• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2015.08a
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• pp.117-120
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• 2015

• Proceedings of the KSRS Conference
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• v.2
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• pp.590-593
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• 2006

Timber inventory management includes to measure and update forest attributes, which is crucial information for private companies and public organizations in property assessment and environment monitoring. Field measurement would be accurate, but time-consuming and inefficient. For the reason, remote sensing technology has been an alternative to field measurement from an economic perspective. Among several sensors, LiDAR and Radar interferometry are known for their efficiency for forest monitoring because they are less influenced by weather and light conditions, and provide reasonably accurate vertical/horizontal measurement for a large area in a short period. For example, Shuttle Radar Topography Mission (SRTM) and National Elevation Dataset (NED) in the U.S. can provide tree height information and DSM. On the other hand, LiDAR DSM (the first return) and DEM (the last return) can also present tree height estimation. With respect to project site of loblolly pine plantation in Louisiana in the U.S., the accuracy of SRTM C-Band approach estimating tree height was assessed by the LiDAR approaches. In addition, SRTM X-Band and NED were also compared with the results. Plantation year in inventory GIS, which is directly related to forest age, is high correlated with the difference between SRTM C-Band and NED. As a byproduct, several stands of age mismatch could be recognized using an outlier detection algorithm, and optical satellite image (ETM+) were used to verify the mismatch. The findings of this study were (1) the confirmation of usefulness of the SRTM DSM for forest monitoring and (2) Multi-sensors- Radar, LiDAR, ETM+, MODIS can be used for accuracy improvement of forest inventory GIS altogether.

• Journal of Forest and Environmental Science
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• v.24 no.3
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• pp.119-126
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• 2008

Growth and yield prediction methods, ranging from whole-stand models to individual-tree models, have been developed for forest types managed for wood production. The resultant models are used for a host of purposes including inventory updating, management planning, evaluation of silvicultural alternatives, and harvest scheduling. Because of the large investment in developing growth and yield models for improved genotypes and silvicultural practices for loblolly pine (Pinus taeda) in the Southern United States, this region serves to illustrate approaches for modelling intensively managed forests. Analytical methods and computing power generally do not restrict development of reliable growth and yield models. However, long-term empirical observations on stand development, which are time consuming and expensive to obtain, often limit modelling efforts. Given that growth and yield models are used to project present volumes and to evaluate alternative treatment effects, data of both the inventory type and the experimental type are needed. Data for developing stand simulators for loblolly pine plantations have been obtained from a combination of permanent plots in operational forest stands and silvicultural experiments; these data collection efforts are described and summarized. Modelling is essential for integrating and synthesizing diverse information, identifying knowledge gaps, and making informed decisions. The questions being posed today are more complex than in the past, thus further accentuating the need for comprehensive models for stand development.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.311-320
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• 2009

This study was carried out to estimate forest biomass and to produce forest biomass thematic map for Muju county by combining field data from the 5$^{th}$ National Forest Inventory (2006-2007) and satellite data. For estimating forest biomass, two methods were examined using a Landsat TM-5(taken on April 28th, 2005) and field data: multi-variant regression modeling and t-Nearest Neighbor (k-NN) technique. Estimates of forest biomass by the two methods were compared by a cross-validation technique. The results showed that the two methods provide comparatively accurate estimation with similar RMSE (63.75$\sim$67.26ton/ha) and mean bias ($\pm$1ton/ha). However, it is concluded that the k-NN method for estimating forest biomass is superior in terms of estimation efficiency to the regression model. The total forest biomass of the study site is estimated 8.4 million ton, or 149 ton/ha by the k-NN technique.

• Journal of agriculture & life science
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• v.53 no.2
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• pp.65-75
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• 2019

Stem and root of elm trees have used as traditional medical materials, but there is little information on the distribution and resources of habitats. Korean native growing Ulmus spp. (U. davidiana var. Japonica, U. parvifolia, U. davidiana, and U. macrocarpa) genetic resources studied through The National Forest Inventory of Korea data and field survey. The distributions of U. davidiana var. japonica according to elevation distributed evenly. Both U. parvifolia and U. davidiana were inhabited mostly at less than 200 m of altitude. Each Ulmaceae species widely were distributed nationwide, but a dominant species was different depending on locals. It observed that Ulmaceae inhabits mainly in steep slopes of 31-45 degrees. Most of the habitats regenerated by natural seeding and the most abundant species were a codominant tree. Distribution of trees in U davidiana var. japonica was 7 m-13 m, and in young U. parvifolia and U. macrocarpa, more than 25% of young trees less than 7 m observed. The distribution of the diameter of breast height of the U. davidiana var. japonica was 46.4% for 11-20 cm, 52.6% for 11-20 cm in U. parvifolia. The average T/R ratio was 0.83, and the mean weight ratio of root bark was 62%. As the results of this study, the domestic Ulmaceae biomassare very small. It is difficult to harvest in that the habitat on the slope. Thus, it is too hard to develop functional materials using biomass at present. Therefore, it is necessary to develop technology for the selection and propagation of elite trees of Ulmaceae.

