• Korean Journal of Remote Sensing
• /
• v.31 no.2
• /
• pp.51-64
• /
• 2015

We improved the MODerate resolution Imaging Spectroradiometer (MODIS) land cover map over the Asia-Oceania region through the reclassification of the misclassified pixels. The misclassified pixels are defined where the number of land cover types are greater than 3 from the 12 years of MODIS land cover map. The ratio of misclassified pixels in this region amounts to 17.53%. The MODIS Normalized Difference Vegetation Index (NDVI) time series over the correctly classified pixels showed that continuous variation with time without noises. However, there are so many unreasonable fluctuations in the NDVI time series for the misclassified pixels. To improve the quality of input data for the reclassification, we corrected the MODIS NDVI using Correction based on Spatial and Temporal Continuity (CSaTC) developed by Cho and Suh (2013). Iterative Self-Organizing Data Analysis (ISODATA) was used for the clustering of NDVI data over the misclassified pixels and land cover types was determined based on the seasonal variation pattern of NDVI. The final land cover map was generated through the merging of correctly classified MODIS land cover map and reclassified land cover map. The validation results using the 138 ground truth data showed that the overall accuracy of classification is improved from 68% of original MODIS land cover map to 74% of reclassified land cover map.

• Journal of The Geomorphological Association of Korea
• /
• v.26 no.3
• /
• pp.53-67
• /
• 2019

The purpose of this study is to identify the quality of landslide susceptibility in a landslide-prone area (Jinbu-myeon, Gangwon-do, South Korea) by spatial prediction modeling approach and compare the results obtained. For this goal, a landslide inventory map was prepared mainly based on past historical information and aerial photographs analysis (Daum Map, 2008), as well as some field observation. Altogether, 550 landslides were counted at the whole study area. Among them, 182 landslides are debris flow and each group of landslides was constructed in the inventory map separately. Then, the landslide inventory was randomly selected through Excel; 50% landslide was used for model analysis and the remaining 50% was used for validation purpose. Total 12 contributing factors, such as slope, aspect, curvature, topographic wetness index (TWI), elevation, forest type, forest timber diameter, forest crown density, geology, landuse, soil depth, and soil drainage were used in the analysis. Moreover, to find out the co-relation between landslide causative factors and incidents landslide, pixels were divided into several classes and frequency ratio for individual class was extracted. Eventually, six landslide susceptibility maps were constructed using the Bayesian Predictive Discriminant (BPD), Empirical Likelihood Ratio (ELR), and Linear Regression Method (LRM) models based on different category dada. Finally, in the cross validation process, landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract success rate curve. The result showed that Bayesian, likelihood and linear models were of 85.52%, 85.23%, and 83.49% accuracy respectively for total data. Subsequently, in the category of debris flow landslide, results are little better compare with total data and its contained 86.33%, 85.53% and 84.17% accuracy. It means all three models were reasonable methods for landslide susceptibility analysis. The models have proved to produce reliable predictions for regional spatial planning or land-use planning.

• Journal of Environmental Impact Assessment
• /
• v.14 no.3
• /
• pp.127-134
• /
• 2005

Recently, aquaculture farm sites have been increased with demand of the expensive fish species and sea food like as seaweed, laver and oyster. Therefore coastal water quality have been deteriorated by organic contamination from marine aquaculture farm sites. For protecting of coastal environment, we need to control the location of aquaculture sites. The purpose of this study is to detect the laver aquaculture sites using multispectral remotely sensed data with autodetection algorithm. In order to detect the aquaculture sites, density slice and contour and vegetation index methods were applied with SPOT and IKONOS data of Shinan area. The marine aquaculture farm sites were extracted by density slice and contour methods with one band digital number(DN) carrying 65% accuracy. However, vegetation index algorithm carried out 75% accuracy using near-infra red and red bands. Extraction of the laver aquaculture site using remotely sensed data will provide the efficient digital map for coastal water management strategies and red tide GIS management system.

