Abstract

Forest aboveground biomass (AGB) is critical for assessing forest productivity and evaluating carbon sequestration rates. Discrete-return LiDAR has been widely used to estimate forest AGB, however, fewer studies have estimated the coniferous forest AGB using airborne small-footprint full-waveform LiDAR data. The objective of this study was to extract a suite of newly proposed metrics from airborne small-footprint full-waveform LiDAR data and to evaluate the ability of these metrics in estimating coniferous forest AGB. To achieve this goal, each waveform was first preprocessed, including de-noising, smoothing, and normalization. Next, all the waveforms within each plot were aggregated into a large pseudo waveform and the return energy profile was generated. Then, the foliage profile was retrieved from the return energy profile based on the Geometric Optical and Radiative Transfer (GORT) model. Finally, a series of new return energy profile metrics and foliage profile metrics were extracted to estimate forest AGB. Simple linear regression was conducted to assess the correlation between each LiDAR metric and forest AGB. Stepwise multiple regression analysis was then carried out to select important prediction metrics and establish the optimal forest AGB estimation model. Results indicated that both return energy profile and foliage profile based height-related metrics were strongly correlated to forest AGB. The energy weighted canopy height (HEweight) (R = 0.88) and foliage area weighted height (HFweight) (R = 0.89) all had the highest correlation coefficients with forest AGB in return energy profile metrics and foliage profile metrics respectively. Energy height percentiles and foliage height percentiles also had the ability to explain AGB variation. The energy-related metrics, foliage area-related metrics, and bounding volume-related metrics derived from the return energy profile and foliage profile were not all sensitive to forest AGB. This study also concluded that combining return energy profile metrics and foliage profile metrics could improve the accuracy of forest AGB estimation, and the optimal model contained the metrics of HFweight, HEweight, and VolumemaxHE, which is the product of the maximum canopy return energy profile amplitude (maxCE) and the maximum height of return energy profile (maxHE).

© 2017 Optical Society of America

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References

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2017 (1)

S. Nie, C. Wang, H. Zeng, X. Xi, and G. Li, “Above-ground biomass estimation using airborne discrete-return and full-waveform LiDAR data in a coniferous forest,” Ecol. Indic. 78, 221–228 (2017).
[Crossref]

2016 (1)

S. Nie, C. Wang, P. Dong, and X. Xi, “Estimating leaf area index of maize using airborne full-waveform lidar data,” Remote Sens. Lett. 7(2), 111–120 (2016).
[Crossref]

2015 (1)

W. Y. Yan, A. Shaker, and N. El-Ashmawy, “Urban land cover classification using airborne LiDAR data: A review,” Remote Sens. Environ. 158, 295–310 (2015).
[Crossref]

2014 (8)

L. Cao, N. Coops, T. Hermosilla, J. Innes, J. Dai, and G. She, “Using small-footprint discrete and full-waveform airborne LiDAR metrics to estimate total biomass and biomass components in subtropical forests,” Remote Sens. 6(8), 7110–7135 (2014).
[Crossref]

R. W. Kulawardhana, S. C. Popescu, and R. A. Feagin, “Fusion of lidar and multispectral data to quantify salt marsh carbon stocks,” Remote Sens. Environ. 154, 345–357 (2014).
[Crossref]

R. Sheridan, S. Popescu, D. Gatziolis, C. Morgan, and N.-W. Ku, “Modeling forest aboveground biomass and volume using airborne LiDAR metrics and forest inventory and analysis data in the Pacific Northwest,” Remote Sens. 7(1), 229–255 (2014).
[Crossref]

F. Pirotti, G. Laurin, A. Vettore, A. Masiero, and R. Valentini, “Small footprint full-waveform metrics contribution to the prediction of biomass in tropical forests,” Remote Sens. 6(10), 9576–9599 (2014).
[Crossref]

W. Li, Z. Niu, S. Gao, N. Huang, and H. Chen, “Correlating the horizontal and vertical distribution of LiDAR point clouds with components of biomass in a Picea crassifolia forest,” Forests 5(8), 1910–1930 (2014).
[Crossref]

F. Pirotti, G. V. Laurin, A. Vettore, A. Masiero, and R. Valentini, “Small footprint full-waveform metrics contribution to the prediction of biomass in tropical forests,” Remote Sens. 6(10), 9576–9599 (2014).
[Crossref]

L. Cao, N. C. Coops, T. Hermosilla, J. Innes, J. Dai, and G. She, “Using small-footprint discrete and full-waveform airborne LiDAR metrics to estimate total biomass and biomass components in subtropical forests,” Remote Sens. 6(8), 7110–7135 (2014).
[Crossref]

M. Dinh Ho Tong, T. Thuy Le, F. Rocca, S. Tebaldini, M. M. d’Alessandro, and L. Villard, “Relating P-band synthetic aperture radar tomography to tropical forest biomass,” IEEE Trans. Geosci. Remote Sens. 52(2), 967–979 (2014).
[Crossref]

2013 (4)

