地球信息科学学报

地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (12): 1977-1986.doi: 10.12082/dqxxkx.2019.190409

• 遥感科学与应用技术 • 上一篇    

阴影消除植被指数(SEVI)去除地形本影和落影干扰的性能评估与应用

江洪1,2,*(), 袁亚伟1,2, 王森1,2   

  1. 1. 福州大学 数字中国研究院（福建）,福州 350108
    2. 福州大学 空间数据挖掘与信息共享教育部重点实验室,福州 350108
  • 收稿日期:2019-07-30 修回日期:2019-10-10 出版日期:2019-12-25 发布日期:2019-12-25
  • 通讯作者: 江洪 E-mail:jh910@fzu.edu.cn
  • 作者简介:江 洪（1975-）,男,福建永安人,博士,副研究员,主要从事环境遥感、信息管理研究。E-mail: jh910@fzu.edu.cn
  • 基金资助:
    福建省自然科学基金项目(2017J01658);国家重点研发计划项目子课题(2017YFB0504203)

Shadow-Eliminated Vegetation Index (SEVI) for Removing Terrain Shadow Effect : Evaluation and Application

JIANG Hong1,2,*(), YUAN Yawei1,2, WANG Sen1,2   

  1. 1. Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, China
    2. Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education , Fuzhou University, Fuzhou 350108, China
  • Received:2019-07-30 Revised:2019-10-10 Online:2019-12-25 Published:2019-12-25
  • Contact: JIANG Hong E-mail:jh910@fzu.edu.cn
  • Supported by:
    Natural Science Foundation of Fujian Province, China(2017J01658);National Key Research and Development Program of China(2017YFB0504203)

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摘要：

地形校正是崎岖山区遥感图像预处理的关键步骤。为了评估基于DEM数据的经验校正模型、山地辐射传输模型和波段组合优化计算模型在去除地形阴影效应方面的性能,并将其应用于福州市植被覆盖监测,本文采用C模型（和SCS+C模型）、6S+C模型和阴影消除植被指数（SEVI）进行评估、比较。采用1999年和2014年两期Landsat 5 TM卫星数据和相关的 30 m ASTER GDEM V2高程数据,分别计算了C校正（和SCS+C校正）和6S+C校正后的归一化植被指数（NDVI）和比值植被指数（RVI）以及基于表观反射率数据的SEVI。通过目视比较、光谱特征比较以及太阳入射角余弦值（cos i）与植被指数的线性回归分析,可以看出C模型和SCS+C模型对本影具有较好的校正效果,但对落影的校正效果欠佳。NDVI和RVI的本影与邻近无阴影阳坡的相对误差分别从71.64%、52.57%降至4.80%、6.43%（C模型）和0.50%、9.94%（SCS + C模型）,而落影与邻近无阴影阳坡的相对误差分别从62.01%、47.57%降至31.05%、24.40%（C模型）和33.42%、16.01%（SCS + C模型）。在NDVI的落影校正效果上,6S+C模型比C模型和SCS+C模型有一定的提升,本影与邻近无阴影阳坡之间的相对误差为8.63%,落影与邻近无阴影阳坡之间的相对误差为14.27%。而SEVI在消除本影和落影方面整体效果更好,本影和落影与邻近无阴影阳坡的相对误差分别为9.86%和10.53%。最后,基于SEVI对福州市1999-2014年的植被覆盖变化进行了监测。监测结果表明： ① 1999-2014年植被覆盖增加了893.61 km 2,植被增加区域主要分布在海拔250~1250 m范围内;② SEVI均值在坡度40°附近达到峰值。

关键词: 阴影消除植被指数（SEVI）, 本影, 落影, 地形校正模型, 植被监测, Landsat 5 TM, NDVI

Abstract:

Topographic correction is a crucial step in the pre-processing of remote sensing imagery of rugged terrain areas. Recently, a Shadow-Eliminated Vegetation Index (SEVI) was proposed to eliminate the influence of the self and cast terrain shadows. To further evaluate the SEVI performance for reducing the terrain shadow effect on regional scales, here we compared the SEVI with classic topographic correction models, including the C model, Sun-Canopy-Sensor (SCS)+C model, and Second Simulation of the Satellite Signal in the Solar Spectrum (6S)+C model via the case study of Fuzhou city, China. Landsat 5 TM satellite data and associated 30-meter Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version 2 (ASTER GDEM V2) were used for the comparison. The satellite imagery were first corrected using the C, SCS+C, and 6S+C models, followed by the calculation of the Normalized Difference Vegetation Index (NDVI) and the Ratio Vegetation Index (RVI). Then, the calculated vegetation indices were evaluated in different ways, including visual comparison, statistical analysis, and linear correlation analysis of the cosine of solar incidence angle ( cos i ) versus vegetation indices. The C and SCS+C models showed accurate correction results over the self shadow areas but less accurate results over the cast shadow areas. Using adjacent sunny slopes as a reference, the relative errors of the NDVI and RVI over the self shadow areas were reduced from 71.64% and 52.57% to 4.80% and 6.43% (C model) and 0.50% and 9.94% (SCS+C model), respectively; the relative errors over cast shadow areas were reduced from 62.01% and 47.57% to 31.05% and 24.40% (C model) and 33.42% and 16.01% (SCS+C model), respectively. The 6S+C model showed better correction results over the cast shadow areas than the C model and the SCS+C model did. The relative errors of the NDVI were 8.63% and 14.27% over self shadow and cast shadow areas, respectively, if the 6S+C model was used. The SEVI seemed the most accurate among these models for corrections of self and cast shadows. The relative errors of the SEVI were 9.86% and 10.53% over the self and cast shadows, respectively. Finally, the SEVI was used to study the vegetation cover change in Fuzhou city from 1999 to 2014. Results show: (1) the vegetation cover in Fuzhou city increased from 1999 to 2014 in general, particularly over the areas with elevation ranging from 250 to 1250 meters; (2) The highest SEVI mean was located on the slope of about 40 degrees.

Key words: Shadow-Eliminated Vegetation Index, self shadow, cast shadow, topographic correction model, vegetation monitor, Landsat 5 TM, NDVI