• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.679-681
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• 2003

The objectives of this research are as follows. First, to investigate methods for a national-scale land cover map based on multi-temporal classification of MODIS data and multi-spectral classification of Landsat TM data. Second, to investigate methods to p roduce ecological zone maps of Korea based on vegetation, climate, and topographic characteristics. The results of this research can be summarized as follows. First, NDVI and EVI of MODIS can be used to ecological mapping of the country by using monthly phenological characteris tics. Second, it was found that EVI is better than NDVI in terms of atmospheric correction and vegetation mapping of dense forests of the country. Third, several ecological zones of the country can be identified from the VI maps, but exact labeling requires much field works, and sufficient field data and macro-environmental data of the country. Finally, relationship between land cover types and natural environmental factors such as temperature, precipitation, elevation, and slope could be identified.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.83-92
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• 2019

Land cover (LC) is an important factor in socioeconomic and environmental studies. According to various studies, a number of LC maps, including global land cover (GLC) datasets, are made using polar orbit satellite data. Due to the insufficiencies of reference datasets in Northeast Asia, several LC maps display discrepancies in that region. In this paper, we performed a feasibility assessment of LC mapping using Geostationary Ocean Color Imager (GOCI) data over Northeast Asia. To produce the LC map, the GOCI normalized difference vegetation index (NDVI) was used as an input dataset and a level-2 LC map of South Korea was used as a reference dataset to evaluate the LC map. In this paper, 7 LC types(urban, croplands, forest, grasslands, wetlands, barren, and water) were defined to reflect Northeast Asian LC. The LC map was produced via principal component analysis (PCA) with K-means clustering, and a sensitivity analysis was performed. The overall accuracy was calculated to be 77.94%. Furthermore, to assess the accuracy of the LC map not only in South Korea but also in Northeast Asia, 6 GLC datasets (IGBP, UMD, GLC2000, GlobCover2009, MCD12Q1, GlobeLand30) were used as comparison datasets. The accuracy scores for the 6 GLC datasets were calculated to be 59.41%, 56.82%, 60.97%, 51.71%, 70.24%, and 72.80%, respectively. Therefore, the first attempt to produce the LC map using geostationary satellite data is considered to be acceptable.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.289-301
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• 2011

In this paper an attempt has been made to classify Asia land cover considering climatic and vegetative characteristics. The sub-class clustering based on the 13 MODIS land cover classes (except water) over Asia was performed with the climate map and the NOVI derived from SPOT 5 VGT D10 data. The unsupervised classification for the sub-class clustering was performed in each land cover class, and total 74 clusters were determined over the study area. Via these clusters, the annual variations (from 1999 to 2007) of precipitation rate and temperature were analyzed as an example by a simple linear regression model. The various annual variations (negative or positive pattern) were represented for each cluster because of the various climate zones and NOVI annual cycles. Therefore, the detailed land cover map as the classification result by the sub-class clustering in this study can be useful information in modelling works for requiring the detailed climatic and vegetative information as a boundary condition.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.1
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• pp.69-88
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• 2023

Efficient spatial planning is one of the necessary factors to successfully respond to climate change. And researchers often use LULC(Land-Use/Cover) data to conduct land use and spatial planning research. However, LULC data has a limited number of grades related to urban surface, so each different urban structure appearing in several cities is not easily analyzed with existing land cover products. This limitation of land cover data seems to be overcome through LCZ(Local Climate Zone) data used in the urban heat island field. Therefore, this study aims to first discuss whether LCZ data can be applied not only to urban heat island fields but also to other fields, and secondly, whether LCZ data still have problems with existing LULC data. Research methodology is largely divided into two categories. First, through literature review, studies in the fields of climate, land use, and urban spatial structure related to LCZ are synthesized to analyze what research LCZ data is currently being used, and how it can be applied and utilized in the fields of land use and urban spatial structure. Next, the GIS spatial analysis methodology is used to analyze whether LCZ still has several errors that are found in the LULC.

• Journal of Environmental Science International
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• v.11 no.9
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• pp.925-931
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• 2002

Land-use types should be included in air pollutant diffusion model because a complex mixture of various land-use patterns with computational grid can make errors in calculation of several parameters. However, the air pollutant diffusion model has generally been treated with a uniform component with land-use type in each mesh because of the complexity of the simulation. This study presents a numerical simulation of the horizontal distribution of $O_3$dry deposition velocity during summertime in Busan metropolitan city. The calculation of the meteorological field was conducted using the land cover data. Simulation has been performed by the following two scenarios : (1) one with current land cover data, and (2) the other with only land and sea for the surface characteristics. The results from each scenario reveals considerable differences on the meteorological fields and these differences can cause changes in the calculation values of $O_3$deposition velocity.

