24.4.13.5 Trees, Forest, Stem Volume, Aboveground Biomass Measurements

Liu, J., Hyyppä, J., Yu, X., Jaakkola, A., Kukko, A., Kaartinen, H., Zhu, L., Liang, X., Wang, Y., Hyyppä, H.,
A Novel GNSS Technique for Predicting Boreal Forest Attributes at Low Cost,
GeoRS(55), No. 9, September 2017, pp. 4855-4867.
IEEE DOI 1709
satellite navigation, vegetation mapping, 2-D remote sensing techniques, GNSS devices, GNSS-derived prediction accuracies, above-ground biomass, basal area, breast height, computational method, tree height, Biomass, Crowdsourcing, Data collection, mobile mapping, radio, propagation, losses BibRef

Kim, G.[Ghiseok], Hong, S.J.[Suk-Ju], Lee, A.Y.[Ah-Yeong], Lee, Y.E.[Ye-Eun], Im, S.J.[Sang-Jun],
Moisture Content Measurement of Broadleaf Litters Using Near-Infrared Spectroscopy Technique,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Hogland, J.[John], Anderson, N.[Nathaniel], Chung, W.[Woodam],
New Geospatial Approaches for Efficiently Mapping Forest Biomass Logistics at High Resolution over Large Areas,
IJGI(7), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Pandit, S.[Santa], Tsuyuki, S.[Satoshi], Dube, T.[Timothy],
Estimating Above-Ground Biomass in Sub-Tropical Buffer Zone Community Forests, Nepal, Using Sentinel 2 Data,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Pandit, S.[Santa], Tsuyuki, S.[Satoshi], Dube, T.[Timothy],
Landscape-Scale Aboveground Biomass Estimation in Buffer Zone Community Forests of Central Nepal: Coupling In Situ Measurements with Landsat 8 Satellite Data,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Knapp, N.[Nikolai], Huth, A.[Andreas], Kugler, F.[Florian], Papathanassiou, K.[Konstantinos], Condit, R.[Richard], Hubbell, S.P.[Stephen P.], Fischer, R.[Rico],
Model-Assisted Estimation of Tropical Forest Biomass Change: A Comparison of Approaches,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Le, A.V.[Anh V.], Paull, D.J.[David J.], Griffin, A.L.[Amy L.],
Exploring the Inclusion of Small Regenerating Trees to Improve Above-Ground Forest Biomass Estimation Using Geospatial Data,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Nguyen, T.H.[Trung H.], Jones, S.[Simon], Soto-Berelov, M.[Mariela], Haywood, A.[Andrew], Hislop, S.[Samuel],
A Comparison of Imputation Approaches for Estimating Forest Biomass Using Landsat Time-Series and Inventory Data,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812

See also Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery. BibRef

Lin, J.Y.[Jia-Yuan], Wang, M.M.[Mei-Mei], Ma, M.G.[Ming-Guo], Lin, Y.[Yi],
Aboveground Tree Biomass Estimation of Sparse Subalpine Coniferous Forest with UAV Oblique Photography,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Saarela, S.[Svetlana], Holm, S.[Sören], Healey, S.P.[Sean P.], Andersen, H.E.[Hans-Erik], Petersson, H.[Hans], Prentius, W.[Wilmer], Patterson, P.L.[Paul L.], Næsset, E.[Erik], Gregoire, T.G.[Timothy G.], Ståhl, G.[Göran],
Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Junttila, V.[Virpi], Kauranne, T.[Tuomo],
Distribution Statistics Preserving Post-Processing Method With Plot Level Uncertainty Analysis for Remotely Sensed Data-Based Forest Inventory Predictions,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Jayathunga, S.[Sadeepa], Owari, T.[Toshiaki], Tsuyuki, S.[Satoshi],
Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Boisvenue, C.[Céline], White, J.C.[Joanne C.],
Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Ou, G.L.[Guang-Long], Li, C.[Chao], Lv, Y.Y.[Yan-Yu], Wei, A.[Anchao], Xiong, H.X.[He-Xian], Xu, H.[Hui], Wang, G.X.[Guang-Xing],
