Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation

Ecological Indicators

Volumn 73, Issue , 2017, Pages 378-387

  Luo, Shezhou ^a,b   Wang, Cheng ^a   Xi, Xiaohuan ^a   Pan, Feifei ^c   Peng, Dailiang ^a   Zou, Jie ^d   Nie, Sheng ^a   Qin, Haiming ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF TORONTO   (Canada)

^c UNIVERSITY OF NORTH TEXAS   (United States)

^d FUZHOU UNIVERSITY   (China)

Author keywords

Aboveground biomass; Airborne LiDAR; Belowground biomass; Fusion; Hyperspectral imagery; Partial least squares regression

Indexed keywords

BIOMASS; FORECASTING; FORESTRY; FUSION REACTIONS; LEAST SQUARES APPROXIMATIONS; REGRESSION ANALYSIS; REMOTE SENSING; SPECTROSCOPY; VEGETATION;

ABOVE GROUND BIOMASS; AIRBORNE LIDAR; BELOWGROUND BIOMASS; HYPER-SPECTRAL IMAGERIES; PARTIAL LEAST SQUARES REGRESSION;

OPTICAL RADAR;

ABOVEGROUND BIOMASS; ACCURACY ASSESSMENT; AIRBORNE SENSING; BELOWGROUND BIOMASS; DATA ACQUISITION; ECOLOGICAL MODELING; ESTIMATION METHOD; LEAST SQUARES METHOD; LIDAR; MULTISPECTRAL IMAGE; REGRESSION ANALYSIS; REMOTE SENSING; RIVER BASIN; SATELLITE IMAGERY; VEGETATION DYNAMICS;

CHINA; GANSU; HEI RIVER;

EID: 85006320934     PISSN: 1470160X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolind.2016.10.001     Document Type: Article

References (95)

