Advanced Processing of Multiplatform Remote Sensing Imagery for the Monitoring of Coastal and Mountain Ecosystems
| العنوان: | Advanced Processing of Multiplatform Remote Sensing Imagery for the Monitoring of Coastal and Mountain Ecosystems |
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| المؤلفون: | Consuelo Gonzalo-Martin, Dionisio Rodríguez-Esparragón, Ferran Marques, Francisco Eugenio, Javier Marcello |
| المساهمون: | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo |
| المصدر: | IEEE Access, Vol 9, Pp 6536-6549 (2021) UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| بيانات النشر: | IEEE, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Imaging systems in geophysics, Teledetecció, 010504 meteorology & atmospheric sciences, General Computer Science, vegetation mapping, Multispectral image, 0211 other engineering and technologies, Context (language use), 02 engineering and technology, 01 natural sciences, remote sensing, General Materials Science, Electrical and Electronic Engineering, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Vegetation mapping, Benthic mapping, Multispectral and hyperspectral imagery, General Engineering, Hyperspectral imaging, Vegetation, multispectral and hyperspectral imagery, Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo [Àrees temàtiques de la UPC], Natural resource, Remote sensing (archaeology), Sustainable management, Cartografia de la vegetació, Environmental science, lcsh:Electrical engineering. Electronics. Nuclear engineering, lcsh:TK1-9971, Global biodiversity |
| الوصف: | Coastal areas are key to sustaining biodiversity, but their complexity and variability makes their analysis challenging. On the other hand, mountain ecosystems include a large percentage of the global biodiversity and their monitoring is essential, as they are especially vulnerable to climate change. In this context, remote sensing offers a cost-effective technology for the conservation of both kinds of natural areas. In this work, multispectral and hyperspectral data recorded by sensors, onboard satellites, aircrafts and remotely piloted aircraft systems (RPAS), have been used for the sustainable management of natural resources. Specifically, a multiplatform methodology has been developed to process multisensor high spatial resolution imagery and the main benefits and drawbacks of each technology have been identified. Advanced processing techniques in each stage of the methodology have been selected to provide accurate and validated benthic and vegetation maps. Two challenging ecosystems, located in Cabrera and Teide National Parks, have been selected for this study. They correspond with a coastal and a mountain island ecosystem, respectively. To address the associated challenges, the use of imagery with the maximum spatial and spectral resolution, provided by Sentinel-2, WorldView-2, CASI and Pika-L, has been considered. Results have been validated with in-situ data and by the National Parks' managers and they have shown the ability of remote sensing to accurately map both Parks when the appropriate imagery and techniques are selected. The best performance was achieved with the Support Vector Machine classifier and, in general, WorldView can be considered the most appropriate platform when factoring in cost, coverage and accuracy. This work was supported in part by the Spanish Agencia Estatal de Investigación (AEI) through the ARTeMISat-1 Project under Grant CGL2013-46674-R and ARTeMISAT-2 Project under Grant CTM2016-77733-R, and partially by the European Regional Development Funds (ERDF). |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2169-3536 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a41489f1a3f29a8b423ee6b3bf0cf84c https://ieeexplore.ieee.org/document/9303372/ |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....a41489f1a3f29a8b423ee6b3bf0cf84c |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21693536 |
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