基于高分一号影像的土壤盐渍化信息提取方法

摘要/Abstract

摘要： 目前土壤盐渍化遥感监测主要使用中低分辨率遥感影像,存在对土壤盐渍化细节信息监测能力不足的问题.通过使用国产"高分一号"卫星高分辨率遥感影像(GF-1 PMS),基于先进的面向对象方法和最大似然法进行了盐渍化信息提取,并比较了两种方法的提取效果,此外,又同Landsat8 OLI的提取结果进行了比较.通过对两种分类方法、两种最新传感器提取结果的比较,可以得出:国产高分一号影像在土壤盐渍化信息提取方面有着巨大的潜力,就提取方法而言,同最大似然法相比,面向对象方法精度更高,更适用于GF-1 PMS影像的盐渍化信息提取,面向对象法的总体精度和Kappa系数为92.94%和0.91,而最大似然法的总体精度和Kappa系数为87.78%和0.77,面向对象方法的精度高出了约5 个百分点;就分辨率而言,同Landsat8 OLI影像相比,Landsat8 OLI 影像分类精度只有63.47%,而GF-1 PMS高分辨影像分类精度要高很多,盐渍化信息细节描述更充分,可提取出受到盐渍化影响的植被信息,对农田尺度盐渍化遥感监测研究有重要意义,也对拓展国产高分一号影像GF-1 PMS的应用范围是有益的.

关键词: 高分一号, 面向对象, 尺度分割, 土壤盐渍化

Abstract: Soil salinization,an important component of the land degradation forms,usually appears in the arid and semi-arid regions where the climate is drought,soil evaporation is very fierce,and the water table is high and contains rich soluble salts. Soil salinization has impeded the development of oasis agriculture in Xinjiang due to its severe effects on agricultural productivity and sustainable development. In order to understand the threat level of soil salinization in oasis agriculture,and ensure the sustainable development of oasis agriculture in the arid and semi-arid regions,it is very necessary to study the method of soil salinization monitoring. At present,remote sensing technology has been widely used to monitor soil salinization and is very useful. However,monitoring soil salinization is mainly based on the low-resolution satellite images,which is not enough for monitoring soil salinization in details. In this study,the domestic GF-1 PMS image was adopted to extract the salinization information based on the advanced object-oriented method. First,fractal net evolution approach was used to segment image and build classification rules for salinization information extraction. Then,by using the maximum likelihood method, soil salinization information was extracted from the domestic GF-1 PMS image and Landsat OLI image in the same region,respectively. Finally,the results of two different methods and different latest sensors for salinization information extraction were compared. The results show as follows:(1)The overall accuracy of object-oriented method for salinization information extraction based on GF-1 PMS image is 92.94% and the kappa coefficient is 0.91. The overall accuracy of maximum likelihood method for salinization information extraction based on GF-1 PMS image is 87.78% and the kappa coefficient is 0.77. Compared with the maximum likelihood method,object-oriented method is better for soil salinization extraction based on GF-1 PMS image and the accuracy is overall improved by 5 percentage points. This illustrates that the object-oriented method is more suitable for GF-1 PMS image when monitoring soil salinization. In addition,object-oriented method can make full use of the relationship between pixels through scale segmentation technology,and can more fully utilize the information contained in the image to improve the accuracy of salinization extraction from high-resolution remote sensing image information.(2)The overall accuracy of Landsat8 OLI image for soil salinization extraction is only 63.47%. Compared with Landsat OLI image,the overall accuracy of GF-1 PMS image is improved by 30 percentage points. The ability of GF-1 image for soil salinization extraction is stronger than that of Landsat image.We can extract the vegetation that is affected by soil salinization,which is meaningful for study of agricultural field scale salinization. This illustrates that GF-1 images has great potential for salinization monitoring on agricultural field scale. In this study,we use domestic GF-1 image to extract soil salinization information based on the advanced object-oriented method for the first time. The result is positive. This shows domestic GF-1 image can be one of data sources to monitor soil salinization in the arid and semi-arid regions.

Key words: GF-1, object-oriented, scale segmentation, soil salinization

中图分类号:

TP79

牛增懿, 丁建丽, 李艳华, 王爽, 王璐, 马成霞. 基于高分一号影像的土壤盐渍化信息提取方法[J]. 干旱区地理, 2016, 39(1): 171-181.

NIU Zeng-yi, DING Jian-li, LI Yan-hua, WANG Shuang, WANG Lu, MA Cheng-xia. Soil salinization information extraction method based on GF-1 image[J]. , 2016, 39(1): 171-181.

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