1. Ahmed, R., Siqueira, P., Hensley, S., A study of forest biomass estimates from lidar in the northern temperate forests of New England. Remote Sens. Environ. 130 (2013), 121–135.
2. Anderson, J., Plourde, L., Martin, M., Braswell, B., Smith, M., Dubayah, R., Hofton, M., Blair, J., Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest. Remote Sens. Environ. 112 (2008), 1856–1870.
3. Asner, G., Mascaro, J., Muller-Landau, H., Vieilledent, G., Vaudry, R., Rasamoelina, M., Hall, J., van Breugel, M., A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia 168 (2012), 1147–1160.
4. Basuki, T.M., van Laake, P.E., Skidmore, A.K., Hussin, Y.A., Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests. For. Ecol. Manage. 257 (2009), 1684–1694.
5. Burnham, K.P., Anderson, D.R., Model Selection and Multimodel Inference: a Practical Information-Theoretic Approach. second ed., 2002, Springer, New York, USA.
6. Cao, L., Coops, N., Hermosilla, T., Innes, J., Dai, J., She, G., Using small-footprint discrete and full-waveform airborne LiDAR metrics to estimate total biomass and biomass components in subtropical forests. Remote Sens. 6 (2014), 7110–7135.
7. Chen, J., Gu, S., Shen, M., Tang, Y., Matsushita, B., Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data. Int. J. Remote Sens. 30 (2009), 6497–6517.
8. Chen, Q., Lu, D., Keller, M., dos-Santos, M., Bolfe, E., Feng, Y., Wang, C., Modeling and mapping agroforestry aboveground biomass in the brazilian amazon using airborne lidar data. Remote Sens., 8, 2016, 21.
9. Cheng, T., Research on Forest Biomass and Carbon Storage in Xiaolong Mountain, Gansu Province. Ph.D. Dissertation. 2007, College of Natural Resources and Environment, Beijing Forestry University, Beijing, China.
10. Cho, M.A., Skidmore, A., Corsi, F., van Wieren, S.E., Sobhan, I., Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. Int. J. Appl. Earth Observ. Geoinf. 9 (2007), 414–424.
11. Clark, M.L., Roberts, D.A., Ewel, J.J., Clark, D.B., Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors. Remote Sens. Environ. 115 (2011), 2931–2942.
12. Dalponte, M., Bruzzone, L., Gianelle, D., Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sens. Environ. 123 (2012), 258–270.
13. Dubayah, R.O., Drake, J.B., Lidar remote sensing for forestry. J. For. 98 (2000), 44–46.
14. Duncanson, L.I., Dubayah, R.O., Cook, B.D., Rosette, J., Parker, G., The importance of spatial detail: assessing the utility of individual crown information and scaling approaches for lidar-based biomass density estimation. Remote Sens. Environ. 168 (2015), 102–112.
15. Ediriweera, S., Pathirana, S., Danaher, T., Nichols, D., Estimating above-ground biomass by fusion of LiDAR and multispectral data in subtropical woody plant communities in topographically complex terrain in North-Eastern Australia. J. For. Res. 25 (2014), 761–771.
16. Englhart, S., Keuck, V., Siegert, F., Aboveground biomass retrieval in tropical forests—the potential of combined X- and L-band SAR data use. Remote Sens. Environ. 115 (2011), 1260–1271.
17. Estornell, J., Ruiz, L.A., Velázquez-Martí, B., Fernández-Sarría, A., Estimation of shrub biomass by airborne LiDAR data in small forest stands. For. Ecol. Manage. 262 (2011), 1697–1703.
18. Foody, G.M., Boyd, D.S., Cutler, M.E.J., Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sens. Environ. 85 (2003), 463–474.
19. 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 Sens. Environ. 115 (2011), 636–649.
20. Gao, S., Niu, Z., Huang, N., Hou, X., Estimating the Leaf Area Index, height and biomass of maize using HJ-1 and RADARSAT-2. Int. J. Appl. Earth Observ. Geoinf. 24 (2013), 1–8.
21. García, M., Riaño, D., Chuvieco, E., Danson, F.M., Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sens. Environ. 114 (2010), 816–830.
22. Geerling, G.W., Labrador-Garcia, M., Clevers, J.G.P.W., Ragas, A.M.J., Smits, A.J.M., Classification of floodplain vegetation by data fusion of spectral (CASI) and LiDAR data. Int. J. Remote Sens. 28 (2007), 4263–4284.
23. Ghamisi, P., Benediktsson, J.A., Phinn, S., Land-cover classification using both hyperspectral and LiDAR data. Int. J. Image Data Fusion 6 (2015), 189–215.
24. Glenn, N.F., Spaete, L.P., Sankey, T.T., Derryberry, D.R., Hardegree, S.P., Mitchell, J.J., Errors in LiDAR-derived shrub height and crown area on sloped terrain. J. Arid Environ. 75 (2011), 377–382.
