본문 바로가기
KERD
논문KCI 등재

Mapping the Potential Distribution of Raccoon Dog Habitats: Spatial Statistics and Optimized Deep Learning Approaches

이사로|Widya Liadira Kusuma|Rezaie Fatemah
국립생태원 (National Institute of Ecology)|2023.11|국립생태원회보 (Proceedings of the National Institute of Ecology of the Republic of Korea)|제4권 제4호|pp. 159-176|DOI 10.22920/PNIE.2023.4.4.159

1

조회

0

다운로드

0

인용

0

참고문헌

키워드

Raccoon dogConvolutional neural networkLong short-term memory networkFrequency ratioGroup method of data handlingHabitat mapping

초록

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.

Abstract

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.

참고문헌 (110)

  1. Abdollahi, M., "Imperialist competitive algorithm for solving systems of nonlinear equations", Computers & Mathematics with Applications, 65, pp.1894~1908 (2013)[DOI]
  2. Ahearn, D. S., "Land use and land cover influence on water quality in the last free-flowing river draining the western Sierra Nevada, California", Journal of Hydrology, 313, pp.234~247 (2005)[DOI]
  3. Ahmed, R., "Remote Sensing and GIScience", pp.3~7 (2021), Springer
  4. Akay, H., "Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods", Soft Computing, 25, pp.9325~9346 (2021)[DOI]
  5. Althuwaynee, O. F., "A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison", International Journal of Remote Sensing, 37, pp.1190~1209 (2016)[DOI]
  6. Alzubaidi, L., "Review of deep learning : concepts, CNN architectures, challenges, applications, future directions", Journal of Big Data, 8, pp.53~ (2021)[DOI]
  7. Arabameri, A., "GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches", Science of The Total Environment, 658, pp.160~177 (2019)[DOI]
  8. Arabameri, A., "GIS-based gully erosion susceptibility mapping : a comparison among three data-driven models and AHP knowledge-based technique", Environmental Earth Sciences, 77, pp.628~ (2018)[DOI]
  9. Arora, A., "Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India", Science of The Total Environment, 750, pp.141565~ (2021)[DOI]
  10. Asmare, D., "Application and validation of AHP and FR methods for landslide susceptibility mapping around Choke Mountain, Northwestern Ethiopia", Scientific African, 19, pp.e01470~ (2023)[DOI]
  11. Atashpaz-Gargari, E., "Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition", Paper presented at 2007 IEEE Congress on Evolutionary Computation (2007)
  12. Barve, S, "Reef-insight:a framework for reef habitat mapping with clustering methods using remote sensing", Information, 14, pp.373~ (2023)[DOI]
  13. Beasom, S. L., "A technique for assessing land surface ruggedness", The Journal of Wildlife Management, 47, pp.1163~1166 (1983)[DOI]
  14. Bonnot, N., "Habitat use under predation risk : hunting, roads and human dwellings influence the spatial behaviour of roe deer", European Journal of Wildlife Research, 59, pp.185~193 (2013)[DOI]
  15. Bradley, A. P., "The use of the area under the ROC curve in the evaluation of machine learning algorithms", Pattern Recognition, 30, pp.1145~1159 (1997)[DOI]
  16. Burnett, B. N., "Aspectrelated microclimatic influences on slope forms and processes, northeastern Arizona", Journal of Geophysical Research, 113, pp.F03002~ (2008)[DOI]
  17. Cai, T., "Using stochastic gradient boosting to infer stopover habitat selection and distribution of Hooded Cranes Grus monacha during spring migration in Lindian, Northeast China", PLoS One, 9, pp.e89913~ (2014)[DOI]
  18. Červinka, J., "Largescale evaluation of carnivore road mortality : the effect of landscape and local scale characteristics", Mammal Research, 60, pp.233~243 (2015)[DOI]
  19. Chen, W., "Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility", Catena, 172, pp.212~231 (2019)[DOI]
  20. Choi, J. K., "Crustacean habitat potential mapping in a tidal flat using remote sensing and GIS", Ecological Modelling, 222, pp.1522~1533 (2011)[DOI]
  21. Choi, J. K., "Spatial polychaeta habitat potential mapping using probabilistic models", Estuarine , Coastal and Shelf Science, 93, pp.98~105 (2011)[DOI]
  22. Dahl, F., "Nest predation by raccoon dog Nyctereutes procyonoides in the archipelago of northern Sweden", Biological Invasions, 21, pp.743~755 (2019)[DOI]
  23. Dargan, S., "A survey of deep learning and its applications : a new paradigm to machine learning", Archives of Computational Methods in Engineering, 27, pp.1071~1092 (2020)[DOI]
  24. Diao, Y., "Predicting current and future species distribution of the raccoon dog(Nyctereutes procyonoides)in Shanghai, China", Landscape and Urban Planning, 228, pp.104581~ (2022)[DOI]
