• Journal of Korean Society for Geospatial Information Science
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• v.23 no.4
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• pp.25-33
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• 2015

The study of ecosystem assessment(ES) is based on land cover information, and primarily it is performed at the global scale. However, these results as data for decision making have a limitation at the aspects of range and scale to solve the regional issue. Although the Ministry of Environment provides available land cover data at the regional scale, it is also restricted in use due to the intrinsic limitation of on screen digitizing method and temporal and spatial difference. This study of objective is to generate UAV land cover map. In order to classify the imagery, we have performed resampling at 5m resolution using UAV imagery. The results of object-based image segmentation showed that scale 20 and merge 34 were the optimum weight values for UAV imagery. In the case of RapidEye imagery;we found that the weight values;scale 30 and merge 30 were the most appropriate at the level of land cover classes for sub-category. We generated land cover imagery using example-based classification method and analyzed the accuracy using stratified random sampling. The results show that the overall accuracies of RapidEye and UAV classification imagery are each 90% and 91%.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.116-119
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• 2005

As to the synthetical estimation of land covering parameters or the compounded land covering classification for multi-resolution satellite data, former researches mainly adopted linear or nonlinear regression models to describe the regression relationship of land covering parameters caused by the degradation of spatial resolution, in order to improve the retrieval accuracy of global land covering parameters based on 1;he lower resolution satellite data. However, these methods can't authentically represent the complementary characteristics of spatial resolutions among different satellite data at arithmetic level. To resolve the problem above, a new compounded land covering classification method at arithmetic level for multi-resolution satellite data is proposed in this .paper. Firstly, on the basis of unsupervised clustering analysis of the higher resolution satellite data, the likelihood distribution scatterplot of each cover type is obtained according to multiple-to-single spatial correspondence between the higher and lower resolution satellite data in some local test regions, then Parzen window approach is adopted to derive the real likelihood functions from the scatterplots, and finally the likelihood functions are extended from the local test regions to the full covering area of the lower resolution satellite data and the global covering area of the lower resolution satellite is classified under the maximum likelihood rule. Some experimental results indicate that this proposed compounded method can improve the classification accuracy of large-scale lower resolution satellite data with the support of some local-area higher resolution satellite data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.4
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• pp.215-219
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• 2001

Land surface parameterization schemes for atmospheric models as well as decision support tools for ecosystem management require a frequent updating of land cover classification data for regional to global scales. Rice paddies have not been treated independently from other agricultural land classes in many classification systems, despite their atmospheric and ecological significance. A simple but improved method over conventional land cover classification schemes for rice paddy is suggested. Normalized difference vegetation index (NDVI) was calculated for the land area of South Korea at a 1km by 1 km resolution from the visible and the near-infrared channel reflectances of NOAA-AVHRR (Advanced Very High Resolution Radiometer). Monthly composite images of daily maximum NDVI were prepared for May and August, and used to classify 4 major land cover classes : urban, farmland, forests and water body. Among the pixels classified as "forests" in August, those classified as "water body" in May were assigned a "rice paddy" class. The distribution pattern of "rice paddy" pixels was very similar to the reported rice acreage of 1,455 Myons, which is the smallest administrative land unit in Korea. The correlation coefficient between the estimated and the reported acreage of Myons was 0.7, while 0.5 was calculated from the USGS classification.calculated from the USGS classification.

• Journal of the Korean Association of Geographic Information Studies
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• v.3 no.2
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• pp.73-86
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• 2000

Using monthly AVHRR-NDVI composite images, global vector data and statistical information, land cover change patterns in Asia during the major growing season (June, July and August) were analyzed for each country. Specifically, explanations on NDVI changes were developed at a regional scale emphasizing human impacts on ground vegetation. The annual mean change in each country showed NDVI-gain trends in high latitude areas and some parts of eastern China and northern/western India. On the contrary, NDVI-loss trends were distinctive in Japan, Korea, some parts of southeastern China, Vietnam, Laos, Cambodia, Thailand, Myanmar and some parts in southwestern/eastern India. These patterns largely coincided with socio-economic information reflected by human behavior. The NDVI change trends showed significant correlation with forest area changes. Also, a multiple regression model showed that the NDVI change patterns were significantly dependent on the changes in forest area and total population.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.5
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• pp.15-27
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• 2022

Climate change is considered a severe global problem closely related to carbon storage. However, recent urbanization and land-use changes reduce carbon stocks in terrestrial ecosystems. Recently, the role of protected areas has been emphasized as a countermeasure to the climate change, and protected areas allow the area to continue to serve as a carbon sink due to legal restrictions. This study attempted to expand the scope of these protected areas to an evaluation-based environmental spatial information theme map. In this study, the area of each grade was compared, and the distribution of land cover for each grade was analyzed using the Ecological and Nature Map, Environmental Conservation Value Assessment Map and Urban Ecological Map of Sejong Special Self-Governing City. Based on this, the average carbon storage for each grade was derived using the InVEST Carbon model. As a result of the analysis, the high-grade area of the environmental spatial information generally showed a wide area of the natural area represented by the forest area, and accordingly, the carbon storage amount was evaluated to be high. However, there are differences in the purpose of production, evaluation items, and evaluation methods between each environmental spatial information, there are differences in area, land cover, and carbon storage. Through this study, environmental spatial information based on the evaluation map can be used for land use management in the carbon aspect, and it is expected that a management plan for each grade suitable for the characteristics of each environmental spatial information is required.

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

• 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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• 2005.10a
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• pp.402-402
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• 2005

The purpose of this study is to analyze the land cover environment changes of tidal flat in the Hampyeong Bay. Especially, it centers on the changes in the sedimentary environment using remote sensing data. Multi-temporal Landsat data (Path-Row: 116-034) were used in this study. Remote sensing data can be effectively applied for quantitative analysis of geological environment changes in the Hampyeong-bay.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.569-573
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• 2005

Land surface temperature(LST) is one of the key parameters in physics and meteorology of land-surface processes on regional and global scales. Urban Heat Island(UHI), a meteorological phenomenon by which the air temperature in an urban area increases beyond that in the suburbs, grows with the progress of urbanization. Satellite remote sensing has been expected to be effective for obtaining thermal information of the earth's surface with a high resolution. The main purpose of this study is to produce LST map of Cheongju and to analyze the spatial distributions of surface heat fluxes in urban areas. This study, taking Cheongju as the study area, aims to examine relationship between vegetation cover rates and surface temperature, and to clarify a method for calculation surface temperature with Landsat TM thermal images.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.207-214
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• 2004

This study focused on providing a methodology to utilize temporal information obtained from remotely sensed data for monitoring a wide variety of targets on the earth's surface. Generally, a methodology in understanding of global changes is composed of mapping, quantifying, and monitoring changes in the physical characteristics of land cover. The selected processing and analysis technique affects the quality of the obtained information. In this research, feature extraction methodology is proposed based on segmentation. It requires a series of processing of multitempotal images: preprocessing of geometric and radiometric correction, image subtraction/thresholding technique, and segmentation/thresholding. It results in the mapping of the change-detected areas. Here, the appropriate methods are studied for each step and especially, in segmentation process, a method to delineate the exact boundaries of features is investigated in multiresolution framework to reduce computational complexity for multitemporal images of large size.