• Korean Journal of Remote Sensing
• /
• v.33 no.6_2
• /
• pp.1061-1073
• /
• 2017

In this study, images were classified using convolutional neural network (CNN) - a deep learning technique - to investigate the feasibility of information production through a combination of artificial intelligence and spatial data. CNN determines kernel attributes based on a classification criterion and extracts information from feature maps to classify each pixel. In this study, a CNN network was constructed to classify materials with similar spectral characteristics and attribute information; this is difficult to achieve by conventional image processing techniques. A Compact Airborne Spectrographic Imager(CASI) and an Airborne Imaging Spectrometer for Application (AISA) were used on the following three study sites to test this method: Site 1, Site 2, and Site 3. Site 1 and Site 2 were agricultural lands covered in various crops,such as potato, onion, and rice. Site 3 included different buildings,such as single and joint residential facilities. Results indicated that the classification of crop species at Site 1 and Site 2 using this method yielded accuracies of 96% and 99%, respectively. At Site 3, the designation of buildings according to their purpose yielded an accuracy of 96%. Using a combination of existing land cover maps and spatial data, we propose a thematic environmental map that provides seasonal crop types and facilitates the creation of a land cover map.

• Korean Journal of Soil Science and Fertilizer
• /
• v.34 no.5
• /
• pp.342-349
• /
• 2001

This study was carried out to classify and evaluate the land cover map using Landsat TM data in Pyeongtaeg City. DGPS data, aerial photography, topographical map were used for selection the training sets and accuracy assessment. The overall accuracy and Kappa coefficient of the land cover classification map(using supervised classification with 13 classes) with Landsat TM data(16 June. 1997) were respectively, 86.8%, 85.4%, but the user's accuracy of urban/village and vinyl-house was below 60%, and the producer's accuracy of read and vinyl-house below 70%. Maybe it was caused the spectral reflectance characteristics, heterogeneity and small distribution area on the artificial things such as urban/village, vinyl_house and road, etc. And then, the agricultural land cover classification system using remote sensing data in Korea was to classify level I and II. Level I consisted of 5 classes such as agricultural land, forest land, water, barren land, urban and built-up land.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.4 no.3
• /
• pp.1-12
• /
• 2001

This thesis represents the integrated 3D DEM using both the process of satellite image and the real value of topographic maps. This DEM is draped on satellite image processed to improve representations of the real world. The 3D visualization and 3D animation with satellite imagery data enables to depict more vivid and realistic world. The paper also describes and implements the natural environmental/ecological information system that consists of 7 modules to manage environmental data systematically through an enhanced user interface. We make use of topographic map, satellite imagery data and several thematic maps. Each module has a user interface enabling to assist particular needs of decision-making for ecological/environmental assessments associated with spatial analysis of ecosystem and classification of the environmental status quo and other purposes.

• Journal of Korean Society for Geospatial Information Science
• /
• v.21 no.4
• /
• pp.13-20
• /
• 2013

Due to the global warming, international agreements have been propelled by industrialized countries. These days, there are various studies and projects to reduce the carbon emission quantity in South Korea, because South Korea is a strong candidate for a newly industrialized nation by Kyoto Protocol. Therefore, this study arranges plans to create various thematic map by producing database that can manage various datum based on grid spatial objects to manage quantity of forest biomass and carbon dioxide. Moreover, this study designs a system to create forest biomass by using the best method of calculation with LiDAR data and KOMPSAT-2 satellite images. In addition, this study designs a biomass monitoring system for public institutions to register biomass, suggesting actual plans to extract, manage, and utilized forest biomass.

• Journal of the Korean Geosynthetics Society
• /
• v.16 no.4
• /
• pp.129-137
• /
• 2017

In this study, the prediction of landslide probability was performed to the study area located in ${\bigcirc}{\bigcirc}$ area of Muan-gun, Jeonnam Province around Honam railway using the computer program SHAPP ver 1.0 developed by a decision tree model. The soil samples were collected at total 8 points, and soil tests were performed to measure soil properties. The thematic maps of soil properties such as coefficient of permeability and void ratio were made on the basis of soil test results. The slope angle analysis of topography was performed using a digital map. As the prediction result of landslide probability, 435 cells among total 15,552 cells were predicted to be in the event of landslides. Therefore, the predicted area of occurring landslides may be $43,500m^2$ because the analyzed cell size was $10m{\times}10m$.

• Journal of Korean Society of Forest Science
• /
• v.96 no.5
• /
• pp.591-601
• /
• 2007

Spatial information on forest biomass is an important factor to evaluate the capability of forest as a carbon sequestrator and is a core independent variable required to drive models which describe ecological processes such as carbon budget, hydrological budget, and energy flow. The objective of this study is to understand the relationship between satellite image and field data, and to quantitatively estimate and map the spatial distribution of forest biomass. Landsat Enhanced Thematic Mapper (ETM+) derived vegetation indices and field survey data were applied to estimate the biomass distribution of mountainous forest located in Gwangneung Experimental Forest (230 ha). Field survey data collected from the ground plots were used as the dependent variable, forest biomass, while satellite image reflectance data (Band 1~5 and Band 7), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and RVI (Ratio Vegetation Index) were used as the independent variables. The mean and total biomass of Gwangneung catchment area were estimated to be about 229.5 ton/ha and $52.8{\times}10^3$ tons respectively. Regression analysis revealed significant relationships between the measured biomass and Landsat derived variables in both of deciduous forest ($R^2=0.76$, P < 0.05) and coniferous forest ($R^2=0.75$, P < 0.05). However, there still exist many uncertainties in the estimation of forest ecosystem parameters based on vegetation remote sensing. Developing remote sensing techniques with adequate filed survey data over a long period are expected to increase the estimation accuracy of spatial information of the forest ecosystem.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.3 no.2
• /
• pp.11-23
• /
• 2000

