• Journal of Forest and Environmental Science
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• v.35 no.3
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• pp.181-188
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• 2019

This study was performed to construct tree species classification map according to three information types (spectral information, texture information, and spectral and texture information) by altitude (30 m, 60 m, 90 m) using the unmanned aerial vehicle images and the object-based classification method, and to evaluate the concordance rate through field survey data. The object-based, optimal weighted values by altitude were 176 for 30 m images, 111 for 60 m images, and 108 for 90 m images in the case of Scale while 0.4/0.6, 0.5/0.5, in the case of the shape/color and compactness/smoothness respectively regardless of the altitude. The overall accuracy according to the type of information by altitude, the information on spectral and texture information was about 88% in the case of 30 m and the spectral information was about 98% and about 86% in the case of 60 m and 90 m respectively showing the highest rates. The concordance rate with the field survey data per tree species was the highest with about 92% in the case of Pinus densiflora at 30 m, about 100% in the case of Prunus sargentii Rehder tree at 60 m, and about 89% in the case of Robinia pseudoacacia L. at 90 m.

• 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 Forest and Environmental Science
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• v.37 no.3
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• pp.260-268
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• 2021

This study was conducted to grasp users' preferences and behavior of forest paths and to provide basic data for forest path construction in the future. Among forest path users, males and females over 20 years old were surveyed on forest path preferences, motives for visiting, using time and distance, companions, and sources for obtaining information on forest paths. A total of 587 people participated in the questionnaire, and cross-analysis was conducted simultaneously using the SPSS statistical program. In analyzing the preference of forest paths, it was found that male and the general public preferred trekking path for activities while female and foresters preferred forest path for relaxation and healing (p<0.05). As for the motives for visiting forest paths, the response that they visited for a walk/rest was the highest in gender, occupation, and age group. As for the number of annual visits, less than 5 was the highest in overall, and foresters tended to visit forest path more often than the general public, and the number of visits to the forest paths increased with age (p<0.01). The most common route to acquire information about forest paths is the Internet, while the Internet use is relatively low in those over 60s (p<0.05). The response ratio of accompanying their family in visiting forest paths were the highest, while the response in their 20s and 60s was the most in accompanying their friends (p<0.05).

• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.15-23
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• 2024

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approx-imately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.

• Journal of Korean Society of Forest Science
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• v.102 no.4
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• pp.608-616
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• 2013

The objective of this study was to suggest contingent valuation method, simple random sampling method, systematic sampling method, maximum practice area method, and overlay practice area method as the estimation methods for forest management area to estimate carbon dioxide sink by forest management as stated in Article 3.4 of the Kyoto Protocol. A case study for Chungcheongbuk-do was performed using maximum practice area method which was thought of as the most rational method within MRV(Measurable, reportable and verifiable). The forest management ratio was defined as forest management area over the total forest area. Based on the definition of forest management activity, forest area was divided into practice forest land and non-practice forest land. The area of forest mangement for practices was based on the 'National Forest Management Information System' and the history of practices from the 'Proposals on Private Management Information Database'. The area of forest management for non-practices was based on the public used forest land from 'Categorial map of forest land'. It was shown that the forest management area of Chuncheongbuk-do was 115,566 ha for practice forest land, and 131,008 ha for non-practice forest land. The forest managent ratio was 49.7% of the total forest area of 495,806 ha. This study made possible to the estimation of the forest management ratio under Kyoto Protocol, and had an advantage in searching for a practical method of forest management plan in which the forest management ratio could be enhanced.

