大庆盐渍土壤pH值的空间异质性[J].东北林业大学学报,2007,35(3):71-74.
[4]潘保原.土壤改良物质对盐渍化土壤改良的作用[D].哈尔滨:东北林业大学,2006.
[5]朱庭芸.灌区土壤盐渍化防治[M].北京农业出版社,1992.32-38.
[6]林年丰,汤洁,卞建民,等.东北平原第四纪环境演化与荒漠化问题[J].第四纪研究,1999,5:448-455.
[7]姜琦刚,刘占声,邱凤民.松辽平原中西部地区生态环境逐渐恶化的地学机理[J].吉林大学学报(地球科学版),2004,24(3):430-434.
[8]戚浩平,翁永玲,赵福岳,等.茶卡—共和盆地土壤盐分与光谱特征研究[J].国土资源遥感,2010,86(11):4-8.
[9] Agassi, M., Shainberg, I.. Effect of electrolyte concentration and soil solidity oninfiltration rate and crust formation[J]. Soil Science Society of America Journal,1981,45:848851.
[10] Farifteh J., Faarshad A., George R.J.. Assessing salt-affected soils using remotesensing, solute modeling, and geophysics[J]. GEODERMA,2006,130:191-206.
[11] Bennett D. L., George R. J.. Using the EM38to measure the effect of soil salinityon Eucalyptus globules in south-western Australia[J]. Agricultural WaterManagement,1995,27(1):69-85.
[12] Gates T. K., Burkhalter J. P., Labadie J. W., et al. Monitoring and Modeling Flowand Salt Transport in a Salinity-Threatened Irrigated Valley[J]. Journal ofIrrigation and Drain Engineering,2002,128(2):87-99.
[13] Ben-Dor R., Patkin A., Banin, A., et al. Mapping of several soil properties usingDAIS-7915hyperspectral scanner data-a case study over clayey soils in Israel[J].International Journal of Remote Sensing,2002(a),23(6):1043-1062.
[14] Dehaan R. L., Taylor G. R.. Field-derived spectra of salinized soils andvegetation as indicators of irrigation-induced soil salinization [J].RemoteSensing of Environment,2002,80:406-417.
[15] Metternicht G., Zinck J. A.. Spatial discrimination of salt-and sodium-affectedsoil surfaces[J]. International Journal of Remote Sensing,1997,18(12):2571-2586.
[16]张敏.苹果褐斑病的高光谱遥感监测估算分析研究[D].西安:西安科技大学,2001.
[17]翁永玲,宫鹏.土壤盐渍化遥感应用研究进展[J].地理科学,2006,3(6):369-374.
[18]童庆禧,张兵郑,芬兰.高光谱遥感—原理、技术与应用[M],北京:高等教育出版社,40-43.
[19] Kruse F. A., Boardman J. W., Huntington J. F., et al. Evaluation and Validation ofEO-1Hyperion for Geologic Mapping. In Proceedings[C], IGARSS, Toronto,Canada,2002,1:593-595.
[20] Gong P., Pu R., Biging G. S., et al. Estimation of Forest Leaf Area Index UsingVegetation Indices Derived From Hyperion Hyperspectral Data[J]. IEEETransactions on Geoscience and Remote Sensing,2003,41(6):1355-1362.
[21] Wu Y. Z.,Chen J., Ji J. F., et al. Feasibility of Reflectance Spectroscopy for theAssessment of Soil Mercury Contamination[J]. Environ. Sci. Technol.2005,39:873-878.
[22] Weng Y. L., Gong P., Zhu Z. L.. Reflectance spectroscopy for the assessment ofsalt content in soils of the Yellow River Delta of China[J]. International Journalof Remote Sensing.2007,29:5511–5531.
[23]王静.基于BP神经网络的盐碱土盐分遥感反演模型[D].长春:东北师范大学,2005.
[24]江红南.基于3S技术的干旱区土壤盐渍化时空演变研究[D].新疆:新疆大学,2007.
