干旱区土壤盐渍化遥感监测与预警网络传输系统研究
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摘要
土壤盐渍化是干旱半干旱地区土地资源退化的主要形式之一。目前,在开发和利用土地和水资源的过程中,特别是新疆等干旱和半干旱地区,土壤盐渍化问题仍然非常突出,是必须重视的关键问题。近年来国内外大量实践表明,利用遥感技术监测土壤盐渍化不仅省时、省力,而且具有快速、宏观、动态等特点,具有其他手段不可替代的优越性。所以利用遥感技术监测盐渍化土壤的性质、而积、程度、地理分布、时空变化及其治理、防止扩散等工作具有重大意义。
当前,国内外区域尺度土壤盐渍化遥感监测应用已趋于主流,但各种信息提取方法结果不确定性较强,其通用性及定量化并不令人满意。同时,目前土壤盐渍化遥感监测的各种研究结果及决策方案等信息的数字化、智能化、计算机决策自动化技术基础研究比较薄弱,还存在着传输时效差、传播不畅、信息覆盖面有限、受各种制约条件限制等问题。
本文针对以上科学问题,首先,利用时间序列遥感影像和野外调查数据,通过分析土壤盐渍化程度与地表参数之间的定量关系；提出综合反映盐渍化土壤生物物理特征的指数—盐渍化遥感监测指数SDI；提出了基于遥感影像融合的盐渍地信息提取方法；提出面向对象的土壤盐渍化信息提取方法。其次,分析土壤盐渍化时空演变、预测和预警等问题,对研究区域的土壤盐渍化情况进行评价。最后,利用计算网络和Web GIS技术开发土壤盐渍化多源遥感监测与预警网络传输系统。主要结论有：
(1)利用面向对象方法的盐渍地信息提取总体精度为89.38%,较传统方法提高8.24%; KAPPA系数为0.88,较传统方法提高0.08。该信息提取方法能实现较为精确的盐渍地信息提取,在高空间分辨率遥感影像盐渍地信息提取中具有一定的优势。(2)与HIS, PCA, Gram-Schmid等传统的影像融合方法相比,WAVELET(小波)融合方法不仅能更好地保持影像光谱扭曲最小,而且能提高空间分辨率。在影像信息熵、清晰度等方面都表现更好,这对盐渍地信息的提取和分类是非常有利的；基于小波融合方法的影像分类相对于单纯的ALOS多光谱影像分类精度而言,分类精度有了一定的提高(提高5%),因此它必将在实际中得到更广泛的应用
(3)本文选取归一化植被指数(NDVI)和地表反照率(Albedo)等两个地表参数作为揭示土壤盐渍化发生过程和程度的重要参数,并对盐渍化土壤在NDVI-Albedo二维特征空间的分布规律进行相关性分析发现盐渍化土壤在此空间上具有较显著的线性分布规律特征,因此提出了综合反映盐渍化土壤生物物理特征的遥感监测指数(Soil Detection Index,SDI)。由于所选取的指标简单易懂、易于获取,具有明确的生物物理意义,因此该模型比较客观地反映了盐渍化土壤地表覆盖、水热组合及其变化情况,有助于综合分析土壤盐渍化发生的过程和对其进行遥感监测。
(4)对研究区域从1989年到2010年,通过各类地物的相互转化,动态度以及重心偏移进行定量的分析,从而揭示21a间的土壤盐渍化的时空变化情况。①1989—2001年,各类地物面积呈现五增二减的趋势：耕地、林地、中度盐渍地、水体和其他均有所增加；而轻度盐渍地和重度盐渍地有所减少；2001—2006年,呈现四增三减的趋势：耕地,轻度盐渍地,中度盐渍地和重度盐渍地均有所增加；林地、水体和其他均有所减少；2006—2010年,呈现四增三减的趋势：中度盐渍地,重度盐渍地、水体和其他均有所增加；耕地、林地、轻度盐渍地均有所减少。②通过Markov模型分析,结果表明：从2015年到2030年,耕地从原本占总面积的3.87%,降低至占总面积的2.98%。林地和轻度盐渍地所占总面积的比例也在逐年稳定降低。中度盐渍地、重度盐渍地、水体和其它占总面积的比例均逐步递增。尤其其它类(G)的增长比例最为显著,从2015年的440920hm2,显著增长到2030年的497440hm2,所占比例也完全吻合逐步增长的趋势。③随着时间的推移,各类地物的重心均有所偏移。其中水体的重心于1989—2001年间向北迁移的距离最远,为23.12km；同期重度盐渍地重心迁移距离次之,为22.04km。林地的重心于1989—-2001年间逐年向西南方向迁移,并且迁移的距离逐年增大,中度盐渍地重心总体向西北方向偏移。
(5)研究区中的盐渍化土壤预警主要以中警、轻警为主,其中重警区域面积达到总面积的15%,主要在库车河下游东南部一带分布；中度警区域面积为总面积的31%,主要在塔里木河的北部和渭干河下游一带分布；轻警区域则占总面积的30%,分布在洪冲积扇下部、库车河两岸和塔里木河灌区；不需要警戒的区域面积较大,占研究区总面积的35%,包括洪冲积扇下部和渭干河、库车河中游的平原地区。从整个研究区来看,盐渍化的警区的分布呈以下特点,一是盐渍化的中警、重警区域呈块状分布,分布的区域较为集中,而轻警区域呈片状或者斑点状断续分布。二是盐渍化预警等级呈现出北部地区高于南部地区,东部地区高于西部地区的趋势。
(6)给出了土壤盐渍化遥感监测和预测、预警结果的网络发布技术及流程。可视化显示主要实现了在Web GIS支持下的远程土壤盐渍化信息在Internet上快捷的数据发布、地图浏览、历史数据管理、空间数据和属性数据双向定位查询、空间分析、评价等基本功能。该系统具有界面清晰、操作方便、实用性强和功能强大等特点,加强了土壤盐渍化信息资源的计算机管理水平。
Soil salinization is one of the land degradation types in arid and semi arid regions. The global extent of primary salt-affected soils is about955M ha, while secondary salinization affects some77M ha, with58%of these in irrigated areas. The damage of soil salinization to the economy, environment and agricultural ecological system is concerned by some scholars year by year. This requires careful monitoring of the soil salinity status and variation to curb degradation trends, and secure sustainable land use and management. It is very important to study the quality, the scope, the area, the distribution, the dynamic change and the degree of saline soils. The combination of remote sensing with some traditional methods has some important meaning for studying the characteristics, areas, degree, distribution, time and space variation, management and protecting from enlarging of soil salinization. This study aiming at the problems mentioned above, firstly, using time sequence remote sensing image and field measured data, analyze the quantify relationship between the salinization degree and land surface parameters, propose