25. Gobakken, T., Næsset, E., Nelson, R., Bollandsås, O.M., Gregoire, T.G., Ståhl, G., Holm, S., Ørka, H.O., Astrup, R., Estimating biomass in Hedmark County, Norway using national forest inventory field plots and airborne laser scanning. Remote Sens. Environ. 123 (2012), 443–456.
26. Guyot, G., Baret, F., Major, D.J., High spectral resolution: determination of spectral shifts between the red and the near infrared. Int. Arch. Photogramm. Remote Sens. 11 (1988), 750–760.
27. Hansen, E., Gobakken, T., Bollandsås, O., Zahabu, E., Næsset, E., Modeling aboveground biomass in dense tropical submontane rainforest using airborne laser scanner data. Remote Sens. 7 (2015), 788–807.
28. Hayashi, R., Kershaw, J.A., Weiskittel, A., Evaluation of alternative methods for using LiDAR to predict aboveground biomass in mixed species and structurally complex forests in northeastern North America. Math. Comput. For. Nat. Resour. Sci. 7 (2015), 49–65.
29. He, Q., Chen, E., An, R., Li, Y., Above-ground biomass and biomass components estimation using LiDAR data in a coniferous forest. Forests 4 (2013), 984–1002.
30. Hernández-Stefanoni, J., Dupuy, J., Johnson, K., Birdsey, R., Tun-Dzul, F., Peduzzi, A., Caamal-Sosa, J., Sánchez-Santos, G., López-Merlín, D., Improving species diversity and biomass estimates of tropical dry forests using airborne LiDAR. Remote Sens. 6 (2014), 4741–4763.
31. Hopkinson, C., Chasmer, L., Lim, K., Treitz, P., Creed, I., Towards a universal lidar canopy height indicator. Can. J. Remote Sens. 32 (2006), 139–152.
32. Houghton, R.A., Aboveground forest biomass and the global carbon balance. Glob. Chang. Biol. 11 (2005), 945–958.
33. Hudak, A.T., Strand, E.K., Vierling, L.A., Byrne, J.C., Eitel, J.U.H., Martinuzzi, S., Falkowski, M.J., Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys. Remote Sens. Environ. 123 (2012), 25–40.
34. Huete, A., Justice, C., Liu, H., Development of vegetation and soil indices for MODIS-EOS. Remote Sens. Environ. 49 (1994), 224–234.
35. Jensen, J., Humes, K., Vierling, L., Hudak, A., Discrete return lidar-based prediction of leaf area index in two conifer forests. Remote Sens. Environ. 112 (2008), 3947–3957.
36. Jochem, A., Hollaus, M., Rutzinger, M., Hofle, B., Estimation of aboveground biomass in alpine forests: a semi-empirical approach considering canopy transparency derived from airborne LiDAR data. Sensors (Basel) 11 (2011), 278–295.
37. Jordan, C., Derivation of leaf-area index from quality of light on the forest floor. Ecology 50 (1969), 663–666.
38. Kajimoto, T., Matsuura, Y., Sofronov, M.A., Volokitina, A.V., Mori, S., Osawa, A., Abaimov, A.P., Above- and belowground biomass and net primary productivity of a Larix gmelinii stand near Tura central Siberia. Tree Physiol. 19 (1999), 815–822.
39. Kaufman, Y.J., Tanre, D., Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 30 (1992), 261–270.
40. Kelloway, E.K., Using Mplus for Structural Equation Modeling: A Researcher's Guide. 2014, SAGE Publications.
41. Koch, B., Status and future of laser scanning: synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS J. Photogramm. Remote Sens. 65 (2010), 581–590.
42. Koetz, B., Morsdorf, F., van der Linden, S., Curt, T., Allgöwer, B., Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data. For. Ecol. Manage. 256 (2008), 263–271.
43. 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. Int. J. Appl. Earth Observ. Geoinf. 18 (2012), 37–48.
44. Kulawardhana, R.W., Popescu, S.C., Feagin, R.A., Fusion of lidar and multispectral data to quantify salt marsh carbon stocks. Remote Sens. Environ. 154 (2014), 345–357.
45. Latifi, H., Fassnacht, F., Koch, B., Forest structure modeling with combined airborne hyperspectral and LiDAR data. Remote Sens. Environ. 121 (2012), 10–25.
46. Lefsky, M.A., Cohen, W.B., Parker, G.G., Harding, D.J., Lidar remote sensing for ecosystem studies: lidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists. Bioscience 52 (2002), 19–30.
47. Li, X., Cheng, G., Liu, S., Xiao, Q., Ma, M., Jin, R., Che, T., Liu, Q., Wang, W., Qi, Y., Wen, J., Li, H., Zhu, G., Guo, J., Ran, Y., Wang, S., Zhu, Z., Zhou, J., Hu, X., Xu, Z., Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design. Am. Meteorol. Soc. 94 (2013), 1145–1160.
