• Journal of Environmental Impact Assessment
• /
• v.26 no.2
• /
• pp.160-170
• /
• 2017

The purpose of this study is to perform clustering of the habitat types and to identify the characteristics of species in the habitat types using mammal data (70,562) of the 3rd National Ecosystem Survey conducted from 2006 to 2012. The 15 habitat types recorded in the field-paper of the 3rd National ecosystem survey were reclassified, which was followed by the statistical analysis of mammal habitat types. In the habitat types cluster analysis, non-hierarchical cluster analysis (k-means cluster analysis), hierarchical cluster analysis, and non-metric multidimensional scaling method were applied to 14 habitat types recorded more than 30 times. A total of 7 Orders, 16 Families, and 39 Species of mammals were identified in the 3rd National Ecosystem Survey collected nationwide. When 11 clusters were classified by habitat types, the simple structure index was the highest (ssi = 0.07). As a result of the similarities and hierarchies between habitat types suggested by the hierarchical clustering analysis, the residential areas were the most different habitat types for mammals; the next following type was a cluster together with rivers and coasts. The results of the non-metric multidimensional scaling analysis demonstrated that both Mus musculus and Rattus norvegicus restrictively appeared in a residential area, which is the most discriminating habitat type. Lutra lutra restrictively appeared in coastal and river areas. In summary, according to our results, the mammalian habitat can be divided into the following four types: (1) the forest type (using forest as the main habitat and migration route); (2) the river type (using water as the main habitat); (3) the residence habitat (living near residential area); and (4) the lowland type (consuming grain or seeds as the main feeding resource).

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.11 no.5
• /
• pp.38-59
• /
• 2008

This research has a meaning to provide basic data for eco-friendly way of district unit plans and ecological landscape planning by evaluation of biotope preservation value at the level of district unit and designating land development of the site, the whole area of Sinseo-dong (Dong-gu, Daegu metropolitan city) for research site. The summary of analysis result is as follows. As a result of classification of biotope types on the research site, it is divided into 11 biotope groups such as a residential biotope group and 51 specific biotope types which is subordinate to the groups. As a result of the first value assessment on classified biotope types, there are 16 types of natural rivers which is full of vegetation as a I class. Also it is analysed as 9 types of IIclass, 14 of IIIclass, 8 of IVclass, and 4 of Vclass. In particular, in light of a wildlife habitat, EB, in case of broad-leaved tree of mixed forest assessed as a II class, was classified into Iclass which is one-step upgraded as a final class with the analysis as there is a structural characteristic (more than 71% of low density, 50 years of age-class). As a result of second assessment, it is analysed that there are 17 special sites (1a,1b) and 33 special sites (2a, 2b, 2c) respectively for preservation of species and biotope. Particularly, in case of the No. 27 space, it was assessed that it has the value of about medium (IIIclass) level, but its value was upgraded with the on-spot detailed investigation that most of Aristolochia contorta, designated as a rare plant by Ministry of Environment, is growing. It is regarded that the above-mentioned research result on evaluation of biotope preservation value is expected to provide very important basic materials for future district unit plans and smooth integration with landscape ecology plans and eco-friendly space development.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.11
• /
• pp.1599-1607
• /
• 2022

The data mainly used for the model are as follows: the personal information, the information of insurance product, etc. With the data, we suggest three types of models: content-based filtering model, collaborative filtering model and classification models-based model. The content-based filtering model finds the cosine of the angle between the users and items, and recommends items based on the cosine similarity; however, before finding the cosine similarity, we divide into several groups by their features. Segmentation is executed by K-means clustering algorithm and manually operated algorithm. The collaborative filtering model uses interactions that users have with items. The classification models-based model uses decision tree and random forest classifier to recommend items. According to the results of the research, the contents-based filtering model provides the best result. Since the model recommends the item based on the demographic and user features, it indicates that demographic and user features are keys to offer more appropriate items.

