• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1095-1106
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• 2019

Damage of forest fire should be investigated quickly and accurately for recovery, compensation and prevention of secondary disaster. Using remotely sensed data, burn severity is investigated based on the difference of reflectance or spectral indices before and after forest fire. Recently, the use of high resolution satellite and UAV imagery is increasing, but it is not easy to obtain an image before forest fire that cannot be predicted where and when. This study tried to analyze availability of high-resolution images and supervised classifiers on the burn severity classification. Two supervised classifiers were applied to the KOMPSAT-3A image and the UAV multispectral image acquired after the forest fire. The maximum likelihood (MLH) classifier use absolute value of spectral reflectance and the spectral angle mapper (SAM) classifier use pattern of spectra. As a result, in terms of spatial resolution, the classification accuracy of the UAV image was higher than that of the satellite image. However, both images shown very high classification accuracy, which means that they can be used for classification of burn severity. In terms of the classifier, the maximum likelihood method showed higher classification accuracy than the spectral angle mapper because some classes have similar spectral pattern although they have different absolute reflectance. Therefore, burn severity can be classified using the high resolution multispectral images after the fire, but an appropriate classifier should be selected to get high accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.474-480
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• 2019

We propose a method to evaluate a Modulation Transfer Function (MTF) using a circular target. In addition, a MATLAB GUI-based resolution analysis tool was developed to enhance the reliability of UAV image quality and the efficiency of the work. For this purpose, images were taken with an FC-6310 during flights at altitudes of 80 m, 120 m, and 150 m and by an iXM-100 at altitudes of 150 m, 200 m, and 400 m. The MTFs of UAV images were compared with traditional photogrammetry by measuring and analyzing MTFs on images taken by the UltraCAM Eagle Mark-2 sensor at a flight altitude of 1000 m. The results show that ${\sigma}MTF$ of the FC-6310 were 0.431(80 m), 0.524(120 m), and 0.699(150 m), and those of the iXM-100 were 0.332(150 m), 0.393(200 m), and 0.631(400 m), respectively. At the altitude of 150 m, the image quality of the iXM-100, which has a high-performance camera, was very high, and the effect of the camera performance on the image quality was confirmed. In addition, the ${\sigma}MTF$ of the UltraCAM Eagle Mark-2 was 0.711 due to the high flight altitude. This was the worst value among all UAV images. However, the ${\sigma}MTF$ of the FC-6310 at 150-m altitude was 0.699, which is almost the same as that of a manned aerial image.

• Journal of Environmental Impact Assessment
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• v.20 no.3
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• pp.349-356
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• 2011

This research is aim to analyze of changing landscape and according to phenological cycle from image information of coastal environment obtained by multi-media were analyzed by camera and satellite image. The digital camera and satellite image were used for tidal flat vegetation monitoring during the construction of Sihwa lake. The vegetation type and phenological cycle of Sihwa tidal flat have been changed with the Sihwa lake ecosystem. The environment changes of Sihwa tidal flat area and ecological change were analyzed by field work digital camera images and satellite images. The airborne, UAV and satellite images were classified with the changed elements of coastal ecological environment and tidal flat vegetation monitoring carried out the changed area and shape of vegetation distribution with time series images.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.1-14
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• 2017

A land cover map provides basic information to help understand the current state of a region, but its utilization in the ecological research field has deteriorated due to limited temporal and spatial resolutions. The purpose of this study was to investigate the possibility of using a land cover map with data based on high resolution images acquired by UAV. Using the UAV, 10.5 cm orthoimages were obtained from the $2.5km^2$ study area, and land cover maps were obtained from object-based and pixel-based classification for comparison and analysis. From accuracy verification, classification accuracy was shown to be high, with a Kappa of 0.77 for the pixel-based classification and a Kappa of 0.82 for the object-based classification. The overall area ratios were similar, and good classification results were found in grasslands and wetlands. The optimal image segmentation weights for object-based classification were Scale=150, Shape=0.5, Compactness=0.5, and Color=1. Scale was the most influential factor in the weight selection process. Compared with the pixel-based classification, the object-based classification provides results that are easy to read because there is a clear boundary between objects. Compared with the land cover map from the Ministry of Environment (subdivision), it was effective for natural areas (forests, grasslands, wetlands, etc.) but not developed areas (roads, buildings, etc.). The application of an object-based classification method for land cover using UAV images can contribute to the field of ecological research with its advantages of rapidly updated data, good accuracy, and economical efficiency.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.1
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• pp.53-62
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• 2016

In recent years, UAV Photogrammetry based on an ultra-light UAS(Unmanned Aerial System) installed with a low-cost compact navigation device and a camera has attracted great attention through fast and accurate acquirement of geo-spatial data. In particular, UAV Photogrammetry do gradually replace the traditional aerial photogrammetry because it is able to produce DEMs(Digital Elevation Models) and Orthophotos rapidly owing to large amounts of high resolution image collection by a low-cost camera and image processing software combined with computer vision technique. With these advantages, UAV-Photogrammetry has therefore been applying to a large scale mapping and cadastral surveying that require accurate position information. This paper presents experimental results of an accuracy performance test with images of 4cm GSD from a fixed wing UAS to demarcate parcel boundaries in agricultural area. Consequently, the accuracy of boundary point extracted from UAS orthoimage has shown less than 8cm compared with that of terrestrial cadastral surveying. This means that UAV images satisfy the tolerance limit of distance error in cadastral surveying for the scale of 1: 500. And also, the area deviation is negligible small, about 0.2%(3.3m²), against true area of 1,969m² by cadastral surveying. UAV-Photogrammetry is therefore as a promising technology to demarcate parcel boundaries.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.249-250
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• 2010

