24.4.13.5.1 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
BibRef

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
BibRef

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
BibRef

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
BibRef

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,
UAV-g19(649-655).
DOI Link 1912
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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,
MMMod21(II:307-317).
Springer DOI 2106
BibRef

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.
IEEE DOI 1912
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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

Wen, Z., Zhao, L., Zhang, W., Chen, E., Xu, K.,
The Effects of Coherence Calculation on Forest Height Estimation Using Sinc Model,
ISPRS20(B1:637-642).
DOI Link 2012
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.K.[Yun-Kai], 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
BibRef

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.
IEEE DOI 2106
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
BibRef

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
BibRef

Quan, Y.[Ying], Li, M.Z.[Ming-Ze], Hao, Y.S.[Yuan-Shuo], 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
BibRef

Chen, F.[Fade], Guo, F.[Fei], Liu, L.L.[Li-Long], 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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.M.[Guo-Ming],
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
BibRef

Xi, Z.L.[Zhi-Long], Xu, H.D.[Hua-Dong], Xing, Y.Q.[Yan-Qiu], Gong, W.S.[Wei-Shu], Chen, G.Z.[Gui-Zhen], Yang, S.H.[Shu-Hang],
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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

Zhang, J.S.[Jian-Shuang], Zhang, Y.J.[Yang-Jian], Fan, W.Y.[Wen-Yi], He, L.Y.[Li-Yuan], Yu, Y.[Ying], Mao, X.G.[Xue-Gang],
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
BibRef

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
BibRef

Huang, Z.H.[Zeng-Hui], Yun, Y.[Ye], Chai, H.M.[Hui-Ming], Lv, X.L.[Xiao-Lei],
The Iterative Extraction of the Boundary of Coherence Region and Iterative Look-Up Table for Forest Height Estimation Using Polarimetric Interferometric Synthetic Aperture Radar Data,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Adrah, E.[Esmaeel], Jaafar, W.S.W.M.[Wan Shafrina Wan Mohd], Omar, H.[Hamdan], Bajaj, S.[Shaurya], Leite, R.V.[Rodrigo Vieira], Mazlan, S.M.[Siti Munirah], Silva, C.A.[Carlos Alberto], Ooi, M.C.G.[Maggie Chel Gee], Said, M.N.M.[Mohd Nizam Mohd], Maulud, K.N.A.[Khairul Nizam Abdul], Cardil, A.[Adrián], Mohan, M.[Midhun],
Analyzing Canopy Height Patterns and Environmental Landscape Drivers in Tropical Forests Using NASA's GEDI Spaceborne LiDAR,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Strunk, J.L.[Jacob L.], Bell, D.M.[David M.], Gregory, M.J.[Matthew J.],
Pushbroom Photogrammetric Heights Enhance State-Level Forest Attribute Mapping with Landsat and Environmental Gradients,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Xu, K.P.[Kun-Peng], Zhao, L.[Lei], Chen, E.[Erxue], Li, K.[Kun], Liu, D.C.[Da-Cheng], Li, T.[Tao], Li, Z.Y.[Zeng-Yuan], Fan, Y.X.[Ya-Xiong],
Forest Height Estimation Approach Combining P-Band and X-Band Interferometric SAR Data,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Wang, C.J.[Cang-Jiao], Elmore, A.J.[Andrew J.], Numata, I.[Izaya], Cochrane, M.A.[Mark A.], Lei, S.G.[Shao-Gang], Hakkenberg, C.R.[Christopher R.], Li, Y.Y.[Yuan-Yuan], Zhao, Y.[Yibo], Tian, Y.[Yu],
A Framework for Improving Wall-to-Wall Canopy Height Mapping by Integrating GEDI LiDAR,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Torres de Almeida, C.[Catherine], Gerente, J.[Jéssica], Rodrigo dos Prazeres Campos, J.[Jamerson], Caruso Gomes Junior, F.[Francisco], Providelo, L.A.[Lucas Antonio], Marchiori, G.[Guilherme], Chen, X.J.[Xin-Jian],
Canopy Height Mapping by Sentinel 1 and 2 Satellite Images, Airborne LiDAR Data, and Machine Learning,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Wang, Z.[Zhao], Shi, J.[Jiang], Sun, S.[Sashuang], Zhu, L.J.[Li-Jun], He, Y.[Yiyin], Jin, R.[Rong], Luo, L.[Letan], Zhao, L.[Lin], Peng, J.X.[Jun-Xiang], Zhou, Z.J.[Zhen-Jiang],
Estimation of Dry Matter and N Nutrient Status of Choy Sum by Analyzing Canopy Images and Plant Height Information,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Li, B.[Bin], Zhao, T.Z.[Tian-Zhong], Su, X.H.[Xiao-Hui], Fan, G.P.[Guang-Peng], Zhang, W.J.[Wen-Jie], Deng, Z.[Zhuo], Yu, Y.H.[Yong-Hui],
Correction of Terrain Effects on Forest Canopy Height Estimation Using ICESat-2 and High Spatial Resolution Images,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Zhang, N.[Nan], Chen, M.J.[Ming-Jie], Yang, F.[Fan], Yang, C.C.[Can-Can], Yang, P.H.[Peng-Hui], Gao, Y.S.[Yu-Shan], Shang, Y.[Yue], Peng, D.[Daoli],
Forest Height Mapping Using Feature Selection and Machine Learning by Integrating Multi-Source Satellite Data in Baoding City, North China,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Managhebi, T.[Tayebe], Maghsoudi, Y.[Yasser], Amani, M.[Meisam],
Forest Height Retrieval Based on the Dual PolInSAR Images,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Xie, J.[Jinwei], Li, L.[Lei], Zhuang, L.[Long], Zheng, Y.[Yu],
A New Strategy for Forest Height Estimation Using Airborne X-Band PolInSAR Data,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Sothe, C.[Camile], Gonsamo, A.[Alemu], Lourenço, R.B.[Ricardo B.], Kurz, W.A.[Werner A.], Snider, J.[James],
Spatially Continuous Mapping of Forest Canopy Height in Canada by Combining GEDI and ICESat-2 with PALSAR and Sentinel,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
BibRef