a new index-salinization detecting index (SDI) which can comprehensively reflect salinized soil biophysical features; point out salinized soil information extracting method which based on remote sensing image fusion; establish object-oriented soil salinization information extracting method. Secondly, analyze the problems of soil salinization time and space evolution, early warning and alarming,, evaluate the soil salinization situation at the study area. At the end, using computer network and Web GIS techniques, develop monitoring of the soil salinization using remote sensing and early warning network transmission system.
(1) The object-oriented classification method was used to extract soil salinization information from the ALOS images, and the results were compared with the traditional information extraction method. The results show that the overall accuracy was89.38%, with a kappa coefficient value of0.88of this method. And the overall accuracy and kappa coefficient value were increased8.24%and0.08, respectively, compared with the traditional method; the object-oriented method is superior to traditional method in high resolution remote sensing information extraction.
(2) The ALOS Panchromatic image and Multi-spectral images are selected for the image fusion method, including HIS, PCA, Gram-Schmidt and Wavelet. The performance of each method was analysed by qualitatively and quantitatively.Also supervised classification method used to extract the soil salinization Information from multispectral images and fused images respectively. The results show that the Wavelet fusion method can provide more information, higher spatial resolution and higher classification resultsis accuracy compared with multi-spectral images.It is most effective image fusion method for monitoring the salinized soil, an advantage over the common data fution methods such as HIS, PCA and Gram-Schmidt.
(3) The relationship between the salinization degree and land surface parameters such as NDVI and albedo, was analyzed quantitatively, using Landsat ETM+and field measured data.The salinization detecion index (SDI) is developed.
(4) Using4temporal images combined with field survey data. With the application of Remote Sensing and Geographic Information Systems, classified statistic and LUCC transform matrix were acquired. In the period from1989to2001, the areas of farmland,woodland, moderate saline land, water and the other (Gobi, sand and clay, etc.) have increased; slight saline land and heavy saline land has reduced. In the period from2001to2006, the areas of slight saline land, moderate saline land and heavy saline land have increased; the other types of land use/covers have reduced. In the period from2006to2010, the areas of moderate saline land, heavy saline land, water and the other have increased; the other types of land use/covers have reduced; As time went by, the gravity center of every type of land use/covers has changed. The farthest distance is the change of gravity center of water between1989and2001, which is23.12km. At the same period, the distance of the gravity center of heavy saline land moved to the northwest is smaller than that of water, which is22.04km. The gravity center of woodland moved to the southwest, and the transferred distances increased year by year. The gravity center of moderately and slightly saline land in essence transferred to northwest during these21years.
