skip to main content

ADVANCED LAND COVER MAPPING OF TROPICAL PEAT SWAMP ECOSYSTEM USING AIRBORNE DISCRETE RETURN LIDAR

*Solichin Manuri  -  Fenner School of Environment and Society, The Australian National University, ACT, 2601, Australia
Hans-Erik Andersen  -  US Forest Service Pacific Northwest Research Station, University of Washington, Seattle, WA 98195, United States
Cris Brack  -  Fenner School of Environment and Society, The Australian National University, ACT, 2601, Australia
Bruce Doran  -  Fenner School of Environment and Society, The Australian National University, ACT, 2601, Australia

Citation Format:
Abstract
The ability to better understand tropical peat ecosystems for restoration and climate change mitigation is often hampered by the lack of availability accurate and detailed data on vegetation cover and hydrologys, which is typically only derived from detailed and high-resolution imaging or field-based measurements. The aims of this study were to explore the potential advantage of airborne discrete-return lidar for mapping of forest cover in peat swamp forests. We used 2.8 pulse.m-1 lidar and the associated 1-m DTM derived from an airborne platform. The lidar dataset fully covered a 120 thousand hectare protection forest in Central Kalimantan. We extracted maximum vegetation heights in 5-m grid resolution to allow detailed mapping of the forest. We followed forest definition from FAO for forest and non-forest classification. We found that lidar was able to capture detail variation of canopy height in high-resolution, thus provide more accurate classification. A comparison with existing maps suggested that the lidar-derived vegetation map was more consistent in defining canopy structure of the vegetation, with small standard deviations of the mean height of each class.
Fulltext View|Download | HTML
Keywords: Lidar; FAO forest definition; canopy height; forest and land cover mapping; peat swamp forests

Article Metrics:

  1. Asner, G. P., et al. (2012). High-resolution mapping of forest carbon stocks in the Colombian Amazon. Biogeosciences, 9(7), 2683–2696. [https://doi.org/10.5194/bg-9-2683-2012">CrossRef]

  2. Ballhorn, U., et al. (2014). LiDAR Survey of the Kalimantan Forests and Climate Partnership (KFCP) Project Site and EMRP Area in Central Kalimantan, Indonesia.

  3. Clark, M. L., et al. (2011). Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors. Remote Sensing of Environment, 115(11), 2931–2942. [https://doi.org/10.1016/j.rse.2010.08.029">CrossRef]

  4. Di Gregorio, A., & Jansen, L. J. M. (1998). Land Cover Classification System (LCCS): classification concepts and user manual. FAO, Rome.

  5. Dowling, R., & Accad, A. (2003). Vegetation classification of the riparian zone along the Brisbane River, Queensland, Australia, using light detection and ranging (lidar) data and forward looking digital video. Canadian Journal of Remote Sensing, 29(5), 556–563. [https://doi.org/10.5589/m03-029">CrossRef]

  6. Ellenberg, H., & Mueller-Dombois, D. (1966). Tentative physiognomic-ecological classification of plant formations of the earth. Instituts der Eidg. Techn. Hochshule Stiftung Rb̈er.

  7. ESRI. (2015). ArcGIS for Desktop: An overview of the generalization toolset. Retrieved from http://pro.arcgis.com/en/pro-app/tool-reference/data-management/an-overview-of-the-generalization-toolset.htm">

  8. FAO. (2010a). Global Forest Resources Assessment 2010. Rome: Food and Agriculture Organization of the United Nations Forestry. Retrieved from http://www.fao.org/docrep/013/i1757e/i1757e.pdf">

  9. FAO. (2010b). Global Forest Resources Assessment 2010: Country Report. Food and Agriculture Organization of the United Nations Forestry. Retrieved from http://www.fao.org/docrep/013/al531E/al531E.pdf">

  10. Franke, J., et al. (2012). Monitoring Fire and Selective Logging Activities in Tropical Peat Swamp Forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(6), 1811–1820. [https://doi.org/10.1109/JSTARS.2012.2202638">CrossRef]

  11. Hirano, A., Madden, M., & Welch, R. (2003). Hyperspectral image data for mapping wetland vegetation. Wetlands, 23(2), 436–448. [https://doi.org/10.1672/18-20">CrossRef]

  12. Holmgren, J., Persson, Å., & Söderman, U. (2008). Species identification of individual trees by combining high resolution LiDAR data with multi‐spectral images. International Journal of Remote Sensing, 29(5), 1537–1552. [https://doi.org/10.1080/01431160701736471">CrossRef]

  13. Hoscilo, A., et al. (2011). Effect of repeated fires on land-cover change on peatland in southern Central Kalimantan, Indonesia, from 1973 to 2005. International Journal of Wildland Fire, 20(4), 578. [https://doi.org/10.1071/WF10029">CrossRef]

  14. Jaenicke, J., et al. (2008). Determination of the amount of carbon stored in Indonesian peatlands. Geoderma, 147(3–4), 151–158. [https://doi.org/10.1016/j.geoderma.2008.08.008">CrossRef]

