The advent of a fourth industrial revolution, built on advances in digital technology, has coincided with studies using various unmanned aerial vehicles (UAVs) being performed worldwide. However, the accuracy of different sensors and their suitability for particular research studies are factors that need to be carefully evaluated. In this study, we evaluated UAV photogrammetry using smart technology. To assess the performance of digital photogrammetry, the accuracy of common procedures for generating orthomosaic images and digital surface models (DSMs) using terrestrial laser scanning (TLS) techniques was measured. Two different type of non-surveying camera(Smartphone camera, fisheye camera) were attached to UAV platform. For fisheye camera, lens distortion was corrected by considering characteristics of lens. Accuracy of orthoimage and DSM generated were comparatively analyzed using aerial and TLS data. Accuracy comparison analysis proceeded as follows. First, we used Ortho mosaic image to compare the check point with a certain area. In addition, vertical errors of camera DSM were compared and analyzed based on TLS. In this study, we propose and evaluate the feasibility of UAV photogrammetry which can acquire 3 - D spatial information at low cost in a construction site.

By applying the method of SAR interferometry to X-band synthetic aperture radar (SAR) image of COSMO-SkyMed, detailed motion patterns of five glaciers in the Parlung Zangbo River basin, Tibetan Plateau, in January 2010 have been derived. The results indicate that flow patterns are generally constrained by the valley geometry and terrain complexity. The maximum of $123.9ma^{-1}$ is observed on glacier No.1 and the minimum of $39.4ma^{-1}$ is found on glacier No.3. The mean values of five glaciers are between 22.9 and $98.2ma^{-1}$. Glaciers No.1, No.2, No.4 and No.5 exhibit high velocities in their upper sections with big slope and low velocities in the lower sections. A moraine lake accelerates the speed of mass exchange leading to a fast flow at the terminal of glacier No.3. These glaciers generally move along the direction of decreased elevation and present a macroscopic illustration of the motion from the northwest to the southeast. The accuracy of DEM and registration conditions of DEM-simulated terrain phases has certain effects on calculations of glacier flow direction and velocity. The error field is relatively fragmented in areas inconsistent with the main flow line of the glaciers, and the shape and uniformity of glacier are directly related to the continuous distribution of flow velocity errors.

Synthetic Aperture Radar (SAR) observations are powerful tools to monitor surface's displacement very accurately, induced by earthquake, volcano, ground subsidence, glacier movement, etc. Especially, radar interferometry (InSAR) which utilizes phase information related to distance from sensor to target, can generate displacement map in line-of-sight direction with accuracy of a few cm or mm. Due to decorrelation effect, however, degradation of coherence in the InSAR application often prohibit from construction of differential interferogram. Offset tracking method is an alternative approach to make a two-dimensional displacement map using intensity information instead of the phase. However, there is limitation in that the offset tracking requires very intensive computation power and time. In this paper, efficiency of parallel computing has been investigated using high performance computer for estimation of glacier velocity. Two TanDEM-X SAR observations which were acquired on September 15, 2013 and September 26, 2013 over the Narsap Sermia in Southwestern Greenland were collected. Atotal of 56 of 2.4 GHz Intel Xeon processors(28 physical processors with hyperthreading) by operating with linux environment were utilized. The Gamma software was used for application of offset tracking by adjustment of the number of processors for the OpenMP parallel computing. The processing times of the offset tracking at the 256 by 256 pixels of window patch size at single and 56 cores are; 26,344 sec and 2,055 sec, respectively. It is impressive that the processing time could be reduced significantly about thirteen times (12.81) at the 56 cores usage. However, the parallel computing using all the processors prevent other background operations or functions. Except the offset tracking processing, optimum number of processors need to be evaluated for computing efficiency.

In this study, we have proposed an improved method to detect forest fires by correcting the reflected signals of day images using the middle-wavelength infrared (MWIR) channel. The proposed method is allowed to remove the reflected signals only using the image itself without an existing data source such as a land-cover map or atmospheric data. It includes the processing steps for calculating a solar-reflected signal such as 1) a simple correction model of the atmospheric transmittance for the MWIR channel and 2) calculating the image-based reflectance. We tested the performance of the method using the MODIS product. When compared to the conventional MODIS fire detection algorithm (MOD14 collection 6), the total number of detected fires was improved by approximately 17%. Most of all, the detection of fires improved by approximately 30% in the high reflection areas of the images. Moreover, the false alarm caused by artificial objects was clearly reduced and a confidence level analysis of the undetected fires showed that the proposed method had much better performance. The proposed method would be applicable to most satellite sensors with MWIR and thermal infrared channels. Especially for geostationary satellites such as GOES-R, HIMAWARI-8/9 and GeoKompsat-2A, the short acquisition time would greatly improve the performance of the proposed fire detection algorithm because reflected signals in the geostationary satellite images frequently vary according to solar zenith angle.

