• Journal of Forest and Environmental Science
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• v.24 no.3
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• pp.143-149
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• 2008

We conduct economic analysis of the snow damage on sugi (Cryptomeria japonica) forest stands in Toyama Prefecture, Japan. We utilize a single tree and distant independent growth simulator called "Silv-Forest." With this growth simulator, we developed an optimization model by dynamic programming, called DP-Silv (Dynamic Programming Silv-Forest). The MS-PATH (multiple stage projection alternative technique) algorithm was embedded as a searching algorithm of dynamic programming. The height / DBH ratio was used to constrain the thinning regime for snow damage protection. The optimal rotation age turned out to be 65 years for the non-restricted case, while it was 50 years for the restricted case. The difference in NPV of these two cases as the induced costs ranged from 179,867 to 1,910,713yen/ha over the rotation age of 20 to 75 years. Under the optimal rotation of 65 years, the cost became 914,226 yen/ha. The estimated annual payment based on the difference in NPV, was from 9,869 yen/ha/yr to 85,900 yen/ha/yr. All in all, 10,000 yen/ha/yr to 20,000 yen/ha/yr seems to cover the payment from the rotation age of 35 to 75 years.

• Korean Journal of Remote Sensing
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• v.16 no.4
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• pp.327-338
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• 2000

The forest conditions of North Korea has been a great concern since it was known to be closely related to many environmental problems of the disastrous flooding, soil erosion, and food shortage. To assess the long-term changes of forest area as well as the canopy conditions, several sources of multitemporal satellite data were applied to the study area near Kaesung. KOMPSAT-1 EOC data were overlaid with 1981 topographic map showing the boundaries of forest to assess the deforestation area. Delineation of the cleared forest was performed by both visual interpretation and unsupervised classification. For analyzing the change of forest canopy condition, multiple scenes of Landsat and SPOT data were selected. After preprocessing of the multitemporal satellite data, such as image registration and normalization, the normalized difference vegetation index (NDVI) was derived as a representation of forest canopy conditions. Although the panchromatic EOC data had radiometric limitation to classify diverse cover types, they can be effectively used t detect and delineate the deforested area. The results showed that a large portion of forest land has been cleared for the urban and agricultural uses during the last twenty years. It was also found that the canopy condition of remaining forests has not been improved for the last twenty years. It was also found that the canopy condition of remaining forests has not been improved for the last twenty years. Possible causes of the deforestation and the temporal pattern of canopy conditions are discussed.

• Journal of Forest and Environmental Science
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• v.26 no.2
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• pp.117-129
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• 2010

The present study was undertaken in moist temperate forest of Mandal-Chopta area in the Garhwal region of Uttarakhand, India. The aim of the present study was to assess the physical properties of soils in relation to the forest structure and composition. Twelve forest types according to the altitude, slope aspect and species compositions were selected for the study. Physical properties of soil i.e., soil colour, soil texture (per cent of sand, silt and clay), moisture content, water holding capacity, porosity, bulk density (gm/$cm^3$) and void ratio were analyzed for three different depths viz., (i) 'upper' (0-10 cm), (ii) 'middle' (11-30 cm) and (iii) 'lower' (31-60 cm) in all the selected forest types. Phytosociological and diversity parameters viz. total basal cover ($Gha^{-1}$), stem density ($Nha^{-1}$), tree species richness, Simpson concentration of dominance and Shannon-Wiener diversity index were also calculated for each forest type. This study also provides the comparisons between the results of physical analysis of the present study with numerous other previous studies in the temperate Himalayan region of the Uttarakhand.

• Journal of Korean Society of Forest Science
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• v.98 no.2
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• pp.133-141
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• 2009

This study was conducted to support for determining an efficient sampling design for forest resources assessments in South Korea with respect to statistical efficiency. For this objective, different systematic sampling designs were simulated and compared based on an artificial forest population that had been built from field sample data and satellite data in Yang-Pyeong County, Korea. Using the k-NN technique, two thematic maps (growing stock and forest cover type per pixel unit) across the test area were generated; field data (n=191) and Landsat ETM+ were used as source data. Four sampling designs (systematic sampling, systematic sampling for post-stratification, systematic cluster sampling, and stratified systematic sampling) were employed as optimum sampling design candidates. In order to compute error variance, the Monte Carlo simulation was used (k=1,000). Then, sampling error and relative efficiency were compared. When the objective of an inventory was to obtain estimations for the entire population, systematic cluster sampling was superior to the other sampling designs. If its objective is to obtain estimations for each sub-population, post-stratification gave a better estimation. In order to successfully perform this procedure, it requires clear definitions of strata of interest per field observation unit for efficient stratification.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.2
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• pp.44-55
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• 2018

The importance of establishing a greenhouse gas inventory is emerging for policymaking and its implementation to cope with climate change. Thus, it is needed to establish Approach 3 level Land Use, Land-Use Change and Forestry (LULUCF) matrix that is spatially explicit regarding land use classifications and changes. In this study, four types of spatial information suitable for establishing the LULUCF matrix were analyzed - Cadastral Map, Land Cover Map, Forest Map, and Biotope Map. This research analyzed the classification properties of each type of spatial information and compared the quantitative and qualitative characteristics of the maps in Boryeong city. Drawn from the conclusions of the quantitative comparison, the forest area showed the maximum difference of 50.42% ($303.79km^2$) in the forest map and 46.09%($276.65km^2$) in the cadastral map. The qualitative comparison drew five qualitative characteristics: data construction scope difference, data construction purpose difference, classification standard difference, and classification item difference. As a result of the study, it was evident that the biotope map was the most appropriate spatial information for the establishment of the LULUCF matrix. In addition, if the LULUCF matrix is made by integrating the biotope, the forest map, and the land cover map, the limitations of each spatial information would be improved. The accuracy of the LULUCF matrix is expected to be improved when the map of the level-3 land cover map and the biotope map of 1:5,000 covering the whole country are completed.