Zhang, Q.[Qi], Hensley, S.[Scott], Zhang, R.[Ruiheng], Liu, C.[Chang], Ge, L.L.[Lin-Lin],
Improved Model-Based Forest Height Inversion Using Airborne L-Band Repeat-Pass Dual-Baseline Pol-InSAR Data,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
BibRef

Ge, S.J.[Shao-Jia], Su, W.M.[Wei-Min], Gu, H.[Hong], Rauste, Y.[Yrjö], Praks, J.[Jaan], Antropov, O.[Oleg],
Improved LSTM Model for Boreal Forest Height Mapping Using Sentinel-1 Time Series,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Tiwari, K.[Kasip], Narine, L.L.[Lana L.],
A Comparison of Machine Learning and Geostatistical Approaches for Mapping Forest Canopy Height over the Southeastern US Using ICESat-2,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Luo, H.B.[Hong-Bin], Yue, C.R.[Cai-Rong], Xie, F.M.[Fu-Ming], Zhu, B.D.[Bo-Dong], Chen, S.[Si],
A Method for Forest Canopy Height Inversion Based on Machine Learning and Feature Mining Using UAVSAR,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Luo, H.B.[Hong-Bin], Yue, C.R.[Cai-Rong], Wang, N.[Ning], Luo, G.F.[Guang-Fei], Chen, S.[Si],
Correcting Underestimation and Overestimation in PolInSAR Forest Canopy Height Estimation Using Microwave Penetration Depth,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Ghosh, S.M.[Sujit M.], Behera, M.D.[Mukunda D.], Kumar, S.[Subham], Das, P.[Pulakesh], Prakash, A.J.[Ambadipudi J.], Bhaskaran, P.K.[Prasad K.], Roy, P.S.[Parth S.], Barik, S.K.[Saroj K.], Jeganathan, C.[Chockalingam], Srivastava, P.K.[Prashant K.], Behera, S.K.[Soumit K.],
Predicting the Forest Canopy Height from LiDAR and Multi-Sensor Data Using Machine Learning over India,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Lahssini, K.[Kamel], Baghdadi, N.[Nicolas], le Maire, G.[Guerric], Fayad, I.[Ibrahim],
Influence of GEDI Acquisition and Processing Parameters on Canopy Height Estimates over Tropical Forests,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Wang, L.[Lei], Zhou, Y.S.[Yu-Shan], Shen, G.[Gaoyun], Xiong, J.[Junnan], Shi, H.T.[Hong-Tao],
Forest Height Inversion Based on Time-Frequency RVoG Model Using Single-Baseline L-Band Sublook-InSAR Data,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Sui, A.[Ao], Michel, O.O.[Opelele Omeno], Mao, Y.[Yu], Fan, W.[Wenyi],
An Improved Forest Height Model Using L-Band Single-Baseline Polarimetric InSAR Data for Various Forest Densities,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Malambo, L.[Lonesome], Popescu, S.[Sorin], Liu, M.[Meng],
