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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:

Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania
Ernest William Mauya, Endre Hansen, Terje Gobakken, et al.
Carbon Balance and Management (2015) Vol. 10, Iss. 1
Open Access | Times Cited: 81

Showing 1-25 of 81 citing articles:

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
Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest
Sujit Madhab Ghosh, Mukunda Dev Behera
Applied Geography (2018) Vol. 96, pp. 29-40
Closed Access | Times Cited: 166
Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon
Marcos Longo, Michael Keller, M. N. dos-Santos, et al.
Global Biogeochemical Cycles (2016) Vol. 30, Iss. 11, pp. 1639-1660
Open Access | Times Cited: 148
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
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: 118
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: 111
Mapping and estimating forest area and aboveground biomass in miombo woodlands in Tanzania using data from airborne laser scanning, TanDEM-X, RapidEye, and global forest maps: A comparison of estimated precision
Erik Næsset, Hans Ole Ørka, Svein Solberg, et al.
Remote Sensing of Environment (2016) Vol. 175, pp. 282-300
Closed Access | Times Cited: 109
El Niño drought increased canopy turnover in Amazon forests
Veronika Leitold, Douglas C. Morton, Marcos Longo, et al.
New Phytologist (2018) Vol. 219, Iss. 3, pp. 959-971
Open Access | Times Cited: 95
Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests
Marcos Longo, Sassan Saatchi, Michael Keller, et al.
Journal of Geophysical Research Biogeosciences (2020) Vol. 125, Iss. 8
Open Access | Times Cited: 77
Human impacts as the main driver of tropical forest carbon
Marcela V. Pyles, Luiz Fernando Silva Magnago, Vinícius Andrade Maia, et al.
Science Advances (2022) Vol. 8, Iss. 24
Open Access | Times Cited: 43
SAR-Based Estimation of Above-Ground Biomass and Its Changes in Tropical Forests of Kalimantan Using L- and C-Band
Anna Berninger, Sandra Lohberger, Matthias Stängel, et al.
Remote Sensing (2018) Vol. 10, Iss. 6, pp. 831-831
Open Access | Times Cited: 74
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 novel approach for estimation of aboveground biomass of a carbon-rich mangrove site in India
Sujit Madhab Ghosh, Mukunda Dev Behera, Buddola Jagadish, et al.
Journal of Environmental Management (2021) Vol. 292, pp. 112816-112816
Closed Access | Times Cited: 42
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
Biomass and InSAR height relationship in a dense tropical forest
Svein Solberg, Endre Hansen, Terje Gobakken, et al.
Remote Sensing of Environment (2017) Vol. 192, pp. 166-175
Closed Access | Times Cited: 58
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: 57
Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory
António Ferraz, Sassan Saatchi, Clément Mallet, et al.
Remote Sensing (2016) Vol. 8, Iss. 8, pp. 653-653
Open Access | Times Cited: 55
Lidar supported estimators of wood volume and aboveground biomass from the Danish national forest inventory (2012–2016)
Steen Magnussen, Thomas Nord‐Larsen, Torben Riis‐Nielsen
Remote Sensing of Environment (2018) Vol. 211, pp. 146-153
Closed Access | Times Cited: 54
Aboveground Carbon Storage and Its Links to Stand Structure, Tree Diversity and Floristic Composition in South-Eastern Tanzania
Iain M. McNicol, Casey M. Ryan, Kyle G. Dexter, et al.
Ecosystems (2017) Vol. 21, Iss. 4, pp. 740-754
Open Access | Times Cited: 53
Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland
Darío Domingo, Hans Ole Ørka, Erik Næsset, et al.
Remote Sensing (2019) Vol. 11, Iss. 8, pp. 948-948
Open Access | Times Cited: 52
Mapping Forest Structure and Composition from Low-Density LiDAR for Informed Forest, Fuel, and Fire Management at Eglin Air Force Base, Florida, USA
Andrew T. Hudak, Benjamin C. Bright, Scott Pokswinski, et al.
Canadian Journal of Remote Sensing (2016) Vol. 42, Iss. 5, pp. 411-427
Closed Access | Times Cited: 50
Effects of Sample Plot Size and GPS Location Errors on Aboveground Biomass Estimates from LiDAR in Tropical Dry Forests
José Luis Hernández‐Stefanoni, Gabriela Reyes-Palomeque, Miguel Santiago, et al.
Remote Sensing (2018) Vol. 10, Iss. 10, pp. 1586-1586
Open Access | Times Cited: 46
Effects of Pulse Density on Digital Terrain Models and Canopy Metrics Using Airborne Laser Scanning in a Tropical Rainforest
Endre Hansen, Terje Gobakken, Erik Næsset
Remote Sensing (2015) Vol. 7, Iss. 7, pp. 8453-8468
Open Access | Times Cited: 44
Using synthetic data to evaluate the benefits of large field plots for forest biomass estimation with LiDAR
Fabian Ewald Fassnacht, Hooman Latifi, Florian Härtig
Remote Sensing of Environment (2018) Vol. 213, pp. 115-128
Closed Access | Times Cited: 38
Integrating airborne laser scanning data, space-borne radar data and digital aerial imagery to estimate aboveground carbon stock in Hyrcanian forests, Iran
Maryam Poorazimy, Shaban Shataee, Ronald E. McRoberts, et al.
Remote Sensing of Environment (2020) Vol. 240, pp. 111669-111669
Closed Access | Times Cited: 36
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