한국환경복원기술학회지 (Journal of the Korean Society of Environmental Restoration Technology)

  • 제20권1호
  • /
  • Pages.1-12
  • /
  • 2017
  • /
  • 1229-3032(pISSN)
  • /
  • 2733-5011(eISSN)
한국환경복원기술학회 (The Korea Society of Environmental Restoration Technology)
권호 표지
DOI QR코드

DOI QR Code

위성영상과 Maxent를 활용한 생태계교란생물 분포지역 예측 : DMZ의 단풍잎돼지풀을 대상으로

Predicting the Potential Distributions of Invasive Species Using the Landsat Imagery and Maxent : Focused on "Ambrosia trifida L. var. trifida" in Korean Demilitarized Zone

  • 투고 : 2016.10.12
  • 심사 : 2017.02.21
  • 발행 : 2017.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This study has been carried out for the purpose of predicting the potential habitat sites of invasive alien plants in the DMZ and providing the basic data for decision-making in managing the future DMZ natural environment. From 2007 to 2015, this study collected the data for the advent of Ambrosia trifida var. trifida through fieldwork around the DMZ area, and simulated the potential distribution area of Ambrosia trifida var. trifida using Maxent model among the models of species distributions. As a result, it showed that the potential distribution area of the Ambrosia trifida var. trifida was concentrated in the western DMZ with relatively low altitude and scanty in the central east regions with relatively high elevation and forest cover rate. Because the invasive alien vegetation is a significant threatening factor in the agriculture and restoration of ecology and it costs a lot to restore the area already invaded by invasive alien vegetation, advance precautions are necessary to prevent biological invasions. It is expected that it is possible to predict the disturbed ecosystems through this study for the efficient land use within DMZ in the future and to apply this study in setting up the areas for the development and conservation within the DMZ.

