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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:

Assessing components of the model-based mean square error estimator for remote sensing assisted forest applications
Ronald E. McRoberts, Erik Næsset, Terje Gobakken, et al.
Canadian Journal of Forest Research (2018) Vol. 48, Iss. 6, pp. 642-649
Closed Access | Times Cited: 49

Showing 1-25 of 49 citing articles:

Changes in global terrestrial live biomass over the 21st century
Liang Xu, Sassan Saatchi, Yan Yang, et al.
Science Advances (2021) Vol. 7, Iss. 27, pp. eabe9829-eabe9829
Open Access | Times Cited: 253
Modelling lidar-derived estimates of forest attributes over space and time: A review of approaches and future trends
Nicholas C. Coops, Piotr Tompalski, Tristan R.H. Goodbody, et al.
Remote Sensing of Environment (2021) Vol. 260, pp. 112477-112477
Open Access | Times Cited: 241
Recent gains in global terrestrial carbon stocks are mostly stored in nonliving pools
Yinon M. Bar-On, Xiaojun Li, Michael O’Sullivan, et al.
Science (2025) Vol. 387, Iss. 6740, pp. 1291-1295
Closed Access | Times Cited: 2
Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data
Jéssica Esteban, Ronald E. McRoberts, Alfredo Fernández-Landa, et al.
Remote Sensing (2019) Vol. 11, Iss. 16, pp. 1944-1944
Open Access | Times Cited: 88
Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory
Andrew J. Lister, Hans Henrik Andersen, Tracey S. Frescino, et al.
Forests (2020) Vol. 11, Iss. 12, pp. 1364-1364
Open Access | Times Cited: 75
Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time – and how this affects applications
Göran Ståhl, Terje Gobakken, Svetlana Saarela, et al.
Forest Ecosystems (2024) Vol. 11, pp. 100164-100164
Open Access | Times Cited: 10
A comparison of UAV laser scanning, photogrammetry and airborne laser scanning for precision inventory of small-forest properties
Stefano Puliti, Jonathan P. Dash, Michael S. Watt, et al.
Forestry An International Journal of Forest Research (2019) Vol. 93, Iss. 1, pp. 150-162
Closed Access | Times Cited: 58
Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors
Svetlana Saarela, André Wästlund, Emma Holmström, et al.
Forest Ecosystems (2020) Vol. 7, Iss. 1
Open Access | Times Cited: 55
From comprehensive field inventories to remotely sensed wall-to-wall stand attribute data — a brief history of management inventories in the Nordic countries
Matti Maltamo, Petteri Packalén, Annika Kangas
Canadian Journal of Forest Research (2020) Vol. 51, Iss. 2, pp. 257-266
Open Access | Times Cited: 52
Estimating aboveground biomass density using hybrid statistical inference with GEDI lidar data and Paraguay’s national forest inventory
Eric L. Bullock, Sean P. Healey, Zhiqiang Yang, et al.
Environmental Research Letters (2023) Vol. 18, Iss. 8, pp. 085001-085001
Open Access | Times Cited: 18
How many bootstrap replications are necessary for estimating remote sensing-assisted, model-based standard errors?
Ronald E. McRoberts, Erik Næsset, Zhengyang Hou, et al.
Remote Sensing of Environment (2023) Vol. 288, pp. 113455-113455
Closed Access | Times Cited: 17
Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees
Martin Krůček, Kamil Král, K. C. Cushman, et al.
Remote Sensing (2020) Vol. 12, Iss. 19, pp. 3260-3260
Open Access | Times Cited: 40
Comparing frameworks for biomass prediction for the Global Ecosystem Dynamics Investigation
Svetlana Saarela, Sören Holm, Sean P. Healey, et al.
Remote Sensing of Environment (2022) Vol. 278, pp. 113074-113074
Open Access | Times Cited: 26
Statistically rigorous, model-based inferences from maps
Ronald E. McRoberts, Erik Næsset, Sassan Saatchi, et al.
Remote Sensing of Environment (2022) Vol. 279, pp. 113028-113028
Closed Access | Times Cited: 23
Using bi-temporal ALS and NFI-based time-series data to account for large-scale aboveground carbon dynamics: the showcase of mediterranean forests
Juan Guerra-Hernández, Adrián Pascual, Frederico Tupinambá‐Simões, et al.
European Journal of Remote Sensing (2024) Vol. 57, Iss. 1
Open Access | Times Cited: 5
Assessment of UAV photogrammetric DTM-independent variables for modelling and mapping forest structural indices in mixed temperate forests
Francesca Giannetti, Nicola Puletti, Stefano Puliti, et al.
Ecological Indicators (2020) Vol. 117, pp. 106513-106513
Open Access | Times Cited: 30
A framework for a forest ecological base map – An example from Norway
Hans Ole Ørka, Marie-Claude Jutras-Perreault, Erik Næsset, et al.
Ecological Indicators (2022) Vol. 136, pp. 108636-108636
Open Access | Times Cited: 17
Large-scale high-resolution yearly modeling of forest growing stock volume and above-ground carbon pool
Elia Vangi, Giovanni D’Amico, Saverio Francini, et al.
Environmental Modelling & Software (2022) Vol. 159, pp. 105580-105580
Closed Access | Times Cited: 17
Harnessing data assimilation and spatial autocorrelation for forest inventory
Qing Xu, Bo Li, Ronald E. McRoberts, et al.
Remote Sensing of Environment (2023) Vol. 288, pp. 113488-113488
Open Access | Times Cited: 11
A Closer Look at Uncertainties in Forest Ecosystem Surveys Using Remotely Sensed Data and Model-Based Inference
Göran Ståhl, Léna Gozé, Emanuele Papucci, et al.
(2025)
Closed Access
Local validation of global biomass maps
Ronald E. McRoberts, Erik Næsset, Sassan Saatchi, et al.
International Journal of Applied Earth Observation and Geoinformation (2019) Vol. 83, pp. 101931-101931
Closed Access | Times Cited: 29
Improving the Results of the Earned Value Management Technique Using Artificial Neural Networks in Construction Projects
Amirhossein Balali, Alireza Valipour, Jurgita Antuchevičienė, et al.
Symmetry (2020) Vol. 12, Iss. 10, pp. 1745-1745
Open Access | Times Cited: 22
Using a Finer Resolution Biomass Map to Assess the Accuracy of a Regional, Map-Based Estimate of Forest Biomass
Ronald E. McRoberts, Erik Næsset, Greg C. Liknes, et al.
Surveys in Geophysics (2019) Vol. 40, Iss. 4, pp. 1001-1015
Open Access | Times Cited: 20
Allometric Models and Biomass Conversion and Expansion Factors to Predict Total Tree-level Aboveground Biomass for Three Conifers Species in Iran
Hassan Ali, J. Mohammadi, Shaban Shataee
Forest Science (2023) Vol. 69, Iss. 4, pp. 355-370
Closed Access | Times Cited: 5
Catering Information Needs from Global to Local Scales—Potential and Challenges with National Forest Inventories
Annika Kangas, Minna Räty, Kari Korhonen, et al.
Forests (2019) Vol. 10, Iss. 9, pp. 800-800
Open Access | Times Cited: 15
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