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Compare NDVI Extracted from Landsat 8 Imagery with that from Landsat 7 Imagery
American Journal of Remote Sensing
Volume 2, Issue 2, April 2014, Pages: 10-14
Received: Aug. 28, 2014; Accepted: Sep. 10, 2014; Published: Sep. 20, 2014
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Dandan Xu, Department of Geography and Planning, University of Saskatchewan, Saskatoon, Canada
Xulin Guo, Department of Geography and Planning, University of Saskatchewan, Saskatoon, Canada
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Landsat 8, the ongoing mission of the Landsat satellites that have provided over 40 years of images, continues to benefit long-term research. However, it is important to know if the spectral features of Landsat 8 are of the same standard as previous Landsat imagery because Landsat 8 images have narrower bands, especially because of the normalized difference vegetation index (NDVI) calculation which is the most popular vegetation index. In this study NDVI values derived from Landsat 8 images were compared with those calculated from Landsat 7 and ground measured hyperspectral data. The result shows that Landsat 8 NDVI is larger than Landsat 7 NDVI in lower vegetation covered areas and the difference becomes smaller as the value of NDVI increases. This indicates that NDVI of Landsat 7 and Landsat 8 is consistent when dealing with high vegetation covered areas (e.g. forest area and tall grass prairie) because the difference between Landsat 7 and 8 NDVI is close to zero when the value of NDVI is high, but this needs to be further investigated. There is also further need for calibration of NDVI in low vegetation covered areas in order to achieve consistency between Landsat 7 and Landsat 8 images.
Remote Sensing, Landsat 8, OLI, LDCM, Landsat 7, ETM+, NDVI, ATCOR
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Dandan Xu, Xulin Guo, Compare NDVI Extracted from Landsat 8 Imagery with that from Landsat 7 Imagery, American Journal of Remote Sensing. Vol. 2, No. 2, 2014, pp. 10-14. doi: 10.11648/j.ajrs.20140202.11
Frank, A.B., et al., Vegetation indices, CO2 flux, and biomass for Northern Plains Grasslands. Journal of Range Management, 2003. 56(4): p. 382-387.
Chen, J., et al., Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data. International Journal of Remote Sensing, 2009. 30(24): p. 6497-6517.
Jiang, Z., et al., Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote sensing of Environment, 2006. 101(3): p. 366-378.
Sims, D.A., et al., A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS. Remote sensing of Environment, 2008. 112(4): p. 1633-1646.
Ma, X., et al., Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect. Remote sensing of Environment, 2013. 139: p. 97-115.
Cord, A.F., et al., Modelling species distributions with remote sensing data: bridging disciplinary perspectives. Journal of Biogeography, 2013. 40(12): p. 2226-2227.
Jordan, Y.C., et al., Traits of surface water pollution under climate and land use changes: A remote sensing and hydrological modeling approach. Earth-Science Reviews, 2014. 128: p. 181-195.
Barichivich, J., et al., Large‐scale variations in the vegetation growing season and annual cycle of atmospheric CO2 at high northern latitudes from 1950 to 2011. Global change biology, 2013.
Silva, F.B., et al., Large-scale heterogeneity of Amazonian phenology revealed from 26-year long AVHRR/NDVI time-series. Environmental Research Letters, 2013. 8(2): p. 024011.
USGS. NDVI from AVHRR. 2013 2013/08/30; Available from:
Chander, G., et al., A Procedure for Radiometric Recalibration of Landsat 5 TM Reflective-Band Data. Ieee Transactions on Geoscience and Remote Sensing, 2010. 48(1): p. 556-574.
Kong, X., et al. Cloud and shadow detection and removal for Landsat-8 data. in Eighth International Symposium on Multispectral Image Processing and Pattern Recognition. 2013. International Society for Optics and Photonics.
Lulla, K., et al., The Landsat 8 is ready for geospatial science and technology researchers and practitioners. Geocarto International, 2013. 28(3): p. 191-191.
Markham, B.L., et al. Landsat Data Continuity Mission, now Landsat-8: six months on-orbit. in SPIE Optical Engineering+ Applications. 2013. International Society for Optics and Photonics.
USGS. Frequently Asked Questions about the Landsat Missions. 2013 11/27/13; Available from:
Guo, X.L., et al., Comparison of Laboratory and Field Remote Sensing Methods to Measure Forage Quality. International Journal of Environmental Research and Public Health, 2010. 7(9): p. 3513-3530.
Black, S.C., et al., Estimation of grassland CO(2) exchange rates using hyperspectral remote sensing techniques. International Journal of Remote Sensing, 2008. 29(1): p. 145-155.
Xu, D., et al., Measuring the dead component of mixed grassland with Landsat imagery. Remote Sensing of Environment, 2014. 142: p. 33-43.
Richter, R., et al. Atmospheric/Topographic Correction for Satellite Imagery. ATCOR-2/3 User Guide, Version 8.3. 2013; October 2013:[Available from:
NASA. Preliminary Spectral Response of the Operational Land Imager In-Band, Band-Average Relative Spectral Response. 2014; Available from:
Bogrekci, I., et al. The effects of soil moisture content on reflectance spectra of soils using UV-VIS-NIR spectroscopy. in Proc. 7th Int. Conf. Precision Agric. 2004.
Guo, X., et al., Monitoring grassland health with remote sensing approaches. Prairie Perspectives, 2005. 8: p. 11-22.
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