Satellite images can be integrated into a crop model to strengthen the advantages of each technique for crop monitoring and to compensate for weaknesses of each other, which can be systematically applied for monitoring inaccessible croplands. The objective of this study was to outline the productivity of paddy rice based on simulation of the yield of all paddy fields in North Korea, using a grid crop model combined with optical satellite imagery. The grid GRAMI-rice model was used to simulate paddy rice yields for inaccessible North Korea based on the bidirectional reflectance distribution function-adjusted vegetation indices (VIs) and the solar insolation. VIs and solar insolation for the model simulation were obtained from the Geostationary Ocean Color Imager (GOCI) and the Meteorological Imager (MI) sensors of the Communication Ocean and Meteorological Satellite (COMS). Reanalysis data of air temperature were achieved from the Korea Local Analysis and Prediction System (KLAPS). Study results showed that the yields of paddy rice were reproduced with a statistically significant range of accuracy. The regional characteristics of crops for all of the sites in North Korea were successfully defined into four clusters through a spatial analysis using the K-means clustering approach. The current study has demonstrated the potential effectiveness of characterization of crop productivity based on incorporation of a crop model with satellite images, which is a proven consistent technique for monitoring of crop productivity in inaccessible regions.

Land cover (LC) is an important factor in socioeconomic and environmental studies. According to various studies, a number of LC maps, including global land cover (GLC) datasets, are made using polar orbit satellite data. Due to the insufficiencies of reference datasets in Northeast Asia, several LC maps display discrepancies in that region. In this paper, we performed a feasibility assessment of LC mapping using Geostationary Ocean Color Imager (GOCI) data over Northeast Asia. To produce the LC map, the GOCI normalized difference vegetation index (NDVI) was used as an input dataset and a level-2 LC map of South Korea was used as a reference dataset to evaluate the LC map. In this paper, 7 LC types(urban, croplands, forest, grasslands, wetlands, barren, and water) were defined to reflect Northeast Asian LC. The LC map was produced via principal component analysis (PCA) with K-means clustering, and a sensitivity analysis was performed. The overall accuracy was calculated to be 77.94%. Furthermore, to assess the accuracy of the LC map not only in South Korea but also in Northeast Asia, 6 GLC datasets (IGBP, UMD, GLC2000, GlobCover2009, MCD12Q1, GlobeLand30) were used as comparison datasets. The accuracy scores for the 6 GLC datasets were calculated to be 59.41%, 56.82%, 60.97%, 51.71%, 70.24%, and 72.80%, respectively. Therefore, the first attempt to produce the LC map using geostationary satellite data is considered to be acceptable.

The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms(ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.

In this study, a hybrid-type of day fog detection algorithm (DFDA) was developed based on the optical and textural characteristics of fog top, using the Himawari-8 /Advanced Himawari Imager data. Supplementary data, such as temperatures of numerical weather prediction model and sea surface temperatures of operational sea surface temperature and sea ice analysis, were used for fog detection. And 10 minutes data from visibility meter from the Korea Meteorological Administration were used for a quantitative verification of the fog detection results. Normalized albedo of fog top was utilized to distinguish between fog and other objects such as clouds, land, and oceans. The normalized local standard deviation of the fog surface and temperature difference between fog top and air temperature were also assessed to separate the fog from low cloud. Initial threshold values (ITVs) for the fog detection elements were selected using hat-shaped threshold values through frequency distribution analysis of fog cases.And the ITVs were optimized through the iteration method in terms of maximization of POD and minimization of FAR. The visual inspection and a quantitative verification using a visibility meter showed that the DFDA successfully detected a wide range of fog. The quantitative verification in both training and verification cases, the average POD (FAR) was 0.75 (0.41) and 0.74 (0.46), respectively. However, sophistication of the threshold values of the detection elements, as well as utilization of other channel data are necessary as the fog detection levels vary for different fog cases(POD: 0.65-0.87, FAR: 0.30-0.53).