Improving Aboveground Biomass Estimation of Pinus densata Forests in Yunnan Using Landsat 8 Imagery by Incorporating Age Dummy Variable and Method Comparison,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Durante, P.[Pilar], Martín-Alcón, S.[Santiago], Gil-Tena, A.[Assu], Algeet, N.[Nur], Tomé, J.L.[José Luis], Recuero, L.[Laura], Palacios-Orueta, A.[Alicia], Oyonarte, C.[Cecilio],
Improving Aboveground Forest Biomass Maps: From High-Resolution to National Scale,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Ni, W.J.[Wen-Jian], Dong, J.C.[Jia-Chen], Sun, G.Q.[Guo-Qing], Zhang, Z.Y.[Zhi-Yu], Pang, Y.[Yong], Tian, X.[Xin], Li, Z.Y.[Zeng-Yuan], Chen, E.[Erxue],
Synthesis of Leaf-on and Leaf-off Unmanned Aerial Vehicle (UAV) Stereo Imagery for the Inventory of Aboveground Biomass of Deciduous Forests,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Domingo, D.[Darío], Ørka, H.O.[Hans Ole], Næsset, E.[Erik], Kachamba, D.[Daud], Gobakken, T.[Terje],
Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Ibrahim, S.[Sa'ad], Balzter, H.[Heiko], Tansey, K.[Kevin], Mathieu, R.[Renaud], Tsutsumida, N.[Narumasa],
Impact of Soil Reflectance Variation Correction on Woody Cover Estimation in Kruger National Park Using MODIS Data,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Yu, X.H.[Xiao-Hui], Ge, H.L.[Hong-Li], Lu, D.S.[Deng-Sheng], Zhang, M.Z.[Mao-Zhen], Lai, Z.X.[Zhou-Xiang], Yao, R.[Rentu],
Comparative Study on Variable Selection Approaches in Establishment of Remote Sensing Model for Forest Biomass Estimation,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Narine, L.L.[Lana L.], Popescu, S.C.[Sorin C.], Malambo, L.[Lonesome],
Synergy of ICESat-2 and Landsat for Mapping Forest Aboveground Biomass with Deep Learning,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Malambo, L.[Lonesome], Popescu, S.C.[Sorin C.],
PhotonLabeler: An Inter-Disciplinary Platform for Visual Interpretation and Labeling of ICESat-2 Geolocated Photon Data,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Adame-Campos, R.L.[Rita Libertad], Ghilardi, A.[Adrian], Gao, Y.[Yan], Paneque-Gálvez, J.[Jaime], Mas, J.F.[Jean-François],
Variables Selection for Aboveground Biomass Estimations Using Satellite Data: A Comparison between Relative Importance Approach and Stepwise Akaike's Information Criterion,
IJGI(8), No. 6, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Esteban, J.[Jessica], McRoberts, R.E.[Ronald E.], Fernández-Landa, A.[Alfredo], Tomé, J.L.[José Luis], Næsset, E.[Erik],
Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Fu, Y.Y.[Yuan-Yuan], He, H.S.[Hong S.], Hawbaker, T.J.[Todd J.], Henne, P.D.[Paul D.], Zhu, Z.L.[Zhi-Liang], Larsen, D.R.[David R.],
Evaluating k-Nearest Neighbor (kNN) Imputation Models for Species-Level Aboveground Forest Biomass Mapping in Northeast China,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Li, G.Y.[Gui-Ying], Xie, Z.L.[Zhu-Li], Jiang, X.D.[Xian-Die], Lu, D.S.[Deng-Sheng], Chen, E.[Erxue],
Integration of ZiYuan-3 Multispectral and Stereo Data for Modeling Aboveground Biomass of Larch Plantations in North China,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Peng, D.L.[Dai-Liang], Zhang, H.[Helin], Liu, L.Y.[Liang-Yun], Huang, W.J.[Wen-Jiang], Huete, A.R.[Alfredo R.], Zhang, X.Y.[Xiao-Yang], Wang, F.M.[Fu-Min], Yu, L.[Le], Xie, Q.Y.[Qiao-Yun], Wang, C.[Cheng], Luo, S.Z.[She-Zhou], Li, C.J.[Cun-Jun], Zhang, B.[Bing],
Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Carvajal-Ramírez, F.[Fernando], Serrano, J.M.P.R.[João Manuel Pereira Ramalho], Agüera-Vega, F.[Francisco], Martínez-Carricondo, P.[Patricio],