• 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.12 no.2
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• pp.169-198
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• 1996

The objectives of this investigation are : 1. To analyze spectral signature and the associated vegetation index for geometric illumination conditions inf1uenced by low solar elevation and high slope orientations in mountainous forest. 2. To assess the accuracy of the spectral angle mapper classification for the a winter land cover in comparison with the maximum likelihood classification. 3. To produce the image of water quality and water properties that could be used to estimate the water pollution sources and the tide-included by turbid water in estuarine and coastal areas. These objectives are to characterize environmental and ecological monitoring applications of the Nak-Dong River Basin by using Fuyo-1 OPS VNIR data acquired on December 26, 1992. The results of this paper are as follows : 1. The spectral digital numbers and vegetation indexes (NDVI and TVI) of mountainous forest are higher on the slope facing the sun than on the slope hidden the sun under low sun elevation condition. 2. The spectral angle mapper algorithm produces a more accurate land cover classification of areas with steep slope, various aspects and low solar elevation than the maximum likelihood classifier. 3. The maximum likelihood classification images can be used for identifying the location and movement of both freshwater and salt water, regardless of geometric illumination conditions. 4. The color-coded density sliced image of selected water bodies by using the near-infrared band 3 can provide distribution of the water quality of the Lower Nak-Dong River. 5. The color-coded normalized difference vegetation index image of the selected mountain forest is suitable to classify winter vegetation cover types, i.e., forest canopy densities for slope orientations.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.271-280
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• 1999

The topographical effect resulted from rugged terrains and inhomogeneous spectral characteristics due to the complexly mixed land cover condition of Korea substantially lower the remotely sensed land cover classification accuracy In this study, a topographic correction method using digital elevation model to alleviate the topographic effects. To deal with inhomogeneous spectral characteristic, a hybrid classifier with inclusion of prior probabilities was introduced. This investigation concluded that the topographical normalization and hybrid classification with prior probabilities are effective on rugged landscape. The overall and average classification accuracies were improved by 0.92％ and 1.016％ respectively. The most substantial and noticeable accuracy improvement was observed in forest areas.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.933-940
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• 2020

Land-use change matrix data is important for calculating the LULUCF (land use, land use change and forestry) sector of the national greenhouse gas inventory. In this study, land cover changes in 2004 and 2019 were compared using the Wall-to-Wall technique with a land cover map of Sejong City from the Ministry of Environment. Sejong City was classified into six land use classes according to the Intergovernmental Panel on Climate Change (IPCC) guidelines: Forest land, crop land, grassland, wetland, settlement and other land. The coordinate system of the land cover maps of 2004 and 2019 were harmonized and the land use was reclassified. The results indicate that during the 15 years from 2004 to 2019 forestlands and croplands decreased from 50.4% (234.2 ㎢) and 34.6% (161.0 ㎢) to 43.4% (201.7 ㎢) and 20.7% (96.2 ㎢), respectively, while Settlement and Other land area increased significantly from 8.9% (41.1 ㎢) and 1.4% (6.9 ㎢) to 35.6% (119.0 ㎢) and 6.5% (30.3 ㎢). 79.㎢ of cropland area (96.2 ㎢) in 2019 was maintained as cropland, and 8.8 ㎢, 1.7 ㎢, 0.5 ㎢, 5.4 ㎢, and 0.4 ㎢ were converted from forestland, grassland, wetland, and settlement, respectively. This research, however, is subject to several limitations. The uncertainty of the land use change matrix when using the wall-to-wall technique depends on the accuracy of the utilized land cover map. Also, the land cover maps have different resolutions and different classification criteria for each production period. Despite these limitations, creating a land use change matrix using the Wall-to-Wall technique with a Land cover map has great advantages of saving time and money.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.507-519
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• 2023

Multi-temporal optical images have been utilized for time-series monitoring of croplands. However, the presence of clouds imposes limitations on image availability, often requiring a cloud removal procedure. This study assesses the applicability of various machine learning algorithms for effective cloud removal in optical imagery. We conducted comparative experiments by focusing on two key variables that significantly influence the predictive performance of machine learning algorithms: (1) land-cover types of training data and (2) temporal variability of land-cover types. Three machine learning algorithms, including Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF), were employed for the experiments using simulated cloudy images in paddy fields of Gunsan. GPR and SVM exhibited superior prediction accuracy when the training data had the same land-cover types as the cloud region, and GPR showed the best stability with respect to sampling fluctuations. In addition, RF was the least affected by the land-cover types and temporal variations of training data. These results indicate that GPR is recommended when the land-cover type and spectral characteristics of the training data are the same as those of the cloud region. On the other hand, RF should be applied when it is difficult to obtain training data with the same land-cover types as the cloud region. Therefore, the land-cover types in cloud areas should be taken into account for extracting informative training data along with selecting the optimal machine learning algorithm.