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

Surface temperature has been derived from the MODIS/ASTER airborne simulator (MASTER) mid-wave infrared single channel data using the MODerate resolution atmospheric TRANsmission (MODTRAN) radiative transfer model with input data including the University of Wisconsin (UW) emissivity, the National Centers for Environmental Prediction (NCEP) atmospheric profiles, and solar and line-of-sight geometry. We have selected the study area that covers some surface types such as water, sand, agricultural (vegetated) land, and clouds. Results of the current study show the reasonable geographical distribution of surface temperature over land and water similar to the pattern of the MASTER L2 surface temperature. The thorough quantitative validation of surface temperature retrieved from this study is somehow limited due to the lack of in-situ measurements. One point comparison at the Salton Sea buoy shows that the present estimate is 1.8 K higher than the field data. Further comparison with the MASTER L2 surface temperature over the study area reveals statistically good agreement with mean differences of 4.6 K between two estimates. We further analyze the surface temperature differences between two estimates and find primary factors to be emissivity and atmospheric correction.

As inter-Korean relations progress, the issue of natural disasters which could directly affect the lives of the people in both Koreas, has not yet been discussed. Considering the current status of inter-Korean relations and the ongoing disaster-related damage in North Korea, it is imperative to establish a technical plan at the pan-governmental level to reduce the damage from natural disasters. The purpose of this study is to secure the Korea Peninsula against natural disasters by organizing South Korea's science and technologies related to natural disasters in order to reduce the damage, and to evaluate the applicability of said technologies. The situation of natural disasters in North Korea for 17 years has been summarized and reclassified based on eight types of natural disasters. Technologies related to natural disasters in South Korea were also investigated and reclassified. Based on the data, a priority evaluation was performed and the prioritization of technology application for each natural disaster type in North Korea was calculated through a quadrant analysis. As a result, the three major categories of high-priority technologies were classified as natural disaster monitoring with remote sensing and spatial information technology, construction of research basis and database based on geographic information system (GIS) and integrated management of complex natural disasters.

Landslides are one of the most damaging geological hazards worldwide, threating both humans and property. Hence, there have been many efforts to prevent landslides and mitigate the damage that they cause. Among such efforts, there have been many studies on mapping landslide susceptibility. Geographic information system (GIS)-based techniques have been developed and applied widely, and are now the main tools used to map landslide susceptibility. We reviewed the status of landslide susceptibility mapping using GIS by number of papers, year, study area, number of landslides, cause, and models applied, based on 776 articles over the last 20 years (1999-2018). The number of studies published annually increased rapidly over time. The total study area spanned 65 countries, and 47.7% of study areas were in China, India, South Korea, and Iran, where more than 500 landslides, 27.3% of all landslides, have occurred. Slope (97.6% of total articles) and geology (82.7% of total articles) were most often implicated as causes, and logistic regression (26.9% of total articles) and frequency ratio (24.7% of total article) models were the most widely used models. We analyzed trends in the causes of and models used to simulate landslides. The main causes were similar each year, but machine learning models have increased in popularity over time. In the future, more study areas should be investigated to improve the generalizability and accuracy of the results. Furthermore, more causes, especially those related to topography and soil, should be considered and more machine learning models should be applied. Finally, landslide hazard and risk maps should be studied in addition to landslide susceptibility maps.

The Outgoing Longwave Radiation (OLR) is an important satellite-driven variable for understanding the Earth's energy budget balance. The geostationary OLR retrievals require angular and spectral integration using an empirical equation for irradiance flux-to-OLR from a regression analysis, which determines the accuracy of the narrowband satellite-based OLR. We selected homogeneous pixels which is satisfied less temporal-spatial variability of cloud, on three infrared channels (6.7, 10.8, $12.0{\mu}m$) of the first multipurpose geostationary satellite in Korea, namely the Communication, Ocean and Meteorological Satellite/Meteorological Imager (COMS/MI). Multiple regression analysis was performed to retrieve OLR with improved accuracy using selected parameters based on theoretical and physical significance. This algorithm yielded retrieval with higher accuracy than broadband-based OLR retrieval: RMSE of 10.54 to $3.81W\;m^{-2}$, and bias of -8.49 to $-0.07W\;m^{-2}$.