[25] Dwivedi R. S.. Monitoring of Salt-affected Soils of the Indo-Genetic AlluvialPlains Using Principal Component Analysis[J].International Journal of RemoteSensing,1996,17:1907-1914.
[26] Rao B. R. M., Dwivedi R. S., et al. Mapping the Magnitude of Sodicity in Part ofthe Indo-Gangetic Plain of Uttar Pradesh, Northern India Using Landsat-TMData[J]. International Journal of Remote Sensing,1991,12(3):419-425.
[27] Dwivedi R. S., Rao B. R. M.. The Selection of the Best Possible Landsat TMBand Combination for Delineating Salt-affected Soils[J]. International Journal ofRemote Sensing,1992,13:2051-2058.
[28] Peng W.. Synthetic analysis for extracting information on soil salinity usingremote sensing and GIS: a case study of Yanggao basin in China[J]. Environ.Manage.,1998,22(1):153-159.
[29] Csillag F.,Pasztor L., Biehl L.. Spectral band selection for the characterization ofsalinity status of soils[J]. Remote sensing of environment,1993(43):231-242.
[30] Dehaan R. L., Taylor G. R.. Field-derived Spectra of Salinized Soils andVegetationas Indicators of Irrigation-induced Soil Salinization[J]. Remotesensing of environment,2002,(80):406-417.
[31]胡际荣.盐碱荒地分布规律及变化趋势[J].华东森林理,1988,2(3):35-39.
[32]骆玉霞,陈焕伟. GIS支持下的TM图像土壤盐渍化分级[J].遥感信息,2000,21(4):12-15.
[33]许迪,王少丽.利用NDVI指数识别作物及土壤盐碱分布的应用该研究[J].灌溉排水学报,2003,22(6):5-8.
[34]吴昀昭,田庆久.土壤光学遥感的理论、方法及应用[J].遥感信息,2003,1:40-47.
[35]孙毅,刘亚琴,等.土壤光谱特性及光谱反射率与苏打盐碱土含盐量的相关[J].吉林农业科学,1992(1):53-56.
[36]翁永玲,宫鹏,朱智良.基于光谱特征的高光谱遥感土壤盐分含量估算[C].2007环境遥感学术年会—自然灾害专题研讨会.
[37]陈凤臻,姜琦刚,等.基于遥感技术的松辽平原盐渍化动态研究[J].生态环境2008,17(5):1921-1925.
[38]石勇,石伟,赵亚凤.测土配方施肥土样的采集与制备[J].土壤肥料,2009,3:32.
[39]王耿明.基于BP神经网络的松辽平原盐碱土含盐量遥感反演研究[D].长春:吉林大学,2007.
[40]杜长玉,衷仲贤,全英,等.玉米叶面喷施微肥的效应[J].内蒙古农业科技,1993,6:15-17.
[41]张亚梅.地物反射波谱特征及高光谱成像遥感[J].光电技术应用,2008,23(5):6-11.
[42] Vane G., Goetz A. F. H.. Terrestrial imaging spectrometry: current status, futuretrends[J]. Remote Sensing of Enviroment,1993.44:117-126.
[43]李俊生,张兵,申茜,等.航天成像光谱仪CHRIS在内陆水质监测中的应用.遥感技术与应用[J],2007,22(5):593-597.
[44]刘小丽,沈芳,朱伟健,等. MERIS卫星数据定量反演长江河口的悬沙浓度[J].长江流域资源与环境,2009,18(11):1026-1030.
[45]朱珂. Hyperion高光谱遥感数据的处理及其在喀斯特石漠化等级划分中的应用[D].贵阳:贵州师范大学,2009.
[46]中国资源应用卫星中心. HJ-1-A、 B卫星介绍[EB/OL].(2009-01).http://www.cresda.com/n16/n1130/n1582/8384.html
[47]高海亮,顾行发,等.环境卫星HJ-1A超光谱成像仪在轨辐射定标及光谱响应函数敏感性分析[J].光谱学与光谱分析,2010,30(11):3149-3155.
