The advantages of using drones over space-borne imagery in the mapping of mangrove forests
| Autor: | Dries Raymaekers, Behara Satyanarayana, Aidy M. Muslim, Sulong Ibrahim, A Muhammad Syafiq, Monika Ruwaimana, Nico Koedam, Farid Dahdouh-Guebas, Viviana Otero |
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| Přispěvatelé: | Faculty of Sciences and Bioengineering Sciences, Biology, General Botany and Nature Management |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Topography
010504 meteorology & atmospheric sciences Aircraft Image Processing Psychologie appliquée 0211 other engineering and technologies lcsh:Medicine Marine and Aquatic Sciences Invasive Species Geographic Mapping 02 engineering and technology 01 natural sciences Remote Sensing Satellite imagery lcsh:Science Image resolution Multidisciplinary Data Processing Agricultural and Biological Sciences(all) Vegetation Sciences bio-médicales et agricoles Cameras Data Acquisition Optical Equipment Engineering and Technology Information Technology Biologie Research Article Freshwater Environments Computer and Information Sciences Mangrove Swamps Equipment Image processing satellite imagery Aerial photography Species Colonization Ecosystem 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Landforms Pixel Biochemistry Genetics and Molecular Biology(all) lcsh:R Ecology and Environmental Sciences Malaysia Aquatic Environments Geomorphology Marine Environments Drone Coasts Wetlands Signal Processing Earth Sciences Environmental science Satellite lcsh:Q reproducibility of results |
| Zdroj: | PLoS ONE PLoS ONE, Vol 13, Iss 7, p e0200288 (2018) PloS one, 13 (7 |
| DOI: | 10.1371/journal.pone.0200288 |
| Popis: | Satellite data and aerial photos have proved to be useful in efficient conservation and management of mangrove ecosystems. However, there have been only very few attempts to demonstrate the ability of drone images, and none so far to observe vegetation (species-level) mapping. The present study compares the utility of drone images (DJI-Phantom-2 with SJ4000 RGB and IR cameras, spatial resolution: 5cm) and satellite images (Pleiades-1B, spatial resolution: 50cm) for mangrove mapping—specifically in terms of image quality, efficiency and classification accuracy, at the Setiu Wetland in Malaysia. Both object- and pixel-based classification approaches were tested (QGIS v.2.12.3 with Orfeo Toolbox). The object-based classification (using a manual rule-set algorithm) of drone imagery with dominant land-cover features (i.e. water, land, Avicennia alba, Nypa fruticans, Rhizophora apiculata and Casuarina equisetifolia) provided the highest accuracy (overall accuracy (OA): 94.0 ±0.5% and specific producer accuracy (SPA): 97.0±9.3%) as compared to the Pleiades imagery (OA: 72.2±2.7% and SPA: 51.9±22.7%). In addition, the pixel-based classification (using a maximum likelihood algorithm) of drone imagery provided better accuracy (OA: 90.0±1.9% and SPA: 87.2±5.1%) compared to the Pleiades (OA: 82.8±3.5% and SPA: 80.4 ±14.3%). Nevertheless, the drone provided higher temporal resolution images, even on cloudy days, an exceptional benefit when working in a humid tropical climate. In terms of the user-costs, drone costs are much higher, but this becomes advantageous over satellite data for long-term monitoring of a small area. Due to the large data size of the drone imagery, its processing time was about ten times greater than that of the satellite image, and varied according to the various image processing techniques employed (in pixel-based classification, drone >50 hours, Pleiades SCOPUS: ar.j info:eu-repo/semantics/published |
| Databáze: | OpenAIRE |
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