DOI QR코드

DOI QR Code

Detection of Pine Wilt Disease tree Using High Resolution Aerial Photographs - A Case Study of Kangwon National University Research Forest -

시계열 고해상도 항공영상을 이용한 소나무재선충병 감염목 탐지 - 강원대학교 학술림 일원을 대상으로 -

  • PARK, Jeong-Mook (Dept. of Forest Management, Division of Forest Sciences, College of Forest and Environmental Sciences, Kangwon National University) ;
  • CHOI, In-Gyu (Korean society of forest environment research) ;
  • LEE, Jung-Soo (Dept. of Forest Management, Division of Forest Sciences, College of Forest and Environmental Sciences, Kangwon National University)
  • 박정묵 (강원대학교 산림환경과학대학 산림과학부 산림경영학과) ;
  • 최인규 (한국산지환경조사연구회) ;
  • 이정수 (강원대학교 산림환경과학대학 산림과학부 산림경영학과)
  • Received : 2019.03.15
  • Accepted : 2019.05.15
  • Published : 2019.06.30

Abstract

The objectives of this study were to extract "Field Survey Based Infection Tree of Pine Wilt Disease(FSB_ITPWD)" and "Object Classification Based Infection Tree of Pine Wilt Disease(OCB_ITPWD)" from the Research Forest at Kangwon National University, and evaluate the spatial distribution characteristics and occurrence intensity of wood infested by pine wood nematode. It was found that the OCB optimum weights (OCB) were 11 for Scale, 0.1 for Shape, 0.9 for Color, 0.9 for Compactness, and 0.1 for Smoothness. The overall classification accuracy was approximately 94%, and the Kappa coefficient was 0.85, which was very high. OCB_ITPWD area is approximately 2.4ha, which is approximately 0.05% of the total area. When the stand structure, distribution characteristics, and topographic and geographic factors of OCB_ITPWD and those of FSB_ITPWD were compared, age class IV was the most abundant age class in FSB_ITPWD (approximately 55%) and OCB_ITPWD (approximately 44%) - the latter was 11% lower than the former. The diameter at breast heigh (DBH at 1.2m from the ground) results showed that (below 14cm) and (below 28cm) DBH trees were the majority (approximately 93%) in OCB_ITPWD, while medium and (more then 30cm) DBH trees were the majority (approximately 87%) in FSB_ITPWD, indicating different DBH distribution. On the other hand, the elevation distribution rate of OCB_ITPWD was mostly between 401 and 500m (approximately 30%), while that of FSB_ITPWD was mostly between 301 and 400m (approximately 45%). Additionally, the accessibility from the forest road was the highest at "100m or less" for both OCB_ITPWD (24%) and FSB_ITPWD (31%), indicating that more trees were infected when a stand was closer to a forest road with higher accessibility. OCB_ITPWD hotspots were 31 and 32 compartments, and it was highly distributed in areas with a higher age class and a higher DBH class.

본 연구는 강원대학교 학술림을 대상으로 현장조사 기반(Field Survey Based)에 의한 감염목(FSB_감염목)과 객체분류기반(Object Classification Based)에 의한 감염목(OCB_감염목)을 추출하고 감염목에 대한 공간적 분포특성 및 발생강도 평가를 목적으로 하였다. OCB 최적 가중치는 Scale 11, Shape 0.1, Color 0.9, Compactness 0.9, Smoothness 0.1로 선정되었으며, 전체 분류정확도는 약 94%, Kappa 계수는 0.88로 매우 높았다. OCB_감염목 지역은 약 2.4ha로 전체 면적의 약 0.05% 발생하였다. OCB_감염목와 FSB_감염목의 임분구조 분포특성 및 지형 지리적 요인을 비교 하면, OCB_감염목 영급은 IV영급의 분포비율이 약 44%로 가장 높았으며, FSB_감염목의 영급도 IV영급의 분포비율이 약 55%로 가장 높았다. OCB_감염목의 IV영급 비율은 FSB_감염목보다 약 11% 낮았다. OCB_감염목 경급은 소경목과 중경목이 약 93%로 대부분을 차지한 반면, FSB_감염목 경급은 중경목과 대경목이 약 87%로 전체 대상지의 경급 분포와 상이하였다. 한편, OCB_감염목 표고 분포비율은 401-500m에서 약 30%로 가장 높은 반면, FSB_감염목은 301-400m에서 약 45%로 상이하였으며, 임도로부터 접근성 분포 비율은 OCB_감염목과 FSB_감염목 모두 100m이하에서 각각 약 24%와 31%로 가장 높아 임도로부터 접근성이 높을수록 감염목이 높았다. OCB_감염목 핫스팟은 31임반과 32임반으로 영급과 경급이 높은 지역에서 높게 분포하였다.

