• Korean Journal of Environment and Ecology
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• v.37 no.4
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• pp.289-301
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• 2023

Forests within watersheds are essential in maintaining ecosystems and are the central infrastructure for constructing an ecological network system. However, due to indiscriminate development projects carried out over past decades, forest fragmentation and land use changes have accelerated, and their original functions have been lost. Since a forest's structural pattern directly impacts ecological processes and functions in understanding forest ecosystems, identifying and analyzing change patterns is essential. Therefore, this study analyzed structural changes in the forest landscape according to the time-series land cover changes using the FRAGSTATS model for the dam watershed of the Geum River upstream. Land cover changes in the dam watershed of the Geum River upstream through land cover change detection showed an increase of 33.12 square kilometers (0.62%) of forests and 67.26 square kilometers (1.26%) of urbanized dry areas and a decrease of 148.25 square kilometers (2.79%) in agricultural areas from the 1980s to the 2010s. The results of no-sampling forest landscape analysis within the watershed indicated landscape percentage (PLAND), area-weighted proximity index (CONTIG_AM), average central area (CORE_MN), and adjacency index (PLADJ) increased, and the number of patches (NP), landscape shape index (LSI), and cohesion index (COHESION) decreased. Identification of structural change patterns through a moving window analysis showed the forest landscape in Sangju City, Gyeongsangbuk Province, Boeun County in Chungcheongbuk Province, and Jinan Province in Jeollabuk Province was relatively well preserved, but fragmentation was ongoing at the border between Okcheon County in Chungcheongbuk Province, Yeongdong and Geumsan Counties in Chungcheongnam Province, and the forest landscape in areas adjacent to Muju and Jangsu Counties in Jeollabuk Province. The results indicate that it is necessary to establish afforestation projects for fragmented areas when preparing a future regional forest management strategy. This study derived areas where fragmentation of forest landscapes is expected and the results may be used as basic data for assessing the health of watershed forests and establishing management plans.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.242-245
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• 2009

본 연구는 한반도 국토 전역을 모니터링 하기 위해 MODIS(Moderate Resolution Imaging Spectroradiometer) 위성영상을 이용하는데 있어서 필요한 기본적 자료처리 및 모니터링 시스템 구축을 목표로 한다. 현재 MODIS관련 product는 대부분 NASA에서 제공하는 알고리즘을 구현하여 제작되며 이러한 product들은 여러 웹싸이트에 접근하여 획득하도록 되어있다. 이러한 방식은 장기적으로 국토를 모니터링 하는데 필요한 자료를 원활하게 공급하지 못하게 되는 단점을 가지고 있다. 그러므로 이 연구는 한반도의 국토환경을 모니터링 하는데 MODIS 자료를 원활히 공급하기 위한 처리 시스템을 구축하고자 한다. 본 연구에서는 Windows환경을 기반으로 사용자 인터페이스를 제공하고 다양한 MODIS 관련 모듈을 내부적으로 자동화 처리하여 보다 쉽게 사용할 수 있도록 개선하였다. 특히 MODIS 원시영상(Production Data Set) Level0 취득부터 보정된 영상자료 Level1B까지의 자동화 처리에 중점을 두었고, Level2이후의 주로 육상(Land)부분에 해당하는 자료를 생성하는 부분을 구현한다. 이러한 MODIS 자료의 생성 이후에 국토 모니터링에 필요한 토지 피복 변화, 산불 탐지 등의 정보를 생성하는 알고리즘 구현 등으로 범위를 확장하려고 한다.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.2
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• pp.81-94
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• 2005