V. Kankare, M. Vastaranta, M. Holopainen, M. Räty, X. Yu, J. Hyyppä, H. Hyyppä, P. Alho, and R. Viitala, “Retrieval of forest aboveground biomass and stem volume with airborne scanning LiDAR,” Remote Sens. 5(5), 2257–2274 (2013).
[Crossref]

W. Huang, G. Sun, R. Dubayah, B. Cook, P. Montesano, W. Ni, and Z. Zhang, “Mapping biomass change after forest disturbance: Applying LiDAR footprint-derived models at key map scales,” Remote Sens. Environ. 134, 319–332 (2013).
[Crossref]

T. Allouis, S. Durrieu, C. Vega, and P. Couteron, “Stem volume and above-ground biomass estimation of individual pine trees from LiDAR data: contribution of full-Waveform signals,” IEEE J-STARTS 6, 924–934 (2013).

Q. He, E. Chen, R. An, and Y. Li, “Above-ground biomass and biomass components estimation using LiDAR data in a coniferous forest,” Forests 4(4), 984–1002 (2013).
[Crossref]

2012 (4)

A. Peduzzi, R. H. Wynne, T. R. Fox, R. F. Nelson, and V. A. Thomas, “Estimating leaf area index in intensively managed pine plantations using airborne laser scanner data,” For. Ecol. Manage. 270, 54–65 (2012).
[Crossref]

H. Tang, R. Dubayah, A. Swatantran, M. Hofton, S. Sheldon, D. B. Clark, and B. Blair, “Retrieval of vertical LAI profiles over tropical rain forests using waveform lidar at La Selva, Costa Rica,” Remote Sens. Environ. 124, 242–250 (2012).
[Crossref]

S. Eckert, “Improved forest biomass and carbon estimations using texture measures from WorldView-2 satellite data,” Remote Sens. 4(12), 810–829 (2012).
[Crossref]

O. W. Tsui, N. C. Coops, M. A. Wulder, P. L. Marshall, and A. McCardle, “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. 69, 121–133 (2012).
[Crossref]

2011 (3)

S. Englhart, V. Keuck, and F. Siegert, “Aboveground biomass retrieval in tropical forests - The potential of combined X- and L-band SAR data use,” Remote Sens. Environ. 115(5), 1260–1271 (2011).
[Crossref]

A. Swatantran, R. Dubayah, D. Roberts, M. Hofton, and J. B. Blair, “Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion,” Remote Sens. Environ. 115(11), 2917–2930 (2011).
[Crossref]

C. J. Gleason and J. Im, “A review of remote sensing of forest biomass and biofuel: options for small-area applications,” GIsci. Remote Sens. 48(2), 141–170 (2011).
[Crossref]

2010 (6)

A. Jochem, M. Hollaus, M. Rutzinger, and B. Höfle, “Estimation of aboveground biomass in alpine forests: a semi-empirical approach considering canopy transparency derived from airborne LiDAR data,” Sensors (Basel) 11(1), 278–295 (2010).
[Crossref] [PubMed]

M. García, D. Riano, E. Chuvieco, and F. M. Danson, “Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data,” Remote Sens. Environ. 114(4), 816–830 (2010).
[Crossref]

S. Solberg, R. Astrup, T. Gobakken, E. Naesset, and D. J. Weydahl, “Estimating spruce and pine biomass with interferometric X-band SAR,” Remote Sens. Environ. 114(10), 2353–2360 (2010).
[Crossref]

R. O. Dubayah, S. L. Sheldon, D. B. Clark, M. A. Hofton, J. B. Blair, G. C. Hurtt, and R. L. Chazdon, “Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica,” J. Geophys. Res. 115(G2), 933 (2010).
[Crossref]

L. A. Magruder, A. L. Neuenschwander, and S. P. Marmillion, “Lidar waveform stacking techniques for faint ground return extraction,” J. Appl. Remote Sens. 4(1), 043501 (2010).
[Crossref]

W. Ni-Meister, S. Lee, A. H. Strahler, C. E. Woodcock, C. Schaaf, T. Yao, K. J. Ranson, G. Sun, and J. B. Blair, “Assessing general relationships between aboveground biomass and vegetation structure parameters for improved carbon estimate from lidar remote sensing,” J. Geophys. Res. 115, 936 (2010).
[Crossref]

2009 (4)

J. J. Riggins, J. A. Tullis, and F. M. Stephen, “Per-segment aboveground forest biomass estimation using LiDAR-derived height percentile statistics,” GIsci. Remote Sens. 46(2), 232–248 (2009).
[Crossref]

X. Li, X. Li, Z. Li, M. Ma, J. Wang, Q. Xiao, Q. Liu, T. Che, E. Chen, G. Yan, Z. Hu, L. Zhang, R. Chu, P. Su, Q. Liu, S. Liu, J. Wang, Z. Niu, Y. Chen, R. Jin, W. Wang, Y. Ran, X. Xin, and H. Ren, “Watershed allied telemetry experimental research,” J. Geophys. Res. 114, D22103 (2009).
[Crossref]

G. Zheng and L. M. Moskal, “Retrieving leaf area index (LAI) using remote sensing: theories, methods and sensors,” Sensors (Basel) 9(4), 2719–2745 (2009).
[Crossref] [PubMed]