[48]张茂鑫,李国春.基于HDF5文件格式的MERSI影像数据提取的研究与实现[J].现代农业科学,2009,16(3):189-192.
[49]王永韬,刘良明. HDF5格式特点及其对遥感数据格式标准化的几点启示[J].国土资源遥感,2005,3(9):39-43.
[50] Datt B., McVicar T.R., Niel T.G., et al. Preprocessing EO-1HyperionHyperspectral Data to Support the Application of Agricultural Indexes[J]. IEEETransactions on Geoscience and Remote Sensing,2003,41(6):1246-1259.
[51]谭炳香,李增元,陈尔学,等. EO-1Hyperion高光谱数据的预处理[J].遥感信息应用技术,2005,6:36-41.
[52]陈三明,钱建平,陈宏毅.桂东南植被覆盖区的抗干扰遥感蚀变信息优化提取与找矿预测[J].桂林理工大学学报,2010,30(1):33-40.
[53]王红,祝民强.基于混合像元分解的火星南极矿物制图研究[J].东华理工大学学报(自然科学版),2010,33(1):43-47.
[54]余旭初,冯伍法,林丽霞.高光谱--遥感测绘的新机遇[J].测绘科学技术学报,2006,23(2):101-105.
[55]罗慧芬,苗放,等.基于FLAASH模型的ASTER卫星影像大气校正[J].安徽农业科学,2009,37(17):8101-8133.
[56] Kruse F. A.. Comparison of ATREM, ACORN, and FLAASH AtmosphericCorrections Using Low-altitude AVIRIS Data of Boulder, Co[R]. In proceedings13th JPL Airborne Geoscience Workshop,2004.
[57]彭杰,张杨珠,等.土壤理化特性与土壤光谱特征关系的研究进展[J].土壤通报,2009,40(5):1203-1208.
[58]中国科学院南京土壤研究所.土壤专报[C].北京:科学出版社,1987.
[59] Rao B. R. M., Ravisankar T., Dwivedi R. S. et al. Spectral behaviour ofsalt-affected soils[J]. International Journal of Remote Sensing,1995,16(12):2125-2136.
[60]胡耀强.含油污泥固化处理后油的迁移规律研究[D].西安:西安石油大学,2007.
[61]高金方.新构造运动与松辽平原的土壤发生[J].土壤学报,1985,22(3):258-263.
[62]程伯容,王汝墉,等.东北松嫩平原盐演土的盐分累积[J].土壤学报,1963,11(1):19-24.
[63]吴阳春,姜琦刚,杨佳佳,等.大庆地区盐渍土光谱特征研究[J].江西农业学报,2011,23(9):94-97.
[64]杜培军,唐宏,方涛.高光谱遥感光谱相似性度量算法与若干新方法研究[J].武汉大学学报(信息科学版),2006,(2):112-115.
[65] Cortes C., Vapnik V. N.. Support-Vector Networks[J]. Machine Learning,1995,20(3):273-297.
[66] Boser B., Guyon I., Vapnik V. N.. A training algorithm for optimalmarginclassifiers[R]. Proceedings of Fifth Annual Workshop on ComputationalLearning Theory. Pittsburgh, PA: ACM Press,1992:144-152.
[67] Suykens J. A. K.. Vandewalle J. Least Squares Support Vector MachineClassfiers[J]. Neural Processing Letter,1999,9(3):293-300.
[68]周奇.对支持向量机几种常用核函数和参数选择的比较研究[J].福建电脑,2009,6:42-43.
[69]杨福刚.基于人工免疫算法的最小二乘支持向量机参数优化算法[J].计算机应用研究,2010,27(5):1702-1704.
[2]丁翠华.基于GIS的LUCC在县域环境研究中的应用[D].武汉:华中师范大学,2005.
