Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data

Remote Sensing of Environment

Volumn 156, Issue , 2015, Pages 322-334

  Bouvier, Marc ^a   Durrieu, Sylvie ^a   Fournier, Richard A ^b   Renaud, Jean Pierre ^c  

^a AGROPARISTECH   (France)

^b UNIVERSITÉ DE SHERBROOKE   (Canada)

^c Research and Development   (France)

Author keywords

Airborne LiDAR; Area based approach; Canopy structure; Foliage density profile; Forest inventory; Model generalization

Indexed keywords

AIRBORNE LIDAR; AREA-BASED; CANOPY STRUCTURE; FOLIAGE DENSITY; FOREST INVENTORY; MODEL GENERALIZATION;

EID: 84908374490     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2014.10.004     Document Type: Review

References (102)

1. Allouis T., Durrieu S., Véga C., Couteron P. Stem volume and above-ground biomass estimation of individual pine trees from LiDAR data: Contribution of full-waveform signals. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2013, 6(2):924-934. 10.1109/JSTARS.2012.2211863.
2. Asner G.P. Tropical forest carbon assessment: Integrating satellite and airborne mapping approaches. Environmental Research Letters 2009, 4(3):034009. 10.1088/1748-9326/4/3/034009.
3. Asner G.P., Mascaro J., Muller-Landau H.C., Vieilledent G., Vaudry R., Rasamoelina M., et al. A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia 2012, 168(4):1147-1160.
4. Avery T.E., Burkhart H.E. Forest measurements 2001, McGraw-Hill, Boston, MA. 5th ed.
5. Axelsson P. DEM generation from laser scanner data using adaptive TIN models. International Archives of Photogrammetry and Remote Sensing 2000, 33(B4/1; PART 4):111-118.
6. Baskerville G.L. Use of logarithmic regression in the estimation of plant biomass. Canadian Journal of Forest Research 1972, 2(1):49-53.
7. Bréda N.J. Ground-based measurements of leaf area index: A review of methods, instruments and current controversies. Journal of Experimental Botany 2003, 54(392):2403-2417.
8. Brown S., Lugo A.E. Biomass of tropical forests: A new estimate based on forest volumes. Science 1984, 223(4642):1290-1293. 10.2307/1692790.
9. Campbell G.S., Norman J.M. The description and measurement of plant canopy structure. Plant canopies: Their growth, form and function 1989, 1-19. Cambridge University Press, Cambridge.
10. Chave J., Andalo C., Brown S., Cairns M.A., Chambers J.Q., Eamus D., et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 2005, 145(1):87-99. 10.1007/s00442-005-0100-x.
11. Chave J., Condit R., Aguilar S., Hernandez A., Lao S., Perez R. Error propagation and scaling for tropical forest biomass estimates. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 2004, 359(1443):409-420.
12. Chen Q. Retrieving vegetation height of forests and woodlands over mountainous areas in the Pacific Coast region using satellite laser altimetry. Remote Sensing of Environment 2010, 114(7):1610-1627. 10.1016/j.rse.2010.02.016.
13. Chen Q. Lidar remote sensing of vegetation biomass 2013, 399-420. Remote Sensing of Natural Resources. CRC Press, Taylor & Francis.
14. Chen Q., Gong P., Baldocchi D., Tian Y.Q. Estimating basal area and stem volume for individual trees from lidar data. Photogrammetric Engineering and Remote Sensing 2007, 73(12):1355.
15. Cohen W.B., Spies T.A. Estimating structural attributes of Douglas-fir/western hemlock forest stands from Landsat and SPOT imagery. Remote Sensing of Environment 1992, 41(1):1-17. 10.1016/0034-4257(92)90056-P.
16. Deleuze C., Senga Kiessé T., Renaud J.-P., Morneau F., Rivoire M., Santenoise P., et al. Rapport final EMERGE sur les modèles de volumes. Projet ANR-08-BIOE-003, Programme BIOE 2008 2013.
17. Dubayah R., Knox R., Hofton M., Blair J.B., Drake J. Land surface characterization using lidar remote sensing. Spatial information for land use management 2000, 25-38. International Publishers Direct, Singapore.
