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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:

Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data
Gaia Vaglio Laurin, Qi Chen, Jeremy Lindsell, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2014) Vol. 89, pp. 49-58
Closed Access | Times Cited: 279

Showing 1-25 of 279 citing articles:

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems
Dengsheng Lu, Qi Chen, Guangxing Wang, et al.
International Journal of Digital Earth (2014) Vol. 9, Iss. 1, pp. 63-105
Open Access | Times Cited: 683
Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass
Fabian Ewald Fassnacht, Florian Härtig, Hooman Latifi, et al.
Remote Sensing of Environment (2014) Vol. 154, pp. 102-114
Closed Access | Times Cited: 366
Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices
Jibo Yue, Guijun Yang, Qingjiu Tian, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2019) Vol. 150, pp. 226-244
Closed Access | Times Cited: 328
Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa
Timothy Dube, Onisimo Mutanga
ISPRS Journal of Photogrammetry and Remote Sensing (2014) Vol. 101, pp. 36-46
Closed Access | Times Cited: 268
A review of UAV monitoring in mining areas: current status and future perspectives
He Ren, Zhao Yanling, Wu Xiao, et al.
International Journal of Coal Science & Technology (2019) Vol. 6, Iss. 3, pp. 320-333
Open Access | Times Cited: 264
Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission
Laura Duncanson, James R. Kellner, John Armston, et al.
Remote Sensing of Environment (2022) Vol. 270, pp. 112845-112845
Open Access | Times Cited: 253
Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges
Tien Dat Pham, Naoto Yokoya, Dieu Tien Bui, et al.
Remote Sensing (2019) Vol. 11, Iss. 3, pp. 230-230
Open Access | Times Cited: 239
Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data
David A. Coomes, Michele Dalponte, Tommaso Jucker, et al.
Remote Sensing of Environment (2017) Vol. 194, pp. 77-88
Open Access | Times Cited: 174
Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations
Kun Liu, Xin Shen, Lin Cao, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2018) Vol. 146, pp. 465-482
Closed Access | Times Cited: 170
Integration of UAV, Sentinel-1, and Sentinel-2 Data for Mangrove Plantation Aboveground Biomass Monitoring in Senegal
José Antonio Suárez Navarro, Nur Algeet, Alfredo Fernández-Landa, et al.
Remote Sensing (2019) Vol. 11, Iss. 1, pp. 77-77
Open Access | Times Cited: 155
Deep Learning Based Retrieval of Forest Aboveground Biomass from Combined LiDAR and Landsat 8 Data
Linjing Zhang, Zhenfeng Shao, Jianchen Liu, et al.
Remote Sensing (2019) Vol. 11, Iss. 12, pp. 1459-1459
Open Access | Times Cited: 145
Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion
Danilo Roberti Alves de Almeida, Eben N. Broadbent, Matheus Pinheiro Ferreira, et al.
Remote Sensing of Environment (2021) Vol. 264, pp. 112582-112582
Open Access | Times Cited: 118
Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing
Yichao Tian, Huang Hu, Guoqing Zhou, et al.
The Science of The Total Environment (2021) Vol. 781, pp. 146816-146816
Closed Access | Times Cited: 110
Nation-wide mapping of tree-level aboveground carbon stocks in Rwanda
Maurice Mugabowindekwe, Martin Brandt, Jérôme Chave, et al.
Nature Climate Change (2022) Vol. 13, Iss. 1, pp. 91-97
Open Access | Times Cited: 86
Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery
Xiaoli Zhang, Hanwen Shen, Tian‐Bao Huang, et al.
Ecological Indicators (2024) Vol. 159, pp. 111752-111752
Open Access | Times Cited: 26
Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI
Chu Wang, Wangfei Zhang, Yongjie Ji, et al.
Forests (2024) Vol. 15, Iss. 1, pp. 215-215
Open Access | Times Cited: 19
Review of the use of remote sensing for biomass estimation to support renewable energy generation
Lalit Kumar, Priyakant Sinha, Subhashni Taylor, et al.
Journal of Applied Remote Sensing (2015) Vol. 9, Iss. 1, pp. 097696-097696
Open Access | Times Cited: 171
Quantifying Forest Biomass Carbon Stocks From Space
Pedro Rodríguez‐Veiga, James Wheeler, Valentin Louis, et al.
Current Forestry Reports (2017) Vol. 3, Iss. 1, pp. 1-18
Open Access | Times Cited: 142
Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data
Gaia Vaglio Laurin, Johannes Balling, Piermaria Corona, et al.
Journal of Applied Remote Sensing (2018) Vol. 12, Iss. 01, pp. 1-1
Open Access | Times Cited: 136
Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms
Catherine Torres de Almeida, Lênio Soares Galvão, Luiz Eduardo de Oliveira Cruz e Aragão, et al.
Remote Sensing of Environment (2019) Vol. 232, pp. 111323-111323
Closed Access | Times Cited: 133
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: 131
Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data
Panpan Zhao, Dengsheng Lu, Guangxing Wang, et al.
International Journal of Applied Earth Observation and Geoinformation (2016) Vol. 53, pp. 1-15
Closed Access | Times Cited: 130
Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery
Lin Chen, Chunying Ren, Bai Zhang, et al.
Forests (2018) Vol. 9, Iss. 10, pp. 582-582
Open Access | Times Cited: 128
Individual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images
Fabien Wagner, Matheus Pinheiro Ferreira, Alber Sánchez, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2018) Vol. 145, pp. 362-377
Open Access | Times Cited: 128
Estimating Above-Ground Biomass in Sub-Tropical Buffer Zone Community Forests, Nepal, Using Sentinel 2 Data
Santa Pandit, Satoshi Tsuyuki, Timothy Dube
Remote Sensing (2018) Vol. 10, Iss. 4, pp. 601-601
Open Access | Times Cited: 122
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