Qi Chen

Orcid: 0000-0003-0110-7996

Affiliations:
  • University of Hawaii at Manoa, Department of Geography, Honolulu, HI, USA


According to our database1, Qi Chen authored at least 17 papers between 2013 and 2020.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2020
Estimating aboveground and organ biomass of plant canopies across the entire season of rice growth with terrestrial laser scanning.
Int. J. Appl. Earth Obs. Geoinformation, 2020

2019
A Survey of Mobile Laser Scanning Applications and Key Techniques over Urban Areas.
Remote. Sens., 2019

A Hierarchical unsupervised method for power line classification from airborne LiDAR data.
Int. J. Digit. Earth, 2019

Detection of wheat height using optimized multi-scan mode of LiDAR during the entire growth stages.
Comput. Electron. Agric., 2019

2018
Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models.
Remote. Sens., 2018

Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM<sub>2.5</sub> Concentrations at an Urban Scale.
Remote. Sens., 2018

Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data.
Remote. Sens., 2018

A Forest Attribute Mapping Framework: A Pilot Study in a Northern Boreal Forest, Northwest Territories, Canada.
Remote. Sens., 2018

Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region.
Remote. Sens., 2018

2017
Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery.
Remote. Sens., 2017

Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas.
Remote. Sens., 2017

Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates.
Remote. Sens., 2017

Examining effective use of data sources and modeling algorithms for improving biomass estimation in a moist tropical forest of the Brazilian Amazon.
Int. J. Digit. Earth, 2017

2016
Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data.
Remote. Sens., 2016

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems.
Int. J. Digit. Earth, 2016

Above ground biomass and tree species richness estimation with airborne lidar in tropical Ghana forests.
Int. J. Appl. Earth Obs. Geoinformation, 2016

2013
Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa.
Int. J. Appl. Earth Obs. Geoinformation, 2013


  Loading...