Table of Contents Author Guidelines Submit a Manuscript
Journal of Sensors
Volume 2018, Article ID 1593129, 9 pages
https://doi.org/10.1155/2018/1593129
Research Article

Forest Canopy Height Estimation Using Multiplatform Remote Sensing Dataset

1Earthquake and Volcano Research Division, Korea Meteorological Administration, 61 16-Gil, Yeouidaebang-ro, Dongjak-gu, Seoul 07062, Republic of Korea
2Division of Science Education, 1 Kangwondaehak-gil, Chuncheon, 24341 Gangwon, Republic of Korea

Correspondence should be addressed to Chang-Wook Lee; rk.ca.nowgnak@eelwc

Received 6 February 2017; Accepted 5 February 2018; Published 11 April 2018

Academic Editor: Oleg Lupan

Copyright © 2018 Won-Jin Lee and Chang-Wook Lee. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. W. A. Mugasha, E. E. Mwakalukwa, E. Luoga et al., “Allometric models for estimating tree volume and aboveground biomass in lowland forests of Tanzania,” International Journal of Forestry Research, vol. 2016, Article ID 8076271, 13 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  2. I. Shendryk, M. Hellstrӧm, L. Klemedtsson, and N. Kljun, “Low-density LiDAR and optical imagery for biomass estimation over boreal forest in Sweden,” Forests, vol. 5, no. 12, pp. 992–1010, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. T. L. Erdody and L. M. Moskal, “Fusion of LiDAR and imagery for estimating forest canopy fuels,” Remote Sensing of Environment, vol. 114, no. 4, pp. 725–737, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. M. García, S. Saatchi, S. Ustin, and H. Balzter, “Modelling forest canopy height by integrating airborne LiDAR samples with satellite radar and multispectral imagery,” International Journal of Applied Earth Observation and Geoinformation, vol. 66, pp. 159–173, 2018. View at Publisher · View at Google Scholar
  5. J. M. Kellndorfer, W. S. Walker, E. LaPoint, K. Kirsch, J. Bishop, and G. Fiske, “Statistical fusion of Lidar, InSAR, and optical remote sensing data for forest stand height characterization: a regional-scale method based on LVIS, SRTM, Landsat ETM+, and ancillary data sets,” Journal of Geophysical Research, vol. 115, no. G2, 2010. View at Publisher · View at Google Scholar
  6. S. Luo, C. Wang, X. Xi et al., “Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation,” Ecological Indicators, vol. 73, pp. 378–387, 2017. View at Publisher · View at Google Scholar · View at Scopus
  7. J. O. Sexton, T. Bax, P. Siqueira, J. J. Swenson, and S. Hensley, “A comparison of Lidar, radar, and field measurements of canopy height in pine and hardwood forests of southeastern North America,” Forest Ecology and Management, vol. 257, no. 3, pp. 1136–1147, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. W. S. Walker, J. M. Kellndorfer, E. LaPoint, M. Hoppus, and J. Westfall, “An empirical InSAR-optical fusion approach to mapping vegetation canopy height,” Remote Sensing of Environment, vol. 109, no. 4, pp. 482–499, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Sprintsin, P. Berliner, S. Cohen, and A. Karnieli, “Using multispectral spaceborne imagery to assess mean tree height in a dryland plantation,” ISRN Forestry, vol. 2013, Article ID 485264, 8 pages, 2013. View at Publisher · View at Google Scholar
  10. K. Lim, P. Treitz, M. Wulder, B. St-Onge, and M. Flood, “LiDAR remote sensing of forest structure,” Progress in Physical Geography, vol. 27, no. 1, pp. 88–106, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. F. Garestier, P. C. Dubois-Fernandez, and K. P. Papathanassiou, “Pine forest height inversion using single-pass X-band PolInSAR data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 1, pp. 59–68, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Berardino, G. Fornaro, R. Lanari, and E. Sansosti, “A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms,” IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 11, pp. 2375–2383, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. J. W. Rouse, R. H. Haas, J. A. Schell, and D. W. Deering, “Monitoring vegetation systems in the great plains with ERTS,” Third ERTS Symposium NASA, vol. SP-351, no. I, pp. 309–317, 1973. View at Google Scholar
  14. J. Wang, P. M. Rich, K. P. Price, and W. D. Kettle, “Relations between NDVI and tree productivity in the central Great Plains,” International Journal of Remote Sensing, vol. 25, no. 16, pp. 3127–3138, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. R. C. Parker and D. L. Evans, “LiDAR forest inventory with single-tree, double-, and single-phase procedures,” International Journal of Forestry Research, vol. 2009, Article ID 864108, 6 pages, 2009. View at Publisher · View at Google Scholar
  16. H. S. Jung, C. W. Lee, J. W. Park, K. D. Kim, and J. S. Won, “Improvement of small baseline subset (SBAS) algorithm for measuring time-series surface deformation from differential SAR interferograms,” Korean Society of Remote Sensing, vol. 24, no. 2, pp. 165–177, 2008. View at Google Scholar