  15. Jaenicke, J., et al. (2010). Planning hydrological restoration of peatlands in Indonesia to mitigate carbon dioxide emissions. Mitigation and Adaptation Strategies for Global Change, 15(3), 223–239. [https://doi.org/10.1007/s11027-010-9214-5">CrossRef]

  16. Jones, T. G., Coops, N. C., & Sharma, T. (2010). Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada. Remote Sensing of Environment, 114(12), 2841–2852. [https://doi.org/10.1016/j.rse.2010.07.002">CrossRef]

  17. Jubanski, J., et al. (2013). Detection of large above-ground biomass variability in lowland forest ecosystems by airborne LiDAR. Biogeosciences, 10(6), 3917–3930. [https://doi.org/10.5194/bg-10-3917-2013">CrossRef]

  18. Kronseder, K., et al. (2012). Above ground biomass estimation across forest types at different degradation levels in Central Kalimantan using LiDAR data. International Journal of Applied Earth Observation and Geoinformation, 18, 37–48. [https://doi.org/10.1016/j.jag.2012.01.010">CrossRef]

  19. Lang, M. W., & McCarty, G. W. (2009). Lidar intensity for improved detection of inundation below the forest canopy. Wetlands, 29(4), 1166–1178. [https://doi.org/10.1672/08-197.1">CrossRef]

  20. Langner, A., Miettinen, J., & Siegert, F. (2007). Land cover change 2002–2005 in Borneo and the role of fire derived from MODIS imagery. Global Change Biology, 13(11), 2329–2340. [https://doi.org/10.1111/j.1365-2486.2007.01442.x">CrossRef]

  21. Lund, H. G. (2002). When Is a Forest Not a Forest? Journal of Forestry, 100(8), 21-28

  22. Miettinen, J., Shi, C., & Liew, S. C. (2012). Two decades of destruction in Southeast Asia’s peat swamp forests. Frontiers in Ecology and the Environment, 10(3), 124–128. [https://doi.org/10.1890/100236">CrossRef]

  23. Ministry of Forestry. (2012). Improvement of Land Cover Change Estimate of Indonesia for Year 2011 (In Indonesian).

  24. MoEF. (2015). National Forest Reference Emission Level for Deforestation and Forest Degradation: In the Context of Decision 1/CP.16 para 70 UNFCCC (Encourages developing country Parties to contribute to mitigation actions in the forest sector).

  25. Murdiyarso, D., Hergoualc’h, K., & Verchot, L. V. (2010). Opportunities for reducing greenhouse gas emissions in tropical peatlands. Proceedings of the National Academy of Sciences, 107(46), 19655–19660. [https://doi.org/10.1073/pnas.0911966107">CrossRef]

  26. Nagendra, H., & Rocchini, D. (2008). High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail. Biodiversity and Conservation, 17(14), 3431–3442. [https://doi.org/10.1007/s10531-008-9479-0">CrossRef]

  27. Page, S. E., et al. (2002). The amount of carbon released from peat and forest fires in Indonesia during 1997. Nature, 420(6911), 61–65. [https://doi.org/10.1038/nature01131">CrossRef]

  28. Saremi, H., et al. (2014). Airborne LiDAR derived canopy height model reveals a significant difference in radiata pine (Pinus radiata D. Don) heights based on slope and aspect of sites. Trees, 28(3), 733–744. [https://doi.org/10.1007/s00468-014-0985-2">CrossRef]

  29. Shimada, M., et al. (2014). New global forest/non-forest maps from ALOS PALSAR data (2007–2010). Remote Sensing of Environment, 155, 13–31. [https://doi.org/10.1016/j.rse.2014.04.014">CrossRef]

  30. Siegert, F., et al. (2013). Historical Land Cover Classification and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas Hulu District.

  31. Takahashi, T., et al. (2005). Estimating individual tree heights of sugi ( Cryptomeria japonica D. Don) plantations in mountainous areas using small-footprint airborne LiDAR. Journal of Forest Research, 10(2), 135–142. [https://doi.org/10.1007/s10310-004-0125-8">CrossRef

  32. Woodwell, G. M. (1984). The Role of terrestrial vegetation in the global carbon cycle: measurement by remote sensing. Published on behalf of the Scientific Committee on Problems of the Environment (SCOPE) of the International Council of Scientific Unions (ICSU) by Wiley. Retrieved from https://books.google.co.id/books?id=AkNRAAAAMAAJ">      

  33. Wösten, J. H. M., et al. (2006). Interrelationships between Hydrology and Ecology in Fire Degraded Tropical Peat Swamp Forests. International Journal of Water Resources Development, 22(1), 157–174. [https://doi.org/10.1080/07900620500405973">CrossRef

  34. Wulder, M. A., et al. (2012). Lidar sampling for large-area forest characterization: A review. Remote Sensing of Environment, 121, 196–209. [https://doi.org/10.1016/j.rse.2012.02.001">CrossRef]


Last update:

No citation recorded.

Last update: 2024-11-21 00:55:36

No citation recorded.