Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems

Abstract

Ecosystems provide a wide variety of useful resources that enhance human welfare, but these resources are declining due to climate change and anthropogenic pressure. In this work, three vulnerable ecosystems, including shrublands, coastal areas with dunes systems and areas of shallow water, are studied. As far as these resources' reduction is concerned, remote sensing and image processing techniques could contribute to the management of these natural resources in a practical and cost-effective way, although some improvements are needed for obtaining a higher quality of the information available. An important quality improvement is the fusion at the pixel level. Hence, the objective of this work is to assess which pansharpening technique provides the best fused image for the different types of ecosystems. After a preliminary evaluation of twelve classic and novel fusion algorithms, a total of four pansharpening algorithms was analyzed using six quality indices. The quality assessment was implemented not only for the whole set of multispectral bands, but also for the subset of spectral bands covered by the wavelength range of the panchromatic image and outside of it. A better quality result is observed in the fused image using only the bands covered by the panchromatic band range. It is important to highlight the use of these techniques not only in land and urban areas, but a novel analysis in areas of shallow water ecosystems. Although the algorithms do not show a high difference in land and coastal areas, coastal ecosystems require simpler algorithms, such as fast intensity hue saturation, whereas more heterogeneous ecosystems need advanced algorithms, as weighted wavelet 'à trous' through fractal dimension maps for shrublands and mixed ecosystems. Moreover, quality map analysis was carried out in order to study the fusion result in each band at the local level. Finally, to demonstrate the performance of these pansharpening techniques, advanced Object-Based (OBIA) support vector machine classification was applied, and a thematic map for the shrubland ecosystem was obtained, which corroborates wavelet 'à trous' through fractal dimension maps as the best fusion algorithm for this ecosystem.

Temporal dynamic analysis of a mountain ecosystem based on multi‐source and multi‐scale remote sensing data

“…After a detailed review of the state‐of‐art in pansharpening techniques, pansharpening algorithms achieving optimal performance were assessed and selected in previous studies performed for the Teide National Park (Ibarrola‐Ulzurrun et al. , ). Thus, it was decided to use the Wavelet “à trous” algorithm to perform the pansharpening process in QB and WV‐2 imagery to increase the spatial resolution by a factor of four with the minimum degradation of the spectral information.…”

Spatiotemporal event detection: a review

“…Very-high-resolution (VHR) optical images (such as Geoeye, Quickbird, and Worldview) and very-high-resolution SAR images (such as Cosmo-Skymed and TerraSAR-X) offer meter or submeter resolution (Tapete and Cigna 2018) for monitoring detailed surface features that could not be captured before, such as inner water areas, complex land ecosystems, and maritime zones (Nguyen, Kopec, and Netzband 2016;Ibarrola-Ulzurrun et al 2017).…”

“…Remote sensing Rule-based (Giglio, Schroeder, and Justice 2016;Soulard et al 2016;Qiu et al 2019) Image processing (Nguyen, Kopec, and Netzband 2016;Ibarrola-Ulzurrun et al 2017;Yu and Yang 2017;Yu, Yang, and Jin 2018) Machine learning (Hill, Minsker, and Amir 2007;Solberg 2012;Garello and Kerbaol 2017;Chen et al 2018;Cong et al 2019;Tian et al 2019) Simulation (Younis, Anquetin, and Thielen 2008;Daniel et al 2009;Alfieri, Thielen, and Pappenberger 2012;Al Amin et al 2015;Basart et al 2012;Semmens et al 2013;Rivera, Dominguez, and Castro 2014;Wang et al 2015…”

Aplicación de sensores remotos para el análisis de cobertura vegetal y cuerpos de agua

“…Permiten detectar características y diferencias muy sutiles entre los rasgos de la superficie especialmente en lo que se refiere a vegetación, suelo y rocas. La diferencia entre una imagen multiespectral y una imagen hiperespectral radica fundamentalmente en el número y ancho de las bandas (Ibarrola-Ulzurrun et al, 2017).…”