• Journal of Korean Society of Forest Science
• /
• v.88 no.2
• /
• pp.157-168
• /
• 1999

This paper analyzed vegetation and land use classification, slope and permanent preservation and legal reserves on the farm Jangada and Jamaica-Mato Grosso do Sul, Brazil, using satellite image for assisting the planning of planting Eucalyptus spp. This part of the State of Mato Grosso do Sul represents an important geopolitcal area, since it is located on the borders of Bolivia and Paraguay. Also exportation of goods can be achieved through hydrovias extending to Buenos Aires, Argentina-through the Paraguay River. Also there are road and railroad connection which link the soutreastern part of Brazil to the Andean countries. The vegetation map from sheet SF 21-Campo Grande of the RADAMBRASIL Project was used as the basis for the preliminary interpretation of coverage, and complemented by a visit of the field. After the initial interpretation of the image, definition of classes of use and land occupation were made, and files of spectral signatures were created. On the farms Jamaica and Jangada Open Arboreal Savanna and Grass Savanna are the predominant physiognomies occupying 68% of total area. In spite of the results being satisfactory at the present moment, the development of this project should be revised and adjusted based on the evaluations already made, including a greater detailing of environmental components, principally with respect to soil and topography.

• Korean Journal of Remote Sensing
• /
• v.16 no.3
• /
• pp.281-291
• /
• 2000

In this study tree plantation planning for the plantation blocks of Eucalyptus species was constructed in order to apply to the two farms Jangada and Jamaica, where are located in the western district of the state Mato Grosso do Sul in Brazil. At first the satellite photo was analyzed for the land use classification and the forest ecosystem was classified with GIS technique, and then on the basis of this result the planting available area was accounted for the two farms. According to the request of the land owner the planting planning was established for the planting available area for 3 years. The total area for the two farms is 5,301 ha, and the planting available area is estimated to be 3,913ha(74%). The rest area is 1,388ha(26%), and should be classified to the permanent legal reserve forest area. In order to minimize the soil loss and the erosion, the planting blocks were divided according to the parallel to the contour line： for the first planing year the plantation area was divided to the 27 blocks and the total area was 1,308ha, for the second planing year the area also divided to 27 blocks(1,327.4ha) and for the third planning year 30 blocks divided (1276.5).

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.1-4
• /
• 2001

The classification performance of Artificial Neural Network (ANN) and RBF-NN was compared for Landsat TM image. The RBF-NN was validated for three unique landuse types (e.g. Mixed landuse area, Cultivated area, Urban area), different input band combinations and classification class. The bootstrap resampling technique was employed to estimate the confidence intervals and distribution for unit load, The pollutant generation was varied significantly according to the classification accuracy and percentile unit load applied. Especially in urban area, where mixed landuse is dominant, the difference of estimated pollutant load is largely varied.

• Journal of Korean Society for Geospatial Information Science
• /
• v.23 no.3
• /
• pp.85-93
• /
• 2015

This paper mainly introduces the methods of extracting landslide information using ALOS(Advanced Land Observing Satellite) images and GIS(Geographical Information System) technology. In this study, we classified images using three different methods which are the unsupervised the supervised and the PCA(Principal Components Analysis) for extracting landslide information based on characteristics of ALOS image. From the image classification results, we found out that the quality of classified image extracted with PCA supervised method was superior than the other images extracted with the other methods. But the accuracy of landslide information extracted from this image classification was still very low as the pixels were very similar between the landslide and safety regions. It means that it is really difficult to distinguish those areas with an image classification method alone because the values of pixels between the landslide and other areas were similar, particularly in a region where the landslide and other areas coexist. To solve this problem, we used the LSM(Landslide Susceptibility Map) created with ArcView software through weighted overlay GIS method in the areas. Finally, the developed LSM was applied to the image classification process using the ALOS images. The accuracy of the extracted landslide information was improved after adopting the PCA and LSM methods. Finally, we found that the landslide region in the study area can be calculated and the accuracy can also be improved with the LSM and PCA image classification methods using GIS tools.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.38 no.5
• /
• pp.407-414
• /
• 2020

SVM (Support vector machine) analysis was performed after applying a deep learning technique based on an Inception-based model (GoogLeNet). The accuracy of automatic image classification was analyzed using an SVM with multiple virtual grid sizes. Six classes were selected from a standard land cover map. Cars were added as a separate item to increase the classification accuracy of roads. The virtual grid size was 2-5 m for natural areas, 5-10 m for traffic areas, and 10-15 m for building areas, based on the size of items and the resolution of input images. The results demonstrate that automatic classification accuracy can be increased by adopting an integrated approach that utilizes weighted virtual grid sizes for different classes.

