• Journal of Korean Society of Forest Science
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• v.108 no.2
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• pp.177-188
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• 2019

The aim of our study was to identify the changing trends in the composition, structure, and spatial distribution of forest vegetation in Apsan Park, a representative urban forest in Daegu, South Korea. A vegetation survey was conducted in 1997 and 2016 using phytosociological methods, and a detailed vegetation map was created using the physiognomic dominant species. There were 22 vegetation types in both 1997 and 2016, but two of those types increased and two decreased. The total coverage per unit area ($100m^2$) of the component vegetation species increased from 163% in 1997 to 182% in 2016, and natural vegetation tended to be more than twice that of artificial vegetation. The average number of species decreased by seven from 25 in 1997 to 18 in 2016. Species diversity (H') increased only slightly from 1,654 in 1997 to 1,680 in 2016, while species dominance (D) decreased by 9% from 0.304 in 1997 to 0.276 in 2016. The similarity in the composition of the forest vegetation was about 78%, which was nearly the same. The life form spectrums of vascular plants changed from '$G-R_5-D_4-e$' in 1997 to '$MM-R_5-D_4-e$' in 2016 and the central dormancy type changed from geophytes (G) to megaphanerophytes (MM). The spatial distribution of the forest vegetation was reduced by approximately four times that of artificial vegetation. The number of forest landscape elements (patches) increased from 269 in 1997 to 294 in 2016, while the average area decreased by 12% from 5.8 ha in 1997 to 5.1 ha in 2016.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1107-1115
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• 2019

Satellite data play a major role in supporting knowledge about forest fire by delivering rapid information to map areas damaged. This study, we used 7 Sentinel-2A images to detect change area in forests of Sokcho on April 4, 2019. The process of classify forest fire severity used 7 levels from Sentinel-2A dNBR(differenced Normalized Burn Ratio). In the process of classifying forest fire damage areas, the study selected three areas with high regrowth of vegetation level and conducted a detailed spatial analysis of the areas concerned. The results of dNBR analysis, regrowth of coniferous forest was greater than broad-leaf forest, but NDVI showed the lowest level of vegetation. This is the error of dNBR classification of dNBR. The results of dNBR time series, an area of forest fire damage decreased to a large extent between April 20th and May 3rd. This is an example of the regrowth by developing rare-plants and recovering broad-leaf plants vegetation. The results showed that change area was detected through the change detection of danage area by forest category and the classification errors of the coniferous forest were reached through the comparison of NDVI and dNBR. Therefore, the need to improve the precision Korean forest fire damage rating table accompanied by field investigations was suggested during the image classification process through dNBR.

• Korean Journal of Remote Sensing
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• v.25 no.5
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• pp.445-454
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• 2009

Localized unprecedented torrential rain and heavy rainfall cause repeated damages and make it difficult to detect and predict the landslide caused by heavy rainfall. To analyze the landslide characteristics of Inje area this study used satellite images photographed after the occurrence of landslide caused by the typhoon Ewiniar occurred in July, 2006, and for GIS analysis purpose, interpreted the satellite images (SPOT5) visually to digitize into developing parts, water traveling parts and sediment parts. For analysis of spatial characteristics, landslide areas obtained from visual interpretation of digital map, 3rd & 4th forest vegetation maps and detailed soil map and grids were overlaid and analyzed. As a result, in regard to topographic features, landslide occurred at places, of which average slope is $26.34^{\circ}$, had south, south-east, south-west aspects and average altitude of 627m. From hydrological analysis, it was found out that water traveling area rapidly spread approaching water traveling area and sediment area. From forest type analysis, it was found out that landslide occurrence was high in pine woods, and in terms of girth class attribute, landslide occurred in small-sized woods, in which the crown occupancy of trees that have the diameter at breast height, 6~16cm, was greater than 50%. From the analysis of soil series, landslide areas constitute 37.85% of OdF and 37.35% of SmF, which had sandy loam soil and excellent drainage capacity. Through this study, landslides in Inje area were characterized and SPOT5 images of 2.5m resolution could be used. But there was a difficulty in determining water traveling parts adjacent to urban area.

• Journal of Conservation Science
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• v.31 no.4
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• pp.395-402
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• 2015

This study was considered utilization of hyperspectral image analysis for monitoring. Accordingly we applied to stone cultural properties to data correction methods, image classification techniques, NDVI computation techniques using hyperspectral image. As the results, hyperspectral image analysis was possible making detailed deterioration map, accurate calculation of deterioration rate, mapping of normalized difference vegetation index on the basis of reflectance of each materials. Therefore, hyperspectral image analysis will be used for effective monitoring techniques of stone cultural heritages.

