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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:

Estimation of Forest Aboveground Biomass in Changbai Mountain Region Using ICESat/GLAS and Landsat/TM Data
Hong Chi, Guoqing Sun, Jinliang Huang, et al.
Remote Sensing (2017) Vol. 9, Iss. 7, pp. 707-707
Open Access | Times Cited: 50

Showing 1-25 of 50 citing articles:

Review of Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation: Progress, Challenges, and Prospects
Lei Tian, Xiaocan Wu, Tao Yu, et al.
Forests (2023) Vol. 14, Iss. 6, pp. 1086-1086
Open Access | Times Cited: 78
Mapping high-resolution forest aboveground biomass of China using multisource remote sensing data
Qiuli Yang, Chunyue Niu, Xiaoqiang Liu, et al.
GIScience & Remote Sensing (2023) Vol. 60, Iss. 1
Open Access | Times Cited: 47
LiDAR Data Fusion to Improve Forest Attribute Estimates: A Review
Mattia Balestra, Suzanne Marselis, Temuulen Tsagaan Sankey, et al.
Current Forestry Reports (2024) Vol. 10, Iss. 4, pp. 281-297
Open Access | Times Cited: 26
Significant wave heights from Sentinel‐1 SAR: Validation and applications
Justin E. Stopa, A. Mouche
Journal of Geophysical Research Oceans (2016) Vol. 122, Iss. 3, pp. 1827-1848
Open Access | Times Cited: 101
Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations
Shengbiao Wu, Jing Wang, Zhengbing Yan, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2020) Vol. 171, pp. 36-48
Open Access | Times Cited: 79
Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation
Sean P. Healey, Zhiqiang Yang, Noel Gorelick, et al.
Remote Sensing (2020) Vol. 12, Iss. 17, pp. 2840-2840
Open Access | Times Cited: 75
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
Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China
Luodan Cao, Jianjun Pan, Ruijuan Li, et al.
Remote Sensing (2018) Vol. 10, Iss. 4, pp. 532-532
Open Access | Times Cited: 66
Estimation and Mapping of Forest Structure Parameters from Open Access Satellite Images: Development of a Generic Method with a Study Case on Coniferous Plantation
David Morin, Milena Planells, Dominique Guyon, et al.
Remote Sensing (2019) Vol. 11, Iss. 11, pp. 1275-1275
Open Access | Times Cited: 60
Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map
Yuzhen Zhang, Shunlin Liang
Remote Sensing (2020) Vol. 12, Iss. 16, pp. 2559-2559
Open Access | Times Cited: 57
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
Improved Object-Based Estimation of Forest Aboveground Biomass by Integrating LiDAR Data from GEDI and ICESat-2 with Multi-Sensor Images in a Heterogeneous Mountainous Region
Lin Chen, Chunying Ren, Guangdao Bao, et al.
Remote Sensing (2022) Vol. 14, Iss. 12, pp. 2743-2743
Open Access | Times Cited: 30
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: 7
Machine learning feature importance selection for predicting aboveground biomass in African savannah with landsat 8 and ALOS PALSAR data
Saad Eddin Ibrahim, Heiko Balzter, Kevin Tansey
Machine Learning with Applications (2024) Vol. 16, pp. 100561-100561
Open Access | Times Cited: 7
Annual forest aboveground biomass changes mapped using ICESat/GLAS measurements, historical inventory data, and time-series optical and radar imagery for Guangdong province, China
Wenjuan Shen, Mingshi Li, Chengquan Huang, et al.
Agricultural and Forest Meteorology (2018) Vol. 259, pp. 23-38
Open Access | Times Cited: 59
Estimation of Forest Canopy Height and Aboveground Biomass from Spaceborne LiDAR and Landsat Imageries in Maryland
Mengjia Wang, Rui Sun, Zhiqiang Xiao
Remote Sensing (2018) Vol. 10, Iss. 2, pp. 344-344
Open Access | Times Cited: 48
Remote Sensing Applications in Monitoring of Protected Areas
Yeqiao Wang, Zhong Lu, Yongwei Sheng, et al.
Remote Sensing (2020) Vol. 12, Iss. 9, pp. 1370-1370
Open Access | Times Cited: 33
Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data
Jialong Zhang, Chi Lu, Hui Xu, et al.
Journal of Forestry Research (2018) Vol. 30, Iss. 5, pp. 1689-1706
Closed Access | Times Cited: 36
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: 16
LiDAR as a Tool for Assessing Timber Assortments: A Systematic Literature Review
Cesar Alvites, Marco Marchetti, Bruno Lasserre, et al.
Remote Sensing (2022) Vol. 14, Iss. 18, pp. 4466-4466
Open Access | Times Cited: 14
Divergent trends of water bodies and their driving factors in a high-latitude water tower, Changbai Mountain
Peng Qi, Xiaoran Huang, Y. Jun Xu, et al.
Journal of Hydrology (2021) Vol. 603, pp. 127094-127094
Closed Access | Times Cited: 18
Integrating Multi-Source Remote Sensing to Assess Forest Aboveground Biomass in the Khingan Mountains of North-Eastern China Using Machine-Learning Algorithms
Xiaoyi Wang, Caixia Liu, Guanting Lv, et al.
Remote Sensing (2022) Vol. 14, Iss. 4, pp. 1039-1039
Open Access | Times Cited: 11
Deep and machine learning prediction of forest above-ground biomass using multi-source remote sensing data in coniferous planted forests in Iran
Hassan Ali, J. Mohammadi, Shaban Shataee
European Journal of Forest Research (2024) Vol. 143, Iss. 6, pp. 1731-1745
Closed Access | Times Cited: 2
Incorporating Forest Mapping-Related Uncertainty into the Error Propagation of Wall-to-Wall Biomass Maps: A General Approach for Large and Small Areas
Hassan Camil David, Alexander Christian Vibrans, Rorai Pereira Martins-Neto, et al.
Remote Sensing (2024) Vol. 16, Iss. 22, pp. 4295-4295
Open Access | Times Cited: 2
Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR
Jinyan Tian, Le Wang, Xiaojuan Li, et al.
Remote Sensing (2019) Vol. 11, Iss. 12, pp. 1446-1446
Open Access | Times Cited: 12
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