Canopy Height Measurement

Chapter Contents (Back)
Height. Canopy Height. LiDAR Canopy height:
See also Forest Analysis, Canopy Heights, LiDAR.
See also Tree Diameter, Tree Width, Stem Diameter, Diameter at Breast Height, DBH.

Yu, X.W.[Xiao-Wei], Hyyppä, J.[Juha], Kukko, A.[Antero], Maltamo, M.[Matti], Kaartinen, H.[Harri],
Change Detection Techniques for Canopy Height Growth Measurements Using Airborne Laser Scanner Data,
PhEngRS(72), No. 12, December 2006, pp. 1339-1348.
WWW Link. 0704
The individual tree height growth of Scots pine was estimated from two laser surveys with three different techniques, and the accuracy of the estimation was evaluated with sample trees. BibRef

Simard, M., Pinto, N., Fisher, J.B., Baccini, A.,
Mapping forest canopy height globally with spaceborne lidar,
GeopResSpacePh(116), 2011, pp. 04021.
DOI Link
WWW Link. BibRef 1100

Simard, M., Fatoyinbo, T.L., Smetanka, C., Rivera-Monroy, V.H., Castañeda-Moya, E., Thomas, N., van der Stocken, T.,
Mangrove canopy height globally related to precipitation, temperature and cyclone frequency,
NatGeosci(12), 2018, pp. 40-45.
DOI Link BibRef 1800

Miliaresis, G., Delikaraoglou, D.,
Effects of Percent Tree Canopy Density and DEM Misregistration on SRTM/NED Vegetation Height Estimates.,
RS(1), No. 2, June 2009, pp. 36-49.
DOI Link 1203

Neuenschwander, A.L.[Amy L.], Magruder, L.A.[Lori A.],
The Potential Impact of Vertical Sampling Uncertainty on ICESat-2/ATLAS Terrain and Canopy Height Retrievals for Multiple Ecosystems,
RS(8), No. 12, 2016, pp. 1039.
DOI Link 1612

Chen, C.F.[Chuan-Fa], Wang, Y.[Yifu], Li, Y.Y.[Yan-Yan], Yue, T.X.[Tian-Xiang], Wang, X.[Xin],
Robust and Parameter-Free Algorithm for Constructing Pit-Free Canopy Height Models,
IJGI(6), No. 7, 2017, pp. xx-yy.
DOI Link 1708

Sumnall, M.[Matthew], Fox, T.R.[Thomas R.], Wynne, R.H.[Randolph H.], Thomas, V.A.[Valerie A.],
Mapping the height and spatial cover of features beneath the forest canopy at small-scales using airborne scanning discrete return Lidar,
PandRS(133), No. Supplement C, 2017, pp. 186-200.
Elsevier DOI 1711
Managed forest, Loblolly pine, Lidar, Voxel, Height-bin, Understorey layer, Height, Horizontal, cover BibRef

Wilke, N.[Norman], Siegmann, B.[Bastian], Klingbeil, L.[Lasse], Burkart, A.[Andreas], Kraska, T.[Thorsten], Muller, O.[Onno], van Doorn, A.[Anna], Heinemann, S.[Sascha], Rascher, U.[Uwe],
Quantifying Lodging Percentage and Lodging Severity Using a UAV-Based Canopy Height Model Combined with an Objective Threshold Approach,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903

Wilke, N.[Norman], Siegmann, B.[Bastian], Frimpong, F., Muller, O., Klingbeil, L.[Lasse], Rascher, U.,
Quantifying Lodging Percentage, Lodging Development and Lodging Severity Using a Uav-based Canopy Height Model,
DOI Link 1912

Liu, M.B.[Ming-Bo], Cao, C.X.[Chun-Xiang], Chen, W.[Wei], Wang, X.J.[Xue-Jun],
Mapping Canopy Heights of Poplar Plantations in Plain Areas Using ZY3-02 Stereo and Multispectral Data,
IJGI(8), No. 3, 2019, pp. xx-yy.
DOI Link 1903

Swinfield, T.[Tom], Lindsell, J.A.[Jeremy A.], Williams, J.V.[Jonathan V.], Harrison, R.D.[Rhett D.], Agustiono, Habibi, Gemita, E.[Elva], Schönlieb, C.B.[Carola B.], Coomes, D.A.[David A.],
Accurate Measurement of Tropical Forest Canopy Heights and Aboveground Carbon Using Structure From Motion,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905

Neuenschwander, A.L.[Amy L.], Magruder, L.A.[Lori A.],
Canopy and Terrain Height Retrievals with ICESat-2: A First Look,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908

Cui, L.[Lei], Jiao, Z.[Ziti], Dong, Y.D.[Ya-Dong], Sun, M.[Mei], Zhang, X.N.[Xiao-Ning], Yin, S.Y.[Si-Yang], Ding, A.X.[An-Xin], Chang, Y.X.[Ya-Xuan], Guo, J.[Jing], Xie, R.[Rui],
Estimating Forest Canopy Height Using MODIS BRDF Data Emphasizing Typical-Angle Reflectances,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910

Wang, Q.A.[Qi-Ang], Ni-Meister, W.[Wenge],
Forest Canopy Height and Gaps from Multiangular BRDF, Assessed with Airborne LiDAR Data,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
(Short Title: Vegetation Structure from LiDAR and Multiangular Data) BibRef
And: Erratum: RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101

Fradette, M.S.[Marie-Soleil], Leboeuf, A.[Antoine], Riopel, M.[Martin], Bégin, J.[Jean],
Method to Reduce the Bias on Digital Terrain Model and Canopy Height Model from LiDAR Data,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904

Osinska-Skotak, K.[Katarzyna], Bakula, K.[Krzysztof], Jelowicki, L.[Lukasz], Podkowa, A.[Anna],
Using Canopy Height Model Obtained with Dense Image Matching of Archival Photogrammetric Datasets in Area Analysis of Secondary Succession,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909

Boutsoukis, C.[Christos], Manakos, I.[Ioannis], Heurich, M.[Marco], Delopoulos, A.[Anastasios],
Canopy Height Estimation from Single Multispectral 2D Airborne Imagery Using Texture Analysis and Machine Learning in Structurally Rich Temperate Forests,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912

Alagialoglou, L.[Leonidas], Manakos, I.[Ioannis], Heurich, M.[Marco], Cervenka, J.[Jaroslav], Delopoulos, A.[Anastasios],
Canopy Height Estimation from Spaceborne Imagery Using Convolutional Encoder-decoder,
Springer DOI 2106

Nie, S., Wang, C., Xi, X., Luo, S., Zhu, X., Li, G., Liu, H., Tian, J., Zhang, S.,
Assessing the Impacts of Various Factors on Treetop Detection Using LiDAR-Derived Canopy Height Models,
GeoRS(57), No. 12, December 2019, pp. 10099-10115.
Vegetation, Surface topography, Biological system modeling, Shape, Forestry, Remote sensing, Canopy height models (CHM), topographic normalization BibRef

Ghosh, S.M.[Sujit Madhab], Behera, M.D.[Mukunda Dev], Paramanik, S.[Somnath],
Canopy Height Estimation Using Sentinel Series Images through Machine Learning Models in a Mangrove Forest,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005

Yang, W.[Wei], Kondoh, A.[Akihiko],
Evaluation of the Simard et al. 2011 Global Canopy Height Map in Boreal Forests,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004

Kashongwe, H.B.[Herve B.], Roy, D.P.[David P.], Bwangoy, J.R.B.[Jean Robert B.],
Democratic Republic of the Congo Tropical Forest Canopy Height and Aboveground Biomass Estimation with Landsat-8 Operational Land Imager (OLI) and Airborne LiDAR Data: The Effect of Seasonal Landsat Image Selection,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005

Shimizu, K.[Katsuto], Ota, T.[Tetsuji], Mizoue, N.[Nobuya], Saito, H.[Hideki],
Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006

Liu, Q.W.[Qing-Wang], Fu, L.Y.[Li-Yong], Chen, Q.[Qiao], Wang, G.X.[Guang-Xing], Luo, P.[Peng], Sharma, R.P.[Ram P.], He, P.[Peng], Li, M.[Mei], Wang, M.X.[Meng-Xi], Duan, G.S.[Guang-Shuang],
Analysis of the Spatial Differences in Canopy Height Models from UAV LiDAR and Photogrammetry,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009

Arjasakusuma, S.[Sanjiwana], Kusuma, S.S.[Sandiaga Swahyu], Phinn, S.[Stuart],
Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data,
IJGI(9), No. 9, 2020, pp. xx-yy.
DOI Link 2009

Lin, X.J.[Xiao-Juan], Xu, M.[Min], Cao, C.X.[Chun-Xiang], Dang, Y.F.[Yong-Feng], Bashir, B.[Barjeece], Xie, B.[Bo], Huang, Z.B.[Zhi-Bin],
Estimates of Forest Canopy Height Using a Combination of ICESat-2/ATLAS Data and Stereo-Photogrammetry,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011

Adam, M.[Markus], Urbazaev, M.[Mikhail], Dubois, C.[Clémence], Schmullius, C.[Christiane],
Accuracy Assessment of GEDI Terrain Elevation and Canopy Height Estimates in European Temperate Forests: Influence of Environmental and Acquisition Parameters,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012

