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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:

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

Showing 1-25 of 88 citing articles:

GEDI launches a new era of biomass inference from space
R. Dubayah, John Armston, Sean P. Healey, et al.
Environmental Research Letters (2022) Vol. 17, Iss. 9, pp. 095001-095001
Open Access | Times Cited: 137
Remote sensing in forestry: current challenges, considerations and directions
Fabian Ewald Fassnacht, Joanne C. White, Michael A. Wulder, et al.
Forestry An International Journal of Forest Research (2023) Vol. 97, Iss. 1, pp. 11-37
Open Access | Times Cited: 133
A novel CNN-LSTM-based approach to predict urban expansion
Wadii Boulila, Hamza Ghandorh, Mehshan Ahmed Khan, et al.
Ecological Informatics (2021) Vol. 64, pp. 101325-101325
Open Access | Times Cited: 123
Estimating Changes in Forest Attributes and Enhancing Growth Projections: a Review of Existing Approaches and Future Directions Using Airborne 3D Point Cloud Data
Piotr Tompalski, Nicholas C. Coops, Joanne C. White, et al.
Current Forestry Reports (2021) Vol. 7, Iss. 1, pp. 1-24
Open Access | Times Cited: 59
A remote sensing tool for near real‐time monitoring of harmful algal blooms and turbidity in reservoirs
Abhiram Siva Prasad Pamula, Hamed Gholizadeh, Mark J. Krzmarzick, et al.
JAWRA Journal of the American Water Resources Association (2023) Vol. 59, Iss. 5, pp. 929-949
Closed Access | Times Cited: 25
A New Forest Growing Stock Volume Estimation Model Based on AdaBoost and Random Forest Model
Xiaorui Wang, Chao Zhang, Zhenping Qiang, et al.
Forests (2024) Vol. 15, Iss. 2, pp. 260-260
Open Access | Times Cited: 12
Forest Aboveground Biomass Estimation and Inventory: Evaluating Remote Sensing-Based Approaches
Muhammad Nouman Khan, Yumin Tan, Ahmad Ali Gul, et al.
Forests (2024) Vol. 15, Iss. 6, pp. 1055-1055
Open Access | Times Cited: 10
National mapping and estimation of forest area by dominant tree species using Sentinel-2 data
Johannes Breidenbach, Lars T. Waser‬, Misganu Debella-Gilo, et al.
Canadian Journal of Forest Research (2020) Vol. 51, Iss. 3, pp. 365-379
Open Access | Times Cited: 65
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
An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review
Hafiz Suliman Munawar, Ahmed W. A. Hammad, S. Travis Waller, et al.
Sustainability (2021) Vol. 13, Iss. 14, pp. 7925-7925
Open Access | Times Cited: 44
Improved Semisupervised UNet Deep Learning Model for Forest Height Mapping With Satellite SAR and Optical Data
Shaojia Ge, Hong Gu, Weimin Su, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2022) Vol. 15, pp. 5776-5787
Open Access | Times Cited: 32
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
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
An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass
Aliasghar Bazrafkan, Nadia Delavarpour, Peter G. Oduor, et al.
Remote Sensing (2023) Vol. 15, Iss. 14, pp. 3543-3543
Open Access | Times Cited: 14
Estimation of boreal forest biomass from ICESat-2 data using hierarchical hybrid inference
Petri Varvia, Svetlana Saarela, Matti Maltamo, et al.
Remote Sensing of Environment (2024) Vol. 311, pp. 114249-114249
Open Access | Times Cited: 5
High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013
Hong Wang, Jiawen Li, Zhiqiu Gao, et al.
Remote Sensing (2019) Vol. 11, Iss. 23, pp. 2724-2724
Open Access | Times Cited: 36
Regional-Scale Forest Mapping over Fragmented Landscapes Using Global Forest Products and Landsat Time Series Classification
Viktor Myroniuk, Mykola Kutia, Arbi J. Sarkissian, et al.
Remote Sensing (2020) Vol. 12, Iss. 1, pp. 187-187
Open Access | Times Cited: 35
Spatiotemporal variation and driving factors of vegetation net primary productivity in a typical karst area in China from 2000 to 2010
Liyi Song, Mingyang Li, Xu Hai, et al.
Ecological Indicators (2021) Vol. 132, pp. 108280-108280
Open Access | Times Cited: 28
Evaluating the performance of airborne and spaceborne lidar for mapping biomass in the United States' largest dry woodland ecosystem
Michael J. Campbell, Jessie F. Eastburn, Philip E. Dennison, et al.
Remote Sensing of Environment (2024) Vol. 308, pp. 114196-114196
Closed Access | Times Cited: 4
Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon
Francisco Mauro, Andrew T. Hudak, Patrick A. Fekety, et al.
Remote Sensing (2021) Vol. 13, Iss. 2, pp. 261-261
Open Access | Times Cited: 25
An AI/ML-Based Strategy for Disaster Response and Evacuation of Victims in Aged Care Facilities in the Hawkesbury-Nepean Valley: A Perspective
Hafiz Suliman Munawar, Mohammad Mojtahedi, Ahmed W. A. Hammad, et al.
Buildings (2022) Vol. 12, Iss. 1, pp. 80-80
Open Access | Times Cited: 19
Comparison of Multiple Machine Learning Models for Estimating the Forest Growing Stock in Large-Scale Forests Using Multi-Source Data
Huajian Huang, Dasheng Wu, Luming Fang, et al.
Forests (2022) Vol. 13, Iss. 9, pp. 1471-1471
Open Access | Times Cited: 18
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
Predicting eucalyptus plantation growth and yield using Landsat imagery in Minas Gerais, Brazil
Jeangelis Silva Santos, Adriano Ribeiro de Mendonça, F. G. Gonçalves, et al.
Ecological Informatics (2023) Vol. 75, pp. 102120-102120
Closed Access | Times Cited: 11
Assessing the effect of ensemble learning algorithms and validation approach on estimating forest aboveground biomass: a case study of natural secondary forest in Northeast China
Hung-Il Jin, Yinghui Zhao, Unil Pak, et al.
Geo-spatial Information Science (2024), pp. 1-20
Open Access | Times Cited: 4
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