Multi-temporal Analysis of High-resolution Satellite Images for Detecting and Monitoring Canopy Decline by Pine Pitch Canker

  • Lee, Hwa-Seon (Department of Geoinformatic Engineering, Inha University) ;
  • Lee, Kyu-Sung (Department of Geoinformatic Engineering, Inha University)
  • Received : 2019.07.16
  • Accepted : 2019.08.19
  • Published : 2019.08.31


Unlike other critical forest diseases, pine pitch canker in Korea has shown rather mild symptoms of partial loss of crown foliage and leaf discoloration. This study used high-resolution satellite images to detect and monitor canopy decline by pine pitch canker. To enhance the subtle change of canopy reflectance in pitch canker damaged tree crowns, multi-temporal analysis was applied to two KOMPSAT multispectral images obtained in 2011 and 2015. To assure the spectral consistency between the two images, radiometric corrections of atmospheric and shadow effects were applied prior to multi-temporal analysis. The normalized difference vegetation index (NDVI) of each image and the NDVI difference (${\Delta}NDVI=NDVI_{2015}-NDVI_{2011}$) between two images were derived. All negative ΔNDVI values were initially considered any pine stands, including both pitch canker damaged trees and other trees, that showed the decrease of crown foliage from 2011 to 2015. Next, $NDVI_{2015}$ was used to exclude the canopy decline unrelated to the pitch canker damage. Field survey data were used to find the spectral characteristics of the damaged canopy and to evaluate the detection accuracy from further analysis.Although the detection accuracy as assessed by limited number of field survey on 21 sites was 71%, there were also many false alarms that were spectrally very similar to the damaged canopy. The false alarms were mostly found at the mixed stands of pine and young deciduous trees, which might invade these sites after the pine canopy had already opened by any crown damages. Using both ${\Delta}NDVI$ and $NDVI_{2015}$ could be an effective way to narrow down the potential area of the pitch canker damage in Korea.


Supported by : Korea Agency for Infrastructure Technology Advancement (KAIA)


