Pan-Sharpening of Landsat-8 Images and Its Application in Calculating Vegetation Greenness and Canopy Water Contents

Pan-sharpening is the process of fusing higher spatial resolution panchromatic (PAN) with lower spatial resolution multispectral (MS) imagery to create higher spatial resolution MS images. Here, our overall objective was to pan-sharpen Landsat-8 images and calculate vegetation greenness (i.e., normalized difference vegetation index (NDVI)), canopy structure (i.e., enhanced vegetation index (EVI)), and canopy water content (i.e., normalized difference water index (NDWI))-related variables. Our proposed methods consisted of: (i) evaluating the relationships between PAN band (0.503–0.676 µm) with a spatial resolution of 15 m and individual MS bands of Landsat-8 from blue (i.e., acquiring in the range 0.452–0.512 µm), green (i.e., 0.533–0.590 µm), red (i.e., 0.636–0.673 µm), near infrared (NIR: 0.851–0.879 µm), shortwave infrared-I (SWIR-I: 1.566–1.651 µm), and SWIR-II (2.107–2.294 µm) bands with a spatial resolution of 30 m; (ii) determining the suitable individual MS bands to be enhanced into the spatial resolution of the PAN band; and (iii) calculating several vegetation greenness and canopy moisture indices (i.e., NDVI, EVI, NDWI-I, and NDWI-II) at 15 m spatial resolution and subsequent validation using their equivalent-values at a spatial resolution of 30 m. Our analysis revealed that strong linear relationships existed between the PAN and most of the MS individual bands of interest except NIR. For example, r2 values were 0.86–0.89 for blue band; 0.89–0.95 for green band; 0.84–0.96 for red band; 0.71–0.79 for SWIR-I band; and 0.71–0.83 for SWIR-II band. As a result, we performed smoothing filter-based intensity modulation method of pan-sharpening to enhance the spatial resolution of 30 m to 15 m. In calculating the vegetation indices, we used the enhanced MS images and resampled the NIR to 15 m. Finally, we evaluated these indices with their equivalents at 30 m spatial resolution and observed strong relationships (i.e., r2 values in the range 0.98–0.99 for NDVI, 0.95–0.98 for EVI, 0.98–1.00 for NDWI).

[1]  N. Tripathi,et al.  Built-up area extraction using Landsat 8 OLI imagery , 2014 .

[2]  Ronald G. Prinn,et al.  Analysis of Visible/SWIR surface reflectance ratios for aerosol retrievals from satellite in Mexico City urban area , 2007 .

[3]  Jocelyn Chanussot,et al.  A Critical Comparison Among Pansharpening Algorithms , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Quazi K. Hassan,et al.  Spatial mapping of growing degree days: an application of MODIS-based surface temperatures and enhanced vegetation index , 2007 .

[5]  Yun Zhang.  PROBLEMS IN THE FUSION OF COMMERCIAL HIGH-RESOLUTION SATELLITE AS WELL AS LANDSAT 7 IMAGES AND INITIAL SOLUTIONS , 2002 .

[6]  Jaco Kemp,et al.  Effect of pan-sharpening multi-temporal Landsat 8 imagery for crop type differentiation using different classification techniques , 2017, Comput. Electron. Agric..

[7]  Qian Du,et al.  Performance evaluation for pan-sharpening techniques , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[8]  Filip Sabo,et al.  Impact analysis of pansharpening Landsat ETM+, Landsat OLI, WorldView-2, and Ikonos images on vegetation indices , 2016, International Conference on Remote Sensing and Geoinformation of Environment.

[9]  Richard Bamler,et al.  A Sparse Image Fusion Algorithm With Application to Pan-Sharpening , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Massimiliano Pittore,et al.  Large-area settlement pattern recognition from Landsat-8 data , 2016 .

[11]  Yun Zhang,et al.  An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images , 2005, Inf. Fusion.

[12]  Intae Yang,et al.  Exploring Landsat 8 , 2015 .