The study aims to propose a method that shall rapidly acquire 3D spatial information of the frequently changing city areas by using the low altitude aerial images taken by the UAV. The artificial 3D model of the artificial structures was constructed using the aerial image data photographed at the test area, calibration data of the non-metric camera and the results of the ground control point survey. Also, the digital surface model was created for areas that were changed due to a number of civil works. Through the above studies, the possibilities of constructing a 3D virtual city model, renewal of 3D GIS database, abstraction of changed information in geographic features and on-demand updating of the digital map were suggested.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.1
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• pp.29-38
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• 2014

UAV(Unmanned Aerial vehicle) could be effectively applied in a field of land monitoring for analyzing disaster area and mapping, because it can quickly acquire image data at low costs. For this reason, we reviewed the legal system related to mapping, and proposed suggestions for improving in legal system, due to introducing the UAV to Korean land-monitoring through this paper. Also, we evaluated spatial and time accuracy of the digital map, which are generated from UAV images that were taken for occasional map updates and disaster detections. As a result, the mean error is about 10m if only GPS/INS data used, while using GCP(Ground Control Points) it is about 10cm. Therefore, we conclude that the UAV could be effective method in korea land-monitoring field.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.5
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• pp.422-433
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• 2017

Unmanned Aerial Vehicle (UAV) has several advantages over conventional remote sensing techniques. They can acquire high-resolution images quickly and repeatedly. And with a comparatively lower flight altitude, they can obtain good quality images even in cloudy weather. In this paper, we developed for estimating garlic growth at field scale model in major cultivation regions. We used the $NDVI_{UAV}$ that reflects the crop conditions, and seven meteorological elements for 3 major cultivation regions from 2015 to 2017. For this study, UAV imagery was taken at Taean, Changnyeong, and Hapcheon regions nine times from early February to late June during the garlic growing season. Four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.), and fresh weight (F.W.) were measured for twenty plants per plot for each field campaign. The multiple linear regression models were suggested by using backward elimination and stepwise selection in the extraction of independent variables. As a result, model of cold type explain 82.1%, 65.9%, 64.5%, and 61.7% of the P.H., F.W., L.N., P.D. with a root mean square error (RMSE) of 7.98 cm, 5.91 g, 1.05, and 3.43 cm. Especially, model of warm type explain 92.9%, 88.6%, 62.8%, 54.6% of the P.H., P.D., L.N., F.W. with a root mean square error (RMSE) of 16.41 cm, 9.08 cm, 1.12, 19.51 g. The spatial distribution map of garlic growth was in strong agreement with the field measurements in terms of field variation and relative numerical values when $NDVI_{UAV}$ was applied to multiple linear regression models. These results will also be useful for determining the UAV multi-spectral imagery necessary to estimate growth parameters of garlic.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.503-514
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• 2021

Rice production with adequate level of area is important for decision making of rice supply and demand policy. It is essential to grasp rice cultivation areas in advance for estimating rice production of the year. This study was carried out to classify paddy rice cultivation in Gimje-si using sentinel-1 SAR (synthetic aperture radar) and UAV imagery in early July. Time-series Sentinel-1A and 1B images acquired from early May to early July were processed to convert into sigma naught (dB) images using SNAP (SeNtinel application platform, Version 8.0) toolbox provided by European Space Agency. Farm map and parcel map, which are spatial data of vector polygon, were used to stratify paddy field population for classifying rice paddy cultivation. To distinguish paddy rice from other crops grown in the paddy fields, we used the decision tree method using threshold levels and random forest model. Random forest model, trained by mainly rice cultivation area and rice and soybean cultivation area in UAV image area, showed the best performance as overall accuracy 89.9%, Kappa coefficient 0.774. Through this, we were able to confirm the possibility of early estimation of rice cultivation area in Gimje-si using UAV image.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.103-113
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• 2019

Spatial resolution is still one of the most important parameters for evaluating image quality. In this study, we propose an approach to evaluate spatial resolution and MTF(Modulation Transfer Function) using bar target and Siemens star chart as a part of quality evaluation for UAV images. To this end, images were taken with a fixed-wing eBee(Canon IXUS) at the flight height of 130m and 260m, and with a rotary-wing GD-800(SONY NEX-5N) at flight height of 130m, with a Phantom 4 pro(FC 6310) at flight height of 90m, respectively. Spatial resolution was measured on orthoimages produced from this data. Results show that the resolution measured on the Siemens star and bar target was accurately degraded in proportion to the flight height regardless of the cameras. In the words, the spatial resolution of images taken at the same altitude of 130m with the eBee(Canon IXUS) and the GD-800(SONY NEX-5N) equipped with different cameras was the same as 4.1cm, and that of the eBee(Canon IXUS) at 260m was 8.0cm. In addition, the resolution measured on the Siemens star was about 1~2cm lower than that of the bar target at every flight height. The general tendency was also found to be proportional to the flight height in the measurement of the ${\sigma}_{MTF}$ from MTF, which simultaneously represents the resolution and contrast information of the image. However, at the same altitude of 130m, the ${\sigma}_{MTF}$ of the GD-800(SONY NEX-5N) is 0.36 and the eBee(Canon IXUS) is 0.59, which shows that the GD-800(SONY NEX-5N) has better camera performance. It is expected that study results will contribute to the analysis of spatial resolution of UAV images and to improve the reliability of quality.