K. G. Zhao, S. Popescu, and R. Nelson, “Lidar remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers,” Remote Sens. Environ. 113(1), 182–196 (2009).
[Crossref]

2008 (3)

G. Sun, K. J. Ranson, D. S. Kimes, J. B. Blair, and K. Kovacs, “Forest vertical structure from GLAS: An evaluation using LVIS and SRTM data,” Remote Sens. Environ. 112(1), 107–117 (2008).
[Crossref]

V. H. Duong, R. Lindenbergh, N. Pfeifer, and G. Vosselman, “Single and two epoch analysis of ICESat full waveform data over forested areas,” Int. J. Remote Sens. 29(5), 1453–1473 (2008).
[Crossref]

M. A. Brovelli, M. Crespi, F. Fratarcangeli, F. Giannone, and E. Realini, “Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method,” ISPRS J. Photogramm. 63(4), 427–440 (2008).
[Crossref]

2007 (3)

M. A. Cho, A. Skidmore, F. Corsi, S. E. van Wieren, and I. Sobhan, “Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression,” Int. J. Appl. Earth Obs. 9(4), 414–424 (2007).
[Crossref]

K. Tan, S. L. Piao, C. H. Peng, and J. Y. Fang, “Satellite-based estimation of biomass carbon stocks for northeast China’s forests between 1982 and 1999,” For. Ecol. Manage. 240(1-3), 114–121 (2007).
[Crossref]

C. Mallet and F. Bretar, “Full waveform topographic lidar: state-of-the-art,” Trait. Signal 24, 385–409 (2007).

2006 (2)

D. S. Lu, “The potential and challenge of remote sensing-based biomass estimation,” Int. J. Remote Sens. 27(7), 1297–1328 (2006).
[Crossref]

W. Wagner, A. Ullrich, V. Ducic, T. Melzer, and N. Studnicka, “Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner,” ISPRS J. Photogramm 60(2), 100–112 (2006).
[Crossref]

2005 (2)

J. M. Chen, C. H. Menges, and S. G. Leblanc, “Global mapping of foliage clumping index using multi-angular satellite data,” Remote Sens. Environ. 97(4), 447–457 (2005).
[Crossref]

T. Neeff, L. V. Dutra, J. R. dos Santos, C. D. Freitas, and L. S. Araujo, “Tropical forest measurement by interferometric height modeling and P-band radar backscatter,” For. Sci. 51, 585–594 (2005).

2004 (1)

K. S. Lim and P. M. Treitz, “Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators,” Scand. J. For. Res. 19(6), 558–570 (2004).
[Crossref]

2003 (1)

P. M. Hansen and J. K. Schjoerring, “Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression,” Remote Sens. Environ. 86(4), 542–553 (2003).
[Crossref]

2002 (2)

M. A. Lefsky, W. B. Cohen, G. G. Parker, and D. J. Harding, “Lidar remote sensing for ecosystem studies,” Bioscience 52(1), 19–30 (2002).
[Crossref]

J. B. Drake, R. O. Dubayah, R. G. Knox, D. B. Clark, and J. B. Blair, “Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest,” Remote Sens. Environ. 81(2-3), 378–392 (2002).
[Crossref]

1999 (2)

T. Nilson, “Inversion of gap frequency data in forest stands,” Agric. For. Meteorol. 98, 437–448 (1999).
[Crossref]

M. A. Lefsky, D. Harding, W. B. Cohen, G. Parker, and H. H. Shugart, “Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA,” Remote Sens. Environ. 67(1), 83–98 (1999).
[Crossref]

1998 (1)

J. Wang and K. Ju, “Study on biomass of water conservation forest on north slope of Qilian mountains,” J. Fujian Coll. For. 18, 319–323 (1998).

1997 (2)

E. S. Kasischke, J. M. Melack, and M. C. Dobson, “The use of imaging radars for ecological applications - A review,” Remote Sens. Environ. 59(2), 141–156 (1997).
[Crossref]

J. M. Chen, P. M. Rich, S. T. Gower, J. M. Norman, and S. Plummer, “Leaf area index of boreal forests: Theory, techniques, and measurements,” J. Geophys. Res. 102(D24), 29429–29443 (1997).
[Crossref]

1996 (1)

M. Nilsson, “Estimation of tree heights and stand volume using an airborne lidar system,” Remote Sens. Environ. 56(1), 1–7 (1996).
[Crossref]

1995 (1)

1993 (1)

R. K. Dixon, K. J. Andrasko, F. G. Sussman, M. A. Lavinson, M. C. Trexler, and T. S. Vinson, “Forest sector carbon offset projects-near-term opportunities to mitigate greenhouse-gas emissions,” Water Air Soil Pollut. 70(1-4), 561–577 (1993).
[Crossref]

1992 (1)

T. Le Toan and A. Beaudoin, “Relating forest biomass to SAR data,” IEEE Trans. Geosci. Rem. Sens. 30, 403-411 (1992).

1982 (1)

W. M. Post, W. R. Emanuel, P. J. Zinke, and A. G. Stangenberger, “Soil carbon pools and world life zones,” Nature 298(5870), 156–159 (1982).
[Crossref]

Alho, P.

