• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.195-203
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• 2008

Burn severity is an important role for rehabilitation of burned forest area. This factor led to the pilot study to determine if high resolution IKONOS images could be used to classify and delinenate the bum severity over burned areas of Samchock Fire and Cheongyang-Yesan Fire. The results of this study can be summarized as follows: 1. The modified Normalized Bum Ratio (NBR) for IKONOS imagery can be evaluated using burn severity mapping. 2. IKONOS-derived NBR imagery could provide fire scar and detail mapping of burned areas at Samchock fire and Cheongyang-Yesan Burns.

• Journal of Korean Society of Forest Science
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• v.100 no.4
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• pp.540-547
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• 2011

This study was conducted to compare classification accuracy by tree species using QuickBird imagery for pixel- and segment-based classifications that have been mostly applied to classify land covers. A total of 398 points was used as training and reference data. Based on this points, the points were classified into fourteen land cover classes: four coniferous and seven deciduous tree species in forest classes, and three non-forested classes. In pixel-based classification, three images obtained by using raw spectral values, three tasseled indices, and three components from principal component analysis were produced. For the both classification processes, the maximum likelihood method was applied. In the pixel-based classification, it was resulted that the classification accuracy with raw spectral values was better than those by the other band combinations. As resulted that, the segment-based classification with a scale factor of 50% provided the most accurate classification (overall accuracy:76% and ${\hat{k}}$ value:0.74) compared to the other scale factors and pixel-based classification.

• Journal of the Korean earth science society
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• v.21 no.2
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• pp.188-200
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• 2000

On the basis of the Landsat TM scenes with 15 year's time differences, the topographic maps with 50 years differences, and the air photos with 25 years differences, we carried out the field survey for geology and forestry and analyzed the topographical change and the variation of the forest resource in Anmyundo. In terms of the discrimination of forest trees in Anmyumdo, the NDVI with larger than 0.5 in the winter season is the indicator of the surface of the pine tree land-cover. The peak values of NDVI appear on the surface of the pine aging 30 through 50 years and decrease a little and grossly stabilized over the more aging trees. The distinction of the deciduous forest and grass land from the pine tree was capable with the correlation with the abrupt seasonal variation of NDVI and the surface aspect. The great change of topography is detected in the region Changgiri due to the continuous tidal erosion since the canal construction about 370 years ago and along the all around coast of Anmyundo due to the reclamation for the paddy field. The surface area of the pine tree land-cover in Anmyundo was estimated 35.91 km$^2$ in 1986 and 33.15 km$^2$ in 1993, which is originated from the grassland development in the southeastern part of Anmyundo where the pine tree dominated by 1986. In the northen part of Anmyundo the surface area of the pine land-cover increased a little in 1993 comparing to 1986.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.651-664
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• 2014

Recently, the demands of accurate forest carbon stock estimation and mapping are increasing in Korea. This study investigates the feasibility of two methods, k-Nearest Neighbor (kNN) and Regression Tree Analysis (RTA), for carbon stock estimation of pilot areas, Gongju and Sejong cities. The 3rd and 5th ~ 6th NFI data were collected together with Landsat TM acquired in 1992, 2010 and Aster in 2009. Additionally, various vegetation indices and tasseled cap transformation were created for better estimation. Comparison between two methods was conducted by evaluating carbon statistics and visualizing carbon distributions on the map. The comparisons indicated clear strengths and weaknesses of two methods: kNN method has produced more consistent estimates regardless of types of satellite images, but its carbon maps were somewhat smooth to represent the dense carbon areas, particularly for Aster 2009 case. Meanwhile, RTA method has produced better performance on mean bias results and representation of dense carbon areas, but they were more subject to types of satellite images, representing high variability in spatial patterns of carbon maps. Finally, in order to identify the increases in carbon stock of study area, we created the difference maps by subtracting the 1992 carbon map from the 2009 and 2010 carbon maps. Consequently, it was found that the total carbon stock in Gongju and Sejong cities was drastically increased during that period.