Research on multispectral-image-based NDVI shadow-effect-eliminating model

Junnan JIAO; Jing SHI; Qingjiu TIAN; Lin GAO; Nianxu XU

doi:10.11834/jrs.20208225

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Research on multispectral-image-based NDVI shadow-effect-eliminating model
Vol. 24, Issue 1, Pages: 53-66(2020)
Published： 07 January 2020 ，
DOI： 10.11834/jrs.20208225

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焦俊男,石静,田庆久,高林,徐念旭.2020.多光谱影像NDVI阴影影响去除模型.遥感学报,24(1): 53-66

JIAO Junnan,SHI Jing,TIAN Qingjiu,GAO Lin,XU Nianxu. 2020. Research on multispectral-image-based NDVI shadow-effect-eliminating model. Journal of Remote Sensing(Chinese). 24(1): 53-66
焦俊男,石静,田庆久,高林,徐念旭.2020.多光谱影像NDVI阴影影响去除模型.遥感学报,24(1): 53-66 DOI： 10.11834/jrs.20208225.

JIAO Junnan,SHI Jing,TIAN Qingjiu,GAO Lin,XU Nianxu. 2020. Research on multispectral-image-based NDVI shadow-effect-eliminating model. Journal of Remote Sensing(Chinese). 24(1): 53-66 DOI： 10.11834/jrs.20208225.

摘要

归一化植被指数(NDVI)在植被多光谱遥感反演中占据尤为重要的地位，而遥感影像中普遍存在的阴影对NDVI的精度产生很大的影响，因此去除阴影对植被NDVI的影响对更精确的定量化研究具有应用价值。本文基于光照区和阴影区的太阳辐射能量差异，模拟出同一植被在光照区和阴影区的辐亮度，分析阴影对NDVI的影响机理；利用植被固有反射率谱间关系，引入对阴影极敏感的且与植被信息相关性小的归一化暗像元指数NDPI (Normalized Dark Pixel Index)，分析同一植被处于光照区与阴影区的NDVI关系，构建以光照区植被NDVI为基准的NDVI阴影影响去除模型NSEE (NDVI Shadow-Effect-Eliminating)，并应用于Landsat 8 OLI影像进行验证。结果表明：NDVI阴影影响基本去除，阴影区NDVI接近正常值，且光照区NDVI保持稳定；有效解决了阴影导致NDVI统计直方图的偏态问题，使其更接近正态分布；与验证影像NDVI沿剖面线逐像元比对发现，植被NDVI阴影影响基本去除；均方根误差RMSE为0.067。本模型能够将本身NDVI值很低的像元与阴影导致NDVI降低的植被像元区分开，符合实际地物情况；模型基于影像自身信息，去除NDVI阴影影响的同时，有效保持了NDVI的相对空间关系；本文基于物理机理构建模型，模型表达简洁、易于应用，且仅依赖于影像自身信息，无需异源数据，计算方便且高效。

Abstract

This paper presents a multispectral-image-based model for eliminating the shadow effect on NDVI. This NDVI Shadow-Effect-Eliminating model (NSEE) is derived from simulated data and then applied on two Landsat 8 OLI images (one for the experiment and one for verification).

NDVI plays a key role in the multispectral remote sensing retrieval of vegetation and has been widely used in many areas. However

the shadow normally existing in remote sensing images always influences the accuracy of NDVI. These effects will be transmitted in the process of further remote sensing retrieval

thereby resulting in errors. In this case

eliminating the shadow effect on vegetation is crucial and has a positive application value and high necessity.

The difference between the shaded region and non-shaded regions in an image depends on how much solar irradiance these regions have received (i.e.

supposing that the shaded region receives a solar diffuse radiation whereas the bright region receives total solar radiation

including direct and diffuse ones). The total solar irradiance (

)

solar direct irradiance (

)

and solar diffuse irradiance (

) are simulated by using MODTRAN 4.0

the typical vegetation reflectance spectra (

) are selected from the spectra library in ENVI 5.3

and the radiances of vegetations (

′) in the shaded and non-shaded regions are calculated (using

and

. The mechanism behind the shadow effects on the NDVI of vegetation is analyzed by using the aforementioned simulated data. A normalized dark pixel index (NDPI) that shows high sensitivity in shadow detection and low relativity to NDVI is introduced. By analyzing the relationships between two sets of simulated NDVI (under solar diffuse radiation and under total solar radiation) of the same vegetation spectrum (to simulate shaded and non-shaded situations in remote sensing image)

the NSEE model of NDVI Shadow-Effect-Eliminating(NSEE) is constructed to correct the NDVI in shaded regions based on the NDVI in the bright regions of an image.