키워드

참고문헌

  1. Abul-Fatih HA and Bazzaz FA. 1979. The Biology Of Ambrosia Trifida L.. I. Influence Of Species Removal On The Organization Of The Plant Community. New Phytol 83: 813-816. https://doi.org/10.1111/j.1469-8137.1979.tb02312.x
  2. Bean WT . Stafford R and Brashares JS. 2012. The effects of small sample size and sample bias on threshold selection and accuracy assessment of species distribution models. Ecography 35: 250-258. https://doi.org/10.1111/j.1600-0587.2011.06545.x
  3. Brown JL. 2014. SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods in Ecology and Evolution. 5(7): 694-700. https://doi.org/10.1111/2041-210X.12200
  4. Cao Y . DeWalt RE . Robinson JL . Tweddale T . Hinz L and Pessino M. 2013. Using Maxent to model the historic distributions of stonefly species in Illinois streams: The effects of regularization and threshold selections. Ecol Model 259: 30-39. https://doi.org/10.1016/j.ecolmodel.2013.03.012
  5. CBD. 2008. CBD-Convention on Biological Diversity. Alien Species that Threaten Ecosystems, Habitats or Species [Article 8(h)]. United Nations.
  6. Choi HJ . Lim SH . Kim KH and Kim S. 2007. Distribution of Giant Ragweed(Ambrosia trifida L.) at Northwest of Gangwon, Korea. Korean J Weed Sci 27: 241-247.
  7. D'Antonio CM . Jackson NE . Horvitz CC and Hedberg R. 2004. Invasive plants in wildland ecosystems: merging the study of invasion processes with management needs. Front Ecol Environ 2: 513-521. https://doi.org/10.1890/1540-9295(2004)002[0513:IPIWEM]2.0.CO;2
  8. Diekmann F . Ford RA . Harrison SK . Regnier EE and Venkatesh R. 2013. Bibliometric Analysis of the Literature on Giant Ragweed (Ambrosia trifida L.). J Agric Food Inf 14: 290-320. https://doi.org/10.1080/10496505.2013.823865
  9. Elith J and Leathwick JR. 2009. Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40: 677. https://doi.org/10.1146/annurev.ecolsys.110308.120159
  10. Evangelista PH . Stohlgren TJ . Morisette JT and Kumar S. 2009. Mapping invasive tamarisk (Tamarix): a comparison of single-scene and time-series analyses of remotely sensed data. Remote Sensing. 1(3): 519-533. https://doi.org/10.3390/rs1030519
  11. Everitt JH and Deloach CJ. 1990. Remote sensing of Chinese tamarisk (Tamarix chinensis) and associated vegetation. Weed Sci: 273-278.
  12. Forman RTT and Alexander LE. 1998. Roads and Their Major Ecological Effects. Annu Rev Ecol Syst 29: 207. https://doi.org/10.1146/annurev.ecolsys.29.1.207
  13. Franklin J. 2010. Mapping species distributions: spatial inference and prediction. Cambridge University Press.
  14. Guo WY . Lambertini C . Li XZ . Meyerson LA and Brix H. 2013. Invasion of Old World Phragmites australis in the New World: precipitation and temperature patterns combined with human influences redesign the invasive niche. Global Change Biology. 19(11): 3406-3422. https://doi.org/10.1111/gcb.12295
  15. IUCN. 2011. IUCN-International Union for Conservation of Nature. Invasive Species.
  16. Jaynes ET. 1957. Information theory and statistical mechanics. Phys Rev 106: 620. https://doi.org/10.1103/PhysRev.106.620
  17. Jeon SW. 2007. Policies on Conservation of the DMZ District Ecosystem. Environ Policy Bull 5.
  18. Kam K and Kim K. 2008. Detection of buried objects using ultra-wideband radar: Newly launched mine detection project in South Korea. Pages 695308-695308 in SPIE Defense and Security Symposium. International Society for Optics and Photonics.
  19. Kim JH. 2011. DMZ Control and Utilities for the Preparation of a Basis for Peaceful Unification. The Journal of Northeast Asia Research. 26(2): 31-49. https://doi.org/10.18013/jnar.2011.26.2.002
  20. Kim KC. 1997. Preserving Biodiversity in Korea's Demilitarized Zone. Science 278: 242-243. https://doi.org/10.1126/science.278.5336.242
  21. Khanum R . Mumtaz AS and Kumar S. 2013. Predicting impacts of climate change on medicinal asclepiads of Pakistan using Maxent modeling. Acta Oecologica. 49: 23-31. https://doi.org/10.1016/j.actao.2013.02.007
  22. Kramer-Schadt S . Niedballa J . Pilgrim JD . Schroder B . Lindenborn J . Reinfelder V . Stillfried M, Heckmann I . Scharf AK . Augeri DM and others. 2013. The importance of correcting for sampling bias in Maxent species distribution models. Divers Distrib 19: 1366-1379. https://doi.org/10.1111/ddi.12096
  23. Kwon HS. 2014. Applying Ensemble Model for Identifying Uncertainty in the Species Distribution Models. Journal of Korean Society for Geospatial Information System. 22(4): 47-52 (In Korean).
