Abstract

Crown base height (CBH) is an essential tree biophysical parameter for many applications in forest management, forest fuel treatment, wildfire modeling, ecosystem modeling and global climate change studies. Accurate and automatic estimation of CBH for individual trees is still a challenging task. Airborne light detection and ranging (LiDAR) provides reliable and promising data for estimating CBH. Various methods have been developed to calculate CBH indirectly using regression-based means from airborne LiDAR data and field measurements. However, little attention has been paid to directly calculate CBH at the individual tree scale in mixed-species forests without field measurements. In this study, we propose a new method for directly estimating individual-tree CBH from airborne LiDAR data. Our method involves two main strategies: 1) removing noise and understory vegetation for each tree; and 2) estimating CBH by generating percentile ranking profile for each tree and using a spline curve to identify its inflection points. These two strategies lend our method the advantages of no requirement of field measurements and being efficient and effective in mixed-species forests. The proposed method was applied to a mixed conifer forest in the Sierra Nevada, California and was validated by field measurements. The results showed that our method can directly estimate CBH at individual tree level with a root-mean-squared error of 1.62 m, a coefficient of determination of 0.88 and a relative bias of 3.36%. Furthermore, we systematically analyzed the accuracies among different height groups and tree species by comparing with field measurements. Our results implied that taller trees had relatively higher uncertainties than shorter trees. Our findings also show that the accuracy for CBH estimation was the highest for black oak trees, with an RMSE of 0.52 m. The conifer species results were also good with uniformly high R2 ranging from 0.82 to 0.93. In general, our method has demonstrated high accuracy for individual tree CBH estimation and strong potential for applications in mixed species over large areas.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (3)

C. Alexander, A. H. Korstjens, and R. A. Hill, “Influence of micro-topography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models,” Int. J. Appl. Earth Obs. Geoinf. 65, 105–113 (2018).
[Crossref]

G. Goldbergs, S. R. Levick, M. Lawes, and A. Edwards, “Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR,” Remote Sens. Environ. 205, 141–150 (2018).
[Crossref]

K. Zhao, J. C. Suarez, M. Garcia, T. Hu, C. Wang, and A. Londo, “Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux,” Remote Sens. Environ. 204, 883–897 (2018).
[Crossref]

2017 (5)

Q. Ma, Y. Su, and Q. Guo, “Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(9), 4225–4236 (2017).
[Crossref]

A. A. Plowright, N. C. Coops, C. M. Chance, S. R. Sheppard, and N. W. Aven, “Multi-scale analysis of relationship between imperviousness and urban tree height using airborne remote sensing,” Remote Sens. Environ. 194, 391–400 (2017).
[Crossref]

R. E. McRoberts, Q. Chen, and B. F. Walters, “Multivariate inference for forest inventories using auxiliary airborne laser scanning data,” For. Ecol. Manage. 401, 295–303 (2017).
[Crossref]

M. Kelly, Y. Su, S. Di Tommaso, D. L. Fry, B. M. Collins, S. L. Stephens, and Q. Guo, “Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA,” Remote Sens. 10(2), 10 (2017).
[Crossref]

Q. Ma, Y. Su, S. Tao, and Q. Guo, “Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains, California,” Int. J. Digit. Earth 5, 1–19 (2017).
[Crossref]

2016 (10)

S. Luo, J. M. Chen, C. Wang, X. Xi, H. Zeng, D. Peng, and D. Li, “Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters,” Opt. Express 24(11), 11578–11593 (2016).
[Crossref] [PubMed]

W. Li, Z. Niu, J. Li, H. Chen, S. Gao, M. Wu, and D. Li, “Generating pseudo large footprint waveforms from small footprint full-waveform airborne LiDAR data for the layered retrieval of LAI in orchards,” Opt. Express 24(9), 10142–10156 (2016).
[Crossref] [PubMed]

C. Véga, J.-P. Renaud, S. Durrieu, and M. Bouvier, “On the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters,” Remote Sens. Environ. 175, 32–42 (2016).
[Crossref]

J.-H. Lee, G. S. Biging, and J. B. Fisher, “An individual tree-based automated registration of aerial images to lidar data in a forested area,” Photogramm. Eng. Remote Sensing 82(9), 699–710 (2016).
[Crossref]

W. Xiao, S. Xu, S. O. Elberink, and G. Vosselman, “Individual tree crown modeling and change detection from airborne lidar data,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(8), 3467–3477 (2016).
[Crossref]

J. Breidenbach, R. E. McRoberts, and R. Astrup, “Empirical coverage of model-based variance estimators for remote sensing assisted estimation of stand-level timber volume,” Remote Sens. Environ. 173, 274–281 (2016).
[Crossref] [PubMed]

Y. Su, Q. Guo, B. M. Collins, D. L. Fry, T. Hu, and M. Kelly, “Forest fuel treatment detection using multi-temporal airborne lidar data and high-resolution aerial imagery: a case study in the Sierra Nevada Mountains, California,” Int. J. Remote Sens. 37(14), 3322–3345 (2016).
[Crossref]

M.-V. Piazza, L. A. Garibaldi, T. Kitzberger, and E. J. Chaneton, “Impact of introduced herbivores on understory vegetation along a regional moisture gradient in Patagonian beech forests,” For. Ecol. Manage. 366, 11–22 (2016).
[Crossref]

D. Reed, L. Washburn, A. Rassweiler, R. Miller, T. Bell, and S. Harrer, “Extreme warming challenges sentinel status of kelp forests as indicators of climate change,” Nat. Commun. 7, 13757 (2016).
[Crossref] [PubMed]

A. Ferraz, S. Saatchi, C. Mallet, S. Jacquemoud, G. Gonçalves, C. A. Silva, P. Soares, M. Tomé, and L. Pereira, “Airborne lidar estimation of aboveground forest biomass in the absence of field inventory,” Remote Sens. 8(8), 653 (2016).
[Crossref]

2015 (4)

F. Pimont, J.-L. Dupuy, E. Rigolot, V. Prat, and A. Piboule, “Estimating leaf bulk density distribution in a tree canopy using terrestrial LiDAR and a straightforward calibration procedure,” Remote Sens. 7(6), 7995–8018 (2015).
[Crossref]

A. S. Maguya, K. Tegel, V. Junttila, T. Kauranne, M. Korhonen, J. Burns, V. Leppanen, and B. Sanz, “Moving Voxel Method for Estimating Canopy Base Height from Airborne Laser Scanner Data,” Remote Sens. 7(7), 8950–8972 (2015).
[Crossref]