18. Dupuy S., Lainé G., Tassin J., Sarrailh J.-M. Characterization of the horizontal structure of the tropical forest canopy using object-based LiDAR and multispectral image analysis. International Journal of Applied Earth Observation and Geoinformation 2013, 25(0):76-86. 10.1016/j.jag.2013.04.001.
19. 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. Canadian Journal of Remote Sensing 2006, 32(2):153-161.
20. Fang J.Y., Liu G.H., Xu S.L. Biomass and net production of forest vegetation in China. Acta Ecologica Sinica 1996, 16(5):497-508.
21. Ferster C.J., Coops N.C., Trofymow J.A. Aboveground large tree mass estimation in a coastal forest in British Columbia using plot-level metrics and individual tree detection from lidar. Canadian Journal of Remote Sensing 2009, 35(3):270-275.
22. Finney D.J. On the distribution of a variate whose logarithm is normally distributed. Supplement to the Journal of the Royal Statistical Society 1941, 155-161.
23. Fisher R.A. The design of experiments 1935, Oliver & Boyd, Edinburgh.
24. Franklin S.E. Remote sensing for sustainable forest management 2001, CRC Press, Boca Raton.
25. Frazer G.W., Magnussen S., Wulder M.A., Niemann K.O. Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass. Remote Sensing of Environment 2011, 115(2):636-649.
26. Genet A., Wernsdörfer H., Jonard M., Pretzsch H., Rauch M., Ponette Q., et al. Ontogeny partly explains the apparent heterogeneity of published biomass equations for Fagus sylvatica in central Europe. Forest Ecology and Management 2011, 261(7):1188-1202.
27. Gleason C.J., Im J. A review of remote sensing of forest biomass and biofuel: options for small-area applications. GIScience & Remote Sensing 2011, 48(2):141-170.
28. Goerndt M.E., Monleon V.J., Temesgen H. Relating forest attributes with area- and tree-based light detection and ranging metrics for western Oregon. Western Journal of Applied Forestry 2010, 25(3):105-111.
29. Hall F.G., Bergen K., Blair J.B., Dubayah R., Houghton R., Hurtt G., et al. Characterizing 3D vegetation structure from space: Mission requirements. Remote Sensing of Environment 2011, 115(11):2753-2775.
30. Hall S.A., Burke I.C., Box D.O., Kaufmann M.R., Stoker J.M. Estimating stand structure using discrete-return lidar: An example from low density, fire prone ponderosa pine forests. Forest Ecology and Management 2005, 208(1):189-209.
31. Hopkinson C., Chasmer L. Testing LiDAR models of fractional cover across multiple forest ecozones. Remote Sensing of Environment 2009, 113(1):275-288.
32. Hopkinson C., Lovell J., Chasmer L., Jupp D., Kljun N., van Gorsel E. Integrating terrestrial and airborne lidar to calibrate a 3D canopy model of effective leaf area index. Remote Sensing of Environment 2013, 136:301-314.
33. Hounzandji A., Jonard M., Nys C., Saint-André L., Ponette Q. Improving the robustness of biomass function: From empirical to functional approaches. Annals of Forest Science 2014.
34. IGN La cartographie forestière http://inventaire-forestier.ign.fr/spip/IMG/pdf/Guide_technique_cartographie.pdf.
35. Jensen J.L.W.V. Sur les fonctions convexes et les inégalités entre les valeurs moyennes. Acta Mathematica 1906, 30(1):175-193.
36. Joly D., Brossard T., Cardot H., Cavailhes J., Hilal M., Wavresky P. Les types de climats en France, une construction spatiale. Cybergeo: European Journal of Geography 2010, 10.4000/cybergeo.23155.
37. Kangas A., Maltamo M. Forest inventory: Methodology and applications 2006, Vol. 10. Springer, Dordrecht.
38. Khan J.A., Van Aelst S., Zamar R.H. Robust linear model selection based on least angle regression. Journal of the American Statistical Association 2007, 102(480):1289-1299.