• Korean Journal of Remote Sensing
• /
• v.20 no.4
• /
• pp.275-288
• /
• 2004

This paper presents a fuzzy relaxation labeling approach incorporated to the fuzzy logic fusion scheme for the classification of multi-sensor remote sensing images. The fuzzy logic fusion and iterative relaxation labeling techniques are adopted to effectively integrate multi-sensor remote sensing images and to incorporate spatial neighboring information into spectral information for contextual classification, respectively. Especially, the iterative relaxation labeling approach can provide additional information that depicts spatial distributions of pixels updated by spatial information. Experimental results for supervised land-cover classification using optical and multi-frequency/polarization images indicate that the use of multi-sensor images and spatial information can improve the classification accuracy.

• Journal of Integrative Natural Science
• /
• v.14 no.2
• /
• pp.41-49
• /
• 2021

The causes of climate change are natural and artificial. Natural causes include changes in temperature and sunspot activities caused by changes in solar radiation due to large-scale volcanic activities, while artificial causes include increased greenhouse gas concentrations and land use changes. Studies have shown that excessive carbon use among artificial causes has accelerated global warming. Climate change is rapidly under way because of this. Due to climate change, the frequency and cycle of infectious disease viruses are greater and faster than before. Currently, the world is suffering greatly from coronavirus infection-19 (COVID-19). Korea is no exception. The first confirmed case occurred on January 20, 2020, and the number of infected people has steadily increased due to several waves since then, and many confirmed cases are occurring in 2021. In this study, we conduct a study on climate change before and after COVID-19 using weather data from Korea to determine whether climate change affects infectious disease viruses through logistic regression analysis. Based on this, we want to classify before and after COVID-19 through a logistic regression model to see how much classification rate we have. In addition, we compare monthly classification rates to see if there are seasonal classification differences.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.6
• /
• pp.507-516
• /
• 2019

The investigation of land characteristics is an important task for the calculation of officially land prices and standard comparison table of land price. Therefore, it should be done objectively and consistently. However, the current investigation system is mainly done by researcher's subjective judgment. Therefore, the objectivity and consistency of this investigation is not guaranteed and questionable. In this study, we first defined the problem by analyzing the current land topography investigation method. In addition, in order to investigate the land topography, the geometry of the parcel is quantified by spatial information and applied to the decision tree based method(C4.5) to produce the final result. This study intended to extract the parcel characteristics data of the topographic by the use of spatial information and to apply the information to the C4.5, there by suggesting a method for addressing the problems. The findings showed approximately 93.5% between the results of topography classification estimated with rules learned by C4.5.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.7 no.3
• /
• pp.78-85
• /
• 2004

The non-planned development of the rural area surrounding the Metropolitan area has become a social problem. The land development program until now has an aspect of not combining the spacial plan and the environmental plan. The land use and city development system based on development should change into a form that combines developing the area and conserving the environment. Therefore, this research attempts to compare the results of the overlay analysis and the gravity matrix which are ways to evaluate the value land that has a high environmental conservation value. The research area is the town of Seonggeo-eup, Cheonan City, and the reason for selecting this area is because it is expected to be densely populated as a connected area to the Metropolitan and the development pressure, such as expanding the industrial area, is high due to convenient transportation. The environmental factors used in the research are the relative altitude, incline, age-class, natural degree of the ecology, classification of the land covering and the NDVI, and the research methods used are the overlay analysis of the GIS and the statistical method. The overlay analysis results showed level 1 13.2%, level 2 30.7%, level 3 47.4%, level 4 1.0%, level 5 2.4%, level 6 5.4% and so on. The gravity matrix was classified as level 1 27.0%, level 2 9.3%, level 3 58.2%, level 4 2.4%, level 5 2.3%, level 6 0.9% and so on. These results are more appropriate than current methods for plans that value the environment because the analyzed results of the gravity matrix have a tendency to highly condense the environmentally valuable area. Consequently, if the spacial and environmental plans combine and therefore expand the efficient use of the land in the current state where the nation's concern in environment is getting higher, it is thought that it will contribute highly on the development of the nation's life quality.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.17 no.4
• /
• pp.189-199
• /
• 2014

Floodplain is a flat plain between levees and rivers. This paper suggests a methodology for analyzing the land uses in the Nakdong River floodplain using the RapidEye imagery and the given LiDAR(LIght Detection And Ranging) DEM(Digital Elevation Models). First, the levee boundaries are generated using the LiDAR DEM, and the area of the floodplain is extracted from the given RapidEye imagery. The land uses in the floodplain are identified in the extracted RapidEye imagery by the ISODATA(Iterative Self-Organizing Data Analysis Technique Analysis) clustering. The overall accuracy of the identified land uses by the ISODATA clustering is 91%. Analysis of the identified land uses in the floodplain is implemented by counting the number of the pixels constituting the land cover clusters. The results of this research shows that the area of the river occupies 46%, the area of the bare soil occupies 36%, the area of the marsh occupies 11%, and the area of the grass occupies 7% in the identified floodplain.