The purpose of this study is to test the applicability of landscape metrics for quantifying and monitoring the landscape structure in the Geumho River watershed, which has undergone heavy environmental disturbances. Landscape metrics were computed from land cover maps(1985, 1999) for the forest patches. The number of variables were reduced from 12 metrics to 3 factors through factor analysis. These factors accounted for above 91% of the variation in the original metrics. We also determined the relative effects of land development on the changes of forest landscape structure using multiple linear regression analysis. At the forest patches, the conversion of forest to urban areas and agriculture resulted in increased fragmentation. Patch area and patch size decreased. and patch density increased as a result of the conversion of forest to agriculture($R^2=0.696$, p<0.01). The heterogeneity of patch size and complexity of patch shape mainly decreased as a result of the conversion of forest to urban areas($R^2=0.405$, p<0.01). The density of core area and edge showed the tendency increase, but there was no relationship with the conversion of forest to urban area and agriculture The future research will be needed to analyze correlations between landscape structures and specific environmental and socioeconomic landscape functions.

• Korean Journal of Environment and Ecology
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• v.32 no.6
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• pp.686-697
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• 2018

Due to the advance in remote sensing technology, it has become easier to more frequently obtain high resolution imagery to detect delicate changes in an extensive area, particularly including forest which is not readily sub-classified. Time-series analysis on high resolution images requires to collect extensive amount of ground truth data. In this study, the potential of land coverage mapas ground truth data was tested in classifying high-resolution imagery. The study site was Wonju-si at Gangwon-do, South Korea, having a mix of urban and natural areas. KOMPSAT-3A imagery taken on March 2015 and land coverage map published in 2017 were used as source data. Two pixel-based classification algorithms, Support Vector Machine (SVM) and Random Forest (RF), were selected for the analysis. Forest only classification was compared with that of the whole study area except wetland. Confusion matrixes from the classification presented that overall accuracies for both the targets were higher in RF algorithm than in SVM. While the overall accuracy in the forest only analysis by RF algorithm was higher by 18.3% than SVM, in the case of the whole region analysis, the difference was relatively smaller by 5.5%. For the SVM algorithm, adding the Majority analysis process indicated a marginal improvement of about 1% than the normal SVM analysis. It was found that the RF algorithm was more effective to identify the broad-leaved forest within the forest, but for the other classes the SVM algorithm was more effective. As the two pixel-based classification algorithms were tested here, it is expected that future classification will improve the overall accuracy and the reliability by introducing a time-series analysis and an object-based algorithm. It is considered that this approach will contribute to improving a large-scale land planning by providing an effective land classification method on higher spatial and temporal scales.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.161-161
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• 2020

최근 기후변화와 여름철 고온 등으로 인한 녹조현상, 화학물질 및 유류 유출 등 화학사고로 인한 하천의 수질오염과 관련된 사회적 관심이 높아지고 있다. 특히, 화학사고로 인한 유해화학물질 유출은 인체에 접촉 시 악영향을 끼치며, 대기·수질·토양을 오염시키고 주변 농작물의 변색이나 괴사를 유발하는 등의 피해를 야기하기 때문에 적절한 조치와 대응이 필요하다. 환경부에서는 유해화학물질 유출사고로 인한 국민건강 및 환경상의 위해를 예방하기 위해 화학물질관리법과 화학물질 등록 및 평가에 관한 법률을 제정하여 유해화학물질을 관리하고 화학사고에 대응하고 있다. 그러나, 화학사고 발생 시 공장 인근의 먼지, 악취 등을 감시하기 위해 현장인력에 의존하거나 화학물질의 유출이 우려되는 곳에 제한적으로 검출센서를 설치해 사고를 감시하고 있어 검출센서 미설치 지역에 대한 능동적 탐지가 어렵고, 화학물질의 공간적 분포 탐지가 불가능하여 초동 대응에 한계가 있다. 한편 최근 초분광 영상을 활용하여 물질 고유의 분광특성을 분석함으로써 토지피복, 식생, 수질 등의 식별에 활용되고 있다. 따라서 초분광 센서를 활용한 화학물질 감지 가능성도 보여주고 있지만, 초분광 센서를 활용한 하천의 화학물질 감지를 위한 연구는 미비한 실정이다. 이에 본 연구에서는 유해화학물질 18종을 대상으로 초분광 영상을 이용한 상호 구분이 가능한 지 확인하고자 해당 유해화학물질의 초분광 영상을 촬영하여 분광라이브러리를 구축하였다. 또한 물질별 특성을 보이는 분광밴드의 범위를 지정해 특성 분광라이브러리를 구축하였으며, 해당 과정에 대한 표준 및 절차를 제시하였다. 본 연구에서 제시한 절차에 따라 18종의 유해화학물질 분광라이브러리와 특성 분광라이브러리를 구축한 결과, 유해화학물질의 식별 가능성을 확인하였다. 향후 연구를 통해 유해화학물질 분광라이브러리 데이터베이스를 확대하고, 실시간 모니터링에 적용할 경우 신속한 화학사고 발생여부 감지 및 대응에 활용할 수 있을 것으로 사료된다.