V. Kankare, M. Vastaranta, M. Holopainen, M. Räty, X. Yu, J. Hyyppä, H. Hyyppä, P. Alho, and R. Viitala, “Retrieval of forest aboveground biomass and stem volume with airborne scanning LiDAR,” Remote Sens. 5(5), 2257–2274 (2013).
[Crossref]

Allouis, T.

T. Allouis, S. Durrieu, C. Vega, and P. Couteron, “Stem volume and above-ground biomass estimation of individual pine trees from LiDAR data: contribution of full-Waveform signals,” IEEE J-STARTS 6, 924–934 (2013).

An, R.

Q. He, E. Chen, R. An, and Y. Li, “Above-ground biomass and biomass components estimation using LiDAR data in a coniferous forest,” Forests 4(4), 984–1002 (2013).
[Crossref]

Andrasko, K. J.

R. K. Dixon, K. J. Andrasko, F. G. Sussman, M. A. Lavinson, M. C. Trexler, and T. S. Vinson, “Forest sector carbon offset projects-near-term opportunities to mitigate greenhouse-gas emissions,” Water Air Soil Pollut. 70(1-4), 561–577 (1993).
[Crossref]

Araujo, L. S.

T. Neeff, L. V. Dutra, J. R. dos Santos, C. D. Freitas, and L. S. Araujo, “Tropical forest measurement by interferometric height modeling and P-band radar backscatter,” For. Sci. 51, 585–594 (2005).

Astrup, R.

S. Solberg, R. Astrup, T. Gobakken, E. Naesset, and D. J. Weydahl, “Estimating spruce and pine biomass with interferometric X-band SAR,” Remote Sens. Environ. 114(10), 2353–2360 (2010).
[Crossref]

Beaudoin, A.

T. Le Toan and A. Beaudoin, “Relating forest biomass to SAR data,” IEEE Trans. Geosci. Rem. Sens. 30, 403-411 (1992).

Blair, B.

H. Tang, R. Dubayah, A. Swatantran, M. Hofton, S. Sheldon, D. B. Clark, and B. Blair, “Retrieval of vertical LAI profiles over tropical rain forests using waveform lidar at La Selva, Costa Rica,” Remote Sens. Environ. 124, 242–250 (2012).
[Crossref]

Blair, J. B.

A. Swatantran, R. Dubayah, D. Roberts, M. Hofton, and J. B. Blair, “Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion,” Remote Sens. Environ. 115(11), 2917–2930 (2011).
[Crossref]

R. O. Dubayah, S. L. Sheldon, D. B. Clark, M. A. Hofton, J. B. Blair, G. C. Hurtt, and R. L. Chazdon, “Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica,” J. Geophys. Res. 115(G2), 933 (2010).
[Crossref]

W. Ni-Meister, S. Lee, A. H. Strahler, C. E. Woodcock, C. Schaaf, T. Yao, K. J. Ranson, G. Sun, and J. B. Blair, “Assessing general relationships between aboveground biomass and vegetation structure parameters for improved carbon estimate from lidar remote sensing,” J. Geophys. Res. 115, 936 (2010).
[Crossref]

G. Sun, K. J. Ranson, D. S. Kimes, J. B. Blair, and K. Kovacs, “Forest vertical structure from GLAS: An evaluation using LVIS and SRTM data,” Remote Sens. Environ. 112(1), 107–117 (2008).
[Crossref]

J. B. Drake, R. O. Dubayah, R. G. Knox, D. B. Clark, and J. B. Blair, “Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest,” Remote Sens. Environ. 81(2-3), 378–392 (2002).
[Crossref]

Bretar, F.

C. Mallet and F. Bretar, “Full waveform topographic lidar: state-of-the-art,” Trait. Signal 24, 385–409 (2007).

Brovelli, M. A.

M. A. Brovelli, M. Crespi, F. Fratarcangeli, F. Giannone, and E. Realini, “Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method,” ISPRS J. Photogramm. 63(4), 427–440 (2008).
[Crossref]

Cao, L.

L. Cao, N. Coops, T. Hermosilla, J. Innes, J. Dai, and G. She, “Using small-footprint discrete and full-waveform airborne LiDAR metrics to estimate total biomass and biomass components in subtropical forests,” Remote Sens. 6(8), 7110–7135 (2014).
[Crossref]

L. Cao, N. C. Coops, T. Hermosilla, J. Innes, J. Dai, and G. She, “Using small-footprint discrete and full-waveform airborne LiDAR metrics to estimate total biomass and biomass components in subtropical forests,” Remote Sens. 6(8), 7110–7135 (2014).
[Crossref]

Chazdon, R. L.

R. O. Dubayah, S. L. Sheldon, D. B. Clark, M. A. Hofton, J. B. Blair, G. C. Hurtt, and R. L. Chazdon, “Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica,” J. Geophys. Res. 115(G2), 933 (2010).
[Crossref]

Che, T.

