• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1089-1100
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• 2017

This paper presented a method using past land cover maps in image segmentation and training set collection for updating land cover maps. In this method, the object boundaries in past land cover maps were used for segmenting image clearly. Also, the classes of past land cover maps were used to extract additional informative training set from the initial classification result using a small number of initial training set. To evaluate the applicability of proposed method, a case study for updating land cover maps was carried out using middle-level land cover maps and WorldView-2 image in the Taean-gun, South Korea. As a result of the case study, the confusions between urban and barren, paddy/dry field and grassland in the initial classification result were reduced by adding training set. In addition, the object segmentation using boundaries of past land cover map cleared land cover boundaries and improved classification accuracy. Based on the result of case study, the proposed method using past land cover maps is expected to be useful for updating land cover maps.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.397-408
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• 2003

The information about land use presents future development and vision being the basis of nation development; therefore, it is necessary to more active research that can detect wide land use and changes for the information and efficient management about land use. In this study, we wished to analyze effectively land use changes to Ansan city that is fast changing land use by the latest national land development and urbanization. this study executed land-cover classification using 4 year's Landsat TM images including Ansan city, and efficiently could manage the result of land-cover changes through Arc/Info GRID analysis. Especially, by using change detection system that is developed in this research, we could variously detect land-cover changes, and query and search easily past land-cover changes of pixels that correspond to specific region.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.3
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• pp.25-39
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• 2013

In this study, the change of the discharge according to the land cover change which acts as one of dominant factors for the outlook of future discharge was analyzed using SWAT(Soil and Water Assessment Tool) model for Yongdam and Daecheong Dam Watershed in the Geum River Basin. The land cover maps generated by Landsat TM satellite images in the past 1990 and 1995 were used as observed data to simulate the land cover in 2000 by CA-Markov serial technique and after they were compared and verified, the changes of land cover in 2050 and 2100 in the future were simulated. The discharge before and after the change of land cover by using input data of SWAT model was compared and analyzed under the A1B scenario. As a result of analyzing the trend in the elapses of year on the land cover in the Geum River Basin, the forest and rice paddy class area steadily decreased while the urban, bare ground and grassland classes increased. As a result of analyzing the change of discharge considering the future change of the land cover, it appeared that the discharge considering the change of land cover increases by 1.83~2.87% on the whole compared to the discharge not considering the change of land cover.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.445-455
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• 2011

A probabilistic classification framework is presented that can combine temporal contextual information derived from an existing land-cover map in order to improve the classification accuracy of land-cover classes that can not be discriminated well when using spectral information only. The transition probability is computed by using the existing land-cover map and training data, and considered as a priori probability. By combining the a priori probability with conditional probability computed from spectral information via a Bayesian combination rule, the a posteriori probability is finally computed and then the final land-cover types are determined. The method presented in this paper can be adopted to any probabilistic classification algorithms in a simple way, compared with conventional classification methods that require heavy computational loads to incorporate the temporal contextual information. A case study for crop classification using time-series MODIS data sets is carried out to illustrate the applicability of the presented method. The classification accuracies of the land-cover classes, which showed lower classification accuracies when using only spectral information due to the low resolution MODIS data, were much improved by combining the temporal contextual information. It is expected that the presented probabilistic method would be useful both for updating the existing past land-cover maps, and for improving the classification accuracy.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.4 s.34
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• pp.59-65
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• 2005

It is very important to detect land cover/land use change from the past and to use it for future urban plan. This paper investigated the application of Landsat satellite imagery for detecting urban growth and assessing its impact on surface temperature in the region. Land cover/land use change detection was carried out by using 30m resolution Landsat satellite images and hierarchial approach was introduced to detect more detail change on the changing area through high resolution aerial photos. Also, surface temperature according to land cover/land use was calculated from Landsat TM thermal infrared data and compared with real temperature to analyze the relationship between urban expansion and surface temperature. As a result, the urban expansion has raised surface radiant temperature in the urbanized area. The method using remote sensing data based on GIS was found to be effective in monitoring and analysing urban growth and in evaluating urbanization impact on surface temperature.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.65-77
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• 2012

This study examined changes in land cover for the past 25 years in Busan and subsequently evaluated heat island potential by using land surface temperature and observation temperature data. The results were as below. The urban area of Busan increased by more than 2.5 times for the past 25 years from 1975 to 2000. It was believed that an increase in the pavement area of city within such a short period of time was an unprecedented phenomenon unique to our country. It could be assumed that urban heat island would be worsened through this process. After analyzing the land temperature according to the land cover, it was shown that there were noticeable changes in the temperature of urban & built-up and mountain & forest areas. In particular, the temperature rose to $36{\sim}39^{\circ}C$ in industrial areas during the summer, whereas it went down to $22{\sim}24^{\circ}C$ in the urban areas at whose center there were mountains. It was found that heat island potential according to the level of land cover had various values depending on the conditions of land cover. Among the areas of urbanization, the industrial area's heat island potential is 6 to $8^{\circ}C$, and the residential and commercial area's is $0{\sim}5^{\circ}C$, so it has been found that there is high possibility to induce urban heat islands. Meanwhile, in the forest or agricultural area or the waterside, the heat island potential is $-6{\sim}-3^{\circ}C$. With this study result, it is possible to evaluate the effects of temperature increase according to the urban land use, and it can be used as foundational data to improve urban thermal environment and plan eco-friendly urban development.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.10 no.2
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• pp.97-104
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• 2007