• Atmosphere
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• v.33 no.1
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• pp.61-72
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• 2023

Accurate estimation of forest carbon stocks is important in establishing greenhouse gas reduction plans. In this study, we estimate the spatial distribution of forest carbon stocks using machine learning techniques based on high-resolution remote sensing data and detailed field survey data. The high-resolution remote sensing data used in this study are Landsat indices (EVI, NDVI, NDII) for monitoring vegetation vitality and Shuttle Radar Topography Mission (SRTM) data for describing topography. We also used the forest growing stock data from the National Forest Inventory (NFI) for estimating forest biomass. Based on these data, we built a model based on machine learning methods and optimized for Korean forest types to calculate the forest carbon stocks per grid unit. With the newly developed estimation model, we created forest carbon stocks maps and estimated the forest carbon stocks in South Korea. As a result, forest carbon stock in South Korea was estimated to be 432,214,520 tC in 2020. Furthermore, we estimated the loss of forest carbon stocks due to the Donghae-Uljin forest fire in 2022 using the forest carbon stock map in this study. The surrounding forest destroyed around the fire area was estimated to be about 24,835 ha and the loss of forest carbon stocks was estimated to be 1,396,457 tC. Our model serves as a tool to estimate spatially distributed local forest carbon stocks and facilitates accounting of real-time changes in the carbon balance as well as managing the LULUCF part of greenhouse gas inventories.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1389-1403
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• 2021

Disaster management using VHR (very high resolution) satellite images supports rapid damage assessment and also offers detailed information of the damages. However, the acquisition of pre-event VHR satellite images is usually limited due to the long revisit time of VHR satellites. The absence of the pre-event data can reduce the accuracy of damage assessment since it is difficult to distinguish the changed region from the unchanged region with only post-event data. To address this limitation, in this study, we conducted the wildfire-induced change detection on national wildfire cases using post-fire VHR satellite images and GIS (Geographic Information System) data. For GIS data, a national land cover map was selected to simulate the pre-fire NIR (near-infrared) images using the spatial information of the pre-fire land cover. Then, the simulated pre-fire NIR images were used to analyze bi-temporal NDVI (Normalized Difference Vegetation Index) correlation for unsupervised change detection. The whole process of change detection was performed on a superpixel basis considering the advantages of superpixels being able to reduce the complexity of the image processing while preserving the details of the VHR images. The proposed method was validated on the 2019 Gangwon wildfire cases and showed a high overall accuracy over 98% and a high F1-score over 0.97 for both study sites.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.4
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• pp.469-476
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• 2009

The new laser scanning technology allows to attain 3D information faster with higher accuracy on surface ground, vegetation and buildings of the earth surface. This acquired information can be used in many areas after modifying them appropriately by users. The contour production for accurate landform is an advanced technology that can reveal the mountain area landscapes hidden by the trees in detail. However, if extremely precise LiDAR data is used in constructing the contour, massive-sized data intricates the contour diagram and could amplify the data size inefficiently. This study illustrates the algorithm producing contour that is filtered in stages for more efficient utilization using the LiDAR contour produced by the detailed landscape data. This filtering stages allow to preserve the original landscape shape and to keep the data size small. Point Filtering determines the produced contour diagram shape and could minimize data size. Thus, in this study we compared experimentally filtered contour with the current digital map(1:5,000).

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.136-142
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• 2021

Open-pit mine development requires continuous management because of topographical changes and there is a risk of accidents if the current status survey is performed directly in the process of calculating the earthwork. In this study, the application of UAV photogrammetry, which can acquire spatial information without direct human access, was applied to open-pit mines development area and analyzed the accuracy, earthwork, and mountain restoration plan to determine its applicability. As a result of accuracy analysis at checkpoint using ortho image and Digital Surface Model(DSM) by UAV photogrammetry, Root Mean Square Error(RMSE) is 0.120 m in horizontal and 0.150 m in vertical coordinates. This satisfied the tolerance range of 1:1,000 digital map. As a result of the comparison of the earthwork, UAV photogrammetry yielded 11.7% more earthwork than the conventional survey method. It is because UAV photogrammetry shows more detailed topography. And result of monitoring mountain restoration showed possible to determine existence of rockfall prevention nets and vegetation. If the terrain changes are monitored by acquiring images periodically, the utility of UAV photogrammetry will be further useful to open-pit mine development.