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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:

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: 136

Showing 1-25 of 136 citing articles:

Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion
Danilo Roberti Alves de Almeida, Eben N. Broadbent, Matheus Pinheiro Ferreira, et al.
Remote Sensing of Environment (2021) Vol. 264, pp. 112582-112582
Open Access | Times Cited: 123
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
Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models
Mi Luo, Shoaib Ahmad Anees, Qiuyan Huang, et al.
Forests (2024) Vol. 15, Iss. 6, pp. 975-975
Open Access | Times Cited: 30
An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products
Yuzhen Zhang, Jun Ma, Shunlin Liang, et al.
Remote Sensing (2020) Vol. 12, Iss. 24, pp. 4015-4015
Open Access | Times Cited: 109
Estimating leaf chlorophyll content of crops via optimal unmanned aerial vehicle hyperspectral data at multi-scales
Wanxue Zhu, Zhigang Sun, Ting Yang, et al.
Computers and Electronics in Agriculture (2020) Vol. 178, pp. 105786-105786
Closed Access | Times Cited: 92
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: 78
Delineation of management zones in agricultural fields using cover–crop biomass estimates from PlanetScope data
Fábio Marcelo Breunig, Lênio Soares Galvão, Ricardo Dalagnol, et al.
International Journal of Applied Earth Observation and Geoinformation (2019) Vol. 85, pp. 102004-102004
Open Access | Times Cited: 77
AM³Net: Adaptive Mutual-Learning-Based Multimodal Data Fusion Network
Jinping Wang, Jun Li, Yanli Shi, et al.
IEEE Transactions on Circuits and Systems for Video Technology (2022) Vol. 32, Iss. 8, pp. 5411-5426
Closed Access | Times Cited: 65
A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets
Yuzhen Zhang, Jun Ma, Shunlin Liang, et al.
GIScience & Remote Sensing (2022) Vol. 59, Iss. 1, pp. 234-249
Open Access | Times Cited: 55
The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing
Bin Yang, Wanxue Zhu, Ehsan Eyshi Rezaei, et al.
Remote Sensing (2022) Vol. 14, Iss. 7, pp. 1559-1559
Open Access | Times Cited: 46
Integrating Sentinel-1 and 2 with LiDAR data to estimate aboveground biomass of subtropical forests in northeast Guangdong, China
Linjing Zhang, Xiaoxue Zhang, Zhenfeng Shao, et al.
International Journal of Digital Earth (2023) Vol. 16, Iss. 1, pp. 158-182
Open Access | Times Cited: 32
A comparative analysis of machine learning techniques for aboveground biomass estimation: A case study of the Western Ghats, India
Kurian Ayushi, Kanda Naveen Babu, Narayanan Ayyappan, et al.
Ecological Informatics (2024) Vol. 80, pp. 102479-102479
Open Access | Times Cited: 17
A brief overview and perspective of using airborne Lidar data for forest biomass estimation
Dengsheng Lu, Xiandie Jiang
International Journal of Image and Data Fusion (2024) Vol. 15, Iss. 1, pp. 1-24
Closed Access | Times Cited: 14
A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture
Sheikh Muhammad Farhan, Jianjun Yin, Zhijian Chen, et al.
Sensors (2024) Vol. 24, Iss. 16, pp. 5409-5409
Open Access | Times Cited: 13
Estimating aboveground biomass of tropical urban forests with UAV-borne hyperspectral and LiDAR data
Matheus Pinheiro Ferreira, Gabriela Barbosa Martins, Thaís Moreira Hidalgo de Almeida, et al.
Urban forestry & urban greening (2024) Vol. 96, pp. 128362-128362
Closed Access | Times Cited: 12
Mapping tree species diversity in a typical natural secondary forest by combining multispectral and LiDAR data
Lang Ming, Jianyang Liu, Ying Quan, et al.
Ecological Indicators (2024) Vol. 159, pp. 111711-111711
Open Access | Times Cited: 11
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
Advances in Laser Scanning to Assess Carbon in Forests: From Ground-Based to Space-Based Sensors
Nicholas C. Coops, Liam Irwin, Harry Seely, et al.
Current Forestry Reports (2025) Vol. 11, Iss. 1
Closed Access | Times Cited: 1
UAV-based individual shrub aboveground biomass estimation calibrated against terrestrial LiDAR in a shrub-encroached grassland
Yujin Zhao, Xiaoliang Liu, Yang Wang, et al.
International Journal of Applied Earth Observation and Geoinformation (2021) Vol. 101, pp. 102358-102358
Open Access | Times Cited: 47
UAV-based indicators of crop growth are robust for distinct water and nutrient management but vary between crop development phases
Wanxue Zhu, Ehsan Eyshi Rezaei, Hamideh Nouri, et al.
Field Crops Research (2022) Vol. 284, pp. 108582-108582
Closed Access | Times Cited: 38
Estimating Aboveground Biomass of Two Different Forest Types in Myanmar from Sentinel-2 Data with Machine Learning and Geostatistical Algorithms
Phyo Wai, Huiyi Su, Mingshi Li
Remote Sensing (2022) Vol. 14, Iss. 9, pp. 2146-2146
Open Access | Times Cited: 35
Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes
Qiuli Yang, Yanjun Su, Tianyu Hu, et al.
Forest Ecosystems (2022) Vol. 9, pp. 100059-100059
Open Access | Times Cited: 35
Forest disturbance and growth processes are reflected in the geographical distribution of large canopy gaps across the Brazilian Amazon
Cristiano Rodrigues Reis, Toby Jackson, Eric Bastos Görgens, et al.
Journal of Ecology (2022) Vol. 110, Iss. 12, pp. 2971-2983
Open Access | Times Cited: 34
Remote sensing and machine learning applications for aboveground biomass estimation in agroforestry systems: a review
Bhuwan Thapa, Sarah Taylor Lovell, Jeffrey J. Wilson
Agroforestry Systems (2023) Vol. 97, Iss. 6, pp. 1097-1111
Closed Access | Times Cited: 20
Towards accurate individual tree parameters estimation in dense forest: optimized coarse-to-fine algorithms for registering UAV and terrestrial LiDAR data
Yuting Zhao, Jungho Im, Zhen Zhen, et al.
GIScience & Remote Sensing (2023) Vol. 60, Iss. 1
Open Access | Times Cited: 18
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