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


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


Pan-Sharpening of Landsat-8 Images and Its Application in Calculating Vegetation Greenness and Canopy Water Contents - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

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

Abstract 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





Autor: Khan Rubayet Rahaman * , Quazi K. Hassan and M. Razu Ahmed

Fuente: http://mdpi.com/



DESCARGAR PDF




Documentos relacionados