• Korean Journal of Remote Sensing
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• v.28 no.1
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• pp.171-180
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• 2012

The field of wildlife habitat conservation research has attracted attention as integrated biodiversity management strategies. Considering the status of the species surveying data and the environmental variables in Korea, the GARP and Maxent models optimized for presence-only data could be one of the most suitable models in habitat modeling. For make sure applicability in the domestic environment we applied the machine learning species distribution model for analyzing habitats of the Korea water deer($Hydropotes$ $inermis$ $argyropus$) in the $Sapgyocheon$ watershed, $Chungcheong$ province. We used the $3^{rd}$ National Natural Environment Survey data and 10 environment variables by literature review for the modelling. Analysis results showed that habitats for the Korea water deer were predicted 16.3%(Maxent) and 27.1%(GARP), respectively. In terms of accuracy(training/test) the Maxent(0.85/0.69) was higher than the GARP(0.65/0.61), and the Spearman's rank correlation coefficient result of the Maxent(${\rho}$=0.71, p<0.01) was higher than the result of GARP(${\rho}$=0.55, p<0.05). However results could be depended on sites and target species, therefore selection of the appropriate model considering on the situation will be important to analyzing habitats.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.4
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• pp.59-64
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• 2008

We need to use the strength of each Species Distribution Model(SDM) because presence location data were only collected due to time and economic limitations in Korea. This study investigated and compared GAM(Generalized Additive Model) which is one of presence-absence models with Maxent(Maximum Entropy Model) which is one of presence only models according to location data(presence/absence data). The target species was Fisher(Martes pennanti) which is an endangered species in California, USA. We implemented environmental data such as topography, climate and vegetation, and applied models to sub-regions and study area. The results of this study were as follows. Firstly, GAM which used real presence and absence data was better than GAM which used pseudo-absence data and Maxent which used presence-only data. Secondly, Maxent was better than GAM when presence-only data were used. Lastly, each model which applied to different regions didn't predict other area well due to the difference of habitat environment and over-predicted outside of study area. We need to select an optimal model to predict a suitable habitat according to the type and distribution of location data.

• Journal of Environmental Impact Assessment
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• v.25 no.6
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• pp.432-441
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• 2016

In this study, we predicted species distributions in Mt. Inwang and Mt. An as preceding research to build ecological corridor by considering connectivity of habitats which have been fragmented in the city. We analyzed species distributions by using Maxent (Maximum Entropy Approach) model with species presence. We used 23 points of mammals and 15 points of Titmouse (Parus major, P. palustris, P. varius) as target species from appearance points of species examined. We build 4 geography factors, 4 vegetation factors, and 2 distance factors as model variables In case of mammals, factors that affected species distribution model was Digital Elevation Model(DEM, 34%) followed by Distance from edge forest to interior (24.8%) and Species of tree (10%). On the other hand, in case of Parus species, factors that affected species distribution model were DEM (39.6%) followed by distance from road (35.4%) and Density-class (8.2%). Therefore, birds and mammals prefer interior of mountain, and this area needs to be protected.

• Journal of Environmental Impact Assessment
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• v.24 no.1
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• pp.35-50
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• 2015

The purposes of this study are to apply species distribution modeling in urban management planning for habitat conservation in non-urban area and to provide a detailed classification method for management zone. To achieve these objectives, Species Distribution Model was used to generate species richness and then to compare with the results from land suitability assessment. 59 species distribution models were developed by Maxent. This study used 15 model variables (5 topographical variables, 4 vegetation variables, and 6 distance variables) for Maxent models. Then species richness was created by sum of predicted species distributions. Land suitability assessment was conducted with criteria from type I of "Guidelines for land suitability assessment". After acquiring evaluation values from species richness and land suitability assessment, the results from these two models were compared according to the five grades of classification. The areas with the identical grade in Species richness and land suitability assessment are categorized and then compared each other. The comparison results are Grade1 10.92%, Grade2 37.10%, Grade3 34.56%, Grade4 20.89% and Grade5 1.73%. Grade1 and Grade5 showed the lowest agreement rate. Namely, development or conservation grade showed high disagreement between two assessment system. Therefore, the areas located between urban, agriculture, forest, and reserve have a tendency to change easily by development plans. Even though management areas are not the core area of reserve, it is important to provide a venue for species habitat and eco-corridor to protect and improve biodiversity in terms of landscape ecology. Consequently, adoption of species richness in three levels of management area classification such as conservation, production, planning should be considered in urban management plan.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.47-52
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• 2014