39. Kim Y., Yang Z., Cohen W.B., Pflugmacher D., Lauver C.L., Vankat J.L. Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data. Remote Sensing of Environment 2009, 113(11):2499-2510.
40. Korhonen L., Korpela I., Heiskanen J., Maltamo M. Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index. Remote Sensing of Environment 2011, 115(4):1065-1080.
41. Kronseder K., Ballhorn U., Böhm V., Siegert F. 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 2012, 18:37-48.
42. Le Toan T., Beaudoin A., Riom J., Guyon D. Relating forest biomass to SAR data. Geoscience and Remote Sensing, IEEE Transactions on 1992, 30(2):403-411.
43. Leboeuf A., Fournier R.A., Luther J.E., Beaudoin A., Guindon L. Forest attribute estimation of northeastern Canadian forests using QuickBird imagery and a shadow fraction method. Forest Ecology and Management 2012, 266:66-74.
44. Leeuwen M., Nieuwenhuis M. Retrieval of forest structural parameters using LiDAR remote sensing. European Journal of Forest Research 2010, 129(4):749-770. 10.1007/s10342-010-0381-4.
45. Lefsky M.A., Cohen W.B., Acker S.A., Parker G.G., Spies T.A., Harding D. Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests. Remote Sensing of Environment 1999, 70(3):339-361.
46. Lefsky M.A., Cohen W.B., Harding D.J., Parker G.G., Acker S.A., Gower S.T. Lidar remote sensing of above-ground biomass in three biomes. Global Ecology and Biogeography 2002, 11(5):393-399.
47. Lefsky M.A., Harding D., Cohen W.B., Parker G.G., Shugart H.H. Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA. Remote Sensing of Environment 1999, 67(1):83-98.
48. Lexerød N.L., Eid T. An evaluation of different diameter diversity indices based on criteria related to forest management planning. Forest Ecology and Management 2006, 222(1):17-28.
49. Li W., Guo Q., Jakubowski M.K., Kelly M. A new method for segmenting individual trees from the lidar point cloud. Photogrammetric Engineering and Remote Sensing 2012, 78(1):75-84.
50. Lim K.S., Treitz P.M. Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Scandinavian Journal of Forest Research 2004, 19(6):558-570.
51. Lim K., Treitz P., Baldwin K., Morrison I., Green J. Lidar remote sensing of biophysical properties of tolerant northern hardwood forests. Canadian Journal of Remote Sensing 2003, 29(5):658-678.
52. Lim K., Treitz P., Wulder M., St-Onge B., Flood M. LiDAR remote sensing of forest structure. Progress in Physical Geography 2003, 27(1):88-106. 10.1191/0309133303pp360ra.
53. Longuetaud F., Santenoise P., Mothe F., Senga Kiessé T., Rivoire M., Saint-André L., et al. Modeling volume expansion factors for temperate tree species in France. Forest Ecology and Management 2013, 292:111-121.
54. Lovell J.L., Jupp D.L., Culvenor D.S., Coops N.C. Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests. Canadian Journal of Remote Sensing 2003, 29(5):607-622.
55. Lu D., Chen Q., Wang G., Moran E., Batistella M., Zhang M., et al. Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates. International Journal of Forestry Research 2012, (2012). 10.1155/2012/436537.
56. Lucas R.M., Cronin N., Lee A., Moghaddam M., Witte C., Tickle P. Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia. Remote Sensing of Environment 2006, 100(3):407-425.
57. Magnussen S., Boudewyn P. Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators. Canadian Journal of Forest Research 1998, 28(7):1016-1031.
58. Magnussen S., Næsset E., Gobakken T., Frazer G. A fine-scale model for area-based predictions of tree-size-related attributes derived from LiDAR canopy heights. Scandinavian Journal of Forest Research 2012, 27(3):312-322.
59. Magurran A.E. Measuring biological diversity 2004, 285-286. Blackwell Publishing, Oxford.