X. Li, X. Li, Z. Li, M. Ma, J. Wang, Q. Xiao, Q. Liu, T. Che, E. Chen, G. Yan, Z. Hu, L. Zhang, R. Chu, P. Su, Q. Liu, S. Liu, J. Wang, Z. Niu, Y. Chen, R. Jin, W. Wang, Y. Ran, X. Xin, and H. Ren, “Watershed allied telemetry experimental research,” J. Geophys. Res. 114, D22103 (2009).
[Crossref]

Chen, E.

Q. He, E. Chen, R. An, and Y. Li, “Above-ground biomass and biomass components estimation using LiDAR data in a coniferous forest,” Forests 4(4), 984–1002 (2013).
[Crossref]

X. Li, X. Li, Z. Li, M. Ma, J. Wang, Q. Xiao, Q. Liu, T. Che, E. Chen, G. Yan, Z. Hu, L. Zhang, R. Chu, P. Su, Q. Liu, S. Liu, J. Wang, Z. Niu, Y. Chen, R. Jin, W. Wang, Y. Ran, X. Xin, and H. Ren, “Watershed allied telemetry experimental research,” J. Geophys. Res. 114, D22103 (2009).
[Crossref]

Chen, H.

W. Li, Z. Niu, S. Gao, N. Huang, and H. Chen, “Correlating the horizontal and vertical distribution of LiDAR point clouds with components of biomass in a Picea crassifolia forest,” Forests 5(8), 1910–1930 (2014).
[Crossref]

Chen, J. M.

J. M. Chen, C. H. Menges, and S. G. Leblanc, “Global mapping of foliage clumping index using multi-angular satellite data,” Remote Sens. Environ. 97(4), 447–457 (2005).
[Crossref]

J. M. Chen, P. M. Rich, S. T. Gower, J. M. Norman, and S. Plummer, “Leaf area index of boreal forests: Theory, techniques, and measurements,” J. Geophys. Res. 102(D24), 29429–29443 (1997).
[Crossref]

J. M. Chen and J. Cihlar, “Plant canopy gap-size analysis theory for improving optical measurements of leaf-area index,” Appl. Opt. 34(27), 6211–6222 (1995).
[Crossref] [PubMed]

Chen, Y.

X. Li, X. Li, Z. Li, M. Ma, J. Wang, Q. Xiao, Q. Liu, T. Che, E. Chen, G. Yan, Z. Hu, L. Zhang, R. Chu, P. Su, Q. Liu, S. Liu, J. Wang, Z. Niu, Y. Chen, R. Jin, W. Wang, Y. Ran, X. Xin, and H. Ren, “Watershed allied telemetry experimental research,” J. Geophys. Res. 114, D22103 (2009).
[Crossref]

Cho, M. A.

M. A. Cho, A. Skidmore, F. Corsi, S. E. van Wieren, and I. Sobhan, “Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression,” Int. J. Appl. Earth Obs. 9(4), 414–424 (2007).
[Crossref]

Chu, R.

X. Li, X. Li, Z. Li, M. Ma, J. Wang, Q. Xiao, Q. Liu, T. Che, E. Chen, G. Yan, Z. Hu, L. Zhang, R. Chu, P. Su, Q. Liu, S. Liu, J. Wang, Z. Niu, Y. Chen, R. Jin, W. Wang, Y. Ran, X. Xin, and H. Ren, “Watershed allied telemetry experimental research,” J. Geophys. Res. 114, D22103 (2009).
[Crossref]

Chuvieco, E.

M. García, D. Riano, E. Chuvieco, and F. M. Danson, “Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data,” Remote Sens. Environ. 114(4), 816–830 (2010).
[Crossref]

Cihlar, J.

Clark, D. B.

H. Tang, R. Dubayah, A. Swatantran, M. Hofton, S. Sheldon, D. B. Clark, and B. Blair, “Retrieval of vertical LAI profiles over tropical rain forests using waveform lidar at La Selva, Costa Rica,” Remote Sens. Environ. 124, 242–250 (2012).
[Crossref]

R. O. Dubayah, S. L. Sheldon, D. B. Clark, M. A. Hofton, J. B. Blair, G. C. Hurtt, and R. L. Chazdon, “Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica,” J. Geophys. Res. 115(G2), 933 (2010).
[Crossref]

J. B. Drake, R. O. Dubayah, R. G. Knox, D. B. Clark, and J. B. Blair, “Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest,” Remote Sens. Environ. 81(2-3), 378–392 (2002).
[Crossref]

Cohen, W. B.

M. A. Lefsky, W. B. Cohen, G. G. Parker, and D. J. Harding, “Lidar remote sensing for ecosystem studies,” Bioscience 52(1), 19–30 (2002).
[Crossref]

M. A. Lefsky, D. Harding, W. B. Cohen, G. Parker, and H. H. Shugart, “Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA,” Remote Sens. Environ. 67(1), 83–98 (1999).
[Crossref]

Cook, B.

