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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:

Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana
Ibrahim Fayad, Nicolas Baghdadi, Jean‐Stéphane Bailly, et al.
Remote Sensing (2016) Vol. 8, Iss. 3, pp. 240-240
Open Access | Times Cited: 48

Showing 1-25 of 48 citing articles:

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
Forest biomass retrieval approaches from earth observation in different biomes
Pedro Rodríguez‐Veiga, S. Quegan, João M. B. Carreiras, et al.
International Journal of Applied Earth Observation and Geoinformation (2019) Vol. 77, pp. 53-68
Open Access | Times Cited: 96
Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data
Sheng Nie, Cheng Wang, Xiaohuan Xi, et al.
Optics Express (2018) Vol. 26, Iss. 10, pp. A520-A520
Open Access | Times Cited: 95
Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning
Maryam Pourshamsi, Junshi Xia, Naoto Yokoya, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2020) Vol. 172, pp. 79-94
Open Access | Times Cited: 79
Modelling aboveground biomass of a multistage managed forest through synergistic use of Landsat-OLI, ALOS-2 L-band SAR and GEDI metrics
Hitendra Padalia, Ankit Prakash, Taibanganba Watham
Ecological Informatics (2023) Vol. 77, pp. 102234-102234
Closed Access | Times Cited: 22
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: 70
Comparing spatial regression to random forests for large environmental data sets
Eric W. Fox, Jay M. Ver Hoef, Anthony R. Olsen
PLoS ONE (2020) Vol. 15, Iss. 3, pp. e0229509-e0229509
Open Access | Times Cited: 69
Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data
Ibrahim Fayad, Nicolas Baghdadi, Stéphane Guitet, et al.
International Journal of Applied Earth Observation and Geoinformation (2016) Vol. 52, pp. 502-514
Open Access | Times Cited: 64
Random Forests for Spatially Dependent Data
Arkajyoti Saha, Sumanta Basu, Abhirup Datta
Journal of the American Statistical Association (2021) Vol. 118, Iss. 541, pp. 665-683
Open Access | Times Cited: 55
Influence of GEDI Acquisition and Processing Parameters on Canopy Height Estimates over Tropical Forests
Kamel Lahssini, Nicolas Baghdadi, Guerric Le Maire, et al.
Remote Sensing (2022) Vol. 14, Iss. 24, pp. 6264-6264
Open Access | Times Cited: 35
An Assessment of the GEDI Lasers’ Capabilities in Detecting Canopy Tops and Their Penetration in a Densely Vegetated, Tropical Area
Ibrahim Fayad, Nicolas Baghdadi, Kamel Lahssini
Remote Sensing (2022) Vol. 14, Iss. 13, pp. 2969-2969
Open Access | Times Cited: 32
Improving Heterogeneous Forest Height Maps by Integrating GEDI-Based Forest Height Information in a Multi-Sensor Mapping Process
David Morin, Milena Planells, Nicolas Baghdadi, et al.
Remote Sensing (2022) Vol. 14, Iss. 9, pp. 2079-2079
Open Access | Times Cited: 30
Modeling forest above-ground biomass using freely available satellite and multisource datasets
Ai Hojo, Ram Avtar, Tatsuro Nakaji, et al.
Ecological Informatics (2023) Vol. 74, pp. 101973-101973
Closed Access | Times Cited: 19
Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images
Xuedi Zhao, Wenmin Hu, Jiang Han, et al.
Remote Sensing (2024) Vol. 16, Iss. 7, pp. 1229-1229
Open Access | Times Cited: 6
Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA
Ram K. Deo, Matthew B. Russell, Grant M. Domke, et al.
Remote Sensing (2017) Vol. 9, Iss. 6, pp. 598-598
Open Access | Times Cited: 47
Urban aerobiological risk mapping of ornamental trees using a new index based on LiDAR and Kriging: A case study of plane trees
Raúl Pecero-Casimiro, Santiago Fernández-Rodríguez, Rafael Tormo-Molina, et al.
The Science of The Total Environment (2019) Vol. 693, pp. 133576-133576
Closed Access | Times Cited: 33
A Comparison of Machine Learning and Geostatistical Approaches for Mapping Forest Canopy Height over the Southeastern US Using ICESat-2
Kasip Tiwari, Lana L. Narine
Remote Sensing (2022) Vol. 14, Iss. 22, pp. 5651-5651
Open Access | Times Cited: 15
Soil quality estimation using environmental covariates and predictive models: an example from tropical soils of Nigeria
Isong Abraham Isong, Kingsley John, Paul B. Okon, et al.
Ecological Processes (2022) Vol. 11, Iss. 1
Open Access | Times Cited: 14
Multi-Sensor Prediction of Stand Volume by a Hybrid Model of Support Vector Machine for Regression Kriging
Lin Chen, Chunying Ren, Bai Zhang, et al.
Forests (2020) Vol. 11, Iss. 3, pp. 296-296
Open Access | Times Cited: 22
Study on the Estimation of Forest Volume Based on Multi-Source Data
Tao Hu, Yuman Sun, Weiwei Jia, et al.
Sensors (2021) Vol. 21, Iss. 23, pp. 7796-7796
Open Access | Times Cited: 18
Modeling wildland fire burn severity in California using a spatial Super Learner approach
Nicholas Simafranca, Bryant Willoughby, E. G. O’Neil, et al.
Environmental and Ecological Statistics (2024) Vol. 31, Iss. 2, pp. 387-408
Closed Access | Times Cited: 2
Predicting species abundance using machine learning approach: a comparative assessment of random forest spatial variants and performance metrics
Ciza Arsène Mushagalusa, Adandé Belarmain Fandohan, Romain Glèlè Kakaï
Modeling Earth Systems and Environment (2024) Vol. 10, Iss. 4, pp. 5145-5171
Closed Access | Times Cited: 2
Quality Assessment of Acquired GEDI Waveforms: Case Study over France, Tunisia and French Guiana
Ibrahim Fayad, Nicolas Baghdadi, J. Riédi
Remote Sensing (2021) Vol. 13, Iss. 16, pp. 3144-3144
Open Access | Times Cited: 16
An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height
Junpeng Zhao, Lei Zhao, Erxue Chen, et al.
Remote Sensing (2022) Vol. 14, Iss. 3, pp. 568-568
Open Access | Times Cited: 12
A new statistical downscaling approach for short‐term forecasting of summer air temperatures through a fusion of deep learning and spatial interpolation
Dongjin Cho, Jungho Im, Sihun Jung
Quarterly Journal of the Royal Meteorological Society (2023) Vol. 150, Iss. 760, pp. 1222-1242
Closed Access | Times Cited: 6
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