• Atmosphere
• /
• v.19 no.2
• /
• pp.169-182
• /
• 2009

To improve the performance of climate and numerical models, concerns on the land-atmosphere schemes are steadily increased in recent years. For the realistic calculation of land-atmosphere interaction, a land surface information of high quality is strongly required. In this study, a new land cover map over the Korean peninsula was developed using MODIS (MODerate resolution Imaging Spectroradiometer) data. The seven phenological data set (maximum, minimum, amplitude, average, growing period, growing and shedding rate) derived from 15-day normalized difference vegetation index (NDVI) were used as a basic input data. The ISOData (Iterative Self-Organizing Data Analysis), a kind of unsupervised non-hierarchical clustering method, was applied to the seven phenological data set. After the clustering, assignment of land cover type to the each cluster was performed according to the phenological characteristics of each land cover defined by USGS (US. Geological Survey). Most of the Korean peninsula are occupied by deciduous broadleaf forest (46.5%), mixed forest (15.6%), and dryland crop (13%). Whereas, the dominant land cover types are very diverse in South-Korea: evergreen needleleaf forest (29.9%), mixed forest (26.6%), deciduous broadleaf forest (16.2%), irrigated crop (12.6%), and dryland crop (10.7%). The 38 in-situ observation data-base over South-Korea, Environment Geographic Information System and Google-earth are used in the validation of the new land cover map. In general, the new land cover map over the Korean peninsula seems to be better classified compared to the USGS land cover map, especially for the Savanna in the USGS land cover map.

• Journal of Korean Society of Rural Planning
• /
• v.17 no.3
• /
• pp.91-101
• /
• 2011

The purpose of this study is to suggest a landscape resources evaluation strategy of rural waterfront villages along the river. This strategy consists of three phases: 1) an evaluation of rural amenity landscape resources, 2) an evaluation of water landscape resources, and 3) development of a positioning map based on the results of phase 1) and 2) the study result as follows. First, the evaluation method used in phase 1) was modified as a set of proposed evaluation indicators to assess development potential on rural waterfront villages. Second, to evaluate water landscape resources in rural waterfront villages, a series of evaluation index was developed including water area, diversity of water resources, biodiversity, and landscape quality. And the last, the positioning map showed relative position of waterfront villages obtained from two evaluation results: rural amenity landscape resources and water landscape resources. The study examined the proposed strategy as a possible alternative to evaluate landscape quality to 398 rural waterfront villages along the Han River. Landscape resources evaluation strategy proposed here could contribute to government officials and planners to operate systematic planning and management of rural waterfront villages.

• Journal of Environmental Impact Assessment
• /
• v.22 no.3
• /
• pp.277-290
• /
• 2013

This study was carried out to propose the new procedure to apply Habitat Evaluation Procedure(HEP) of target species using delphi technique, which is suitable to develop endangered species with few researches and ecological knowledges. To identify habitat quality of specific species in development project site, we can develop habitat model and create habitat suitability maps. In this study, we select the Common Kestrel(Falco tinnunculus) as target species in four areas(Paju, Siheung, Ansan, Hwaseong) which is located near the Seoul metropolitan area. The Delphi technique was selected to get the reliable information on the species and habitats requirements. Through the delphi approach, seven habitat components were determined as suitable variables for the Common Kestrel: density($n/km^2$) of small mammals, area($km^2$) of bare-grounds, pasturelands and riparian, and open area(%), spatial distribution and area of croplands, landscape diversity, breeding sites(tall trees, cliffs, high-rise buildings), and the length of shelf. Habitat variables used in this model were classified into two categories: % of suitable land-cover type(open areas, croplands, pasturelands, wetlands, and baregrounds) and the quality of feeding sites(within 250m from edges of woodlands). Habitat quality of the Common Kestrel was assessed against occurred sites derived from the nationwide survey. Predicted habitat suitability map were closely related to the observed sites of the endangered avian species in the study areas. With the habitat suitability map of the Common Kestrel, we assess the environmental impacts with habitat loss after development project in environmental impact assessment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.34 no.4
• /
• pp.431-441
• /
• 2016

