Ling Wu
Orcid: 0000-0003-1712-191XAffiliations:
- China University of Geoscience (Beijing), School of Information Engineering, China (PhD 2013)
According to our database1,
Ling Wu
authored at least 35 papers
between 2011 and 2024.
Collaborative distances:
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Bibliography
2024
EWMACD Algorithm in Early Detection of Defoliation Caused by Dendrolimus tabulaeformis Tsai et Liu.
Remote. Sens., July, 2024
Historical Dynamic Mapping of Eucalyptus Plantations in Guangxi during 1990-2019 Based on Sliding-Time-Window Change Detection Using Dense Landsat Time-Series Data.
Remote. Sens., March, 2024
IEEE Trans. Geosci. Remote. Sens., 2024
Spatiotemporal Cube Model Based on Stress Features for Identification of Heavy Metal Stress in Rice.
IEEE Trans. Geosci. Remote. Sens., 2024
2023
Site Selection Prediction for Coffee Shops Based on Multi-Source Space Data Using Machine Learning Techniques.
ISPRS Int. J. Geo Inf., August, 2023
Sub-Annual Scale LandTrendr: Sub-Annual Scale Deforestation Detection Algorithm Using Multi-Source Time Series Data.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2023
2022
Online Forest Disturbance Detection at the Sub-Annual Scale Using Spatial Context From Sparse Landsat Time Series.
IEEE Trans. Geosci. Remote. Sens., 2022
State-and-Evolution Detection Models: A Framework for Continuously Monitoring Landscape Pattern Change.
IEEE Trans. Geosci. Remote. Sens., 2022
Reconstruction of Optical Image Time Series With Unequal Lengths SAR Based on Improved Sequence-Sequence Model.
IEEE Trans. Geosci. Remote. Sens., 2022
Hybrid Spatiotemporal Graph Convolutional Network for Detecting Landscape Pattern Evolution From Long-Term Remote Sensing Images.
IEEE Trans. Geosci. Remote. Sens., 2022
2021
Spectral analysis of seasonal rock and vegetation changes for detecting karst rocky desertification in southwest China.
Int. J. Appl. Earth Obs. Geoinformation, 2021
Long-term Landsat monitoring of mining subsidence based on spatiotemporal variations in soil moisture: A case study of Shanxi Province, China.
Int. J. Appl. Earth Obs. Geoinformation, 2021
2020
Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland.
Remote. Sens., 2020
An Improved Spatiotemporal Data Fusion Method Using Surface Heterogeneity Information Based on ESTARFM.
Remote. Sens., 2020
Parallel Computing for Obtaining Regional Scale Rice Growth Conditions Based on WOFOST and Satellite Images.
IEEE Access, 2020
2019
Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery.
Remote. Sens., 2019
An approach for heavy metal pollution detected from spatio-temporal stability of stress in rice using satellite images.
Int. J. Appl. Earth Obs. Geoinformation, 2019
2018
Scaling Correction of Remotely Sensed Leaf Area Index for Farmland Landscape Pattern With Multitype Spatial Heterogeneities Using Fractal Dimension and Contextural Parameters.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2018
Classification of Rice Heavy Metal Stress Levels Based on Phenological Characteristics Using Remote Sensing Time-Series Images and Data Mining Algorithms.
Sensors, 2018
Sensors, 2018
A Modified Spatiotemporal Fusion Algorithm Using Phenological Information for Predicting Reflectance of Paddy Rice in Southern China.
Remote. Sens., 2018
Method for Mapping Rice Fields in Complex Landscape Areas Based on Pre-Trained Convolutional Neural Network from HJ-1 A/B Data.
ISPRS Int. J. Geo Inf., 2018
2017
Distinguishing Heavy-Metal Stress Levels in Rice Using Synthetic Spectral Index Responses to Physiological Function Variations.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2017
Extraction of Rice Phenological Differences under Heavy Metal Stress Using EVI Time-Series from HJ-1A/B Data.
Sensors, 2017
Estimating FAPAR of Rice Growth Period Using Radiation Transfer Model Coupled with the WOFOST Model for Analyzing Heavy Metal Stress.
Remote. Sens., 2017
2016
Optimizing the Temporal Scale in the Assimilation of Remote Sensing and WOFOST Model for Dynamically Monitoring Heavy Metal Stress in Rice.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2016
Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model.
Sensors, 2016
Remote. Sens., 2016
2015
The Dynamic Assessment Model for Monitoring Cadmium Stress Levels in Rice Based on the Assimilation of Remote Sensing and the WOFOST Model.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2015
Analyzing the Spatial Scaling Bias of Rice Leaf Area Index From Hyperspectral Data Using Wavelet-Fractal Technique.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2015
An improved assimilation method with stress factors incorporated in the WOFOST model for the efficient assessment of heavy metal stress levels in rice.
Int. J. Appl. Earth Obs. Geoinformation, 2015
Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium, 2015
2013
The assimilation of spectral sensing and the WOFOST model for the dynamic simulation of cadmium accumulation in rice tissues.
Int. J. Appl. Earth Obs. Geoinformation, 2013
2011
Wavelet-based detection of crop zinc stress assessment using hyperspectral reflectance.
Comput. Geosci., 2011
Monitoring stress levels on rice with heavy metal pollution from hyperspectral reflectance data using wavelet-fractal analysis.
Int. J. Appl. Earth Obs. Geoinformation, 2011