[3]张子峰,宫伟光.大庆盐渍土壤pH值的空间异质性[J].东北林业大学学报,2007,35(3):71-74.
[4]潘保原.土壤改良物质对盐渍化土壤改良的作用[D].哈尔滨:东北林业大学,2006.
[5]朱庭芸.灌区土壤盐渍化防治[M].北京农业出版社,1992.32-38.
[6]林年丰,汤洁,卞建民,等.东北平原第四纪环境演化与荒漠化问题[J].第四纪研究,1999,5:448-455.
[7]姜琦刚,刘占声,邱凤民.松辽平原中西部地区生态环境逐渐恶化的地学机理[J].吉林大学学报(地球科学版),2004,24(3):430-434.
[8]戚浩平,翁永玲,赵福岳,等.茶卡—共和盆地土壤盐分与光谱特征研究[J].国土资源遥感,2010,86(11):4-8.
[9] Agassi, M., Shainberg, I.. Effect of electrolyte concentration and soil solidity oninfiltration rate and crust formation[J]. Soil Science Society of America Journal,1981,45:848851.
[10] Farifteh J., Faarshad A., George R.J.. Assessing salt-affected soils using remotesensing, solute modeling, and geophysics[J]. GEODERMA,2006,130:191-206.
[11] Bennett D. L., George R. J.. Using the EM38to measure the effect of soil salinityon Eucalyptus globules in south-western Australia[J]. Agricultural WaterManagement,1995,27(1):69-85.
[12] Gates T. K., Burkhalter J. P., Labadie J. W., et al. Monitoring and Modeling Flowand Salt Transport in a Salinity-Threatened Irrigated Valley[J]. Journal ofIrrigation and Drain Engineering,2002,128(2):87-99.
[13] Ben-Dor R., Patkin A., Banin, A., et al. Mapping of several soil properties usingDAIS-7915hyperspectral scanner data-a case study over clayey soils in Israel[J].International Journal of Remote Sensing,2002(a),23(6):1043-1062.
[14] Dehaan R. L., Taylor G. R.. Field-derived spectra of salinized soils andvegetation as indicators of irrigation-induced soil salinization [J].RemoteSensing of Environment,2002,80:406-417.
[15] Metternicht G., Zinck J. A.. Spatial discrimination of salt-and sodium-affectedsoil surfaces[J]. International Journal of Remote Sensing,1997,18(12):2571-2586.
[16]张敏.苹果褐斑病的高光谱遥感监测估算分析研究[D].西安:西安科技大学,2001.
[17]翁永玲,宫鹏.土壤盐渍化遥感应用研究进展[J].地理科学,2006,3(6):369-374.
[18]童庆禧,张兵郑,芬兰.高光谱遥感—原理、技术与应用[M],北京:高等教育出版社,40-43.
[19] Kruse F. A., Boardman J. W., Huntington J. F., et al. Evaluation and Validation ofEO-1Hyperion for Geologic Mapping. In Proceedings[C], IGARSS, Toronto,Canada,2002,1:593-595.
[20] Gong P., Pu R., Biging G. S., et al. Estimation of Forest Leaf Area Index UsingVegetation Indices Derived From Hyperion Hyperspectral Data[J]. IEEETransactions on Geoscience and Remote Sensing,2003,41(6):1355-1362.
[21] Wu Y. Z.,Chen J., Ji J. F., et al. Feasibility of Reflectance Spectroscopy for theAssessment of Soil Mercury Contamination[J]. Environ. Sci. Technol.2005,39:873-878.
[22] Weng Y. L., Gong P., Zhu Z. L.. Reflectance spectroscopy for the assessment ofsalt content in soils of the Yellow River Delta of China[J]. International Journalof Remote Sensing.2007,29:5511–5531.
[23]王静.基于BP神经网络的盐碱土盐分遥感反演模型[D].长春:东北师范大学,2005.