The subjects of this study are to examine and to apply the methods of making 1 : 10,000 scale digital maps using Russian's 2 m resolution satellite images of Alternative and 8 m resolution stereo satellite images of MK-4 for the Kyoha area of Paju-city where aerial-photo surveying is not possible. A digital elevation model (DEM) was calculated from MK-4 images. With this DEM, the Alternative images were orthorectified. Ground control points (GCP) were acquired from GPS surveyings and were used to perform geometric corrections on Alternative images. From field investigation, thematic attributes are digitized on the monitor. RMS errors of the planar and vertical positions are estimated to ${\pm}0.4$ m and ${\pm}15$ m, respectively. The planar accuracy is better than an accuracy required by NGIS (national GIS) programs. Local information from field investigation was added and the resulting maps should be good as base maps for, such as, regional and urban plannings.

• Journal of Environmental Impact Assessment
• /
• v.7 no.1
• /
• pp.81-91
• /
• 1998

The objective of this study was to use remotely sensed data, combined with in situ data, for the assessment of trophic state for Daecheong reservoir. Three Landsat TM(Thematic Mapper) imagery data were processed to portray trophic state conditions. The remotely sensed data and the measured data were obtained on 20 June 1995. Regression models have been developed between the chlorophyll-a concentration and reflectance which was converted to Landsat TM digital data. The regression model was determined based on the correlation coefficient which was higher than 0.7 and was applied to the entire study area to generate a distribution map of chlorophyll-a and trophic state. The equation, providing estimates of chlorophyll-a concentration, represented the year-to-year spatial variation of trophic zones in the reservoir. Satellite remote sensing data derived from Landsat TM had been successfully used for trophic slate mapping in Daecheong reservoir.

• Journal of Environmental Impact Assessment
• /
• v.14 no.6
• /
• pp.387-402
• /
• 2005

In this study, we tried to predict the change of future land-cover and relationships between land-cover change and geo-spatial information in the Gongju area by using fuzzy logic operation. Quantitative evaluation of prediction models was carried out using a prediction rate curve using. Based on the analysis of correlations between the geo-spatial information and land-cover change, the class with the highest correlation was extracted. Fuzzy operations were used to predict land-cover change and determine the land-cover prediction maps that were the most suitable. It was predicted that in urban areas, the urban expansion of old and new towns would occur centering on the Gem-river, and that urbanization of areas along the interchange and national roads would also expand. Among agricultural areas, areas adjacent to national roads connected to small tributaries of the Gem-river and neighboring areas would likely experience changes. Most of the forest areas are located in southeast and from this result we can guess why the wide chestnut-tree cultivation complex is located in these areas and the possibility of forest damage is very high. As a result of validation using the prediction rate curve, it was indicated that among fuzzy operators, the maximum fuzzy operator was the most suitable for analyzing land-cover change in urban and agricultural areas. Other fuzzy operators resulted in the similar prediction capabilities. However, in the prediction rate curve of integrated models for land-cover prediction in the forest areas, most fuzzy operators resulted in poorer prediction capabilities. Thus, it is necessary to apply new thematic maps or prediction models in connection with the effective prediction of changes in the forest areas.

• Korean Journal of Remote Sensing
• /
• v.29 no.6
• /
• pp.611-619
• /
• 2013

Although the need of crop production has increased in Mongolia, crop cultivation is very limited because of the harsh climatic and topographic conditions. Crop lands are sparsely distributed with relatively small sizes and, therefore, it is difficult to survey the exact area of crop lands. The study aimed to find an easy and effective way of accurate classification to map crop lands in Mongolia using satellite images. To classify the crop lands over the study area in northern Mongolia, four classifications were carried out by using 1) Thematic Mapper (TM) image August 23, 2) TM image of July 6, 3) combined 12 bands of TM images of July and August, and 4) both TM images of July and August by layered classification. Wheat and potato are the major crop types and they show relatively high variation in crop conditions between July and August. On the other hands, other land cover types (forest, riparian vegetation, grassland, water and bare soil) do not show such difference between July and August. The results of four classifications clearly show that the use of multi-temporal images is essential to accurately classify the crop lands. The layered classification method, in which each class is separated by a subset of TM images, shows the highest classification accuracy (93.7%) of the crop lands. The classification accuracies are lower when we use only a single TM image of either July or August. Because of the different planting practice of potato and the growth condition of wheat, the spectral characteristics of potato and wheat cannot be fully separated from other cover types with TM image of either July or August. Further refinements on the spatial characteristics of existing crop lands may enhance the crop mapping method in Mongolia.