A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Yan, E.[Enping], Zhao, Y.L.[Yun-Lin], Lin, H.[Hui], Wang, G.X.[Guang-Xing], Mo, D.K.[Deng-Kui],
Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Ou, G.L.[Guang-Long], Lv, Y.Y.[Yan-Yu], Xu, H.[Hui], Wang, G.X.[Guang-Xing],
Improving Forest Aboveground Biomass Estimation of Pinus densata Forest in Yunnan of Southwest China by Spatial Regression using Landsat 8 Images,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Zhang, Y.Z.[Yu-Zhen], Liang, S.L.[Shun-Lin], Yang, L.[Lu],
A Review of Regional and Global Gridded Forest Biomass Datasets,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Zhang, Y.Z.[Yu-Zhen], Liang, S.L.[Shun-Lin],
Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Piedelobo, L.[Laura], Taramelli, A.[Andrea], Schiavon, E.[Emma], Valentini, E.[Emiliana], Molina, J.L.[José-Luis], Xuan, A.N.[Alessandra Nguyen], González-Aguilera, D.[Diego],
Assessment of Green Infrastructure in Riparian Zones Using Copernicus Programme,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Nguyen, T.H.[Trung H.], Jones, S.[Simon], Soto-Berelov, M.[Mariela], Haywood, A.[Andrew], Hislop, S.[Samuel],
Landsat Time-Series for Estimating Forest Aboveground Biomass and Its Dynamics across Space and Time: A Review,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001
BibRef

Zhang, Q.[Qi], Xu, L.H.[Li-Hua], Zhang, M.Z.[Mao-Zhen], Wang, Z.[Zhi], Gu, Z.F.[Zhang-Feng], Wu, Y.Q.[Ya-Qi], Shi, Y.J.[Yi-Jun], Lu, Z.W.[Zhang-Wei],
Uncertainty Analysis of Remote Sensing Pretreatment for Biomass Estimation on Landsat OLI and Landsat ETM+,
IJGI(9), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Bascietto, M.[Marco], Sperandio, G.[Giulio], Bajocco, S.[Sofia],
Efficient Estimation of Biomass from Residual Agroforestry,
IJGI(9), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Hu, Y.[Yang], Xu, X.L.[Xue-Lei], Wu, F.Y.[Fa-Yun], Sun, Z.Q.[Zhong-Qiu], Xia, H.M.[Hao-Ming], Meng, Q.M.[Qing-Min], Huang, W.L.[Wen-Li], Zhou, H.[Hua], Gao, J.P.[Jin-Ping], Li, W.T.[Wei-Tao], Peng, D.L.[Dao-Li], Xiao, X.M.[Xiang-Ming],
Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Jurjevic, L.[Luka], Gašparovic, M.[Mateo], Milas, A.S.[Anita Simic], Balenovic, I.[Ivan],
Impact of UAS Image Orientation on Accuracy of Forest Inventory Attributes,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Li, X.Y.[Xin-Yu], Liu, Z.H.[Zhao-Hua], Lin, H.[Hui], Wang, G.X.[Guang-Xing], Sun, H.[Hua], Long, J.P.[Jiang-Ping], Zhang, M.[Meng],
Estimating the Growing Stem Volume of Chinese Pine and Larch Plantations based on Fused Optical Data Using an Improved Variable Screening Method and Stacking Algorithm,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Poley, L.G.[Lucy G.], McDermid, G.J.[Gregory J.],
A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Pham, T.D.[Tien Dat], Yokoya, N.[Naoto], Xia, J.[Junshi], Ha, N.T.[Nam Thang], Le, N.N.[Nga Nhu], Nguyen, T.T.T.[Thi Thu Trang], Dao, T.H.[Thi Huong], Vu, T.T.P.[Thuy Thi Phuong], Pham, T.D.[Tien Duc], Takeuchi, W.[Wataru],
Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Iizuka, K.[Kotaro], Hayakawa, Y.S.[Yuichi S.], Ogura, T.[Takuro], Nakata, Y.[Yasutaka], Kosugi, Y.[Yoshiko], Yonehara, T.[Taichiro],
Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms-Case Study of Evergreen Conifer Planted Forests in Japan,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Narine, L.L.[Lana L.], Popescu, S.C.[Sorin C.], Malambo, L.[Lonesome],
Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Issa, S.[Salem], Dahy, B.[Basam], Ksiksi, T.[Taoufik], Saleous, N.[Nazmi],
A Review of Terrestrial Carbon Assessment Methods Using Geo-Spatial Technologies with Emphasis on Arid Lands,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Poley, L.G.[Lucy G.], Laskin, D.N.[David N.], McDermid, G.J.[Gregory J.],
Quantifying Aboveground Biomass of Shrubs Using Spectral and Structural Metrics Derived from UAS Imagery,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Rodríguez-Veiga, P.[Pedro], Carreiras, J.[Joao], Smallman, T.L.[Thomas Luke], Exbrayat, J.F.[Jean-François], Ndambiri, J.[Jamleck], Mutwiri, F.[Faith], Nyasaka, D.[Divinah], Quegan, S.[Shaun], Williams, M.[Mathew], Balzter, H.[Heiko],
Carbon Stocks and Fluxes in Kenyan Forests and Wooded Grasslands Derived from Earth Observation and Model-Data Fusion,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Gao, Y.K.[Yu-Kun], Lu, D.S.[Deng-Sheng], Li, G.Y.[Gui-Ying], Wang, G.X.[Guang-Xing], Chen, Q.[Qi], Liu, L.J.[Li-Juan], Li, D.Q.[Deng-Qiu],
Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Banerjee, B.P.[Bikram Pratap], Spangenberg, G.[German], Kant, S.[Surya],
Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Gargiulo, J.[Juan], Clark, C.[Cameron], Lyons, N.[Nicolas], de Veyrac, G.[Gaspard], Beale, P.[Peter], Garcia, S.[Sergio],
Spatial and Temporal Pasture Biomass Estimation Integrating Electronic Plate Meter, Planet CubeSats and Sentinel-2 Satellite Data,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Taddese, H.[Habitamu], Asrat, Z.[Zerihun], Burud, I.[Ingunn], Gobakken, T.[Terje], Ørka, H.O.[Hans Ole], Dick, Ø.B.[Øystein B.], Næsset, E.[Erik],
Use of Remotely Sensed Data to Enhance Estimation of Aboveground Biomass for the Dry Afromontane Forest in South-Central Ethiopia,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Hawrylo, P.[Pawel], Francini, S.[Saverio], Chirici, G.[Gherardo], Giannetti, F.[Francesca], Parkitna, K.[Karolina], Krok, G.[Grzegorz], Mitelsztedt, K.[Krzysztof], Lisanczuk, M.[Marek], Sterenczak, K.[Krzysztof], Ciesielski, M.[Mariusz], Wezyk, P.[Piotr], Socha, J.[Jaroslaw],
The Use of Remotely Sensed Data and Polish NFI Plots for Prediction of Growing Stock Volume Using Different Predictive Methods,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Zhang, Y.Z.[Yu-Zhen], Ma, J.[Jun], Liang, S.L.[Shun-Lin], Li, X.S.[Xi-Sheng], Li, M.Y.[Man-Yao],
An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Menlove, J.[James], Healey, S.P.[Sean P.],
A Comprehensive Forest Biomass Dataset for the USA Allows Customized Validation of Remotely Sensed Biomass Estimates,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Zhao, X.[Xuan], Liu, J.J.[Jian-Jun], Hao, H.[Hongke], Yang, Y.Z.[Yan-Zheng],
Quantifying the Spatial Heterogeneity and Driving Factors of Aboveground Forest Biomass in the Urban Area of Xi'an, China,
IJGI(9), No. 12, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Kumar, P.[Praveen], Krishna, A.P.[Akhouri P.], Rasmussen, T.M.[Thorkild M.], Pal, M.K.[Mahendra K.],
Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India,
IJGI(10), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Naik, P.[Parth], Dalponte, M.[Michele], Bruzzone, L.[Lorenzo],
Prediction of Forest Aboveground Biomass Using Multitemporal Multispectral Remote Sensing Data,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Park, J.H.[Jin Han], Gan, J.[Jianbang], Park, C.[Chan],
