Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China
Hong Chi, Guoqing Sun, Jinliang Huang, et al.
Remote Sensing (2015) Vol. 7, Iss. 5, pp. 5534-5564
Open Access | Times Cited: 75

Showing 1-25 of 75 citing articles:

Mapping global forest canopy height through integration of GEDI and Landsat data
Peter Potapov, Xinyuan Li, Andrés Hernández-Serna, et al.
Remote Sensing of Environment (2020) Vol. 253, pp. 112165-112165
Open Access | Times Cited: 927
Modelling lidar-derived estimates of forest attributes over space and time: A review of approaches and future trends
Nicholas C. Coops, Piotr Tompalski, Tristan R.H. Goodbody, et al.
Remote Sensing of Environment (2021) Vol. 260, pp. 112477-112477
Open Access | Times Cited: 241
Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data
Tianyu Hu, Yanjun Su, Baolin Xue, et al.
Remote Sensing (2016) Vol. 8, Iss. 7, pp. 565-565
Open Access | Times Cited: 156
Future biomass carbon sequestration capacity of Chinese forests
Yitong Yao, Shilong Piao, Tao Wang
Science Bulletin (2018) Vol. 63, Iss. 17, pp. 1108-1117
Closed Access | Times Cited: 148
Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery
Lin Chen, Chunying Ren, Bai Zhang, et al.
Forests (2018) Vol. 9, Iss. 10, pp. 582-582
Open Access | Times Cited: 129
Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China
Huabing Huang, Caixia Liu, Xiaoyi Wang, et al.
Remote Sensing of Environment (2018) Vol. 221, pp. 225-234
Closed Access | Times Cited: 111
Forest biomass retrieval approaches from earth observation in different biomes
Pedro Rodríguez‐Veiga, S. Quegan, João M. B. Carreiras, et al.
International Journal of Applied Earth Observation and Geoinformation (2019) Vol. 77, pp. 53-68
Open Access | Times Cited: 96
Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example
Lana L. Narine, Sorin C. Popescu, Lonesome Malambo
Remote Sensing (2020) Vol. 12, Iss. 11, pp. 1824-1824
Open Access | Times Cited: 90
Estimation of LAI with the LiDAR Technology: A Review
Yao Wang, Hongliang Fang
Remote Sensing (2020) Vol. 12, Iss. 20, pp. 3457-3457
Open Access | Times Cited: 90
Synergy of ICESat-2 and Landsat for Mapping Forest Aboveground Biomass with Deep Learning
Lana L. Narine, Sorin C. Popescu, Lonesome Malambo
Remote Sensing (2019) Vol. 11, Iss. 12, pp. 1503-1503
Open Access | Times Cited: 85
Forest Height Mapping Using Feature Selection and Machine Learning by Integrating Multi-Source Satellite Data in Baoding City, North China
Nan Zhang, Mingjie Chen, Fan Yang, et al.
Remote Sensing (2022) Vol. 14, Iss. 18, pp. 4434-4434
Open Access | Times Cited: 41
State-wide forest canopy height and aboveground biomass map for New York with 10 m resolution, integrating GEDI, Sentinel-1, and Sentinel-2 data
Haifa Tamiminia, Bahram Salehi, Masoud Mahdianpari, et al.
Ecological Informatics (2023) Vol. 79, pp. 102404-102404
Open Access | Times Cited: 31
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
Estimating Forest Growing Stock Volume Using Feature Selection and Advanced Remote Sensing Algorithm
Yabing Zhao, Famiao Guo, Yin Wang, et al.
Remote Sensing Applications Society and Environment (2025), pp. 101458-101458
Closed Access | Times Cited: 1
Slope-adaptive waveform metrics of large footprint lidar for estimation of forest aboveground biomass
Yao Wang, Wenjian Ni, Guoqing Sun, et al.
Remote Sensing of Environment (2019) Vol. 224, pp. 386-400
Closed Access | Times Cited: 67
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
Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models
Yang Hu, Xuelei Xu, Fayun Wu, et al.
Remote Sensing (2020) Vol. 12, Iss. 1, pp. 186-186
Open Access | Times Cited: 63
Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data
Rodrigo Vieira Leite, Carlos Alberto Silva, Eben N. Broadbent, et al.
Remote Sensing of Environment (2021) Vol. 268, pp. 112764-112764
Open Access | Times Cited: 48
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
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: 23
Modeling the Spatial Distribution of Acacia decurrens Plantation Forests Using PlanetScope Images and Environmental Variables in the Northwestern Highlands of Ethiopia
Bireda Alemayehu, Juan Carlos Pinilla Suárez, J. Rosette
Forests (2024) Vol. 15, Iss. 2, pp. 277-277
Open Access | Times Cited: 7
Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data
Kaili Liu, Jindi Wang, Weisheng Zeng, et al.
Remote Sensing (2017) Vol. 9, Iss. 4, pp. 341-341
Open Access | Times Cited: 62
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
Twenty‐first century remote sensing technologies are revolutionizing the study of tropical forests
Arturo Sánchez‐Azofeifa, Jose Antonio Guzmán, Carlos Campos-Vargas, et al.
Biotropica (2017) Vol. 49, Iss. 5, pp. 604-619
Closed Access | Times Cited: 58
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
Page 1 - Next Page

Scroll to top