• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.129-136
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• 2014

This study deals with red tide detection by using the remote sensing imagery from the Geostationary Ocean Color Imager (GOCI), the world's first geostationary orbit satellite, around the southern coast of Korea where the most severe red tide occurred recently. The red tide zone was determined by the available data selection from the GOCI imagery during the period of red tide occurrence and also the severe red tide zone was detected through the spatial analysis by temporal change out of the red tide zone. This study results showed that the coast in the vicinity of the Hansan and Yokji in Tongyeong-si was classified into the severe red tide zone, and that the red tide was likely to spread from the coast of Hansan and Yokji to the one of Sanyang-eub. In addition, the comparative analysis between the area of red tide occurrence, the prevention activities of Gyeongsangnam-do provincial government and the amount of the damage cost over time showed close correlation among them. It is still early to conclude that the study is showing the severe red tide zone and the spread path exactly due to various factors for red tide occurrence and activities. In order to improve the reliability of the results, the more data analysis is required.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.66-80
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• 2012

It is necessary to normalize spectral image values derived from multi-temporal satellite data to a common scale in order to apply remote sensing methods for change detection, disaster mapping, crop monitoring and etc. There are two main approaches: absolute radiometric normalization and relative radiometric normalization. This study focuses on the multi-temporal satellite image processing by the use of relative radiometric normalization. Three scenes of KOMPSAT-2 imagery were processed using the Multivariate Alteration Detection(MAD) method, which has a particular advantage of selecting PIFs(Pseudo Invariant Features) automatically by canonical correlation analysis. The scenes were then applied to detect disaster areas over Sendai, Japan, which was hit by a tsunami on 11 March 2011. The case study showed that the automatic extraction of changed areas after the tsunami using relatively normalized satellite data via the MAD method was done within a high accuracy level. In addition, the relative normalization of multi-temporal satellite imagery produced better results to rapidly map disaster-affected areas with an increased confidence level.

• 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.

• Journal of Korea Water Resources Association
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• v.54 no.8
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• pp.553-566
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• 2021

River confluence is often characterized by shear layer and the associated strong mixing. In natural rivers, the main channel and its tributary can be separated by the shear layer using contrasting colors. The shear layer can be easily observed using aerial images from satellite or unmanned aerial vehicles. This study proposes a low-cost identification method extracting geographic features of the shear layer using RGB aerial image. The method consists of three stages. At first, in order to identify the shear layer, it performs image segmentation using a Gaussian mixture model and extracts the water bodies of the main channel and tributary. Next, the self-organizing map simplifies the flow line of the water bodies into the 1-dimensional curve grid. After that, the curvilinear coordinate transformation is performed using the water body pixels and the curve grid. As a result, the shear layer identification method was successfully applied to the confluence between Nakdong River and Nam River to extract geometric shear layer features (confluence angle, upstream- and downstream- channel widths, shear layer length, maximum shear layer thickness).

• Journal of Korea Water Resources Association
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• v.56 no.spc1
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• pp.1027-1036
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• 2023

Coastal topographic information is crucial in coastal management, but point measurment based approeaches, which are labor intensive, are generally applied to land and underwater, separately. This study introduces an efficient method enabling land and undetwater surveys using an unmanned aerial vehicle (UAV). This method involves applying two different algorithms to measure the topography on land and water depth, respectively, using UAV imagery and merge them to reconstruct whole coastal digital elevation model. Acquisition of the landside terrain is achieved using the Structure-from-Motion Multi-View Stereo technique with spatial scan imagery. Independently, underwater bathymetry is retrieved by employing a depth inversion technique with a drone-acquired wave field video. After merging the two digital elevation models into a local coordinate, interpolation is performed for areas where terrain measurement is not feasible, ultimately obtaining a continuous nearshore terrain. We applied the proposed survey technique to Jangsa Beach, South Korea, and verified that detailed terrain characteristics, such as berm, can be measured. The proposed UAV-based survey method has significant efficiency in terms of time, cost, and safety compared to existing methods.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.5
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• pp.13-25
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• 2024