W. Huang, G. Sun, R. Dubayah, B. Cook, P. Montesano, W. Ni, and Z. Zhang, “Mapping biomass change after forest disturbance: Applying LiDAR footprint-derived models at key map scales,” Remote Sens. Environ. 134, 319–332 (2013).
[Crossref]

Coops, N.

L. Cao, N. Coops, T. Hermosilla, J. Innes, J. Dai, and G. She, “Using small-footprint discrete and full-waveform airborne LiDAR metrics to estimate total biomass and biomass components in subtropical forests,” Remote Sens. 6(8), 7110–7135 (2014).
[Crossref]

Coops, N. C.

L. Cao, N. C. Coops, T. Hermosilla, J. Innes, J. Dai, and G. She, “Using small-footprint discrete and full-waveform airborne LiDAR metrics to estimate total biomass and biomass components in subtropical forests,” Remote Sens. 6(8), 7110–7135 (2014).
[Crossref]

O. W. Tsui, N. C. Coops, M. A. Wulder, P. L. Marshall, and A. McCardle, “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. 69, 121–133 (2012).
[Crossref]

Corsi, F.

M. A. Cho, A. Skidmore, F. Corsi, S. E. van Wieren, and I. Sobhan, “Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression,” Int. J. Appl. Earth Obs. 9(4), 414–424 (2007).
[Crossref]

Couteron, P.

T. Allouis, S. Durrieu, C. Vega, and P. Couteron, “Stem volume and above-ground biomass estimation of individual pine trees from LiDAR data: contribution of full-Waveform signals,” IEEE J-STARTS 6, 924–934 (2013).

Crespi, M.

M. A. Brovelli, M. Crespi, F. Fratarcangeli, F. Giannone, and E. Realini, “Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method,” ISPRS J. Photogramm. 63(4), 427–440 (2008).
[Crossref]

d’Alessandro, M. M.

M. Dinh Ho Tong, T. Thuy Le, F. Rocca, S. Tebaldini, M. M. d’Alessandro, and L. Villard, “Relating P-band synthetic aperture radar tomography to tropical forest biomass,” IEEE Trans. Geosci. Remote Sens. 52(2), 967–979 (2014).
[Crossref]

Dai, J.

L. Cao, N. C. Coops, T. Hermosilla, J. Innes, J. Dai, and G. She, “Using small-footprint discrete and full-waveform airborne LiDAR metrics to estimate total biomass and biomass components in subtropical forests,” Remote Sens. 6(8), 7110–7135 (2014).
[Crossref]

L. Cao, N. Coops, T. Hermosilla, J. Innes, J. Dai, and G. She, “Using small-footprint discrete and full-waveform airborne LiDAR metrics to estimate total biomass and biomass components in subtropical forests,” Remote Sens. 6(8), 7110–7135 (2014).
[Crossref]

Danson, F. M.

M. García, D. Riano, E. Chuvieco, and F. M. Danson, “Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data,” Remote Sens. Environ. 114(4), 816–830 (2010).
[Crossref]

Dinh Ho Tong, M.

M. Dinh Ho Tong, T. Thuy Le, F. Rocca, S. Tebaldini, M. M. d’Alessandro, and L. Villard, “Relating P-band synthetic aperture radar tomography to tropical forest biomass,” IEEE Trans. Geosci. Remote Sens. 52(2), 967–979 (2014).
[Crossref]

Dixon, R. K.

R. K. Dixon, K. J. Andrasko, F. G. Sussman, M. A. Lavinson, M. C. Trexler, and T. S. Vinson, “Forest sector carbon offset projects-near-term opportunities to mitigate greenhouse-gas emissions,” Water Air Soil Pollut. 70(1-4), 561–577 (1993).
[Crossref]

Dobson, M. C.

E. S. Kasischke, J. M. Melack, and M. C. Dobson, “The use of imaging radars for ecological applications - A review,” Remote Sens. Environ. 59(2), 141–156 (1997).
[Crossref]

Dong, P.

S. Nie, C. Wang, P. Dong, and X. Xi, “Estimating leaf area index of maize using airborne full-waveform lidar data,” Remote Sens. Lett. 7(2), 111–120 (2016).
[Crossref]

dos Santos, J. R.

T. Neeff, L. V. Dutra, J. R. dos Santos, C. D. Freitas, and L. S. Araujo, “Tropical forest measurement by interferometric height modeling and P-band radar backscatter,” For. Sci. 51, 585–594 (2005).

Drake, J. B.

J. B. Drake, R. O. Dubayah, R. G. Knox, D. B. Clark, and J. B. Blair, “Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest,” Remote Sens. Environ. 81(2-3), 378–392 (2002).
[Crossref]

Dubayah, R.

W. Huang, G. Sun, R. Dubayah, B. Cook, P. Montesano, W. Ni, and Z. Zhang, “Mapping biomass change after forest disturbance: Applying LiDAR footprint-derived models at key map scales,” Remote Sens. Environ. 134, 319–332 (2013).
[Crossref]

H. Tang, R. Dubayah, A. Swatantran, M. Hofton, S. Sheldon, D. B. Clark, and B. Blair, “Retrieval of vertical LAI profiles over tropical rain forests using waveform lidar at La Selva, Costa Rica,” Remote Sens. Environ. 124, 242–250 (2012).
[Crossref]

A. Swatantran, R. Dubayah, D. Roberts, M. Hofton, and J. B. Blair, “Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion,” Remote Sens. Environ. 115(11), 2917–2930 (2011).
[Crossref]

Dubayah, R. O.

