• Journal of Korean Society of Forest Science
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• v.104 no.1
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• pp.117-126
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• 2015

Forest statistics of local administrative districts have many social needs, nevertheless we have some difficulties for working out an accurate statistics because of insufficient data in small-area level. Thus, new small-area estimation method has to set aside additional data, decrease errors of statistics and consider the local forest characteristics at the same time. In this study, we researched the spatial divisions that can set aside additional data for statistics production and satisfy the major premise, which is "forest characteristics of spatial divisions have to be equal to that of small-area". And we compared synthetic estimation methods based on three different spatial divisions(provinces, neighbor districts and new expanded districts). New expanded districts were divided based on the criteria of climate, soil type and tree species composition that affects local forest characteristics. Small-area statistics were assessed in terms of the ability to estimate local forest characteristics and consistency within large-area statistics. As a result, new expanded districts synthetic estimation was assessed to calculate statistics that reflects local forest characteristics better than other two estimation methods. Moreover, this synthetic estimation method produced the statistics that was included within 95% confidence interval of large-area statistics and was the closer to large-area statistics than the neighbor districts synthetic estimation.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.4
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• pp.86-100
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• 2014

The purposes of this study are to provide a guideline for the suitability of wind farm facilities in forest lands and to suggest improvement plans of policies and systems to minimize the damage of forest lands. First, we implemented a literature review and field surveys to examine and select factors for the suitability of wind farm facilities in forest lands. Spatial database for selected location factors of wind farm facilities in forest lands was constructed to develop the suitability model for locating wind farm facilities focusing on Gangwon-do. Data used in this study include wind power resource, legal mountainous preserved area, forest roads, developed areas, forest class, and other spatial data. In order to find specific-sized potential areas for a certain number of wind farm turbines, we used block statistics and focal statistics methods. As a result, the areas for potential wind farm locations were 1,261ha from a block statistics method and 1,411ha from a focal statistics method. Based on the outputs of this research, it is required to make an urgent solution for the prevention of forest disaster and to prepare reduction measures for the destruction of ridge landscape.

• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.4
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• pp.235-239
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• 2005

An accurate fire danger rating model can contribute to effective forest fire prevention activities. This study evaluates the national forest fire danger rating index based on forest fire statistics data from 1999 to 2002. The number of fires was related to the forest fire danger rating index $(R^2=0.67)$, and no correlation was found with burned areas. A one-way ANOVA test between forest fire danger rating levels and forest fire statistics data indicated that a difference in the number of fires was found among 'danger', 'precaution' and 'none' levels, but 'precaution' and 'none' levels could not be delineated. In the case of a burned area, no difference was found among the three levels.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.239-239
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• 2019

• Journal of Integrative Natural Science
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• v.13 no.1
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• pp.34-40
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• 2020

This study aims to create a model for predicting the number of extinguishment manpower to put out forest fires by taking into account the climate, the situation, and the extent of the damage at the time of the forest fires. Past research has been approached to determine the cause of the forest fire or to predict the occurrence of a forest fire. How to deal with forest fires is also a very important part of how to deal with them, so predicting the number of extinguishment manpower is important. Therefore predicting the number of extinguishment manpower that have been put into the forest fire is something that can be presented as a new perspective. This study presents a model for predicting the number of extinguishment manpower inputs considering the scale of the damage with forest fire on a scale bigger than 0.1 ha as data based on the forest fire annual report(Korea Forest Service; KFS) from 2015 to 2018 using the moderated multiple regression analysis. As a result, weather factors and extinguished time considering the damage show that affect forest fire extinguishment manpower.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1479-1481
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• 2003

The aim of this paper was mainly to interpret the real information-missing patch of image by using the kriging interpolation technology of spatial statistics. The TM Image of the Jingouling Forest Farm of Wangqing Forestry Bureau of Northeast China on 1 July 1997 was used as the tested material in this paper. Based on the classification for the TM image, the information pixel-missing patch of image was interpolated by the kriging interpolation technology of spatial statistics theory under the image treatment software-ERDAS and the geographic information system software-Arc/Info. The interpolation results were already passed precise examination. This paper would provide a method and means for interpreting the information-missing patch of image.

• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.153-160
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• 2007

Forest cover density studies using high resolution satellite data and object oriented classification are limited in India. This article focuses on the potential use of QuickBird satellite data and object oriented classification in forest density mapping. In this study, the high-resolution satellite data was classified based on NDVI/pixel based and object oriented classification methods and results were compared. The QuickBird satellite data was found to be suitable in forest density mapping. Object oriented classification was superior than the NDVI/pixel based classification. The Object oriented classification method classified all the density classes of forest (dense, open, degraded and bare soil) with higher producer and user accuracies and with more kappa statistics value compared to pixel based method. The overall classification accuracy and Kappa statistics values of the object oriented classification were 83.33% and 0.77 respectively, which were higher than the pixel based classification (68%, 0.56 respectively). According to the Z statistics, the results of these two classifications were significantly different at 95% confidence level.

• Journal of Forest and Environmental Science
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• v.24 no.3
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• pp.165-169
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• 2008

This study attempts to analyze the relationship between forest volume and age based on 5th NFI data and spectral characteristics of satellite imagery using ASTER sensor in Korea. Forest stand volume and age had the negative correlation with the spectral reflectance in all of the band (Blue, Green, Red, SWIR). With increasing of stand volume and age, spectral reflectance decrease. The spectral reflectance of band1 showed the highest correlation between stand volume and spectral reflectance among the VNIR wavelength. The spectral reflectance band 1, 2 (visible wavelength) and stand age have high correlation compared to other bands. The correlation coefficients between forest volume and vegetation indices have low relationship. This result indicates that the reflectance of blue band may be important factor to improve the potential of optical remote sensing data to estimate forest volume and age.

• Journal of Korean Society of Forest Science
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• v.99 no.6
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• pp.827-835
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• 2010

Reliable forest statistics provides important information to meet the UNFCCC. In this respect, the national forest inventory has played a crucial role to provide the reliable forest statistics for several decades. However, the previous forest statistics calculated by administrative district has not provided spatial information in a small scale. Thus, this study focused on developing models to estimate an explicit spatial distribution of forest growing stock. For this, first, stand volume model by stand types was developed using National Forest Inventory(NFI) data. Second, forest type map was integrated with this model. NFI data were used to calculate plot-level stand volume and basal area. The stand types of NFI plot including the species composition, age class, DBH class and crown density class are very crucial data to be connected with forest type map. Finally, polygonlevel stand volume map was developed with spatial uncertainty map. Average stand volume was estimated at 85.7 $m^3$/ha in the study area, and at 95% significance interval it was ranged from 79.7 $m^3$/ha to 91.8 $m^3$/ha.

• Journal of Korean Society of Forest Science
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• v.97 no.1
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• pp.1-9
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• 2008

It is necessary to examine relationship between spatial patterns of forest types and characteristics of forest fires for efficient management of fire and forest. By the ecoregions of South Korea, we computed landscape indices for whole types of forests(landscape level) and pine forests(class level), and analyzed characteristics of forest fires using statistics of forest fires from 1991 to 2006. We performed canonical correlation analysis to model the relationship between the landscape indices and the statistics of forest fires. At landscape level, forest patches were larger and more complex in the ecoregions which had higher percentage of forest area. At class level, pine forest patches were more complex and closer to neighbor patches in the coastal ecoregions. The ecoregions including metropolitan areas and cities had more frequent fire occurrences per 1,000ha, while mountainous coastal ecoregions had more burned areas and faster spread of fire growth rate. The canonical correlation between the landscape indices for pine forests and the statistics of forest fires was statistically significant at the 0.05 level and explained more than 70% of the variation in fire variables. The results showed that combustion time per fire was longer in the ecoregions which had larger and more aggregated pine forest patches.