2,019
Views
11
CrossRef citations to date
0
Altmetric
Original Articles

Multi-sensor forest vegetation height mapping methods for Tanzania

ORCID Icon, ORCID Icon, , , ORCID Icon & ORCID Icon
Pages 587-606 | Received 16 Aug 2017, Accepted 03 Apr 2018, Published online: 22 May 2018

References

  • Aase, J.K., & Siddoway, F.H. (1981). Assessing winter wheat dry matter production via spectral reflectance measurements. Remote Sensing of Environment, 11, 267–277.
  • Campbell, J.B. (2006). Introduction to remote sensing (4th ed.). Oxon, UK: Taylor and Francis.
  • Cartus, O., Kellndorfer, J., Rombach, M., & Walker, W. (2012). Mapping canopy height and growing stock volume using airborne lidar, ALOS PALSAR and Landsat ETM+. Remote Sensing, 4, 3320–3345.
  • Chopping, M., Nolin, A., Moisen, G.G., Martonchik, J.V., & Bull, M. (2009). Forest canopy height from the Multiangle Imaging SpectroRadiometer (MISR) assessed with high resolution discrete return lidar. Remote Sensing of Environment, 113, 2172–2185.
  • Ene, L.T., Næsset, E., Gobakken, T., Bollandsås, O.M., Mauya, E.W., & Zahabu, E. (2017). Large-scale estimation of change in aboveground biomass in miombo woodlands using airborne laser scanning and national forest inventory data. Remote Sensing of Environment, 188, 106–117.
  • Hansen, E.H., Gobakken, T., Bollandsås, O.M., Zahabu, E., & Næsset, E. (2015). Modeling aboveground biomass in dense tropical submontane rainforest using airborne laser scanner data. Remote Sensing, 7, 788–807.
  • Hansen, M.C., & Loveland, T.R. (2012). A review of large area monitoring of land cover change using landsat data. Remote Sensing Of Environment, 122, 66–74. doi:10.1016/j.rse.2011.08.024
  • Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., … Townshend, J.R.G. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342, 850–853.
  • Helmer, E.H., Ruzycki, T.S., Wunderle, J.M., Jr., Vogesser, S., Ruefenacht, B., Kwit, C., … Ewert, D.N. (2010). Mapping tropical dry forest height, foliage height profiles and disturbance type and age with a time series of cloud-cleared Landsat and ALI image mosaics to characterize avian habitat. Remote Sensing of Environment, 114, 2457–2473.
  • Hopkinson, C., Chasmer, L., Lim, K., Treitz, P., & Creed, I. (2006). Towards a universal lidar canopy height indicator. Canadian Journal of Remote Sensing, 32(2), 139–152.
  • Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., & Zhu, Y.-H. (2000). Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128, 109–120.
  • Ioki, K., Tsuyuki, S., Hirata, Y., Phua, M.-H., Wong, W.V.C., Ling, Z.-Y., … Takao., G. (2014). Estimationg above-ground biomass of tropical rainforest of different degradation levels in Northern Borneo using airborne LiDAR. Forest Ecology and Management, 328, 335–341.
  • Jin, S., & Sader, S.A. (2005). Comparison of time series tasseled cap wetness and the normalizeddifference moisture index in detecting forest disturbances. Remote Sensing Of Environment, 94, 364 – 372. doi:10.1016/j.rse.2004.10.012
  • Kalacska, M., Sanchez-Azofeifa, G.A., Rivard, B., Caelli, T., White, H.P., & Calvo-Alvarado, J.C. (2007). Ecological fingerprinting of ecosystem succession: Estimating secondary tropical dry forest structure and diversity using imaging spectroscopy. Remote Sensing of Environment, 108, 82–96.
  • Kalman, R.E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45.
  • Kellndorfer, J., Walker, W., Pierce, L., Dobson, C., Fites, J.A., Hunsaker, C., … Clutter, M. (2004). Vegetation height estimation from Shuttle Radar Topography Mission and National Elevation Datasets. Remote Sensing of Environment, 93, 339–358.
  • Kleynhans, W., Olivier, J.C., Wessels, K.J., Salmon, B.P., Van den Bergh, F., & Steenkamp, K. (2011). Detecting land cover change using an extended Kalman filter on MODIS NDVI time-series data. IEEE Geoscience and Remote Sensing Letters, 8(3), 507–511.
  • Kugler, F., Schulze, D., Hajnsek, I., Pretzsch, H., & Papathanassiou, K.P. (2014). TanDEM-X pol-inSAR performance for forest height estimation. IEEE Transactions on Geoscience and Remote Sensing, 52(10), 6404–6422.
  • Kwak, D.-A., Lee, W.-K., Lee, J.-H., Biging, G.S., & Gong, P. (2007). Detection of individual trees and estimation of tree height using LiDAR data. Journal of Forest Research, 12, 425–434.
