• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.38-38
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• 2001

The rainfall estimates give sufficiently accurate information to map areas which have received the minimum rainfall necessary for outbreaks of pests such as locusts, thus cutting down the cost of searching for likely outbreak sites. At the other end of the scale, satellite rainfall estimates can be used to give timely warnings of changes in river levels and the likelihood of floods in large river catchments.(omitted)

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.184-185
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• 2019

• Korean Journal of Ecology and Environment
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• v.56 no.4
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• pp.330-338
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• 2023

To investigate the impact of forestry projects on the carbon stocks of forests, we estimated the carbon stock change of above-ground and soil before and after forestry projects using forest type maps, forestry project information, and soil information. First, we selected six map sheet with large areas and declining age class based on forest type map information. Then, we collected data such as forest type maps, growth coefficients, soil organic matter content, and soil bulk density of the estimated areas to calculate forest carbon storage. As a result, forest carbon stocks decreased by about 34.1~70.0% after forestry projects at all sites. In addition, compared to reference studies, domestic forest soils store less carbon than the above-ground, so it is judged that domestic forest soils have great potential to store more carbon and strategies to increase carbon storage are needed. It was estimated that the amount of carbon stored before forestry projects is about 1.5 times more than after forestry projects. The study estimated that it takes about 27 years for forests to recover to their pre-thinning carbon stocks following forestry projects. Since it takes a long time for forests to recover to their original carbon stocks once their carbon stocks are reduced by physical damage, it is necessary to plan to preserve them as much as possible, especially for highly conservative forests, so that they can maintain their carbon storage function.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.698-701
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• 2005

This paper examines that is extracted certain information in forest areas within high resolution imagery based on wavelet transformation. First of all, study areas are selected one more species distributed spots refer to forest type map. Next, study area is cut 256 x 256 pixels size because of image processing problem in large volume data. Prior to wavelet transformation, five texture parameters (contrast, dissimilarity, entropy, homogeneity, Angular Second Moment (ASM≫ calculated by using Gray Level Co-occurrence Matrix (GLCM). Five texture images are set that shifting window size is 3x3, distance .is 1 pixel, and angle is 45 degrees used. Wavelet function is selected Daubechies 4 wavelet basis functions. Result is summarized 3 points; First, Wavelet transformation images derived from contrast, dissimilarity (texture parameters) have on effect on edge elements detection and will have probability used forest road detection. Second, Wavelet fusion images derived from texture parameters and original image can apply to forest area classification because of clustering in Homogeneous forest type structure. Third, for grading evaluation in forest fire damaged area, if data fusion of established classification method, GLCM texture extraction concept and wavelet transformation technique effectively applied forest areas (also other areas), will obtain high accuracy result.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.123-139
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• 2024

Forest vertical structure is vital for comprehending ecosystems and biodiversity, in addition to fundamental forest information. Currently, the forest vertical structure is predominantly assessed via an in-situ method, which is not only difficult to apply to inaccessible locations or large areas but also costly and requires substantial human resources. Therefore, mapping systems based on remote sensing data have been actively explored. Recently, research on analyzing and classifying images using machine learning techniques has been actively conducted and applied to map the vertical structure of forests accurately. In this study, Sentinel-2 and digital surface model images were obtained on two different dates separated by approximately one month, and the spectral index and tree height maps were generated separately. Furthermore, according to the acquisition time, the input data were separated into cases 1 and 2, which were then combined to generate case 3. Using these data, forest vetical structure mapping models based on random forest, support vector machine, and extreme gradient boost(XGBoost)were generated. Consequently, nine models were generated, with the XGBoost model in Case 3 performing the best, with an average precision of 0.99 and an F1 score of 0.91. We confirmed that generating a forest vertical structure mapping model utilizing bi-seasonal data and an appropriate model can result in an accuracy of 90% or higher.

• Spatial Information Research
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• v.8 no.1
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• pp.1-14
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• 2000

Zoning data such as national land use planning map, urban land use planning map, agricultural promotion zoning ap, forest land zoning map has a relationship each other in law and spatial context. But difficulties in data share and the lack of accuracy of manual work makes serious inconsistence son zoning data relationship. This causes many trial and error in land use. For resolving this problem the data modellingmethod is presented as a technical solution.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.11 no.1
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• pp.1-13
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• 2008

This study review of Compositional Evaluation Items for Environmental Conservation Value Assessment Map (ECVAM) in Korea. The ECVAM is composed of legal assessment and environmental/ecological assessment items. ECVAM basically adapts an overlay method for environmental/ecological assessment items. The objective of this study is to suggest supplementary items for the ECVAM with the following process : Overlapping rates of the assessment items in the ECVAM are calculated to understand the grade distribution of the environmental conservation value assessment and to analyze the overlapping rates among the assessment items, as a result it is found that various items are overlapped each other. In order to reflect effectively each assessment item to the ECVAM, Analyzed the overlapping degree among assessment items to be applied to this map. On the concrete we gripped results to be assessed by various items, which were overlapped each other. In order to reflect effectively each assessment item to the environmental conservation value assessment map of national land, we analyzed the overlapping degree on environmental/ecological items, and investigated the grade distribution by field survey. In this study we assessed the ECVAM by 5 kinds of method. Method 1 is Grade 1 areas of each administrative district, Method 2 is Comparing overlapping areas of each assessment items Grade 1, 2 and Permission of each assessment items' duplication, Method 3 is Grade 1, 2 areas by only singular assessment items, Method 4 is Only Grade 1 areas of Method 2 and Method 5 is Only Grade 2 areas of Method 2. As results, Method 1 showed Seoul and other metropolitan cities reveal a high proportion of Grade I regions by the legal assessment items. Kangwon-Do, show a high proportion of Grade I regions by the environmental/ecological assessment item. Method 2 showed 93.4% of diameter Grade II(standard for stability), forest diameter item was accounted for 99.9% by Method 3, Method 4 showed 95.7% of forest diameter and forest density was accounted for 66.4% by Method 5. From now on, this study will contribute to reduce the complexity in the process of manufacturing ECVAM of National Land, and to raise the pliability in the process of managing and updating this map.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.364-380
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• 2016

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.

• Journal of Korean Society of Forest Science
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• v.97 no.4
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• pp.445-451
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• 2008

This study was carried out to develop Tablet PC based customizing system for fine mapping of forest cover type. The major contents and characteristics of FIS developed in this study were as follows. Field Investigation System (FIS) has a merit of accessibility to display exact location in various spatial data with position information received from the GPS. FIS can be used to record and manage many field information on which field investigation is done, with the help of the memo tool, field-sheet tool, calculating distance and area with measuring tool as well as editing forest type. It is possible to do field investigation effectively using FIS developed in this study. Accordingly, investigation and time costs can be reduced and field-work productivity will be improved.

• 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.