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.179-197
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• 2020

Topographic normalization reduces the terrain effects on reflectance by adjusting the brightness values of the image pixels to be equal if the pixels cover the same land-cover. Topographic effects are induced by the imaging conditions and tend to be large in high mountainousregions. Therefore, image analysis on mountainous terrain such as estimation of wildfire damage assessment requires appropriate topographic normalization techniques to yield accurate image processing results. However, most of the previous studies focused on the evaluation of topographic normalization on satellite images with moderate-low spatial resolution. Thus, the alleviation of topographic effects on multi-temporal high-resolution images was not dealt enough. In this study, the evaluation of terrain normalization was performed for each band to select the optimal technical combinations for rapid and accurate wildfire damage assessment using PlanetScope images. PlanetScope has considerable potential in the disaster management field as it satisfies the rapid image acquisition by providing the 3 m resolution daily image with global coverage. For comparison of topographic normalization techniques, seven widely used methods were employed on both pre-fire and post-fire images. The analysis on bi-temporal images suggests the optimal combination of techniques which can be applied on images with different land-cover composition. Then, the vegetation index was calculated from the images after the topographic normalization with the proposed method. The wildfire damage detection results were obtained by thresholding the index and showed improvementsin detection accuracy for both object-based and pixel-based image analysis. In addition, the burn severity map was constructed to verify the effects oftopographic correction on a continuous distribution of brightness values.

• Journal of the Korean Geographical Society
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• v.40 no.3 s.108
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• pp.296-309
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• 2005

In this study, we investigated the change of future land-surface and relationships of land-surface change with geo-spatial information, using a Bayesian prediction model based on a likelihood ratio function, for analysing the land-surface change of the Gongju area. We classified the land-surface satellite images, and then extracted the changing area using a way of post classification comparison. land-surface information related to the land-surface change is constructed in a GIS environment, and the map of land-surface change prediction is made using the likelihood ratio function. As the results of this study, the thematic maps which definitely influence land-surface change of rural or urban areas are elevation, water system, population density, roads, population moving, the number of establishments, land price, etc. Also, thematic maps which definitely influence the land-surface change of forests areas are elevation, slope, population density, population moving, land price, etc. As a result of land-surface change analysis, center proliferation of old and new downtown is composed near Gum-river, and the downtown area will spread around the local roads and interchange areas in the urban area. In case of agricultural areas, a small tributary of Gum-river or an area of local roads which are attached with adjacent areas showed the high probability of change. Most of the forest areas are located in southeast and from this result we can guess why the wide chestnut-tree cultivation complex is located in these areas and the capability of forest damage is very high. As a result of validation using a prediction rate curve, a capability of prediction of urban area is $80\%$, agriculture area is $55\%$, forest area is $40\%$ in higher $10\%$ of possibility which the land-surface change would occur. This integration model is unsatisfactory to Predict the forest area in the study area and thus as a future work, it is necessary to apply new thematic maps or prediction models In conclusion, we can expect that this way can be one of the most essential land-surface change studies in a few years.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.35-45
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• 2018