Discrepancies between Global Forest Net Primary Productivity Estimates Derived from MODIS and Forest Inventory Data and Underlying Factors,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Lin, M.Z.[Mei-Zhi], Ling, Q.P.[Qing-Ping], Pei, H.Q.[Hui-Qing], Song, Y.[Yanni], Qiu, Z.X.[Zi-Xuan], Wang, C.[Cai], Liu, T.D.[Tie-Dong], Gong, W.F.[Wen-Feng],
Remote Sensing of Tropical Rainforest Biomass Changes in Hainan Island, China from 2003 to 2018,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Dorado-Roda, I.[Iván], Pascual, A.[Adrián], Godinho, S.[Sergio], Silva, C.A.[Carlos A.], Botequim, B.[Brigite], Rodríguez-Gonzálvez, P.[Pablo], González-Ferreiro, E.[Eduardo], Guerra-Hernández, J.[Juan],
Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Li, X.Y.[Xin-Yu], Lin, H.[Hui], Long, J.P.[Jiang-Ping], Xu, X.D.[Xiao-Dong],
Mapping the Growing Stem Volume of the Coniferous Plantations in North China Using Multispectral Data from Integrated GF-2 and Sentinel-2 Images and an Optimized Feature Variable Selection Method,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Hashemi-Beni, L.[Leila], Kurkalova, L.A.[Lyubov A.], Mulrooney, T.J.[Timothy J.], Azubike, C.S.[Chinazor S.],
Combining Multiple Geospatial Data for Estimating Aboveground Biomass in North Carolina Forests,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Chang, Z.B.[Zhong-Bing], Hobeichi, S.[Sanaa], Wang, Y.P.[Ying-Ping], Tang, X.L.[Xu-Li], Abramowitz, G.[Gab], Chen, Y.[Yang], Cao, N.N.[Nan-Nan], Yu, M.X.[Meng-Xiao], Huang, H.B.[Hua-Bing], Zhou, G.Y.[Guo-Yi], Wang, G.X.[Gen-Xu], Ma, K.P.[Ke-Ping], Du, S.[Sheng], Li, S.G.[Sheng-Gong], Han, S.J.[Shi-Jie], Ma, Y.X.[You-Xin], Wigneron, J.P.[Jean-Pierre], Fan, L.[Lei], Saatchi, S.S.[Sassan S.], Yan, J.H.[Jun-Hua],
New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Lv, G.T.[Guan-Ting], Cui, G.S.[Gui-Shan], Wang, X.Y.[Xiao-Yi], Yu, H.[Hangnan], Huang, X.[Xiao], Zhu, W.H.[Wei-Hong], Lin, Z.[Zhehao],
Signatures of Wetland Impact: Spatial Distribution of Forest Aboveground Biomass in Tumen River Basin,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Hernández-Stefanoni, J.L.[José Luis], Castillo-Santiago, M.Á.[Miguel Ángel], Andres-Mauricio, J.[Juan], Portillo-Quintero, C.A.[Carlos A.], Tun-Dzul, F.[Fernando], Dupuy, J.M.[Juan Manuel],
Carbon Stocks, Species Diversity and Their Spatial Relationships in the Yucatán Peninsula, Mexico,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Tang, R.[Rong], Zhao, Y.T.[Yu-Ting], Lin, H.[Huilong],
Spatio-Temporal Variation Characteristics of Aboveground Biomass in the Headwater of the Yellow River Based on Machine Learning,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Gara, T.W.[Tawanda W.], Rahimzadeh-Bajgiran, P.[Parinaz], Darvishzadeh, R.[Roshanak],
Forest Leaf Mass per Area (LMA) through the Eye of Optical Remote Sensing: A Review and Future Outlook,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Li, X.Y.[Xin-Yu], Long, J.P.[Jiang-Ping], Zhang, M.[Meng], Liu, Z.H.[Zhao-Hua], Lin, H.[Hui],
Coniferous Plantations Growing Stock Volume Estimation Using Advanced Remote Sensing Algorithms and Various Fused Data,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Li, X.Y.[Xin-Yu], Zhang, M.[Meng], Long, J.P.[Jiang-Ping], Lin, H.[Hui],
A Novel Method for Estimating Spatial Distribution of Forest Above-Ground Biomass Based on Multispectral Fusion Data and Ensemble Learning Algorithm,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Gregory, J.[Joe], Berthoud, L.[Lucy], Tryfonas, T.[Theo], Prezzavento, A.[Antonio], Faure, L.[Ludovic],
Investigating the Flexibility of the MBSE Approach to the Biomass Mission,
SMCS(51), No. 11, November 2021, pp. 6946-6961.
