• 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 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.112 no.3
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• pp.267-279
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• 2023

This research assessed the feasibility of using high-resolution aerial images and deep learning algorithms for estimating the land-use and land-cover areas at the Approach 3 level, as outlined by the Intergovernmental Panel on Climate Change. The results from different sampling densities of high-resolution (51 cm) aerial images were compared with the land-cover map, provided by the Ministry of Environment, and analyzed to estimate the accuracy of the land-use and land-cover areas. Transfer learning was applied to the VGG16 architecture for the deep learning model, and sampling densities of 4 × 4 km, 2 × 4 km, 2 × 2 km, 1 × 2 km, 1 × 1 km, 500 × 500 m, and 250 × 250 m were used for estimating and evaluating the areas. The overall accuracy and kappa coefficient of the deep learning model were 91.1% and 88.8%, respectively. The F-scores, except for the pasture category, were >90% for all categories, indicating superior accuracy of the model. Chi-square tests of the sampling densities showed no significant difference in the area ratios of the land-cover map provided by the Ministry of Environment among all sampling densities except for 4 × 4 km at a significance level of p = 0.1. As the sampling density increased, the standard error and relative efficiency decreased. The relative standard error decreased to ≤15% for all land-cover categories at 1 × 1 km sampling density. These results indicated that a sampling density more detailed than 1 x 1 km is appropriate for estimating land-cover area at the local level.

• Journal of Environmental Science International
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• v.5 no.2
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• pp.103-112
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• 1996

Observational date is integral in our understanding of present climate, its natural variability and any cnange roue to anturopogenic effects. This study incorporates a brief overview of sampling requirements using data from the first ISLSCP Field Experiment (FIFE) in 1987, which was a multi-disciplinary field experiment over a 15km grid in Konza Prairie, USA. Sampling strategies were designed for precipitation and soil moisture measurements and also detecting land cover type. It was concludes that up to 8 raingages would be needed for valuable precipitation measurements covering the whole FIFE catchment, but only one soil moisture station. Results show that as new gages or station are added to the catchment then the sampling error is reduced, but the Improvement in error performance is less as the number of gages or stations increases. Sampling from remoteiy sensed instruments shows different results. It can be seen that the sampling error at 1arger resolution sizes are small due to competing error contribution from both commission and omission error.

• Journal of Climate Change Research
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• v.8 no.4
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• pp.329-337
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• 2017

In this study, land-use changes from 1990 to 2010 in Jeju Island by different approaches were produced and compared to suggest a more efficient approach. In a sample-based method, land-use changes were analyzed with different sampling intensities of 2 km and 4 km grids, which were distributed by the fifth National Forest Inventory (NFI5), and their uncertainty was assessed. When comparing the uncertainty for different sampling intensities, the one with the grid of 2 km provided more precise information; ranged from 6.6 to 31.3% of the relative standard error for remaining land-use categories for 20 years. On the other hand, land-cover maps by a wall-to-wall approach were produced by using time-series Landsat imageries. Forest land increased from 34,194 ha to 44,154 ha for 20 years, where about 69% of total forest land were remained as forest land and 19% and 8% within forest lands were converted to grassland and cropland, respectively. In the case of grassland, only about 40% of which were remained as grassland and most of the area were converted to forest land and cropland. When comparing land-cover area by land-use categories with land-use statistics, forest areas were underestimated while areas of cropland and grassland were overestimated. In order to analyze land use change, it is necessary to establish a clear and consistent definition on the six land use classification.

• Journal of Climate Change Research
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• v.6 no.1
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• pp.33-40
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• 2015

Forests are to be recognized as an important carbon sink under the UNFCCC that consist of above- and below-biomass, dead organic matter (DOM) such as dead wood and litter, and soil organic matter (SOM). In order to asses for DOM and SOM, however, it is relevant to land-use change matrices over last 20 years for each land-use category. In this study, a land-use change matrix was produced and its uncertainty was assessed using a point sampling technique with permanent sample plots in national forest inventory at Chungbuk province. With point sampling estimated areas at 2012 year for each land-use category were significantly similar to the true areas by given six land-use categories. Relative standard error in terms of uncertainty of land-use change among land-use categories ranged in 4.3~44.4%, excluding the other land. Forest and cropland covered relatively large areas showed lower uncertainty compared to the other land-use categories. This result showed that selected permanent samples in the NFI are able to support for producing land-use change matrix at a national or province level. If the $6^{th}$ NFI data are fully collected, the uncertainty of estimated area should be improved.

