• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.175-175
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• 2023

Detection and monitoring of wildfires are essential for limiting their harmful effects on ecosystems, human lives, and property. In this research, we propose a novel method running in the Google Earth Engine platform for identifying and characterizing burnt regions using a hybrid of Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (multispectral photography) images. The 2022 Uljin wildfire, the severest event in South Korean history, is the primary area of our investigation. Given its documented success in remote sensing and land cover categorization applications, we select the Random Forest (RF) method as our primary classifier. Next, we evaluate the performance of our model using multiple accuracy measures, including overall accuracy (OA), Kappa coefficient, and area under the curve (AUC). The proposed method shows the accuracy and resilience of wildfire identification compared to traditional methods that depend on survey data. These results have significant implications for the development of efficient and dependable wildfire monitoring systems and add to our knowledge of how machine learning and remote sensing-based approaches may be combined to improve environmental monitoring and management applications.

• 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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.83-89
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• 2012

Natural Vegetation cover, which is very important earth resource, has been significantly altered by humans in some manner. Since this has currently resulted in a significant effect on global climate, various studies on vegetation environment including forest have been performed and the results are utilized in policy decision making. Remotely sensed data can detect, identify and map vegetation cover change based on the analysis of spectral characteristics and thus are vigorously utilized for monitoring vegetation resources. Among various vegetation indices extracted from spectral reponses of remotely sensed data, NDVI is the most popular index which provides a measure of how much photosynthetically active vegetation is present in the scene. In this study, for change detection in vegetation cover, a Multi-layer Perceptron Network (MLPN) as a nonparametric approach has been designed and applied to MODIS/Aqua vegetation indices 16-day L3 global 250m SIN Grid(v005) (MYD13Q1) data. The feature vector for change detection is constructed with the direct NDVI diffenrence at a pixel as well as the differences in some subset of NDVI series data. The research covered 5 years (2006-20110) over Korean peninsular.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.2
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• pp.108-120
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• 2008

This paper discusses the prediction of deforestation areas using probability models from forest census database, Geographic information system (GIS) database and the land cover database. The land cover data was analyzed using remotely-sensed (RS) data of the Landsat TM data from 1989 to 2001. Over the analysis period of 12 years, the deforestation area was about 40ha. Most of the deforestation areas were attributable to road construction and residential development activities. About 80% of the deforestation areas for residential development were found within 100m of the road network. More than 20% of the deforestation areas for forest road construction were within 100m of the road network. Geographic factors and vegetation change detection (VCD) factors were used in probability models to construct deforestation occurrence map. We examined the size effect of area partition as training area and validation area for the probability models. The Bayes model provided a better deforestation prediction rate than that of the regression model.

• Spatial Information Research
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• v.16 no.1
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• pp.19-32
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• 2008

Usually in South Korea, land cover type and topographic undulation are frequently changed even in a narrow area. However, most of researches using aerial LIDAR(LIght Detection And Ranging) data in abroad had been acquired in the study areas to be changed infrequently. This research was performed to explore reconstruction methodologies of 3D surface models considering the distribution of land cover type and topographic undulation. Composed of variously undulatory forests, rocky river beds and man-made land cover such as streets, trees, buildings, parking lots and so on, an area was selected for the research. First of all, the area was divided into three zones based on land cover type and topographic undulation using its aerial ortho-photo. Then, aerial LIDAR data was clipped by each zone and different 3D modeling processes were applied to each clipped data before integration of each models and reconstruction of overall model. These kinds of processes might be effectively applied to landscape management, forest inventory and digital map composition. Besides, they would be useful to resolve less- or over-extracted problems caused by simple rectangle zoning when an usual data processing of aerial LIDAR.

• 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 Remote Sensing
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• v.27 no.3
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• pp.259-276
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• 2011

Short-term land cover changes, such as forest fire scar and crop harvesting, can be detected by high temporal resolution satellite imagery like MODIS and AVHRR. Because these optical satellite images are often obscured by clouds, the static cloud-free composite methods (maximum NDVI, minblue, minVZA, etc.) has been used based on non-overlapping composite period (8-day, 16-day, or a month). Due to relatively long time lag between successive images, these methods are not suitable for observing short-term land cover changes in near-real time. In this study, we suggested a new dynamic cloud-free composite algorithm that uses cut-and-patch method of cloud-masked daily MODIS data using MOD35 products. Because this dynamic composite algorithm generates daily cloud-free MODIS images with the most recent information, it can be used to monitor short-term land cover changes in near-real time. The dynamic composite algorithm also provides information on the date of each pixel used in compositing, thereby makes accurately identify the date of short-term event.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.2
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• pp.197-206
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• 2010

Recently, developed countries have continuously been trying to recognize many issues about heat island in urban area and to make up countermeasures for them. This research is designed to extract change of land cover in the area under condition of land development with satellite images and to analyze its effect on the heat change in there. Heat change upon change of land cover in daejeon was analyzed with the four Landsat satellite images taken in April 1985, August 1994, May 2001, and May 2009. In order to measure the temperature on the surface in the city, the land surface temperature was produced with Landsat TM Band 6. Heat change is to detected with it. As a result, The urban area has been increased up to 23.59 percent. On the other hand, the forest area has been decreased up to 27.91%. Due to the urbanization, the temperature on the surface in urban center was higher than surrounding area. In that case, the temperature of urban center area was higher 2.4 to $5.7^{\circ}C$ compared with the forest area.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.883-896
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• 2023

As human activities in Korea are spread throughout the mountains, forest fires often affect residential areas, infrastructure, and other facilities. Hence, it is necessary to detect fire-damaged areas quickly to enable support and recovery. Remote sensing is the most efficient tool for this purpose. Fire damage detection experiments were conducted on the east coast of Korea. Because this area comprises a mixture of forest and artificial land cover, data with low resolution are not suitable. We used Sentinel-2 multispectral instrument (MSI) data, which provide adequate temporal and spatial resolution, and the k-nearest neighbor (kNN) algorithm in this study. Six bands of Sentinel-2 MSI and two indices of normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as features for kNN classification. The kNN classifier was trained using 2,000 randomly selected samples in the fire-damaged and undamaged areas. Outliers were removed and a forest type map was used to improve classification performance. Numerous experiments for various neighbors for kNN and feature combinations have been conducted using bi-temporal and uni-temporal approaches. The bi-temporal classification performed better than the uni-temporal classification. However, the uni-temporal classification was able to detect severely damaged areas.