Detecting geological structures in coastal area of Buan, South Korea using unmanned aerial vehicle images

2020 ◽  
Author(s):  
Hong-Jin Lee ◽  
Kyoungtae Ko
Keyword(s):  
South Korea ◽  
Unmanned Aerial Vehicle ◽  
West Coast ◽  
Tidal Range ◽  
Structure Mapping ◽  
The West ◽  
Traditional Structure ◽  
Aerial Vehicle ◽  
Mapping Techniques ◽  
Uav Images

<p>This study attempted to use unmanned aerial vehicle (UAV) photogrammetry for structural mapping at limited exposure outcrops in the west coast area of Buan, South Korea. The west coast area of Buan has a large tidal range, and there are restrictions for traditional structure mapping. High spatial resolution (about 4.5 cm per pixel) UAV images were obtained at low tide from a selected study site. The UAV survey identified 50 brittle structures (fractures and faults that were divided into three groups) and changes in the bedding trace. The bedding trace demonstrates various directional verging of the fold geometry that indicates slump-fault structures. While more research is still necessary, this study demonstrated that UAV mapping techniques are very useful for geological structural analysis in coastal areas.</p>

scholarly journals Comparison of Outdoor Compost Pile Detection Using Unmanned Aerial Vehicle Images and Various Machine Learning Techniques

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 31
Author(s):  
Bonggeun Song ◽  
Kyunghun Park
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
South Korea ◽  
Unmanned Aerial Vehicle ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Efficient Detection ◽  
Learning Techniques ◽  
Aerial Vehicle ◽  
Uav Images

Since outdoor compost piles (OCPs) contain large amounts of nitrogen and phosphorus, they act as a major pollutant that deteriorates water quality, such as eutrophication and green algae, when the OCPs enter the river during rainfall. In South Korea, OCPs are frequently used, but there is a limitation that a lot of manpower and budget are consumed to investigate the current situation, so it is necessary to efficiently investigate the OCPs. This study compared the accuracy of various machine learning techniques for the efficient detection and management of outdoor compost piles (OCPs), a non-point pollution source in agricultural areas in South Korea, using unmanned aerial vehicle (UAV) images. RGB, multispectral, and thermal infrared UAV images were taken in August and October 2019. Additionally, vegetation indices (NDVI, NDRE, ENDVI, and GNDVI) and surface temperature were also considered. Four machine learning techniques, including support vector machine (SVM), decision tree (DT), random forest (RF), and k-NN, were implemented, and the machine learning technique with the highest accuracy was identified by adjusting several variables. The accuracy of all machine learning techniques was very high, reaching values of up to 0.96. Particularly, the accuracy of the RF method with the number of estimators set to 10 was highest, reaching 0.989 in August and 0.987 in October. The proposed method allows for the prediction of OCP location and area over large regions, thereby foregoing the need for OCP field measurements. Therefore, our findings provide highly useful data for the improvement of OCP management strategies and water quality.

scholarly journals Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4442
Author(s):  
Zijie Niu ◽  
Juntao Deng ◽  
Xu Zhang ◽  
Jun Zhang ◽  
Shijia Pan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Distribution Maps ◽  
Time Operation ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Segmentation Image

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images

2021 ◽  
Vol 173 ◽  
pp. 95-121
Author(s):  
Juepeng Zheng ◽  
Haohuan Fu ◽  
Weijia Li ◽  
Wenzhao Wu ◽  
Le Yu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Oil Palm ◽  
Palm Trees ◽  
Aerial Vehicle ◽  
Uav Images

scholarly journals Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV): Norway Spruce Forest Case Study

2021 ◽  
Author(s):  
Veronika Kopačková-Strnadová ◽  
Lucie Koucká ◽  
Jan Jelenek ◽  
Zuzana Lhotakova ◽  
Filip Oulehle
Keyword(s):  
Norway Spruce ◽  
Unmanned Aerial Vehicle ◽  
Linear Models ◽  
Photosynthetic Pigment ◽  
Ground Truth ◽  
Needle Age ◽  
Vegetation Indexes ◽  
Pigment Contents ◽  
Aerial Vehicle ◽  
Uav Images

Remote sensing is one of the modern methods that have significantly developed over the last two decades and nowadays provides a new means for forest monitoring. High spatial and temporal resolutions are demanded for accurate and timely monitoring of forests. In this study multi-spectral Unmanned Aerial Vehicle (UAV) images were used to estimate canopy parameters (definition of crown extent, top and height as well as photosynthetic pigment contents). The UAV images in Green, Red, Red-Edge and NIR bands were acquired by Parrot Sequoia camera over selected sites in two small catchments (Czech Republic) covered dominantly by Norway spruce monocultures. Individual tree extents, together with tree tops and heights, were derived from the Canopy Height Model (CHM). In addition, the following were tested i) to what extent can the linear relationship be established between selected vegetation indexes (NDVI and NDVIred edge) derived for individual trees and the corresponding ground truth (e.g., biochemically assessed needle photosynthetic pigment contents), and ii) whether needle age selection as a ground truth and crown light conditions affect the validity of linear models. The results of the conducted statistical analysis show that the two vegetation indexes (NDVI and NDVIred edge) tested here have a potential to assess photosynthetic pigments in Norway spruce forests at a semi-quantitative level, however the needle-age selection as a ground truth was revealed to be a very important factor. The only usable results were obtained for linear models when using the 2nd year needle pigment contents as a ground truth. On the other hand, the illumination conditions of the crown proved to have very little effect on the model’s validity. No study was found to directly compare these results conducted on coniferous forest stands. This shows that there is a further need for studies dealing with a quantitative estimation of the biochemical variables of nature coniferous forests when employing spectral data acquired by the UAV platform at a very high spatial resolution.

scholarly journals Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm

2019 ◽  
Vol 11 (10) ◽  
pp. 1226 ◽  
Author(s):  
Jianqing Zhao ◽  
Xiaohu Zhang ◽  
Chenxi Gao ◽  
Xiaolei Qiu ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Near Infrared ◽  
Similarity Index ◽  
Crop Growth ◽  
Contrast Threshold ◽  
Growth Monitoring ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Aerial Vehicle ◽  
Uav Images

To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.

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