Landsat-Scale Regional Forest Canopy Height Mapping Using ICESat-2 Along-Track Heights: Case Study of Eastern Texas,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Zhou, X.C.[Xiao-Cheng], Hao, Y.[Youzhuang], Di, L.P.[Li-Ping], Wang, X.Q.[Xiao-Qin], Chen, C.C.[Chong-Cheng], Chen, Y.Z.[Yun-Zhi], Nagy, G.[Gábor], Jancso, T.[Tamas],
Improving GEDI Forest Canopy Height Products by Considering the Stand Age Factor Derived from Time-Series Remote Sensing Images: A Case Study in Fujian, China,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Cheng, K.[Kai], Su, Y.J.[Yan-Jun], Guan, H.[Hongcan], Tao, S.L.[Sheng-Li], Ren, Y.[Yu], Hu, T.Y.[Tian-Yu], Ma, K.P.[Ke-Ping], Tang, Y.H.[Yan-Hong], Guo, Q.H.[Qing-Hua],
Mapping China's planted forests using high resolution imagery and massive amounts of crowdsourced samples,
PandRS(196), 2023, pp. 356-371.
Elsevier DOI 2302
Planted forest, Crowdsourced samples, Landsat, Sentinel-1, Digital Elevation Model, Forest canopy height BibRef

Ngo, Y.N.[Yen-Nhi], Minh, D.H.T.[Dinh Ho Tong], Baghdadi, N.[Nicolas], Fayad, I.[Ibrahim],
Tropical Forest Top Height by GEDI: From Sparse Coverage to Continuous Data,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Goldbergs, G.[Grigorijs],
Comparison of Canopy Height Metrics from Airborne Laser Scanner and Aerial/Satellite Stereo Imagery to Assess the Growing Stock of Hemiboreal Forests,
RS(15), No. 6, 2023, pp. 1688.
DOI Link 2304
BibRef

Pourrahmati, M.R.[Manizheh Rajab], Baghdadi, N.[Nicolas], Fayad, I.[Ibrahim],
Comparison of GEDI LiDAR Data Capability for Forest Canopy Height Estimation over Broadleaf and Needleleaf Forests,
RS(15), No. 6, 2023, pp. 1522.
DOI Link 2304
BibRef

Xing, C.[Cheng], Wang, H.[Hongmiao], Zhang, Z.J.[Zhan-Jie], Yin, J.J.[Jun-Jun], Yang, J.[Jian],
A Review of Forest Height Inversion by PolInSAR: Theory, Advances, and Perspectives,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308
BibRef

Guan, X.[Xuebing], Yang, X.[Xiguang], Yu, Y.[Ying], Pan, Y.[Yan], Dong, H.[Hanyuan], Yang, T.[Tao],
Canopy-Height and Stand-Age Estimation in Northeast China at Sub-Compartment Level Using Multi-Resource Remote Sensing Data,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308
BibRef

Zhu, X.X.[Xiao-Xiao], Nie, S.[Sheng], Zhu, Y.M.[Ya-Min], Chen, Y.M.[Yi-Ming], Yang, B.[Bo], Li, W.[Wang],
Evaluation and Comparison of ICESat-2 and GEDI Data for Terrain and Canopy Height Retrievals in Short-Stature Vegetation,
RS(15), No. 20, 2023, pp. 4969.
DOI Link 2310
BibRef


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

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


Last update:Mar 16, 2024 at 20:36:19