  1. Asner, G.P. and A.S. Warner, 2003. Canopy shadow in IKONOS satellite observations of tropical forests and savannas, Remote Sensing of Environment, 87(4): 521-533.
  2. Bernstein, L.S., S.M. Adler-Golden, X. Jin, B. Gregor, and R.L. Sundberg, 2012. Quick atmospheric correction (QUAC) code for VNIR-SWIR spectral imagery: Algorithm details, Proc. of 2012 IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Shanghai, China, Jun. 4-7.
  3. Choi, W., S. Ko, S. Lee, and S. Shin, 2010. Annual report of monitoring for forest insect pests and disease in Korea, Korea Forest Research Institute, Seoul, Korea, 10(40) (in Korean).
  4. Choi, W., Y. Nam, J. Park, S. Ko, and S. Lee, 2015. Annual report of monitoring for forest insect pests and disease in Korea, Korea Forest Research Institute, Seoul, Korea, 16(03) (in Korean).
  5. Coops, N.C., M. Johnson, M.A. Wulder, and J.C. White, 2006. Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation, Remote Sensing of Environment, 103(1): 67-80.
  6. Crowley, B.J., B.J. Harvey, and B.A. Holzman, 2009. Dynamics of pitch canker disease in bishop pines (Pinus muricata) at Point Reyes National Seashore, CA, Proc. of 2009 Association of American Geographers Annual Meeting, Las Vegas, NV, Mar. 22-27.
  7. Dennison, P.E., A.R. Brunelle, and V.A. Carter, 2010. Assessing canopy mortality during a mountain pine beetle outbreak using GeoEye-1 high spatial resolution satellite data, Remote Sensing of Environment, 114(11): 2431-2435.
  8. DeRose, R.J., J.N. Long, and R.D. Ramsey, 2011. Combining dendrochronological data and the disturbance index to assess Engelmann spruce mortality caused by a spruce beetle outbreak in southern Utah, USA, Remote Sensing of Environment, 115(9): 2342-2349.
  9. Fan, Y., T. Koukal, and P.J. Weisberg, 2014. A sun-crown-sensor model and adapted C-correction logic for topographic correction of high resolution forest imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 96: 94-105.
  10. Franklin, S., M. Wulder, R. Skakun, and A. Carroll, 2003. Mountain pine beetle red-attack forest damage classification using stratified Landsat TM data in British Columbia, Canada, Photo - grammetric Engineering & Remote Sensing, 69(3): 283-288.
  11. Gooshbor, L., M.P. Bavaghar, J. Amanollahi, and H. Ghobari, 2016. Monitoring infestations of oak forests by Tortrix viridana (Lepidoptera: Tortricidae) using remote sensing, Plant Protection Science, 52(4): 270-276.
  12. Gordon, T., A. Storer, and D. Wood, 2001. The pitch canker epidemic in California, Plant Disease, 85(11): 1128-1139.
  13. Gu, D. and A. Gillespie, 1998. Topographic normalization of Landsat TM images of forest based on subpixel sun-canopy-sensor geometry, Remote Sensing of Environment, 64(2): 166-175.
  14. Hart, S.J. and T.T. Veblen, 2015. Detection of spruce beetle-induced tree mortality using high-and medium-resolution remotely sensed imagery, Remote Sensing of Environment, 168: 134-145.
  15. Hicke, J.A. and J. Logan, 2009. Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery, International Journal of Remote Sensing, 30(17): 4427-4441.
  16. Kim, J., Y. Kim, M. Jo, and I. Kim, 2003. A Study on the Extraction of Damaged Area by Pine Wood Nematode Using High Resolution IKONOS Stellite Images and GPS, Journal of Korean Forestry Society, 92(4): 362-366 (in Korean with English abstract).
  17. Kim, M., H. Bang, and J. Lee, 2017. Use of unmanned aerial vehicle for forecasting pine wood nematode in boundary area: A case study of Sejong Metropolitan Autonomous City, Journal of Korean Society of Forest Science, 106(1): 100-109 (in Korean with English abstract).
  18. Kim, S., E. Kim, Y. Nam, W.I. Choi, and C. Kim, 2015. Distribution characteristics analysis of pine wilt disease using time series hyperspectral aerial imagery, Korean Journal of Remote Sensing, 31(5): 385-394 (in Korean with English abstract).
  19. Lee, J. K., S.H. Lee, S. I. Yang, and Y.W. Lee, 2000. First report of pitch canker disease on Pinus rigida in Korea, The Plant Pathology Journal, 16(1): 52-54.
  20. Lee, J.B., E.S. Kim, and S.H. Lee, 2014. An analysis of spectral pattern for detecting pine wilt disease using ground-based hyperspectral camera, Korean Journal of Remote Sensing, 30(5): 665-675 (in Korean with English abstract).
  21. Lee, S.K., S.J. Park, G.M. Baek, H.B. Kim, and C.W. Lee, 2019. Detection of damaged pine tree by the pine wilt disease using UAV Image, Korean Journal of Remote Sensing, 35(3): 359-373 (in Korean with English abstract).
  22. McCarthy, M., B. Dimmitt, and F. Muller-Karger, 2018. Rapid Coastal Forest Decline in Florida's Big Bend, Remote Sensing, 10(11): 1721.
  23. Meddens, A.J., J.A. Hicke, and L.A. Vierling, 2011. Evaluating the potential of multispectral imagery to map multiple stages of tree mortality, Remote Sensing of Environment, 115(7): 1632-1642.
  24. Meddens, A.J., J.A. Hicke, L.A. Vierling, and A.T. Hudak, 2013. Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery, Remote Sensing of Environment, 132: 49-58.
  25. Nasi, R., E. Honkavaara, P. Lyytikainen-Saarenmaa, M. Blomqvist, P. Litkey, T. Hakala, N. Viljanen, T. Kantola, T. Tanhuanpaa, and M. Holopainen, 2015. Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level, Remote Sensing, 7(11): 15467-15493.
  26. Ortiz, S., J. Breidenbach, and G. Kandler, 2013. Early detection of bark beetle green attack using TerraSAR-X and RapidEye data, Remote Sensing, 5(4): 1912-1931.
  27. Pause, M., C. Schweitzer, M. Rosenthal, V. Keuck, J. Bumberger, P. Dietrich, M. Heurich, A. Jung, and A. Lausch, 2016. In situ/remote sensing integration to assess forest health A review, Remote Sensing, 8(6): 471.
  28. Poona, N.K. and R. Ismail, 2013. Discriminating the occurrence of pitch canker fungus in Pinus radiata trees using QuickBird imagery and artificial neural networks, Southern Forests: A Journal of Forest Science, 75(1): 29-40.
  29. Ryu, J., S. Kim, W. Shim, J. Kim, S. Seo, and B. Yu, 2010. Production manual for the 5th digital forest stand map (1:25,000), Korea Forest Research Institute, Daejeon, Korea (in Korean).
  30. Safonova, A., S. Tabik, D. Alcaraz-Segura, A. Rubtsov, Y. Maglinets, and F. Herrera, 2019. Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning, Remote Sensing, 11(6): 643.
  31. Skakun, R.S., M.A. Wulder, and S.E. Franklin, 2003. Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage, Remote Sensing of Environment, 86(4): 433-443.
  32. Vaughn, N., G. Asner, P. Brodrick, R. Martin, J. Heckler, D. Knapp, and R. Hughes, 2018. An Approach for High-Resolution Mapping of Hawaiian Metrosideros Forest Mortality Using Laser-Guided Imaging Spectroscopy, Remote Sensing, 10(4): 502.
  33. Wang, C., Z. Lu, and T.L. Haithcoat, 2007. Using landsat images to detect oak decline in the Mark Twain national forest, Ozark highlands, Forest Ecology and Management, 240(1-3): 70-78.
  34. White, J.C., M.A. Wulder, D. Brooks, R. Reich, and R.D. Wheate, 2005. Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery, Remote Sensing of Environment, 96(3-4): 340-351.
  35. White, J.C., M.A. Wulder, and D. Grills, 2006. Detecting and mapping mountain pine beetle red-attack damage with SPOT-5 10-m multispectral imagery, Journal of Ecosystems and Management, 7(2): 105-118.
  36. Wingfield, M., A. Hammerbacher, R. Ganley, E. Steenkamp, T. Gordon, B. Wingfield, and T. Coutinho, 2008. Pitch canker caused by Fusarium circinatum-a growing threat to pine plantations and forests worldwide, Australasian Plant Pathology, 37(4): 319-334.
  37. Woo, K., J. Yoon, S. Han, and S. Woo, 2011. Effects of Fusarium circinatum on Disease Development and Gas Exchange in the Seedlings of Pinus spp, Research in Plant Disease, 17(2): 177-183.
  38. Wulder, M.A., J.C. White, N.C. Coops, and C.R. Butson, 2008. Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring, Remote Sensing of Environment, 112(6): 2729-2740.
  39. Xi, Z., D. Lu, L. Liu, and H. Ge, 2016. Detection of drought-induced hickory disturbances in western Lin An county, China, using multitemporal Landsat imagery, Remote Sensing, 8(4): 345.