Species distribution models have been widely applied in order to assess biodiversity, design reserve, manage habitat and predict climate change. However, SDMs has been used restrictively to the public and policy sectors owing to model uncertainty. Recent studies on ensemble and consensus models have been increased to reduce model uncertainty. This paper was carried out single model and multi model for Corylopsis coreana and compares two models. First, model evaluation was used AUC, kappa and TSS. TSS was the most effective method because it was easy to compare several models and convert binary maps. Second, both single and ensemble model show good performance and RF, Maxent and GBM was evaluated higher, GAM and SRE was evaluated lower relatively. Third, ensemble model tended to overestimate over single model. This problem can be solved by the suitable model selection and weighting through collaboration between field experts and modeler. Finally, we should identify causes and magnitude of model uncertainty and improve data quality and model methods in order to apply special decision-making support system and conservation planning, and when we make policy decisions using SDMs, we should recognize uncertainty and risk.

• Journal of Environmental Impact Assessment
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• v.21 no.4
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• pp.593-607
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• 2012

The Ministry of Environment have started the 'National Ecosystem Survey' since 1986. It has been carried out nationwide every ten years as the largest survey project in Korea. The second one and the third one produced the GIS-based inventory of species. Three survey methods were different from each other. There were few studies for species distribution using national survey data in Korea. The purposes of this study are to test species distribution models for finding the most suitable modeling methods for the National Ecosystem Survey data and to investigate the modeling results according to survey methods and taxonominal group. Occurrence data of nine species were extracted from the National Ecosystem Survey by taxonomical group (plant, mammal, and bird). Plants are Korean winter hazel (Corylopsis coreana), Iris odaesanensis (Iris odaesanensis), and Berchemia (Berchemia berchemiaefolia). Mammals are Korean Goral (Nemorhaedus goral), Marten (Martes flavigula koreana), and Leopard cat (Felis bengalensis). Birds are Black Woodpecker (Dryocopus martius), Eagle Owl (Bubo Bubo), and Common Buzzard (Buteo buteo). Environmental variables consisted of climate, topography, soil and vegetation structure. Two modeling methods (GAM, Maxent) were tested across nine species, and predictive species maps of target species were produced. The results of this study were as follows. Firstly, Maxent showed similar 5 cross-validated AUC with GAM. Maxent is more useful model to develop than GAM because National Ecosystem Survey data has presence-only data. Therefore, Maxent is more useful species distribution model for National Ecosystem Survey data. Secondly, the modeling results between the second and third survey methods showed sometimes different because of each different surveying methods. Therefore, we need to combine two data for producing a reasonable result. Lastly, modeling result showed different predicted distribution pattern by taxonominal group. These results should be considered if we want to develop a species distribution model using the National Ecosystem Survey and apply it to a nationwide biodiversity research.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.13 no.6
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• pp.161-172
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• 2010

As the interest on biodiversity has increased around the world, researches about evaluating potential for habitat are also increasing to find and comprehend the valuable habitats. This study focus on comprehending the significance of stream in evaluating habitat's potential. The purpose of this study is to evaluate habitat potential with applying stream as a main variable, and to comprehend the relationship between the variables and habitat potential. Basin is a unit that has hydrological properties and dynamic interaction with ecosystem. Especially, biodiversity and suitability of habitat in basin area has direct correlation with stream. Existing studies also are proposing for habitat potential evaluation in basin unit, they applied forest, slope and road as main variables. Despite stream is considered the most important factor in basin area, researchers haven't applied stream as a main variable. Therefore, in this study, three variables that can demonstrate hydrological properties are selected, which are, riparian distance, stream order and land use disturbance, and evaluate habitat potential. Habitat potential is analyzed by using Maxent (Maximum entropy model), and vertebrate's presence data is used as dependent variables and stream order map and land cover map is used as base data of independent variables. As a result of analysis, habitat potential is higher at riparian and upstream area, and lower at frequently disturbed area. Result indicates that adjacent to stream, upstream, and less disturbed area is the habitat that vertebrate prefer. In particular, mammals prefer adjacent area of stream and forest and reptiles prefer upriver area. Birds prefer adjacent area of stream and midstream and amphibians prefer adjacent area of stream and upriver. The result of this research could help to establish habitat conservation strategy around basin unit in the future.

