基于Landsat ETM遥感数据的大柳塔煤炭开发区土壤水分信息提取
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摘要
土壤水分是监测土壤质量的一个重要指标,同时,土壤水分状况也是生态环境健康状态敏感指示因子之一。传统的土壤水分监测方法有一定的局限性,随着RS和GIS技术的发展,利用遥感技术,结合地理信息系统分析功能有效地对土壤水分进行实时的动态的大范围的监测,对环境保护和重建具有十分重要的意义。
本文采用Landsat7 ETM遥感数据,结合野外实测土壤数据和光谱数据,对陕北神木县大柳塔煤炭开发区的土壤含水量进行遥感定量化分析研究,得出研究区土壤含水量的分布规律,为矿区土壤水分损失的研究提供依据,同时也为该地区的经济发展和环境保护提供科学决策依据。
1、神木大柳塔煤炭开发区土壤水分的光谱特性；
2、神木大柳塔煤炭开发区土壤的光谱反射率与土壤含水量的关系模型；
3、神木大柳塔煤炭开发区土壤遥感信息处理方法；
4、神木大柳塔煤炭开发区土壤水分光谱法的遥感定量分析模型；
5、神木大柳塔煤炭开发区土壤水分的分布特征。
本研究通过实测的土壤光谱反射率与实测土壤水分的相关分析表明,土壤光谱反射率随着土壤含水量的增加而减少,成负相关关系,这个结论符合一般的变化规律；采用ETM3、ETM4、ETM5的光谱反射率R3、R4、R5分别同实测土壤含水量建立相关关系模型,结果显示ETM4的指数模型对土壤水分较敏感；运用“光学植被盖度”模型,消除用遥感的方法监测土壤水分时植被盖度所造成的干扰；运用ETM4波段的指数模式S=5.84705e-0.0144*B4进行反演,反演结果的平均绝对误差为0.206667,平均相对误差为15.46144%；根据反演模型和ETM影像对研究区土壤水分进行反演,获得研究区土壤水分空间分布等级图,该图直观地反映了土壤水分的空间分布状况；同时利用NDVI对研究区的植被盖度进行分类,结果表明,反演结果与植被覆盖度呈一定的正相关性,与实测的情况相符。
The soil moisture is a major index to monitor the soil quality,in the meantime, the condition of soil moisture is one of indicative factor of Eco-environment statement of health.Traditional monitor method of the soil moisture has certain limitations.With the development of the technology of the RS and GIS, using the technology of RS and combining the analysis function of GIS, we can effectively carry out the real-time soil moisture dynamics of large-scale monitoring.It's very meaningful for Environmental protection and rebuilding.
This paper using the RS date from Landsat ETM,combining the date of the field survey and the spectral data, do a research of RS quantitative analysis on the soil moisture in Daliuta mining area in northern Shaanxi province, retrieval the distribution of water content of soil in this area. This not only can provide parameters for the loss of soil water study, but also provide decision parameters for economic development and environment protection in this area.
1.The spectral characteristics of soil moisture in Shenmu county;
2.The relational model of the soil spectral reflectance and the soil water content;
3.The processing method of the soil RS information;
4.The RS quantitative analysis model of the soil moisture;
5.The distribution characteristics of the soil moisture;
By the correlation analysis of soil spectral reflectance and the soil moisture messured from the field, this research show that the soil spectral reflectance is lowering with the raising of the soil water content, and the both is negative correlation. This is follow the general rule; Using the RS reflectance(R2,R3 and R4) of ETM2,ETM3 and ETM4 and the tested soil water content, relationship model were built respectively. It shows that the exponential model of ETM4 is very sensitive to soil moisture; Using the optics vegetation coverage model to remove the vegetation coverage interference, which is resulted from vegetation coverage monitoring the soil moisture with RS method; Using the exponential model of ETM4: S=5.84705e"00144*B4 for retrieval, the average absolute error of result is 0.206667, the average relative error is 15.46144%; According to the retrieval model and ETM image of this area, soil moisture was estimated, and distribution rank chart was made which intuitively displayed the distribution rule of soil moisture; At mean time, taking advantage of the NDVI to classify the vegetation coverage, the result shows that the retrieval result and the vegetation demonstrate a definite positive correlation, and the result match up the practical situation.
