基于SEVI的复杂地形山区植被FPAR遥感反演与地形效应评估

doi:10.12082/dqxxkx.2020.190316

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (8): 1725-1734.doi: 10.12082/dqxxkx.2020.190316

基于SEVI的复杂地形山区植被FPAR遥感反演与地形效应评估

蒋世豪(), 江洪^*(), 陈慧

福州大学数字中国研究院（福建）,空间数据挖掘与信息共享教育部重点实验室,卫星空间信息技术综合应用国家地方联合工程研究中心,福州 350108

收稿日期:2019-06-20 修回日期:2019-10-31 出版日期:2020-08-25 发布日期:2020-10-25
通讯作者: 江洪 E-mail:jzkkll@qq.com;jh910@fzu.edu.cn
作者简介:蒋世豪（1994— ）,男,河南开封人,硕士生,研究方向为遥感技术与应用。E-mail:jzkkll@qq.com
基金资助:
国家重点研发计划课题(2017YFB0504203);福建省自然科学基金项目(2017J01658)

Vegetation FPAR Retrieval based on SEVI in Rugged Terrain and Terrain Effects Assessment

JIANG Shihao(), JIANG Hong^*(), CHEN Hui

Fuzhou University The Academy of Digital China (Fujian), Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou 350108, China

Received:2019-06-20 Revised:2019-10-31 Online:2020-08-25 Published:2020-10-25
Contact: JIANG Hong E-mail:jzkkll@qq.com;jh910@fzu.edu.cn
Supported by:
National Key Research and Development Program of China(2017YFB0504203);Natural Science Foundation of Fujian Province, China(2017J01658)

摘要/Abstract

摘要：

植物吸收性光合有效辐射分量（FPAR）的遥感反演是生态环境领域的核心研究内容之一,但在复杂地形山区,其估算精度严重受到地形效应的影响（包括本影与落影）。本文利用能够消除地形阴影影响的阴影消除植被指数（SEVI）对山区遥感影像进行FPAR反演,并分别与基于不同影像预处理程度计算的归一化植被指数（NDVI）、比值型植被指数（RVI）反演的FPAR做对比分析,以评估复杂山区反演FPAR存在的地形效应。结果表明：在不做地形校正的情况下,基于NDVI与RVI反演FPAR会使得本影及落影区域的值远小于非阴影区域的值,它们的相对误差均大于70%;基于C校正后的NDVI与RVI反演FPAR可以较好地校正本影区域,相对误差降至约6.974%,但落影处的校正效果不明显,相对误差约为48.133 %;而基于SEVI反演FPAR无需DEM数据的支持,可以达到经FLAASH+C组合校正后NDVI与RVI反演FPAR相似的结果,且能改善落影区域的地形校正效果,相对误差降至约2.730%。

关键词: FPAR, 植被指数, SEVI, 地形校正, 本影, 落影, 复杂地形山区

Abstract:

The retrival of Fraction of Absorbed Photosynthetically Active Radiation (FPAR) by remote sensing is one of the major research fields in ecological environment. However, in mountainous areas with rugged terrain, the estimation accuracy is seriously affected by terrain effect, including the influence of self and cast shadow. In this paper, Shadow-Eliminated Vegetation Index (SEVI), which can effectually remove the influence of terrain shadow, was used to conduct FPAR inversion in mountainous areas from remote sensing data. The inversion result based on SEVI was compared and analyzed with the inversion results based on Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) with different pre-processing degrees and evaluate the topographic effect of FPAR inversion based on different indexes in complex mountainous areas from remote sensing images. The results show that the FPAR inversion based on NDVI and RVI have much smaller values in self and cast shadow areas than that in non-shadow area without terrain correction using DEM data, their relative error are both greater than 70%. C correction can be better used in the pre-processing of NDVI and RVI deriving and effectually corrected the FPAR inversion results based on these two indexes, its relative error dropped to about 6.974%. But the results after C correction not performed well in cast shadow areas, its relative error is about 48.133%. The FPAR inversion based on the SEVI without DEM data can achieve similar results with the FPAR inversion based on NDVI and RVI after the atmospheric correction of the FLAASH and C combination, and the result shows a better terrain correction effect in shadow area where relative error dropped to about 2.730%.

Key words: FPAR, vegetation indices, SEVI, topographic correction, self shadow, cast shadow

蒋世豪, 江洪, 陈慧. 基于SEVI的复杂地形山区植被FPAR遥感反演与地形效应评估[J]. 地球信息科学学报, 2020, 22(8): 1725-1734.DOI:10.12082/dqxxkx.2020.190316

JIANG Shihao, JIANG Hong, CHEN Hui. Vegetation FPAR Retrieval based on SEVI in Rugged Terrain and Terrain Effects Assessment[J]. Journal of Geo-information Science, 2020, 22(8): 1725-1734.DOI:10.12082/dqxxkx.2020.190316

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结果	数据类别	M_self	M_cast	M_sunny	M_self/%	M_cast/%
FPAR-t	TOA	0.217	0.240	0.838	74.046	71.340
FPAR-c	C	0.558	0.311	0.600	6.974	48.133
FPAR-fc	FLAASH+C	0.582	0.486	0.643	9.560	24.413
FPAR-s	TOA	0.634	0.619	0.637	0.382	2.730

结果	数据类别	r²	k	d	Mean	Std	Std/Mean/%
FPAR-t	TOA	0.830	1.123	-0.146	0.432	0.292	67.627
FPAR-c	C	0.034	0.444	0.121	0.490	0.149	30.359
FPAR-fc	FLAASH+C	0.080	0.849	-0.146	0.570	0.120	21.042
FPAR-s	TOA	0.002	0.396	0.089	0.630	0.039	6.202

基于SEVI的复杂地形山区植被FPAR遥感反演与地形效应评估

Vegetation FPAR Retrieval based on SEVI in Rugged Terrain and Terrain Effects Assessment

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