48. Li, W., Niu, Z., Huang, N., Wang, C., Gao, S., Wu, C., Airborne LiDAR technique for estimating biomass components of maize A case study in Zhangye City, Northwest China. Ecol. Indic. 57 (2015), 486–496.
49. Lim, K.S., Treitz, P.M., Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Scand. J. For. Res. 19 (2004), 558–570.
50. Lin, Y., West, G., Reflecting conifer phenology using mobile terrestrial LiDAR A case study of Pinus sylvestris growing under the Mediterranean climate in Perth, Australia. Ecol. Indic. 70 (2016), 1–9.
51. Listopad, C.M.C.S., Masters, R.E., Drake, J., Weishampel, J., Branquinho, C., Structural diversity indices based on airborne LiDAR as ecological indicators for managing highly dynamic landscapes. Ecol. Indic. 57 (2015), 268–279.
52. Lu, D., Chen, Q., Wang, G., Moran, E., Batistella, M., Zhang, M., Vaglio Laurin, G., Saah, D., Aboveground forest biomass estimation with landsat and LiDAR data and uncertainty analysis of the estimates. Int. J. For. Res. 2012 (2012), 1–16.
53. Lucas, R.M., Lee, A.C., Bunting, P.J., Retrieving forest biomass through integration of CASI and LiDAR data. Int. J. Remote Sens. 29 (2008), 1553–1577.
54. Luedeling, E., Gassner, A., Partial least squares regression for analyzing walnut phenology in california. Agric. For. Meteorol. 158–159 (2012), 43–52.
55. Luo, S., Wang, C., Xi, X., Pan, F., Estimating FPAR of maize canopy using airborne discrete-return LiDAR data. Opt. Express 22 (2014), 5106–5117.
56. Luo, S., Wang, C., Pan, F., Xi, X., Li, G., Nie, S., Xia, S., Estimation of wetland vegetation height and leaf area index using airborne laser scanning data. Ecol. Indic. 48 (2015), 550–559.
57. Luo, S., Chen, J.M., Wang, C., Xi, X., Zeng, H., Peng, D., Li, D., Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters. Opt. Express 24 (2016), 11578–11593.
58. Luo, S., Wang, C., Xi, X., Zeng, H., Li, D., Xia, S., Wang, P., Fusion of airborne discrete-return LiDAR and hyperspectral data for land cover classification. Remote Sens., 8, 2016, 3.
59. Mauya, E., Hansen, E., Gobakken, T., Bollandsas, O., Malimbwi, R., Naesset, E., Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania. Carbon Balance Manag., 10, 2015, 10.
60. Mitchard, E.T.A., Saatchi, S.S., Woodhouse, I.H., Nangendo, G., Ribeiro, N.S., Williams, M., Ryan, C.M., Lewis, S.L., Feldpausch, T.R., Meir, P., Using satellite radar backscatter to predict above-ground woody biomass: a consistent relationship across four different African landscapes. Geophys. Res. Lett., 36, 2009.
61. 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 Sens. Environ. 104 (2006), 50–61.
62. Mutanga, O., Skidmore, A.K., Narrow band vegetation indices overcome the saturation problem in biomass estimation. Int. J. Remote Sens. 25 (2004), 3999–4014.
63. Næsset, E., Gobakken, T., Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sens. Environ. 112 (2008), 3079–3090.
64. Næsset, E., Bollandsås, O.M., Gobakken, T., Comparing regression methods in estimation of biophysical properties of forest stands from two different inventories using laser scanner data. Remote Sens. Environ. 94 (2005), 541–553.
65. Næsset, E., Gobakken, T., Bollandsås, O.M., Gregoire, T.G., Nelson, R., Ståhl, G., Comparison of precision of biomass estimates in regional field sample surveys and airborne LiDAR-assisted surveys in Hedmark County, Norway. Remote Sens. Environ. 130 (2013), 108–120.
66. Nelson, R.F., Hyde, P., Johnson, P., Emessiene, B., Imhoff, M.L., Campbell, R., Edwards, W., Investigating RaDAR–LiDAR synergy in a North Carolina pine forest. Remote Sens. Environ. 110 (2007), 98–108.
67. Nie, S., Wang, C., Dong, P., Xi, X., Estimating leaf area index of maize using airborne full-waveform lidar data. Remote Sens. Lett. 7 (2016), 111–120.
68. Olsoy, P.J., Mitchell, J.J., Levia, D.F., Clark, P.E., Glenn, N.F., Estimation of big sagebrush leaf area index with terrestrial laser scanning. Ecol. Indic. 61:Part 2 (2016), 815–821.
69. Parresol, B.R., Additivity of nonlinear biomass equations. Can. J. For. Res. 31 (2001), 865–878.
70. Popescu, S.C., Zhao, K., Neuenschwander, A., Lin, C., Satellite lidar vs. small footprint airborne lidar: comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level. Remote Sens. Environ. 115 (2011), 2786–2797.
71. Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H., Sorooshian, S., A modified soil adjusted vegetation index. Remote Sens. Environ. 48 (1994), 119–126.
72. Qin, Y., Li, S., Vu, T.-T., Niu, Z., Ban, Y., Synergistic application of geometric and radiometric features of LiDAR data for urban land cover mapping. Opt. Express 23 (2015), 13761–13775.
73. Riaño, D., Valladares, F., Condés, S., Chuvieco, E., Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests. Agric. For. Meteorol. 124 (2004), 269–275.
74. Richardson, J.J., Moskal, L.M., Kim, S.-H., Modeling approaches to estimate effective leaf area index from aerial discrete-return LIDAR. Agric. For. Meteorol. 149 (2009), 1152–1160.
75. Rondeaux, G., Steven, M., Baret, F., Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 55 (1996), 95–107.
76. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite-1 Symposium, 1973, 309–317.
77. Ruiz, L., Hermosilla, T., Mauro, F., Godino, M., Analysis of the influence of plot size and LiDAR density on forest structure attribute estimates. Forests 5 (2014), 936–951.
78. Sarrazin, M.J.D., van Aardt, J.A.N., Asner, G.P., McGlinchy, J., Messinger, D.W., Wu, J., Fusing small-footprint waveform LiDAR and hyperspectral data for canopy-level species classification and herbaceous biomass modeling in savanna ecosystems. Can. J. Remote Sens. 37 (2011), 653–665.
79. Strunk, J., Temesgen, H., Andersen, H.-E., Flewelling, J.P., Madsen, L., Effects of lidar pulse density and sample size on a model-assisted approach to estimate forest inventory variables. Can. J. Remote Sens. 38 (2012), 644–654.
80. Sun, G., Ranson, K.J., Guo, Z., Zhang, Z., Montesano, P., Kimes, D., Forest biomass mapping from lidar and radar synergies. Remote Sens. Environ. 115 (2011), 2906–2916.
81. Swatantran, A., Dubayah, R., Roberts, D., Hofton, M., Blair, J.B., Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion. Remote Sens. Environ. 115 (2011), 2917–2930.
82. Thomas, V., Finch, D.A., McCaughey, J.H., Noland, T., Rich, L., Treitz, P., Spatial modelling of the fraction of photosynthetically active radiation absorbed by a boreal mixedwood forest using a lidar–hyperspectral approach. Agric. For. Meteorol. 140 (2006), 287–307.
83. Tsui, O.W., Coops, N.C., Wulder, M.A., Marshall, P.L., McCardle, A., Using multi-frequency radar and discrete-return LiDAR measurements to estimate above-ground biomass and biomass components in a coastal temperate forest. ISPRS J. Photogramm. Remote Sens. 69 (2012), 121–133.
84. Tucker, C.J., Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8 (1979), 127–150.
85. Vaglio Laurin, G., Chen, Q., Lindsell, J.A., Coomes, D.A., Frate, F.D., Guerriero, L., Pirotti, F., Valentini, R., Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data. ISPRS J. Photogramm. Remote Sens. 89 (2014), 49–58.
86. Wang, C., Menenti, M., Stoll, M.P., Feola, A., Belluco, E., Marani, M., Separation of ground and low vegetation signatures in LiDAR measurements of salt-marsh environments. IEEE Trans. Geosci. Remote Sens. 47 (2009), 2014–2023.
87. Weng, Q., Scale Issues in Remote Sensing. 2014, Wiley.
88. Wold, S., PLS for multivariate linear modelin. van der Waterbeemd, H., (eds.) Chemometric Methods in Molecular Design: Methods and Principles in Medicinal Chemistry, 1995, Chemie, Weinheim, Germany, 195–218.
89. Woodcock, C.E., Strahler, A.H., The factor of scale in remote sensing. Remote Sens. Environ. 21 (1987), 311–332.
90. Wu, H., Li, Z.L., Scale issues in remote sensing: a review on analysis, processing and modeling. Sensors (Basel) 9 (2009), 1768–1793.
91. Xiao, Q., Wen, J., HiWATER: Airborne LiDAR Raw Data in the Middle Reaches of the Heihe River Basin. 2014, Heihe Plan Science Data Center.
92. 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 Sens. Environ. 113 (2009), 1628–1645.
93. Zhao, K., Popescu, S., Nelson, R., Lidar remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers. Remote Sens. Environ. 113 (2009), 182–196.
94. Zolkos, S.G., Goetz, S.J., Dubayah, R., A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sens. Environ. 128 (2013), 289–298.
95. d'Oliveira, M.V.N., Reutebuch, S.E., McGaughey, R.J., Andersen, H.-E., Estimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest Acre State, Western Brazilian Amazon. Remote Sens. Environ. 124 (2012), 479–491.