O. S. Ahmed, S. E. Franklin, M. A. Wulder, and J. C. White, “Characterizing stand-level forest canopy cover and height using landsat time series, samples of airborne LiDAR, and the random forest algorithm,” ISPRS J. Photogramm. Remote Sens. 101, 89–101 (2015).
[Crossref]

M. Kelly and S. Di Tommaso, “Mapping forests with Lidar provides flexible, accurate data with many uses,” Calif. Agric. 69(1), 14–20 (2015).
[Crossref]

2014 (3)

H. Tang, M. Brolly, F. Zhao, A. H. Strahler, C. L. Schaaf, S. Ganguly, G. Zhang, and R. Dubayah, “Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA,” Remote Sens. Environ. 143, 131–141 (2014).
[Crossref]

A. Matkan, M. Hajeb, B. Mirbagheri, S. Sadeghian, and M. Ahmadi, “Spatial analysis for outlier removal from LiDAR data,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 40(W3), 187–190 (2014).
[Crossref]

X. Lu, Q. Guo, W. Li, and J. Flanagan, “A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data,” ISPRS J. Photogramm. Remote Sens. 94, 1–12 (2014).
[Crossref]

2013 (4)

M. K. Jakubowski, W. Li, Q. Guo, and M. Kelly, “Delineating individual trees from LiDAR data: A comparison of vector-and raster-based segmentation approaches,” Remote Sens. 5(9), 4163–4186 (2013).
[Crossref]

M. K. Jakubowksi, Q. Guo, B. Collins, S. Stephens, and M. Kelly, “Predicting surface fuel models and fuel metrics using Lidar and CIR imagery in a dense, mountainous forest,” Photogramm. Eng. Remote Sensing 79(1), 37–49 (2013).
[Crossref]

J. P. Dandois and E. C. Ellis, “High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision,” Remote Sens. Environ. 136, 259–276 (2013).
[Crossref]

L. Korhonen, J. Vauhkonen, A. Virolainen, A. Hovi, and I. Korpela, “Estimation of tree crown volume from airborne lidar data using computational geometry,” Int. J. Remote Sens. 34(20), 7236–7248 (2013).
[Crossref]

2012 (2)

C. J. Gleason and J. Im, “Forest biomass estimation from airborne LiDAR data using machine learning approaches,” Remote Sens. Environ. 125, 80–91 (2012).
[Crossref]

W. Li, Q. Guo, M. K. Jakubowski, and M. Kelly, “A new method for segmenting individual trees from the lidar point cloud,” Photogramm. Eng. Remote Sensing 78(1), 75–84 (2012).
[Crossref]

2011 (1)

J. D. Muss, D. J. Mladenoff, and P. A. Townsend, “A pseudo-waveform technique to assess forest structure using discrete lidar data,” Remote Sens. Environ. 115(3), 824–835 (2011).
[Crossref]

2010 (2)

J. Vauhkonen, “Estimating crown base height for Scots pine by means of the 3D geometry of airborne laser scanning data,” Int. J. Remote Sens. 31(5), 1213–1226 (2010).
[Crossref]

Q. Guo, W. Li, H. Yu, and O. Alvarez, “Effects of topographic variability and lidar sampling density on several DEM interpolation methods,” Photogramm. Eng. Remote Sensing 76(6), 701–712 (2010).
[Crossref]

2009 (2)

J. R. Ben-Arie, G. J. Hay, R. P. Powers, G. Castilla, and B. St-Onge, “Development of a pit filling algorithm for LiDAR canopy height models,” Comput. Geosci. 35(9), 1940–1949 (2009).
[Crossref]

T. J. Dean, Q. V. Cao, S. D. Roberts, and D. L. Evans, “Measuring heights to crown base and crown median with LiDAR in a mature, even-aged loblolly pine stand,” For. Ecol. Manage. 257(1), 126–133 (2009).
[Crossref]

2008 (4)

J. Hyyppä, H. Hyyppä, D. Leckie, F. Gougeon, X. Yu, and M. Maltamo, “Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests,” Int. J. Remote Sens. 29(5), 1339–1366 (2008).
[Crossref]

L. A. Arroyo, C. Pascual, and J. A. Manzanera, “Fire models and methods to map fuel types: the role of remote sensing,” For. Ecol. Manage. 256(6), 1239–1252 (2008).
[Crossref]

S. C. Popescu and K. Zhao, “A voxel-based lidar method for estimating crown base height for deciduous and pine trees,” Remote Sens. Environ. 112(3), 767–781 (2008).
[Crossref]

J. Holmgren, Å. Persson, and U. Söderman, “Species identification of individual trees by combining high resolution LiDAR data with multi‐spectral images,” Int. J. Remote Sens. 29(5), 1537–1552 (2008).
[Crossref]

2006 (2)

S. Solberg, E. Naesset, and O. M. Bollandsas, “Single tree segmentation using airborne laser scanner data in a structurally heterogeneous spruce forest,” Photogramm. Eng. Remote Sensing 72(12), 1369–1378 (2006).
[Crossref]

X. Yu, J. Hyyppä, A. Kukko, M. Maltamo, and H. Kaartinen, “Change detection techniques for canopy height growth measurements using airborne laser scanner data,” Photogramm. Eng. Remote Sensing 72(12), 1339–1348 (2006).
[Crossref]

2005 (3)

H.-E. Andersen, R. J. McGaughey, and S. E. Reutebuch, “Estimating forest canopy fuel parameters using LIDAR data,” Remote Sens. Environ. 94(4), 441–449 (2005).
[Crossref]

S. D. Roberts, T. J. Dean, D. L. Evans, J. W. McCombs, R. L. Harrington, and P. A. Glass, “Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurements of height and crown dimensions,” For. Ecol. Manage. 213(1-3), 54–70 (2005).
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2004 (2)

S. J. Zarnoch, W. A. Bechtold, and K. Stolte, “Using crown condition variables as indicators of forest health,” Can. J. For. Res. 34(5), 1057–1070 (2004).
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J. Holmgren and Å. Persson, “Identifying species of individual trees using airborne laser scanner,” Remote Sens. Environ. 90(4), 415–423 (2004).
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2002 (2)

E. Næsset and T. Økland, “Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve,” Remote Sens. Environ. 79, 105–115 (2002).
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J. B. Drake, R. O. Dubayah, D. B. Clark, R. G. Knox, J. B. Blair, M. A. Hofton, R. L. Chazdon, J. F. Weishampel, and S. Prince, “Estimation of tropical forest structural characteristics using large-footprint lidar,” Remote Sens. Environ. 79(2-3), 305–319 (2002).
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2001 (1)

P. Foster, “The potential negative impacts of global climate change on tropical montane cloud forests,” Earth Sci. Rev. 55(1-2), 73–106 (2001).
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1999 (1)

M. A. Lefsky, W. Cohen, S. Acker, G. G. Parker, T. Spies, and D. Harding, “Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests,” Remote Sens. Environ. 70(3), 339–361 (1999).
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S. L. Stephens, “Evaluation of the effects of silvicultural and fuels treatments on potential fire behaviour in Sierra Nevada mixed-conifer forests,” For. Ecol. Manage. 105(1-3), 21–35 (1998).
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G. S. Biging and M. Dobbertin, “Evaluation of competition indices in individual tree growth models,” For. Sci. 41, 360–377 (1995).