• Journal of Korean Society of Forest Science
• /
• v.111 no.2
• /
• pp.201-223
• /
• 2022

Currently, species extinctions are increasing due to climate change and continued anthropogenic impact. We selected 300 species for conservation with emphasis on plants co-occurring in the Baekdudaegan area, which is a large ecological axis of Korea. We aimed to investigate the vegetation community and environmental characteristics of Zabelia tyaihyonii in the limestone habitat among the target plant species in the Baekdudaegan region to derive effective conservation strategies. In Danyang-gun, Yeongwol-gun, and Jecheon-si, we selected 36 investigation sites where Z. tyaihyonii was present. We investigated the vegetation, flora, soil and physical environment. We also found notable plants such as Thalictrum petaloideum, Sillaphyton podagraria, and Neillia uekii at the investigation sites. We classified forest vegetation community types into 4 vegetation units and 7 species group types. With canonical correspondence analysis (CCA) of the vegetation community and habitat factors, we determined the overall explanatory power to be 75.2%, and we classified the environmental characteristics of the habitat of Z. tyaihyonii into a grouping of three. Among these, we detected a relationship between the environmental factors elevation, slope, organic matter, rock ratio, pH, potassium, and sodium. We identified numerous rare and endemic plants, including Thalictrum petaloideum, in the investigation site, and determined that these groups needed to be preserved at the habitat level. In the classification of the vegetation units analyzed based on the emerging plants and the CCA, we reaffirmed the uniqueness and specificity of the vegetation community in the habitat of Z. tyaihyonii. We anticipate that our results will be used as scientific evidence for the empirical conservation of the native habitats of Z. tyaihyonii.

• Journal of Korean Society of Forest Science
• /
• v.89 no.2
• /
• pp.275-284
• /
• 2000

This study was carried out to analyze the mechanical properties of soil and the slope stability on the fill slope of forest road constructed in the regions which consist of igneous and metamorphic rock area. The results were summarized as follows. 1) Soil type by Unified Soil Classification System(USCS) was classified as SW in soil slope, GP in weathered rock slope, GP in soft rock slope for both types of parent rock, but gravelly soil slopes in igneous and metamorphic rock area were classified as SP and GW, respectively. 2) Dry unit weight was $1.34g/cm^2{\sim}1.59g/cm^2$, specific gravity 0.57~0.61, and void ratio 0.66~0.93 in the case of igneous rock area, a dry unit weight was $1.35g/cm^2{\sim}1.51g/cm^2$, specific gravity 2.67~2.77, and void ratio 0.78~1.01 in the case of metamorphic rock area. 3) The strength parameters such as internal friction angle(${\phi}$) and cohesion(c) were selected and tested for slope stability analysis. ${\phi}$ and c of soil in igneous rock area were within the range of $29.51^{\circ}{\sim}41.82^{\circ}$ and $0.03kg/cm^2{\sim}0.38kg/cm^2$, respectively, and $21.43^{\circ}{\sim}41.43^{\circ}$ and $0.05kg/cm^2{\sim}0.44kg/cm^2$ in metamorphic rock area, respectively. 4) Result of the slope stability analysis of forest road showed that, in the weathered rock slope of igneous rock and the weathered rock and soil slope of metamorphic rock area, the possibility of slope failure was high as safety factor was below 1.0.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.26 no.2
• /
• pp.55-67
• /
• 2023

This study aims to develop a model that can automatically identify the rooftop shape of apartment buildings using GIS and machine learning algorithms, and apply it to analyze the relationship between rooftop shape and characteristics of apartment complexes. A database of rooftop data for each building in an apartment complex was constructed using geospatial data, and individual buildings within each complex were classified into flat type, tower type, and mixed types using the random forest algorithm. In addition, the relationship between the proportion of rooftop shapes, development density, height, and other characteristics of apartment complexes was analyzed to propose the potential application of geospatial information in the real estate field. This study is expected to serve as a basic research on AI-based building type classification and to be utilized in various spatial and real estate analyses.

