Tree parameter determinations using airborne Light Detection and Ranging (LiDAR) have been conducted in many forest types, including coniferous, boreal, and deciduous. However, there are only a few scientific articles discussing the application of LiDAR to mangrove biophysical parameter extraction at an individual tree level. The main objective of this study was to investigate the potential of using LiDAR data to estimate the biophysical parameters of mangrove trees at an individual tree scale. The Variable Window Filtering (VWF) and Inverse Watershed Segmentation (IWS) methods were investigated by comparing their performance in individual tree detection and in deriving tree position, crown diameter, and tree height using the LiDAR-derived Canopy Height Model (CHM). The results demonstrated that each method performed well in mangrove forests with a low percentage of crown overlap conditions. The VWF method yielded a slightly higher accuracy for mangrove parameter extractions from LiDAR data compared with the IWS method. This is because the VWF method uses an adaptive circular filtering window size based on an allometric relationship. As a result of the VWF method, the position measurements of individual tree indicated a mean distance error value of 1.10 m. The individual tree detection showed a kappa coefficient of agreement ( K) value of 0.78. The estimation of crown diameter produced a coefficient of determination ( R 2) value of 0.75, a Root Mean Square Error of the Estimate (RMSE) value of 1.65 m, and a Relative Error (RE) value of 19.7%. Tree height determination from LiDAR yielded an R 2 value of 0.80, an RMSE value of 1.42 m, and an RE value of 19.2%. However, there are some limitations in the mangrove parameters derived from LiDAR. The results indicated that an increase in the percentage of crown overlap (C OL) results in an accuracy decrease of the mangrove parameters extracted from the LiDAR-derived CHM, particularly for crown measurements. In this study, the accuracy of LiDAR-derived biophysical parameters in mangrove forests using the VWF and IWS methods is lower than in coniferous, boreal, pine, and deciduous forests. An adaptive allometric equation that is specific for the level of tree density and percentage of crown overlap is a solution for improving the predictive accuracy of the VWF method.
References
[1]
Kuenzer, C.; Bluemel, A.; Gebhardt, S.; Quoc, T.V.; Dech, S. Remote sensing of mangrove ecosystems: A Review. Remote Sens 2011, 3, 878–928.
[2]
Food and Agricultural Organization (FAO). Management and Utilization of Mangroves in Asia and Pacific. FAO Environment Paper No.3;; Agriculture Department: Rome, Italy, 1982; p. 166.
[3]
Harada, K.; Imamura, F. Experimental Study on the Effect in Reducing Tsunami by the Coastal Permeable Structures. Proceedings of the Twelfth International Offshore and Polar Engineering Conference, Kitakyushu, Japan, 26–31 May 2002; pp. 652–658.
[4]
Danielsen, F.; S？rensen, M.K.; Olwig, M.F.; Selvam, V.; Parish, F.; Burgess, N.D.; Hiraishi, T.; Karunagaran, V.M.; Rasmussen, M.S.; Hansen, L.B.; et al. The Asian Tsunami: A protective role for coastal vegetation. Science 2005, doi:10.1126/science.1118387.
[5]
Primary Production of the Major Vegetation Units of the World. In Primary Productivity of the Biosphere; Lieth, H., Whittaker, R.H., Eds.; Springer: Berlin/Heidelberg, Germany, 1975; pp. 305–328.
[6]
McNally, R.; McEwin, A.; Holland, T. The Potential for Mangrove Carbon Projects in Vietnam; SNV-Netherlands Development Organisation REDD+ Programme: Ha Noi, Vietnam, 2011.
[7]
Kristensen, E.; Bouillon, S.; Dittmar, T.; Marchand, C. Organic carbon dynamics in mangrove ecosystems: A review. Aquat. Bot 2008, 89, 201–219.
[8]
Komiyama, A.; Poungparn, S.; Kato, S. Common allometric equations for estimating the tree weight of mangroves. J. Trop. Ecol 2005, 21, 471–477.
[9]
Komiyama, A.; Ong, J.E.; Poungparn, S. Allometry, biomass, and productivity of mangrove forests: A review. Aquat. Bot 2007, 89, 128–137.
[10]
Zhang, K.; Simard, M.; Ross, M.; Rivera-Monroy, V.H.; Houle, P.; Ruiz, P.; Twilley, R.R.; Whelan, K. Airborne laser scanning quantification of disturbances from hurricanes and lightning strikes to mangrove forests in Everglades National Park, USA. Sensors 2008, 8, 2262–2292.