A/R CDM(Afforestation/Reforestation Clean Development Mechanism) in Kyoto Mechanism means, either afforestation in the area used for other purposes more than 50 years or reforestation in the area used for other purposes on December 31st in 1989. South Korea has few sites due to the successful forestation in the past, but North Korea has not reforested the deforested lands since the mid-1970's. So these areas need to apply A/R CDM Project for restoration. The purposes of this study are to make a time series analysis in deforested areas and to estimate a feasibility of A/R CDM. To find the site satisfying A/R CDM business definition, land cover classification was applied using satellite images of the mid-1970's with good forestation, late 1980's including A/R CDM base year, and recent 2000's, and the chronological change was analyzed to categorize the possible sites. The North Korean topographical map of 1977 was used to verify land cover classification degree of 1970's, the land cover classification results made by the Ministry of Environment in 2000 were compared to verify the accuracy of 1980's results, and the land cover classification results in 2000's were verified by 2 site visits. The results of this study can be summarized as follows. The eligible A/R CDM sites are 605,156ha on the basis of the forestation change analysis in North Korea. Since the mid-1970's, 30.8% of the decreased forestation area of 1,966,306ha was classified into A/R CDM eligible sites. While other countries have the limited eligible sites, which has not been used for forestation since 1989 or which is being scattered, North Korea has large scale sites. Deforested sites are mainly around road and residential area, consequently give better accessibility for forestation than other countries. In conclusion, it is found that North Korea can provide efficient site for applying A/R COM Project to forestation restoring deforested land because of easy accessibility and existence of many possible sites due to artificial deforestation. Also, it is meaningful that the study suggests the application possibility of A/R COM Project to restore deforested land in North Korea and the related basic information through the chronological classification of the mid-1970's with good forestation, the late-1980's including A/R COM base year, and recent 2000's. It is expected that the study contributes to revitalization of A/R CDM Project and related research on North Korea forestation.

• Spatial Information Research
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• v.10 no.1
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• pp.139-152
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• 2002

The purpose of this study is to estimate temporal soil loss change according to long-term land cover changes using G1S and RS. Revised USLE(Universal Soil Loss Equation) factors were prepared by using point rainfall data, DEM(Digital Elevation Model), soil map and land cover map. During the past two decades, land cover changes were traced by using Landsat MSS and TM data. As a result, forest area in 2000 has decreased 25.3 $km^2$ compared with that in 1990. Soil loss has decreased 3751.2 tou/yr. On the other hand, upland area has increased 22.5 $km^2$. Soil loss of upland has increased 5395.4 to/yr. Therefore, soil loss in 2000 increased 6.3 kg/$m^2$/yr compared with that in 1990. This was mainly caused by the increased upland area.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.461-469
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• 2022

This paper presents a novel training data extraction approach using semi-supervised learning (SSL)-based classification without the analyst intervention for time-series land-cover mapping. The SSL-based approach first performs initial classification using initial training data obtained from past images including land-cover characteristics similar to the image to be classified. Reliable training data from the initial classification result are then extracted from SSL-based iterative classification using classification uncertainty information and class labels of neighboring pixels as constraints. The potential of the SSL-based training data extraction approach was evaluated from a classification experiment using unmanned aerial vehicle images in croplands. The use of new training data automatically extracted by the proposed SSL approach could significantly alleviate the misclassification in the initial classification result. In particular, isolated pixels were substantially reduced by considering spatial contextual information from adjacent pixels. Consequently, the classification accuracy of the proposed approach was similar to that of classification using manually extracted training data. These results indicate that the SSL-based iterative classification presented in this study could be effectively applied to automatically extract reliable training data for time-series land-cover mapping.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.3
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• pp.76-88
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• 2015

This paper presents a classification method based on the combination of active learning with temporal contextual information extracted from past land-cover maps for the classification of crop cultivation areas. Iterative classification based on active learning is designed to extract reliable training data and cultivation rules from past land-cover maps are quantified as temporal contextual information to be used for not only assignment of training data but also relaxation of spectral ambiguity. To evaluate the applicability of the classification method proposed in this paper, a case study with MODIS time-series vegetation index data sets and past cropland data layers(CDLs) is carried out for the classification of corn and soybean in Illinois state, USA. Iterative classification based on active learning could reduce misclassification both between corn and soybean and between other crops and non crops. The combination of temporal contextual information also reduced the over-estimation results in major crops and led to the best classification accuracy. Thus, these case study results confirm that the proposed classification method can be effectively applied for crop cultivation areas where it is not easy to collect the sufficient number of reliable training data.