• 한국지리정보학회지
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• 제13권1호
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• pp.62-73
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• 2010

조류는 복잡한 생태계의 상태를 평가하는 대표적인 생물 지표종으로써, 서식지 관리를 통한 효율적인 보전이 필요하다. 이에 본 연구는 창원시를 대상으로 산림성 조류의 서식지에 영향을 미치는 서식지 변수를 GIS기법으로 추출하여 서식지 예측 모형을 제시함으로써 향후 서식지 보존을 위한 유용한 기초자료를 제공하고자 하였다. 연구결과, 135지점에 출현한 산림성 조류는 총 5목 15과 26종 922개체로 나타났다. 또한 산림성 조류의 종다양도를 종속변수, 서식지 변수들을 독립변수로 하여 서식지 예측모형을 구축한 결과, '식생지수', '계곡으로부터의 거리', '혼효림으로부터의 거리', '밭 면적' 등 4개의 변수가 유의성을 가지는 것으로 분석되었으며, 이들의 설명력은 51.3%로 나타났다. 다음으로 모형의 정확도를 검증한 결과, 상관계수 0.735, 절대평균오차비율(MAPE) 20.7%로 비교적 합리적인 예측으로 판단되었으며, 구축된 모형을 활용하여 서식지 예측지도를 제작하였다. 이 지도는 현장조사를 근거로 조사되지 않은 지역의 종다양도를 예측 할 수 있어 향후 서식지 보존을 위한 전략수립에 유용한 기초자료로 활용 가능하리라 판단된다.

• Ecology and Resilient Infrastructure
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• 제4권4호
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• pp.200-206
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• 2017

도시화로 인한 서식지의 훼손과 단편화는 전세계적으로 양서류에게 큰 위협이 되고 있다. 본 연구에서는 도시화가 청개구리의 분포와 수도에 미치는 영향을 파악하고자, 인천와 그 주변에 위치한 18개 논에서 청음으로 청개구리의 수도를 측정하고 서식지 환경과 토지 이용을 조사하였다. 인천과 주변 논에서 청개구리의 수도는 0 - 17마리 / 서식지 또는 0 - 41마리 / ha이었다. 청개구리의 수도는 서식지와 도로와의 거리가 멀어짐에 따라서 서식지의 둘레길이 면적이 켜질수록 증가하였다. 일반적인 예측과는 달리 청개구리의 밀도는 서식지의 크기와 음의 상관을, 주변의 토지이용 강도와는 양의 상관관계를 보였다. 따라서 도시화에 의하여 서식지 면적이 감소하고 주변이 개발됨에 따라서 청개구리리가 좁은 서식지로 집중화될 수 있다고 생각된다.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• 제4권4호
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• pp.159-176
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• 2023

The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.

• 한국환경복원기술학회지
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• 제24권5호
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• pp.1-14
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• 2021

Excessive development and urbanization have destroyed animal, plant, habitats and reduced biodiversity. In order to preserve species diversity, habitat prediction studies are have been conducted at home and overseas using various modeling techniques. This study was conducted to suggest optimal habitat modeling research by comparing HSI and MaxEnt, which are widely used among habitat modeling techniques. The study was targeted on the endangered species of Prionailurus bengalensis in nearby areas (5460.35km2) including Cheonan City, and the same data were used for analysis to compare those models. According to the HSI analysis, Prionailurus bengalensis's habitat probability was 74.65% for less than 0.5 and 25.34% for more than 0.5 and the top 30% were forest (99.07%). MaxEnt's analysis showed that 56.22% of those below 0.5 and 43.79% of those above 0.5 were found to have a high explanatory power of 78.3% of AUC. The Paired Wilcoxn test, which evaluated the significance of thoes models, confirmed that the mean difference between the two models was statistically significant (p<0.05). Analysis of the differences in the results of those models using the matrix table shows that score 24.43% HSI and MaxEnt was accordance,12.44% of the 0.0 to 0.2 section, 7.22% of the 0.2 to 0.4 section, 2.73% of the 0.4 to 0.6 section, 1.96% of the 0.6 to 0.8, and 0.08% of the 0.9 to 1.0. To verify where the score difference appears, the result values of those models were reset to values from 1 to 5 and overlaid. Overlapping analysis resulted in 30.26% of the Strongly agree values, 56.77% of the agree values, and 11.92% of the Disagree values. The places where the difference in scores occurs were analyzed in the order of forest (45.23%), agricultural land (34.57%), and urbanization area (7.65%). This confirmed that the analysis of the same target species within the same target site also has differences in forecasts depending on the modelling method. Therefore, a novel analysis method combining the advantages of each modeling in habitat prediction studies should be developed, and future study may be used to select Prionailurus bengalensis and species-protected areas and species protection areas in the future. Further research is judged to require higher accuracy studies through the use of various modeling techniques and on-site verification.