With the increasing wildfire damage driven by climate change, it is crucial to assess the effectiveness of restoration efforts on a large scale. The majority of forests in Korea are situated in rugged mountainous regions, making it challenging to monitor large-scale wildfires. Consequently, establishing methodologies that use satellite imagery to evaluate restoration effectiveness is essential. This study aims to assess the recovery trends of ecosystems in wildfire-affected areas using NDVI mean-variance plots, which monitor changes in NDVI mean and variance over time through satellite imagery and visually represent the restoration process. The analysis of NDVI mean-variance plots for different restoration methods revealed that landscape restoration had the slowest recovery. This slower recovery is likely due to reduced growth from the complete removal of damaged trees. In contrast to High Severity (HS) areas, Moderate High Severity (MHS) areas showed that commercial afforestation, revegetation, ecological forest treatment led to a more stable recovery state post-disturbance, suggesting that areas with lower wildfire severity may recover more quickly. Furthermore, the recovery trends between artificial and natural restoration showed no significant difference, indicating that natural restoration can have similar restoration effects to artificial restoration in appropriate areas. Therefore, the study emphasizes the need to expand natural restoration areas, considering ecological and economic benefits such as increased biodiversity and genetic resource conservation. This research provides critical baseline data for the formulation and implementation of restoration policies in large-scale wildfire-affected regions and is expected to contribute significantly to the development of effective management strategies and monitoring techniques.

• Korean Journal of Environmental Agriculture
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• v.27 no.2
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• pp.120-132
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• 2008

Remotely sensed images from a satellite can be applied for detecting and quantifying spatial and temporal variations in terms of landuse & landcover, crop growth, and disaster for agricultural applications. The purposes of this study were to analyze topography using DEM(digital elevation model) and classify landuse & landcover into 10 classes-paddy field, dry field, forest, bare land, grass & bush, water body, reclaimed land, salt farm, residence & building, and others-using Landsat TM images in North Korea. Elevation was greater than 1,000 meters in the eastern part of North Korea around Ranggang-do where Kaemagowon was located. Pyeongnam and Hwangnam in the western part of North Korea were low in elevation. Topography of North Korea showed typical 'east-high and west-low' landform characteristics. Landcover classification of North Korea using spectral reflectance of multi-temporal Landsat TM images was performed and the statistics of each landcover by administrative district, slope, and agroclimatic zone were calculated in terms of area. Forest areas accounted for 69.6 percent of the whole area while the areas of dry fields and paddy fields were 15.7 percent and 4.2 percent, respectively. Bare land and water body occupied 6.6 percent and 1.6 percent, respectively. Residence & building reached less than 1 percent of the country. Paddy field areas concentrated in the A slope ranged from 0 to 2 percent(greater than 80 percent). The dry field areas were shown in the A slope the most, followed by D, E, C, B, and F slopes. According to the statistics by agroclimatic zone, paddy and dry fields were mainly distributed in the North plain region(N-6) and North western coastal region(N-7). Forest areas were evenly distributed all over the agroclimatic regions. Periodic landcover analysis of North Korea based on remote sensing technique using satellite imagery can produce spatial and temporal statistics information for future landuse management and planning of North Korea.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.517-525
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• 2013

Time-series data of Normal Difference Vegetation Index (NDVI) obtained by the Moderate-resolution Imaging Spectroradiometer(MODIS) satellite imagery gives a waveform that reveals the characteristics of the phenology. The waveform can be decomposed into harmonics of various periods by the Fourier transformation. The resulting $n^{th}$ harmonics represent the amount of NDVI change in a period of a year divided by n. The values of each harmonics or their relative relation have been used to classify the vegetation species and to build a vegetation map. Here, we propose a method to estimate the annual amount of carbon absorbed on the forest from the $1^{st}$ harmonic NDVI value. The $1^{st}$ harmonic value represents the amount of growth of the leaves. By the allometric equation of trees, the growth of leaves can be considered to be proportional to the total amount of carbon absorption. We compared the $1^{st}$ harmonic NDVI values of the 6220 sample points with the reference data of the carbon absorption obtained by the field survey in the forest of South Korea. The $1^{st}$ harmonic values were roughly proportional to the amount of carbon absorption irrespective of the species and ages of the vegetation. The resulting proportionality constant between the carbon absorption and the $1^{st}$ harmonic value was 236 tCO2/5.29ha/year. The total amount of carbon dioxide absorption in the forest of South Korea over the last ten years has been estimated to be about 56 million ton, and this coincides with the previous reports obtained by other methods. Considering that the amount of the carbon absorption becomes a kind of currency like carbon credit, our method is very useful due to its generality.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.989-1006
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• 2020

