Remote Sensing of Ecological Hotspots: Producing Value-added Information from Multiple Data SourcesChandi Witharana1*, Uchitha S. Nishshanka2 and Jagath Gunatilaka2
- *Corresponding Author:
- Chandi Witharana
Center for Integrative Geosciences
University of Connecticut, Storrs
CT 06269-4087, USA
Fax: +1 860-486-1383
E-mail: [email protected]
Received Date: April 10, 2013; Accepted Date: May 27, 2013; Published Date: June 05, 2013
Citation: Witharana C, Nishshanka US, Gunatilaka J (2013) Remote Sensing of Ecological Hotspots: Producing Value-added Information from Multiple Data Sources. J Geogr Nat Disast 3: 108 doi:10.4172/2167-0587.1000108
Copyright: © 2013 Witharana C, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Fusing high-spatial resolution panchromatic and high-spectral resolution multispectral images with complementary characteristics provides basis for complex land-use and land-cover type classifications. In this research, we investigated how well different pan sharpening algorithms perform when applied to single-sensor single-date and multi-senor multi–date images that encompass the Horton Plains national park (HPNP), a highly fragile eco-region that has been experiencing severe canopy depletion since 1970s, in Sri Lanka. Our aim was to deliver resolution-enhanced multitemporal images from multiple earth observation (EO) data sources in support of long-term dieback monitoring in the HPNP. We selected six candidate fusion algorithms: Brovey transform, Ehlers fusion algorithm, high-pass filter (HPF) fusion algorithm, modified intensity-hue-saturation (MIHS) fusion algorithm, principal component analysis (PCA) fusion algorithm, and the wavelet-PCA fusion algorithm. These algorithms were applied to eight different aerial and satellite images taken over the HPNP during last five decades. Fused images were assessed for spectral and spatial fidelity using fifteen quantitative quality indicators and visual inspection methods. Spectral quality metrics include correlation coefficient, root-mean-square-error (RMSE), relative difference to mean, relative difference to standard deviation, spectral discrepancy, deviation index, peak signal-to-noise ratio index, entropy, mean structural similarity index, spectral angle mapper, and relative dimensionless global error in synthesis. The spatial integrity of fused images was assessed using Canny edge correspondence, high-pass correlation coefficient, RMSE of Sobel-filtered edge images, and Fast Fourier Transform correlation. The Wavelet-PCA algorithm exhibited the worst spatial improvement while the Ehlers.MIHS and PCA fusion algorithms showed mediocre results. With respect to our multidimensional quality assessment,the HPF emerged as the best performing algorithm for single-sensor single-date and multi-sensor multi-date data fusion.We further examined the effect of fusion in the object-based image analysis framework. Our subjective analysis showed the improvement of image object candidates when panchromatic images’ high-frequency information is injected to low resolution multispectral images.