R. O. Dubayah, S. L. Sheldon, D. B. Clark, M. A. Hofton, J. B. Blair, G. C. Hurtt, and R. L. Chazdon, “Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica,” J. Geophys. Res. 115(G2), 933 (2010).
[Crossref]

J. B. Drake, R. O. Dubayah, R. G. Knox, D. B. Clark, and J. B. Blair, “Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest,” Remote Sens. Environ. 81(2-3), 378–392 (2002).
[Crossref]

Ducic, V.

W. Wagner, A. Ullrich, V. Ducic, T. Melzer, and N. Studnicka, “Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner,” ISPRS J. Photogramm 60(2), 100–112 (2006).
[Crossref]

Duong, V. H.

V. H. Duong, R. Lindenbergh, N. Pfeifer, and G. Vosselman, “Single and two epoch analysis of ICESat full waveform data over forested areas,” Int. J. Remote Sens. 29(5), 1453–1473 (2008).
[Crossref]

Durrieu, S.

T. Allouis, S. Durrieu, C. Vega, and P. Couteron, “Stem volume and above-ground biomass estimation of individual pine trees from LiDAR data: contribution of full-Waveform signals,” IEEE J-STARTS 6, 924–934 (2013).

Dutra, L. V.

T. Neeff, L. V. Dutra, J. R. dos Santos, C. D. Freitas, and L. S. Araujo, “Tropical forest measurement by interferometric height modeling and P-band radar backscatter,” For. Sci. 51, 585–594 (2005).

Eckert, S.

S. Eckert, “Improved forest biomass and carbon estimations using texture measures from WorldView-2 satellite data,” Remote Sens. 4(12), 810–829 (2012).
[Crossref]

El-Ashmawy, N.

W. Y. Yan, A. Shaker, and N. El-Ashmawy, “Urban land cover classification using airborne LiDAR data: A review,” Remote Sens. Environ. 158, 295–310 (2015).
[Crossref]

Emanuel, W. R.

W. M. Post, W. R. Emanuel, P. J. Zinke, and A. G. Stangenberger, “Soil carbon pools and world life zones,” Nature 298(5870), 156–159 (1982).
[Crossref]

Englhart, S.

S. Englhart, V. Keuck, and F. Siegert, “Aboveground biomass retrieval in tropical forests - The potential of combined X- and L-band SAR data use,” Remote Sens. Environ. 115(5), 1260–1271 (2011).
[Crossref]

Fang, J. Y.

K. Tan, S. L. Piao, C. H. Peng, and J. Y. Fang, “Satellite-based estimation of biomass carbon stocks for northeast China’s forests between 1982 and 1999,” For. Ecol. Manage. 240(1-3), 114–121 (2007).
[Crossref]

Feagin, R. A.

R. W. Kulawardhana, S. C. Popescu, and R. A. Feagin, “Fusion of lidar and multispectral data to quantify salt marsh carbon stocks,” Remote Sens. Environ. 154, 345–357 (2014).
[Crossref]

Fox, T. R.

A. Peduzzi, R. H. Wynne, T. R. Fox, R. F. Nelson, and V. A. Thomas, “Estimating leaf area index in intensively managed pine plantations using airborne laser scanner data,” For. Ecol. Manage. 270, 54–65 (2012).
[Crossref]

Fratarcangeli, F.

M. A. Brovelli, M. Crespi, F. Fratarcangeli, F. Giannone, and E. Realini, “Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method,” ISPRS J. Photogramm. 63(4), 427–440 (2008).
[Crossref]

Freitas, C. D.

T. Neeff, L. V. Dutra, J. R. dos Santos, C. D. Freitas, and L. S. Araujo, “Tropical forest measurement by interferometric height modeling and P-band radar backscatter,” For. Sci. 51, 585–594 (2005).

Gao, S.

W. Li, Z. Niu, S. Gao, N. Huang, and H. Chen, “Correlating the horizontal and vertical distribution of LiDAR point clouds with components of biomass in a Picea crassifolia forest,” Forests 5(8), 1910–1930 (2014).
[Crossref]

García, M.

M. García, D. Riano, E. Chuvieco, and F. M. Danson, “Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data,” Remote Sens. Environ. 114(4), 816–830 (2010).
[Crossref]

Gatziolis, D.

R. Sheridan, S. Popescu, D. Gatziolis, C. Morgan, and N.-W. Ku, “Modeling forest aboveground biomass and volume using airborne LiDAR metrics and forest inventory and analysis data in the Pacific Northwest,” Remote Sens. 7(1), 229–255 (2014).
[Crossref]

Giannone, F.

M. A. Brovelli, M. Crespi, F. Fratarcangeli, F. Giannone, and E. Realini, “Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method,” ISPRS J. Photogramm. 63(4), 427–440 (2008).
[Crossref]

Gleason, C. J.

