地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (9): 1128-1134.doi: 10.3724/SP.J.1047.2015.01128

• • 上一篇    

水土流失区马尾松林植被提取的土壤调节指数分析

李晶, 徐涵秋*, 李霞, 郭燕滨   

  1. 福州大学环境与资源学院福州大学遥感信息工程研究所,福建省水土流失遥感监测评估与灾害防治重点实验室,福州 350108
  • 收稿日期:2015-01-04 修回日期:2015-03-11 出版日期:2015-09-10 发布日期:2015-09-07
  • 通讯作者: 徐涵秋
  • 作者简介:

    作者简介:李晶(1992-),女,硕士生,主要从事环境与资源遥感研究。E-mail: 1239188161@qq.com

  • 基金资助:
    国家科技支撑计划课题“南方红壤水土流失治理技术研究与示范”(2013BAC08B01-05);福建省教育厅项目“南方典型红壤水土流失区遥感动态监测与生态环境评价——以长汀县为例”(JA3030)

Vegetation Information Extraction of Pinus Massoniana Forest in Soil Erosion Areas Using Soil-adjusted Vegetation Index

LI Jing, XU Hanqiu*, LI Xia, GUO Yanbin   

  1. College of Environment and Resources, Fuzhou University Institute of Remote Sensing Information Engineering, Fuzhou University Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection, Fuzhou University Fuzhou 350108, China
  • Received:2015-01-04 Revised:2015-03-11 Online:2015-09-10 Published:2015-09-07
  • Contact: XU Hanqiu
  • About author:

    *The author: SHEN Jingwei, E-mail:jingweigis@163.com

摘要:

为实现水土流失区植被遥感信息的准确提取,本文采用2007年ALOS 10 m多光谱影像,利用土壤调节植被指数SAVI和MSAVI,对福建长汀水土流失区马尾松林不同植被覆盖密度的3个实验区进行植被提取,并选用不同的土壤调节因子(L=0.25,0.5,0.75,1)做实验,将结果和以NDVI植被指数提取的结果进行对比,分析了提取效果及受土壤噪音的影响程度。实验表明,SAVI指数能提高水土流失区的植被提取精度。在中、低植被覆盖区,其提取的总精度比NDVI高出2%~7%,Kappa系数高出7%~18%;而土壤调节因子L的取值对植被信息的提取也呈现出一定的规律性,即:随着L从0向1递增,SAVI提取稀疏植被的能力上升而探测阴坡植被的能力下降。总体来看,对于低植被覆盖和中等植被覆盖地区,可分别用SAVI(L取0.75)和SAVI(L取0.5)来提取植被信息,对于高植被覆盖区,仍可直接用NDVI进行植被信息提取;研究发现MSAVI在植被信息提取中并不具有特别的优势。

关键词: 植被提取, 土壤调节因子, SAVI, NDVI, MSAVI

Abstract:

Vegetation indices are frequently used in remote sensing applications. Nevertheless, vegetation monitoring based on vegetation indices may be affected by different soil background conditions. The aim of this study is to investigate the relationships between vegetation abundance and vegetation indices, and evaluate the performance of vegetation indices along with different values for soil-adjusted factors in vegetation-feature extractions. Changting county of Fujian, a typical reddish soil erosion region in southern China, was taken as a test site for the study. Three subtest sites were selected to represent the low, moderate and dense vegetation coverage areas, respectively. After converting the original digital numbers of the ALOS image to at-satellite reflectance, vegetation indices including the normalized difference vegetation index (NDVI), the modified soil-adjusted vegetation index (MSAVI) and the soil-adjusted vegetation index (SAVI) with soil adjustment factor (L) values of 0.25, 0.5, 0.75 and 1 were calculated. The vegetation features were then extracted from the above vegetation-enhanced images and compared to high resolution images to assess their accuracies. The results suggest that SAVI gives a better performance in both low and moderate vegetation coverage area while using a L value of 0.75 for low vegetation coverage area and 0.5 for moderate vegetation coverage area, with the overall accuracies of 76.26% and 80.65%, respectively. NDVI gives a better performance in the dense vegetation coverage area with an overall extraction accuracy of 84.01%. Whereas MSAVI does not perform well in any of the three selected test sites. The soil adjustment factor L of SAVI has significant influence on the accurate extraction of vegetation information. As increasing L from 0 to 1, the ability of the SAVI in detecting sparse vegetation is gradually enhanced, however its ability in detecting vegetation information in shaded slope areas is decreased.

Key words: vegetation extraction, soil adjustment factor, SAVI, NDVI, MSAVI