1990 (1)

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Acker, S.

M. A. Lefsky, W. Cohen, S. Acker, G. G. Parker, T. Spies, and D. Harding, “Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests,” Remote Sens. Environ. 70(3), 339–361 (1999).
[Crossref]

Ahmadi, M.

A. Matkan, M. Hajeb, B. Mirbagheri, S. Sadeghian, and M. Ahmadi, “Spatial analysis for outlier removal from LiDAR data,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 40(W3), 187–190 (2014).
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Ahmed, O. S.

O. S. Ahmed, S. E. Franklin, M. A. Wulder, and J. C. White, “Characterizing stand-level forest canopy cover and height using landsat time series, samples of airborne LiDAR, and the random forest algorithm,” ISPRS J. Photogramm. Remote Sens. 101, 89–101 (2015).
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Alexander, C.

C. Alexander, A. H. Korstjens, and R. A. Hill, “Influence of micro-topography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models,” Int. J. Appl. Earth Obs. Geoinf. 65, 105–113 (2018).
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Alvarez, O.

Q. Guo, W. Li, H. Yu, and O. Alvarez, “Effects of topographic variability and lidar sampling density on several DEM interpolation methods,” Photogramm. Eng. Remote Sensing 76(6), 701–712 (2010).
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Andersen, H.-E.

H.-E. Andersen, R. J. McGaughey, and S. E. Reutebuch, “Estimating forest canopy fuel parameters using LIDAR data,” Remote Sens. Environ. 94(4), 441–449 (2005).
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Arroyo, L. A.

L. A. Arroyo, C. Pascual, and J. A. Manzanera, “Fire models and methods to map fuel types: the role of remote sensing,” For. Ecol. Manage. 256(6), 1239–1252 (2008).
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Astrup, R.

J. Breidenbach, R. E. McRoberts, and R. Astrup, “Empirical coverage of model-based variance estimators for remote sensing assisted estimation of stand-level timber volume,” Remote Sens. Environ. 173, 274–281 (2016).
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Aven, N. W.

A. A. Plowright, N. C. Coops, C. M. Chance, S. R. Sheppard, and N. W. Aven, “Multi-scale analysis of relationship between imperviousness and urban tree height using airborne remote sensing,” Remote Sens. Environ. 194, 391–400 (2017).
[Crossref]

Bechtold, W. A.

S. J. Zarnoch, W. A. Bechtold, and K. Stolte, “Using crown condition variables as indicators of forest health,” Can. J. For. Res. 34(5), 1057–1070 (2004).
[Crossref]

Bell, T.

D. Reed, L. Washburn, A. Rassweiler, R. Miller, T. Bell, and S. Harrer, “Extreme warming challenges sentinel status of kelp forests as indicators of climate change,” Nat. Commun. 7, 13757 (2016).
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Ben-Arie, J. R.

J. R. Ben-Arie, G. J. Hay, R. P. Powers, G. Castilla, and B. St-Onge, “Development of a pit filling algorithm for LiDAR canopy height models,” Comput. Geosci. 35(9), 1940–1949 (2009).
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Biging, G. S.

J.-H. Lee, G. S. Biging, and J. B. Fisher, “An individual tree-based automated registration of aerial images to lidar data in a forested area,” Photogramm. Eng. Remote Sensing 82(9), 699–710 (2016).
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G. S. Biging and M. Dobbertin, “Evaluation of competition indices in individual tree growth models,” For. Sci. 41, 360–377 (1995).

Blair, J. B.

J. B. Drake, R. O. Dubayah, D. B. Clark, R. G. Knox, J. B. Blair, M. A. Hofton, R. L. Chazdon, J. F. Weishampel, and S. Prince, “Estimation of tropical forest structural characteristics using large-footprint lidar,” Remote Sens. Environ. 79(2-3), 305–319 (2002).
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Bollandsas, O. M.

S. Solberg, E. Naesset, and O. M. Bollandsas, “Single tree segmentation using airborne laser scanner data in a structurally heterogeneous spruce forest,” Photogramm. Eng. Remote Sensing 72(12), 1369–1378 (2006).
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Bouvier, M.

C. Véga, J.-P. Renaud, S. Durrieu, and M. Bouvier, “On the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters,” Remote Sens. Environ. 175, 32–42 (2016).
[Crossref]

Breidenbach, J.

J. Breidenbach, R. E. McRoberts, and R. Astrup, “Empirical coverage of model-based variance estimators for remote sensing assisted estimation of stand-level timber volume,” Remote Sens. Environ. 173, 274–281 (2016).
[Crossref] [PubMed]

Brolly, M.

H. Tang, M. Brolly, F. Zhao, A. H. Strahler, C. L. Schaaf, S. Ganguly, G. Zhang, and R. Dubayah, “Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA,” Remote Sens. Environ. 143, 131–141 (2014).
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Burns, J.

A. S. Maguya, K. Tegel, V. Junttila, T. Kauranne, M. Korhonen, J. Burns, V. Leppanen, and B. Sanz, “Moving Voxel Method for Estimating Canopy Base Height from Airborne Laser Scanner Data,” Remote Sens. 7(7), 8950–8972 (2015).
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Cao, Q. V.

T. J. Dean, Q. V. Cao, S. D. Roberts, and D. L. Evans, “Measuring heights to crown base and crown median with LiDAR in a mature, even-aged loblolly pine stand,” For. Ecol. Manage. 257(1), 126–133 (2009).
[Crossref]

Castilla, G.

J. R. Ben-Arie, G. J. Hay, R. P. Powers, G. Castilla, and B. St-Onge, “Development of a pit filling algorithm for LiDAR canopy height models,” Comput. Geosci. 35(9), 1940–1949 (2009).
[Crossref]

Chance, C. M.

A. A. Plowright, N. C. Coops, C. M. Chance, S. R. Sheppard, and N. W. Aven, “Multi-scale analysis of relationship between imperviousness and urban tree height using airborne remote sensing,” Remote Sens. Environ. 194, 391–400 (2017).
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Chaneton, E. J.

