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Research Article

A comparative study on the applicability and effectiveness of NSVI and NDVI for estimating fractional vegetation cover based on multi-source remote sensing image

Zhang-hua Xua College of Environment and Safety Engineering, Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou, China;b Fujian Provincial Key Laboratory of Resources and Environment Monitoring & Sustainable Management and Utilization, Sanming, ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-9017-6920View further author information
ORCID Icon,
Yi-fan Lia College of Environment and Safety Engineering, Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou, ChinaView further author information
,
Bin Lia College of Environment and Safety Engineering, Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou, ChinaView further author information
,
Zhen-bang Haoc College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, ChinaView further author information
,
Lili Lind Department of Biological Science and Biotechnology, Minnan Normal University, Zhangzhou, ChinaView further author information
,
Xin-yu Hua College of Environment and Safety Engineering, Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou, ChinaView further author information
,
Xin Zhoua College of Environment and Safety Engineering, Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou, ChinaView further author information
,
Hui Yue Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, The Academy of Digital China, Fuzhou University, Fuzhou, ChinaView further author information
,
Song-yang Xiange Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, The Academy of Digital China, Fuzhou University, Fuzhou, ChinaView further author information
,
Mpomboum -Lingom -Fils Pascalf College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, ChinaView further author information
,
Wan-ling Shena College of Environment and Safety Engineering, Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou, ChinaView further author information
,
An-qi Hea College of Environment and Safety Engineering, Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou, ChinaView further author information
,
Ling-yan Chene Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, The Academy of Digital China, Fuzhou University, Fuzhou, ChinaView further author information
&
Zeng-lu Lib Fujian Provincial Key Laboratory of Resources and Environment Monitoring & Sustainable Management and Utilization, Sanming, ChinaView further author information
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Article: 2184501 | Received 05 Sep 2022, Accepted 17 Feb 2023, Published online: 15 Mar 2023