  25. Dilts, T. E., "Improved topographic ruggedness indices more accurately model fine-scale ecological patterns", Landscape Ecology, 38, pp.1395~1410 (2023)[DOI]
  26. Dodangeh, E., "Novel hybrid intelligence models for flood-susceptibility prediction : meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search", Journal of Hydrology, 590, pp.125423~ (2020)[DOI]
  27. Du, P., "Advances of four machine learning methods for spatial data handling: a review", Journal of Geovisualization and Spatial Analysis, 4, pp.13~ (2020)[DOI]
  28. Ellerman, J. R., "Checklist of Palaearctic and Indian Mammals, 1758-1946" (1951), Trustees of the British Museum
  29. Farlow, S.J., "Self-Organizing Methods in Modeling: GMDH-Type Algorithms" (1984), Marcel Dekker
  30. Alexander Fernald, "Modeling Sustainability of Water, Environment, Livelihood, and Culture in Traditional Irrigation Communities and Their Linked Watersheds", Sustainability, 4(11), pp.2998~3022 (2012), MDPI AG[DOI]
  31. Ghebrezgabher, M. G., "Assessment of NDVI variations in responses to climate change in the Horn of Africa", The Egyptian Journal of Remote Sensing and Space Science, 23, pp.249~261 (2020)[DOI]
  32. Graves, A, "Supervised Sequence Labelling with Recurrent Neural Networks", pp.37~45 (2012), Springer
  33. Eric B. Henderson, "A learning heuristic for integrating spatial and temporal detail in forest planning", Natural Resource Modeling, 34(1), pp.e12299~ (2021), Wiley[DOI]
  34. YoonJee Hong, "Population genetic study of the raccoon dog (<i>Nyctereutes procyonoides<", i>) in South Korea using newly developed 12 microsatellite markers, 2013, pp.88(1), 69~76[DOI]
  35. Hong, Y. J., "Population structure of the raccoon dog(Nyctereutes procyonoides)using microsatellite loci analysis in South Korea : implications for disease management", The Journal of Veterinary Medical Science, 80, pp.1631~1638 (2018)[DOI]
  36. Huang, F., "Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping", Catena, 191, pp.104580~ (2020)[DOI]
  37. Ismail, A. A., "Improving long-horizon forecasts with expectation-biased LSTM networks" (2018)[DOI]
  38. Ivakhnenko A. G., "Problems of further development of the group method of data handling algorithms. Part I", Pattern Recognition and Image Analysis, 10, pp.187~194 (2000)
  39. Ivakhnenko, A. G., "Heuristic self-organization in problems of engineering cybernetics", Automatica, 6, pp.207~219 (1970)[DOI]
  40. Ivakhnenko, A. G., "The group method of data handling in long-range forecasting", Technological Forecasting and Social Change, 12, pp.213~227 (1978)[DOI]
  41. 정우성, "Home range differences by habitat type of raccoon dogs Nyctereutes procyonoides (Carnivora: Canidae)", Journal of Asia-Pacific Biodiversity, 10(3), pp.349~354 (2017), 국립중앙과학관
  42. Jie, C., "Feature selection in machine learning : a new perspective", Neurocomputing, 300, pp.70~79 (2018)[DOI]
  43. Khosravi, K., "A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and machine learning methods", Journal of Hydrology, 573, pp.311~323 (2019)[DOI]
  44. Kong, W., "Short-term residential load forecasting based on LSTM recurrent neural network", IEEE Transactions on Smart Grid, 10, pp.841~851 (2019)[DOI]
  45. Kopecký, M., "Topographic Wetness Index calculation guidelines based on measured soil moisture and plant species composition", Science of The Total Environment, 757, pp.143785~ (2021)[DOI]
  46. Kusuma, W. L., "PM2.5 pollutant in Asia-a comparison of metropolis cities in Indonesia and Taiwan", International Journal of Environmental Research and Public Health, 16, pp.4924~ (2019)[DOI]
  47. Lecun, Y., "Gradient-based learning applied to document recognition", Proceedings of the IEEE, 86, pp.2278~2324 (1998)[DOI]
  48. Lee, S., "Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models", Landslides, 4, pp.33~41 (2007)[DOI]
  49. 이사로, "Application of Statistical and Machine Learning Techniques for Habitat Potential Mapping of Siberian Roe Deer in South Korea", 국립생태원회보, 2(1), pp.1~14 (2021), 국립생태원[DOI]
  50. Lee, S., "Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models", Environmental Geology, 50, pp.847~855 (2006)[DOI]
  51. Lee, S., "Probabilistic landslide susceptibility and factor effect analysis", Environmental Geology, 47, pp.982~990 (2005)[DOI]
  52. Lee, S., "Macrobenthos habitat potential mapping using GIS-based artificial neural network models", Marine Pollution Bulletin, 67, pp.177~186 (2013)[DOI]
  53. Lemly, A. D., "Agriculture and wildlife : ecological impli-cations of subsurface irrigation drainage", Journal of Arid Environments, 28, pp.85~94 (1994)[DOI]
  54. Li, X., "Interpretable deep learning : interpretation, interpretability, trustworthiness, and beyond", Knowledge and Information Systems, 64, pp.3197~3234 (2022)[DOI]