Since the use of UAV (Unmanned Aerial Vehicle) is convenient for the acquisition of data on broad or inaccessible regions, it is nowadays used to establish spatial information for various fields, such as the environment, ecosystem, forest, or for military purposes. In this study, the process of estimating FVC (Fractional Vegetation Cover), based on multi-spectral UAV, to overcome the limitations of conventional methods is suggested. Hence, we propose that the FVC map is generated by using multi-spectral imaging. First, two types of result classifications were obtained based on RF (Random Forest) using RGB images and NDVI (Normalized Difference Vegetation Index) with RGB images. Then, the result map was reclassified into vegetation and non-vegetation. Finally, an FVC map-based RF were generated by using pixel calculation and FVC map-based GI (Gutman and Ignatov) model were indirectly made by fixed parameters. The method of adding NDVI shows a relatively higher accuracy compared to that of adding only RGB, and in particular, the GI model shows a lower RMSE (Root Mean Square Error) with 0.182 than RF. In this regard, the availability of the GI model which uses only the values of NDVI is higher than that of RF whose accuracy varies according to the results of classification. Our results showed that the GI mode ensures the quality of the FVC if the NDVI maintained at a uniform level. This can be easily achieved by using a UAV, which can provide vegetation data to improve the estimation of FVC.

• Spatial Information Research
• /
• v.21 no.3
• /
• pp.45-53
• /
• 2013

Web GIS, based on the latest web-technology, has evolved to provide efficient and accurate spatial information to users. Furthermore, Web GIS Server has improved the performance constantly to respond user web requests and to offer spatial information service. This research aims to create a designed and implemented Web GIS Server that is named as Nodemap which uses the emergent technology, Node.js, which has been issued for an event-oriented, non-blocking I/O model framework for coding JavaScript on the server development. Basically, NodeMap is Web GIS Server that supports OGC implementation specification. It is designed to process GIS data by using DBMS, which supports spatial index and standard spatial query function. And NodeMap uses Node-Canvas module supported HTML5 canvas to render spatial information on tile map. Lastly, NodeMap uses Express module based connect module framework. NodaMap performance demonstration confirmed a possibility of applying Node.js as a (next/future) Web GIS Server development technology through the benchmarking. Having completed its quality test of NodeMap, this study has shown the compatibility and potential for Node.js as a Web GIS server development technology, and has shown the bright future of internet GIS service.

• Journal of the Korean earth science society
• /
• v.36 no.1
• /
• pp.109-124
• /
• 2015

A novel approach, hybrid surface rainfall (KNU-HSR) technique developed by Kyungpook Natinal University, was utilized for improving the radar rainfall estimation. The KNU-HSR technique estimates radar rainfall at a 2D hybrid surface consistings of the lowest radar bins that is immune to ground clutter contaminations and significant beam blockage. Two HSR techniques, static and dynamic HSRs, were compared and evaluated in this study. Static HSR technique utilizes beam blockage map and ground clutter map to yield the hybrid surface whereas dynamic HSR technique additionally applies quality index map that are derived from the fuzzy logic algorithm for a quality control in real time. The performances of two HSRs were evaluated by correlation coefficient (CORR), total ratio (RATIO), mean bias (BIAS), normalized standard deviation (NSD), and mean relative error (MRE) for ten rain cases. Dynamic HSR (CORR=0.88, BIAS= $-0.24mm\;hr^{-1}$, NSD=0.41, MRE=37.6%) shows better performances than static HSR without correction of reflectivity calibration bias (CORR=0.87, BIAS= $-2.94mm\;hr^{-1}$, NSD=0.76, MRE=58.4%) for all skill scores. Dynamic HSR technique overestimates surface rainfall at near range whereas it underestimates rainfall at far ranges due to the effects of beam broadening and increasing the radar beam height. In terms of NSD and MRE, dynamic HSR shows the best results regardless of the distance from radar. Static HSR significantly overestimates a surface rainfall at weaker rainfall intensity. However, RATIO of dynamic HSR remains almost 1.0 for all ranges of rainfall intensity. After correcting system bias of reflectivity, NSD and MRE of dynamic HSR are improved by about 20 and 15%, respectively.

• Korean Journal of Remote Sensing
• /
• v.39 no.4
• /
• pp.425-440
• /
• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.