基于国产高分一号卫星数据的区域土壤盐渍化信息提取与建模  被引量:28

Extraction and Modeling of Regional Soil Salinization Based on Data from GF-1 Satellite

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作  者:曹雷[1,2,3] 丁建丽[1,2] 玉米提·哈力克[1,2,3] 苏雯[1,2] 宁娟[1,2] 缪琛[1,2] 李焕[1,2] CAO Lei DINGJianli UMUT Halik SU Wen NING Juan MIAO Chen LI Huan(College of Resources and Environment Sciences, Xinjiang University, Urumqi 830046, China Key Laboratory of Oasis Ecosystem of Ministry of Education, Xinjiang University, Urumqi 830046, China Faculty of Mathematics and Geography, Catholic University ofEichstaett-Ingolstadt, Eichstaett 85071, Germany)

机构地区:[1]新疆大学资源与环境科学学院,乌鲁木齐830046 [2]新疆大学绿洲生态教育部重点实验室,乌鲁木齐830046 [3]德国埃希施塔特-因戈尔施塔特大学数学与地理学院,埃希施塔特85071

出  处:《土壤学报》2016年第6期1399-1409,共11页Acta Pedologica Sinica

基  金:新疆维吾尔自治区科技支疆项目(201504051064);高分辨率对地观测重大专项(民用部分)(95-Y40B02-9001-13/15-03-01);国家留学基金委创新型人才培养国际合作项目(201505990312)资助~~

摘  要:土壤盐渍化是干旱半干旱地区土地退化的主要原因之一,给当地生态系统和社会经济的可持续发展带来了严重的威胁,而对盐渍化空间分布信息的提取是治理盐渍化的基础。因此,选取生态脆弱区渭—库绿洲为研究区,利用2014年7月19日GF-1多光谱影像数据,提取光谱指数及波段信息,结合实际采样点的土壤表层电导率数据(0~10 cm),采用偏最小二乘回归模型(partial least squares regression,PLSR)对土壤盐渍化进行模拟,并对研究区盐渍化分布进行模拟和评估。结果表明:实测土壤表层电导率与光谱指数相关性较好;利用PLSR对渭—库绿洲土壤表层盐渍信息建模,对土壤盐渍化信息提取效果较好,精度较高;充分利用了GF-1影像包含的信息,提高了高分辨率遥感影像盐渍化信息提取的精度;非盐渍化和轻度盐渍化面积分别占总面积的42.88%和17.16%,绿洲中部偏东及东南区域,盐渍化现象稍弱,可成为今后绿洲扩张的重点方向;而中度盐渍化、重度盐渍化和盐土面积分别占总面积的29.51%、8.57%和1.88%,绿洲北部/西部及西南方向的重度盐渍化区域紧挨绿洲区域,已严重威胁了绿洲经济的健康发展,亟待治理。【Objective】Soil salinization,being one of the main causes of land degradation in arid and semi-arid regions,poses a great threat to sustainable development of the local social economy and ecological system. 【Method】How to extract the information of spatial distribution of soil salinization is the foundation for management of soil salinization. Therefore,the Weigan-Kuqa Oasis,an area rather fragile in ecology,was selected as an object in this study,using the GF-1 satellite multispectral images of the date of July 19,2014 as the main data source. A total of 16 spectral indices i.e. Normalized difference vegetation index(NDVI),soil adjusted vegetation index(SAVI),normalized differential salinity index(NDSI),salinity index(SI-T),brightness index(BI),salinity index(SI),salinity index 1(SI1),salinity index 2(SI2),salinity index 3(SI3),salinity index(S1),salinity index(S2),salinity index(S3),salinity index(S5),salinity index(S6),intensity index 1(Int1),intensity index 2(Int2),and four bands,i.e. band 1(B1),band 2(B2),band 3(B3)and band 4(B4),were chosen for analysis. The images in pretreatment were computed by band in line with the spectral index formulas with the aid of software ENVI4.8. Hence,gray scale maps of different spectral indices were derived and pixel values of the 36 sampling points corresponding to the gray scale maps were extracted. The data of electrical conductivities in the surface soil layers(0~10 cm)of those sampling sites during 22~28 July 2014 were also collected for analysis of Pearson correlation with the pixel values using software SPSS 19.0. Thus sensitivities of different spectral indices to the data of soil salinization were figured out. PLSR models were built and validated for relationships of the mathematical formulas for five different electrical conductivities(i.e. measured conductivity,reciprocal of measured conductivity,logarithm of measured conductivity,MSR of measured conductivity and reciproc

关 键 词:土壤盐渍化 高分一号 光谱指数 偏最小二乘回归法(PLSR) 渭—库绿洲 

分 类 号:TP79[自动化与计算机技术—检测技术与自动化装置]

 

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