Next Article in Journal
Geospatial Analysis of Earthquake Damage Probability of Water Pipelines Due to Multi-Hazard Failure
Next Article in Special Issue
Trajectory Data Mining via Cluster Analyses for Tropical Cyclones That Affect the South China Sea
Previous Article in Journal
A High Performance, Spatiotemporal Statistical Analysis System Based on a Spatiotemporal Cloud Platform
Previous Article in Special Issue
Management System for Dam-Break Hazard Mapping in a Complex Basin Environment
Open AccessArticle

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

Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Jason K. Levy and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(6), 168; https://doi.org/10.3390/ijgi6060168
Received: 4 April 2017 / Revised: 18 May 2017 / Accepted: 5 June 2017 / Published: 7 June 2017
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). View Full-Text
Keywords: data fusion; panchromatic; multispectral; imageries; spatial resolution data fusion; panchromatic; multispectral; imageries; spatial resolution
Show Figures

Figure 1

MDPI and ACS Style

Rahaman, K.R.; Hassan, Q.K.; Ahmed, M.R. Pan-Sharpening of Landsat-8 Images and Its Application in Calculating Vegetation Greenness and Canopy Water Contents. ISPRS Int. J. Geo-Inf. 2017, 6, 168.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop