Abstract

Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R2 = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m2), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data.

© 2016 Optical Society of America

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References

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2015 (6)

S. Luo, C. Wang, X. Xi, S. Nie, S. Xia, and W. Yiping, “Forest leaf area index estimation using combined ICESat/GLAS and optical remote sensing image,” J. Inf. Millim. Waves 34(2), 243–249 (2015).

Y. Qin, S. Li, T.-T. Vu, Z. Niu, and Y. Ban, “Synergistic application of geometric and radiometric features of LiDAR data for urban land cover mapping,” Opt. Express 23(11), 13761–13775 (2015).
[Crossref] [PubMed]

W. Li, Z. Niu, N. Huang, C. Wang, S. Gao, and C. Wu, “Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China,” Ecol. Indic. 57, 486–496 (2015).
[Crossref]

P. Wilkes, S. D. Jones, L. Suarez, A. Haywood, W. Woodgate, M. Soto-Berelov, A. Mellor, and A. K. Skidmore, “Understanding the effects of ALS pulse density for metric retrieval across diverse forest types,” Photogramm. Eng. Remote Sensing 81(8), 625–635 (2015).
[Crossref]

K. K. Singh, G. Chen, J. B. McCarter, and R. K. Meentemeyer, “Effects of LiDAR point density and landscape context on estimates of urban forest biomass,” ISPRS J. Photogramm. Remote Sens. 101, 310–322 (2015).
[Crossref]

S. Luo, C. Wang, F. Pan, X. Xi, G. Li, S. Nie, and S. Xia, “Estimation of wetland vegetation height and leaf area index using airborne laser scanning data,” Ecol. Indic. 48, 550–559 (2015).
[Crossref]

2014 (7)

S. Luo, C. Wang, X. Xi, and F. Pan, “Estimating FPAR of maize canopy using airborne discrete-return LiDAR data,” Opt. Express 22(5), 5106–5117 (2014).
[Crossref] [PubMed]

G. W. Sileshi, “A critical review of forest biomass estimation models, common mistakes and corrective measures,” For. Ecol. Manage. 329, 237–254 (2014).
[Crossref]

M. Watt, A. Meredith, P. Watt, and A. Gunn, “The influence of LiDAR pulse density on the precision of inventory metrics in young unthinned Douglas-fir stands during initial and subsequent LiDAR acquisitions,” N. Z. J. For. Sci. 44(1), 1–9 (2014).
[Crossref]

L. Ruiz, T. Hermosilla, F. Mauro, and M. Godino, “Analysis of the influence of plot size and LiDAR density on forest structure attribute estimates,” Forests 5(5), 936–951 (2014).
[Crossref]

N. Tilly, D. Hoffmeister, Q. Cao, S. Huang, V. Lenz-Wiedemann, Y. Miao, and G. Bareth, “Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice,” J. Appl. Remote Sens. 8(1), 083671 (2014).
[Crossref]

J. U. H. Eitel, T. S. Magney, L. A. Vierling, T. T. Brown, and D. R. Huggins, “LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status,” Field Crops Res. 159, 21–32 (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]

2013 (6)

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]

C. Atzberger, “Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs,” Remote Sens. 5(2), 949–981 (2013).
[Crossref]

S. Gao, Z. Niu, N. Huang, and X. Hou, “Estimating the leaf area index, height and biomass of maize using HJ-1 and RADARSAT-2,” Int. J. Appl. Earth Obs. Geoinf. 24, 1–8 (2013).
[Crossref]

G. Pope and P. Treitz, “Leaf area index (LAI) Estimation in boreal mixedwood forest of Ontario, Canada using light detection and ranging (LiDAR) and worldview-2 imagery,” Remote Sens. 5(10), 5040–5063 (2013).
[Crossref]

M. K. Jakubowski, Q. Guo, and M. Kelly, “Tradeoffs between lidar pulse density and forest measurement accuracy,” Remote Sens. Environ. 130, 245–253 (2013).
[Crossref]

X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
[Crossref]

2012 (8)

E. Luedeling and A. Gassner, “Partial least squares regression for analyzing walnut phenology in California,” Agric. For. Meteorol. 158–159, 43–52 (2012).
[Crossref]

J. Strunk, H. Temesgen, H.-E. Andersen, J. P. Flewelling, and L. Madsen, “Effects of lidar pulse density and sample size on a model-assisted approach to estimate forest inventory variables,” Can. J. Rem. Sens. 38(5), 644–654 (2012).
[Crossref]

L. Zhang and T. E. Grift, “A LIDAR-based crop height measurement system for Miscanthus giganteus,” Comput. Electron. Agric. 85, 70–76 (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. Remote Sens. 69, 121–133 (2012).
[Crossref]

F. Zhao, Q. Guo, and M. Kelly, “Allometric equation choice impacts lidar-based forest biomass estimates: a case study from the Sierra National Forest, CA,” Agric. For. Meteorol. 165, 64–72 (2012).
[Crossref]

T. Hakala, J. Suomalainen, S. Kaasalainen, and Y. Chen, “Full waveform hyperspectral LiDAR for terrestrial laser scanning,” Opt. Express 20(7), 7119–7127 (2012).
[Crossref] [PubMed]

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]

Q. Chen, G. Vaglio Laurin, J. J. Battles, and D. Saah, “Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass,” Remote Sens. Environ. 121, 108–117 (2012).
[Crossref]

2011 (7)

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]

S. C. Popescu, K. Zhao, A. Neuenschwander, and C. Lin, “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(11), 2786–2797 (2011).
[Crossref]

N. F. Glenn, L. P. Spaete, T. T. Sankey, D. R. Derryberry, S. P. Hardegree, and J. J. Mitchell, “Errors in LiDAR-derived shrub height and crown area on sloped terrain,” J. Arid Environ. 75(4), 377–382 (2011).
[Crossref]

C. Edson and M. G. Wing, “Airborne light detection and ranging (LiDAR) for individual tree stem location, height, and biomass measurements,” Remote Sens. 3(12), 2494–2528 (2011).
[Crossref]

G. W. Frazer, S. Magnussen, M. A. Wulder, and K. O. Niemann, “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(2), 636–649 (2011).
[Crossref]

A. C. Morel, S. S. Saatchi, Y. Malhi, N. J. Berry, L. Banin, D. Burslem, R. Nilus, and R. C. Ong, “Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data,” For. Ecol. Manage. 262(9), 1786–1798 (2011).
[Crossref]

J. Estornell, L. A. Ruiz, B. Velázquez-Martí, and A. Fernández-Sarría, “Estimation of shrub biomass by airborne LiDAR data in small forest stands,” For. Ecol. Manage. 262(9), 1697–1703 (2011).
[Crossref]

2010 (2)

S. Solberg, “Mapping gap fraction, LAI and defoliation using various ALS penetration variables,” Int. J. Remote Sens. 31(5), 1227–1244 (2010).
[Crossref]

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 11(1), 278–295 (2010).
[Crossref] [PubMed]

2009 (4)

J. Chen, S. Gu, M. Shen, Y. Tang, and B. Matsushita, “Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data,” Int. J. Remote Sens. 30(24), 6497–6517 (2009).
[Crossref]

C. Wang, M. Menenti, M. P. Stoll, A. Feola, E. Belluco, and M. Marani, “Separation of ground and low vegetation signatures in LiDAR measurements of salt-marsh environments,” IEEE Trans. Geosci. Rem. Sens. 47(7), 2014–2023 (2009).
[Crossref]

J. J. Richardson, L. M. Moskal, and S.-H. Kim, “Modeling approaches to estimate effective leaf area index from aerial discrete-return LIDAR,” Agric. For. Meteorol. 149(6–7), 1152–1160 (2009).
[Crossref]

K. Zhao and S. Popescu, “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(8), 1628–1645 (2009).
[Crossref]

2008 (6)

J. Jensen, K. Humes, L. Vierling, and A. Hudak, “Discrete return lidar-based prediction of leaf area index in two conifer forests,” Remote Sens. Environ. 112(10), 3947–3957 (2008).
[Crossref]

M. Chopping, G. G. Moisen, L. Su, A. Laliberte, A. Rango, J. V. Martonchik, and D. P. C. Peters, “Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA multiangle imaging spectro-radiometer,” Remote Sens. Environ. 112(5), 2051–2063 (2008).
[Crossref]

L. Chasmer, C. Hopkinson, P. Treitz, H. McCaughey, A. Barr, and A. Black, “A lidar-based hierarchical approach for assessing MODIS fPAR,” Remote Sens. Environ. 112(12), 4344–4357 (2008).
[Crossref]

T. Gobakken and E. Næsset, “Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data,” Can. J. For. Res. 38(5), 1095–1109 (2008).
[Crossref]

E. Næsset and T. Gobakken, “Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser,” Remote Sens. Environ. 112(6), 3079–3090 (2008).
[Crossref]

J. Anderson, L. Plourde, M. Martin, B. Braswell, M. Smith, R. Dubayah, M. Hofton, and J. Blair, “Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest,” Remote Sens. Environ. 112(4), 1856–1870 (2008).
[Crossref]

2007 (1)

M. Magnusson, J. E. S. Fransson, and J. Holmgren, “Effects on estimation accuracy of forest variables using different pulse density of laser data,” For. Sci. 53(6), 619–626 (2007).

2006 (2)

J. Heiskanen, “Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data,” Int. J. Remote Sens. 27(6), 1135–1158 (2006).
[Crossref]

J. M. Chen, A. Govind, O. Sonnentag, Y. Zhang, A. Barr, and B. Amiro, “Leaf area index measurements at Fluxnet-Canada forest sites,” Agric. For. Meteorol. 140(1–4), 257–268 (2006).
[Crossref]

2005 (3)

M. A. Lefsky, D. P. Turner, M. Guzy, and W. B. Cohen, “Combining lidar estimates of aboveground biomass and Landsat estimates of stand age for spatially extensive validation of modeled forest productivity,” Remote Sens. Environ. 95(4), 549–558 (2005).
[Crossref]

P. Hyde, R. Dubayah, B. Peterson, J. Blair, M. Hofton, C. Hunsaker, R. Knox, and W. Walker, “Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems,” Remote Sens. Environ. 96(3–4), 427–437 (2005).
[Crossref]

C. Hopkinson, L. E. Chasmer, G. Sass, I. F. Creed, M. Sitar, W. Kalbfleisch, and P. Treitz, “Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment,” Can. J. Rem. Sens. 31(2), 191–206 (2005).
[Crossref]

2004 (2)

I. Jonckheere, S. Fleck, K. Nackaerts, B. Muys, P. Coppin, M. Weiss, and F. Baret, “Review of methods for in situ leaf area index determination. Part I. Theories, sensors and hemispherical photography,” Agric. For. Meteorol. 121(1–2), 19–35 (2004).
[Crossref]

D. Riaño, F. Valladares, S. Condés, and E. Chuvieco, “Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests,” Agric. For. Meteorol. 124(3–4), 269–275 (2004).
[Crossref]

2003 (3)

G. M. Foody, D. S. Boyd, and M. E. J. Cutler, “Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions,” Remote Sens. Environ. 85(4), 463–474 (2003).
[Crossref]

A. A. Gitelson, A. Viña, T. J. Arkebauer, D. C. Rundquist, G. Keydan, and B. Leavitt, “Remote estimation of leaf area index and green leaf biomass in maize canopies,” Geophys. Res. Lett. 30(5), 1248 (2003).
[Crossref]

J. B. Drake, R. G. Knox, R. O. Dubayah, D. B. Clark, R. Condit, J. B. Blair, and M. Hofton, “Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships,” Glob. Ecol. Biogeogr. 12(2), 147–159 (2003).
[Crossref]

2002 (1)

N. Zhang, M. Wang, and N. Wang, “Precision agriculture—a worldwide overview,” Comput. Electron. Agric. 36(2–3), 113–132 (2002).
[Crossref]

2001 (1)

S. Wold, M. Sjöström, and L. Eriksson, “PLS-regression: a basic tool of chemometrics,” Chemom. Intell. Lab. Syst. 58(2), 109–130 (2001).
[Crossref]

2000 (1)

I. J. Davenport, R. B. Bradbury, G. Q. A. Anderson, G. R. F. Hayman, J. R. Krebs, D. C. Mason, J. D. Wilson, and N. J. Veck, “Improving bird population models using airborne remote sensing,” Int. J. Remote Sens. 21(13–14), 2705–2717 (2000).
[Crossref]

1996 (1)

J. M. Chen and J. Cihlar, “Retrieving leaf area index of boreal conifer forests using Landsat TM images,” Remote Sens. Environ. 55(2), 153–162 (1996).
[Crossref]

1991 (1)

J. M. Chen and T. A. Black, “Measuring leaf area index of plant canopies with branch architecture,” Agric. For. Meteorol. 57(1–3), 1–12 (1991).
[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]

Amiro, B.

J. M. Chen, A. Govind, O. Sonnentag, Y. Zhang, A. Barr, and B. Amiro, “Leaf area index measurements at Fluxnet-Canada forest sites,” Agric. For. Meteorol. 140(1–4), 257–268 (2006).
[Crossref]

Andersen, H.-E.

J. Strunk, H. Temesgen, H.-E. Andersen, J. P. Flewelling, and L. Madsen, “Effects of lidar pulse density and sample size on a model-assisted approach to estimate forest inventory variables,” Can. J. Rem. Sens. 38(5), 644–654 (2012).
[Crossref]

Anderson, G. Q. A.

I. J. Davenport, R. B. Bradbury, G. Q. A. Anderson, G. R. F. Hayman, J. R. Krebs, D. C. Mason, J. D. Wilson, and N. J. Veck, “Improving bird population models using airborne remote sensing,” Int. J. Remote Sens. 21(13–14), 2705–2717 (2000).
[Crossref]

Anderson, J.

J. Anderson, L. Plourde, M. Martin, B. Braswell, M. Smith, R. Dubayah, M. Hofton, and J. Blair, “Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest,” Remote Sens. Environ. 112(4), 1856–1870 (2008).
[Crossref]

Arkebauer, T. J.

A. A. Gitelson, A. Viña, T. J. Arkebauer, D. C. Rundquist, G. Keydan, and B. Leavitt, “Remote estimation of leaf area index and green leaf biomass in maize canopies,” Geophys. Res. Lett. 30(5), 1248 (2003).
[Crossref]

Atzberger, C.

C. Atzberger, “Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs,” Remote Sens. 5(2), 949–981 (2013).
[Crossref]

Ban, Y.

Banin, L.

A. C. Morel, S. S. Saatchi, Y. Malhi, N. J. Berry, L. Banin, D. Burslem, R. Nilus, and R. C. Ong, “Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data,” For. Ecol. Manage. 262(9), 1786–1798 (2011).
[Crossref]

Baret, F.

I. Jonckheere, S. Fleck, K. Nackaerts, B. Muys, P. Coppin, M. Weiss, and F. Baret, “Review of methods for in situ leaf area index determination. Part I. Theories, sensors and hemispherical photography,” Agric. For. Meteorol. 121(1–2), 19–35 (2004).
[Crossref]

Bareth, G.

N. Tilly, D. Hoffmeister, Q. Cao, S. Huang, V. Lenz-Wiedemann, Y. Miao, and G. Bareth, “Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice,” J. Appl. Remote Sens. 8(1), 083671 (2014).
[Crossref]

Barr, A.

L. Chasmer, C. Hopkinson, P. Treitz, H. McCaughey, A. Barr, and A. Black, “A lidar-based hierarchical approach for assessing MODIS fPAR,” Remote Sens. Environ. 112(12), 4344–4357 (2008).
[Crossref]

J. M. Chen, A. Govind, O. Sonnentag, Y. Zhang, A. Barr, and B. Amiro, “Leaf area index measurements at Fluxnet-Canada forest sites,” Agric. For. Meteorol. 140(1–4), 257–268 (2006).
[Crossref]

Battles, J. J.

Q. Chen, G. Vaglio Laurin, J. J. Battles, and D. Saah, “Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass,” Remote Sens. Environ. 121, 108–117 (2012).
[Crossref]

Belluco, E.

C. Wang, M. Menenti, M. P. Stoll, A. Feola, E. Belluco, and M. Marani, “Separation of ground and low vegetation signatures in LiDAR measurements of salt-marsh environments,” IEEE Trans. Geosci. Rem. Sens. 47(7), 2014–2023 (2009).
[Crossref]

Berry, N. J.

A. C. Morel, S. S. Saatchi, Y. Malhi, N. J. Berry, L. Banin, D. Burslem, R. Nilus, and R. C. Ong, “Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data,” For. Ecol. Manage. 262(9), 1786–1798 (2011).
[Crossref]

Black, A.

L. Chasmer, C. Hopkinson, P. Treitz, H. McCaughey, A. Barr, and A. Black, “A lidar-based hierarchical approach for assessing MODIS fPAR,” Remote Sens. Environ. 112(12), 4344–4357 (2008).
[Crossref]

Black, T. A.

J. M. Chen and T. A. Black, “Measuring leaf area index of plant canopies with branch architecture,” Agric. For. Meteorol. 57(1–3), 1–12 (1991).
[Crossref]

Blair, J.

J. Anderson, L. Plourde, M. Martin, B. Braswell, M. Smith, R. Dubayah, M. Hofton, and J. Blair, “Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest,” Remote Sens. Environ. 112(4), 1856–1870 (2008).
[Crossref]

P. Hyde, R. Dubayah, B. Peterson, J. Blair, M. Hofton, C. Hunsaker, R. Knox, and W. Walker, “Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems,” Remote Sens. Environ. 96(3–4), 427–437 (2005).
[Crossref]

Blair, J. B.

J. B. Drake, R. G. Knox, R. O. Dubayah, D. B. Clark, R. Condit, J. B. Blair, and M. Hofton, “Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships,” Glob. Ecol. Biogeogr. 12(2), 147–159 (2003).
[Crossref]

Boyd, D. S.

G. M. Foody, D. S. Boyd, and M. E. J. Cutler, “Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions,” Remote Sens. Environ. 85(4), 463–474 (2003).
[Crossref]

Bradbury, R. B.

I. J. Davenport, R. B. Bradbury, G. Q. A. Anderson, G. R. F. Hayman, J. R. Krebs, D. C. Mason, J. D. Wilson, and N. J. Veck, “Improving bird population models using airborne remote sensing,” Int. J. Remote Sens. 21(13–14), 2705–2717 (2000).
[Crossref]

Braswell, B.

J. Anderson, L. Plourde, M. Martin, B. Braswell, M. Smith, R. Dubayah, M. Hofton, and J. Blair, “Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest,” Remote Sens. Environ. 112(4), 1856–1870 (2008).
[Crossref]

Brown, T. T.

J. U. H. Eitel, T. S. Magney, L. A. Vierling, T. T. Brown, and D. R. Huggins, “LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status,” Field Crops Res. 159, 21–32 (2014).
[Crossref]

Burslem, D.

A. C. Morel, S. S. Saatchi, Y. Malhi, N. J. Berry, L. Banin, D. Burslem, R. Nilus, and R. C. Ong, “Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data,” For. Ecol. Manage. 262(9), 1786–1798 (2011).
[Crossref]

Cao, Q.

N. Tilly, D. Hoffmeister, Q. Cao, S. Huang, V. Lenz-Wiedemann, Y. Miao, and G. Bareth, “Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice,” J. Appl. Remote Sens. 8(1), 083671 (2014).
[Crossref]

Chasmer, L.

L. Chasmer, C. Hopkinson, P. Treitz, H. McCaughey, A. Barr, and A. Black, “A lidar-based hierarchical approach for assessing MODIS fPAR,” Remote Sens. Environ. 112(12), 4344–4357 (2008).
[Crossref]

Chasmer, L. E.

C. Hopkinson, L. E. Chasmer, G. Sass, I. F. Creed, M. Sitar, W. Kalbfleisch, and P. Treitz, “Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment,” Can. J. Rem. Sens. 31(2), 191–206 (2005).
[Crossref]

Che, T.

X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
[Crossref]

Chen, G.

K. K. Singh, G. Chen, J. B. McCarter, and R. K. Meentemeyer, “Effects of LiDAR point density and landscape context on estimates of urban forest biomass,” ISPRS J. Photogramm. Remote Sens. 101, 310–322 (2015).
[Crossref]

Chen, J.

J. Chen, S. Gu, M. Shen, Y. Tang, and B. Matsushita, “Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data,” Int. J. Remote Sens. 30(24), 6497–6517 (2009).
[Crossref]

Chen, J. M.

J. M. Chen, A. Govind, O. Sonnentag, Y. Zhang, A. Barr, and B. Amiro, “Leaf area index measurements at Fluxnet-Canada forest sites,” Agric. For. Meteorol. 140(1–4), 257–268 (2006).
[Crossref]

J. M. Chen and J. Cihlar, “Retrieving leaf area index of boreal conifer forests using Landsat TM images,” Remote Sens. Environ. 55(2), 153–162 (1996).
[Crossref]

J. M. Chen and T. A. Black, “Measuring leaf area index of plant canopies with branch architecture,” Agric. For. Meteorol. 57(1–3), 1–12 (1991).
[Crossref]

Chen, Q.

Q. Chen, G. Vaglio Laurin, J. J. Battles, and D. Saah, “Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass,” Remote Sens. Environ. 121, 108–117 (2012).
[Crossref]

Chen, Y.

Cheng, G.

X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
[Crossref]

Chopping, M.

M. Chopping, G. G. Moisen, L. Su, A. Laliberte, A. Rango, J. V. Martonchik, and D. P. C. Peters, “Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA multiangle imaging spectro-radiometer,” Remote Sens. Environ. 112(5), 2051–2063 (2008).
[Crossref]

Chuvieco, E.

D. Riaño, F. Valladares, S. Condés, and E. Chuvieco, “Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests,” Agric. For. Meteorol. 124(3–4), 269–275 (2004).
[Crossref]

Cihlar, J.

J. M. Chen and J. Cihlar, “Retrieving leaf area index of boreal conifer forests using Landsat TM images,” Remote Sens. Environ. 55(2), 153–162 (1996).
[Crossref]

Clark, D. B.

J. B. Drake, R. G. Knox, R. O. Dubayah, D. B. Clark, R. Condit, J. B. Blair, and M. Hofton, “Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships,” Glob. Ecol. Biogeogr. 12(2), 147–159 (2003).
[Crossref]

Cohen, W. B.

M. A. Lefsky, D. P. Turner, M. Guzy, and W. B. Cohen, “Combining lidar estimates of aboveground biomass and Landsat estimates of stand age for spatially extensive validation of modeled forest productivity,” Remote Sens. Environ. 95(4), 549–558 (2005).
[Crossref]

Condés, S.

D. Riaño, F. Valladares, S. Condés, and E. Chuvieco, “Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests,” Agric. For. Meteorol. 124(3–4), 269–275 (2004).
[Crossref]

Condit, R.

J. B. Drake, R. G. Knox, R. O. Dubayah, D. B. Clark, R. Condit, J. B. Blair, and M. Hofton, “Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships,” Glob. Ecol. Biogeogr. 12(2), 147–159 (2003).
[Crossref]

Coops, N. C.

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. Remote Sens. 69, 121–133 (2012).
[Crossref]

Coppin, P.

I. Jonckheere, S. Fleck, K. Nackaerts, B. Muys, P. Coppin, M. Weiss, and F. Baret, “Review of methods for in situ leaf area index determination. Part I. Theories, sensors and hemispherical photography,” Agric. For. Meteorol. 121(1–2), 19–35 (2004).
[Crossref]

Creed, I. F.

C. Hopkinson, L. E. Chasmer, G. Sass, I. F. Creed, M. Sitar, W. Kalbfleisch, and P. Treitz, “Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment,” Can. J. Rem. Sens. 31(2), 191–206 (2005).
[Crossref]

Cutler, M. E. J.

G. M. Foody, D. S. Boyd, and M. E. J. Cutler, “Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions,” Remote Sens. Environ. 85(4), 463–474 (2003).
[Crossref]

Davenport, I. J.

I. J. Davenport, R. B. Bradbury, G. Q. A. Anderson, G. R. F. Hayman, J. R. Krebs, D. C. Mason, J. D. Wilson, and N. J. Veck, “Improving bird population models using airborne remote sensing,” Int. J. Remote Sens. 21(13–14), 2705–2717 (2000).
[Crossref]

Derryberry, D. R.

N. F. Glenn, L. P. Spaete, T. T. Sankey, D. R. Derryberry, S. P. Hardegree, and J. J. Mitchell, “Errors in LiDAR-derived shrub height and crown area on sloped terrain,” J. Arid Environ. 75(4), 377–382 (2011).
[Crossref]

Drake, J. B.

J. B. Drake, R. G. Knox, R. O. Dubayah, D. B. Clark, R. Condit, J. B. Blair, and M. Hofton, “Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships,” Glob. Ecol. Biogeogr. 12(2), 147–159 (2003).
[Crossref]

Dubayah, R.

J. Anderson, L. Plourde, M. Martin, B. Braswell, M. Smith, R. Dubayah, M. Hofton, and J. Blair, “Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest,” Remote Sens. Environ. 112(4), 1856–1870 (2008).
[Crossref]

P. Hyde, R. Dubayah, B. Peterson, J. Blair, M. Hofton, C. Hunsaker, R. Knox, and W. Walker, “Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems,” Remote Sens. Environ. 96(3–4), 427–437 (2005).
[Crossref]

Dubayah, R. O.

J. B. Drake, R. G. Knox, R. O. Dubayah, D. B. Clark, R. Condit, J. B. Blair, and M. Hofton, “Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships,” Glob. Ecol. Biogeogr. 12(2), 147–159 (2003).
[Crossref]

Edson, C.

C. Edson and M. G. Wing, “Airborne light detection and ranging (LiDAR) for individual tree stem location, height, and biomass measurements,” Remote Sens. 3(12), 2494–2528 (2011).
[Crossref]

Eitel, J. U. H.

J. U. H. Eitel, T. S. Magney, L. A. Vierling, T. T. Brown, and D. R. Huggins, “LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status,” Field Crops Res. 159, 21–32 (2014).
[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]

Eriksson, L.

S. Wold, M. Sjöström, and L. Eriksson, “PLS-regression: a basic tool of chemometrics,” Chemom. Intell. Lab. Syst. 58(2), 109–130 (2001).
[Crossref]

Estornell, J.

J. Estornell, L. A. Ruiz, B. Velázquez-Martí, and A. Fernández-Sarría, “Estimation of shrub biomass by airborne LiDAR data in small forest stands,” For. Ecol. Manage. 262(9), 1697–1703 (2011).
[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]

Feola, A.

C. Wang, M. Menenti, M. P. Stoll, A. Feola, E. Belluco, and M. Marani, “Separation of ground and low vegetation signatures in LiDAR measurements of salt-marsh environments,” IEEE Trans. Geosci. Rem. Sens. 47(7), 2014–2023 (2009).
[Crossref]

Fernández-Sarría, A.

J. Estornell, L. A. Ruiz, B. Velázquez-Martí, and A. Fernández-Sarría, “Estimation of shrub biomass by airborne LiDAR data in small forest stands,” For. Ecol. Manage. 262(9), 1697–1703 (2011).
[Crossref]

Fleck, S.

I. Jonckheere, S. Fleck, K. Nackaerts, B. Muys, P. Coppin, M. Weiss, and F. Baret, “Review of methods for in situ leaf area index determination. Part I. Theories, sensors and hemispherical photography,” Agric. For. Meteorol. 121(1–2), 19–35 (2004).
[Crossref]

Flewelling, J. P.

J. Strunk, H. Temesgen, H.-E. Andersen, J. P. Flewelling, and L. Madsen, “Effects of lidar pulse density and sample size on a model-assisted approach to estimate forest inventory variables,” Can. J. Rem. Sens. 38(5), 644–654 (2012).
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G. W. Frazer, S. Magnussen, M. A. Wulder, and K. O. Niemann, “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(2), 636–649 (2011).
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Gao, S.

W. Li, Z. Niu, N. Huang, C. Wang, S. Gao, and C. Wu, “Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China,” Ecol. Indic. 57, 486–496 (2015).
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S. Gao, Z. Niu, N. Huang, and X. Hou, “Estimating the leaf area index, height and biomass of maize using HJ-1 and RADARSAT-2,” Int. J. Appl. Earth Obs. Geoinf. 24, 1–8 (2013).
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E. Luedeling and A. Gassner, “Partial least squares regression for analyzing walnut phenology in California,” Agric. For. Meteorol. 158–159, 43–52 (2012).
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A. A. Gitelson, A. Viña, T. J. Arkebauer, D. C. Rundquist, G. Keydan, and B. Leavitt, “Remote estimation of leaf area index and green leaf biomass in maize canopies,” Geophys. Res. Lett. 30(5), 1248 (2003).
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N. F. Glenn, L. P. Spaete, T. T. Sankey, D. R. Derryberry, S. P. Hardegree, and J. J. Mitchell, “Errors in LiDAR-derived shrub height and crown area on sloped terrain,” J. Arid Environ. 75(4), 377–382 (2011).
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E. Næsset and T. Gobakken, “Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser,” Remote Sens. Environ. 112(6), 3079–3090 (2008).
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T. Gobakken and E. Næsset, “Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data,” Can. J. For. Res. 38(5), 1095–1109 (2008).
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J. Chen, S. Gu, M. Shen, Y. Tang, and B. Matsushita, “Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data,” Int. J. Remote Sens. 30(24), 6497–6517 (2009).
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M. K. Jakubowski, Q. Guo, and M. Kelly, “Tradeoffs between lidar pulse density and forest measurement accuracy,” Remote Sens. Environ. 130, 245–253 (2013).
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F. Zhao, Q. Guo, and M. Kelly, “Allometric equation choice impacts lidar-based forest biomass estimates: a case study from the Sierra National Forest, CA,” Agric. For. Meteorol. 165, 64–72 (2012).
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M. A. Lefsky, D. P. Turner, M. Guzy, and W. B. Cohen, “Combining lidar estimates of aboveground biomass and Landsat estimates of stand age for spatially extensive validation of modeled forest productivity,” Remote Sens. Environ. 95(4), 549–558 (2005).
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Hardegree, S. P.

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I. J. Davenport, R. B. Bradbury, G. Q. A. Anderson, G. R. F. Hayman, J. R. Krebs, D. C. Mason, J. D. Wilson, and N. J. Veck, “Improving bird population models using airborne remote sensing,” Int. J. Remote Sens. 21(13–14), 2705–2717 (2000).
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P. Wilkes, S. D. Jones, L. Suarez, A. Haywood, W. Woodgate, M. Soto-Berelov, A. Mellor, and A. K. Skidmore, “Understanding the effects of ALS pulse density for metric retrieval across diverse forest types,” Photogramm. Eng. Remote Sensing 81(8), 625–635 (2015).
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N. Tilly, D. Hoffmeister, Q. Cao, S. Huang, V. Lenz-Wiedemann, Y. Miao, and G. Bareth, “Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice,” J. Appl. Remote Sens. 8(1), 083671 (2014).
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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 11(1), 278–295 (2010).
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J. Anderson, L. Plourde, M. Martin, B. Braswell, M. Smith, R. Dubayah, M. Hofton, and J. Blair, “Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest,” Remote Sens. Environ. 112(4), 1856–1870 (2008).
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P. Hyde, R. Dubayah, B. Peterson, J. Blair, M. Hofton, C. Hunsaker, R. Knox, and W. Walker, “Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems,” Remote Sens. Environ. 96(3–4), 427–437 (2005).
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J. B. Drake, R. G. Knox, R. O. Dubayah, D. B. Clark, R. Condit, J. B. Blair, and M. Hofton, “Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships,” Glob. Ecol. Biogeogr. 12(2), 147–159 (2003).
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M. Magnusson, J. E. S. Fransson, and J. Holmgren, “Effects on estimation accuracy of forest variables using different pulse density of laser data,” For. Sci. 53(6), 619–626 (2007).

Holopainen, M.

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).
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L. Chasmer, C. Hopkinson, P. Treitz, H. McCaughey, A. Barr, and A. Black, “A lidar-based hierarchical approach for assessing MODIS fPAR,” Remote Sens. Environ. 112(12), 4344–4357 (2008).
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C. Hopkinson, L. E. Chasmer, G. Sass, I. F. Creed, M. Sitar, W. Kalbfleisch, and P. Treitz, “Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment,” Can. J. Rem. Sens. 31(2), 191–206 (2005).
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Hou, X.

S. Gao, Z. Niu, N. Huang, and X. Hou, “Estimating the leaf area index, height and biomass of maize using HJ-1 and RADARSAT-2,” Int. J. Appl. Earth Obs. Geoinf. 24, 1–8 (2013).
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X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
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Huang, N.

W. Li, Z. Niu, N. Huang, C. Wang, S. Gao, and C. Wu, “Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China,” Ecol. Indic. 57, 486–496 (2015).
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S. Gao, Z. Niu, N. Huang, and X. Hou, “Estimating the leaf area index, height and biomass of maize using HJ-1 and RADARSAT-2,” Int. J. Appl. Earth Obs. Geoinf. 24, 1–8 (2013).
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Huang, S.

N. Tilly, D. Hoffmeister, Q. Cao, S. Huang, V. Lenz-Wiedemann, Y. Miao, and G. Bareth, “Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice,” J. Appl. Remote Sens. 8(1), 083671 (2014).
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J. Jensen, K. Humes, L. Vierling, and A. Hudak, “Discrete return lidar-based prediction of leaf area index in two conifer forests,” Remote Sens. Environ. 112(10), 3947–3957 (2008).
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J. U. H. Eitel, T. S. Magney, L. A. Vierling, T. T. Brown, and D. R. Huggins, “LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status,” Field Crops Res. 159, 21–32 (2014).
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Humes, K.

J. Jensen, K. Humes, L. Vierling, and A. Hudak, “Discrete return lidar-based prediction of leaf area index in two conifer forests,” Remote Sens. Environ. 112(10), 3947–3957 (2008).
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Hunsaker, C.

P. Hyde, R. Dubayah, B. Peterson, J. Blair, M. Hofton, C. Hunsaker, R. Knox, and W. Walker, “Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems,” Remote Sens. Environ. 96(3–4), 427–437 (2005).
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P. Hyde, R. Dubayah, B. Peterson, J. Blair, M. Hofton, C. Hunsaker, R. Knox, and W. Walker, “Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems,” Remote Sens. Environ. 96(3–4), 427–437 (2005).
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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).
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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).
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Jakubowski, M. K.

M. K. Jakubowski, Q. Guo, and M. Kelly, “Tradeoffs between lidar pulse density and forest measurement accuracy,” Remote Sens. Environ. 130, 245–253 (2013).
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Jensen, J.

J. Jensen, K. Humes, L. Vierling, and A. Hudak, “Discrete return lidar-based prediction of leaf area index in two conifer forests,” Remote Sens. Environ. 112(10), 3947–3957 (2008).
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X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
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Jochem, A.

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 11(1), 278–295 (2010).
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P. Wilkes, S. D. Jones, L. Suarez, A. Haywood, W. Woodgate, M. Soto-Berelov, A. Mellor, and A. K. Skidmore, “Understanding the effects of ALS pulse density for metric retrieval across diverse forest types,” Photogramm. Eng. Remote Sensing 81(8), 625–635 (2015).
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Kaasalainen, S.

Kalbfleisch, W.

C. Hopkinson, L. E. Chasmer, G. Sass, I. F. Creed, M. Sitar, W. Kalbfleisch, and P. Treitz, “Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment,” Can. J. Rem. Sens. 31(2), 191–206 (2005).
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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).
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M. K. Jakubowski, Q. Guo, and M. Kelly, “Tradeoffs between lidar pulse density and forest measurement accuracy,” Remote Sens. Environ. 130, 245–253 (2013).
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F. Zhao, Q. Guo, and M. Kelly, “Allometric equation choice impacts lidar-based forest biomass estimates: a case study from the Sierra National Forest, CA,” Agric. For. Meteorol. 165, 64–72 (2012).
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A. A. Gitelson, A. Viña, T. J. Arkebauer, D. C. Rundquist, G. Keydan, and B. Leavitt, “Remote estimation of leaf area index and green leaf biomass in maize canopies,” Geophys. Res. Lett. 30(5), 1248 (2003).
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P. Hyde, R. Dubayah, B. Peterson, J. Blair, M. Hofton, C. Hunsaker, R. Knox, and W. Walker, “Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems,” Remote Sens. Environ. 96(3–4), 427–437 (2005).
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Knox, R. G.

J. B. Drake, R. G. Knox, R. O. Dubayah, D. B. Clark, R. Condit, J. B. Blair, and M. Hofton, “Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships,” Glob. Ecol. Biogeogr. 12(2), 147–159 (2003).
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I. J. Davenport, R. B. Bradbury, G. Q. A. Anderson, G. R. F. Hayman, J. R. Krebs, D. C. Mason, J. D. Wilson, and N. J. Veck, “Improving bird population models using airborne remote sensing,” Int. J. Remote Sens. 21(13–14), 2705–2717 (2000).
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Kulawardhana, R. W.

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).
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M. Chopping, G. G. Moisen, L. Su, A. Laliberte, A. Rango, J. V. Martonchik, and D. P. C. Peters, “Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA multiangle imaging spectro-radiometer,” Remote Sens. Environ. 112(5), 2051–2063 (2008).
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Leavitt, B.

A. A. Gitelson, A. Viña, T. J. Arkebauer, D. C. Rundquist, G. Keydan, and B. Leavitt, “Remote estimation of leaf area index and green leaf biomass in maize canopies,” Geophys. Res. Lett. 30(5), 1248 (2003).
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Lefsky, M. A.

M. A. Lefsky, D. P. Turner, M. Guzy, and W. B. Cohen, “Combining lidar estimates of aboveground biomass and Landsat estimates of stand age for spatially extensive validation of modeled forest productivity,” Remote Sens. Environ. 95(4), 549–558 (2005).
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Lenz-Wiedemann, V.

N. Tilly, D. Hoffmeister, Q. Cao, S. Huang, V. Lenz-Wiedemann, Y. Miao, and G. Bareth, “Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice,” J. Appl. Remote Sens. 8(1), 083671 (2014).
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Li, G.

S. Luo, C. Wang, F. Pan, X. Xi, G. Li, S. Nie, and S. Xia, “Estimation of wetland vegetation height and leaf area index using airborne laser scanning data,” Ecol. Indic. 48, 550–559 (2015).
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X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
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Li, S.

Li, W.

W. Li, Z. Niu, N. Huang, C. Wang, S. Gao, and C. Wu, “Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China,” Ecol. Indic. 57, 486–496 (2015).
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X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
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S. C. Popescu, K. Zhao, A. Neuenschwander, and C. Lin, “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(11), 2786–2797 (2011).
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X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
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Liu, S.

X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
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Luedeling, E.

E. Luedeling and A. Gassner, “Partial least squares regression for analyzing walnut phenology in California,” Agric. For. Meteorol. 158–159, 43–52 (2012).
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Luo, S.

S. Luo, C. Wang, X. Xi, S. Nie, S. Xia, and W. Yiping, “Forest leaf area index estimation using combined ICESat/GLAS and optical remote sensing image,” J. Inf. Millim. Waves 34(2), 243–249 (2015).

S. Luo, C. Wang, F. Pan, X. Xi, G. Li, S. Nie, and S. Xia, “Estimation of wetland vegetation height and leaf area index using airborne laser scanning data,” Ecol. Indic. 48, 550–559 (2015).
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S. Luo, C. Wang, X. Xi, and F. Pan, “Estimating FPAR of maize canopy using airborne discrete-return LiDAR data,” Opt. Express 22(5), 5106–5117 (2014).
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X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
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Madsen, L.

J. Strunk, H. Temesgen, H.-E. Andersen, J. P. Flewelling, and L. Madsen, “Effects of lidar pulse density and sample size on a model-assisted approach to estimate forest inventory variables,” Can. J. Rem. Sens. 38(5), 644–654 (2012).
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Magney, T. S.

J. U. H. Eitel, T. S. Magney, L. A. Vierling, T. T. Brown, and D. R. Huggins, “LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status,” Field Crops Res. 159, 21–32 (2014).
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Magnussen, S.

G. W. Frazer, S. Magnussen, M. A. Wulder, and K. O. Niemann, “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(2), 636–649 (2011).
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Magnusson, M.

M. Magnusson, J. E. S. Fransson, and J. Holmgren, “Effects on estimation accuracy of forest variables using different pulse density of laser data,” For. Sci. 53(6), 619–626 (2007).

Malhi, Y.

A. C. Morel, S. S. Saatchi, Y. Malhi, N. J. Berry, L. Banin, D. Burslem, R. Nilus, and R. C. Ong, “Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data,” For. Ecol. Manage. 262(9), 1786–1798 (2011).
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Marani, M.

C. Wang, M. Menenti, M. P. Stoll, A. Feola, E. Belluco, and M. Marani, “Separation of ground and low vegetation signatures in LiDAR measurements of salt-marsh environments,” IEEE Trans. Geosci. Rem. Sens. 47(7), 2014–2023 (2009).
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Marshall, P. L.

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. Remote Sens. 69, 121–133 (2012).
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Martin, M.

J. Anderson, L. Plourde, M. Martin, B. Braswell, M. Smith, R. Dubayah, M. Hofton, and J. Blair, “Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest,” Remote Sens. Environ. 112(4), 1856–1870 (2008).
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K. K. Singh, G. Chen, J. B. McCarter, and R. K. Meentemeyer, “Effects of LiDAR point density and landscape context on estimates of urban forest biomass,” ISPRS J. Photogramm. Remote Sens. 101, 310–322 (2015).
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M. Chopping, G. G. Moisen, L. Su, A. Laliberte, A. Rango, J. V. Martonchik, and D. P. C. Peters, “Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA multiangle imaging spectro-radiometer,” Remote Sens. Environ. 112(5), 2051–2063 (2008).
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A. C. Morel, S. S. Saatchi, Y. Malhi, N. J. Berry, L. Banin, D. Burslem, R. Nilus, and R. C. Ong, “Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data,” For. Ecol. Manage. 262(9), 1786–1798 (2011).
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M. Chopping, G. G. Moisen, L. Su, A. Laliberte, A. Rango, J. V. Martonchik, and D. P. C. Peters, “Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA multiangle imaging spectro-radiometer,” Remote Sens. Environ. 112(5), 2051–2063 (2008).
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P. Hyde, R. Dubayah, B. Peterson, J. Blair, M. Hofton, C. Hunsaker, R. Knox, and W. Walker, “Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems,” Remote Sens. Environ. 96(3–4), 427–437 (2005).
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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).
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S. C. Popescu, K. Zhao, A. Neuenschwander, and C. Lin, “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(11), 2786–2797 (2011).
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M. Chopping, G. G. Moisen, L. Su, A. Laliberte, A. Rango, J. V. Martonchik, and D. P. C. Peters, “Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA multiangle imaging spectro-radiometer,” Remote Sens. Environ. 112(5), 2051–2063 (2008).
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D. Riaño, F. Valladares, S. Condés, and E. Chuvieco, “Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests,” Agric. For. Meteorol. 124(3–4), 269–275 (2004).
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J. J. Richardson, L. M. Moskal, and S.-H. Kim, “Modeling approaches to estimate effective leaf area index from aerial discrete-return LIDAR,” Agric. For. Meteorol. 149(6–7), 1152–1160 (2009).
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J. Estornell, L. A. Ruiz, B. Velázquez-Martí, and A. Fernández-Sarría, “Estimation of shrub biomass by airborne LiDAR data in small forest stands,” For. Ecol. Manage. 262(9), 1697–1703 (2011).
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A. A. Gitelson, A. Viña, T. J. Arkebauer, D. C. Rundquist, G. Keydan, and B. Leavitt, “Remote estimation of leaf area index and green leaf biomass in maize canopies,” Geophys. Res. Lett. 30(5), 1248 (2003).
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A. C. Morel, S. S. Saatchi, Y. Malhi, N. J. Berry, L. Banin, D. Burslem, R. Nilus, and R. C. Ong, “Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data,” For. Ecol. Manage. 262(9), 1786–1798 (2011).
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N. F. Glenn, L. P. Spaete, T. T. Sankey, D. R. Derryberry, S. P. Hardegree, and J. J. Mitchell, “Errors in LiDAR-derived shrub height and crown area on sloped terrain,” J. Arid Environ. 75(4), 377–382 (2011).
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C. Hopkinson, L. E. Chasmer, G. Sass, I. F. Creed, M. Sitar, W. Kalbfleisch, and P. Treitz, “Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment,” Can. J. Rem. Sens. 31(2), 191–206 (2005).
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J. Chen, S. Gu, M. Shen, Y. Tang, and B. Matsushita, “Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data,” Int. J. Remote Sens. 30(24), 6497–6517 (2009).
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K. K. Singh, G. Chen, J. B. McCarter, and R. K. Meentemeyer, “Effects of LiDAR point density and landscape context on estimates of urban forest biomass,” ISPRS J. Photogramm. Remote Sens. 101, 310–322 (2015).
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C. Hopkinson, L. E. Chasmer, G. Sass, I. F. Creed, M. Sitar, W. Kalbfleisch, and P. Treitz, “Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment,” Can. J. Rem. Sens. 31(2), 191–206 (2005).
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J. Anderson, L. Plourde, M. Martin, B. Braswell, M. Smith, R. Dubayah, M. Hofton, and J. Blair, “Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest,” Remote Sens. Environ. 112(4), 1856–1870 (2008).
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N. F. Glenn, L. P. Spaete, T. T. Sankey, D. R. Derryberry, S. P. Hardegree, and J. J. Mitchell, “Errors in LiDAR-derived shrub height and crown area on sloped terrain,” J. Arid Environ. 75(4), 377–382 (2011).
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C. Wang, M. Menenti, M. P. Stoll, A. Feola, E. Belluco, and M. Marani, “Separation of ground and low vegetation signatures in LiDAR measurements of salt-marsh environments,” IEEE Trans. Geosci. Rem. Sens. 47(7), 2014–2023 (2009).
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M. Chopping, G. G. Moisen, L. Su, A. Laliberte, A. Rango, J. V. Martonchik, and D. P. C. Peters, “Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA multiangle imaging spectro-radiometer,” Remote Sens. Environ. 112(5), 2051–2063 (2008).
[Crossref]

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P. Wilkes, S. D. Jones, L. Suarez, A. Haywood, W. Woodgate, M. Soto-Berelov, A. Mellor, and A. K. Skidmore, “Understanding the effects of ALS pulse density for metric retrieval across diverse forest types,” Photogramm. Eng. Remote Sensing 81(8), 625–635 (2015).
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Tang, Y.

J. Chen, S. Gu, M. Shen, Y. Tang, and B. Matsushita, “Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data,” Int. J. Remote Sens. 30(24), 6497–6517 (2009).
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N. Tilly, D. Hoffmeister, Q. Cao, S. Huang, V. Lenz-Wiedemann, Y. Miao, and G. Bareth, “Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice,” J. Appl. Remote Sens. 8(1), 083671 (2014).
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G. Pope and P. Treitz, “Leaf area index (LAI) Estimation in boreal mixedwood forest of Ontario, Canada using light detection and ranging (LiDAR) and worldview-2 imagery,” Remote Sens. 5(10), 5040–5063 (2013).
[Crossref]

L. Chasmer, C. Hopkinson, P. Treitz, H. McCaughey, A. Barr, and A. Black, “A lidar-based hierarchical approach for assessing MODIS fPAR,” Remote Sens. Environ. 112(12), 4344–4357 (2008).
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C. Hopkinson, L. E. Chasmer, G. Sass, I. F. Creed, M. Sitar, W. Kalbfleisch, and P. Treitz, “Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment,” Can. J. Rem. Sens. 31(2), 191–206 (2005).
[Crossref]

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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. Remote Sens. 69, 121–133 (2012).
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Q. Chen, G. Vaglio Laurin, J. J. Battles, and D. Saah, “Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass,” Remote Sens. Environ. 121, 108–117 (2012).
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D. Riaño, F. Valladares, S. Condés, and E. Chuvieco, “Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests,” Agric. For. Meteorol. 124(3–4), 269–275 (2004).
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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).
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I. J. Davenport, R. B. Bradbury, G. Q. A. Anderson, G. R. F. Hayman, J. R. Krebs, D. C. Mason, J. D. Wilson, and N. J. Veck, “Improving bird population models using airborne remote sensing,” Int. J. Remote Sens. 21(13–14), 2705–2717 (2000).
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Velázquez-Martí, B.

J. Estornell, L. A. Ruiz, B. Velázquez-Martí, and A. Fernández-Sarría, “Estimation of shrub biomass by airborne LiDAR data in small forest stands,” For. Ecol. Manage. 262(9), 1697–1703 (2011).
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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).
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A. A. Gitelson, A. Viña, T. J. Arkebauer, D. C. Rundquist, G. Keydan, and B. Leavitt, “Remote estimation of leaf area index and green leaf biomass in maize canopies,” Geophys. Res. Lett. 30(5), 1248 (2003).
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Vu, T.-T.

Walker, W.

P. Hyde, R. Dubayah, B. Peterson, J. Blair, M. Hofton, C. Hunsaker, R. Knox, and W. Walker, “Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems,” Remote Sens. Environ. 96(3–4), 427–437 (2005).
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Wang, C.

S. Luo, C. Wang, X. Xi, S. Nie, S. Xia, and W. Yiping, “Forest leaf area index estimation using combined ICESat/GLAS and optical remote sensing image,” J. Inf. Millim. Waves 34(2), 243–249 (2015).

S. Luo, C. Wang, F. Pan, X. Xi, G. Li, S. Nie, and S. Xia, “Estimation of wetland vegetation height and leaf area index using airborne laser scanning data,” Ecol. Indic. 48, 550–559 (2015).
[Crossref]

W. Li, Z. Niu, N. Huang, C. Wang, S. Gao, and C. Wu, “Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China,” Ecol. Indic. 57, 486–496 (2015).
[Crossref]

S. Luo, C. Wang, X. Xi, and F. Pan, “Estimating FPAR of maize canopy using airborne discrete-return LiDAR data,” Opt. Express 22(5), 5106–5117 (2014).
[Crossref] [PubMed]

C. Wang, M. Menenti, M. P. Stoll, A. Feola, E. Belluco, and M. Marani, “Separation of ground and low vegetation signatures in LiDAR measurements of salt-marsh environments,” IEEE Trans. Geosci. Rem. Sens. 47(7), 2014–2023 (2009).
[Crossref]

Wang, M.

N. Zhang, M. Wang, and N. Wang, “Precision agriculture—a worldwide overview,” Comput. Electron. Agric. 36(2–3), 113–132 (2002).
[Crossref]

Wang, N.

N. Zhang, M. Wang, and N. Wang, “Precision agriculture—a worldwide overview,” Comput. Electron. Agric. 36(2–3), 113–132 (2002).
[Crossref]

Wang, S.

X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
[Crossref]

Wang, W.

X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
[Crossref]

Watt, M.

M. Watt, A. Meredith, P. Watt, and A. Gunn, “The influence of LiDAR pulse density on the precision of inventory metrics in young unthinned Douglas-fir stands during initial and subsequent LiDAR acquisitions,” N. Z. J. For. Sci. 44(1), 1–9 (2014).
[Crossref]

Watt, P.

M. Watt, A. Meredith, P. Watt, and A. Gunn, “The influence of LiDAR pulse density on the precision of inventory metrics in young unthinned Douglas-fir stands during initial and subsequent LiDAR acquisitions,” N. Z. J. For. Sci. 44(1), 1–9 (2014).
[Crossref]

Weiss, M.

I. Jonckheere, S. Fleck, K. Nackaerts, B. Muys, P. Coppin, M. Weiss, and F. Baret, “Review of methods for in situ leaf area index determination. Part I. Theories, sensors and hemispherical photography,” Agric. For. Meteorol. 121(1–2), 19–35 (2004).
[Crossref]

Wen, J.

X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
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Wilkes, P.

P. Wilkes, S. D. Jones, L. Suarez, A. Haywood, W. Woodgate, M. Soto-Berelov, A. Mellor, and A. K. Skidmore, “Understanding the effects of ALS pulse density for metric retrieval across diverse forest types,” Photogramm. Eng. Remote Sensing 81(8), 625–635 (2015).
[Crossref]

Wilson, J. D.

I. J. Davenport, R. B. Bradbury, G. Q. A. Anderson, G. R. F. Hayman, J. R. Krebs, D. C. Mason, J. D. Wilson, and N. J. Veck, “Improving bird population models using airborne remote sensing,” Int. J. Remote Sens. 21(13–14), 2705–2717 (2000).
[Crossref]

Wing, M. G.

C. Edson and M. G. Wing, “Airborne light detection and ranging (LiDAR) for individual tree stem location, height, and biomass measurements,” Remote Sens. 3(12), 2494–2528 (2011).
[Crossref]

Wold, S.

S. Wold, M. Sjöström, and L. Eriksson, “PLS-regression: a basic tool of chemometrics,” Chemom. Intell. Lab. Syst. 58(2), 109–130 (2001).
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Woodgate, W.

P. Wilkes, S. D. Jones, L. Suarez, A. Haywood, W. Woodgate, M. Soto-Berelov, A. Mellor, and A. K. Skidmore, “Understanding the effects of ALS pulse density for metric retrieval across diverse forest types,” Photogramm. Eng. Remote Sensing 81(8), 625–635 (2015).
[Crossref]

Wu, C.

W. Li, Z. Niu, N. Huang, C. Wang, S. Gao, and C. Wu, “Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China,” Ecol. Indic. 57, 486–496 (2015).
[Crossref]

Wulder, M. A.

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. Remote Sens. 69, 121–133 (2012).
[Crossref]

G. W. Frazer, S. Magnussen, M. A. Wulder, and K. O. Niemann, “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(2), 636–649 (2011).
[Crossref]

Wynne, R. H.

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]

Xi, X.

S. Luo, C. Wang, X. Xi, S. Nie, S. Xia, and W. Yiping, “Forest leaf area index estimation using combined ICESat/GLAS and optical remote sensing image,” J. Inf. Millim. Waves 34(2), 243–249 (2015).

S. Luo, C. Wang, F. Pan, X. Xi, G. Li, S. Nie, and S. Xia, “Estimation of wetland vegetation height and leaf area index using airborne laser scanning data,” Ecol. Indic. 48, 550–559 (2015).
[Crossref]

S. Luo, C. Wang, X. Xi, and F. Pan, “Estimating FPAR of maize canopy using airborne discrete-return LiDAR data,” Opt. Express 22(5), 5106–5117 (2014).
[Crossref] [PubMed]

Xia, S.

S. Luo, C. Wang, X. Xi, S. Nie, S. Xia, and W. Yiping, “Forest leaf area index estimation using combined ICESat/GLAS and optical remote sensing image,” J. Inf. Millim. Waves 34(2), 243–249 (2015).

S. Luo, C. Wang, F. Pan, X. Xi, G. Li, S. Nie, and S. Xia, “Estimation of wetland vegetation height and leaf area index using airborne laser scanning data,” Ecol. Indic. 48, 550–559 (2015).
[Crossref]

Xiao, Q.

X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
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Xu, Z.

X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
[Crossref]

Yiping, W.

S. Luo, C. Wang, X. Xi, S. Nie, S. Xia, and W. Yiping, “Forest leaf area index estimation using combined ICESat/GLAS and optical remote sensing image,” J. Inf. Millim. Waves 34(2), 243–249 (2015).

Yu, X.

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).
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Zhang, L.

L. Zhang and T. E. Grift, “A LIDAR-based crop height measurement system for Miscanthus giganteus,” Comput. Electron. Agric. 85, 70–76 (2012).
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Zhang, N.

N. Zhang, M. Wang, and N. Wang, “Precision agriculture—a worldwide overview,” Comput. Electron. Agric. 36(2–3), 113–132 (2002).
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Zhang, Y.

J. M. Chen, A. Govind, O. Sonnentag, Y. Zhang, A. Barr, and B. Amiro, “Leaf area index measurements at Fluxnet-Canada forest sites,” Agric. For. Meteorol. 140(1–4), 257–268 (2006).
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Zhao, F.

F. Zhao, Q. Guo, and M. Kelly, “Allometric equation choice impacts lidar-based forest biomass estimates: a case study from the Sierra National Forest, CA,” Agric. For. Meteorol. 165, 64–72 (2012).
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Zhao, K.

S. C. Popescu, K. Zhao, A. Neuenschwander, and C. Lin, “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(11), 2786–2797 (2011).
[Crossref]

K. Zhao and S. Popescu, “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(8), 1628–1645 (2009).
[Crossref]

Zhou, J.

X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
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Zhu, G.

X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
[Crossref]

Zhu, Z.

X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
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Agric. For. Meteorol. (7)

I. Jonckheere, S. Fleck, K. Nackaerts, B. Muys, P. Coppin, M. Weiss, and F. Baret, “Review of methods for in situ leaf area index determination. Part I. Theories, sensors and hemispherical photography,” Agric. For. Meteorol. 121(1–2), 19–35 (2004).
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J. M. Chen, A. Govind, O. Sonnentag, Y. Zhang, A. Barr, and B. Amiro, “Leaf area index measurements at Fluxnet-Canada forest sites,” Agric. For. Meteorol. 140(1–4), 257–268 (2006).
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F. Zhao, Q. Guo, and M. Kelly, “Allometric equation choice impacts lidar-based forest biomass estimates: a case study from the Sierra National Forest, CA,” Agric. For. Meteorol. 165, 64–72 (2012).
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J. J. Richardson, L. M. Moskal, and S.-H. Kim, “Modeling approaches to estimate effective leaf area index from aerial discrete-return LIDAR,” Agric. For. Meteorol. 149(6–7), 1152–1160 (2009).
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E. Luedeling and A. Gassner, “Partial least squares regression for analyzing walnut phenology in California,” Agric. For. Meteorol. 158–159, 43–52 (2012).
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Bull. Am. Meteorol. Soc. (1)

X. Li, G. Cheng, S. Liu, Q. Xiao, M. Ma, R. Jin, T. Che, Q. Liu, W. Wang, Y. Qi, J. Wen, H. Li, G. Zhu, J. Guo, Y. Ran, S. Wang, Z. Zhu, J. Zhou, X. Hu, and Z. Xu, “Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design,” Bull. Am. Meteorol. Soc. 94(8), 1145–1160 (2013).
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T. Gobakken and E. Næsset, “Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data,” Can. J. For. Res. 38(5), 1095–1109 (2008).
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J. Strunk, H. Temesgen, H.-E. Andersen, J. P. Flewelling, and L. Madsen, “Effects of lidar pulse density and sample size on a model-assisted approach to estimate forest inventory variables,” Can. J. Rem. Sens. 38(5), 644–654 (2012).
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C. Hopkinson, L. E. Chasmer, G. Sass, I. F. Creed, M. Sitar, W. Kalbfleisch, and P. Treitz, “Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment,” Can. J. Rem. Sens. 31(2), 191–206 (2005).
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S. Wold, M. Sjöström, and L. Eriksson, “PLS-regression: a basic tool of chemometrics,” Chemom. Intell. Lab. Syst. 58(2), 109–130 (2001).
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Comput. Electron. Agric. (2)

L. Zhang and T. E. Grift, “A LIDAR-based crop height measurement system for Miscanthus giganteus,” Comput. Electron. Agric. 85, 70–76 (2012).
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N. Zhang, M. Wang, and N. Wang, “Precision agriculture—a worldwide overview,” Comput. Electron. Agric. 36(2–3), 113–132 (2002).
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Ecol. Indic. (2)

W. Li, Z. Niu, N. Huang, C. Wang, S. Gao, and C. Wu, “Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China,” Ecol. Indic. 57, 486–496 (2015).
[Crossref]

S. Luo, C. Wang, F. Pan, X. Xi, G. Li, S. Nie, and S. Xia, “Estimation of wetland vegetation height and leaf area index using airborne laser scanning data,” Ecol. Indic. 48, 550–559 (2015).
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Field Crops Res. (1)

J. U. H. Eitel, T. S. Magney, L. A. Vierling, T. T. Brown, and D. R. Huggins, “LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status,” Field Crops Res. 159, 21–32 (2014).
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A. C. Morel, S. S. Saatchi, Y. Malhi, N. J. Berry, L. Banin, D. Burslem, R. Nilus, and R. C. Ong, “Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data,” For. Ecol. Manage. 262(9), 1786–1798 (2011).
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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).
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J. Estornell, L. A. Ruiz, B. Velázquez-Martí, and A. Fernández-Sarría, “Estimation of shrub biomass by airborne LiDAR data in small forest stands,” For. Ecol. Manage. 262(9), 1697–1703 (2011).
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G. W. Sileshi, “A critical review of forest biomass estimation models, common mistakes and corrective measures,” For. Ecol. Manage. 329, 237–254 (2014).
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M. Magnusson, J. E. S. Fransson, and J. Holmgren, “Effects on estimation accuracy of forest variables using different pulse density of laser data,” For. Sci. 53(6), 619–626 (2007).

Forests (1)

L. Ruiz, T. Hermosilla, F. Mauro, and M. Godino, “Analysis of the influence of plot size and LiDAR density on forest structure attribute estimates,” Forests 5(5), 936–951 (2014).
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A. A. Gitelson, A. Viña, T. J. Arkebauer, D. C. Rundquist, G. Keydan, and B. Leavitt, “Remote estimation of leaf area index and green leaf biomass in maize canopies,” Geophys. Res. Lett. 30(5), 1248 (2003).
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Glob. Ecol. Biogeogr. (1)

J. B. Drake, R. G. Knox, R. O. Dubayah, D. B. Clark, R. Condit, J. B. Blair, and M. Hofton, “Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships,” Glob. Ecol. Biogeogr. 12(2), 147–159 (2003).
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IEEE Trans. Geosci. Rem. Sens. (1)

C. Wang, M. Menenti, M. P. Stoll, A. Feola, E. Belluco, and M. Marani, “Separation of ground and low vegetation signatures in LiDAR measurements of salt-marsh environments,” IEEE Trans. Geosci. Rem. Sens. 47(7), 2014–2023 (2009).
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Int. J. Appl. Earth Obs. Geoinf. (1)

S. Gao, Z. Niu, N. Huang, and X. Hou, “Estimating the leaf area index, height and biomass of maize using HJ-1 and RADARSAT-2,” Int. J. Appl. Earth Obs. Geoinf. 24, 1–8 (2013).
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Int. J. Remote Sens. (4)

S. Solberg, “Mapping gap fraction, LAI and defoliation using various ALS penetration variables,” Int. J. Remote Sens. 31(5), 1227–1244 (2010).
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J. Heiskanen, “Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data,” Int. J. Remote Sens. 27(6), 1135–1158 (2006).
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I. J. Davenport, R. B. Bradbury, G. Q. A. Anderson, G. R. F. Hayman, J. R. Krebs, D. C. Mason, J. D. Wilson, and N. J. Veck, “Improving bird population models using airborne remote sensing,” Int. J. Remote Sens. 21(13–14), 2705–2717 (2000).
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J. Chen, S. Gu, M. Shen, Y. Tang, and B. Matsushita, “Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data,” Int. J. Remote Sens. 30(24), 6497–6517 (2009).
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ISPRS J. Photogramm. Remote Sens. (2)

K. K. Singh, G. Chen, J. B. McCarter, and R. K. Meentemeyer, “Effects of LiDAR point density and landscape context on estimates of urban forest biomass,” ISPRS J. Photogramm. Remote Sens. 101, 310–322 (2015).
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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. Remote Sens. 69, 121–133 (2012).
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J. Appl. Remote Sens. (1)

N. Tilly, D. Hoffmeister, Q. Cao, S. Huang, V. Lenz-Wiedemann, Y. Miao, and G. Bareth, “Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice,” J. Appl. Remote Sens. 8(1), 083671 (2014).
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J. Arid Environ. (1)

N. F. Glenn, L. P. Spaete, T. T. Sankey, D. R. Derryberry, S. P. Hardegree, and J. J. Mitchell, “Errors in LiDAR-derived shrub height and crown area on sloped terrain,” J. Arid Environ. 75(4), 377–382 (2011).
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J. Inf. Millim. Waves (1)

S. Luo, C. Wang, X. Xi, S. Nie, S. Xia, and W. Yiping, “Forest leaf area index estimation using combined ICESat/GLAS and optical remote sensing image,” J. Inf. Millim. Waves 34(2), 243–249 (2015).

N. Z. J. For. Sci. (1)

M. Watt, A. Meredith, P. Watt, and A. Gunn, “The influence of LiDAR pulse density on the precision of inventory metrics in young unthinned Douglas-fir stands during initial and subsequent LiDAR acquisitions,” N. Z. J. For. Sci. 44(1), 1–9 (2014).
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Opt. Express (3)

Photogramm. Eng. Remote Sensing (1)

P. Wilkes, S. D. Jones, L. Suarez, A. Haywood, W. Woodgate, M. Soto-Berelov, A. Mellor, and A. K. Skidmore, “Understanding the effects of ALS pulse density for metric retrieval across diverse forest types,” Photogramm. Eng. Remote Sensing 81(8), 625–635 (2015).
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Remote Sens. (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).
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C. Atzberger, “Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs,” Remote Sens. 5(2), 949–981 (2013).
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C. Edson and M. G. Wing, “Airborne light detection and ranging (LiDAR) for individual tree stem location, height, and biomass measurements,” Remote Sens. 3(12), 2494–2528 (2011).
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G. Pope and P. Treitz, “Leaf area index (LAI) Estimation in boreal mixedwood forest of Ontario, Canada using light detection and ranging (LiDAR) and worldview-2 imagery,” Remote Sens. 5(10), 5040–5063 (2013).
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Remote Sens. Environ. (16)

J. Jensen, K. Humes, L. Vierling, and A. Hudak, “Discrete return lidar-based prediction of leaf area index in two conifer forests,” Remote Sens. Environ. 112(10), 3947–3957 (2008).
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G. W. Frazer, S. Magnussen, M. A. Wulder, and K. O. Niemann, “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(2), 636–649 (2011).
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G. M. Foody, D. S. Boyd, and M. E. J. Cutler, “Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions,” Remote Sens. Environ. 85(4), 463–474 (2003).
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K. Zhao and S. Popescu, “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(8), 1628–1645 (2009).
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L. Chasmer, C. Hopkinson, P. Treitz, H. McCaughey, A. Barr, and A. Black, “A lidar-based hierarchical approach for assessing MODIS fPAR,” Remote Sens. Environ. 112(12), 4344–4357 (2008).
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M. Chopping, G. G. Moisen, L. Su, A. Laliberte, A. Rango, J. V. Martonchik, and D. P. C. Peters, “Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA multiangle imaging spectro-radiometer,” Remote Sens. Environ. 112(5), 2051–2063 (2008).
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S. C. Popescu, K. Zhao, A. Neuenschwander, and C. Lin, “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(11), 2786–2797 (2011).
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Q. Chen, G. Vaglio Laurin, J. J. Battles, and D. Saah, “Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass,” Remote Sens. Environ. 121, 108–117 (2012).
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M. A. Lefsky, D. P. Turner, M. Guzy, and W. B. Cohen, “Combining lidar estimates of aboveground biomass and Landsat estimates of stand age for spatially extensive validation of modeled forest productivity,” Remote Sens. Environ. 95(4), 549–558 (2005).
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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).
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[Crossref] [PubMed]

Other (1)

Q. Xiao and J. Wen, “HiWATER: Airborne LiDAR-DEM&DSM data production in the middle reaches of the Heihe River Basin,” (Heihe Plan Science Data Center, 2013).

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Figures (6)

Fig. 1
Fig. 1 Schematic diagram illustrating the selected 11 corn plants for height measurements.
Fig. 2
Fig. 2 VIP values of LiDAR-derived metrics in fitting the PLS model.
Fig. 3
Fig. 3 Scatterplots of the predicted biophysical parameters against field-observed corn biophysical parameters and regression lines of the best prediction models for (a) LAI, (b) height, and (c) AGB. Solid lines indicate the best-fit regression line. Dotted lines denote the 1:1 line.
Fig. 4
Fig. 4 Changes in R2 and RMSE with point densities for (a) LAI, (b) height, and (c) AGB.
Fig. 5
Fig. 5 Effects of varying sampling sizes on the estimation accuracy of biophysical parameters (a) LAI, (b) height, and (c) AGB. The estimation models were constructed based on the original LiDAR point density of 7.32 points/m2.
Fig. 6
Fig. 6 Effects of varying height thresholds on the estimation accuracy of biophysical parameters (a) LAI, (b) height, and (c) AGB. The estimation models were constructed based on the original LiDAR point density of 7.32 points/m2.

Tables (6)

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Table 1 Statistics of field-measured biophysical parameters at the plot level (n = 42).

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Table 2 LiDAR data acquisition parameters used in this study.

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Table 3 Statistics of LiDAR point density and post spacing at the plot level (n = 42).

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Table 4 LiDAR-derived metrics for estimating biophysical parameters.

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Table 5 LAI, height and aboveground biomass estimation accuracies using single linear and PLS regression methods from the original LiDAR point density data (n = 42).

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Table 6 Estimation accuracies of LAI, height and biomass with varying point density.

Equations (4)

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B=129.72H131.16
R_cover= N canopy (h) N all
RMS E r = RMSE y ¯
RMS E cv = i=1 n ( y ^ i y i ) 2 n

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