M.-V. Piazza, L. A. Garibaldi, T. Kitzberger, and E. J. Chaneton, “Impact of introduced herbivores on understory vegetation along a regional moisture gradient in Patagonian beech forests,” For. Ecol. Manage. 366, 11–22 (2016).
[Crossref]

Chazdon, R. L.

J. B. Drake, R. O. Dubayah, D. B. Clark, R. G. Knox, J. B. Blair, M. A. Hofton, R. L. Chazdon, J. F. Weishampel, and S. Prince, “Estimation of tropical forest structural characteristics using large-footprint lidar,” Remote Sens. Environ. 79(2-3), 305–319 (2002).
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Chen, H.

Chen, J. M.

Chen, Q.

R. E. McRoberts, Q. Chen, and B. F. Walters, “Multivariate inference for forest inventories using auxiliary airborne laser scanning data,” For. Ecol. Manage. 401, 295–303 (2017).
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Clark, D. B.

J. B. Drake, R. O. Dubayah, D. B. Clark, R. G. Knox, J. B. Blair, M. A. Hofton, R. L. Chazdon, J. F. Weishampel, and S. Prince, “Estimation of tropical forest structural characteristics using large-footprint lidar,” Remote Sens. Environ. 79(2-3), 305–319 (2002).
[Crossref]

Cohen, W.

M. A. Lefsky, W. Cohen, S. Acker, G. G. Parker, T. Spies, and D. Harding, “Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests,” Remote Sens. Environ. 70(3), 339–361 (1999).
[Crossref]

Collins, B.

M. K. Jakubowksi, Q. Guo, B. Collins, S. Stephens, and M. Kelly, “Predicting surface fuel models and fuel metrics using Lidar and CIR imagery in a dense, mountainous forest,” Photogramm. Eng. Remote Sensing 79(1), 37–49 (2013).
[Crossref]

Collins, B. M.

M. Kelly, Y. Su, S. Di Tommaso, D. L. Fry, B. M. Collins, S. L. Stephens, and Q. Guo, “Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA,” Remote Sens. 10(2), 10 (2017).
[Crossref]

Y. Su, Q. Guo, B. M. Collins, D. L. Fry, T. Hu, and M. Kelly, “Forest fuel treatment detection using multi-temporal airborne lidar data and high-resolution aerial imagery: a case study in the Sierra Nevada Mountains, California,” Int. J. Remote Sens. 37(14), 3322–3345 (2016).
[Crossref]

Coops, N. C.

A. A. Plowright, N. C. Coops, C. M. Chance, S. R. Sheppard, and N. W. Aven, “Multi-scale analysis of relationship between imperviousness and urban tree height using airborne remote sensing,” Remote Sens. Environ. 194, 391–400 (2017).
[Crossref]

Dandois, J. P.

J. P. Dandois and E. C. Ellis, “High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision,” Remote Sens. Environ. 136, 259–276 (2013).
[Crossref]

Dean, T. J.

T. J. Dean, Q. V. Cao, S. D. Roberts, and D. L. Evans, “Measuring heights to crown base and crown median with LiDAR in a mature, even-aged loblolly pine stand,” For. Ecol. Manage. 257(1), 126–133 (2009).
[Crossref]

S. D. Roberts, T. J. Dean, D. L. Evans, J. W. McCombs, R. L. Harrington, and P. A. Glass, “Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurements of height and crown dimensions,” For. Ecol. Manage. 213(1-3), 54–70 (2005).
[Crossref]

Di Tommaso, S.

M. Kelly, Y. Su, S. Di Tommaso, D. L. Fry, B. M. Collins, S. L. Stephens, and Q. Guo, “Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA,” Remote Sens. 10(2), 10 (2017).
[Crossref]

M. Kelly and S. Di Tommaso, “Mapping forests with Lidar provides flexible, accurate data with many uses,” Calif. Agric. 69(1), 14–20 (2015).
[Crossref]

Dobbertin, M.

M. Dobbertin, “Tree growth as indicator of tree vitality and of tree reaction to environmental stress: a review,” Eur. J. For. Res. 124(4), 319–333 (2005).
[Crossref]

G. S. Biging and M. Dobbertin, “Evaluation of competition indices in individual tree growth models,” For. Sci. 41, 360–377 (1995).

Drake, J. B.

J. B. Drake, R. O. Dubayah, D. B. Clark, R. G. Knox, J. B. Blair, M. A. Hofton, R. L. Chazdon, J. F. Weishampel, and S. Prince, “Estimation of tropical forest structural characteristics using large-footprint lidar,” Remote Sens. Environ. 79(2-3), 305–319 (2002).
[Crossref]

Dubayah, R.

H. Tang, M. Brolly, F. Zhao, A. H. Strahler, C. L. Schaaf, S. Ganguly, G. Zhang, and R. Dubayah, “Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA,” Remote Sens. Environ. 143, 131–141 (2014).
[Crossref]

Dubayah, R. O.

J. B. Drake, R. O. Dubayah, D. B. Clark, R. G. Knox, J. B. Blair, M. A. Hofton, R. L. Chazdon, J. F. Weishampel, and S. Prince, “Estimation of tropical forest structural characteristics using large-footprint lidar,” Remote Sens. Environ. 79(2-3), 305–319 (2002).
[Crossref]

Dupuy, J.-L.

F. Pimont, J.-L. Dupuy, E. Rigolot, V. Prat, and A. Piboule, “Estimating leaf bulk density distribution in a tree canopy using terrestrial LiDAR and a straightforward calibration procedure,” Remote Sens. 7(6), 7995–8018 (2015).
[Crossref]

Durrieu, S.

C. Véga, J.-P. Renaud, S. Durrieu, and M. Bouvier, “On the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters,” Remote Sens. Environ. 175, 32–42 (2016).
[Crossref]

Edwards, A.

G. Goldbergs, S. R. Levick, M. Lawes, and A. Edwards, “Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR,” Remote Sens. Environ. 205, 141–150 (2018).
[Crossref]

Elberink, S. O.

W. Xiao, S. Xu, S. O. Elberink, and G. Vosselman, “Individual tree crown modeling and change detection from airborne lidar data,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(8), 3467–3477 (2016).
[Crossref]

Ellis, E. C.

J. P. Dandois and E. C. Ellis, “High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision,” Remote Sens. Environ. 136, 259–276 (2013).
[Crossref]

Evans, D. L.

