How to utilize vegetation survey using drone image and image analysis software
© The Author(s) 2016
Received: 11 October 2016
Accepted: 31 October 2016
Published: 17 April 2017
This study tried to analyze error range and resolution of drone images using a rotary wing by comparing them with field measurement results and to analyze stands patterns in actual vegetation map preparation by comparing drone images with aerial images provided by National Geographic Information Institute of Korea. A total of 11 ground control points (GCPs) were selected in the area, and coordinates of the points were identified. In the analysis of aerial images taken by a drone, error per pixel was analyzed to be 0.284 cm. Also, digital elevation model (DEM), digital surface model (DSM), and orthomosaic image were abstracted. When drone images were comparatively analyzed with coordinates of ground control points (GCPs), root mean square error (RMSE) was analyzed as 2.36, 1.37, and 5.15 m in the direction of X, Y, and Z. Because of this error, there were some differences in locations between images edited after field measurement and images edited without field measurement. Also, drone images taken in the stream and the forest and 51 and 25 cm resolution aerial images provided by the National Geographic Information Institute of Korea were compared to identify stands patterns. To have a standard to classify polygons according to each aerial image, image analysis software (eCognition) was used. As a result, it was analyzed that drone images made more precise polygons than 51 and 25 cm resolution images provided by the National Geographic Information Institute of Korea. Therefore, if we utilize drones appropriately according to characteristics of subject, we can have advantages in vegetation change survey and general monitoring survey as it can acquire detailed information and can take images continuously.
KeywordsVegetation survey Actual vegetation map Drone image Aerial image Image analysis software
A drone is an air plane or a helicopter-shaped flying object which flies by radio waves as unmanned aerial vehicle (UAV) or remotely piloted aircraft (RPA) (Lee 2015). Drones were initially developed for the military purpose such as combat or reconnaissance but now they are used in diverse parts including surveillance, transportation, observation, agriculture, and leisure. It is expected that they will be used in more diversified areas in the future. Drones can be classified as fixed wing drones and rotary wing drones according to the method of operation. The most popular fixed wing drone in Korea is eBee developed by Sensefly, a Swiss company. On the other hand, popular rotary wing drones are Phantom series and Inspire series by DJI, a Chinese company. Fixed wing drones and rotary wing drones have some strengths and weaknesses. Fixed wing drones can fly longer than rotary wing drones because of energy efficiency as they fly using air lift force but they need a certain space for taking-off and landing. Although rotary wing drones can take off and land vertically, they can fly for shorter time than fixed wing drones because of low energy efficiency.
Recently, many universities and laboratories have performed various researches on drones. There are studies on topographic survey of open cast mines using relatively cheap drones (Phantom 2 vision+, DJI, China) (Lee and Choi 2015), on comparison of topographic survey results by a fixed wing drone (eBee, SenseFly, Switzerland) and a rotary wing drone (Phantom 2 vision+, DJI, China) (Lee and Choi 2016), and on sight analysis and utilization method (Kim 2014). In particular, many studies have been made in ecology field relating with preparation of actual vegetation map and classification of stands patterns such as monitoring invasive alien species (The National Institute of Ecology 2015), precise vegetation survey in forest swamps (Korea National Arboretum 2015), and a study on forest swamp biotope (Korea National Arboretum 2016).
It is said that images taken by drones have some exterior orientation distortion because of tilting caused by wind or movement and some interior orientation distortion according to camera and lens mounted on the drone. Therefore, the images taken by drones should be corrected (Kim et al. 2015). Additionally, as it is reported that drone images have multicentimeter level higher space resolution compared with satellite images (Tahar et al. 2011, Kim et al. 2015), drones can provide higher resolution images than portal site maps provided by Daum, Naver, or Google or aerial images provided by National Geographic Information Institute of Korea. Especially, the aforementioned aerial images are not current images updated real time so there is a tendency to show different pictures from current status in highly disturbed areas by invasive alien species such as stream or lowland forests. In case of streams, it is difficult to analyze geomorphological landscapes or fine landscapes in the field of mathematical ecology with 1/25,000 scaled and 1/5000 scaled digital maps distributed by National Geographic Information Institute because the minimal contour line unit is 10 cm in those maps (Kim 2014). It is supposed that we can save time and efforts while obtaining highly precise survey results if we perform studies based on drone images taken in advance. However, the use of drones in ecological field has been used without any understanding the error range and resolution for each model.
Accordingly, this study tried to analyze error range and resolution of drone images using a most popular rotary wing drone (Phantom 3 Professional, DJI, China) by comparing them with field measurement results and to analyze stands patterns in actual vegetation map preparation by comparing drone images with aerial images provided by National Geographic Information Institute of Korea.
Materials and methods
Research equipment and subject area
Specifications of Phantom 3 Professional aircraft used in this study
16 m/s (ATTI mode, no wind)
Max. flight altitude
Sony EXMOR 1/2.3
4000 × 3000
Coordinates of the ground control points (GCPs) set in the study area
ID of GCP
For vegetation pattern analysis in actual vegetation map, aerial images by National Geographic Information Institute of Korea (51 and 25 cm grade) and drone images were used. The analysis was made using image analysis software (eCognition, Trimble, USA). Image editing of aerial images taken in the field was performed using photogrammetry software such as Pix4Dmapper Pro (Pix4D, Switzerland) and PhotoScan Professional (Agisoft, Russia). In case of program editing mechanism, a single image can be obtained as each drone image-taking point has a GPS coordinate and a drone takes images overlapped. Additionally, an orthomosaic image can be made as the aforementioned program allows geometric correction by automatic aerial triangulation (AAT) (Siebert and Teizer 2014, Lee and Choi 2015).
Results and discussion
Analysis of error range and resolution of drone images through field measurement
Accuracy of orthomosaic images made from ground control points (GCPs)
Comparison of community classification according to aerial images in preparation of actual vegetation map
Automatic aerial triangulation
Digital elevation model
Digital surface model
Ground control points
Root mean square error
Remotely piloted aircraft
Unmanned aerial vehicle
Unified control points
This subject is supported by Korea Ministry of Environment (MOE) as “Public Technology Program based on Environmental Policy (2014000210003)”.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This subject is supported by the Korea Ministry of Environment (MOE) as “Public Technology Program based on Environmental Policy (2014000210003)”.
Availability of data and materials
Y-GH mainly analyzed and interpreted the image data using diverse software and was a major contributor in writing the manuscript. S-HJ mainly collected the image data with the drone and arranged the data using some software to analyze. OK mainly designed this work and revised the paper totally. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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