首页 | 本学科首页   官方微博 | 高级检索  
   检索      

干旱区土壤盐渍化特征空间建模
引用本文:丁建丽,姚远,王飞.干旱区土壤盐渍化特征空间建模[J].生态学报,2014,34(16):4620-4631.
作者姓名:丁建丽  姚远  王飞
作者单位:新疆大学资源与环境科学学院绿洲生态教育部重点实验室, 乌鲁木齐830046;新疆大学资源与环境科学学院绿洲生态教育部重点实验室, 乌鲁木齐830046;新疆大学资源与环境科学学院绿洲生态教育部重点实验室, 乌鲁木齐830046
基金项目:新疆维吾尔自治区青年科技创新人才培养工程(2013711014);国家自然科学基金项目(U1303381,41261090,41130531);教育部新世纪优秀人才支持计划(NCET-12-1075);霍英东青年教师基金项目(121018)
摘    要:当前,土壤盐渍化以及因灌溉引起的土壤次生盐渍化问题是我国干旱、半干旱区所面临的主要生态环境问题。在特征空间理论的支持下,以波谱分解技术为基础,以Landsat-TM、Landsat-ETM+多光谱遥感影像和野外调查数据为基础数据源,通过分析干旱区土壤盐渍化对地表生物物理特征的影响,探讨了表征盐渍化过程与地表生物物理特征之间的规律及定量关系,进而利用土壤盐渍化遥感监测中关键的3个指标——经过波谱分解技术获得的直接表征盐渍化的土壤盐渍化光谱、间接表征盐渍化的植被覆盖度和土壤水分含量协同构建了二维特征空间支持下的土壤盐渍化遥感监测模型VSSI(Vegetation fraction and Soil fraction Soil Index)、SVSI(Soil water contents and Vegetation fraction Soil Index)、SSSI(Soil water contents and Soil salinization fraction Soil Index)和三维特征空间支持下的土壤盐渍化遥感监测模型SVWSI和SDI。研究结果表明:基于三维特征空间建立的SVWSI(Soil salinization fraction-Vegetation fraction-Water contents Soil Index)和SDI(Soil Distance Index)模型对不同盐渍化程度土壤的敏感程度要高于基于传统二维特征空间建立的VSSI、SVSI和SSSI模型。其中,SVWSI和SDI模型与实测0—10 cm土壤盐分含量决定系数分别为R2=0.8325和R2=0.8646,这充分说明基于高维数特征空间所构建的土壤盐渍化遥感监测模型能更准确地反映盐渍化土壤地表盐量组合及其变化信息,且指标简单、易于获取,对于今后干旱区区域大尺度盐渍地信息提取以及动态监测研究具有重要意义。

关 键 词:特征空间  遥感  土壤盐渍化  渭干河-库车河三角洲绿洲
收稿时间:2012/12/29 0:00:00
修稿时间:2014/6/16 0:00:00

Detecting soil salinization in arid regions using spectral feature space derived from remote sensing data
DING Jianli,YAO Yuan and WANG Fei.Detecting soil salinization in arid regions using spectral feature space derived from remote sensing data[J].Acta Ecologica Sinica,2014,34(16):4620-4631.
Authors:DING Jianli  YAO Yuan and WANG Fei
Institution:College of Resource and Environmental Science, Xinjiang University, Key Laboratory for Oasis Ecology, Xinjiang University, Urumqi 830046, China;College of Resource and Environmental Science, Xinjiang University, Key Laboratory for Oasis Ecology, Xinjiang University, Urumqi 830046, China;College of Resource and Environmental Science, Xinjiang University, Key Laboratory for Oasis Ecology, Xinjiang University, Urumqi 830046, China
Abstract:Soil salinization, especially secondary soil salinization caused by irrigation activities is one of the primary ecological and environmental concerns in the arid and semi-arid regions of China. A critical research question is to quickly and accurately monitor soil salinization in arid and semi-arid regions so that prevention strategies can be developed quickly and deployed efficiently. Traditional techniques based on field soil sampling and laboratory experiments, though could be rather accurate for the sampling sites and their immediate vicinity, can hardly produce real-time evaluation. Monitoring soil salinization using remotely sensed imageries, however, starts to attract scholarly attention during the past decades due to the almost real-time information collection, vast geographic coverage, and rich information contained in the remotely sensed imageries. The current study is an attempt to employ remote sensing technique to monitor soil salinization in the arid/semi-arid Weigan-Kuqa Delta Oasis region in Xinjiang, Western China. Data are collected from Landsat-TM and Landsat-ETM+ multiple-spectral remote sensing imageries. Field soil samples at the 0-10 cm depth are obtained for validation purposes as well. The study intends to establish a statistical relationship between the degrees of soil salinization and surface biophysical reflective characteristics that are captured by the remote sensing imageries. Spectral un-mixing analysis of the multispectral imageries produces three groups of commonly used spectral information for soil salinization monitor and evaluation, i. e., individual spectra that are sensitive to soil salinization, that can be used to derive vegetation cover, and that can be used to derive soil moisture contents. The study then combines these groups of information establish three two-dimensional and two three-dimensional soil salinization monitoring indices. The three two dimensional indices include: Vegetation fraction and Soil Index(VSSI), Soil water contents and Vegetation fraction Soil Index(SVSI) and Soil water contents and Soil salinization fraction Soil Index(SSSI). The two three-dimensional soil salinization monitoring indices include: Soil salinization fraction-vegetation fraction-Water contents Soil Index(SVWSI) and Soil Distance Index(SDI). Statistical analyses using these obtained two dimensional and three dimensional indices with field soil sample data are conducted as well. The result suggests that all the indices are able to provide sufficient monitoring and evaluating performance of the severity of soil salinization in our designated study region. Three dimensional indices, however, tend to be more sensitive to soil salinization than the two dimensional indices. In particular, SVWSI and SDI are highly correlated with soil salt contents at the 0-10 cm depth, with correlation coefficients of R2=0.8325 and R2=0.8646, respectively. The result suggests that higher dimensional indices derived from remote sensing imageries might provide more accurate soil salinization monitoring measurements than lower dimensional indices due to enriched information structure. Since obtaining spectral information from remote sensing imageries is relatively straightforward and is often either real-time of near real-time, our suggests that rich information that can be derived from remotely sensed imageries shall be of invaluable importance to provide real-time and accurate evaluation and monitor for soil salinization monitoring and evaluation might provide timely strategies that can mitigate or even prevent further soil salinization in arid and semi-arid regions.
Keywords:feature space  remote sensing  soil salinization  Weigan-Kuqa river delta oasis
本文献已被 CNKI 等数据库收录!
点击此处可从《生态学报》浏览原始摘要信息
点击此处可从《生态学报》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号