T. J. Dean, Q. V. Cao, S. D. Roberts, and D. L. Evans, “Measuring heights to crown base and crown median with LiDAR in a mature, even-aged loblolly pine stand,” For. Ecol. Manage. 257(1), 126–133 (2009).
[Crossref]

S. D. Roberts, T. J. Dean, D. L. Evans, J. W. McCombs, R. L. Harrington, and P. A. Glass, “Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurements of height and crown dimensions,” For. Ecol. Manage. 213(1-3), 54–70 (2005).
[Crossref]

Ferraz, A.

A. Ferraz, S. Saatchi, C. Mallet, S. Jacquemoud, G. Gonçalves, C. A. Silva, P. Soares, M. Tomé, and L. Pereira, “Airborne lidar estimation of aboveground forest biomass in the absence of field inventory,” Remote Sens. 8(8), 653 (2016).
[Crossref]

Fisher, J. B.

J.-H. Lee, G. S. Biging, and J. B. Fisher, “An individual tree-based automated registration of aerial images to lidar data in a forested area,” Photogramm. Eng. Remote Sensing 82(9), 699–710 (2016).
[Crossref]

Flanagan, J.

X. Lu, Q. Guo, W. Li, and J. Flanagan, “A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data,” ISPRS J. Photogramm. Remote Sens. 94, 1–12 (2014).
[Crossref]

Foster, P.

P. Foster, “The potential negative impacts of global climate change on tropical montane cloud forests,” Earth Sci. Rev. 55(1-2), 73–106 (2001).
[Crossref]

Franklin, S. E.

O. S. Ahmed, S. E. Franklin, M. A. Wulder, and J. C. White, “Characterizing stand-level forest canopy cover and height using landsat time series, samples of airborne LiDAR, and the random forest algorithm,” ISPRS J. Photogramm. Remote Sens. 101, 89–101 (2015).
[Crossref]

Fry, D. L.

M. Kelly, Y. Su, S. Di Tommaso, D. L. Fry, B. M. Collins, S. L. Stephens, and Q. Guo, “Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA,” Remote Sens. 10(2), 10 (2017).
[Crossref]

Y. Su, Q. Guo, B. M. Collins, D. L. Fry, T. Hu, and M. Kelly, “Forest fuel treatment detection using multi-temporal airborne lidar data and high-resolution aerial imagery: a case study in the Sierra Nevada Mountains, California,” Int. J. Remote Sens. 37(14), 3322–3345 (2016).
[Crossref]

Ganguly, S.

H. Tang, M. Brolly, F. Zhao, A. H. Strahler, C. L. Schaaf, S. Ganguly, G. Zhang, and R. Dubayah, “Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA,” Remote Sens. Environ. 143, 131–141 (2014).
[Crossref]

Gao, S.

Garcia, M.

K. Zhao, J. C. Suarez, M. Garcia, T. Hu, C. Wang, and A. Londo, “Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux,” Remote Sens. Environ. 204, 883–897 (2018).
[Crossref]

Garibaldi, L. A.

M.-V. Piazza, L. A. Garibaldi, T. Kitzberger, and E. J. Chaneton, “Impact of introduced herbivores on understory vegetation along a regional moisture gradient in Patagonian beech forests,” For. Ecol. Manage. 366, 11–22 (2016).
[Crossref]

Glass, P. A.

S. D. Roberts, T. J. Dean, D. L. Evans, J. W. McCombs, R. L. Harrington, and P. A. Glass, “Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurements of height and crown dimensions,” For. Ecol. Manage. 213(1-3), 54–70 (2005).
[Crossref]

Gleason, C. J.

C. J. Gleason and J. Im, “Forest biomass estimation from airborne LiDAR data using machine learning approaches,” Remote Sens. Environ. 125, 80–91 (2012).
[Crossref]

Goldbergs, G.

G. Goldbergs, S. R. Levick, M. Lawes, and A. Edwards, “Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR,” Remote Sens. Environ. 205, 141–150 (2018).
[Crossref]

Gonçalves, G.

A. Ferraz, S. Saatchi, C. Mallet, S. Jacquemoud, G. Gonçalves, C. A. Silva, P. Soares, M. Tomé, and L. Pereira, “Airborne lidar estimation of aboveground forest biomass in the absence of field inventory,” Remote Sens. 8(8), 653 (2016).
[Crossref]

Gougeon, F.

J. Hyyppä, H. Hyyppä, D. Leckie, F. Gougeon, X. Yu, and M. Maltamo, “Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests,” Int. J. Remote Sens. 29(5), 1339–1366 (2008).
[Crossref]

Guo, Q.

M. Kelly, Y. Su, S. Di Tommaso, D. L. Fry, B. M. Collins, S. L. Stephens, and Q. Guo, “Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA,” Remote Sens. 10(2), 10 (2017).
[Crossref]

Q. Ma, Y. Su, S. Tao, and Q. Guo, “Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains, California,” Int. J. Digit. Earth 5, 1–19 (2017).
[Crossref]

Q. Ma, Y. Su, and Q. Guo, “Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(9), 4225–4236 (2017).
[Crossref]

Y. Su, Q. Guo, B. M. Collins, D. L. Fry, T. Hu, and M. Kelly, “Forest fuel treatment detection using multi-temporal airborne lidar data and high-resolution aerial imagery: a case study in the Sierra Nevada Mountains, California,” Int. J. Remote Sens. 37(14), 3322–3345 (2016).
[Crossref]

X. Lu, Q. Guo, W. Li, and J. Flanagan, “A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data,” ISPRS J. Photogramm. Remote Sens. 94, 1–12 (2014).
[Crossref]

M. K. Jakubowski, W. Li, Q. Guo, and M. Kelly, “Delineating individual trees from LiDAR data: A comparison of vector-and raster-based segmentation approaches,” Remote Sens. 5(9), 4163–4186 (2013).
[Crossref]

M. K. Jakubowksi, Q. Guo, B. Collins, S. Stephens, and M. Kelly, “Predicting surface fuel models and fuel metrics using Lidar and CIR imagery in a dense, mountainous forest,” Photogramm. Eng. Remote Sensing 79(1), 37–49 (2013).
[Crossref]

W. Li, Q. Guo, M. K. Jakubowski, and M. Kelly, “A new method for segmenting individual trees from the lidar point cloud,” Photogramm. Eng. Remote Sensing 78(1), 75–84 (2012).
[Crossref]

Q. Guo, W. Li, H. Yu, and O. Alvarez, “Effects of topographic variability and lidar sampling density on several DEM interpolation methods,” Photogramm. Eng. Remote Sensing 76(6), 701–712 (2010).
[Crossref]

Hajeb, M.

A. Matkan, M. Hajeb, B. Mirbagheri, S. Sadeghian, and M. Ahmadi, “Spatial analysis for outlier removal from LiDAR data,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 40(W3), 187–190 (2014).
[Crossref]

Harding, D.

