• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.5
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• pp.15-27
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• 2022

Climate change is considered a severe global problem closely related to carbon storage. However, recent urbanization and land-use changes reduce carbon stocks in terrestrial ecosystems. Recently, the role of protected areas has been emphasized as a countermeasure to the climate change, and protected areas allow the area to continue to serve as a carbon sink due to legal restrictions. This study attempted to expand the scope of these protected areas to an evaluation-based environmental spatial information theme map. In this study, the area of each grade was compared, and the distribution of land cover for each grade was analyzed using the Ecological and Nature Map, Environmental Conservation Value Assessment Map and Urban Ecological Map of Sejong Special Self-Governing City. Based on this, the average carbon storage for each grade was derived using the InVEST Carbon model. As a result of the analysis, the high-grade area of the environmental spatial information generally showed a wide area of the natural area represented by the forest area, and accordingly, the carbon storage amount was evaluated to be high. However, there are differences in the purpose of production, evaluation items, and evaluation methods between each environmental spatial information, there are differences in area, land cover, and carbon storage. Through this study, environmental spatial information based on the evaluation map can be used for land use management in the carbon aspect, and it is expected that a management plan for each grade suitable for the characteristics of each environmental spatial information is required.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1119-1126
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• 2002

In this paper, we propose two predictive spatial data mining based on spatial statistics and apply for predicting the forest fire hazardous area. These are conditional probability and likelihood ratio methods. In these approaches, the prediction models and estimation procedures are depending un the basic quantitative relationships of spatial data sets relevant forest fire with respect to selected the past forest fire ignition areas. To make forest fire hazardous area prediction map using the two proposed methods and evaluate the performance of prediction power, we applied a FHR (Forest Fire Hazard Rate) and a PRC (Prediction Rate Curve) respectively. In comparison of the prediction power of the two proposed prediction model, the likelihood ratio method is mort powerful than conditional probability method. The proposed model for prediction of forest fire hazardous area would be helpful to increase the efficiency of forest fire management such as prevention of forest fire occurrence and effective placement of forest fire monitoring equipment and manpower.

• Journal of Korean Society of Forest Science
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• v.89 no.4
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• pp.470-479
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• 2000

Management units of forest vegetation established on Mt. Moodeung (1,186.8m), a typical urban forest at Kwangju city located in the southwestern Korea, was classified phytosociologically and its spatial distribution mapped out with special reference to its ecological conservation and management. Management units of this area were classified into three categories; twenty-one higher units, ten lower units and nine lowest units, giving a total of 31 zones. Total area for detailed mapping was 2,779.5ha, of which natural vegetation accounted for 2192.0ha (78.9%), residing in most part of this area, artificial vegetation for 159.1ha (5.7%), and non-forested area including arable area, burned area and others for 428.4ha (15.5%). The ratio of natural forest element showed 93.2%, which is much higher when compared with those of other urban forests.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.274-274
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• 2002

Damage to forest trees, caused by wildfire, changes their spectral reflectance signature. This factor led to the initiation of a research project at the Remote Sensing & GIS Laboratory, Kookmin University, to determine if multispectral data acquired by IKONOS could provide fire scar and bum severity mapping. This paper will present detail mapping of burned areas in the eastern coast of Korea with IKONOS imagery. In addition, a single post-burn Landsat-7 ETM＋ data was used to compare with IKONOS, the study area. Burn severity map based on IKONOS image was found to be affected by strong topographic illumination effects in the mountain forest. But it has better the delineation of the bum-scarred area. In this study the NDVI was analyzed for geometric illumination conditions influenced by topography(slop, aspect and elevation) and shadow(solar elevation and azimuth angle).

• Animal cells and systems
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• v.15 no.1
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• pp.73-78
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• 2011

Estimating the habitat potential of inland forest patches for birds requires the modeling of species-area relationships, or relationships between habitat size and numbers of bird species in each patch. The accurate estimation of speciesarea relationships significantly reduces the effort required to recognize the number of species living in each patch. The objective of this study was to estimate the relationship between forest patch size and bird species diversity in Dangjin County, in northwest South Korea, based on the sizes of inland forest patches. KOMPSAT-2 images were obtained and ortho-rectified to construct a map of the target forest patches. The numbers of birds per patch were surveyed four times: August 2008, September 2008, February 2009 and May 2009. Regression models were derived to explain the relationships between the numbers of bird species and patch size. A model that was derived using data from all four observation periods had the highest coefficient of determination ($R^2$). According to these models, the numbers of bird species at first increased linearly with increasing patch size; however, the curve then plateaued. Our model including observations from four seasons will be useful for estimating the numbers of bird species in other inland forest patches in South Korea.