• Journal of agriculture & life science
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• v.53 no.6
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• pp.23-33
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• 2019

In accordance with a change in the management information system containing the management record and planning for the entire national forest in South Korea by an amendment of the relevant law (The national forest management planning and methods, Korea Forest Service), in this study, average, the maximum, and the minimum values for DBH were presented while only average values were required before the amendment. In this regard, there is a need for an estimation algorithm by which all the existing values for DBH established before the revision can be converted to the highest and the lowest ones. The purpose of this study is to develop an estimation equation to automatically show the minimum and the maximum values for DBH for 12 main tree species from the data in the national forest management information system. In order to develop the estimation equation for the minimum and the maximum values for DBH, there was exploited the 6,858 fixed sample plots of the fifth and the sixth national forest inventory between in 2006 and 2015. Two estimation models were applied for DBH-tree age and DHB-tree height using such growth variables as DBH, tree age, and height, to draw the estimation equation for the maximum and the minimum values for DBH. The findings showed that the most suitable model to estimate the minimum and the maximum values for DBH was Dmin=a+bD+cH, Dmax=a+bD+cH with the variables of DBH and height. Based on these optimal models, the estimation equation was devised for the minimum and the maximum values for DBH for the 12 main tree species.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.185-191
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• 2015

This study presents an estimate of on-site surface fuel loadings composed of coarse woody debris (CWD) using $5^{th}$ National Forest Inventory (NFI) data in South Korea. We classified CWD data into forest type, region and decay class, and used conversion factors by decay class and tonne of oil equivalent developed in the country. In 2010, the total wildfire fuel load of CWD was estimated as 8.9 million TOE; those of coniferous, deciduous and mixed forests were 3.5 million TOE, 2.8 million TOE and 2.6 million TOE, respectively. Gangwon Province had the highest wildfire fuel load of CWD (2.3 million TOE), whereas Seoul exhibited the lowest wildfire fuel load of CWD (0.02 million TOE). Wildfire fuel loads of CWD were estimated as 2.9 million TOE, 1.9 million TOE, 2.4 million TOE and 1.7 million TOE for decay classes I, II, III and IV, respectively. The total wildfire fuel load of CWD corresponded to the calorific value of 8.2 million tons crude oil, 2.46% of that of living trees. Proportionate to the growing stock, total wildfire fuel load of CWD was in a broad distinction by region, while its TOE $ha^{-1}$ was not. This implies that there is no need to establish different guidelines by region for management of CWD. The results of this work provide a baseline study for scientific policy guidelines on preventing wildfires by proposing CWD as wildfire fuel load.

• Journal of Climate Change Research
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• v.6 no.4
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• pp.303-310
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• 2015

This study was conducted to estimate of carbon stock and greenhouse gas (GHGs) removals by tree species and forest type at Gangwon province. We used a point sampling data with permanent sample plots in national forest inventory and national emission factors. GHGs emissions was caclulated using the stock change method related to K-MRV and IPCC guidance. Total carbon stock and greenhouse gas removals were high in deciduous forest and species than in coniferous. The range of annual net greenhouse gas emissions in other deciduous species was from $-11,564.83Gg\;CO_2\;yr^{-1}$ to $-13,500.60Gg\;CO_2\;yr^{-1}$ during 3 years (2011~2013). On the other hand, coniferous forest was temporally converted to source due to reducing of growing stock in 2012. It was that growing stocks and forest area were likely to reduce by the deforestation and clear cutting. This study did not consider other carbon pools (soil and dead organic matter) due to the lack of data. This study needs to complement the activity data and emission factors, and then will find the way to calculate the greenhouse gas emissions and removals in the near future.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.385-398
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• 2018

Although the importance of developing reliable and systematic GHG inventory has increased, the GIS/RS-based national scale LULUCF (Land Use, Land-Use Change and Forestry) sector analysis is insufficient in the context of the Paris Agreement. In this study, the change in $CO_2$ storage of forest land due to land use change is estimated using two GIS/RS methodologies, Sampling and Wall-to-Wall methods, from 2000 to 2010. Particularly, various imagery with sampling data and land cover maps are used for Sampling and Wall-to-Wall methods, respectively. This land use matrix of these methodologies and the national cadastral statistics are classified by six land-use categories (Forest land, Cropland, Grassland, Wetlands, Settlements, and Other land). The difference of area between the result of Sampling methods and the cadastral statistics decreases as the sample plot distance decreases. However, the difference is not significant under a 2 km sample plot. In the 2000s, the Wall-to-Wall method showed similar results to sampling under a 2 km distance except for the Settlement category. With the Wall-to-Wall method, $CO_2$ storage is higher than that of the Sampling method. Accordingly, the Wall-to-Wall method would be more advantageous than the Sampling method in the presence of sufficient spatial data for GHG inventory assessment. These results can contribute to establish an annual report system of national greenhouse gas inventory in the LULUCF sector.

• Journal of Korean Society of Forest Science
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• v.106 no.4
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• pp.450-456
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• 2017

This study was conducted to develop estimated equation for mortality rates (volume of dead trees, %) on coniferous and broad-leaved forests, representative forest types of South Korea. There were 6 equation models applied for estimating mortality such as a exponential equation, a Hamilton equation and variables using were DBH, basal area, and site index. Raw data used for estimating mortality were $5^{th}$ and $6^{th}$ national forest inventory data, and mortality was calculated with the difference of stocks between lived trees and dead trees by each sample plots. The most applicable equation to describe mortality on coniferous forest and broad-leaved forest was indicated as $P=(1+e^{(a+b{\times}DBH+c{\times}BA+d{\times}no\_ha+e{\times}density)})^{-1}$ and their goodness of fit showed 34% and 51% respectively. Goodness of fit in both equations were not much high because there were various factors which affect the mortality such as topographic conditions, soil characteristic, climatic factors, site quality, and competition. Therefore, it is considered that explaining mortality in forest with only 2 or 3 variables like DBH, basal area used in this analysis could be very difficult facts. However, this study is certainly worth in that there is no useful information on mortality by each forest type throughout the country at the present, and we would make an effort to promote the fitness of estimated equation for mortality adding competition index, tree crown density etc.