M. A. Lefsky, W. Cohen, S. Acker, G. G. Parker, T. Spies, and D. Harding, “Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests,” Remote Sens. Environ. 70(3), 339–361 (1999).
[Crossref]

Harrer, S.

D. Reed, L. Washburn, A. Rassweiler, R. Miller, T. Bell, and S. Harrer, “Extreme warming challenges sentinel status of kelp forests as indicators of climate change,” Nat. Commun. 7, 13757 (2016).
[Crossref] [PubMed]

Harrington, R. L.

S. D. Roberts, T. J. Dean, D. L. Evans, J. W. McCombs, R. L. Harrington, and P. A. Glass, “Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurements of height and crown dimensions,” For. Ecol. Manage. 213(1-3), 54–70 (2005).
[Crossref]

Hay, G. J.

J. R. Ben-Arie, G. J. Hay, R. P. Powers, G. Castilla, and B. St-Onge, “Development of a pit filling algorithm for LiDAR canopy height models,” Comput. Geosci. 35(9), 1940–1949 (2009).
[Crossref]

Hill, R. A.

C. Alexander, A. H. Korstjens, and R. A. Hill, “Influence of micro-topography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models,” Int. J. Appl. Earth Obs. Geoinf. 65, 105–113 (2018).
[Crossref]

Hofton, M. A.

J. B. Drake, R. O. Dubayah, D. B. Clark, R. G. Knox, J. B. Blair, M. A. Hofton, R. L. Chazdon, J. F. Weishampel, and S. Prince, “Estimation of tropical forest structural characteristics using large-footprint lidar,” Remote Sens. Environ. 79(2-3), 305–319 (2002).
[Crossref]

Holmgren, J.

J. Holmgren, Å. Persson, and U. Söderman, “Species identification of individual trees by combining high resolution LiDAR data with multi‐spectral images,” Int. J. Remote Sens. 29(5), 1537–1552 (2008).
[Crossref]

J. Holmgren and Å. Persson, “Identifying species of individual trees using airborne laser scanner,” Remote Sens. Environ. 90(4), 415–423 (2004).
[Crossref]

Hovi, A.

L. Korhonen, J. Vauhkonen, A. Virolainen, A. Hovi, and I. Korpela, “Estimation of tree crown volume from airborne lidar data using computational geometry,” Int. J. Remote Sens. 34(20), 7236–7248 (2013).
[Crossref]

Hu, T.

K. Zhao, J. C. Suarez, M. Garcia, T. Hu, C. Wang, and A. Londo, “Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux,” Remote Sens. Environ. 204, 883–897 (2018).
[Crossref]

Y. Su, Q. Guo, B. M. Collins, D. L. Fry, T. Hu, and M. Kelly, “Forest fuel treatment detection using multi-temporal airborne lidar data and high-resolution aerial imagery: a case study in the Sierra Nevada Mountains, California,” Int. J. Remote Sens. 37(14), 3322–3345 (2016).
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Hyyppä, H.

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M. K. Jakubowksi, Q. Guo, B. Collins, S. Stephens, and M. Kelly, “Predicting surface fuel models and fuel metrics using Lidar and CIR imagery in a dense, mountainous forest,” Photogramm. Eng. Remote Sensing 79(1), 37–49 (2013).
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M. K. Jakubowski, W. Li, Q. Guo, and M. Kelly, “Delineating individual trees from LiDAR data: A comparison of vector-and raster-based segmentation approaches,” Remote Sens. 5(9), 4163–4186 (2013).
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X. Yu, J. Hyyppä, A. Kukko, M. Maltamo, and H. Kaartinen, “Change detection techniques for canopy height growth measurements using airborne laser scanner data,” Photogramm. Eng. Remote Sensing 72(12), 1339–1348 (2006).
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M. Kelly, Y. Su, S. Di Tommaso, D. L. Fry, B. M. Collins, S. L. Stephens, and Q. Guo, “Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA,” Remote Sens. 10(2), 10 (2017).
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Y. Su, Q. Guo, B. M. Collins, D. L. Fry, T. Hu, and M. Kelly, “Forest fuel treatment detection using multi-temporal airborne lidar data and high-resolution aerial imagery: a case study in the Sierra Nevada Mountains, California,” Int. J. Remote Sens. 37(14), 3322–3345 (2016).
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M. K. Jakubowski, W. Li, Q. Guo, and M. Kelly, “Delineating individual trees from LiDAR data: A comparison of vector-and raster-based segmentation approaches,” Remote Sens. 5(9), 4163–4186 (2013).
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W. Li, Q. Guo, M. K. Jakubowski, and M. Kelly, “A new method for segmenting individual trees from the lidar point cloud,” Photogramm. Eng. Remote Sensing 78(1), 75–84 (2012).
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M.-V. Piazza, L. A. Garibaldi, T. Kitzberger, and E. J. Chaneton, “Impact of introduced herbivores on understory vegetation along a regional moisture gradient in Patagonian beech forests,” For. Ecol. Manage. 366, 11–22 (2016).
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X. Yu, J. Hyyppä, A. Kukko, M. Maltamo, and H. Kaartinen, “Change detection techniques for canopy height growth measurements using airborne laser scanner data,” Photogramm. Eng. Remote Sensing 72(12), 1339–1348 (2006).
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A. S. Maguya, K. Tegel, V. Junttila, T. Kauranne, M. Korhonen, J. Burns, V. Leppanen, and B. Sanz, “Moving Voxel Method for Estimating Canopy Base Height from Airborne Laser Scanner Data,” Remote Sens. 7(7), 8950–8972 (2015).
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M. A. Lefsky, W. Cohen, S. Acker, G. G. Parker, T. Spies, and D. Harding, “Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests,” Remote Sens. Environ. 70(3), 339–361 (1999).
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L. A. Arroyo, C. Pascual, and J. A. Manzanera, “Fire models and methods to map fuel types: the role of remote sensing,” For. Ecol. Manage. 256(6), 1239–1252 (2008).
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M.-V. Piazza, L. A. Garibaldi, T. Kitzberger, and E. J. Chaneton, “Impact of introduced herbivores on understory vegetation along a regional moisture gradient in Patagonian beech forests,” For. Ecol. Manage. 366, 11–22 (2016).
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D. Reed, L. Washburn, A. Rassweiler, R. Miller, T. Bell, and S. Harrer, “Extreme warming challenges sentinel status of kelp forests as indicators of climate change,” Nat. Commun. 7, 13757 (2016).
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D. Reed, L. Washburn, A. Rassweiler, R. Miller, T. Bell, and S. Harrer, “Extreme warming challenges sentinel status of kelp forests as indicators of climate change,” Nat. Commun. 7, 13757 (2016).
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F. Pimont, J.-L. Dupuy, E. Rigolot, V. Prat, and A. Piboule, “Estimating leaf bulk density distribution in a tree canopy using terrestrial LiDAR and a straightforward calibration procedure,” Remote Sens. 7(6), 7995–8018 (2015).
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S. D. Roberts, T. J. Dean, D. L. Evans, J. W. McCombs, R. L. Harrington, and P. A. Glass, “Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurements of height and crown dimensions,” For. Ecol. Manage. 213(1-3), 54–70 (2005).
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A. Matkan, M. Hajeb, B. Mirbagheri, S. Sadeghian, and M. Ahmadi, “Spatial analysis for outlier removal from LiDAR data,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 40(W3), 187–190 (2014).
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A. Ferraz, S. Saatchi, C. Mallet, S. Jacquemoud, G. Gonçalves, C. A. Silva, P. Soares, M. Tomé, and L. Pereira, “Airborne lidar estimation of aboveground forest biomass in the absence of field inventory,” Remote Sens. 8(8), 653 (2016).
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J. Holmgren, Å. Persson, and U. Söderman, “Species identification of individual trees by combining high resolution LiDAR data with multi‐spectral images,” Int. J. Remote Sens. 29(5), 1537–1552 (2008).
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M. K. Jakubowksi, Q. Guo, B. Collins, S. Stephens, and M. Kelly, “Predicting surface fuel models and fuel metrics using Lidar and CIR imagery in a dense, mountainous forest,” Photogramm. Eng. Remote Sensing 79(1), 37–49 (2013).
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M. Kelly, Y. Su, S. Di Tommaso, D. L. Fry, B. M. Collins, S. L. Stephens, and Q. Guo, “Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA,” Remote Sens. 10(2), 10 (2017).
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H. Tang, M. Brolly, F. Zhao, A. H. Strahler, C. L. Schaaf, S. Ganguly, G. Zhang, and R. Dubayah, “Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA,” Remote Sens. Environ. 143, 131–141 (2014).
[Crossref]