Image data collected by an airborne hyperspectral camera system have a great usability in coastal line mapping, detection of facilities composed of specific materials, detailed land use analysis, change monitoring and so forh in a complex coastal area because the system provides almost complete spectral and spatial information for each image pixel of tens to hundreds of spectral bands. A few approaches after classifying by a few approaches based on SAM(Spectral Angle Mapper) supervised classification were applied for extracting optimal land cover information from hyperspectral images acquired by CASI-1500 airborne hyperspectral camera on the object of a coastal area which includes both land and sea water areas. We applied three different approaches, that is to say firstly the classification approach of combined land and sea areas, secondly the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas, and thirdly the land area-only classification approach using atmospheric correction images and compared classification results and accuracies. Land cover classification was conducted respectively by selecting not only four band images with the same wavelength range as IKONOS, QuickBird, KOMPSAT and GeoEye satelllite images but also eight band images with the same wavelength range as WorldView-2 from 48 band hyperspectral images and then compared with the classification result conducted with all of 48 band images. As a result, the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas is more effective than classification approach of combined land and sea areas. It is showed the bigger the number of bands, the higher accuracy and reliability in the reclassification approach referred above. The results of higher spectral resolution showed asphalt or concrete roads was able to be classified more accurately.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.847-859
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• 2021

Recently, as the number of high-resolution satellite SAR images increases, the demand for precise matching of SAR imagesin change detection and image fusion is consistently increasing. RMSE (Root Mean Square Error) values using GCPs (Ground Control Points) selected by analysts have been widely used for quantitative evaluation of image registration results, while it is difficult to find an approach for automatically measuring the registration accuracy. In this study, a feasibility analysis was conducted on using the FSIM (Feature Similarity) index as a measure to evaluate the registration accuracy. TerraSAR-X (TSX) staring spotlight data collected from various incidence angles and orbit directions were used for the analysis. FSIM was almost independent on the spatial resolution of the SAR image. Using a single SAR image, the FSIM with respect to registration errors was analyzed, then use it to compare with the value estimated from TSX data with different imaging geometry. FSIM index slightly decreased due to the differencesin imaging geometry such as different look angles, different orbit tracks. As the result of analyzing the FSIM value by land cover type, the change in the FSIM index according to the co-registration error was most evident in the urban area. Therefore, the FSIM index calculated in the urban was mostsuitable for determining the accuracy of image registration. It islikely that the FSIM index has sufficient potential to be used as an index for the co-registration accuracy of SAR image.

• Spatial Information Research
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• v.19 no.5
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• pp.1-11
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• 2011

This study mainly focused on the method of accurately extracting damage information in the im agery change detection process using the constructed high resolution aerial im agery. Bongwha-gun in Gyungsangbuk-do which had been severely damaged from a localized torrential downpour at the end of July, 2008 was selected as study area. This study utilized aerial im agery having photographing scale of 30cm gray image of pre-disaster and 40cm color image of post-disaster. In order to correct errors from the differences of the image resolution of pre-/post-disaster and time series, the prelim inary phase of image processing techniques such as normalizing, contrast enhancement and equalizing were applied to reduce errors. The extent of the damage was calculated using one to one comparison of the intensity of each pixel of pre-/post-disaster im aged. In this step, threshold values which facilitate to extract the extent that damage investigator wants were applied by setting difference values of the intensity of pixel of pre-/post-disaster. The accuracy of optimal image processing and the result of threshold values were verified using the error matrix. The results of the study enabled the early exaction of the extents of the damages using the aerial imagery with identical characteristics. It was also possible to apply to various damage items for imagery change detection in case of utilizing multi-band im agery. Furthermore, more quantitative estimation of the dam ages would be possible with the use of numerous GIS layers such as land cover and cadastral maps.