Keywords

GRJBBB_2019_v22n2_36_f0001.png 이미지

FIGURE 1. Location of study area

GRJBBB_2019_v22n2_36_f0002.png 이미지

FIGURE 2. Schematic methodology for Infection Tree of Pine wilt disease

GRJBBB_2019_v22n2_36_f0003.png 이미지

FIGURE 3. Process of masking

GRJBBB_2019_v22n2_36_f0004.png 이미지

FIGURE 4. Selection process of scale and shape/color and compactness/smoothness

GRJBBB_2019_v22n2_36_f0005.png 이미지

FIGURE 5. OCB_ITPWD region

GRJBBB_2019_v22n2_36_f0006.png 이미지

FIGURE 6. Comparison of ageclass and DBH class

GRJBBB_2019_v22n2_36_f0007.png 이미지

FIGURE 7. Comparison of elevation and Accessibillity with the forest road

GRJBBB_2019_v22n2_36_f0008.png 이미지

FIGURE 8. Hotspot analysis of OCB_ITPWD

TABLE 1. Selection of optimized segmentation parameters on level

GRJBBB_2019_v22n2_36_t0001.png 이미지

TABLE 2. Error Matrix based on TTA Mask

GRJBBB_2019_v22n2_36_t0002.png 이미지

References

  1. Baatz, M., U. Benz., S. Dehghani., M. Heynen., A. Holtje., P. Hofmann., I. Lingenfelder., M. Mimler., M. Sohlbach., M. Weber and G. Willhauck. 2004. eCognition Professional: User Guide 4, Munich:Definiens-Imaging.
  2. Bagstad, K.J., D.J. Semmens., Z.H. Ancona and B.C. Sherrouse. 2017. Evaluating alternative methods for biophysical and cultural ecosystem services hotspot mapping in natural resource planning. Landscape Ecology 32(1):77-97. https://doi.org/10.1007/s10980-016-0430-6
  3. Futai, K. 2003. Role of asymptomatic carrier trees in epidemic spread of pine wilt disease. Journal of Forest Research 8(4):253-260. https://doi.org/10.1007/s10310-003-0034-2
  4. Harris, N.L., E.Goldman., C.Gabris., J.Nordling., S.Minnemeyer., S.Ansari., M.Lippmann., L.Bennett., M.Raad., M.Hansen and P.Potapov. 2017. Using spatial statistics to identify emerging hot spots of forest loss. Environmental Research Letters 12(2):1-13.
  5. Hellesen, T. and L. Matikainen. 2013. Thomas Hellesen Leena An Object-Based Approach for Mapping Shrub and Tree Cover on Grassland Habitats by Use of LiDAR and CIR Orthoimages. Remote Sensing 2013(5):558-583. https://doi.org/10.3390/rs5020558
  6. Hong. G.Y. 2017. A Extraction and Accuracy Assessment of Dead Tree Using UAV Image. Ph.M. Thesis, Univ. of Seoul, Seoul, Korea. pp.63-64.
  7. James, R., N. Tisserat and T. Todd. 2006. Prevention of Pine Wilt of Scots Pine (Pinus sylvestris) with Systemic Abamectin Injections. Arboriculture & Urban Forestry 32(5):195-201.
  8. Jeong, H.S. 2015. Studies on the Damage Analysis and Effective Control System of Pine Wilt Disease in Korea. Ph.D. Thesis, Univ. of Sangji, Wonju, Korea. pp.1-7.
  9. Johnson, B.A., R. Tateishi and N.T. Hoan. 2013. A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees International Journal of Remote Sensing 34(20):6969-6982. https://doi.org/10.1080/01431161.2013.810825
  10. Kansas Forest Service. 2008. A Guide to Aid Local Central/Western Government in Addressing Pine Wilt Disease. pp.4-12.
  11. Kim, E.N. and D.Y. Kim. 2008. An Investigation of Pine Wilt Damage by Using Ground Remote Sensing Technique. Journal of The Korean Association of Regional Geographers 14(1):84-92.
  12. Kim, J.B., D.Y. Kim and N.C. Park. 2010. Development of an Aerial Precision Forecasting Techniques for the Pine Wilt Disease Damaged Area Based on GIS and GPS. Journal of the Korean Association of Geographic Information Studies 13(1):28-34. https://doi.org/10.11108/kagis.2010.13.1.028
