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干旱区土壤盐渍化遥感监测模型构建研究

Remote Sensing-Based Synthesis Index Model for Monitoring of Soil Salinization in Arid Area

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【Author in Chinese】 哈学萍

【Supervisor】 丁建丽

【Author's Information】 新疆大学, 地图学与地理信息系统, 2009, 硕士

【Subtitle】以渭干河—库车河三角洲绿洲为例

【Abstract in Chinese】 土壤盐渍化是干旱半干旱区土地退化的主要形式之一。其发生发展是一个气候、水文、母质、植被等诸多因素耦合的复杂动力学过程。通常在气候干旱、土壤蒸发强度大、地下水位高且含有较多可溶性盐类的地区,容易导致土壤盐渍化的发生。土壤盐渍化直接和间接地影响人类生存、社会稳定、农业生产、资源与环境可持续发展。正确评价或预防土壤盐渍化对促进农业生产和区域可持续发展具有重要的现实意义。遥感具有客观反映土壤盐渍化时空变化的监测能力,遥感监测盐渍化土壤研究取得了很大进步。然而随着定量遥感应用研究的发展,对盐渍化土壤信息的定量化提取要求的提高,像元尺度的信息提取已不能满足需求。同时,国内外盐渍化土壤遥感监测研究也表明,在复杂的地表环境下,单纯采用土壤光谱特征是无法全面、准确反映土壤盐渍化信息,其方法在土壤盐渍化监测中暴露出诸多问题,如盐分信息提取的不确定性、土壤盐分监测的滞后效应等,不能适应全面、动态的盐渍化土壤监测与盐分信息提取的迫切需求。因此有必要对图像识别算法加以修改或发展新的方法,以适应定量提取区域尺度盐渍化土壤信息的新要求。利用定量遥感方法,实现准确的盐渍化土壤信息提取一直是遥感应用领域亟待解决的重要科学问题之一。研究基于波谱分解技术基础,有效利用作为盐渍化土壤遥感监测的两个关键指标——土壤光谱特征和盐渍区盐生植被生物物理特征,同时解决以往传统遥感监测土壤盐渍化的弊端——混合像元问题;在此基础上构建适于盐渍化评价的盐生植被指数(NHVI),充分利用盐生植被的生物物理特征实时监测土壤盐分含量的变化特征,弥补裸土光谱监测盐渍化程度得不足,增强盐渍化遥感监测的敏感度、实时性;同时通过光谱变换与运算,构建能够综合反映盐渍化程度的裸土及植被光谱特征的二维特征空间,并基于二维特征空间发展区域尺度盐渍化土壤的遥感定量提取指数——SDI模型。并用实地观测的土壤含盐量数据对提出的盐生植被指数(NHVI)和SDI模型进行检验和比较分析,结果表明:1)文中提出的盐生植被指数和研究区实地观测的土壤含盐量高度相关,能反映土壤盐渍化程度;(2)SDI充分利用可见光、近红外、植被生物物理特性,在准确获取地表生态物理参数的前提下,盐渍化遥感监测信息提取精度得以提高;其具有简单和反演速度快的优势,能够很好反映土壤盐渍化程度;(3)SDI考虑了地表植被覆盖的影响,因此,在干旱区,不同植被覆盖下地表的盐渍化遥感监测都可选用SDI模型;本研究的开展,以求抛砖引玉,引起学术界同仁和主管部门对此领域研究的关注。为区域尺度盐渍化土壤信息监测提供更丰富、更定量化的土壤盐渍化信息,为土壤盐渍化的遥感监测与评价提供有效的遥感监测模型。

【Abstract】 Soil salinization is an important worldwide environmental problem, especially in arid and semi-arid regions.Quantitative remote sensing provides accurate and up-to-date information of spatio-temporal dynamics of salinity. Studies at both home and overseas showed that it is hard to acquire reliable information of salinity by using a single wavelength in visible, near infrared (NIR), thermal or microwave domain, specifically in a sophisticated surface conditions such as agricultural fields, while the reported methods inherited many limitations in practical applications including time-lag effect, being too complex to calculate, being excessively dependent on meteorological observations and field measurements etc. Therefore, developing of simple, effective and operational methods for the satellite estimation of surface salnity, especially vegetation cover is of great interest for both researchers in remote sensing community and policy makers for the sustainable development of eco-environments.Bare salt-affected Soil reflect spectral and halophytic plants spectral are the most direct and important indicator of salinity events and, therefore, using an integrated algorithm of the spectral response of bare soil and vegetation is critical to the soil salinization estimation. In this paper, an improved soil salt-affected monitoring method, the combined fraction spectral response index (SDI), is developed introducing soil and vegetation fraction, which takes into account both salty-affected soil spectral and halophytic plant growth. To validate the salinity indices proposed by this paper, Enhanced Thematic Mapper Plus and ALOS imagese from different times with various salinization conditions are used to calculate the SDI and to correlate ground measuring salt content (SAL))with SDI. SAL was determined in surface soil samples(0-10cm). Multiple endmember spectral mixture analysis (MESMA) uses linear mixture models to provide bare soil and halophytic vegetation fraction abundances respectively. SDI based on SMA increase in the level of salt-affected soil spectral response by eliminating non-halophytic vegetation affected and combining halophytic vegetation indictor of salinity. Correlation coefficients between SDI and soil salinity were obtained and a model was adjusted to predict soil salinity. It is evident from the results that SDI is highly accordant with in-situ soil SAL values with the highly correlation of 0.9149. Variance accounted for by exponential models for SAL was of 83.7%. The SDI demonstrates a much better performance in measuring salinity soil since it takes into account both soil surface and halophytic vegetation growth in the modeling process. The SDI has the potential to provide a simple and low-cost salt-affected areas monitoring method in the remote estimation of salinization phenomena.

【Keywords in Chinese】 干旱区盐渍化土壤NHVI-SI特征空间监测模型
【Key words】 Arid landsalinity soilNHVI-SI spaceSDI
  • 【Contributor】 新疆大学
  • 【Year of Internet Publish】200912
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