References

  • Andrew DR, Trevor FK, Mirco M, Youngryel R, Oliver S, Michael T. 2013. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric For Meteorol. 169(3):156–173.
  • Arekhi M, Goksel C, Sanli FB, Senel G. 2019. Comparative evaluation of the spectral and spatial consistency of sentinel-2 and Landsat 8 OLI Data for Igneada Longos Forest. IJGI. 8(2):56.
  • Badgley G, Field CB, Berry JA. 2017. Canopy near-infrared reflectance and terrestrial photosynthesis. Sci Adv. 3(3).
  • Barsi JA, Alhammoud B, Czapla-Myers J, Gascon F, Haque MO, Kaewmanee M, Leigh L, Markham BL. 2018. Sentinel-2A MSI and Landsat 8 OLI radiometric cross comparison over desert sites. Eur J Remote Sens. 51(1):822–837.
  • Bausch WC. 1993. Soil background effects on reflectancebased crop coefficients for corn. Remote Sens Environ. 46(2):213–222.
  • Carlson TN, Gillies RR, Perry EM. 1994. A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sens. Rev. 9(1-2):161–173.
  • Chastain R, Housman I, Goldstein J, Finco M, Tenneson K. 2019. Empirical cross sensor comparison of sentinel-2A and 2B MSI, Landsat 8 OLI, and Landsat 7 ETM + top of atmosphere spectral characteristics over the conterminous United States. Remote Sens Environ. 221:274–285.
  • Chen YX, Qin K, Hu ZW, Zeng C. 2019. A method of representing and merging and extracting building blocks from high-resolution images. Geomatics Inf. Sci. Wuhan Univ. 44(6):908–916.
  • Crowther TW, Glick HB, Covey KR, Bettigole C, Maynard DS, Thomas SM, Smith JR, Hintler G, Duguid MC, Amatulli G, et al. 2015. Mapping tree density at a global scale. Nature. 525(7568):201–205.
  • Cui TX, Gong ZN, Zhao WJ, Zhao YL, Lin C. 2013. Research on estimating wetland vegetation abundance based on spectral mixture analysis with different endmember model: a case study in Wild Duck Lake wetland, Beijing Acta Ecol Sin. 33(4):1160–1171.
  • Dong SG, Qin JX, Guo YK. 2019. Shadow detection method for high-resolution remote sensing images supported by generalized stereoscopic image pair. J El Me In. 33(3):105–111.
  • Gitelson AA, Kaufman YJ, Stark R, Rundquist D. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sens Environ. 80(1):76–87.
  • Guerschman JP, Hill MJ, Leys J, Heidenreich S. 2020. Vegetation cover dependence on accumulated antecedent precipitation in Australia: relationships with photosynthetic and non-photosynthetic vegetation fractions. Remote Sens Environ. 240:111670.
  • Gunasekara NK, Al-Wardy MM, Al-Rawas GA, Charabi Y. 2015. Applicability of VI in arid vegetation delineation using shadow-affected SPOT imagery. Environ Monit Assess. 187(7):4662.
  • Guo ZW, Wu CY, Wang XY. 2019. Forest insect-disease monitoring and estimation based on satellite remote sensing data. Geogr Res. 8(4):831–843.
  • Guzinski R, Nieto H. 2019. Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high-resolution evapotranspiration estimations. Remote Sens Environ. 221:157–172.
  • Hansen M, Potapov P, Margono B, et al. 2014. High-resolution global maps of 21st-century forest cover change. Science. 342(6187):850–853.
  • Hirano Y, Yasuoka Y, Ichinose T. 2004. Urban climate simulation by incorporating satellite-derived vegetation cover distribution into a mesoscale –meteorological model. Theor Appl Climatol. 79(3-4):175–184.
  • Hu XY, Xu ZH, Chen WH, Chen QX, Wang L, Liu H, Liu ZC. 2021. Construction and application effect of normalized shaded vegetation index NSVI based on PROBA/CHRIS image. Remote Sens Land Resour. 33(2):55–65.
  • Huete AR. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens Environ. 25(3):295–309.
  • Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira L. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ. 83(1/2):195–213.
  • Jiang H, Yuan YW, Wang S. 2019. Shadow-eliminated vegetation index (SEVI) for removing terrain shadow effect: evaluation and application. J Geoinf Sci. 21(12):1977–1986.
  • Jiao JN, Shi J, Tian QJ, Gao L, Xu NX. 2020. Research on multispectral-image-based NDVI shadow-effect eliminating model. J Remote Sens. 24(1):53–66.
  • Kim TW, Wie GJ, Suh YC. 2014. An adequate band selection for vegetation index of CASI-1500 airborne hyperspectral imagery using image differencing and spectral derivative. J Korean Assoc Geogr Inf Stud. 6(4):16–28.
  • Leprieur C, Kerr YH, Mastorchio S, Meunier JC. 2000. Monitoring vegetation cover across semi-arid regions: comparison of remote observations from various scales. Int J Remote Sens. 21(2):281–300.
  • Li J, Guo YB, Xu HQ, Li X. 2015. Vegetation information extraction of Pinus Massoniana Forest in soil erosion areas using soil-adjusted vegetation index. J Geoinf Sci. 17(9):1128–1134.
  • Li XS, Li ZY, Gao ZH, Bai LN, Wang BY, Li SM. 2010. Estimation of sparse vegetation cover in arid regions based on vegetation indices derived from Hyperion data. J Beijing For Univ. 32(3):95–100.
