Dengqiu Li

Orcid: 0000-0002-9839-759X

According to our database1, Dengqiu Li authored at least 13 papers between 2016 and 2024.

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

Timeline

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Bibliography

2024
Integration of Handheld and Airborne Lidar Data for Dicranopteris Dichotoma Biomass Estimation in a Subtropical Region of Fujian Province, China.
Remote. Sens., June, 2024

Examining the Effects of Soil and Water Conservation Measures on Patterns and Magnitudes of Vegetation Cover Change in a Subtropical Region Using Time Series Landsat Imagery.
Remote. Sens., February, 2024

2023
Integration of Landsat time-series vegetation indices improves consistency of change detection.
Int. J. Digit. Earth, December, 2023

Vertical Characteristics of Vegetation Distribution in Wuyishan National Park Based on Multi-Source High-Resolution Remotely Sensed Data.
Remote. Sens., October, 2023

Forest Structure Simulation of Eucalyptus Plantation Using Remote-Sensing-Based Forest Age Data and 3-PG Model.
Remote. Sens., January, 2023

2022
Examining the Role of UAV Lidar Data in Improving Tree Volume Calculation Accuracy.
Remote. Sens., 2022

Examining Vegetation Change and Associated Spatial Patterns in Wuyishan National Park at Different Protection Levels.
Remote. Sens., 2022

2020
Examining the Roles of Spectral, Spatial, and Topographic Features in Improving Land-Cover and Forest Classifications in a Subtropical Region.
Remote. Sens., 2020

2019
Exploring Bamboo Forest Aboveground Biomass Estimation Using Sentinel-2 Data.
Remote. Sens., 2019

2018
Examining Land Cover and Greenness Dynamics in Hangzhou Bay in 1985-2016 Using Landsat Time-Series Data.
Remote. Sens., 2018

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

2017
Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data.
Remote. Sens., 2017

2016
Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data.
Int. J. Appl. Earth Obs. Geoinformation, 2016


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