60. Maltamo M., Eerikäinen K., Pitkänen J., Hyyppä J., Vehmas M. Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions. Remote Sensing of Environment 2004, 90(3):319-330.
61. Martens S.N., Ustin S.L., Rousseau R.A. Estimation of tree canopy leaf area index by gap fraction analysis. Forest Ecology and Management 1993, 61(1):91-108.
62. Means J.E., Acker S.A., Fitt B.J., Renslow M., Emerson L., Hendrix C.J. Predicting forest stand characteristics with airborne scanning lidar. Photogrammetric Engineering and Remote Sensing 2000, 66(11):1367-1372.
63. Means J.E., Acker S.A., Harding D.J., Blair J.B., Lefsky M.A., Cohen W.B., et al. Use of large-footprint scanning airborne lidar to estimate forest stand characteristics in the Western Cascades of Oregon. Remote Sensing of Environment 1999, 67(3):298-308.
64. Morsdorf F., Kötz B., Meier E., Itten K.I., Allgöwer B. Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sensing of Environment 2006, 104(1):50-61.
65. Mutlu M., Popescu S.C., Zhao K. Sensitivity analysis of fire behavior modeling with LIDAR-derived surface fuel maps. Forest Ecology and Management 2008, 256(3):289-294.
66. Næsset E. Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing 1997, 52(2):49-56. 10.1016/S0924-2716(97)83000-6.
67. Næsset E. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment 2002, 80(1):88-99. 10.1016/S0034-4257(01)00290-5.
68. Næsset E. Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scandinavian Journal of Forest Research 2004, 19(2):164-179.
69. Næsset E. Estimating above-ground biomass in young forests with airborne laser scanning. International Journal of Remote Sensing 2011, 32(2):473-501.
70. Næsset E., Gobakken T. Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sensing of Environment 2008, 112(6):3079-3090. 10.1016/j.rse.2008.03.004.
71. Nelson R., Krabill W., Tonelli J. Estimating forest biomass and volume using airborne laser data. Remote Sensing of Environment 1988, 24(2):247-267.
72. Nilson T. A theoretical analysis of the frequency of gaps in plant stands. Agricultural Meteorology 1971, 8:25-38.
73. Nilson T. Inversion of gap frequency data in forest stands. Agricultural and Forest Meteorology 1999, 98:437-448.
74. Nilsson M. Estimation of tree heights and stand volume using an airborne lidar system. Remote Sensing of Environment 1996, 56(1):1-7. 10.1016/0034-4257(95)00224-3.
75. Nyström M., Holmgren J., Olsson H. Prediction of tree biomass in the forest-tundra ecotone using airborne laser scanning. Remote Sensing of Environment 2012, 123(0):271-279. 10.1016/j.rse.2012.03.008.
76. Patenaude G., Hill R., Milne R., Gaveau D.L.A., Briggs B.B.J., Dawson T.P. Quantifying forest above ground carbon content using LiDAR remote sensing. Remote Sensing of Environment 2004, 93(3):368-380. 10.1016/j.rse.2004.07.016.
77. Patterson P.L., Williams M.S. Effects of registration errors between remotely sensed and ground data on estimators of forest area. Forest Science 2003, 49(1):110-118.
78. Picard R.R., Cook R.D. Cross-validation of regression models. Journal of the American Statistical Association 1984, 79(387):575-583.
79. Picard N., Saint-André L., Henry M. Manual for building tree volume and biomass allometric equations: from field measurement to prediction. Manual for building tree volume and biomass allometric equations: from field measurement to prediction and Centre de Coopération Internationale en Recherche Agronomique pour le Développement 2012, (http://www.fao.org/docrep/018/i3058e/i3058e.pdf).
80. 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. Canadian Journal of Remote Sensing 2003, 29(5):564-577.
81. Reich P.B. Key canopy traits drive forest productivity. Proceedings of the Royal Society Series B: Biological Sciences 2012, 279(1736):2128-2134.
