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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:

Comparison of precision of biomass estimates in regional field sample surveys and airborne LiDAR-assisted surveys in Hedmark County, Norway
Erik Næsset, Terje Gobakken, Ole Martin Bollandsås, et al.
Remote Sensing of Environment (2012) Vol. 130, pp. 108-120
Open Access | Times Cited: 118

Showing 1-25 of 118 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: 692
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: 372
Adapting National Forest Inventories to changing requirements – the case of the Swedish National Forest Inventory at the turn of the 20th century
Jonas Fridman, Sören Holm, Mats B. Nilsson, et al.
Silva Fennica (2014) Vol. 48, Iss. 3
Open Access | Times Cited: 241
Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation
Göran Ståhl, Svetlana Saarela, Sebastian Schnell, et al.
Forest Ecosystems (2016) Vol. 3, Iss. 1
Open Access | Times Cited: 154
Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data
Lin Cao, Nicholas C. Coops, John L. Innes, et al.
Remote Sensing of Environment (2016) Vol. 178, pp. 158-171
Closed Access | Times Cited: 152
Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation
Shezhou Luo, Cheng Wang, Xiaohuan Xi, et al.
Ecological Indicators (2016) Vol. 73, pp. 378-387
Closed Access | Times Cited: 122
Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland
Daud Kachamba, Hans Ole Ørka, Terje Gobakken, et al.
Remote Sensing (2016) Vol. 8, Iss. 11, pp. 968-968
Open Access | Times Cited: 112
Use of partial-coverage UAV data in sampling for large scale forest inventories
Stefano Puliti, Liviu Theodor Ene, Terje Gobakken, et al.
Remote Sensing of Environment (2017) Vol. 194, pp. 115-126
Closed Access | Times Cited: 96
Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China
Zhenfeng Shao, Linjing Zhang
Sensors (2016) Vol. 16, Iss. 6, pp. 834-834
Open Access | Times Cited: 88
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
Very High Density Point Clouds from UAV Laser Scanning for Automatic Tree Stem Detection and Direct Diameter Measurement
Karel Kuželka, Martin Slavík, Peter Surový
Remote Sensing (2020) Vol. 12, Iss. 8, pp. 1236-1236
Open Access | Times Cited: 85
Estimating canopy structure and biomass in bamboo forests using airborne LiDAR data
Lin Cao, Nicholas C. Coops, Yuan Sun, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2019) Vol. 148, pp. 114-129
Closed Access | Times Cited: 82
Algorithm Theoretical Basis Document for GEDI Footprint Aboveground Biomass Density
James R. Kellner, John Armston, Laura Duncanson
Earth and Space Science (2022) Vol. 10, Iss. 4
Open Access | Times Cited: 51
Model-assisted estimation of growing stock volume using different combinations of LiDAR and Landsat data as auxiliary information
Svetlana Saarela, Anton Grafström, Göran Ståhl, et al.
Remote Sensing of Environment (2014) Vol. 158, pp. 431-440
Closed Access | Times Cited: 88
A Comparison of Machine Learning Techniques Applied to Landsat-5 TM Spectral Data for Biomass Estimation
Pablito Marcelo López-Serrano, Carlos A. López‐Sánchez, Juan Gabriél Álvarez‐González, et al.
Canadian Journal of Remote Sensing (2016) Vol. 42, Iss. 6, pp. 690-705
Open Access | Times Cited: 80
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
Large-scale estimation of change in aboveground biomass in miombo woodlands using airborne laser scanning and national forest inventory data
Liviu Theodor Ene, Erik Næsset, Terje Gobakken, et al.
Remote Sensing of Environment (2016) Vol. 188, pp. 106-117
Closed Access | Times Cited: 64
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
Estimating Above-Ground Biomass of the Regional Forest Landscape of Northern Western Ghats Using Machine Learning Algorithms and Multi-sensor Remote Sensing Data
Faseela V. Sainuddin, Guljar Malek, Ankur Rajwadi, et al.
Journal of the Indian Society of Remote Sensing (2024) Vol. 52, Iss. 4, pp. 885-902
Closed Access | Times Cited: 8
Above ground biomass and tree species richness estimation with airborne lidar in tropical Ghana forests
Gaia Vaglio Laurin, Nicola Puletti, Qi Chen, et al.
International Journal of Applied Earth Observation and Geoinformation (2016) Vol. 52, pp. 371-379
Open Access | Times Cited: 58
Estimating stand density, biomass and tree species from very high resolution stereo-imagery – towards an all-in-one sensor for forestry applications?
Fabian Ewald Fassnacht, Daniel Mangold, Jannika Schäfer, et al.
Forestry An International Journal of Forest Research (2017) Vol. 90, Iss. 5, pp. 613-631
Open Access | Times Cited: 58
Comparing echo-based and canopy height model-based metrics for enhancing estimation of forest aboveground biomass in a model-assisted framework
Gherardo Chirici, Ronald E. McRoberts, Lorenzo Fattorini, et al.
Remote Sensing of Environment (2015) Vol. 174, pp. 1-9
Closed Access | Times Cited: 51
Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review
Peter Surový, Karel Kuželka
Forests (2019) Vol. 10, Iss. 3, pp. 273-273
Open Access | Times Cited: 44
Estimation of Individual Tree Biomass in Natural Secondary Forests Based on ALS Data and WorldView-3 Imagery
Yinghui Zhao, Ye Ma, Lindi J. Quackenbush, et al.
Remote Sensing (2022) Vol. 14, Iss. 2, pp. 271-271
Open Access | Times Cited: 27
Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems
José Manuel Fernández‐Guisuraga, Susana Suárez‐Seoane, Paulo M. Fernandes, et al.
Forest Ecosystems (2022) Vol. 9, pp. 100022-100022
Open Access | Times Cited: 25
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