  24. Lahoz-Monfort JJ . Guillera-Arroita G . Milner Gulland EJ . Young RP and Nicholson E. 2010. Satellite imagery as a single source of predictor variables for habitat suitability modelling: how Landsat can inform the conservation of a critically endangered lemur. Journal of Applied Ecology. 47(5): 1094-1102. https://doi.org/10.1111/j.1365-2664.2010.01854.x
  25. Lass LW and Callihan RH. 1997. The effect of phenological stage on detectability of yellow hawkweed (Hieracium pratense) and oxeye daisy (Chrysanthemum leucanthemum) with remote multispectral digital imagery. Weed Technol: 248-256.
  26. Lass LW . Carson HW and Callihan RH. 1996. Detection of yellow starthistle (Centaurea solstitialis) and common St. Johnswort (Hypericum perforatum) with multispectral digital imagery. Weed Technol: 466-474.
  27. Liu C . White M and Newell G. 2013. Selecting thresholds for the prediction of species occurrence with presence-only data. J Biogeogr 40: 778-789. https://doi.org/10.1111/jbi.12058
  28. Merow C . Smith MJ and Silander JA. 2013. A practical guide to Maxent for modeling species' distributions: what it does, and why inputs and settings matter. Ecography 36: 1058-1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x
  29. Naimi B . Skidmore AK . Groen TA and Hamm NA. 2011. Spatial autocorrelation in predictors reduces the impact of positional uncertainty in occurrence data on species distribution modelling. Journal of Biogeography 38(8): 1497-1509. https://doi.org/10.1111/j.1365-2699.2011.02523.x
  30. Park HC . Lee GG and Lee JH. 2015. Regional vulnerability assessment of invasive alien plants in Seoul and Gyeonggi Province. J. Korean Env. Res. Tech. 18(6): 1-13 (In Korean).
  31. Park HC. 2016. Development and application of climate change sensitivity assessment method for plants using the species distribution models. Ph.D. dissertation. Kanwon National University (In Korean).
  32. Peterson EB. 2005. Estimating cover of an invasive grass (Bromus tectorum) using tobit regression and phenology derived from two dates of Landsat ETM+ data. International Journal of Remote Sensing. 26(12): 2491-2507. https://doi.org/10.1080/01431160500127815
  33. Phillips SJ and Dudik M. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography. 31: 161-175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
  34. Phillips SJ . Dudik M and Schapire RE. 2004. A maximum entropy approach to species distribution modeling. Page 83 in Proceedings of the twenty-first international conference on Machine learning. ACM.
  35. Shin D. 2013. The potential impact of a botanical garden in the Korean Demilitarized Zone. Thesis. Master's Theses, University of Delaware.
  36. Swets JA. 1988. Measuring the accuracy of diagnostic systems. Science 240: 1285-1293. https://doi.org/10.1126/science.3287615
  37. Watling JI . Romanach SS . Bucklin DN . Speroterra C . Brandt LA . Pearlstine LG and Mazzotti FJ. 2012. Do bioclimate variables improve performance of climate envelope models? Ecological Modelling. 246: 79-85. https://doi.org/10.1016/j.ecolmodel.2012.07.018
  38. Warren DL and Seifert SN. 2011. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications 21(2): 335-342. https://doi.org/10.1890/10-1171.1
  39. West AM . Evangelista PH . Jarnevich CS . Young NE . Stohlgren TJ . Talbert C and Anderson R. 2016 Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM). Journal of Visualized Experiments: JoVE. (116).
  40. Wilcove DS . Rothstein D . Dubow J . Phillips A and E. 1998. Quantifying threats to imperiled species in the United States. Bio-Science. 48(8): 607-615.
  41. Wopfner N . Gadermaier G . Egger M . Asero R . Ebner C . Jahn-Schmid B and Ferreira F. 2005. The Spectrum of Allergens in Ragweed and Mugwort Pollen. Int Arch Allergy Immunol 138: 337-346. https://doi.org/10.1159/000089188

피인용 문헌

  1. 환경DNA 메타바코딩 기술을 활용한 수생태계 어류종 군집조사의 가능성 - 도시 생태하천 초기분석 자료를 중심으로 vol.22, pp.6, 2017, https://doi.org/10.13087/kosert.2019.22.6.125
  2. Litter decomposition rate and nutrient dynamics of giant ragweed (Ambrosia trifida L.) in the non-native habitat of South Korea vol.449, pp.1, 2017, https://doi.org/10.1007/s11104-020-04502-7
  3. Modeling of Forest Communities’ Spatial Structure at the Regional Level through Remote Sensing and Field Sampling: Constraints and Solutions vol.11, pp.10, 2017, https://doi.org/10.3390/f11101088