[13]  Hiroaki Kuze,et al.  Spectral information analysis of image fusion data for remote sensing applications , 2013 .

[14]  Josaphat Tetuko Sri Sumantyo,et al.  Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[15]  Jagalingam Pushparaj,et al.  Comparison of various pan-sharpening methods using Quickbird-2 and Landsat-8 imagery , 2017, Arabian Journal of Geosciences.

[16]  Jixian Zhang.  Multi-source remote sensing data fusion: status and trends , 2010 .

[17]  Quazi K. Hassan,et al.  Spatial Enhancement of MODIS-based Images of Leaf Area Index: Application to the Boreal Forest Region of Northern Alberta, Canada , 2010, Remote. Sens..

[18]  Quazi K. Hassan,et al.  Quantification of Local Warming Trend: A Remote Sensing-Based Approach , 2017, PloS one.

[19]  Rakesh Kumar Mishra,et al.  From UNB PanSharp to Fuze Go – the success behind the pan-sharpening algorithm , 2014 .

[20]  Yun Zhang,et al.  A review and comparison of commercially available pan-sharpening techniques for high resolution satellite image fusion , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[21]  Victor F. Rodriguez-Galiano,et al.  Downscaling Landsat 7 ETM+ thermal imagery using land surface temperature and NDVI images , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Yuji Murayama,et al.  Evaluation of land cover classification based on multispectral versus pansharpened landsat ETM+ imagery , 2013 .

[23]  E. Vermote,et al.  Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer , 1997 .

[24]  Alberta..  Natural regions and subregions of Alberta , 2006 .

[25]  Hankui K. Zhang,et al.  Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. , 2016, Remote sensing of environment.

[26]  Henry Scheyvens,et al.  An ensemble pansharpening approach for finer-scale mapping of sugarcane with Landsat 8 imagery , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[27]  Quazi K. Hassan,et al.  Application of Landsat-7 ETM+ and MODIS products in mapping seasonal accumulation of growing degree days at an enhanced resolution , 2007 .

[28]  Brian Johnson,et al.  Effects of Pansharpening on Vegetation Indices , 2014, ISPRS Int. J. Geo Inf..

[29]  B. Smith,et al.  The Greenland Ice Mapping Project (GIMP) land classification and surface elevation data sets , 2014 .

[30]  Bruno Aiazzi,et al.  A Comparison Between Global and Context-Adaptive Pansharpening of Multispectral Images , 2009, IEEE Geoscience and Remote Sensing Letters.

[31]  Claudio Parente,et al.  Pan-sharpening WorldView-2: IHS, Brovey and Zhang methods in comparison , 2016 .

[32]  David P. Roy,et al.  Computationally Inexpensive Landsat 8 Operational Land Imager (OLI) Pansharpening , 2016, Remote. Sens..

[33]  Yun Zhang,et al.  Understanding image fusion , 2004 .

[34]  Qiuming Cheng,et al.  Two improvement schemes of PAN modulation fusion methods for spectral distortion minimization , 2009 .

[35]  Conghe Song,et al.  Downscaling real-time vegetation dynamics by fusing multi-temporal MODIS and Landsat NDVI in topographically complex terrain , 2011 .

[36]  V. Karathanassi,et al.  A comparison study on fusion methods using evaluation indicators , 2007 .

[37]  Quazi K. Hassan,et al.  Fusion of MODIS and Landsat-8 Surface Temperature Images: A New Approach , 2015, PloS one.

[38]  Jocelyn Chanussot,et al.  A critical comparison of pansharpening algorithms , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[39]  Runhe Shi,et al.  Integrating pan-sharpening and classifier ensemble techniques to map an invasive plant (Spartina alterniflora) in an estuarine wetland using Landsat 8 imagery , 2016 .

[40]  J. G. Liu,et al.  Smoothing Filter-based Intensity Modulation : a spectral preserve image fusion technique for improving spatial details , 2001 .

[41]  H. Dridi,et al.  Contribution of Landsat 8 data for the estimation of land surface temperature in Batna city, Eastern Algeria , 2017 .