Publicly Available Tool
- TRA approach for reducing the reflectance difference between Landsat 8 and Sentinel-2: https://github.com/GERSL/TRA.
Research Interests
Research Experience |
1. Change Detection / Change Agent Classification
2. Data Preprocessing
3. Vegetation Parameters Retrieval
4. Land cover mapping
5. Change analysis of variables with significant seasonal variations
- Change detection: Integrating the temporal, spectral, and spatial contextual information for near real-time monitoring of land surface change.
- Change Agent Classification: Attributed land disturbances into eight agents: fire, harvest, mechanical, stress, wind, hydrology, debris and others.
2. Data Preprocessing
- BRDF correction: Modified the selection of directional reflectance for BRDF/albedo retrieval, and improved the data availability of BRDF/albedo product largely.
- Time series reconstruction: Conducted global pixel-level evaluations of six gap-filling approaches for the first time, and considered vegetation growth trajectory, protection of key point, noise resistance and curve stability comprehensively.
- Harmonizing Landsat and Sentinel-2 data: Improve the data consistency of harmonized Landsat and Sentinel-2 data.
3. Vegetation Parameters Retrieval
- Land surface phenology retrieval: Demonstrated the constant thresholds at several inflexions for LSP retrieval and simplified the inflexion-based approaches, and proposed a LSP retrieval algorithm which is applicable for global varied vegetation growth trajectories.
4. Land cover mapping
- Water cover extent: Mapped the surface water cover dynamically from MODIS data, and captured the seasonal fluctuations of surface water cover.
5. Change analysis of variables with significant seasonal variations
- Continuous variables such as land surface phenology: Considered the vegetation growth trajectory as a whole for inter-annual change analysis.
- Discrete variables such as surface water cover (binary variable): Defined the open surface water cover frequency, and converted the binary variable within a year as continuous variable, which could avoid misleading seasonal fluctuations as inter-annual changes.
Presentations
- Qiu S., Lin, Y., Shang, R.*, Zhang, J., Ma, L., Zhu, Z. Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data. 2018 AGU Fall Meeting. 10/12/2018-14/12/2018 (Poster)
- Shang, R., Liu, R.*, Liu, Y., Xu, M., Zuo, L. Global retrieval of land surface phenology from MODIS data. The 12th International Congress of Ecology. 20/8/2017-25/8/2017 (Oral)
- Shang, R., Liu, R.*, Liu, Y., Xu, M., Zuo, L. Globally mapping seasonal fluctuations of the open surface water extent independent of cloud mask using MODIS data. 2016 AGU Fall Meeting. 12/12/2016-17/12/2016 (Poster)
- Shang, R., Liu, R.*, Liu, Y., Zuo, L. Extracting the vegetation phenology of India monsoon forest. 2016 IGRASS. 10/7/2016-15/7/2016 (Poster)
- Shang, R., Liu, R.* Zuo, L. Xu, M. Extracting the vegetation phenology of India monsoon forest. 2015 AGU Fall Meeting. 14/12/2015-18/12/2015 (Poster)