• Journal of Korean Society of Forest Science
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• v.102 no.4
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• pp.467-476
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• 2013

Site index information given from forest site map only exist in the sampled locations. In this study, site index for unsampled locations were estimated using kriging interpolation method which can interpolate values between point samples to generate a continuous surface. Site index of Pinus densiplora in Danyang area were calculated using Chapman-Richards model by plot unit. Then site index for unsampled locations were interpolated by theoretical variogram models and ordinary kriging. Also in order to assess parameter selection, cross-validation was performed by calculating mean error (ME), average standard error (ASE) and root mean square error (RMSE). In result, gaussian model was excluded because of the biggest relative nugget (37.40%). Then spherical model (16.80%) and exponential model (8.77%) were selected. Site index estimates of Pinus densiplora throughout the entire area in Danyang showed 4.39~19.53 based on exponential model, and 4.54~19.23 based on spherical model. By cross-validation, RMSE had almost no difference. But ME and ASE from spherical model were slightly lower than exponential model. Therefore site index prediction map from spherical model were finally selected. Average site index from site prediction map was 10.78. It can be expected that regional variance can be considered by site index prediction map in order to estimate forest biomass which has big spatial variance and eventually it is helpful to improve an accuracy of forest carbon estimation.

• Journal of Korean Society of Forest Science
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• v.83 no.3
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• pp.322-330
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• 1994

The Universal Soil Loss Equation (USLE) has been widely used to predict long-term soil loss by incorporating several erosion factors, such as rainfall, soil, topography, and vegetation. This study is aimed to introduce the LISLE within geographic information system(GIS) environment. The Kwangneung Experimental Forest located in Kyongki Province was selected for the study area. Initially, twelve years of hourly rainfall records that were collected from 1982 to 1993 were processed to obtain the rainfall factor(R) value for the LISLE calculation. Soil survey map and topographic map of the study area were digitized and subsequent input values(K, L, S factors) were derived. The cover type and management factor (C) values were obtained from the classification of Landsat Thematic Mapper(CM) satellite imagery. All these input values were geographically registered over a common map coordinate with $25{\times}25m^2$ ground resolution. The USLE was calculated for every grid location by selecting necessary input values from the digital base maps. Once the LISLE was calculated, the resultant soil loss values(A) were represented by both numerical values and map format. Using GIS to run the LISLE, it is possible to pent out the exact locations where soil loss potential is high. In addition, this approach can be a very effective tool to monitor possible soil loss hazard under the situations of forest changes, such as conversion of forest lands to other uses, forest road construction, timber harvesting, and forest damages caused by fire, insect, and diseases.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.667-675
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• 2022

Agroforestry is an ecological and economic land-use system that enables sustainable agriculture by combining forestry, agriculture, and livestock industries. North Korea chose agroforestry as a strategy for the restoration of sloping land and deforested land. Agroforestry was proposed for the inter-Korean forest cooperation subcommittee meeting and is currently highlighting carbon removal and promoting the '2050 Carbon Neutral Strategy' and 'Korea Peninsula Green Détente.' The study area, Suan-gun, Hwanghaebuk-do, is a constant deforestation monitoring area and a pilot site for management by the International Center for Research in Agroforestry. The requirements for agroforestry were analyzed through literature analysis. The agroforestry site-suitability map was visualized by applying GIS overlap analysis. Approximately 8,839 ha of sloping area was selected as suitable for agroforestry management, which is about 15% of Suan. We compared the map with Google Earth images and visually detected the land use status, such as agroforestry in Suan, to verify the results. As a future study, we will consider both natural-environment and socioeconomic factors and evaluate the relative importance of the factors to produce a high-accuracy agroforestry sitesuitability map at the national scale with the goal of producing basic data for the inter-Korea forest cooperation policy for long-term goals.

• Journal of Korean Society of Forest Science
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• v.108 no.3
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• pp.418-428
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• 2019

The purpose of this study was to analyze and clarify the forest information shown in the Korean Peninsula Forest Distribution Map (KPFDM) printed in 1910. First, the background, process, results, and reliability of the Forest Survey Project (1910), which is the basis of the KPFDM, were evaluated. Next, the information of the KPFDM, preserved as a paper map, was digitized to show forest status and forest type. The results of the analysis can be summarized as follows: Analyzing the Korean peninsula of the 1910 period in terms of the present South and North Korean regions, stocked forests were found to be more widely distributed (73%) in the northern region. The southern region largely consisted of deforested areas, with young-growth trees and unstocked forests making up 80% of all forests there. The northern region had abundant natural forests, with 80% of the forests in Yanggang-do, which currently includes Mt. Baekdu and the Hyesan area, composed of stocked forests. Pinus densiflora was found about 2.7 times more often in the southern region than in the northern region. Large numbers of coniferous trees excluding Pinus densiflora were found in the northern region. In particular, 53% of the forests and 72% of the stocking land in the southern region were composed of Pinus densiflora.

• Journal of Korean Society of Forest Science
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• v.101 no.1
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• pp.1-6
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• 2012

To achieve the forest fire management goals such as early detection and quick suppression, fire resources should be allocated at high probability area where forest fires occur. The objective of this study was to develop and validate models to estimate spatially distributed probabilities of occurrence of forest fire. The models were builded by exploring relationships between fire ignition location and forest, terrain and anthropogenic factors using logistic regression. Distance to forest, cemetery, fire history, forest type, elevation, slope were chosen as the significant factors to the model. The model constructed had a good fit and classification accuracy of the model was 63%. This model and map can support the allocation optimization of forest fire resources and increase effectiveness in fire prevention and planning.