Su, Y.

M. Kelly, Y. Su, S. Di Tommaso, D. L. Fry, B. M. Collins, S. L. Stephens, and Q. Guo, “Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA,” Remote Sens. 10(2), 10 (2017).
[Crossref]

Q. Ma, Y. Su, S. Tao, and Q. Guo, “Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains, California,” Int. J. Digit. Earth 5, 1–19 (2017).
[Crossref]

Q. Ma, Y. Su, and Q. Guo, “Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(9), 4225–4236 (2017).
[Crossref]

Y. Su, Q. Guo, B. M. Collins, D. L. Fry, T. Hu, and M. Kelly, “Forest fuel treatment detection using multi-temporal airborne lidar data and high-resolution aerial imagery: a case study in the Sierra Nevada Mountains, California,” Int. J. Remote Sens. 37(14), 3322–3345 (2016).
[Crossref]

Suarez, J. C.

K. Zhao, J. C. Suarez, M. Garcia, T. Hu, C. Wang, and A. Londo, “Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux,” Remote Sens. Environ. 204, 883–897 (2018).
[Crossref]

Tang, H.

H. Tang, M. Brolly, F. Zhao, A. H. Strahler, C. L. Schaaf, S. Ganguly, G. Zhang, and R. Dubayah, “Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA,” Remote Sens. Environ. 143, 131–141 (2014).
[Crossref]

Tao, S.

Q. Ma, Y. Su, S. Tao, and Q. Guo, “Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains, California,” Int. J. Digit. Earth 5, 1–19 (2017).
[Crossref]

Tegel, K.

A. S. Maguya, K. Tegel, V. Junttila, T. Kauranne, M. Korhonen, J. Burns, V. Leppanen, and B. Sanz, “Moving Voxel Method for Estimating Canopy Base Height from Airborne Laser Scanner Data,” Remote Sens. 7(7), 8950–8972 (2015).
[Crossref]

Tomé, M.

A. Ferraz, S. Saatchi, C. Mallet, S. Jacquemoud, G. Gonçalves, C. A. Silva, P. Soares, M. Tomé, and L. Pereira, “Airborne lidar estimation of aboveground forest biomass in the absence of field inventory,” Remote Sens. 8(8), 653 (2016).
[Crossref]

Townsend, P. A.

J. D. Muss, D. J. Mladenoff, and P. A. Townsend, “A pseudo-waveform technique to assess forest structure using discrete lidar data,” Remote Sens. Environ. 115(3), 824–835 (2011).
[Crossref]

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M. H. Zwietering, I. Jongenburger, F. M. Rombouts, and K. van ’t Riet, “Modeling of the bacterial growth curve,” Appl. Environ. Microbiol. 56(6), 1875–1881 (1990).
[PubMed]

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L. Korhonen, J. Vauhkonen, A. Virolainen, A. Hovi, and I. Korpela, “Estimation of tree crown volume from airborne lidar data using computational geometry,” Int. J. Remote Sens. 34(20), 7236–7248 (2013).
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J. Vauhkonen, “Estimating crown base height for Scots pine by means of the 3D geometry of airborne laser scanning data,” Int. J. Remote Sens. 31(5), 1213–1226 (2010).
[Crossref]

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C. Véga, J.-P. Renaud, S. Durrieu, and M. Bouvier, “On the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters,” Remote Sens. Environ. 175, 32–42 (2016).
[Crossref]

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L. Korhonen, J. Vauhkonen, A. Virolainen, A. Hovi, and I. Korpela, “Estimation of tree crown volume from airborne lidar data using computational geometry,” Int. J. Remote Sens. 34(20), 7236–7248 (2013).
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W. Xiao, S. Xu, S. O. Elberink, and G. Vosselman, “Individual tree crown modeling and change detection from airborne lidar data,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(8), 3467–3477 (2016).
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J. B. Drake, R. O. Dubayah, D. B. Clark, R. G. Knox, J. B. Blair, M. A. Hofton, R. L. Chazdon, J. F. Weishampel, and S. Prince, “Estimation of tropical forest structural characteristics using large-footprint lidar,” Remote Sens. Environ. 79(2-3), 305–319 (2002).
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[Crossref]

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H. Tang, M. Brolly, F. Zhao, A. H. Strahler, C. L. Schaaf, S. Ganguly, G. Zhang, and R. Dubayah, “Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA,” Remote Sens. Environ. 143, 131–141 (2014).
[Crossref]

Zhao, K.