[24]江红南.基于3S技术的干旱区土壤盐渍化时空演变研究[D].新疆:新疆大学,2007.
[25] Dwivedi R. S.. Monitoring of Salt-affected Soils of the Indo-Genetic AlluvialPlains Using Principal Component Analysis[J].International Journal of RemoteSensing,1996,17:1907-1914.
[26] Rao B. R. M., Dwivedi R. S., et al. Mapping the Magnitude of Sodicity in Part ofthe Indo-Gangetic Plain of Uttar Pradesh, Northern India Using Landsat-TMData[J]. International Journal of Remote Sensing,1991,12(3):419-425.
[27] Dwivedi R. S., Rao B. R. M.. The Selection of the Best Possible Landsat TMBand Combination for Delineating Salt-affected Soils[J]. International Journal ofRemote Sensing,1992,13:2051-2058.
[28] Peng W.. Synthetic analysis for extracting information on soil salinity usingremote sensing and GIS: a case study of Yanggao basin in China[J]. Environ.Manage.,1998,22(1):153-159.
[29] Csillag F.,Pasztor L., Biehl L.. Spectral band selection for the characterization ofsalinity status of soils[J]. Remote sensing of environment,1993(43):231-242.
[30] Dehaan R. L., Taylor G. R.. Field-derived Spectra of Salinized Soils andVegetationas Indicators of Irrigation-induced Soil Salinization[J]. Remotesensing of environment,2002,(80):406-417.
[31]胡际荣.盐碱荒地分布规律及变化趋势[J].华东森林理,1988,2(3):35-39.
[32]骆玉霞,陈焕伟. GIS支持下的TM图像土壤盐渍化分级[J].遥感信息,2000,21(4):12-15.
[33]许迪,王少丽.利用NDVI指数识别作物及土壤盐碱分布的应用该研究[J].灌溉排水学报,2003,22(6):5-8.
[34]吴昀昭,田庆久.土壤光学遥感的理论、方法及应用[J].遥感信息,2003,1:40-47.
[35]孙毅,刘亚琴,等.土壤光谱特性及光谱反射率与苏打盐碱土含盐量的相关[J].吉林农业科学,1992(1):53-56.
[36]翁永玲,宫鹏,朱智良.基于光谱特征的高光谱遥感土壤盐分含量估算[C].2007环境遥感学术年会—自然灾害专题研讨会.
[37]陈凤臻,姜琦刚,等.基于遥感技术的松辽平原盐渍化动态研究[J].生态环境2008,17(5):1921-1925.
[38]石勇,石伟,赵亚凤.测土配方施肥土样的采集与制备[J].土壤肥料,2009,3:32.
[39]王耿明.基于BP神经网络的松辽平原盐碱土含盐量遥感反演研究[D].长春:吉林大学,2007.
[40]杜长玉,衷仲贤,全英,等.玉米叶面喷施微肥的效应[J].内蒙古农业科技,1993,6:15-17.
[41]张亚梅.地物反射波谱特征及高光谱成像遥感[J].光电技术应用,2008,23(5):6-11.
[42] Vane G., Goetz A. F. H.. Terrestrial imaging spectrometry: current status, futuretrends[J]. Remote Sensing of Enviroment,1993.44:117-126.
[43]李俊生,张兵,申茜,等.航天成像光谱仪CHRIS在内陆水质监测中的应用.遥感技术与应用[J],2007,22(5):593-597.
[44]刘小丽,沈芳,朱伟健,等. MERIS卫星数据定量反演长江河口的悬沙浓度[J].长江流域资源与环境,2009,18(11):1026-1030.
[45]朱珂. Hyperion高光谱遥感数据的处理及其在喀斯特石漠化等级划分中的应用[D].贵阳:贵州师范大学,2009.