The NSEE model is applied on two Landsat 8 OLI images. The results show that

the NDVI values in the shaded regions are basically corrected to be normal

whereas the NDVIs in the bright regions remain stable in both the experimental and verification images. The NSEE model can also normalize the skewness of the NDVI statistical histogram caused by the shadow effect. The NDVI values of the experimental and verification images are compared pixel by pixel along the two transect lines

and the result shows that the reduction in NDVI due to shadow is eliminated and that the NDVI in the bright region belonging to either vegetation or non-vegetation pixels remains stable. The total RMSE is 0.067

thereby validating the effectiveness of the model is effective.

The NSEE model effectively eliminates the shadow effects of shadow on the NDVI of vegetations. This model can also distinguish the NDVI-decreasing pixels of NDVI-decreasing (due to shadow effects) from those pixels with relatively low original NDVI values

thereby suggesting that the model fits well with land type. This model is entirely based on the image information itself

it can effectively maintain the relative spatial relations of NDVI

and effectively eliminate the influence of shadow. The proposed NESS model is based on a physical mechanism

it is concise and can be easily applied. This model only depends on the information of the multispectral image

does not require different data sources

and shows a convenient and efficient calculation.

关键词

遥感植被阴影冠层NDVINDPI阴影Landsat 8 OLI多光谱遥感

Keywords

remote sensingshaded vegetation canopyNDVINDPIshadow effectsLandsat 8 OLImultispectral remote sensing

references

Bian J H, Li A N, Wang S N, Zhao W and Lei G B. 2016. Restoration of information obscured by mountain shadows for landsat TM images based on MODIS NDVI. Remote Sensing Technology and Application, 31(1): 12-22

边金虎, 李爱农, 王少楠, 赵伟, 雷光斌. 2016. 基于MODIS NDVI的Landsat TM影像地形阴影区光谱信息恢复方法研究. 遥感技术与应用, 31(1): 12-22）[DOI: 10.11873/j.issn.1004-0323.2016.1.0012http://dx.doi.org/10.11873/j.issn.1004-0323.2016.1.0012]

Chen Y, Fan J R, Wen X H, Cao W C and Wang L. 2015. Research on cloud removal from landsat TM image based on spatial and temporal data fusion model. Remote Sensing Technology and Application, 30(2): 312-320

陈阳, 范建容, 文学虎, 曹伟超, 王蕾. 2015. 基于时空数据融合模型的TM影像云去除方法研究. 遥感技术与应用, 30(2): 312-320）[DOI: 10.11873/j.issn.1004-0323.2015.2.0312http://dx.doi.org/10.11873/j.issn.1004-0323.2015.2.0312]

Chu Q W, Zhang H Q, Wu Y W, Feng Z K and Chen B. 2013. Application research of Landsat-8. Remote Sensing Information, 28(4): 110-114

初庆伟, 张洪群, 吴业炜, 冯钟葵, 陈勃. 2013. Landsat-8卫星数据应用探讨. 遥感信息, 28(4): 110-114）[DOI: 10.3969/j.issn.1000-3177.2013.04.019http://dx.doi.org/10.3969/j.issn.1000-3177.2013.04.019]

Gao Y N and Zhang W C. 2008. Comparison test and research progress of topographic correction on remotely sensed data. Geographical Research, 27(2): 467-477

高永年, 张万昌. 2008. 遥感影像地形校正研究进展及其比较实验. 地理研究, 27(2): 467-477）[DOI: 10.3321/j.issn:1000-0585.2008.02.024http://dx.doi.org/10.3321/j.issn:1000-0585.2008.02.024]

Guo J H, Tian Q J and Wu J Z. 2006. Study on multispectral detecting shadow areas and a theoretical model of removing shadows from remote sensing images. Journal of Remote Sensing, 10(2): 151-159

虢建宏, 田庆久, 吴昀昭. 2006. 遥感影像阴影多波段检测与去除理论模型研究. 遥感学报, 10(2): 151-159）[DOI: 10.11834/jrs.20060224http://dx.doi.org/10.11834/jrs.20060224]

Guo N. 2003. Vegetation index and its advances. Arid Meteorology, 21(4): 71-75

郭铌. 2003. 植被指数及其研究进展. 干旱气象, 21(4): 71-75

Huemmrich K F. 1996. Effects of shadows on vegetation indices//Proceedings of 1996 International Geoscience and Remote Sensing Symposium. Lincoln: IEEE [DOI: 10.1109/IGARSS.1996.516990http://dx.doi.org/10.1109/IGARSS.1996.516990]

Jiang Z Y, Huete A R, Chen J, Chen Y H, Li J, Yan G J and Zhang X Y. 2006. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of Environment, 101(3): 366-378 [DOI: 10.1016/j.rse.2006.01.003http://dx.doi.org/10.1016/j.rse.2006.01.003]

Liu H Q and Huete A. 1995. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33(2): 457-465 [DOI: 10.1109/36.377946http://dx.doi.org/10.1109/36.377946]

