• Journal of Forest and Environmental Science
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• v.36 no.3
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• pp.187-198
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• 2020

This study extracted deforestation area and degraded forestland area, which are potential REDD+ (Reducing Emissions from Deforestation and Forest Degradation) project candidate areas in Paraguay using Land Cover Map (LCM) and Tree Cover Map (TCM). The REDD+ project objectives scenarios were set three stages: 'afforestation and economic efficiency scenario', 'local capacity reinforcement scenario', and 'Infrastructure-oriented scenario'. And then, we evaluated the project unit suitable area of the REDD+ project. All scenarios selected the evaluation factors for each scenario in addition to the area ratio factors for deforestation area and degraded forestland area and weighted values were extracted by assigning category scores. As a result of the three scenarios comparison analysis, Concepcion state score was the highest. Within Concepcion state, the Belon district had the highest score, making it appropriate as a project unit REDD+ project candidate area in Paraguay, while the San Carlos district had the lowest score. This study can be used as basic data for selecting REDD+ project candidate area in Paraguay, and it is expected to contribute sufficiently to REDD+ project if additional data or information of social, cultural and economic sectors are secured.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.1
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• pp.89-100
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• 2010

The aim of this study is to produce land cover maps using satellite imagery with various degrees of high resolution and then compare the accuracy of the image types and categories. For the land cover map produced on a small-scale classification the estuary area around the Nakdong river, including an urban area, farming land and waters, was selected. The images were classified by analyzing the aerial photos taken from KOMPSAT2, Quickbird and IKONOS satellites, which all have a resolution of over 1m to the naked eye. Once all of the land cover maps with different images and land cover categories had been produced they were compared to each other. Results show that image accuracy from the aerial photos and Quickbird was relatively higher than with KOMPSAT2 and IKONOS. The agreement ratio for the large-scale classification across the classification methods ranged between 0.934 and 0.956 for most cases. The Kappa value ranged between 0.905 and 0.937; the agreement ratio for the middle-scale classification was 0.888~0.913 and the Kappa value was 0.872~0.901. The agreement ratio for the small-scale classification was 0.833~0.901 and the Kappa value was 0.813~0.888. In addition, in terms of the degree of confusion occurrence across the images, there was confusion on the urbanized arid areas and empty land in the large-scale classification. For the middle-scale classification, the confusion mainly occurred on the rice paddies, fields, house cultivating area and artificial grassland. For the small-scale classification, confusion mainly occurred on natural green fields, cultivating land with facilities, tideland and the surface of the sea. The findings of this study indicate that the classification of the high resolution images with the naked eye showed an agreement ratio of over 80%, which means that it can be used in practice. The findings also suggest that the use of higher resolution images can lead to increased accuracy in classification, indicating that the time when the images are taken is important in producing land cover maps.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.378-381
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• 2008

The regulating and relaxing targets in the Land Use Regulation and Total Maximum Daily Loads are influenced by Land cover information. For the providing more accurate land information, this study attempted to generate an impervious surface map using KOMPSAT-2 image which a Korea manufactured high resolution satellite image. The classification progress of this study carried out by tasseled-cap spectral enhancement through each class extraction technique neither existing classification method. KOMPSAT-2 image of this study is enhanced by Soil Brightness Index(SBI), Green vegetation Index(GVI), None-Such wetness Index(NWI). Then ranges of extracted each index in enhanced image are determined. And then, Confidence Interval of classes was determined through the calculating Non-exceedance Probability. Spectral distributions of each class are changed according to changing of Control coefficient(${\alpha}$) at the calculated Non-exceedance Probability. Previously, Land cover classification map was generated based on established ranges of classes, and then, pervious and impervious surface was reclassified. Finally, impervious ratio of reclassified impervious surface map was calculated with blocks in the study area.

• Journal of Korea Water Resources Association
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• v.53 no.3
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• pp.215-223
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• 2020

The objective of this study is to develop a prediction model of specific degradation using data mining classification especially for the rivers in South Korea river. A number of critical predictors such as erosion and sediment transport were extracted for the prediction model considering watershed morphometric characteristics, rainfall, land cover, land use, and bed material. The suggested model includes the elevations at the mid relative area of the hypsometric curve of watershed morphomeric characteristics, the urbanization ratio, and the wetland and water ratio of land cover factors as the condition factors. The proposed model describes well the measured specific degradation of the rivers in South Korea. In addition, the development model was compared with the existing models, since the existing models based on different conditions and purposes show low predictability, they have a limit about the application of Korean River. Therefore, this study is focusing on improving the applicability of the existing model

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.521-527
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• 2013

Rule set is an important step which impacts significantly on accuracy of object-oriented classification result. Therefore, this paper proposes a rule set to extract land cover from Landsat Thematic Mapper (TM) imagery acquired in Donganh, Hanoi, Vietnam. The rules were generated to distinguish five classes, namely river, pond, residential areas, vegetation and paddy. These classes were classified not only based on spectral characteristics of features, but also indices of water, soil, vegetation, and urban. The study selected five indices, including largest difference index max.diff; length/width; hue, saturation and intensity (HSI); normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) based on membership functions of objects. Overall accuracy of classification result is 0.84% as the rule set is used in classification process.