• 한국환경복원기술학회지
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• 제20권3호
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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.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• 제2권1호
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• pp.1-14
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• 2021

The study has been carried out with an objective to prepare Siberian roe deer habitat potential maps in South Korea based on three geographic information system-based models including frequency ratio (FR) as a bivariate statistical approach as well as convolutional neural network (CNN) and long short-term memory (LSTM) as machine learning algorithms. According to field observations, 741 locations were reported as roe deer's habitat preferences. The dataset were divided with a proportion of 70:30 for constructing models and validation purposes. Through FR model, a total of 10 influential factors were opted for the modelling process, namely altitude, valley depth, slope height, topographic position index (TPI), topographic wetness index (TWI), normalized difference water index, drainage density, road density, radar intensity, and morphological feature. The results of variable importance analysis determined that TPI, TWI, altitude and valley depth have higher impact on predicting. Furthermore, the area under the receiver operating characteristic (ROC) curve was applied to assess the prediction accuracies of three models. The results showed that all the models almost have similar performances, but LSTM model had relatively higher prediction ability in comparison to FR and CNN models with the accuracy of 76% and 73% during the training and validation process. The obtained map of LSTM model was categorized into five classes of potentiality including very low, low, moderate, high and very high with proportions of 19.70%, 19.81%, 19.31%, 19.86%, and 21.31%, respectively. The resultant potential maps may be valuable to monitor and preserve the Siberian roe deer habitats.

• Spatial Information Research
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• 제15권2호
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• pp.135-146
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• 2007

국내에 분포하는 진범아속의 식물종 개체군을 대상으로 GIS분석을 통해 서식환경을 분석 검증함으로써 이를 통해 환경변화에 취약한 고산식물종의 보전과 관리를 위한 새로운 방안을 모색하고자 본 연구를 수행하였다. 표본조사와 문헌조사를 바탕으로 작성된 진범아속의 분포도를 중심으로 현장조사에서 정확한 분포좌표와 서식환경 등의 지리적 및 생물학적 인자를 조사하여 이를 바탕으로 수치지형도를 이용하여 서식지 모형을 만들어냈다. 진범아속의 식물개체군은 해발고도 $470{\sim}1320m$ 구간이며 북향의 $15.5{\sim}36^{\circ}$ 사이의 경사지역으로 수계에서 가까운 활엽수림에 주로 분포하였다. 이를 바탕으로 GIS 프로그램을 사용하여 서울인근의 양수와 목동 두 개의 도엽에서 고도 향 경사 등의 요소의 중첩과 수계와의 거리, 토지피복분류에 따른 주변 식생 등을 조합하여 적합서식지를 확인하였고 현장 검증에서 이 적합서식지에 실제 진범아속 식물의 분포를 검증하였다. 이를 통하여 보전을 요하는 식물군의 미확인 서식지의 추측이나 대체서식지의 선정과정에 있어 GIS가 획기적으로 사용될 수 있음을 확인하였다.