[46]中国资源应用卫星中心. HJ-1-A、 B卫星介绍[EB/OL].(2009-01).http://www.cresda.com/n16/n1130/n1582/8384.html
[47]高海亮,顾行发,等.环境卫星HJ-1A超光谱成像仪在轨辐射定标及光谱响应函数敏感性分析[J].光谱学与光谱分析,2010,30(11):3149-3155.
[48]张茂鑫,李国春.基于HDF5文件格式的MERSI影像数据提取的研究与实现[J].现代农业科学,2009,16(3):189-192.
[49]王永韬,刘良明. HDF5格式特点及其对遥感数据格式标准化的几点启示[J].国土资源遥感,2005,3(9):39-43.
[50] Datt B., McVicar T.R., Niel T.G., et al. Preprocessing EO-1HyperionHyperspectral Data to Support the Application of Agricultural Indexes[J]. IEEETransactions on Geoscience and Remote Sensing,2003,41(6):1246-1259.
[51]谭炳香,李增元,陈尔学,等. EO-1Hyperion高光谱数据的预处理[J].遥感信息应用技术,2005,6:36-41.
[52]陈三明,钱建平,陈宏毅.桂东南植被覆盖区的抗干扰遥感蚀变信息优化提取与找矿预测[J].桂林理工大学学报,2010,30(1):33-40.
[53]王红,祝民强.基于混合像元分解的火星南极矿物制图研究[J].东华理工大学学报(自然科学版),2010,33(1):43-47.
[54]余旭初,冯伍法,林丽霞.高光谱--遥感测绘的新机遇[J].测绘科学技术学报,2006,23(2):101-105.
[55]罗慧芬,苗放,等.基于FLAASH模型的ASTER卫星影像大气校正[J].安徽农业科学,2009,37(17):8101-8133.
[56] Kruse F. A.. Comparison of ATREM, ACORN, and FLAASH AtmosphericCorrections Using Low-altitude AVIRIS Data of Boulder, Co[R]. In proceedings13th JPL Airborne Geoscience Workshop,2004.
[57]彭杰,张杨珠,等.土壤理化特性与土壤光谱特征关系的研究进展[J].土壤通报,2009,40(5):1203-1208.
[58]中国科学院南京土壤研究所.土壤专报[C].北京:科学出版社,1987.
[59] Rao B. R. M., Ravisankar T., Dwivedi R. S. et al. Spectral behaviour ofsalt-affected soils[J]. International Journal of Remote Sensing,1995,16(12):2125-2136.
[60]胡耀强.含油污泥固化处理后油的迁移规律研究[D].西安:西安石油大学,2007.
[61]高金方.新构造运动与松辽平原的土壤发生[J].土壤学报,1985,22(3):258-263.
[62]程伯容,王汝墉,等.东北松嫩平原盐演土的盐分累积[J].土壤学报,1963,11(1):19-24.
[63]吴阳春,姜琦刚,杨佳佳,等.大庆地区盐渍土光谱特征研究[J].江西农业学报,2011,23(9):94-97.
[64]杜培军,唐宏,方涛.高光谱遥感光谱相似性度量算法与若干新方法研究[J].武汉大学学报(信息科学版),2006,(2):112-115.
[65] Cortes C., Vapnik V. N.. Support-Vector Networks[J]. Machine Learning,1995,20(3):273-297.
[66] Boser B., Guyon I., Vapnik V. N.. A training algorithm for optimalmarginclassifiers[R]. Proceedings of Fifth Annual Workshop on ComputationalLearning Theory. Pittsburgh, PA: ACM Press,1992:144-152.
[67] Suykens J. A. K.. Vandewalle J. Least Squares Support Vector MachineClassfiers[J]. Neural Processing Letter,1999,9(3):293-300.
[68]周奇.对支持向量机几种常用核函数和参数选择的比较研究[J].福建电脑,2009,6:42-43.
[69]杨福刚.基于人工免疫算法的最小二乘支持向量机参数优化算法[J].计算机应用研究,2010,27(5):1702-1704.
© 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 |