C. J. Gleason and J. Im, “A review of remote sensing of forest biomass and biofuel: options for small-area applications,” GIsci. Remote Sens. 48(2), 141–170 (2011).
[Crossref]

Gobakken, T.

S. Solberg, R. Astrup, T. Gobakken, E. Naesset, and D. J. Weydahl, “Estimating spruce and pine biomass with interferometric X-band SAR,” Remote Sens. Environ. 114(10), 2353–2360 (2010).
[Crossref]

Gower, S. T.

J. M. Chen, P. M. Rich, S. T. Gower, J. M. Norman, and S. Plummer, “Leaf area index of boreal forests: Theory, techniques, and measurements,” J. Geophys. Res. 102(D24), 29429–29443 (1997).
[Crossref]

Hajnsek, I.

T. Mette, K. Papathanassiou, and I. Hajnsek, Biomass estimation from polarimetric SAR interferometry over heterogeneous forest terrain, 2004 IEEE Int. Geosci. Rem. Sens. Symp. (2004), pp. 511–514.
[Crossref]

Hansen, P. M.

P. M. Hansen and J. K. Schjoerring, “Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression,” Remote Sens. Environ. 86(4), 542–553 (2003).
[Crossref]

Harding, D.

M. A. Lefsky, D. Harding, W. B. Cohen, G. Parker, and H. H. Shugart, “Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA,” Remote Sens. Environ. 67(1), 83–98 (1999).
[Crossref]

Harding, D. J.

M. A. Lefsky, W. B. Cohen, G. G. Parker, and D. J. Harding, “Lidar remote sensing for ecosystem studies,” Bioscience 52(1), 19–30 (2002).
[Crossref]

He, Q.

Q. He, E. Chen, R. An, and Y. Li, “Above-ground biomass and biomass components estimation using LiDAR data in a coniferous forest,” Forests 4(4), 984–1002 (2013).
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Figures (8)

Fig. 1
Fig. 1 Airborne ortho charged coupled device (CCD) image of the study area and the distribution of field plots.
Fig. 2
Fig. 2 Technical flow chart of the methods applied in this research.
Fig. 3
Fig. 3 The sample waveforms: (a) original waveform; (b) de-noised waveform; (c) smoothed waveform.
Fig. 4
Fig. 4 The sample small-footprint waveforms (a) and the aggregated pseudo waveform (b) of one plot.
Fig. 5
Fig. 5 The pseudo large waveforms of the 35 plots.
Fig. 6
Fig. 6 Representations of several metrics extracted from the return energy profile (a) and from the foliage profile (b).
Fig. 7
Fig. 7 The correlation coefficient (R) of each LiDAR metric and field-measured AGB.
Fig. 8
Fig. 8 Field-measured AGB vs. predicted AGB: (a) from the return energy profile metrics derived model (Eq. (14)); (b) from the foliage profile metrics derived model (Eq. (15)); (c) from the combo model (Eq. (16)).

Tables (2)

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Table 1 LiDAR metrics used for forest AGB estimation.

Tables Icon

Table 2 Results of the forest AGB estimation models using a single LiDAR metric.

Equations (22)

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s t e m b i o m a s s = 0.0478 × ( D 2 × H ) 0.8665
b r a n c h b i o m a s s = 0.0061 × ( D 2 × H ) 0.8905
l e a f b i o m a s s = 0.2650 × ( D 2 × H ) 0.4701
f r u i t b i o m a s s = 0.0342 × ( D 2 × H ) 0.5779
V N ( i ) = V i V T with V T = i = 1 N V i
P ( θ ) = e G ( θ ) L A I cos ( θ )
f cov e r ( z ) = E v ( z ) E v ( 0 ) + ρ v ρ g E g
L A I c u m ( z ) = log ( 1 f cov e r ( z ) ) G * Ω
f o l i a g e _ p r o f i l e ( z ) = d L A I c u m ( z ) d z
R C E _ T E = t o t a l C E t o t a l E
H E w e i g h t = i = 1 N ( H i × C E i ) t o t a l C E
V o l u m e max H E = max C E × max H E
V o l u m e H E w e i g h t = max C E × H E w e i g h t
H F w e i g h t = i = 1 N ( H i × F i ) t o t a l F
V o l u m e max H F = max C F × max H F
V o l u m e H F w e i g h t = max C F × H F w e i g h t
R = i = 1 n ( x i x ¯ ) ( y i y ¯ ) i = 1 n ( x i x ¯ ) 2 i = 1 n ( y i y ¯ ) 2
R M S E L O O C V = 1 n i = 1 n ( x i y i ) 2
B I A S L O O C V = 1 n i = 1 n ( x i y i )
AGB = 3 . 555 + 23 . 49 0 * H Eweight 11 .0 3 0 * EH75
AGB = 19 .0 92 + 1 . 288 * H Fweight
AGB = 29 .0 61 + 4 .0 27 * H Fweight + 0.0 11 * Volume maxHE 26 . 63 * H Eweight

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