[11]
Cole, T.G.; Ekel, K.C.; Devoe, N.N. Structure of mangrove tree and forest in Micronesia. Forest Ecol. Manage 1999, 117, 95–109.
[12]
Malthus, T.J.; Mumby, P.J. Remote sensing of the coastal zone: An overview and priorities for future research. Int. J. Remote Sens 2003, 24, 2805–2815.
[13]
Lucas, R.M.; Ellison, J.C.; Mitchell, A.; Donnelly, B.; Finlayson, M.; Milne, A.K. Use of stereo aerial photography for quantifying changes in the extent and height of mangroves in tropical Australia. Wetlands Ecol. Manage 2002, 10, 161–175.
[14]
Giri, C.; Pengra, B.; Zhu, Z.; Singh, A.; Tieszen, L.L. Monitoring Mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar. Coast. Shelf Sci 2007, 73, 91–100.
[15]
Wang, L.; Sousa, W.P.; Gong, P.; Biging, G.S. Comparison of IKONOS and QuickBird imagery for mapping mangrove species on the Caribbean coast of Panama. Remote Sens. Environ 2004, 91, 432–440.
[16]
Yang, C.; Everitt, J.H.; Fletcher, R.S.; Jensen, R.R.; Mausel, P.W. Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas Gulf Coast. Photogramm. Eng. Remote Sensing 2009, 75, 425–435.
[17]
Wang, L.; Sousa, W.P. Distinguishing mangrove species with laboratory measurements of hyperspectral leaf reflectance. Int. J. Remote Sens 2009, 30, 1267–1281.
[18]
Meza Diaz, B.; Blackburn, G.A. Remote sensing of mangrove biophysical properties: Evidence from a laboratory simulation of the possible effects of background variation on spectral vegetation indices. Int. J. Remote Sens 2003, 24, 53–73.
[19]
Alongi, D.M. Mangrove forests: Resilience; protection from tsunamis; and responses to global climate change. Estuar. Coast. Shelf Sci 2008, 76, 1–13.
[20]
Cochard, R.; Ranamukhaarachchi, S.L.; Shivakoti, G.P.; Shipin, O.V.; Edwards, P.J.; Seeland, K.T. The 2004 tsunami in Aceh and Southern Thailand: A review on coastal ecosystems; wave hazards and vulnerability. Perspect. Plant Ecol. Evol. Syst 2008, 10, 3–40.
[21]
Lucas, R.M.; Mitchell, A.L.; Rosenqvist, A.; Proisy, C.; Melius, A.; Ticehurst, C. The potential of L-band SAR for quantifying mangrove characteristics and change: Case studies from the tropics. Aquat. Conserv 2007, 17, 245–264.
[22]
Kovacs, J.M.; Vandenberg, C.V.; Wang, J.; Flores-Verdugo, F. The use of multipolarized spaceborne SAR backscatter for monitoring the health of a degraded mangrove forest. J. Coast. Res 2008, 24, 248–254.
[23]
Souza Filho, P.W.; Paradella, W.R. Use of RADARSAT-1 fine mode and Landsat-5 TM selective principal component analysis for geomorphological mapping in a macrotidal mangrove coast in the Amazon Region. Can. J. Remote Sens 2005, 31, 214–224.
[24]
Ozdemir, I. Estimating stem volume by tree crown area and tree shadow area extracted from pan-sharpened Quickbird imagery in open Crimean juniper forests. Int. J. Remote Sens 2008, 29, 5643–5655.
[25]
Myint, S.W.; Giri, C.P.; Wang, L.; Zhu, Z.; Gillette, S. Identifying mangrove species and their surrounding land use and land cover classes using an object oriented approach with a lacunarity spatial measure. GIScience Remote Sens 2008, 45, 188–208.
[26]
Lovell, J.L.; Jupp, D.L.B.; Newnham, G.J.; Coops, N.C.; Culvenor, D.S. Simulation study for finding optimal lidar acquisition parameters for forest height retrieval. Forest Ecol. Manage 2005, 214, 398–412.
[27]
Zhang, K. Identification of gaps in mangrove forests with airborne LiDAR. Remote Sens. Environ 2007, 112, 2309–2325.
[28]
Brandtberg, T.; Warner, T.A.; Landenberger, R.E.; McGraw, J.B. Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America. Remote Sens. Environ 2003, 85, 290–303.
[29]
Edson, C.; Wing, M.G. Airborne Light Detection and Ranging (LiDAR) for individual tree stem location, height, and biomass measurements. Remote Sens 2011, 3, 2494–2528.