Wen, Z., Zhao, L., Zhang, W., Chen, E., Xu, K.,
The Effects of Coherence Calculation on Forest Height Estimation Using Sinc Model,
DOI Link 2012

Kumar, S.[Shashi], Govil, H.[Himanshu], Srivastava, P.K.[Prashant K.], Thakur, P.K.[Praveen K.], Kushwaha, S.P.S.[Satya P. S.],
Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012

Pourshamsi, M.[Maryam], Xia, J.[Junshi], Yokoya, N.[Naoto], Garcia, M.[Mariano], Lavalle, M.[Marco], Pottier, E.[Eric], Balzter, H.[Heiko],
Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning,
PandRS(172), 2021, pp. 79-94.
Elsevier DOI 2101
Polarimetric synthetic aperture radar (PolSAR), LiDAR, L-band, Forest height, Machine learning BibRef

Chen, W.[Wei], Zheng, Q.H.[Qi-Hui], Xiang, H.B.[Hai-Bing], Chen, X.[Xu], Sakai, T.[Tetsuro],
Forest Canopy Height Estimation Using Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) Technology Based on Full-Polarized ALOS/PALSAR Data,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101

Huang, Y.[Yue], Zhang, Q.[Qiaoping], Ferro-Famil, L.[Laurent],
Forest Height Estimation Using a Single-Pass Airborne L-Band Polarimetric and Interferometric SAR System and Tomographic Techniques,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102

Jiang, F.[Fugen], Zhao, F.[Feng], Ma, K.[Kaisen], Li, D.S.[Dong-Sheng], Sun, H.[Hua],
Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104

Lu, H.L.[Hong-Liang], Zhang, H.[Heng], Fan, H.[Huaitao], Liu, D.C.[Da-Cheng], Wang, J.[Jili], Wan, X.X.[Xiang-Xing], Zhao, L.[Lei], Deng, Y.[Yunkai], Zhao, F.J.[Feng-Jun], Wang, R.[Robert],
Forest height retrieval using P-band airborne multi-baseline SAR data: A novel phase compensation method,
PandRS(175), 2021, pp. 99-118.
Elsevier DOI 2105
Forest 3-D structure, Phase errors compensation, Network construction, Phase gradient autofocus, SAR tomography BibRef

Ku, N.W.[Nian-Wei], Popescu, S.[Sorin], Eriksson, M.[Marian],
Regionalization of an Existing Global Forest Canopy Height Model for Forests of the Southern United States,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105

Benson, M.L.[Michael L.], Pierce, L.[Leland], Bergen, K.[Kathleen], Sarabandi, K.[Kamal],
Model-Based Estimation of Forest Canopy Height and Biomass in the Canadian Boreal Forest Using Radar, LiDAR, and Optical Remote Sensing,
GeoRS(59), No. 6, June 2021, pp. 4635-4653.
Forestry, Laser radar, Remote sensing, Biomass, Biological system modeling, Synthetic aperture radar, synthetic aperture radar (SAR) BibRef

Fayad, I.[Ibrahim], Baghdadi, N.[Nicolas], Alvares, C.A.[Clayton Alcarde], Stape, J.L.[Jose Luiz], Bailly, J.S.[Jean Stéphane], Scolforo, H.F.[Henrique Ferraço], Cegatta, I.R.[Italo Ramos], Zribi, M.[Mehrez], Le Maire, G.[Guerric],
Terrain Slope Effect on Forest Height and Wood Volume Estimation from GEDI Data,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106

Peng, X.[Xing], Li, X.[Xinwu], Du, Y.[Yanan], Xie, Q.H.[Qing-Hua],
Forest Height Estimation from a Robust TomoSAR Method in the Case of Small Tomographic Aperture with Airborne Dataset at L-Band,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106

Quan, Y.[Ying], Li, M.Z.[Ming-Ze], Hao, Y.[Yuanshuo], Wang, B.[Bin],
Comparison and Evaluation of Different Pit-Filling Methods for Generating High Resolution Canopy Height Model Using UAV Laser Scanning Data,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106

Chen, F.[Fade], Guo, F.[Fei], Liu, L.[Lilong], Nan, Y.[Yang],
An Improved Method for Pan-Tropical Above-Ground Biomass and Canopy Height Retrieval Using CYGNSS,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107

Goldbergs, G.[Grigorijs],
Impact of Base-to-Height Ratio on Canopy Height Estimation Accuracy of Hemiboreal Forest Tree Species by Using Satellite and Airborne Stereo Imagery,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108

Chen, H.[Hao], Cloude, S.R.[Shane R.], White, J.C.[Joanne C.],
Using GEDI Waveforms for Improved TanDEM-X Forest Height Mapping: A Combined SINC + Legendre Approach,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108