IEEE DOI 2110
Biomass, Biological system modeling, Unified modeling language, Space vehicles, Object oriented modeling, Biomass, systems engineering BibRef

Xu, X.D.[Xiao-Dong], Lin, H.[Hui], Liu, Z.H.[Zhao-Hua], Ye, Z.L.[Zi-Lin], Li, X.Y.[Xin-Yu], Long, J.P.[Jiang-Ping],
A Combined Strategy of Improved Variable Selection and Ensemble Algorithm to Map the Growing Stem Volume of Planted Coniferous Forest,
RS(13), No. 22, 2021, pp. xx-yy.
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Use Remote Sensing and Machine Learning to Study the Changes of Broad-Leaved Forest Biomass and Their Climate Driving Forces in Nature Reserves of Northern Subtropics,
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AGB recovery, Forest age, GWR, Remote sensing, Secondary forests, Surrounding tree cover BibRef

Bauer, L.[Luise], Huth, A.[Andreas], Bogdanowski, A.[André], Müller, M.[Michael], Fischer, R.[Rico],
Edge Effects in Amazon Forests: Integrating Remote Sensing and Modelling to Assess Changes in Biomass and Productivity,
RS(16), No. 3, 2024, pp. 501.
DOI Link 2402
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Suwanlee, S.R.[Savittri Ratanopad], Pinasu, D.[Dusadee], Som-Ard, J.[Jaturong], Borgogno-Mondino, E.[Enrico], Sarvia, F.[Filippo],
Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms,
RS(16), No. 5, 2024, pp. 750.
DOI Link 2403
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Nie, Y.H.[Yong-Hui], Hu, Y.F.[Yi-Fan], Sa, R.[Rula], Fan, W.[Wenyi],
Inversion of Forest above Ground Biomass in Mountainous Region Based on PolSAR Data after Terrain Correction: A Case Study from Saihanba, China,
RS(16), No. 5, 2024, pp. 846.
DOI Link 2403
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Li, Y.Q.[Yuan-Qi], Hu, R.H.[Rong-Hai], Xing, Y.Z.[Yu-Zhen], Pang, Z.[Zhe], Chen, Z.[Zhi], Niu, H.S.[Hai-Shan],
Comparison of Three Approaches for Estimating Understory Biomass in Yanshan Mountains,
RS(16), No. 6, 2024, pp. 1060.
DOI Link 2403
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Tian, X.[Xin], Li, J.[Jiejie], Zhang, F.[Fanyi], Zhang, H.B.[Hai-Bo], Jiang, M.[Mi],
Forest Aboveground Biomass Estimation Using Multisource Remote Sensing Data and Deep Learning Algorithms: A Case Study over Hangzhou Area in China,
RS(16), No. 6, 2024, pp. 1074.
DOI Link 2403
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Ali, M.[Moonis], Lohani, B.[Bharat], Hollaus, M.[Markus], Pfeifer, N.[Norbert],
Benchmarking Geometry-Based Leaf-Filtering Algorithms for Tree Volume Estimation Using Terrestrial LiDAR Scanners,
RS(16), No. 6, 2024, pp. 1021.
DOI Link 2403
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Pilikos, G.[Georgios], Clarizia, M.P.[Maria Paola], Floury, N.[Nicolas],
Biomass Estimation with GNSS Reflectometry Using a Deep Learning Retrieval Model,
RS(16), No. 7, 2024, pp. 1125.
