• Journal of Korean Society of Forest Science
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• v.81 no.4
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• pp.346-356
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• 1992

The extensive Snake River floodplain in Northwest United States has experienced major changes in water channels and vegetation types due to floodings. To detect the change of wetland cover-types for the period of 1985 and 1988, post-classification comparison and image differencing change detection techniques were evaluated using Landsat-5 TM digital data. Differenced infrared-band images indicated better accuracy indices than any visible-band images. A thresholding technique was applied to identify the change and no change categories from the transformed images produced by image differencing. The problems in using different accuracy indices, including the Kappa coefficient of agreement, overall accuracy, producer's accuracy, user's accuracy, and average accuracy(based on both the producer's and user's accuracy approaches) in determining an optimal threshold level, were examined.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.598-601
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• 2007

Urban growth management is essential for sustainable urban growth. Monitoring physical urban built-up area is a task of great significance to manage urban growth. Detecting urbanized area is essential for monitoring urbanized area. Although image classifications using satellite imagery are among the conventional methods for detecting urbanized area, they requires very tedious and hard work, especially if time-series remote sensing data have to be processed. In this paper, we propose an effective urbanized area detecting method based on normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). To verify the proposed method, we extract urbanized area using two methods; one is conventional supervised classification method and the other is the proposed method. Experiments shows that two methods are consistent with 98% in 1998, 99.3% in 2000, namely the consistency of two methods is very high. Because the proposed method requires no more process without band operations, it can reduce time and effort. Compared with the supervised classification method, the proposed method using vegetation indices can serve as quick and efficient alternatives for detecting urbanized area.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1861-1869
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• 2022

Recent climate change can increase the frequency and damage of wildfires worldwide. It can also lead to the deterioration of the forest ecosystem and increase casualties and economic loss. Satellite-based indices for forest damage can facilitate an objective and rapid examination of burned areas and help analyze inaccessible places like North Korea. In this letter, we conducted a detection of burned areas in North Korea using the traditional Normalized Burn Ratio (NBR), the Normalized Difference Vegetation Index (NDVI) to represent vegetation vitality, and the Fire Burn Index (FBI) and Forest Withering Index (FWI) that were recently developed. Also, we suggested a strategy for the satellite-based detection of burned areas in the Korean Peninsula as a result of comparing the four indices. Future work requires the examination of small-size wildfires and the applicability of deep learning technologies.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.295-300
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• 2004

변화탐지를 위하여 기존에 사용하던 화소차 혹은 화소비 변화탐지 기법은 단밴드의 영상을 사용하므로 다중분광 자료를 활용하기 어렵고 변화지역의 유형을 추출하는데 적절하지 못하다는 단점이 있다. 후분류 변화탐지 기법은 다중분팡 영상의 활용이 가능하고 변화지역의 변화 유형을 파악할 수 있지만 변화탐지 성과가 분류 결과의 정확도에 의해 크게 영향을 받는다. 이에 반해 CVA(Change Vector Analysis) 변화탐지는 여러 밴드의 다중분광 영상을 이용하여 변화지역을 탐지할 뿐 아니라 피복 변화의 경향을 파악할 수 있어 보다 효율적인 기법으로 평가받고 있다. 기본적인 CVA 변화탐지는 일반적으로 다중분광 영상의 red 밴드와 infrared 밴드의 영상을 조합하여 변화탐지를 수행하여 식생 및 인공물의 변화를 탐지한다. 본 연구에서는 단순한 red/NIR 밴드간의 조합 외에 식생의 특성을 계수로 변환하는 PDA(Pattern Decomposition Analysis) 변환 및 Tasseled Cap 변환 결과를 이용한 CVA 변화탐지를 수행하고 각 결과의 정확도를 비교하여 보다 효율적인 식생 변화탐지 기법을 제안하고자 하였다.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.819-829
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• 2019

In the disaster of collapse, an immediate response is needed to prevent the damage from worsening, and damage area calculation, response and recovery plan should be established. This requires accurate detection of the damage affected area. This study performed the detection of the damaged area by using UAV which can respond quickly and in real-time to detect the collapse accident. The study area was selected as B-05 housing redevelopment area in Jung-gu, Ulsan, where the demolition of houses and apartments in progress as the redevelopment project began. This area resembles a collapsed state of the building, which clear changes before and after the demolition. UAV images were acquired on May 17 and July 9, 2019, respectively. The changing area was considered as the damaged area before and after the collapse of the building, and the changing area was detected using CVA (Change Vector Analysis) the Representative Change Detection Technique, and SLIC (Simple Linear Iterative Clustering) based superpixel algorithm. In order to accurately perform the detection of the damaged area, the uninterested area (vegetation) was firstly removed using ExG (Excess Green), Among the objects that were detected by change, objects that had been falsely detected by area were finally removed by calculating the minimum area. As a result, the accuracy of the detection of damaged areas was 95.39%. In the future, it is expected to be used for various data such as response and recovery measures for collapse accidents and damage calculation.

• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.601-611
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• 2011

Land cover changes are occurring for a variety of reasons such as urbanization, infrastructure construction, desertification, drought, flood, and so on. Many researchers have studied the cause and effect of land cover changes, and also the methods for change detection. However, most of the detection methods are based on the dichotomy of "change" and "not change" according a threshold value. In this paper, we present a change detection method with the integration of probability, spatial autocorrelation, and hotspot detection. We used the AMOEBA (A Multidirectional Ecotope-Based Algorithm) and developed the AMOEBA-CH (core hotspot) because the original algorithm tends to produce too many clusters. Our method considers the probability of land cover changes and the spatial interactions between each pixel and its neighboring pixels using a local spatial autocorrelation measure. The core hotspots of land cover changes can be delineated by a contiguity-dominance model of our AMOEBA-CH method. We tested our algorithm in a simulation for land cover changes using NDVI (Normalized Difference Vegetation Index) data in South Korea between 2000 and 2008.

• Journal of Environmental Impact Assessment
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• v.10 no.3
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• pp.167-174
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• 2001

National Parks in Korea occupy about four percents of South Korean land. This paper aims to prove the potentiality of the application of remotely sensed data for the effective management of National Parks. Different satellite images such as Landsat TM, IRS-1C, Alternative image, and IKONOS image are analyzed for the detection of changes, the extraction of degraded areas, and the comparison of Normalized Difference Vegetation Index (NDVI) in Bukhansan National Park. The artificial structures such as buildings and paved areas are overvalued in relatively higher resolution data. The result showed that the choice of images should be determined according to specific purposes and the combination of different resolution data may be the solution for the effective management of National Park.

• Atmosphere
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• v.21 no.4
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• pp.337-347
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• 2011

In this paper, we developed a detection and correction method of noisy pixels embedded in the time series of normalized difference vegetation index (NDVI) data based on the spatio-temporal continuity of vegetation conditions. For the application of the method, 25-year (1982-2006) GIMMS (Global Inventory Modeling and Mapping Study) NDVI dataset over the Korean peninsula were used. The spatial resolution and temporal frequency of this dataset are $8{\times}8km^2$ and 15-day, respectively. Also the land cover map over East Asia is used. The noisy pixels are detected by the temporal continuity check with the reference values and dynamic threshold values according to season and location. In general, the number of noisy pixels are especially larger during summer than other seasons. And the detected noisy pixels are corrected by the iterative method until the noisy pixels are completely corrected. At first, the noisy pixels are replaced by the arithmetic weighted mean of two adjacent NDVIs when the two NDVI are normal. After that the remnant noisy pixels are corrected by the weighted average of NDVI of the same land cover according to the distance. After correction, the NDVI values and their variances are increased and decreased by 5% and 50%, respectively. Comparing to the other correction method, this correction method shows a better result especially when the noisy pixels are occurred more than 2 times consistently and the temporal change rates of NDVI are very high. It means that the correction method developed in this study is superior in the reconstruction of maximum NDVI and NDVI at the starting and falling season.

• Journal of the Korean Geographical Society
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• v.33 no.3
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• pp.435-446
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• 1998

This Paper reports that a new remote sensing techlique focused on a narrow and long strip target (e.g. 15m wide and 100km long) has been specifically developed for pipeline ROW (Right-Of-Way) recovery monitoring. With video it was possible to isolate the maior vegetation communities of the narrow pipeline ROW with acceptable spatial precision by visual or quantitative methods. It was particuarly useful when used to assess a variety of spatial patch dynamics for ROW recovery through digital change-detection techniques in a GIS environment. The main conclusion of this paper is that VSM is a realistic operational technique for a pipeline ROW application. The results also indicate that VSM could be extensively used for other examples of linear thematic mapping.

• Korean Journal of Environment and Ecology
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• v.18 no.2
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• pp.231-235
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• 2004

Landsat satellite images were analyzed to study vegetation change patterns of warm-temperate forests from 1991 to 2002 in Wando. For this purpose, Landsat TM satellite image of 1991 and Landsat ETM image of 2002 were used for vegetation classification using ENVI image processing software. Four different forest types were set as a classification criteria; evergreen broadleaf, evergreen conifer, deciduous broadleaf, and others. Unsupervised classification method was applied to classily forest types. Although it was impossible to draw exact forest types in rocky areas because of differences in data detection time and rough resolution of image, 2002 data revealed that total 2,027ha of evergreen broadleaf forests were growing in Wando. Evergreen broadleaves and evergreen conifers increased in total areas compared to 11 years ago, but there was sharp decrease in deciduous broadleaves. GIS-based management system for warm-temperate forest was done using Arc/Info. Geographic and attribute database of Wando such as vegetation, soils, topography, land owners were built with Arc/Info and ArcView. Graphic user interface which manages and queries necessary data was developed using Avenue.