[30]
Lindberg, E.; Hollaus, M. Comparison of methods for Estimation of stem volume, stem number and basal area from Airborne laser scanning data in a hemi-boreal forest. Remote Sens 2012, 4, 1004–1023.
[31]
Pirotti, F. Assessing a template matching approach for tree height and position extraction from lidar-derived canopy height models of pinus pinaster stands. Forests 2010, 1, 194–208.
[32]
Popescu, S.C. Estimating biomass of individual pine and trees using airborne lidar. Biomass Bioenergy 2007, 31, 646–655.
[33]
Tesfamichael, S.G.; van Aardt, J.A.N.; Ahmed, F. Estimating plot-level tree height and volume of Eucalyptus grandis plantations using small-footprint, discrete return lidar data. Progr. Phys. Geogr 2010, 34, 515–540.
[34]
Shrestha, R.; Wynne, R.H. Estimating biophysical parameters of individual trees in an urban environment using small footprint discrete-return imaging lidar. Remote Sens 2012, 4, 484–508.
[35]
Long, G.C.; Larry, J.T. Mapping and monitoring Louisiana’s Mangroves in the aftermath of the 2010 gulf of Mexico oil spill. J. Coastal Res 2011, 27, 1059–1064.
[36]
Benjamin, W.H. Satellite remote sensing of mangrove forests: Recent advances and future opportunities. Progr. Phys. Geogr 2011, 35, 87–108.
[37]
John, C. Integrated LiDAR and IKONOS multispectral imagery for mapping mangrove distribution and physical properties. Int. J. Remote Sens 2011, 32, 6765–6781.
[38]
Zhang, K.; Patricia, A.H.; Michael, S.R.; Pablo, L.R.; Marc, S. Airborne Laser Mapping of Mangroves on the Biscayne Bay Coast, Miami, Florida. Proceedings of IEEE International Conference on Geoscience and Remote Sensing Symposium, 2006 (IGARSS 2006), Miami, FL, USA, 31 July–4 August 2006; pp. 3750–3754.
[39]
Temilola, F.E.; Marc, S. Height and biomass of mangroves in Africa from ICESat/GLAS and SRTM. Int. J. Remote Sens 2013, 34, 668–681.
[40]
Simard, M.; Rivera-Monroy, V.H.; Mancera-Pineda, J.E.; Casta？eda-Moya, E.; Twilley, R.R. A systematic method for 3D mapping of mangrove forests based on Shuttle Radar Topography Mission elevation data, ICEsat/GLAS waveforms and field data. Remote Sens. Environ 2008, 112, 2131–2144.
[41]
Falkowski, M.J.; Smith, A.M.S.; Gessler, P.E.; Hudak, A.T.; Vierling, L.A.; Evans, J.S. The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. Can. J. Remote Sens 2008, 34, S338–S350.
[42]
Popescu, S.C.; Wynne, R.H. Seeing the trees in the forest: Using lidar and multispectral data fusion with local filtering and variable window size for estimating tree height. Photogramm. Eng. Remote Sensing 2004, 70, 589–604.
[43]
Falkowski, M.J.; Smith, A.M.S.; Hudak, A.T.; Gessler, P.E.; Vierling, L.A.; Crookston, N.L. Automated estimation of individual conifer tree height and crown diameter via two dimensional spatial wavelet analysis of lidar data. Can. J. Remote Sens 2006, 32, 153–161.
[44]
Jing, L.; Hu, B.; Noland, T.; Li, J. An individual tree crown delineation method based on multi-scale segmentation of imagery. ISPRS J. Photogramm 2012, 70, 88–98.
[45]
Chen, Q.; Baldicchi, D.; Gong, P.; Kelly, M. Isolation individual tree in a savanna woodland using small footprint LiDAR Data. Photogramm. Eng. Remote Sensing 2006, 72, 923–932.
[46]
Mangrove Resource Convention Office, Department of Marine and Coastal Resources. Mangrove in Thailand; The Communities of Agriculture and Cooperatives: Bangkok, Thailand, 2009; p. 167.
[47]
Kitamura, S.; Anwar, C.; Chaniago, A.; Baba, S. Handbook of Mangroves in Indonesia; The International Society for Mangrove Ecosystem: Saritaksu Denpasar, Bali, Indonesia, 1997; p. 199.
[48]
Optech Incorporated. ALTM 2050 Airborne Laser Terrain Mapper: The Ultimate in Performance; Optech Incorporated: Toronto, ON, Canada, 2002.