Liu W and Yamazaki F. 2012. Object-based shadow extraction and correction of high-resolution optical satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(4): 1296-1302 [DOI: 10.1109/JSTARS.2012.2189558http://dx.doi.org/10.1109/JSTARS.2012.2189558]

Luan H J, Tian Q J, Yu T, Hu X L, Huang Y, Du L T, Zhao L M, Wei X, Han J, Zhang Z W and Li S P. 2013. Modeling continuous scaling of NDVI based on fractal theory. Spectroscopy and Spectral Analysis, 33(7): 1857-1862

栾海军, 田庆久, 余涛, 胡新礼, 黄彦, 杜灵通, 赵利民, 魏曦, 韩杰, 张周威, 李少鹏. 2013. 基于分形理论的NDVI连续空间尺度转换模型研究. 光谱学与光谱分析, 33(7): 1857-1862） [DOI: 10.3964/j.issn.1000-0593(2013)07-1857-06http://dx.doi.org/10.3964/j.issn.1000-0593(2013)07-1857-06]

Tian Q J and Min X J. 1998. Advances in study on vegetation indices. Advance in Earth Sciences, 13(4): 327-333

田庆久, 闵祥军. 1998. 植被指数研究进展. 地球科学进展, 13(4): 327-333） [DOI: 10.3321/j.issn:1001-8166.1998.04.002http://dx.doi.org/10.3321/j.issn:1001-8166.1998.04.002]

Wang L, Zhao G X, Jiang Y M, Zhu X C, Chang C Y and Wang M M. 2016. Detection of cloud shadow in Landsat 8 OLI image by shadow index and azimuth search method. Journal of Remote Sensing, 20(6): 1461-1469

王凌, 赵庚星, 姜远茂, 朱西存, 常春艳, 王明媚. 2016. 利用阴影指数和方位搜索法检测Landsat 8 OLI影像中云影. 遥感学报, 20(6): 1461-1469）[DOI: 10.11834/jrs.20165248http://dx.doi.org/10.11834/jrs.20165248]

Wang Z X, Liu C and Huete A. 2003. From AVHRR-NDVI to MODIS-EVI: advances in vegetation index research. Acta Ecologica Sinica, 23(5): 979-987

王正兴, 刘闯, Huete A. 2003. 植被指数研究进展: 从AVHRR-NDVI到MODIS-EVI. 生态学报, 23(5): 979-987）[DOI: 10.3321/j.issn:1000-0933.2003.05.020http://dx.doi.org/10.3321/j.issn:1000-0933.2003.05.020]

Xu Z H, Liu J, Yu K Y, Liu T, Gong C H, Tang M Y, Xie W J and Li Z L. 2013. Construction of vegetation shadow index (SVI) and application effects in four remote sensing images. Spectroscopy and Spectral Analysis, 33(12): 3359-3365

许章华, 刘健, 余坤勇, 刘涛, 龚从宏, 唐梦雅, 谢婉君, 李增禄. 2013. 阴影植被指数SVI的构建及其在四种遥感影像中的应用效果. 光谱学与光谱分析, 33(12): 3359-3365）[DOI: 10.3964/j.issn.1000-0593(2013)12-3359-07http://dx.doi.org/10.3964/j.issn.1000-0593(2013)12-3359-07]

Yang G J, Pu R L, Zhang J X, Zhao C J, Feng H K and Wang J H. 2013. Remote sensing of seasonal variability of fractional vegetation cover and its object-based spatial pattern analysis over mountain areas. ISPRS Journal of Photogrammetry and Remote Sensing, 77: 79-93 [DOI: 10.1016/j.isprsjprs.2012.11.008http://dx.doi.org/10.1016/j.isprsjprs.2012.11.008]

Yang Q Y, Jiang Z C, Luo W Q, Ma Z L, Cao J H and Shen L N. 2013. Sequential simulation of normal different vegetation index of mountain shadow in Karst Peak cluster area. Transactions of The Chinese Society for Agricultural Machinery, 44(5): 232-236, 225

杨奇勇, 蒋忠诚, 罗为群, 马祖陆, 曹建华, 沈利娜. 2013. 岩溶区峰丛洼地山体阴影区域植被指数随机模拟. 农业机械学报, 44(5): 232-236, 225） [DOI: 10.6041/j.issn.1000-1298.2013.05.040http://dx.doi.org/10.6041/j.issn.1000-1298.2013.05.040]

Zhang L F, Sun X J, Wu T X and Zhang H M. 2015. An analysis of shadow effects on spectral vegetation indexes using a ground-based imaging spectrometer. IEEE Geoscience and Remote Sensing Letters, 12(11): 2188-2192 [DOI: 10.1109/LGRS.2015.2450218http://dx.doi.org/10.1109/LGRS.2015.2450218]

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