  55. Liu, C., "Identifying wildlife corridors for the restoration of regional habitat connectivity: a multispecies approach and comparison of resistance surfaces", PLoS One, 13, pp.e0206071~ (2018)[DOI]
  56. Lodder, R. A., "Quantile analysis : a method for characterizing data distributions", Applied Spectroscopy, 42, pp.1512~1520 (1988)
  57. Marino, A., "Competitive exclusion and herbivore management in a context of livestock-wildlife conflict", Austral Ecology, 47, pp.1208~1221 (2022)[DOI]
  58. Melis, C., "Individual and temporal variation in habitat association of an alien carnivore at its invasion front", PLoS One, 10, pp.e0122492~ (2015)[DOI]
  59. Raccoon Dogs in Norway - Potential Expansion Rate, Distribution Area and Management Implications, "Melis, C.", Norges Teknisk-Naturvitenskapelige Universitet, ~ (2007)
  60. Mohanasundaram, S., "Reconstructing NDVI and land surface temperature for cloud cover pixels of Landsat-8 images for assessing vegetation health index in the Northeast region of Thailand", Environmental monitoring and assessment, 195, pp.211~ (2022)[DOI]
  61. Mohebbian, M. R., "Blind, cuff-less, calibration-free and continuous blood pressure estimation using optimized inductive group method of data handling", Biomedical Signal Processing and Control, 57, pp.101682~ (2020)[DOI]
  62. Mulashani, A. K., "Enhanced group method of data handling(GMDH)for permeability prediction based on the modified Levenberg Marquardt technique from well log data", Energy, 239, pp.121915~ (2022)[DOI]
  63. Murmu, S., "Application of fuzzy logic and neural network in crop classification : a review", Aquatic Procedia, 4, pp.1203~1210 (2015)[DOI]
  64. Oh, H. J., "Ruditapes philippinarum habitat mapping potential using SVM and Naïve Bayes", Journal of Coastal Research, 90, pp.41~48 (2019)[DOI]
  65. Okabe, F., "Habitat use by introduced raccoons and native raccoon dogs in a deciduous forest of Japan", Journal of Mammalogy, 88, pp.1090~1097 (2007)[DOI]
  66. Ookura, S., "An efficient method for wind power generation forecasting by LSTM in consideration of overfitting prevention", IFAC-PapersOnLine, 53, pp.12169~12174 (2020)[DOI]
  67. Panahi, M., "Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms : a study in Western Iran", Journal of Hydrology : Regional Studies, 35, pp.100825~ (2021)[DOI]
  68. Park, I., "Application of an adaptive neuro-fuzzy inference system to ground subsidence hazard mapping", Computers & Geosciences, 48, pp.228~238 (2012)[DOI]
  69. Quarantelli, E. L., "Handbook of Disaster Research", pp.16~41 (2007), Springer
  70. Reich, Y., "Evaluating machine learning models for engineering problems", Artificial Intelligence in Engineering, 13, pp.257~272 (1999)[DOI]
  71. Rezaie, F., "Novel hybrid models by coupling support vector regression(SVR)with meta-heuristic algorithms(WOA and GWO)for flood susceptibility mapping", Natural Hazards, 114, pp.1247~1283 (2022)[DOI]
  72. Rezaie, F., "Spatial modeling of geogenic indoor radon distribution in Chungcheongnam-do, South Korea using enhanced machine learning algorithms", Environment international, 171, pp.107724~ (2023)[DOI]
  73. Rezaie, F., "Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms", Environmental pollution (Barking, Essex : 1987), 292(Pt B), pp.118385~ (2022)[DOI]
  74. Riley, S. J., "A terrain ruggedness index that quantifies topographic heterogeneity", Intermountain Journal of Sciences, 5, pp.23~27 (1999)
  75. Ruymgaart, F. H., "A robust principal component analysis", Journal of Multivariate Analysis, 11, pp.485~497 (1981)[DOI]
  76. Sabzi, S, "Classification of cucumber leaves based on nitrogen content using the hyperspectral imaging technique and majority voting", Plants (Basel, Switzerland), 10, pp.898~ (2021)[DOI]
  77. Saeki, M., "Movements and habitat selection of raccoon dogs(Nyctereutes procyonoides)in a mosaic landscape", Journal of Mammalogy, 88, pp.1098~1111 (2007)[DOI]
  78. Sarker I. H., "Deep learning : a comprehensive overview on techniques, taxonomy, applications and research directions", SN Computer Science, 2, pp.420~ (2021)[DOI]
  79. Schwemmer, P., "Spatio-temporal movement patterns and habitat choice of red foxes(Vulpes vulpes)and racoon dogs(Nyctereutes procyonoides)along the Wadden Sea coast", European Journal of Wildlife Research, 67, pp.49~ (2021)[DOI]
  80. Shi, H., "Deep learning for household load forecasting-a novel pooling deep RNN", IEEE Transactions on Smart Grid, 9, pp.5271~5280 (2018)[DOI]
  81. Sidorovich, V. E., "Seasonal and annual variation in the diet of the raccoon dog Nyctereutes procyonoides in northern Belarus : the role of habitat type and family group", Acta Theriologica, 53, pp.27~38 (2008)[DOI]