K. Zhao, J. C. Suarez, M. Garcia, T. Hu, C. Wang, and A. Londo, “Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux,” Remote Sens. Environ. 204, 883–897 (2018).
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S. C. Popescu and K. Zhao, “A voxel-based lidar method for estimating crown base height for deciduous and pine trees,” Remote Sens. Environ. 112(3), 767–781 (2008).
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M. H. Zwietering, I. Jongenburger, F. M. Rombouts, and K. van ’t Riet, “Modeling of the bacterial growth curve,” Appl. Environ. Microbiol. 56(6), 1875–1881 (1990).
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M. H. Zwietering, I. Jongenburger, F. M. Rombouts, and K. van ’t Riet, “Modeling of the bacterial growth curve,” Appl. Environ. Microbiol. 56(6), 1875–1881 (1990).
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W. Xiao, S. Xu, S. O. Elberink, and G. Vosselman, “Individual tree crown modeling and change detection from airborne lidar data,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(8), 3467–3477 (2016).
[Crossref]

Q. Ma, Y. Su, and Q. Guo, “Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(9), 4225–4236 (2017).
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Q. Ma, Y. Su, S. Tao, and Q. Guo, “Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains, California,” Int. J. Digit. Earth 5, 1–19 (2017).
[Crossref]

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J. Vauhkonen, “Estimating crown base height for Scots pine by means of the 3D geometry of airborne laser scanning data,” Int. J. Remote Sens. 31(5), 1213–1226 (2010).
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L. Korhonen, J. Vauhkonen, A. Virolainen, A. Hovi, and I. Korpela, “Estimation of tree crown volume from airborne lidar data using computational geometry,” Int. J. Remote Sens. 34(20), 7236–7248 (2013).
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O. S. Ahmed, S. E. Franklin, M. A. Wulder, and J. C. White, “Characterizing stand-level forest canopy cover and height using landsat time series, samples of airborne LiDAR, and the random forest algorithm,” ISPRS J. Photogramm. Remote Sens. 101, 89–101 (2015).
[Crossref]

Nat. Commun. (1)

D. Reed, L. Washburn, A. Rassweiler, R. Miller, T. Bell, and S. Harrer, “Extreme warming challenges sentinel status of kelp forests as indicators of climate change,” Nat. Commun. 7, 13757 (2016).
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Q. Guo, W. Li, H. Yu, and O. Alvarez, “Effects of topographic variability and lidar sampling density on several DEM interpolation methods,” Photogramm. Eng. Remote Sensing 76(6), 701–712 (2010).
[Crossref]

X. Yu, J. Hyyppä, A. Kukko, M. Maltamo, and H. Kaartinen, “Change detection techniques for canopy height growth measurements using airborne laser scanner data,” Photogramm. Eng. Remote Sensing 72(12), 1339–1348 (2006).
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M. K. Jakubowski, W. Li, Q. Guo, and M. Kelly, “Delineating individual trees from LiDAR data: A comparison of vector-and raster-based segmentation approaches,” Remote Sens. 5(9), 4163–4186 (2013).
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A. Ferraz, S. Saatchi, C. Mallet, S. Jacquemoud, G. Gonçalves, C. A. Silva, P. Soares, M. Tomé, and L. Pereira, “Airborne lidar estimation of aboveground forest biomass in the absence of field inventory,” Remote Sens. 8(8), 653 (2016).
[Crossref]

A. S. Maguya, K. Tegel, V. Junttila, T. Kauranne, M. Korhonen, J. Burns, V. Leppanen, and B. Sanz, “Moving Voxel Method for Estimating Canopy Base Height from Airborne Laser Scanner Data,” Remote Sens. 7(7), 8950–8972 (2015).
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F. Pimont, J.-L. Dupuy, E. Rigolot, V. Prat, and A. Piboule, “Estimating leaf bulk density distribution in a tree canopy using terrestrial LiDAR and a straightforward calibration procedure,” Remote Sens. 7(6), 7995–8018 (2015).
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M. Kelly, Y. Su, S. Di Tommaso, D. L. Fry, B. M. Collins, S. L. Stephens, and Q. Guo, “Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA,” Remote Sens. 10(2), 10 (2017).
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C. Véga, J.-P. Renaud, S. Durrieu, and M. Bouvier, “On the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters,” Remote Sens. Environ. 175, 32–42 (2016).
[Crossref]

H. Tang, M. Brolly, F. Zhao, A. H. Strahler, C. L. Schaaf, S. Ganguly, G. Zhang, and R. Dubayah, “Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA,” Remote Sens. Environ. 143, 131–141 (2014).
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J. Holmgren and Å. Persson, “Identifying species of individual trees using airborne laser scanner,” Remote Sens. Environ. 90(4), 415–423 (2004).
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[Crossref]

J. B. Drake, R. O. Dubayah, D. B. Clark, R. G. Knox, J. B. Blair, M. A. Hofton, R. L. Chazdon, J. F. Weishampel, and S. Prince, “Estimation of tropical forest structural characteristics using large-footprint lidar,” Remote Sens. Environ. 79(2-3), 305–319 (2002).
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Figures (8)

Fig. 1
Fig. 1 The map of study area and the locations of field measured plots
Fig. 2
Fig. 2 Flow chart of the method for estimating crown base height (CBH).
Fig. 3
Fig. 3 An example of estimating individual tree CBH from LiDAR point cloud data.
Fig. 4
Fig. 4 An example of one individual tree, showing the process of locating the top of understory for subsequential removal.
Fig. 5
Fig. 5 An example of estimating CBH based on the percentile ranking profile of LiDAR points for an individual tree.
Fig. 6
Fig. 6 Comparison of CBH from field measurements and the proposed LiDAR-based method.
Fig. 7
Fig. 7 The CBH from LiDAR estimates compared to the CBH from field measurements, for different tree height groups.
Fig. 8
Fig. 8 CBH estimates from LiDAR compared to those from field measurements for seven tree species (i.e., ABCO, ABMA, CADE, PILA, PIPO, PSME, and QUKE).

Tables (3)

Tables Icon

Table 1 Summary of the field measurements and LiDAR-derived tree structure parameters for the matched trees.

Tables Icon

Table 2 Summary of the field measurements for the matched trees categorized by tree species. Tree height, CBH and DBH are presented as mean values ± standard deviation.

Tables Icon

Table 3 Summary of results from previous CBH estimation method comparing with our method for individual trees

Equations (5)

Equations on this page are rendered with MathJax. Learn more.

d ij = ( x i x j ) 2 + ( y i y j ) 2
D ij = d ij +w×( h i h j )
R 2 =1 i=1 n ( x i y i ) 2 / i=1 n ( x i x m ) 2
RMSE= i=1 n ( x i y i ) 2 /n
bias= y m x m x m ×100%

Metrics