DOI Link 2404
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Liu, J.J.[Jing-Jing], Zhang, Y.Z.[Yu-Zhen],
A Multi-Scale Forest Above-Ground Biomass Mapping Approach: Employing a Step-by-Step Spatial Downscaling Method with Bias-Corrected Ensemble Machine Learning,
RS(16), No. 7, 2024, pp. 1228.
DOI Link 2404
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Zhao, X.[Xuedi], Hu, W.[Wenmin], Han, J.[Jiang], Wei, W.[Wei], Xu, J.X.[Jia-Xing],
Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images,
RS(16), No. 7, 2024, pp. 1229.
DOI Link 2404
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Wu, Y.[Yong], Ou, G.L.[Guang-Long], Lu, T.F.[Teng-Fei], Huang, T.B.[Tian-Bao], Zhang, X.L.[Xiao-Li], Liu, Z.[Zihao], Yu, Z.B.[Zhi-Bo], Guo, B.B.[Bin-Bing], Wang, E.[Er], Feng, Z.H.[Zi-Hang], Luo, H.B.[Hong-Bin], Lu, C.[Chi], Wang, L.G.[Lei-Guang], Xu, W.[Weiheng],
Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna,
RS(16), No. 7, 2024, pp. 1276.
DOI Link 2404
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Wu, Y.[Yong], Ou, G.L.[Guang-Long], Huang, T.[Tianbao], Zhang, X.L.[Xiao-Li], Liu, C.X.[Chun-Xiao], Liu, Z.[Zhi], Yu, Z.B.[Zhi-Bo], Luo, H.B.[Hong-Bin], Lu, C.[Chi], Shi, K.[Kaize], Wang, L.G.[Lei-Guang], Xu, W.[Weiheng],
Climate Interprets Saturation Value Variations Better Than Soil and Topography in Estimating Oak Forest Aboveground Biomass Using Landsat 8 OLI Imagery,
RS(16), No. 8, 2024, pp. 1338.
DOI Link 2405
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Chen, X.Y.[Xin-Yang], Yang, K.M.[Ke-Ming], Ma, J.[Jun], Jiang, K.[Kegui], Gu, X.[Xinru], Peng, L.[Lishun],
Aboveground Biomass Inversion Based on Object-Oriented Classification and Pearson-mRMR-Machine Learning Model,
RS(16), No. 9, 2024, pp. 1537.
DOI Link 2405
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Wu, Z.J.[Zhen-Jiang], Yao, F.M.[Feng-Mei], Zhang, J.H.[Jia-Hua], Liu, H.Y.[Hao-Yu],
Estimating Forest Aboveground Biomass Using a Combination of Geographical Random Forest and Empirical Bayesian Kriging Models,
RS(16), No. 11, 2024, pp. 1859.
DOI Link 2406
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Sa, R.[Rula], Nie, Y.H.[Yong-Hui], Chumachenko, S.[Sergey], Fan, W.[Wenyi],
Biomass Estimation and Saturation Value Determination Based on Multi-Source Remote Sensing Data,
RS(16), No. 12, 2024, pp. 2250.
DOI Link 2406
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Li, X.X.[Xiao-Xue], Wu, J.[Juan], Lu, S.F.[Shun-Fa], Li, D.Q.[Deng-Qiu], Lu, D.S.[Deng-Sheng],
Integration of Handheld and Airborne Lidar Data for Dicranopteris Dichotoma Biomass Estimation in a Subtropical Region of Fujian Province, China,
RS(16), No. 12, 2024, pp. 2088.
DOI Link 2406
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Wang, Y.C.[Ying-Chen], Wang, H.T.[Hong-Tao], Wang, C.[Cheng], Zhang, S.T.[Shu-Ting], Wang, R.X.[Rong-Xi], Wang, S.H.[Shao-Hui], Duan, J.J.[Jing-Jing],
Co-Kriging-Guided Interpolation for Mapping Forest Aboveground Biomass by Integrating Global Ecosystem Dynamics Investigation and Sentinel-2 Data,
RS(16), No. 16, 2024, pp. 2913.
DOI Link 2408
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Zhang, L.J.[Lin-Jing], Yin, X.R.[Xin-Ran], Wang, Y.[Yaru], Chen, J.[Jing],
Aboveground Biomass Mapping in SemiArid Forests by Integrating Airborne LiDAR with Sentinel-1 and Sentinel-2 Time-Series Data,
RS(16), No. 17, 2024, pp. 3241.