• Journal of Korea Water Resources Association
• /
• v.54 no.2
• /
• pp.81-91
• /
• 2021

Synthetic Apreture Radar (SAR) is attracting attentions with its possibility of producing high resolution data that can be used for soil moisture estimation. High resolution soil moisture data enables more specific observation of soil moisture than existing soil moisture products from other satellites. It can also be used for studies of wildfire, landslide, and flood. The SAR based soil moisture estimation should be conducted considering vegetation, which affects backscattering signals from the SAR sensor. In this study, a SAR based soil moisture estimation at regions covered with various vegetation types on the middle area of Korea (Cropland, Grassland, Forest) is conducted. The representative backscattering model, Water Cloud Model (WCM) is used for soil moisture estimation over vegetated areas. Radar Vegetation Index (RVI) and in-situ soil moisture data are used as input factors for the model. Total 6 study areas are selected for 3 vegetation types according to land cover classification with 2 sites per each vegetation type. Soil moisture evaluation result shows that the accuracy of each site stands out in the order of grassland, forest, and cropland. Forested area shows correlation coefficient value higher than 0.5 even with the most dense vegetation, while cropland shows correlation coefficient value lower than 0.3. The proper vegetation and soil moisture conditions for SAR based soil moisture estimation are suggested through the results of the study. Future study, which utilizes additional ancillary vegetation data (vegetation height, vegetation type) is thought to be necessary.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.39 no.2
• /
• pp.10-17
• /
• 2011

The paradigm of the use and management of natural resources is changing. Wise use of natural resources can be achieved by enhancing their conservation value and, at the same time, taking them as an opportunity for regional development. It leads to an idea of pursuing regional development by making good use of natural resources. In this paper, natural resources were classified as living species resources, ecosystem and landscape resources, and non-living resources. The resources were divided into 27 detailed analysis indices. The administrative boundaries of 165 municipalities in Korea were defined as spatial analysis units. Finally, a spatial database of natural resources was built. To classify the regional types, we conducted factor analyses with a detailed index of natural resources and a cluster analysis with the factor value. As the result of the factor analysis, six factors have been deduced as follows: forest resources, landscape resources, coastal ecology resources, inland water resources, landform resources, and ecology visit resources. In addition, the cluster analyses were conducted for the points of the factors drawn. The final classification consists of nine groups, and appropriate methods for each regional development have been suggested. Results of this study will contribute to providing fundamental materials for site selection and objective-setting for regional development policies and planning in consideration of natural resources.

• Korean Journal of Environment and Ecology
• /
• v.35 no.4
• /
• pp.377-386
• /
• 2021

This study was conducted to provide basic data for habitat management and preservation of Jeongmaek. A total of 18 priority research areas were selected with consideration to terrain and habitat environment, and 54 fixed plots were selected for three types of habits: development, valley, and forest road and ridge. The survey was conducted in each season (May, August, and October), excluding the winter season, from 2016 to 2018. The distribution analysis of birds observed in each habitat type using a self-organizing map (SOM) classified them into a total of four groups (MRPP, A=0.12, and p <0.005). The comparative analysis of the number of species, the number of individuals, and the species diversity index for each SOM group showed that they were all the highest in group III (Kruskal-Wallis, the number species: x² = 13.436, P <0.005; the number of individuals: x² = 8.229, P <0.05; the species diversity index: x² = 17.115, P <0.005). Moreover, the analysis by applying the land cover map to the random forest model to examine the index species of each group and identify the characteristics of the habitat environment showed a difference in the ratio of the habitat environment and the indicator species among the four groups. The index species analysis identified a total of 18 bird species as the indicator species in three groups except for group II. When applying the random forest model and indicator species analysis to the results of classification into four groups using the SOM, the composition of the indicator species by the group showed a correlation with the habitat characteristics of each group. Moreover, the distribution patterns and densities of observed species were clearly distinguished according to the dominant habitat for each group. The results of the analysis that applied the SOM, indicator species, and random forest model together can derive useful results for the characterization of bird habitats according to the habitat environment.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_2
• /
• pp.1651-1669
• /
• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.