地球信息科学学报

地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (4): 537-543.doi: 10.3724/SP.J.1047.2016.00537

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WorldView-2近红外光谱波段反演马尾松植被信息的比较研究

胡秀娟(), 徐涵秋(), 黄绍霖, 张灿, 唐菲   

  1. 1. 福州大学环境与资源学院,福州 350116
    2. 福州大学遥感信息工程研究所,福州 350116
    3. 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 350116
  • 收稿日期:2015-06-11 修回日期:2015-08-13 出版日期:2016-04-20 发布日期:2016-04-19
  • 作者简介:

    作者简介：胡秀娟(1982-),女,博士生,研究方向为资源环境遥感与景观规划。E-mail: huxiujuan_fzu@163.com

  • 基金资助:
    国家科技支撑计划项目(2013BAC08B01-05);福州大学科技发展基金项目(2014-XY-10)

Comparison Between the Two Near Infrared Bands of WorldView-2 Imagery in Their Applications in Pinus Massoniana Forest

HU Xiujuan(), XU Hanqiu*(), HUANG Shaolin, ZHANG Can, TANG Fei   

  1. 1. College of Environment and Resources, Fuzhou University, Fuzhou 350116, China
    2. Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350116, China
    3. Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection, Fuzhou University, Fuzhou 350116, China
  • Received:2015-06-11 Revised:2015-08-13 Online:2016-04-20 Published:2016-04-19
  • Contact: XU Hanqiu E-mail:huxiujuan_fzu@163.com;hxu@fzu.edu.cn

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摘要：

WorldView-2卫星自2009年发射至今,已为用户提供了大量高性能的影像产品。与众多高分辨率卫星影像不同,WorldView-2有2个近红外波段,即近红外1(Near-infrared1,NIR1)和近红外2(Near-infrared2,NIR2),但目前这2个波段在应用上的区别并不清楚。因此,本文以福建省长汀县河田地区的马尾松林为例,采用NIR1和NIR2这2个近红外波段分别构建了3种植被指数(NDVI、ARVI和NDMVI),以探索二者在植被信息反演方面的差异。结果表明,NIR1构建的植被指数在马尾松林提取精度上高于NIR2,并具有更丰富的植被信息量。经统计可知,NIR1所构建的植被指数信息量比NIR2分别大8.0%(NDVI)、12.3%(ARVI)和7.3%(NDMVI);在反演植被覆盖度方面,NIR1也比NIR2具有更高的精度,其模拟的植被覆盖度与实际植被覆盖度的拟合度更高,误差更小。NIR1和NIR2所表现出的差异是因为马尾松在这2个近红外波段的光谱反射不同,其反射在NIR1的波长范围内达到最强,而在NIR2的波长范围内则出现了小幅下降。

关键词: WorldView-2, 近红外光谱, 植被指数, 植被覆盖度, 遥感

Abstract:

Since its launch in 2009, the WorldView-2 satellite has provided a large amount of high-quality images to the world. The WorldView-2 has two near infrared spectral bands NIR1 and NIR2, which make it different from the numerous other previously launched satellite sensor data. Up to date, however, the differences between the two NIR bands in applications are not clear. Therefore, taking Pinus Massoniana forest in the Changting county of Fujian, China as an example, this paper utilized the two NIR bands respectively to compute three vegetation indices, which are the Normalized Difference Vegetation Index (NDVI), the Atmospherically Resistant Vegetation Index (ARVI), and the Normalized Difference Mountain Vegetation Index (NDMVI), to explore the differences between the two bands in the retrieval of vegetation information. The results show that the accuracy of the extracted Pinus Massoniana information using the indices derived from the NIR1 band is always higher than that derived from the NIR2, and the NIR1-derived indices can gain more vegetation information than the NIR2-derived indices, which is 8.0% higher in NDVI, 12.3% higher in ARVI, and 7.3% higher in NDMVI, respectively. As for the retrieval of fractional vegetation coverage, NIR1 also show a higher accuracy than NIR2, as it shows a higher degree of agreement and lower root mean square error when compared with the actual fractional vegetation coverage. The performance differences between the two NIR bands are caused by the higher reflection rate of Pinus Massoniana in NIR1 wavelength than in the NIR2 spectral range.

Key words: Worldview-2, near infrared spectrum, vegetation index, fractional vegetation coverage, remote sensing