  • Li XW, Shi H, Zhang Y, Niu ZC, Wang TT, Ding M, Cai K. 2018. Cyanobacteria blooms monitoring in Taihu Lake based on the Sentinel-2A satellite of European Space Agency. Environ Monit China. 34(4):169–176.
  • Liu F, Liu SH, Xiang Y. 2014. Research on UAV remote sensing monitoring of vegetation cover in gardens. Trans Chin Soc Agric Mach. 45(11):250–257.
  • Liu C, Zhang X, Wang T, Chen G, Zhu K, Wang Q, Wang J. 2022. Detection of vegetation coverage changes in the Yellow River Basin from 2003 to 2020. Ecol Indic. 138:108818.
  • Liu XT, Zhou L, Shi H, Wang SQ, Chi YG. 2018. Phenological characteristics of temperate coniferous and broad-leaved mixed forests based on multiple remote sensing vegetation indices, chlorophyII fluorescence and CO2 flux data. Acta Ecol Sin. 38(10):3482–3494.
  • Li CJ, Zhao CJ, Liu LY, Wang JH, Wang RC. 2004. Infrared spectral index inversion of field winter wheat cover and sensitivity analysis. Trans CSAE. 20(5):159–164.
  • Li MX, Zhu XC, Bai XY, Peng YF, Tian ZY, Jiang YM. 2021. Remote sensing inversion of nitrogen content in apple canopy based on shadow removal in UAV multi-spectral remote sensing images. Sci Agric Sin. 54(10):2084–2094.
  • Mancino G, Ferrara A, Padula A, Nolè A. 2020. Cross-comparison between Landsat 8(OLI)and Landsat 7(ETM+)Derived Vegetation Indices in a Mediterranean Environment. Remote Sens. 12(2):291.
  • Oduncu E, Yuksel SE. 2021. An in-depth analysis of hyperspectral target detection with shadow compensation via LiDAR. Signal Process Image Commun. 99(4):116427.
  • Ono A, Kajiwara K, Honda Y. 2010. Development of new vegetation indexes, shadows index (SI) and water stress trend (WST). N W Remote Sens. 38(1):710–714.
  • Owen TW, Carlson TN, Gillies RR. 1998. An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization. Int J Remote Sens. 19(9):1663–1681.
  • Pudmenzky C, King R, Butler H. 2015. Broad scale mapping of vegetation cover across Australia from rainfall and temperature data. J Arid Environ. 120:55–62.
  • Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S. 1994. A modified soil adjusted vegetation index. Remote Sens Environ. 48(2):119–126.
  • Roper T, Andrews M. 2013. Shadow modelling and correction techniques in hyperspectral imaging. Electron Lett. 49(7):458–459.
  • Rouse JW, Haas RH, Schell JA. 1973. Monitoring vegetation systems in the Great Plains with ERTS. The 3rd ERTS Symposium, NASA SP-351, vol. 1, p. 309–317.
  • Rundquist BC. 2002. The influence of canopy green vegetation fraction on spectral measurements over native tallgrass prairie. Remote Sens Environ. 81(1):129–135.
  • Shen JX, Ji X. 2016. Cloud and cloud shadow multi-feature collaborative detection from remote sensing image. J Geoinf Sci. 18(5):599–605.
  • Silvoster ML, Govindan VK. 2012. Enhanced CNN based electron microscopy image segmentation. Cyb Inf Technol. 12(2):84–97.
  • Song WJ, Mu XH, Ruan GY, Gao Z, Li LY, Yan GJ. 2017. Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method. Int J Appl Earth Obs Geoinf. 58:168–176.
  • Tian QJ, Min XJ. 1998. Advances in study on vegetation indices. Adv Earth Sci Contrib Int Conf. 13(4):10–16.
  • Vupparaboina KK, Dansingani KK, Goud A, Rasheed MA, Jawed F, Jana S, Richhariya A, Freund KB, Chhablani J. 2018. Quantitative shadow compensated optical coherence tomography of choroidal vasculature. Sci Rep. 8(1):6461.
  • Xu ZH, Lin L, Wang QF. 2018. Construction and application effects of normalized shaded vegetation index (NSVI). J Infrared Millim Waves. 37(2):154–162.
  • Xu ZH, Liu J, Yu KY, Liu T, Gong CH, Tang MY, Xie WJ, Li ZL. 2013. Construction of Vegetation Shadow Index (SVI) and application effects in four remote sensing images. Spectrosc Spectral Anal (Beijing, China). 33(12):3359–3365.
  • Yang QY, Jiang ZC, Luo WQ, Ma ZL, Cao JH, Shen LN. 2013. Sequential simulation of normal different vegetation index of mountain shadow in Karst Peak Cluster Area. Trans Chin Soc Agric Mach. 44(05):232–236.
  • Zhang XX. 2016. A comparative study on different vegetation indices in capabilities for eliminating soil background noise and detecting vegetation coverage. Highlights Sciencepaper. 9(2):126–132.
  • Zhang HM, Chen LJ, Liu W, Han WT, Zhang SY, Zhang F. 2021. Estimation of summer corn fractional vegetation coverage based on stacking ensemble learning. Trans Chin Soc Agric Mach. 52(7):195–202.
  • Zhang LF, Sun XJ, Wu TX, Huang CP. 2015. An analysis of shadow effects on spectral vegetation indexes using a ground-based imaging spectrometer. IEEE Geosci Remote Sens Lett. 12(11):1–5.
  • Zhao J, Yang HB, Lan YB, Lu LQ, Jia P, Li ZM. 2019. Extraction method of summer corn vegetation coverage based on visible light image of unmanned aerial vehicle. Trans Chin Soc Agric Mach. 50(5):232–240.
  • Zhao LL, Zhang R, Liu YX, Zhu XC. 2020. The differences between extracting vegetation information from GF1-WFV and Landsat8-OLI. Acta Ecol Sin. 40(10):3495–3506.

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