Li, M.[Mei], Li, Z.Y.[Zeng-Yuan], Liu, Q.W.[Qing-Wang], Chen, E.[Erxue],
Comparison of Coniferous Plantation Heights Using Unmanned Aerial Vehicle (UAV) Laser Scanning and Stereo Photogrammetry,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108

Zhang, H.[He], Bauters, M.[Marijn], Boeckx, P.[Pascal], van Oost, K.[Kristof],
Mapping Canopy Heights in Dense Tropical Forests Using Low-Cost UAV-Derived Photogrammetric Point Clouds and Machine Learning Approaches,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109

Parache, H.B.[Helen Blue], Mayer, T.[Timothy], Herndon, K.E.[Kelsey E.], Flores-Anderson, A.I.[Africa Ixmucane], Lei, Y.[Yang], Nguyen, Q.[Quyen], Kunlamai, T.[Thannarot], Griffin, R.[Robert],
Estimating Forest Stand Height in Savannakhet, Lao PDR Using InSAR and Backscatter Methods with L-Band SAR Data,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112

Mao, Y.[Yu], Michel, O.O.[Opelele Omeno], Yu, Y.[Ying], Fan, W.Y.[Wen-Yi], Sui, A.[Ao], Liu, Z.H.[Zhi-Hui], Wu, G.[Guoming],
Retrieval of Boreal Forest Heights Using an Improved Random Volume over Ground (RVoG) Model Based on Repeat-Pass Spaceborne Polarimetric SAR Interferometry: The Case Study of Saihanba, China,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112

Xi, Z.[Zhilong], Xu, H.[Huadong], Xing, Y.Q.[Yan-Qiu], Gong, W.S.[Wei-Shu], Chen, G.Z.[Gui-Zhen], Yang, S.[Shuhang],
Forest Canopy Height Mapping by Synergizing ICESat-2, Sentinel-1, Sentinel-2 and Topographic Information Based on Machine Learning Methods,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201

Zhao, J.P.[Jun-Peng], Zhao, L.[Lei], Chen, E.[Erxue], Li, Z.Y.[Zeng-Yuan], Xu, K.P.[Kun-Peng], Ding, X.Y.[Xiang-Yuan],
An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202

Oh, S.C.[Sung-Chan], Jung, J.[Jinha], Shao, G.[Guofan], Shao, G.[Gang], Gallion, J.[Joey], Fei, S.L.[Song-Lin],
High-Resolution Canopy Height Model Generation and Validation Using USGS 3DEP LiDAR Data in Indiana, USA,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202

Zhang, Q.[Qi], Ge, L.L.[Lin-Lin], Hensley, S.[Scott], Isabel Metternicht, G.[Graciela], Liu, C.[Chang], Zhang, R.H.[Rui-Heng],
PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data,
PandRS(186), 2022, pp. 123-139.
Elsevier DOI 2203
Repeat-pass, L-band, PolInSAR, LiDAR, Forest height, GAN BibRef

Bulluck, L.[Lesley], Lin, B.[Baron], Schold, E.[Elizabeth],
Fine Resolution Imagery and LIDAR-Derived Canopy Heights Accurately Classify Land Cover with a Focus on Shrub/Sapling Cover in a Mountainous Landscape,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204

Schreyer, J.[Johannes], Byron Walker, B.[Blake], Lakes, T.[Tobia],
Implementing urban canopy height derived from a TanDEM-X-DEM: An expert survey and case study,
PandRS(187), 2022, pp. 345-361.
Elsevier DOI 2205

Zhang, J.S.[Jian-Shuang], Zhang, Y.[Yangjian], Fan, W.[Wenyi], He, L.Y.[Li-Yuan], Yu, Y.[Ying], Mao, X.[Xuegang],
A Modified Two-Steps Three-Stage Inversion Algorithm for Forest Height Inversion Using Single-Baseline L-Band PolInSAR Data,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205

Morin, D.[David], Planells, M.[Milena], Baghdadi, N.[Nicolas], Bouvet, A.[Alexandre], Fayad, I.[Ibrahim], Toan, T.L.[Thuy Le], Mermoz, S.[Stéphane], Villard, L.[Ludovic],
Improving Heterogeneous Forest Height Maps by Integrating GEDI-Based Forest Height Information in a Multi-Sensor Mapping Process,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205

Mitsevich, L., Zhukovskaya, N.,
3d Modeling and GIS Analysis for Aerodrome Forest Obstacle Monitoring,
ISPRS21(B2-2021: 753-757).
DOI Link 2201

Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Tree Height Measurement .

Last update:May 21, 2022 at 16:37:58