• Journal of Ecology and Environment
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• 제46권4호
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• pp.316-323
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• 2022

Background: We developed a habitat suitability index (HSI) model for Pedicularis hallaisanensis, a Grade II Endangered Species in South Korea. To determine the habitat variables, we conducted a literature review on P. hallaisanensis with a specific focus on the associated spatial factors, climate, topography, threats, and soil factors to derive five environmental factors that influence P. hallaisanensis habitats. The specific variables were defined based on the collected data and consultations with experts in the field, with the validity of each variable tested through field studies. Results: Mt. Seorak had a suitable habitat area of 2.48 km² for sites with a score of 1 (0.62% of total area) and 0.01 km² for sites with a score of 0.9. Mt. Bangtae had a suitable habitat area of 0.03 km² for sites with a score of 1 (0.02% of total area) and 0 km² for sites with a score of 0.9. Mt. Gaya showed 0.13 km² of suitable habitat for sites with a score of 1 (0.17% of total area) and 0 km² for sites with a score of 0.9. Lastly, Mt. Halla showed 3.12 km² of suitable habitat related to sites with a score of 1 (2.04% of total area) and 4.08 km² of sites with a score of 0.9 (2.66% of total area). Mt. Halla accounts for 73.1% of the total core habitat area. Considering the climatic, soil, and forest conditions together with standardized collection sites, our results indicate that Mt. Halla should be viewed as a core habitat of P. hallaisanensis. Conclusions: The findings in this study provide useful data for the identification of core habitat areas and potential alternative habitats to prevent the extinction of the endangered species, P. hallaisanensis. Furthermore, the developed HSI model allows for the prediction of suitable habitats based on the ecological niche of a given species to identify its unique distribution and causal factors.

• 한국수산과학회지
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• 제30권1호
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• pp.139-147
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• 1997

Sediment transport around artificial habitat which is induced by the change ol flow due to installation of the structure plays a role not only as a defect function of subsidence and burial but also bottom-environment control function. This study examined the characteristics of local scouring and deposition with sediment sizes, current velocities and installation direction of artificial habitat in flow field. Resultant subsidence and burial processes are investigated and discussed with Reynolds number. Together with sediment number and dimensionless time elapse, prediction formulas are established by combining these relationships. Bottom control function as cultivating effects is discussed with installation direction, and applicability of countermeasures is compared and stone pavement method is recommended.

• 한국환경생태학회지
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• 제35권2호
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• pp.154-163
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• 2021

본 연구는 한국 특산식물이고, IUCN Red List의 EN(위기종) 등급에 속하는 개느삼을 대상으로 자생지 분포, 자생지 분포 예측을 하기 위해 수행되었다. 개느삼의 자생지 분포 조사 결과,강원도 양구군 13곳, 인제군 3곳, 춘천시 2곳, 홍천군 1곳 총 19곳에 분포하는 것을 확인하였다. 우리나라에서 가장 북쪽 자생지는 양구군 임당리, 동쪽 인제군 한계리, 서쪽 춘천시 지내리, 남쪽 홍천군 성동리로 각각 확인되었다. 개느삼 자생지의 해발고도는 169-711m에 분포하는 것으로 나타났고, 평균 해발고도는 375m로 조사되었다. 개느삼 자생지의 면적은 8,000-734,000m²인 것으로 분석되었고, 평균 202,789m²로 조사되었다. 대부분의 개느삼 자생지는 간벌, 가지치기 등과 같은 숲가꾸기가 이루어진 곳으로 조사되었다. 개느삼 잠재 분포지 분석을 MaxEnt 프로그램을 이용하여 수행한 결과, AUC값은 0.9762로 분석되었다. 분포예측 자생지는 강원도 양구군, 인제군, 춘천시, 화천군 지역에 집중되어 분포하는 것으로 나타났다. 자생지 분포예측에 가장 영향을 많이 미치는 변수는 연간강수량, 토양탄소함유량, 최한월 기온으로 분석되었다. 본 연구 결과를 토대로 개느삼은 광량이 풍부하고 능선부에 주로 서식하는 것을 확인하였고, 향후 본 연구결과의 자생지 정보를 토대로 개느삼 자생지를 보전하기 위한 보호지역 지정 등을 위한 기초자료로 활용될 수 있을 것으로 판단된다.