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Image dataset acquired from an unmanned aerial vehicle over an experimental site within El Soldado estuary in Guaymas, Sonora, México.

Authors :
Encinas-Lara MS
Méndez-Barroso LA
Yépez EA
Source :
Data in brief [Data Brief] 2020 Mar 14; Vol. 30, pp. 105425. Date of Electronic Publication: 2020 Mar 14 (Print Publication: 2020).
Publication Year :
2020

Abstract

It is well known that remote sensing is a series of procedures which detects physical characteristics of the earth surface by remotely-measuring its reflected and emitted radiation using cameras or sensors. Lately, the increasing use of unmanned aerial vehicles (UAVs) as remote sensing platforms and the development of small-size sensors have resulted in the expansion of continuous monitoring of earth surface at smaller spatial scales. For this reason, the integration of UAV- and consumer-grade cameras can be useful to acquire surface characteristics at plot or footprint scale. This dataset contains 314 aerial images covering an area of aproximately 18,800 m <superscript>2</superscript> within the footprint of an Eddy covariance and meterorological station. The monitoring site was deployed at "El Soldado" estuary (27°57'14.4″ N and 110°58'19.2″ W) located in the southern coast of the Mexican State of Sonora. UAV flight path was programmed to flight in autonomous mode with an altitude of 30 m, a velocity of 5 m/s and a frontal and side overlap of 85 and 75% respectively. This dataset was created to support mapping surveys for surface classification and site description. This dataset is aimed to support researchers, stakeholders and general public interested in coastal areas, natural resources management and ecosystem conservation. Finally, this dataset could be also used for those interested in digital photogrammetry and 3D reconstruction as benchmark example to develop high resolution orthomosaics.<br /> (© 2020 The Authors.)

Details

Language :
English
ISSN :
2352-3409
Volume :
30
Database :
MEDLINE
Journal :
Data in brief
Publication Type :
Academic Journal
Accession number :
32280736
Full Text :
https://doi.org/10.1016/j.dib.2020.105425