• Proceedings of the KSRS Conference
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• v.2
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• pp.840-843
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• 2006

The aim of this study was to detect landslide using satellite image and apply the landslide to probabilistic landslide-susceptibility mapping at Gangneung area, Korea using a Geographic Information System (GIS). Landslide locations were identified by change detection technique of KOMSAT-1 (Korea Multipurpose Satellite) EOC (Electro Optical Camera) images and checked in field. For landslide-susceptibility mapping, maps of the topography, geology, soil, forest, lineaments, and land cover were constructed from the spatial data sets. Then, the sixteen factors that influence landslide occurrence were extracted from the database. Using the factors and detected landslide, the relationships were calculated using frequency ratio, one of the probabilistic model. Then, landslide-susceptibility map was drawn using the frequency ration and finally, the map was verified by comparing with existing landslide locations. As the verification result, the prediction accuracy showed 86.76%. The landslide-susceptibility map can be used to reduce hazards associated with landslides and to land cover planning.

• KCID journal
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• v.14 no.2
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• pp.236-244
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• 2007

Agricultural water demand in North Korea must be considered for the near-future investment in agricultural consolidation projects and to prepare for the future unification. Thus, the objective of this study is to estimate the agricultural water demand of paddy fieldss in North Korea. GIS data including land cover classification map, Thiessen network and administration maps of North Korea, and meteorological data were synthesized. In order to estimate paddy water demand for a 10-year return period, the FAO Blaney-Criddle method and the fixed effective rainfall ratio method were used. The results showed that 4.77 billion $\beta$(c)/year paddy water demand is required for the 512,400 ha of paddy fieldss. Paddy water demand in the three major regions - Hwanghaedo, Pyeongando, Hamgyeongnamdo - was estimated chargong 81.7 percent of total paddy water demand in North Korea.

• Journal of Wetlands Research
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• v.14 no.4
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• pp.561-569
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• 2012

This study estimates landscape indices of the Gyeongan-stream watershed from 1975 to 2000 by classifying the land cover into impervious cover and pervious cover depending on its state using a landscape analysis program. For the indicator of biodiversity this study uses Total Core Area(TCA) among landscape indices. The estimated TCA is then used along with an Impervious Cover Model(ICM) to compare the number of fish species that appear in the Gyeongan-stream watershed. In the relations between TCA and the impervious cover ratio, it has been found that as the impervious cover ratio increases, TCA decreases accordingly. It shows that as the ratio of impervious cover in the landscape increases due to urbanization and development, the critical area that individual species need for isolation from outside has decreased. Also, the monitoring of the number of fish species that appear in the Gyeongan-stream watershed shows that in the areas with low impervious cover ratio there are more fish species appearing that inhabit in clean, uncontaminated water. It has been identified that the Gyeongan-stream watershed falls into the category of Impacted Stream and that its state is worsening, and since the watershed in this area responds to the impervious cover ratio very sensitively, its fish diversity it is required to improve the state of the basin through its proper and careful management.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.45-55
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• 2008

The impact on streamflow and groundwater recharge considering future potential climate and land use change was assessed using SLURP (Semi-distributed Land-Use Runoff Process) continuous hydrologic model. The model was calibrated and verified using 4 years (1999-2002) daily observed streamflow data for a $260.4km^2$ which has been continuously urbanized during the past couple of decades. The model was calibrated and validated with the coefficient of determination and Nash-Sutcliffe efficiency ranging from 0.8 to 0.7 and 0.7 to 0.5, respectively. The CCCma CGCM2 data by two SRES (Special Report on Emissions Scenarios) climate change scenarios (A2 and B2) of the IPCC (Intergovemmental Panel on Climate Change) were adopted and the future weather data was downscaled by Delta Change Method using 30 years (1977 - 2006, baseline period) weather data. The future land uses were predicted by CA (Cellular Automata)-Markov technique using the time series land use data of Landsat images. The future land uses showed that the forest and paddy area decreased 10.8 % and 6.2 % respectively while the urban area increased 14.2 %. For the future vegetation cover information, a linear regression between monthly NDVI (Normalized Difference Vegetation Index) from NOAA/AVHRR images and monthly mean temperature using five years (1998 - 2002) data was derived for each land use class. The future highest NDVI value was 0.61 while the current highest NDVI value was 0.52. The model results showed that the future predicted runoff ratio ranged from 46 % to 48 % while the present runoff ratio was 59 %. On the other hand, the impact on runoff ratio by land use change showed about 3 % increase comparing with the present land use condition. The streamflow and groundwater recharge was big decrease in the future.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.63-63
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• 2003

The aim of this study is to apply and verify of Bayesian probability model, the likelihood ratio and statistical model, at Janghung, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of IRS satellite images, field surveys, and maps of the topography, soil type, forest cover, geology and land use were constructed to spatial database. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography were calculated from the topographic database. Texture, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter and density of forest were extracted from the forest database. Land use was classified from the Landsat TM image satellite image. As each factor's ratings, the likelihood ratio coefficient were overlaid for landslide susceptibility mapping, Then the landslide susceptibility map was verified and compared using the existing landslide location. The results can be used to reduce hazards associated with landslides management and to plan land use and construction.