  82. Singh, R., "Estimation of elastic constant of rocks using an ANFIS approach", Applied Soft Computing, 12, pp.40~45 (2012)[DOI]
  83. Sreedevi, P. D., "Drainage morphometry and its influence on hydrology in an semi arid region : using SRTM data and GIS", Environmental Earth Sciences, 70, pp.839~848 (2013)[DOI]
  84. Süld, K., "Home ranges of raccoon dogs in managed and natural areas", PLoS One, 12, pp.e0171805~ (2017)[DOI]
  85. Sutor, A., "Home ranges of raccoon dogs(Nyctereutes procyonoides, Gray, 1834)in Southern Brandenburg, Germany", European Journal of Wildlife Research, 58, pp.85~97 (2012)[DOI]
  86. Sutor, A., "Diet of the raccoon dog Nyctereutes procyonoides-a canid with an opportunistic foraging strategy", Acta Theriologica, 55, pp.165~176 (2010)[DOI]
  87. Swets J.A., "Measuring the accuracy of diagnostic systems", Science (New York, N.Y.), 240, pp.1285~1293 (1988)[DOI]
  88. Talukdar, S., "Effects of damming on the hydrological regime of Punarbhaba river basin wetlands", Ecological Engineering, 135, pp.61~74 (2019)[DOI]
  89. Teng, J., "Assessing habitat suitability for wintering geese by using Normalized Difference Water Index(NDWI)in a large floodplain wetland, China", Ecological Indicators, 122, pp.107260~ (2021)[DOI]
  90. Thi Ngo, P. T., "Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran", Geoscience Frontiers, 12, pp.505~519 (2021)[DOI]
  91. Tien Bui, D., "A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides", Remote Sensing, 10, pp.1538~ (2018)[DOI]
  92. Traba, J., "Realised niche changes in a native herbivore assemblage associated with the presence of livestock", Oikos, 126, pp.1400~1409 (2017)[DOI]
  93. Tran, T. T. K., "Enhancing predictive ability of optimized group method of data handling(GMDH)method for wildfire susceptibility mapping", Agricultural and Forest Meteorology, 339, pp.109587~ (2023)[DOI]
  94. Van Pham, T., "Southern extension of raccoon dog Nyctereutes procyonoides(Mammalia : Carnivora : Canidae)range in Vietnam with comments on its conservation status in the country", European Journal of Wildlife Research, 69, pp.22~ (2023)[DOI]
  95. Wang, D., "Morphometric characterisation of landform from DEMs", International Journal of Geographical Information Science, 24, pp.305~326 (2010)[DOI]
  96. Wang, Y., "Flood susceptibility mapping in Dingnan County(China)using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm", Journal of environmental management, 247, pp.712~729 (2019)[DOI]
  97. Wilson, J.P, "Environmental Applications of Digital Terrain Modeling" (2018), John Wiley & Sons
  98. Won, P.H., "Illustrated Encyclopedia of Fauna and Flora of Korea. Volume 7, Mammals" (1967), Samhwabook
  99. Xie, Y., "Exploring the dynamic correlation of landscape composition and habitat fragmentation with surface water quality in the Shenzhen river and deep bay cross-border watershed, China", Ecological Indicators, 90, pp.231~246 (2018)[DOI]
  100. Yalcin, A., "A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey", Catena, 85, pp.274~287 (2011)[DOI]
  101. Rikiya Yamashita, "Convolutional neural networks: an overview and application in radiology", Insights into Imaging, 9(4), pp.611~629 (2018), Springer Science and Business Media LLC[DOI]
  102. 양동군, "Detection of viral infections in wild Korean raccoon dogs (Nyctereutes procyonoides koreensis)", 대한수의학회지, 57(4), pp.209~214 (2017), 대한수의학회[DOI]
  103. Yu, D., "Rough Sets and Knowledge Technology", pp.364~375 (2014), Springer
  104. Zabihi, K., "A fuzzy logic approach to analyse the suitability of nesting habitat for greater sage-grouse in western Wyoming", Journal of Spatial Science, 62, pp.215~234 (2017)[DOI]
  105. Zafar, A., "A comparison of pooling methods for convolutional neural networks", Applied Sciences, 12, pp.8643~ (2022)[DOI]
  106. Zhang, C. L., "Improving CNN linear layers with power mean non-linearity", Pattern Recognition, 89, pp.12~21 (2019)[DOI]
  107. Zhang, M., "D-GMDH : a novel inductive modelling approach in the forecasting of the industrial economy", Economic Modelling, 30, pp.514~520 (2013)[DOI]
  108. Zhang, Z., "A fast twostage hybrid meta-heuristic algorithm for robust corridor allocation problem", Advanced Engineering Informatics, 53, pp.101700~ (2022)[DOI]
  109. Zhisheng, A., "Global monsoon dynamics and climate change", Annual review of earth and planetary sciences, 43, pp.29~77 (2015)[DOI]
  110. Zzaman, R. U., "Flood hazard mapping of Sangu River basin in Bangladesh using multi-criteria analysis of hydro-geomorphological factors", Journal of Flood Risk Management, 14, pp.e12715~ (2021)[DOI]