Building change monitoring based on building detection is one of the most important fields in terms of monitoring artificial structures using high-resolution multi-temporal images such as CAS500-1 and 2, which are scheduled to be launched. However, not only the various shapes and sizes of buildings located on the surface of the Earth, but also the shadows or trees around them make it difficult to detect the buildings accurately. Also, a large number of misdetection are caused by relief displacement according to the azimuth and elevation angles of the platform. In this study, object-based building detection was performed using the azimuth angle of the Sun and the corresponding main direction of shadows to improve the results of building change detection. After that, the platform's azimuth and elevation angles were used to detect changed buildings. The object-based segmentation was performed on a high-resolution imagery, and then shadow objects were classified through the shadow intensity, and feature information such as rectangular fit, Gray-Level Co-occurrence Matrix (GLCM) homogeneity and area of each object were calculated for building candidate detection. Then, the final buildings were detected using the direction and distance relationship between the center of building candidate object and its shadow according to the azimuth angle of the Sun. A total of three methods were proposed for the building change detection between building objects detected in each image: simple overlay between objects, comparison of the object sizes according to the elevation angle of the platform, and consideration of direction between objects according to the azimuth angle of the platform. In this study, residential area was selected as study area using high-resolution imagery acquired from KOMPSAT-3 and Unmanned Aerial Vehicle (UAV). Experimental results have shown that F1-scores of building detection results detected using feature information were 0.488 and 0.696 respectively in KOMPSAT-3 image and UAV image, whereas F1-scores of building detection results considering shadows were 0.876 and 0.867, respectively, indicating that the accuracy of building detection method considering shadows is higher. Also among the three proposed building change detection methods, the F1-score of the consideration of direction between objects according to the azimuth angles was the highest at 0.891.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.449-459
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• 2015

The conventional National Forest Inventory(NFI)-based forest carbon stock estimation method is suitable for national-scale estimation, but is not for regional-scale estimation due to the lack of NFI plots. In this study, for the purpose of regional-scale carbon stock estimation, we created grid-based forest carbon stock maps using spatial ancillary data and two types of up-scaling methods. Chungnam province was chosen to represent the study area and for which the $5^{th}$ NFI (2006~2009) data was collected. The first method (method 1) selects forest type map as ancillary data and uses regression model for forest carbon stock estimation, whereas the second method (method 2) uses satellite imagery and k-Nearest Neighbor(k-NN) algorithm. Additionally, in order to consider uncertainty effects, the final AGB carbon stock maps were generated by performing 200 iterative processes with Monte Carlo simulation. As a result, compared to the NFI-based estimation(21,136,911 tonC), the total carbon stock was over-estimated by method 1(22,948,151 tonC), but was under-estimated by method 2(19,750,315 tonC). In the paired T-test with 186 independent data, the average carbon stock estimation by the NFI-based method was statistically different from method2(p<0.01), but was not different from method1(p>0.01). In particular, by means of Monte Carlo simulation, it was found that the smoothing effect of k-NN algorithm and mis-registration error between NFI plots and satellite image can lead to large uncertainty in carbon stock estimation. Although method 1 was found suitable for carbon stock estimation of forest stands that feature heterogeneous trees in Korea, satellite-based method is still in demand to provide periodic estimates of un-investigated, large forest area. In these respects, future work will focus on spatial and temporal extent of study area and robust carbon stock estimation with various satellite images and estimation methods.