DOI Link 2409
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Leditznig, T.[Thomas], Klug, H.[Hermann],
Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing,
RS(16), No. 21, 2024, pp. 3926.
DOI Link 2411
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Guo, W.H.[Wen-Hua], Liu, Z.H.[Zhi-Hua], Xu, W.[Wenru], Wang, W.J.[Wen J.], Shafron, E.[Ethan], Lv, Q.S.[Qiu-Shuang], Li, K.[Kaili], Zhou, S.[Siyu], Guan, R.[Ruhong], Yang, J.[Jian],
Spatial and Temporal Patterns of Forest Biomass Carbon Sink in China from 1990 to 2021,
RS(16), No. 20, 2024, pp. 3811.
DOI Link 2411
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Sun, X.Y.[Xiao-Yu], Li, G.Y.[Gui-Ying], Wu, Q.Q.[Qin-Quan], Ruan, J.Y.[Jing-Yi], Li, D.Q.[Deng-Qiu], Lu, D.S.[Deng-Sheng],
Mapping Forest Carbon Stock Distribution in a Subtropical Region with the Integration of Airborne Lidar and Sentinel-2 Data,
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DOI Link 2411
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Omoniyi, T.O.[Temitope Olaoluwa], Sims, A.[Allan],
Enhancing the Precision of Forest Growing Stock Volume in the Estonian National Forest Inventory with Different Predictive Techniques and Remote Sensing Data,
RS(16), No. 20, 2024, pp. 3794.
DOI Link 2411
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David, H.C.[Hassan C.], Vibrans, A.C.[Alexander C.], Martins-Neto, R.P.[Rorai P.], Corte, A.P.D.[Ana Paula Dalla], Netto, S.P.[Sylvio Péllico],
Incorporating Forest Mapping-Related Uncertainty into the Error Propagation of Wall-to-Wall Biomass Maps: A General Approach for Large and Small Areas,
RS(16), No. 22, 2024, pp. 4295.
DOI Link 2412
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Wu, N.[Nan], Zhang, C.[Chao], Zhuo, W.[Wei], Shi, R.[Runhe], Zhu, F.Q.[Feng-Quan], Liu, S.C.[Shi-Chang],
Assessment of the Impact of Coastal Wetland Saltmarsh Vegetation Types on Aboveground Biomass Inversion,
RS(16), No. 24, 2024, pp. 4762.
DOI Link 2501
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Kilbride, J.B.[John B.], Kennedy, R.E.[Robert E.],
A Large-Scale Inter-Comparison and Evaluation of Spatial Feature Engineering Strategies for Forest Aboveground Biomass Estimation Using Landsat Satellite Imagery,
RS(16), No. 23, 2024, pp. 4586.
DOI Link 2501
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Wang, E.[Er], Huang, T.[Tianbao], Liu, Z.[Zhi], Bao, L.[Lei], Guo, B.[Binbing], Yu, Z.B.[Zhi-Bo], Feng, Z.H.[Zi-Hang], Luo, H.B.[Hong-Bin], Ou, G.L.[Guang-Long],
Improving Forest Above-Ground Biomass Estimation Accuracy Using Multi-Source Remote Sensing and Optimized Least Absolute Shrinkage and Selection Operator Variable Selection Method,
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Pan, L.Y.[Li-Yuan], Liu, L.[Liu], Condon, A.G.[Anthony G.], Estavillo, G.M.[Gonzalo M.], Coe, R.A.[Robert A.], Bull, G.[Geoff], Stone, E.A.[Eric A.], Petersson, L.[Lars], Rolland, V.[Vivien],
Biomass Prediction with 3D Point Clouds from LiDAR,
WACV22(1716-1726)
IEEE DOI 2202
Point cloud compression, Weight measurement, Laser radar, Sociology, Medical Imaging/Imaging for Bioinformatics/Biological and Cell Microscopy Low-level and Physics-based Vision BibRef

Eto, S., Masuda, H., Hiraoka, Y., Matsushita, M., Takahashi, M.,
Precise Calculation of Cross Sections and Volume for Tree Stem Using Point Clouds,
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