  13. Kim, J.B., M.H. Jo., I.H. Kim and J.H. Park. 2002. Analysis of Topography spectral characteristics for Extracting Pine Wilt Disease Using IKONOS satellite image. The Korean Association of Geographic Information Studies 2002. pp.15-22.
  14. Kim S.R., E.S. Kim, Y.W. Nam, W.I. Choi and C.M. Kim. 2015. Distribution Characteristics Analysis of Pine Wilt Disease Using Time Series Hyperspectral Aerial Imagery. Korean Journal of Remote Sensing 31(5):385-394. https://doi.org/10.7780/kjrs.2015.31.5.3
  15. Korea Forest Research Institute. 2009. Pine Wilt Disease of Ecological Characteristics Research. pp.167-193.
  16. Korea Rural Economic Institute. 2014. Pine Wilt Disease and control policy task. pp. 1.
  17. Lee, H.J., L.J. Ho and S.Y. Kim. 2011. Land cover object-oriented base classification using digital aerial photo image. Journal of the Korean Society for Geo-Spatial Information System 19(1):105-113.
  18. Lee, H.J., Y.Y. Geol and R.J. Ho. 2010. Improvement of accuracy in land cover information using an object-based classification. Journal of the Korean Society for Geo-Spatial Information System 18(3):11-12.
  19. Lee, J.B., D.Y. Heo and Y.D. Eo. 2007. Study on selection of optimized segmentation parameters and analysis of classification accuracy for object-oriented classification. Korean Journal of Remote Sensing 23(6):521-528. https://doi.org/10.7780/kjrs.2007.23.6.521
  20. Lee, M.B. 2010. A geomorphic surface analysis using remote sensing in DMZ of Chugaryeong rift valley, Central Korea. Journal of the Korean Geomorphological Association 17(1):1-14.
  21. Lee, S.H., Y.G. Oh., N.Y. Park., S.H. Lee and J.Y. Choi. 2014. Extraction of paddy field in Jaeryeong, North Korea by object-oriented classification with RapidEye NDVI imagery. Journal of the Korean Society of Agricultural Engineers 56(3):55-64. https://doi.org/10.5389/KSAE.2014.56.3.055
  22. Na, H.S. and J.S. Lee. 2014. Analysis of Land Cover Characteristics with Object-Based Classification Method - Focusing on the DMZ in Inje-gun, Gangwon-do -. Journal of the Korean Association of Geographic Information Studies 17(2):121-135. https://doi.org/10.11108/kagis.2014.17.2.121
  23. National Institute of Forest Science, 2014, http://forest.go.kr/newkfsweb/html/HtmlPage.do?pg=/conser/conser_020103.html&mn=KFS_14_03_10_01_03&orgId=kfri (A ccessed March 2019).
  24. Park, G.D. 2016. Studies on Forest Stand Management of Pinus thunbergii in Jeju Island. Ph.D. Thesis, Univ. of Gangwon, Kangwon-do, Korea. pp.40-66.
  25. Son, M.H., W.K. Lee., S.H. Lee., H.K. Cho and J.H. Lee. 2006. Natural Spread Pattern of Damaged Area by Pine Wilt Disease Using Geostatistical Analysis. Journal of Korean Forest Society 95(3): 240-249.
  26. Takenaka, Y., M. Katoh., S. Deng and K. Cheung. 2017. Detecting Forests Damaged by Pine Wilt Disease at the Individual Tree Level using Airborne Laser data and WORLDVIEW-2/3 Images over Two Seasons. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Volume XLII-3/W3.
  27. Walter, V. 2004. Object-based classification of remote sensing data for change detection. ISPRS Journal of Photogrammetry and Remote Sensing 58(3-4):225-238. https://doi.org/10.1016/j.isprsjprs.2003.09.007