• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.170-181
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• 2015

A research on extracting croplands located nearby coastal areas using the spatial information data sets is the important task for managing the agricultural products in coastal areas. This research aims to extract the various croplands(croplands on mountains and croplands on plain areas) located nearby coastal areas using the KOMPSAT-2 imagery, the high-resolution satellite imagery, and the airborne topographic LiDAR(Light Detection And Ranging) data acquired in coastal areas of Uljin, Korea. Firstly, the NDVI(Normalized Difference Vegetation Index) imagery is generated from the KOMPSAT-2 imagery, and the vegetation areas are extracted from the NDVI imagery by using the appropriate threshold. Then, the DSM(Digital Surface Model) and DEM(Digital Elevation Model) are generated from the LiDAR data by using interpolation method, and the CHM(Canopy Height Model) is generated using the differences of the pixel values of the DSM and DEM. Then the plain areas are extracted from the CHM by using the appropriate threshold. The low slope areas are also extracted from the slope map generated using the pixel values of the DEM. Finally, the areas of intersection of the vegetation areas, the plain areas and the low slope areas are extracted with the areas higher than the threshold and they are defined as the croplands located nearby coastal areas. The statistical results show that 85% of the croplands on plain areas and 15% of the croplands on mountains located nearby coastal areas are extracted by using the proposed methodology.

• Proceedings of the KSRS Conference
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• v.1
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• pp.98-101
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• 2006

In general, stereo images are widely used to remote sensing or photogrametric applications for the purpose of image understanding and feature extraction or cognition. However, the most cases of these stereo-based application deal with 2-D satellite images or the airborne photos so that its main targets are generation of small-scaled or large-scaled DEM(Digital Elevation Model) or DSM(Digital Surface Model), in the 2.5-D. Contrast to these previous approaches, the scope of this study is to investigate 3-D stereo processing and visualization of true geo-referenced 3-D features based on anaglyph technique, and the aim is at the prototype development for stereo visualization system of complex typed 3-D GIS features. As for complex typed 3-D features, the various kinds of urban landscape components are taken into account with their geometric characteristics and attributes. The main functions in this prototype are composed of 3-D feature authoring and modeling along with database schema, stereo matching, and volumetric visualization. Using these functions, several technical aspects for migration into actual 3-D GIS application are provided with experiment results. It is concluded that this result will contribute to more specialized and realistic applications by linking 3-D graphics with geo-spatial information.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.119-125
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• 2017

The mud flat in Korea is the geographical feature generated from the sediment of rivers of Korea and China and it is the important topography for pollution purification and fishing industry. The mud flat is difficult to access such that it requires the aerial survey for the high-resolution spatial information of the area. In this study we used drones instead of the conventional aerial and remote sensing approaches which have shortcomings of costs and revisit times. We carried out GPS-based control point survey, temporal image acquisition using drones, bundle adjustment, stereo image processing for DSM and ortho photo generation, followed by co-registration between the spatio-temporal information.

• Journal of Environmental Impact Assessment
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• v.24 no.2
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• pp.134-153
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• 2015

When making urban planning, it is important to understand climate effect caused by urban structural changes. Seoul city applies UPIS(Urban Plan Information System) which provides information on urban planning scenario. Technology for analyzing climate effect resulted from urban planning needs to developed by linking urban planning scenario provided by UPIS and climate analysis model, CAS(Climate Analysis Seoul). CAS develops for analyzing urban climate conditions to provide realistic information considering local air temperature and wind flows. Quantitative analyses conducted by CAS for the production, transportation, and stagnation of cold air, wind flow and thermal conditions by incorporating GIS analysis on land cover and elevation and meteorological analysis from MetPhoMod(Meteorology and atmospheric Photochemistry Meso-scale model). In order to reflect land cover and elevation of the latest information, CAS used to highly accurate raster data (1m) sourced from LiDAR survey and KOMPSAT-2(KOrea Multi-Purpose SATellite) satellite image(4m). For more realistic representation of land surface characteristic, DSM(Digital Surface Model) and DTM(Digital Terrain Model) data used as an input data for CFD(Computational Fluid Dynamics) model. Eight inflow directions considered to investigate the change of flow pattern, wind speed according to reconstruction and change of thermal environment by connecting green area formation. Also, MetPhoMod in CAS data used to consider realistic weather condition. The result show that wind corridors change due to reconstruction. As a whole surface temperature around target area decreases due to connecting green area formation. CFD model coupled with CAS is possible to evaluate the wind corridor and heat environment before/after reconstruction and connecting green area formation. In This study, analysis of climate impact before and after created the green area, which is part of 'Connecting green network across the north and south in Seoul' plan, one of the '2020 Seoul master plan'.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.187-196
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• 2020