• Korean Journal of Ecology and Environment
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• v.54 no.3
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• pp.221-228
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• 2021

Recently, environmental DNA (eDNA)-based metabarcoding approaches have been proposed to evaluate the status of freshwater ecosystems owing to various advantages, including fast and easy sampling and minimal habitat disruption from sampling. Therefore, as a case study, we applied eDNA metabarcoding techniques to evaluate the effects of an abandoned mine land located near a headwater stream of Nakdonggang River, South Korea, by examining benthic macroinvertebrate diversity and compared the results with those obtained using the traditional Surber-net sampling method. The number of genera was higher in Surber-net sampling (29) than in the eDNA analysis (20). The genus richness tended to decrease from headwater to downstream in eDNA analysis, whereas richness tended to decrease at sites with acid-sulfated sediment areas using Surber-net sampling. Through cluster analysis and non-metric multidimensional scaling, the sampling sites were differentiated into two parts: acid-sulfated and other sites using Surber-net sampling, whereas they were grouped into the two lowest downstream and other sites using eDNA sampling. To evaluate freshwater ecosystems using eDNA analysis in practical applications, it is necessary to constantly upgrade the methodologies and compare the data with field survey methods.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.733-741
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• 2012

The successful launch of Arirang satellites allow the acquisition of high resolution satellite imagery of Korean territory and enables the transition from the conventional cultivation area survey method to new image based methods adopted in advanced nations. In this study, we suggested reasonable sizes of the primary sampling unit and the secondary sampling unit for the satellite imagery based sampling design in 8 provinces preselected for this research. The PSU size was determined mainly in consideration of intracorrelation that shows the degree of homogeneity within each cluster and the efficiency of the image process. For the SSU size, we considered the relative standard error and the differences between the land cover maps produced by the Ministry of Environment and the satellite imagery processed by the National Statistical Office.

• Journal of Forest and Environmental Science
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• v.30 no.1
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• pp.45-49
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• 2014

This study was tried to find out the applicability of decision support system for forest land use conversion, which developed based on algorithm for forest land-use conversion. Decision support system developed by Ministry of Safety Administration is free from the existing licensed laws omission. And it made the input requirements for each value of the final result so that you can determine whether the permit was available by the laws and regulations related to the algorithm for forest land use conversion. Also, in order to do field surveys, equal sampling interval method is used to extract samples for the operability by comparing and analyzing the actual area. As a result, 88 areas of total 100 areas are able to get permission by the decision support system for forest land use conversion, and it means if there is enough data with sufficient research, it can make the availability permits easily.

• Journal of Soil and Groundwater Environment
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• v.20 no.7
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• pp.53-60
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• 2015

This study suggested the new site classification system according to land use, type of contamination and contaminants. Because the present site classification system can not cover all the areas, we changed the concept of land use to more detail one and enlarged the concept of other areas to cover all the areas not defined as certain land use. In case of the present industrial area, it was merged as other areas to avoid the confusion with oil and toxic material storage tank farm area. Accident area was separated from other areas and defined as only accident area caused by the mobile storage facility. In addition to classify the sites according to the basic land use, we classify the sites again in lower level according to the type of contamination and contaminants. With this classification system, we proposed different soil sampling strategy with the consideration of the origin of contamination and the interactions between soil and contaminants. We removed the surface soil sample (0~15 cm depth) around above storage tank because it was not a effective sample to assess whether that area contaminated or not. We also proposed to take the deeper soil samples at minimum three sampling points to confirm the depth of contamination in exploratory soil survey. We also proposed to remove the one point of 15 m depth sampling because it is not effective to confirm contaminated soil depth and needs the exhausted labor and cost. Instead of doing this, we added the continuous sampling to uncontaminated subsoil. Soil sampling points and depth in detailed soil survey is determined based on the results of exploratory soil survey. Therefore, effectiveness and reinforcements of exploratory soil survey would play an important role in improving the reliability of detailed soil survey.