[49]
Soininen, A. TerraScan User’s Guide; The National Mapping Agency of Great Britain: Southampton, UK, 2012.
[50]
Hugelschaffer, D. Use of LiDAR in Forestry Applications; Sunpine Forest Products Ltd: Sundre, AB, Canada, 2004.
[51]
Kaewwong, A.; Boonyawat, S.; Aksornkoae, S.; Tabthipwon, P. The Suitable Mangrove Tree Species for Planting on the Abandoned Mining Area, Changwat Ranong. Proceedings of 39th Kasetsart University Annual Conference: Science, Natural Resources and Environmental Economics, Bangkok, Thailand, 4–5 February 2001; 1, pp. 409–417.
[52]
Popescu, S.C.; Wynne, R.H.; Nelson, R.F. Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. Can. J. Remote Sens 2003, 29, 564–577.
[53]
Beucher, S.; Lantuéjoul, C. Use of Watersheds in Contour Detection. Proceedings of International Workshop on Image Processing, Real-Time Edge and Motion Detection, Rennes, France, 17–21 September 1979.
[54]
Andersen, H.E. Using airborne light detection and ranging (lidar) to characterize forest stand condition on the Kenai Peninsula of Alaska. West. J. Appl. Forest 2009, 24, 95–102.
[55]
Ziegler, M.; Konrad, M.; Hofrichter, J.; Wimmer, A.; Ruppert, G.; Schardt, M.; Hyyppa, J. Assessment of forest attributes and single-tree segmentation by means of forest scanning. Proc. SPIE 2000, 4035, 73–84.
[56]
Hypp？, J.; Kelle, O.; Lehikoinen, M.; Inkinen, M. A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Trans. Geosci. Remote Sens 2001, 39, 969–975.
[57]
Schardt, M.; Ziegler, M.; Wimmer, A.; Wack, R.; Hyypp？, J. Assessment of forest parameters by means of laser scanning. Int. Arch. Photogramm. Remote Sens 2002, 34, 302–309.
[58]
Kaartinen, H.; Hyypp？, J.; Yu, X.; Vastaranta, M.; Hyypp？, H.; Kukko, A.; Holopainen, M.; Heipke, C.; Hirschmugl, M.; Morsdorf, F.; et al. An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sens 2012, 4, 950–974.
[59]
Persson, ？.; Holmgren, J.; S？dermann, U. Detecting and measuring individual trees using an airborne laser scanner. Photogramm. Eng. Remote Sensing 2002, 68, 925–932.
[60]
Arzai, A.H.; Aliyu, B.S. The relationship between canopy width, height and trunk size in some tree species growth in the Savana zone of Nigeria. Bayero J. Pure Appl. Sci 2010, 3, 260–263.
[61]
Mugo, J.M.; Njunge, J.T.; Malimbwi, R.E.; Kigomo, B.N.; Mwasi, B.N.; Muchiri, M.N. Models for predicting stem diameter from crown diameter of open grown trees in Sondu-Nyando river catchment, Kenya. Asian J. Agric. Sci 2011, 3, 119–126.
[62]
Eysn, L.; Hollaus, M.; Schadauer, K.; Pfeifer, N. Forest delineation based on airborne lidar data. Remote Sens 2012, 4, 762–783.
[63]
Hyypp？, J.; Inkinen, M. Detecting and estimating attributes for single trees using laser scanner. Photogramm. J. Finl 1999, 16, 27–42.
[64]
Hopkinson, C.; Chasmer, L.; Sass, G.; Creed, I.; Sitar, M.; Kalbfleisch, W.; Treitz, P. Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment. Can. J. Remote Sens 2005, 31, 191–206.
[65]
Maltamo, M.; Mustonen, K.; Hyypp？, J.; Yu, X. The accuracy of estimating individual tree variables with airborne laser scanning in a boreal nature reserve. Can. J. Forest Res 2004, 34, 1791–1801.
[66]
Gaveau, D.L.A.; Hill, R. Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data. Can. J. Remote Sens 2003, 29, 650–657.
[67]
Chasmer, L.; Hopkinson, C.; Treitz, P. Investigating laser pulse penetration through a conifer canopy by integrating airborne and terrestrial lidar. Can. J. Remote Sens 2006, 32, 116–125.
[68]
Hyypp？, J.; Pyysalo, U.; Hyypp？, H.; Samberg, A. Elevation of Accuracy of Laser Scanning-Derived Digital Terrain and Target Models in Forest Environment. Proceedings of EARSeL-SIG-Workshop LIDAR, Dresden, Germany, 16–17 June 2000.