• Journal of Environmental Impact Assessment
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• v.23 no.5
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• pp.379-392
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• 2014

This study was performed to predict the future climate envelope of Pinus pumila, a subalpine plant and a Climate-sensitive Biological Indicator Species (CBIS) of Korea. P. pumila is distributed at Mt. seorak in South Korea. Suitable habitat were predicted under two alternative RCPscenarios (IPCC AR5). The SDM used for future prediction was a Maxent model, and the total number of environmental variables for Maxent was 8. It was found that the distribution range of P. pumila in the South Korean was $38^{\circ}7^{\prime}8^{{\prime}{\prime}}N{\sim}38^{\circ}7^{\prime}14^{{\prime}{\prime}}N$ and $128^{\circ}28^{\prime}2^{{\prime}{\prime}}E{\sim}128^{\circ}27^{\prime}38^{{\prime}{\prime}}E$ and 1,586m~1,688m in altitude. The variables that contribute the most to define the climate envelope are altitude. Climate envelope simulation accuracy was evaluated using the ROC's AUC. The P. pumila model's 5-cv AUC was found to be 0.99966. which showed that model accuracy was very high. Under both the RCP4.5 and RCP8.5 scenarios, the climate envelope for P. pumila is predicted to decrease in South Korea. According to the results of the maxent model has been applied in the current climate, suitable habitat is $790.78km^2$. The suitable habitats, are distributed in the region of over 1,400m. Further, in comparison with the suitable habitat of applying RCP4.5 and RCP8.5 suitable habitat current, reduction of area RCP8.5 was greater than RCP4.5. Thus, climate change will affect the distribution of P. pumila. Therefore, governmental measures to conserve this species will be necessary. Additionally, for CBIS vulnerability analysis and studies using sampling techniques to monitor areas based on the outcomes of this study, future study designs should incorporate the use of climatic predictions derived from multiple GCMs, especially GCMs that were not the one used in this study. Furthermore, if environmental variables directly relevant to CBIS distribution other than climate variables, such as the Bioclim parameters, are ever identified, more accurate prediction than in this study will be possible.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.3
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• pp.55-64
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• 2017

The problem of the population number of honeybees that is decreasing not only domestically but also globally, has a great influence on human beings and the entire ecosystem. The habitat of honeybees is recognized to be superior in urban environment rather than rural environment, and predicting for habitat assessment and conservation is necessary. Based on this, we targeted Cheonan City and neighboring administrative areas where the distribution of agricultural areas, urban areas, and forest areas is displayed equally. In order to predict the habitat preferred by honeybees, we apply the Maxent model what based on the presence information of the species. We also selected 10 environmental variables expected to influence honeybees habitat environment through literature survey. As a result of constructing the species distribution model using the Maxent model, 71.7% of the training data were shown on the AUC(Area Under Cover) basis, and it was be confirmed with an area of 20.73% in the whole target area, based on the 50% probability of presence of honeybees. It was confirmed that the contribution of the variable has influence on land covering, distance from the forest, altitude, aspect. Based on this, the possibility of honeybee's habitat characteristics were confirmed to be higher in wetland environment, in agricultural land, close to forest and lower elevation, southeast and west. The prediction of these habitat environments has significance as a lead research that presents the habitat of honeybees with high conservation value of ecosystems in terms of urban space, and it will be useful for future urban park planning and conservation area selection.

• Journal of Veterinary Clinics
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• v.36 no.1
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• pp.23-29
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• 2019

While research findings suggest that the highly pathogenic avian influenza (HPAI) is the leading cause of economic loss in Korean poultry industry with an estimated cumulative impact of $909 million since 2003, identifying the environmental and anthropogenic risk factors involved remains a challenge. The objective of this study was to identify areas at high risk for potential HPAI outbreaks according to the likelihood of HPAI virus detection in wild birds. This study integrates spatial information regarding HPAI surveillance with relevant demographic and environmental factors collected between 2003 and 2018. The Maximum Entropy (Maxent) species distribution modeling with presence-only data was used to model the spatial risk of HPAI virus. We used historical data on HPAI occurrence in wild birds during the period 2003-2018, collected by the National Quarantine Inspection Agency of Korea. The database contains a total of 1,065 HPAI cases (farms) tied to 168 unique locations for wild birds. Among the environmental variables, the most effective predictors of the potential distribution of HPAI in wild birds were (in order of importance) altitude, number of HPAI outbreaks at farm-level, daily amount of manure processed and number of wild birds migrated into Korea. The area under the receiver operating characteristic curve for the 10 Maxent replicate runs of the model with twelve variables was 0.855 with a standard deviation of 0.012 which indicates that the model performance was excellent. Results revealed that geographic area at risk of HPAI is heterogeneously distributed throughout the country with higher likelihood in the west and coastal areas. The results may help biosecurity authority to design risk-based surveillance and implementation of control interventions optimized for the areas at highest risk of HPAI outbreak potentials.