• Journal of Forest and Environmental Science
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• v.36 no.3
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• pp.187-198
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• 2020

This study extracted deforestation area and degraded forestland area, which are potential REDD+ (Reducing Emissions from Deforestation and Forest Degradation) project candidate areas in Paraguay using Land Cover Map (LCM) and Tree Cover Map (TCM). The REDD+ project objectives scenarios were set three stages: 'afforestation and economic efficiency scenario', 'local capacity reinforcement scenario', and 'Infrastructure-oriented scenario'. And then, we evaluated the project unit suitable area of the REDD+ project. All scenarios selected the evaluation factors for each scenario in addition to the area ratio factors for deforestation area and degraded forestland area and weighted values were extracted by assigning category scores. As a result of the three scenarios comparison analysis, Concepcion state score was the highest. Within Concepcion state, the Belon district had the highest score, making it appropriate as a project unit REDD+ project candidate area in Paraguay, while the San Carlos district had the lowest score. This study can be used as basic data for selecting REDD+ project candidate area in Paraguay, and it is expected to contribute sufficiently to REDD+ project if additional data or information of social, cultural and economic sectors are secured.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.4
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• pp.10-21
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• 2008

A forest functions classification map is an essential element for the management planning of national forests. This study was intended to make out the map at the stand level by utilizing the Forest Functions Evaluation Program(FFEP), developed by Korea Forest Research Institute. In this program, the potential of each function was evaluated in each grid cell, and then a forest functions estimation map was generated based on the optimum grid cell values in each sub-compartment unit. Finally, the program produced a forest functions classification map with consideration of the priority of the functions. The final forest functions classification map required for the national forest management planning made out overlapping those results which the rest of the forest classified referring priority functions classification map to national forest manager and classified according to the local administrative guidance and sustainable forest resources management guidance. The results indicated that the forest function classification using the FFEP program could be an efficient tool for providing the data required for national forest management planning. Also this study made a meaningful progress in the forest function classification by considering the local forest administrative guidance and sustainable forest resources management guidance.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.785-798
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• 2023

Recently, forest fires have become frequent due to climate change, and the size of forest fires is also increasing. Forest fires in Korea continue to cause more than 100 ha of forest fire damage every year. It was found that 90% of the large-scale wildfires that occurred in Gangwon-do over the past five years were concentrated in the east coast area. The east coast area has a climate vulnerable to forest fires such as dry air and intermediate wind, and forest conditions of coniferous forests. In this regard, studies related to various forest fire analysis, such as predicting the risk of forest fires and calculating the risk of forest fires, are being promoted. There are many studies related to risk analysis for forest areas in consideration of weather and forest-related factors, but studies that have conducted risk analysis for forest-friendly areas are still insufficient. Management of forest adjacent areas is important for the protection of human life and property. Forest-adjacent houses and facilities are greatly threatened by forest fires. Therefore, in this study, a grid-based forest fire-related disaster risk map was created using factors affected by forest-neighboring areas using national branch numbers, and differences in risk ratings were compared for forest areas and areas adjacent to forests based on Gangneung forest fire cases.

• Journal of Forest and Environmental Science
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• v.33 no.4
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• pp.295-304
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• 2017

This study compared and analyzed the construction of a land use change matrix for the Intergovernmental Panel on Climate Change's (IPCC) land use, land use change, and forestry area (LULUCF). We used National Forest Inventory (NFI) permanent sample plots (with a sample intensity of 4 km) and permanent sample plots with 500 m sampling intensity. The land use change matrix was formed using the point sampling method, Level-2 Land Cover Maps, and forest aerial photographs (3rd and 4th series). The land use change matrix using the land cover map indicated that the annual change in area was the highest for forests and cropland; the cropland area decreased over time. We evaluated the uncertainty of the land use change matrix. Our results indicated that the forest land use, which had the most sampling, had the lowest uncertainty, while the grassland and wetlands had the highest uncertainty and the least sampling. The uncertainty was higher for the 4 km sampling intensity than for the 500 m sampling intensity, which indicates the importance of selecting the appropriate sample size when constructing a national land use change matrix.

• Journal of Forest and Environmental Science
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• v.10 no.1
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• pp.49-56
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• 1994

This study was carried out to estimate forest road elongation coefficients for providing the basic information of forest road construction. As the results, road elongation coefficients were calculated 0.33 less than 5% of road gradient, 0.39 between the gradient of 6% and 10%, 0.50 between the gradient of 11% and 15%, 0.58 between the gradient of 16% and 20%, and 0.60 greater than the gradient of 20%.