本文采用Landsat7 ETM遥感数据,结合野外实测土壤数据和光谱数据,对陕北神木县大柳塔煤炭开发区的土壤含水量进行遥感定量化分析研究,得出研究区土壤含水量的分布规律,为矿区土壤水分损失的研究提供依据,同时也为该地区的经济发展和环境保护提供科学决策依据。
1、神木大柳塔煤炭开发区土壤水分的光谱特性；
2、神木大柳塔煤炭开发区土壤的光谱反射率与土壤含水量的关系模型；
3、神木大柳塔煤炭开发区土壤遥感信息处理方法；
4、神木大柳塔煤炭开发区土壤水分光谱法的遥感定量分析模型；
5、神木大柳塔煤炭开发区土壤水分的分布特征。
本研究通过实测的土壤光谱反射率与实测土壤水分的相关分析表明,土壤光谱反射率随着土壤含水量的增加而减少,成负相关关系,这个结论符合一般的变化规律；采用ETM3、ETM4、ETM5的光谱反射率R3、R4、R5分别同实测土壤含水量建立相关关系模型,结果显示ETM4的指数模型对土壤水分较敏感；运用“光学植被盖度”模型,消除用遥感的方法监测土壤水分时植被盖度所造成的干扰；运用ETM4波段的指数模式S=5.84705e-0.0144*B4进行反演,反演结果的平均绝对误差为0.206667,平均相对误差为15.46144%；根据反演模型和ETM影像对研究区土壤水分进行反演,获得研究区土壤水分空间分布等级图,该图直观地反映了土壤水分的空间分布状况；同时利用NDVI对研究区的植被盖度进行分类,结果表明,反演结果与植被覆盖度呈一定的正相关性,与实测的情况相符。
The soil moisture is a major index to monitor the soil quality,in the meantime, the condition of soil moisture is one of indicative factor of Eco-environment statement of health.Traditional monitor method of the soil moisture has certain limitations.With the development of the technology of the RS and GIS, using the technology of RS and combining the analysis function of GIS, we can effectively carry out the real-time soil moisture dynamics of large-scale monitoring.It's very meaningful for Environmental protection and rebuilding.
This paper using the RS date from Landsat ETM,combining the date of the field survey and the spectral data, do a research of RS quantitative analysis on the soil moisture in Daliuta mining area in northern Shaanxi province, retrieval the distribution of water content of soil in this area. This not only can provide parameters for the loss of soil water study, but also provide decision parameters for economic development and environment protection in this area.
1.The spectral characteristics of soil moisture in Shenmu county;
2.The relational model of the soil spectral reflectance and the soil water content;
3.The processing method of the soil RS information;
4.The RS quantitative analysis model of the soil moisture;
5.The distribution characteristics of the soil moisture;
By the correlation analysis of soil spectral reflectance and the soil moisture messured from the field, this research show that the soil spectral reflectance is lowering with the raising of the soil water content, and the both is negative correlation. This is follow the general rule; Using the RS reflectance(R2,R3 and R4) of ETM2,ETM3 and ETM4 and the tested soil water content, relationship model were built respectively. It shows that the exponential model of ETM4 is very sensitive to soil moisture; Using the optics vegetation coverage model to remove the vegetation coverage interference, which is resulted from vegetation coverage monitoring the soil moisture with RS method; Using the exponential model of ETM4: S=5.84705e"00144*B4 for retrieval, the average absolute error of result is 0.206667, the average relative error is 15.46144%; According to the retrieval model and ETM image of this area, soil moisture was estimated, and distribution rank chart was made which intuitively displayed the distribution rule of soil moisture; At mean time, taking advantage of the NDVI to classify the vegetation coverage, the result shows that the retrieval result and the vegetation demonstrate a definite positive correlation, and the result match up the practical situation.
引文
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