(5) Under several important principles guidance, using Web GIS technique, the salinization early warning system was made in research area. The system development by the plat-form of Arc GIS server, SQL DBMS, combined with Net. This is very important for decision making for the relevant department.
当前,国内外区域尺度土壤盐渍化遥感监测应用已趋于主流,但各种信息提取方法结果不确定性较强,其通用性及定量化并不令人满意。同时,目前土壤盐渍化遥感监测的各种研究结果及决策方案等信息的数字化、智能化、计算机决策自动化技术基础研究比较薄弱,还存在着传输时效差、传播不畅、信息覆盖面有限、受各种制约条件限制等问题。
本文针对以上科学问题,首先,利用时间序列遥感影像和野外调查数据,通过分析土壤盐渍化程度与地表参数之间的定量关系；提出综合反映盐渍化土壤生物物理特征的指数—盐渍化遥感监测指数SDI；提出了基于遥感影像融合的盐渍地信息提取方法；提出面向对象的土壤盐渍化信息提取方法。其次,分析土壤盐渍化时空演变、预测和预警等问题,对研究区域的土壤盐渍化情况进行评价。最后,利用计算网络和Web GIS技术开发土壤盐渍化多源遥感监测与预警网络传输系统。主要结论有：
(1)利用面向对象方法的盐渍地信息提取总体精度为89.38%,较传统方法提高8.24%; KAPPA系数为0.88,较传统方法提高0.08。该信息提取方法能实现较为精确的盐渍地信息提取,在高空间分辨率遥感影像盐渍地信息提取中具有一定的优势。(2)与HIS, PCA, Gram-Schmid等传统的影像融合方法相比,WAVELET(小波)融合方法不仅能更好地保持影像光谱扭曲最小,而且能提高空间分辨率。在影像信息熵、清晰度等方面都表现更好,这对盐渍地信息的提取和分类是非常有利的；基于小波融合方法的影像分类相对于单纯的ALOS多光谱影像分类精度而言,分类精度有了一定的提高(提高5%),因此它必将在实际中得到更广泛的应用
(3)本文选取归一化植被指数(NDVI)和地表反照率(Albedo)等两个地表参数作为揭示土壤盐渍化发生过程和程度的重要参数,并对盐渍化土壤在NDVI-Albedo二维特征空间的分布规律进行相关性分析发现盐渍化土壤在此空间上具有较显著的线性分布规律特征,因此提出了综合反映盐渍化土壤生物物理特征的遥感监测指数(Soil Detection Index,SDI)。由于所选取的指标简单易懂、易于获取,具有明确的生物物理意义,因此该模型比较客观地反映了盐渍化土壤地表覆盖、水热组合及其变化情况,有助于综合分析土壤盐渍化发生的过程和对其进行遥感监测。
(4)对研究区域从1989年到2010年,通过各类地物的相互转化,动态度以及重心偏移进行定量的分析,从而揭示21a间的土壤盐渍化的时空变化情况。①1989—2001年,各类地物面积呈现五增二减的趋势：耕地、林地、中度盐渍地、水体和其他均有所增加；而轻度盐渍地和重度盐渍地有所减少；2001—2006年,呈现四增三减的趋势：耕地,轻度盐渍地,中度盐渍地和重度盐渍地均有所增加；林地、水体和其他均有所减少；2006—2010年,呈现四增三减的趋势：中度盐渍地,重度盐渍地、水体和其他均有所增加；耕地、林地、轻度盐渍地均有所减少。②通过Markov模型分析,结果表明：从2015年到2030年,耕地从原本占总面积的3.87%,降低至占总面积的2.98%。林地和轻度盐渍地所占总面积的比例也在逐年稳定降低。中度盐渍地、重度盐渍地、水体和其它占总面积的比例均逐步递增。尤其其它类(G)的增长比例最为显著,从2015年的440920hm2,显著增长到2030年的497440hm2,所占比例也完全吻合逐步增长的趋势。③随着时间的推移,各类地物的重心均有所偏移。其中水体的重心于1989—2001年间向北迁移的距离最远,为23.12km；同期重度盐渍地重心迁移距离次之,为22.04km。林地的重心于1989—-2001年间逐年向西南方向迁移,并且迁移的距离逐年增大,中度盐渍地重心总体向西北方向偏移。
(5)研究区中的盐渍化土壤预警主要以中警、轻警为主,其中重警区域面积达到总面积的15%,主要在库车河下游东南部一带分布；中度警区域面积为总面积的31%,主要在塔里木河的北部和渭干河下游一带分布；轻警区域则占总面积的30%,分布在洪冲积扇下部、库车河两岸和塔里木河灌区；不需要警戒的区域面积较大,占研究区总面积的35%,包括洪冲积扇下部和渭干河、库车河中游的平原地区。从整个研究区来看,盐渍化的警区的分布呈以下特点,一是盐渍化的中警、重警区域呈块状分布,分布的区域较为集中,而轻警区域呈片状或者斑点状断续分布。二是盐渍化预警等级呈现出北部地区高于南部地区,东部地区高于西部地区的趋势。
(6)给出了土壤盐渍化遥感监测和预测、预警结果的网络发布技术及流程。可视化显示主要实现了在Web GIS支持下的远程土壤盐渍化信息在Internet上快捷的数据发布、地图浏览、历史数据管理、空间数据和属性数据双向定位查询、空间分析、评价等基本功能。该系统具有界面清晰、操作方便、实用性强和功能强大等特点,加强了土壤盐渍化信息资源的计算机管理水平。
Soil salinization is one of the land degradation types in arid and semi arid regions. The global extent of primary salt-affected soils is about955M ha, while secondary salinization affects some77M ha, with58%of these in irrigated areas. The damage of soil salinization to the economy, environment and agricultural ecological system is concerned by some scholars year by year. This requires careful monitoring of the soil salinity status and variation to curb degradation trends, and secure sustainable land use and management. It is very important to study the quality, the scope, the area, the distribution, the dynamic change and the degree of saline soils. The combination of remote sensing with some traditional methods has some important meaning for studying the characteristics, areas, degree, distribution, time and space variation, management and protecting from enlarging of soil salinization. This study aiming at the problems mentioned above, firstly, using time sequence remote sensing image and field measured data, analyze the quantify relationship between the salinization degree and land surface parameters, propose a new index-salinization detecting index (SDI) which can comprehensively reflect salinized soil biophysical features; point out salinized soil information extracting method which based on remote sensing image fusion; establish object-oriented soil salinization information extracting method. Secondly, analyze the problems of soil salinization time and space evolution, early warning and alarming,, evaluate the soil salinization situation at the study area. At the end, using computer network and Web GIS techniques, develop monitoring of the soil salinization using remote sensing and early warning network transmission system.