관련 논문AI추천

Application of Statistical and Machine Learning Techniques for Habitat Potential Mapping of Siberian Roe Deer in South Korea이사로, Rezaie Fatemeh국립생태원 · 국립생태원회보2021 · 제2권 제1호머신러닝 알고리즘을 이용한 포유류 종 풍부도 매핑 구축 연구김은섭, 김지영, 이동근, 전윤호, 최지영한국환경영향평가학회 · 환경영향평가2024 · 제33권 제2호여우(Vulpes vulpes)의 서식지 적합성 지수(HSI) 모델 개발어여경, 이상돈한국환경영향평가학회 · 환경영향평가2022 · 제31권 제4호Use of GIS to Develop a Multivariate Habitat Model for the Leopard Cat (Prionailurus bengalensis) in Mountainous Region of Korea노백호한국생태학회 · Journal of Ecology and Environment2009 · 제32권 제4호A Systematic Area Prioritization Process for Biodiversity Conservation in the DMZ and the CCZ김진오국토연구원 · 국토연구2010 · 제65권 전국단위의 포유류 생물다양성우수지역 분석 연구권혁수, 김명진, 김지연, 서창완한국환경영향평가학회 · 환경영향평가2014 · 제23권 제6호

이 간행물의 다른 논문 (4건)

Linking Species with the Ecosystem: The Emergence of Big Data and Artificial Intelligence in Ecological Research권오석pp. 135-140A Review of Ecological and Natural Map Grades and Public Appeals in Korea박기현(Kihyun Park), 오우석, 조장삼, 임효선, 김정철, 고의정, 윤혜연, 유창훈pp. 141-145Social Wasp Diversity and Wasp Nest Removal Trends on Ulleungdo Island, South Korea김재희(Jaehee Kim), 권오석, 김일권, 최문보pp. 146-153Non-Invasive Sex Determination of Asiatic Black Bear (Ursus thibetanus) via Sex-Specific Amplification of the Amelogenin Gene김백준pp. 154-158
원문 보기