Jiangping Long

Orcid: 0000-0001-9971-5505

According to our database1, Jiangping Long authored at least 17 papers between 2019 and 2024.

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

Timeline

Legend:

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PhD thesis 
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Bibliography

2024
A Novel Feature Evaluation Method in Mapping Forest AGB by Fusing Multiple Evaluation Metrics Using PolSAR Data.
IEEE Geosci. Remote. Sens. Lett., 2024

2023
Evaluating the Transferability of Spectral Variables and Prediction Models for Mapping Forest Aboveground Biomass Using Transfer Learning Methods.
Remote. Sens., November, 2023

Mapping Growing Stem Volume Using Dual-Polarization GaoFen-3 SAR Images in Evergreen Coniferous Forests.
Remote. Sens., May, 2023

Interpretation and Mapping Tree Crown Diameter Using Spatial Heterogeneity in Relation to the Radiative Transfer Model Extracted from GF-2 Images in Planted Boreal Forest Ecosystems.
Remote. Sens., April, 2023

Evaluating the Sensitivity of Polarimetric Features Related to Rotation Domain and Mapping Chinese Fir AGB Using Quad-Polarimetric SAR Images.
Remote. Sens., March, 2023

Mapping Forest Growing Stem Volume Using Novel Feature Evaluation Criteria Based on Spectral Saturation in Planted Chinese Fir Forest.
Remote. Sens., January, 2023

2022
Analyzing the Saturation of Growing Stem Volume Based on ZY-3 Stereo and Multispectral Images in Planted Coniferous Forest.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022

Mapping Forest Stock Volume Based on Growth Characteristics of Crown Using Multi-Temporal Landsat 8 OLI and ZY-3 Stereo Images in Planted Eucalyptus Forest.
Remote. Sens., 2022

Inversion of Coniferous Forest Stock Volume Based on Backscatter and InSAR Coherence Factors of Sentinel-1 Hyper-Temporal Images and Spectral Variables of Landsat 8 OLI.
Remote. Sens., 2022

2021
A Combined Strategy of Improved Variable Selection and Ensemble Algorithm to Map the Growing Stem Volume of Planted Coniferous Forest.
Remote. Sens., 2021

A Novel Method for Estimating Spatial Distribution of Forest Above-Ground Biomass Based on Multispectral Fusion Data and Ensemble Learning Algorithm.
Remote. Sens., 2021

Mapping the Growing Stem Volume of the Coniferous Plantations in North China Using Multispectral Data from Integrated GF-2 and Sentinel-2 Images and an Optimized Feature Variable Selection Method.
Remote. Sens., 2021

Coniferous Plantations Growing Stock Volume Estimation Using Advanced Remote Sensing Algorithms and Various Fused Data.
Remote. Sens., 2021

2020
Estimating the Growing Stem Volume of the Planted Forest Using the General Linear Model and Time Series Quad-Polarimetric SAR Images.
Sensors, 2020

Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method.
Sensors, 2020

Estimating the Growing Stem Volume of Chinese Pine and Larch Plantations based on Fused Optical Data Using an Improved Variable Screening Method and Stacking Algorithm.
Remote. Sens., 2020

2019
Mapping Growing Stem Volume of Chinese Fir Plantation Using a Saturation-based Multivariate Method and Quad-polarimetric SAR Images.
Remote. Sens., 2019


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