• Journal of The Geomorphological Association of Korea
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• v.18 no.1
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• pp.41-56
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• 2011

This study analyzes the change in land-cover based on satellite imagery to draw up land-cover map in the future, and estimates the change in land category using statistical data of the land category. To estimate land category, this study applied the double exponentially smoothing method. The result of the land cover classification according to year using satellite imagery showed that the type with the largest increase in area of land cover change in the cities of Cheonan and Asan was artificial structure, followed by water, grass field and bare land. However forest, paddy, marsh and dry field were reduced. Further, the result of the time-series analysis of the land category was found to be similar to the result of the land cover classification using satellite imagery. Especially, the result of the estimation of the land category change using the double exponentially smoothing method showed that paddy, dry field, forest and marsh are anticipated to consistently decrease in area from 2010 to 2100, whereas artificial structure, water, bare land and grass field are anticipated to consistently increase. Such results can be utilized as basic data to estimate the change in land cover according to climate change in order to prepare climate change response strategies.

• Proceedings of the KSRS Conference
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• v.2
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• pp.555-558
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• 2006

In recent years, urban development has expanded rapidly in Nawabshah City of Pakistan. A major effect associated with this population trend is transformation of the landscape from natural cover types to increasingly impervious urban land. The core objective of this study are to provide time-series information to define and measure the urban land cover changes of Nawabshah, Pakistan between the years 1992 and 2002, and to examine related urbanization impacts on air quality of the study area. Two multi-temporal Landsat images acquired in 1992 and 2002 together with standard topographical maps to measure land cover changes were used in this study. The image processing and data manipulation were conducted using algorithms supplied with the ERDAS Imagine software. An unsupervised classification approach, which uses a minimum spectral distance to assign pixels to clusters, was used with the overall accuracy ranging from 84 percent to 92 percent. Land cover statistics demonstrate that during the study period (1992-2002) extensive transformation of barren and vegetated lands into urban land have taken place in Nawabshah City. Results revealed that land cover changes due to urbanization has not only contaminated the air quality of the study area but also raised the health concerns for the local residents.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.2
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• pp.113-122
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• 2021

Urban expansion, particularly converting sub-urban areas to residential and commercial land use in metropolitan areas, has been considered as a significant signal of regional economic development. However, this results in urban climate change. One of the key impacts of rapid urbanization on the environment is the effect of UHI (Urban Heat Island). Understanding the effects of urban land cover change on UHI is crucial for improving the ecology and sustainability of cities. This research reports an application of remote sensing data, GIS (Geographic Information Systems) for assessing effects of urban land cover change on the LST (Land Surface Temperature) and heat budget components in Ho Chi Minh City, where is one of the fastest urbanizing region of Vietnam. The change of urban land cover component and LST in the city was derived by using multi-temporal Landsat data for the period of 1998 - 2020. The analysis showed that, from 1998 to 2020 the city had been drastically urbanized into multiple directions, with the urban areas increasing from approximately 125.281 km² in 1998 to 162.6 km² in 2007, and 267.2 km² in 2020, respectively. The results of retrieved LST revealed the radiant temperature for 1998 ranging from 20.2℃ to 31.2℃, while that for 2020 remarkably higher ranging from 22.1℃ to 42.3℃. The results also revealed that given the same percentage of urban land cover components, vegetation area is more effective to reduce the value of LST, meanwhile the impervious surface is the most effective factor to increase the value of the LST.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1035-1046
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• 2022

This study evaluated the applicability of multi-resolution data fusion for land-cover classification. In the applicability evaluation, a spatial time-series geostatistical deconvolution/fusion model (STGDFM) was applied as a multi-resolution data fusion model. The study area was selected as some agricultural lands in Iowa State, United States. As input data for multi-resolution data fusion, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite images were used considering the landscape of study area. Based on this, synthetic Landsat images were generated at the missing date of Landsat images by applying STGDFM. Then, land-cover classification was performed using both the acquired Landsat images and the STGDFM fusion results as input data. In particular, to evaluate the applicability of multi-resolution data fusion, two classification results using only Landsat images and using both Landsat images and fusion results were compared and evaluated. As a result, in the classification result using only Landsat images, the mixed patterns were prominent in the corn and soybean cultivation areas, which are the main land-cover type in study area. In addition, the mixed patterns between land-cover types of vegetation such as hay and grain areas and grass areas were presented to be large. On the other hand, in the classification result using both Landsat images and fusion results, these mixed patterns between land-cover types of vegetation as well as corn and soybean were greatly alleviated. Due to this, the classification accuracy was improved by about 20%p in the classification result using both Landsat images and fusion results. It was considered that the missing of the Landsat images could be compensated for by reflecting the time-series spectral information of the MODIS images in the fusion results through STGDFM. This study confirmed that multi-resolution data fusion can be effectively applied to land-cover classification.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.516-518
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• 2003

This study discusses an effort to build a model to link normalized difference vegetation indices (NDVI) and socio-economic indicators derived from village survey (1990, 1993, 1996, and 2000) statistical data in Bekasi, West Java, Indonesia. Socio-economics indicators of sub-district level, in this study the number of agricultural households (AH), are aggregated from village level data. NDVI from Landsat-TM resolution data (1989 and 1997) are computed to detect land use/land cover (LULC) dynamics in the sub-district areas. Attention is mainly paid on the examination of agricultural land cover changing in the sub-district level. NDVI measurements might be used to predict AH dynamics as showed by computed linear regression models.