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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels
Qi Chen, Gaia Vaglio Laurin, Riccardo Valentini
Remote Sensing of Environment (2015) Vol. 160, pp. 134-143
Closed Access | Times Cited: 133

Showing 1-25 of 133 citing articles:

biomass: an r package for estimating above‐ground biomass and its uncertainty in tropical forests
Maxime Réjou‐Méchain, Ariane Tanguy, Camille Piponiot, et al.
Methods in Ecology and Evolution (2017) Vol. 8, Iss. 9, pp. 1163-1167
Open Access | Times Cited: 356
Tree‐centric mapping of forest carbon density from airborne laser scanning and hyperspectral data
Michele Dalponte, David A. Coomes
Methods in Ecology and Evolution (2016) Vol. 7, Iss. 10, pp. 1236-1245
Open Access | Times Cited: 283
Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation
Panpan Zhao, Dengsheng Lu, Guangxing Wang, et al.
Remote Sensing (2016) Vol. 8, Iss. 6, pp. 469-469
Open Access | Times Cited: 211
Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data
Gaia Vaglio Laurin, Nicola Puletti, W.D. Hawthorne, et al.
Remote Sensing of Environment (2016) Vol. 176, pp. 163-176
Open Access | Times Cited: 174
A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps
Arnan Araza, Sytze de Bruin, Martin Herold, et al.
Remote Sensing of Environment (2022) Vol. 272, pp. 112917-112917
Open Access | Times Cited: 82
Review of Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation: Progress, Challenges, and Prospects
Lei Tian, Xiaocan Wu, Tao Yu, et al.
Forests (2023) Vol. 14, Iss. 6, pp. 1086-1086
Open Access | Times Cited: 78
Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region
Shoaib Ahmad Anees, Kaleem Mehmood, Waseem Razzaq Khan, et al.
Ecological Informatics (2024) Vol. 82, pp. 102732-102732
Open Access | Times Cited: 50
Testing the generality of above‐ground biomass allometry across plant functional types at the continent scale
Keryn I. Paul, Stephen H. Roxburgh, Jérôme Chave, et al.
Global Change Biology (2015) Vol. 22, Iss. 6, pp. 2106-2124
Open Access | Times Cited: 176
Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China
Huabing Huang, Caixia Liu, Xiaoyi Wang, et al.
Remote Sensing of Environment (2018) Vol. 221, pp. 225-234
Closed Access | Times Cited: 111
Variability and uncertainty in forest biomass estimates from the tree to landscape scale: the role of allometric equations
Anthony G. Vorster, Paul Evangelista, Atticus Stovall, et al.
Carbon Balance and Management (2020) Vol. 15, Iss. 1
Open Access | Times Cited: 111
Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them
Maxime Réjou‐Méchain, Nicolas Barbier, Pierre Couteron, et al.
Surveys in Geophysics (2019) Vol. 40, Iss. 4, pp. 881-911
Closed Access | Times Cited: 102
Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data
Lin Chen, Yeqiao Wang, Chunying Ren, et al.
Remote Sensing (2019) Vol. 11, Iss. 4, pp. 414-414
Open Access | Times Cited: 99
A radiative transfer model-based method for the estimation of grassland aboveground biomass
Xingwen Quan, Binbin He, Marta Yebra, et al.
International Journal of Applied Earth Observation and Geoinformation (2016) Vol. 54, pp. 159-168
Closed Access | Times Cited: 95
Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference
Qi Chen, Ronald E. McRoberts, Changwei Wang, et al.
Remote Sensing of Environment (2016) Vol. 184, pp. 350-360
Closed Access | Times Cited: 91
Assessing terrestrial laser scanning for developing non-destructive biomass allometry
Atticus Stovall, Kristina J. Anderson‐Teixeira, Herman H. Shugart
Forest Ecology and Management (2018) Vol. 427, pp. 217-229
Open Access | Times Cited: 88
AdQSM: A New Method for Estimating Above-Ground Biomass from TLS Point Clouds
Guangpeng Fan, Liangliang Nan, Yanqi Dong, et al.
Remote Sensing (2020) Vol. 12, Iss. 18, pp. 3089-3089
Open Access | Times Cited: 86
Detecting vulnerability of humid tropical forests to multiple stressors
Sassan Saatchi, Marcos Longo, Liang Xu, et al.
One Earth (2021) Vol. 4, Iss. 7, pp. 988-1003
Open Access | Times Cited: 65
Carbon balance of rubber (Hevea brasiliensis) plantations: A review of uncertainties at plot, landscape and production level
Sergey Blagodatsky, Jianchu Xu, Georg Cadisch
Agriculture Ecosystems & Environment (2016) Vol. 221, pp. 8-19
Closed Access | Times Cited: 85
Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries
Pierre Ploton, Nicolas Barbier, Stéphane Momo Takoudjou, et al.
Biogeosciences (2016) Vol. 13, Iss. 5, pp. 1571-1585
Open Access | Times Cited: 82
Mapping and estimating the total living biomass and carbon in low-biomass woodlands using Landsat 8 CDR data
Belachew Gizachew, Svein Solberg, Erik Næsset, et al.
Carbon Balance and Management (2016) Vol. 11, Iss. 1
Open Access | Times Cited: 79
Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta
Temilola Fatoyinbo, E. A. Feliciano, David Lagomasino, et al.
Environmental Research Letters (2017) Vol. 13, Iss. 2, pp. 025012-025012
Open Access | Times Cited: 77
Hybrid estimators for mean aboveground carbon per unit area
Ronald E. McRoberts, Qi Chen, Grant M. Domke, et al.
Forest Ecology and Management (2016) Vol. 378, pp. 44-56
Closed Access | Times Cited: 74
Assessment of multi-wavelength SAR and multispectral instrument data for forest aboveground biomass mapping using random forest kriging
Lin Chen, Yeqiao Wang, Chunying Ren, et al.
Forest Ecology and Management (2019) Vol. 447, pp. 12-25
Closed Access | Times Cited: 73
Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates
Gaia Vaglio Laurin, Francesco Pirotti, Mattia Callegari, et al.
Remote Sensing (2016) Vol. 9, Iss. 1, pp. 18-18
Open Access | Times Cited: 71
Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass
Timothy G. Grégoire, Erik Næsset, Ronald E. McRoberts, et al.
Remote Sensing of Environment (2015) Vol. 173, pp. 98-108
Closed Access | Times Cited: 70
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