In this study, collapsed building detection using UAV (Unmanned Aerial Vehicle) and PlanetScope satellite images was carried out, suggesting the possibility of utilization of heterogeneous sensors in object detection located on the surface. To this end, the area where about 20 buildings collapsed due to forest fire damage was selected as study site. First of all, the feature information of objects such as ExG (Excess Green), GLCM (Gray-Level Co-Occurrence Matrix), and DSM (Digital Surface Model) were generated using high-resolution UAV images performed object-based segmentation to detect collapsed buildings. The features were then used to detect candidates for collapsed buildings. In this process, a result of the change detection using PlanetScope were used together to improve detection accuracy. More specifically, the changed pixels acquired by the bitemporal PlanetScope images were used as seed pixels to correct the misdetected and overdetected areas in the candidate group of collapsed buildings. The accuracy of the detection results of collapse buildings using only UAV image and the accuracy of collapse building detection result when UAV and PlanetScope images were used together were analyzed through the manually dizitized reference image. As a result, the results using only UAV image had 0.4867 F1-score, and the results using UAV and PlanetScope images together showed that the value improved to 0.8064 F1-score. Moreover, the Kappa coefficiant value was also dramatically improved from 0.3674 to 0.8225.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.633-636
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• 2004

Airborne laser scanning thechnology has been studied in many applications, DSM(Digital Surface Model) development, building extraction, 3D virtual city modeling. In this paper, we will evaluate the possibility of airborne laser scanning technology for transportation application, especially for recognizing moving vehicles on road. First, we initially segment the region of roads from all LiDAR DATA using the GIS map and intensity image. Secondly, the segmented region is divided into the roads and vehicles using the height threshold value of local based window. Finally, the vehicles will be classified into the several types of vehicles by MDC(Minimum Distance Classification) method using the vehicle's geometry information, height, length, width, etc

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.3-8
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• 2016

Due to the recent technical development and the enhancement of image resolution, Unmanned Airborne Vehicles(UAVs) have been used for various fields. A low altitude UAV system takes advantage of taking riverbed imagery at small rivers as well as land surface imagery on the ground. The bathymetric data are generated from the low altitude UAV system. The accuracy of the data is analyzed along water depths, comparing GPS observations and a DSM generated from UAV images. It is found that the depth accuracy of the geospatial data below 50 cm depth of water satisfies the regulation(${\pm}10cm$ spatial accuracy) of bathymetric surveying. Therefore this research shows that the geospatial data generated from UAV images at shallow regions of rivers can be used for bathymetric surveying.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.123-139
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• 2024

Forest vertical structure is vital for comprehending ecosystems and biodiversity, in addition to fundamental forest information. Currently, the forest vertical structure is predominantly assessed via an in-situ method, which is not only difficult to apply to inaccessible locations or large areas but also costly and requires substantial human resources. Therefore, mapping systems based on remote sensing data have been actively explored. Recently, research on analyzing and classifying images using machine learning techniques has been actively conducted and applied to map the vertical structure of forests accurately. In this study, Sentinel-2 and digital surface model images were obtained on two different dates separated by approximately one month, and the spectral index and tree height maps were generated separately. Furthermore, according to the acquisition time, the input data were separated into cases 1 and 2, which were then combined to generate case 3. Using these data, forest vetical structure mapping models based on random forest, support vector machine, and extreme gradient boost(XGBoost)were generated. Consequently, nine models were generated, with the XGBoost model in Case 3 performing the best, with an average precision of 0.99 and an F1 score of 0.91. We confirmed that generating a forest vertical structure mapping model utilizing bi-seasonal data and an appropriate model can result in an accuracy of 90% or higher.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.181-190
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• 2011

Since the radar satellite missions such as TerraSAR-X and COSMO-SkyMed were launched in 2007, the spatial resolution of spaceborne SAR(Synthetic Aperture Radar) images reaches about 1 meter at spotlight mode. In 2011, the first Korean SAR satellite, KOMPSAT-5, will be launched, operating at X-band with the highest spatial resolution of 1 m as well. The improved spatial resolution of state-of-the-art SAR sensor suggests expanding InSAR(Interferometric SAR) analysis in urban monitoring. By the way, the shadow and layover phenomena are more prominent in urban areas due to building structure because of inherent side-looking geometry of SAR system. Up to date the most conventional algorithms do not consider the return signals at the frontage of building during InSAR phase and SAR intensity simulation. In this study the new algorithm introducing multi-scattering in layover region is proposed for phase and intensity simulation, which is utilized a precise LIDAR DSM(Digital Surface Model) in urban areas. The InSAR phases simulated by the proposed method are compared with TerraSAR-X spotlight data. As a result, both InSAR phases are well matched, even in layover areas. This study will be applied to urban monitoring using high resolution SAR data, in terms of change detection and displacement monitoring at the scale of building unit.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.127-136
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• 2015

This study aims to quantify the stand-level above ground biomass in intact tropical rain forest of Brunei using airborne LiDAR data. Twenty four sub-plots with the size of 0.09ha ($30m{\times}30m$) were located in the 25ha study area along the altitudinal gradients. Field investigated data (Diameter at Breast Height (DBH) and individual tree position data) in sub-plots were used. Digital Surface Model (DSM), Digital Terrain Model (DTM) and Canopy Height Model (CHM) were constructed using airborne LiDAR data. CHM was divided into 24 sub-plots and 12 LiDAR height metrics were built. Multiple regression equation between the variables extracted from the LiDAR data and biomass calculated by using a allometric equation was derived. Stand-level biomass estimated from LiDAR data were distributed from 155.81 Mg/ha to 597.21 Mg/ha with the mean value of 366.48 Mg/ha. R-square value of the verification analysis was 0.84.