  • Larsen, Y., Engen, G., Lauknes, T.R., Malnes, E., & Høgda, K.A. (2005, November 28 – December 2). A generic differential interferometric SAR processing system, with applications to land subsidence and snow-water equivalent retrieval. Proceedings of the Fringe 2005 Workshop, Frascati, Italy.
  • Lefsky, M.A. (2010). A global forest canopy height map from the moderate resolution imaging spectroradiometer and the geoscience laser altimeter system. Geophysical Research Letters, 37, L15401. doi:10.1029/2010GL043622
  • Lefsky, M.A., Keller, M., Pang, Y., Camargo, P.B., & Hunter, M.O. (2007). Revised method for forest canopy height estimation from Geoscience Laser Altimeter System waveforms. Journal of Applied Remote Sensing, 1, 013537.
  • Lei, Y., & Siqueira, P. (2014). Estimation of forest height using spaceborne repeat-pass L-band InSAR correlation magnitude over the US state of Maine. Remote Sensing, 6, 10252–10285.
  • Lymburner, L., Beggs, P.J., & Jacobson, C.R. (2000). Estimation of canopy-average surface-specific leaf area using Landsat TM data. Photogrammetric Engineering & Remote Sensing, 66(2), 183–191.
  • Mauya, E.W., Ene, L.T., Bollandsås, O.M., Gobakken, T., Næsset, E., Malimbwi, R.E., & Zahabu, E. (2015). Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzania. Carbon Balance and Management, 10, paper no. 28. doi:10.1186/s13021-015-0037-2
  • Mitchard, E., Saatchi, S.S., Woodhouse, I., Nangendo, G., Ribeiro, N., Williams, M., … Meir, P. (2009). Using satellite radar backscatter to predict aboveground woody biomass: A consistent relationship across four different African landscapes. Geophysical Research Letters, 36, L23401.
  • Næsset, E. (1997). Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS Journal of Photogrammetry & Remote Sensing, 52, 49–56.
  • Salberg, A.B., & Trier, Ø.D. (2011, July 24–29). Temporal analysis of forest cover using hidden Markov models. In Proceedings of the 2011 IEEE Geoscience and Remote Sensing Symposium (pp. 2322–2325). Vancouver, Canada. doi:10.1109/IGARSS.2011.6049674
  • Salberg, A.B., & Trier, Ø.D. (2012, July 22–27). Temporal analysis of multisensor data for forest change detection using hidden Markov models. In Proceedings of the 2012 IEEE Geoscience and Remote Sensing Symposium (pp. 6749–6752). Munich, Germany. doi:10.1109/IGARSS.2012.6352556
  • Sexton, J.O., Bax, T., Siqueira, P., Swenson, J.J., & Hensley, S. (2009). A comparison of lidar, radar, and field measurements of canopy height in pine and hardwood forests of southeastern North America. Forest Ecology and Management, 257, 1136–1147.
  • Skidmore, A.K., Pettorelli, N., Coops, N.C., Geller, G.N., Hansen, M., Lucas, R., … Wegmann, M. (2015). Agree on biodiversity metrics to track from space. Science, 523, 403–405.
  • Townshend, J.R.G., Goff, T.E., & Tucker, C.J. (1985). Multitemporal dimensionality of images of normalized difference vegetation index at continental scales. IEEE Transactions on Geoscience and Remote Sensing, 23(6), 888–895.
  • Véga, C., & St-Onge, B. (2008). Height growth reconstruction of a boreal forest canopy over a period of 58 years using a combination of photogrammetric and lidar models. Remote Sensing of Environment, 112, 1784–1794.
  • Wilkes, P., Jones, S.D., Suarez, L., Mellor, A., Woodgate, W., Soto-Berelov, M., … Skidmore, A. (2015). Mapping forest canopy height across large areas by upscaling ALS estimates with freely available satellite data. Remote Sensing, 7, 12563–12587.
  • Wolter, P.T., Townsend, P.A., & Sturtevant, B.R. (2009). Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data. Remote Sensing of Environment, 113, 2019–2036.
  • Zhang, G., Ganguly, S., Nemani, R.R., White, M.A., Milesi, C., Hashimoto, H., … Myneni, R.B. (2014). Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data. Remote Sensing of Environment, 151, 44–56.
  • Zhu, Z., Wang, S., & Woodcock, C.E. (2015). Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images. Remote Sensing of Environment, 159, 269–277.
  • Zhu, Z., Woodcock, C.E., & Olofsson, P. (2012). Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sensing of Environment, 122, 75–91.