82. Ross I. The radiation regime and architecture of plant stands 1981, Vol. 3. Dr. W. Junk Publishers, The Hague.
83. Ruel J.J., Ayres M.P. Jensen's inequality predicts effects of environmental variation. Trends in Ecology & Evolution 1999, 14(9):361-366. 10.1016/S0169-5347(99)01664-X.
84. Schumacher F.X., Hall.D.S. Logarithmic expression of timber-tree volume. Journal of Agricultural Research 1933, 47:719-734.
85. Scott C.T., Gove J.H. Forest inventory. Encyclopedia of Environmetrics 2002, 814-820. Wiley, Chichester.
86. Shaiek O., Loustau D., Trichet P., Meredieu C., Bachtobji B., Garchi S., et al. Generalized biomass equations for the main aboveground biomass components of maritime pine across contrasting environments. Annals of Forest Science 2011, 68(3):443-452. 10.1007/s13595-011-0044-8.
87. Snee R.D. Validation of regression models: Methods and examples. Technometrics 1977, 19(4):415-428.
88. Solberg S., Brunner A., Hanssen K.H., Lange H., Næsset E., Rautiainen M., et al. Mapping LAI in a Norway spruce forest using airborne laser scanning. Remote Sensing of Environment 2009, 113(11):2317-2327.
89. Solberg S., Næsset E., Hanssen K.H., Christiansen E. Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning. Remote Sensing of Environment 2006, 102(3):364-376.
90. Sprugel D.G. Correcting for bias in log-transformed allometric equations. Ecology 1983, 64(1):209-210.
91. Tang H., Brolly M., Zhao F., Strahler A.H., Schaaf C.L., Ganguly S., et al. Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA. Remote Sensing of Environment 2014, 143:131-141.
92. Tang H., Dubayah R., Swatantran A., Hofton M., Sheldon S., Clark D.B., et al. Retrieval of vertical LAI profiles over tropical rain forests using waveform lidar at La Selva, Costa Rica. Remote Sensing of Environment 2012, 124:242-250.
93. Ung C.-H., Bernier P., Guo X.-J. Canadian national biomass equations: New parameter estimates that include British Columbia data. Canadian Journal of Forest Research 2008, 38(5):1123-1132.
94. Véga C., Durrieu S. Multi-level filtering segmentation to measure individual tree parameters based on Lidar data: Application to a mountainous forest with heterogeneous stands. International Journal of Applied Earth Observation and Geoinformation 2011, 13(4):646-656. 10.1016/j.jag.2011.04.002.
95. Véga C., Hamrouni A., El Mokhtari S., Morel J., Bock J., Renaud J.-P., et al. PTrees: A point-based approach to forest tree extraction from lidar data. International Journal of Applied Earth Observation and Geoinformation 2014, 33:98-108. 10.1016/j.jag.2014.05.001.
96. Vose J.M., Clinton B.D., Sullivan N.H., Bolstad P.V. Vertical leaf area distribution, light transmittance, and application of the Beer-Lambert Law in four mature hardwood stands in the southern Appalachians. Canadian Journal of Forest Research 1995, 25(6):1036-1043.
97. Waring R.H. Estimating forest growth and efficiency in relation to canopy leaf area. Advances in Ecological Research 1983, 13:327-354.
98. Wulder M.A., White J.C., Nelson R.F., Næsset E., Ørka H.O., Coops N.C., et al. Lidar sampling for large-area forest characterization: A review. Remote Sensing of Environment 2012, 121:196-209.
99. Zanne A.E., Lopez-Gonzalez G., Coomes D.A., Ilic J., Jansen S., Lewis S.L., et al. Global wood density database. Dryad. Identifier 2009, 235. (http://hdl.handle.net/10255/dryad).
100. Zhao K., Popescu S. Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA. Remote Sensing of Environment 2009, 113(8):1628-1645.
101. Zianis D., Mencuccini M. On simplifying allometric analyses of forest biomass. Forest Ecology and Management 2004, 187(2):311-332.
102. Zolkos S.G., Goetz S.J., Dubayah R. A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sensing of Environment 2013, 128(0):289-298. 10.1016/j.rse.2012.10.017.