(1) The object-oriented classification method was used to extract soil salinization information from the ALOS images, and the results were compared with the traditional information extraction method. The results show that the overall accuracy was89.38%, with a kappa coefficient value of0.88of this method. And the overall accuracy and kappa coefficient value were increased8.24%and0.08, respectively, compared with the traditional method; the object-oriented method is superior to traditional method in high resolution remote sensing information extraction.
(2) The ALOS Panchromatic image and Multi-spectral images are selected for the image fusion method, including HIS, PCA, Gram-Schmidt and Wavelet. The performance of each method was analysed by qualitatively and quantitatively.Also supervised classification method used to extract the soil salinization Information from multispectral images and fused images respectively. The results show that the Wavelet fusion method can provide more information, higher spatial resolution and higher classification resultsis accuracy compared with multi-spectral images.It is most effective image fusion method for monitoring the salinized soil, an advantage over the common data fution methods such as HIS, PCA and Gram-Schmidt.
(3) The relationship between the salinization degree and land surface parameters such as NDVI and albedo, was analyzed quantitatively, using Landsat ETM+and field measured data.The salinization detecion index (SDI) is developed.
(4) Using4temporal images combined with field survey data. With the application of Remote Sensing and Geographic Information Systems, classified statistic and LUCC transform matrix were acquired. In the period from1989to2001, the areas of farmland,woodland, moderate saline land, water and the other (Gobi, sand and clay, etc.) have increased; slight saline land and heavy saline land has reduced. In the period from2001to2006, the areas of slight saline land, moderate saline land and heavy saline land have increased; the other types of land use/covers have reduced. In the period from2006to2010, the areas of moderate saline land, heavy saline land, water and the other have increased; the other types of land use/covers have reduced; As time went by, the gravity center of every type of land use/covers has changed. The farthest distance is the change of gravity center of water between1989and2001, which is23.12km. At the same period, the distance of the gravity center of heavy saline land moved to the northwest is smaller than that of water, which is22.04km. The gravity center of woodland moved to the southwest, and the transferred distances increased year by year. The gravity center of moderately and slightly saline land in essence transferred to northwest during these21years.
(5) Under several important principles guidance, using Web GIS technique, the salinization early warning system was made in research area. The system development by the plat-form of Arc GIS server, SQL DBMS, combined with Net. This is very important for decision making for the relevant department.
引文
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