Reserach Projects

Water-related Land Cover Dynamics in Northeast Asia Under Climate Change and Anthropogenic Activities

08/2021-present

Advisor: Prof. Xiangming Xiao

• The overall aim is to enhance our understanding of the impacts of changing climate and increasing human activities on the dynamics of water-related land cover types.
• Water related land cover types include yearlong surface water, paddy rice, and natural wetlands.
• We have developed a set of knowledge-based water-related land cover mapping algorithms based on each type’s unique and stable optical and/or microwave signatures (associated with its biophysical features), by integrating multi-source (optical, SAR, thermal) remote sensing.
• We consider the remote sensing capability and define water-related land cover types in terms of plant growth form (tree, shrub, grass), life cycle (perennial, annual), leaf seasonality (evergreen, deciduous), and canopy type (open, closed).
• We are extending the work in Northeast China to Northest Asia by integrating Deep Learning methods.
• We developed algorithms to detect the duration of surface water disappearance under extreme drought.
• We have constructed a water/non-water database to study the surface and terrestrial water resources dynamics in Northeast Asia during 2000-2023. We have explored the impacts of climate and anthropogenic factors on water resources, and the pressures of water resource changes on populations.

Related publications:

1. Zhang, C., Xiao, X., Wang, X., Qin, Y., Doughty, R., Yang, X., … & Dong, J. (2024). Mapping wetlands in Northeast China by using knowledge-based algorithms and microwave (PALSAR-2, Sentinel-1), optical (Sentinel-2, Landsat), and thermal (MODIS) images. Journal of Environmental Management, 349, 119618. [Full text]
2. Zhang, C., Xiao, X., et al. Mapping paddy rice in Northeast China with a knowledge-based algorithm and time series optical, microwave, and thermal imagery. Frontiers of Earth Sciences (in Press)
3. Zhang, C., Xiao, X., et al. Climate-induced losses of surface water and total water storage in Northeast Asia. (Communications Earth & Environment, in Revision)
4. Zhang, C., Xiao, X., et al. Challenges and benefits of habitat changes for migratory birds in the East Asian-Australasian Flyway (in Prep.)

Eucalyptus Plantation Mapping by Knowledge-based Algorithms and Multi-source Remote Sensing

04/2022-05/2023

Advisor: Prof. Xiangming Xiao

• This is a project I completed in the course Environmental Remote Sensing provided by Prof. Xiangming Xiao.
• We explored the potential of the red edge bands of Sentinel-2 for Eucalyptus plantation mapping.
• We integrated PALSAR-2, Sentinel-2, Landsat 7/8 to develop Eucalyptus mapping algorithm.
• We developed a knowledge-based Eucalyptus mapping algorithm.

Related publications:

1. Zhang, C., Xiao, X., Zhao, L., Qin, Y., Doughty, R., Wang, X., … & Yang, X. (2023). Mapping Eucalyptus plantation in Guangxi, China by using knowledge-based algorithms and PALSAR-2, Sentinel-2, and Landsat images in 2020. International Journal of Applied Earth Observation and Geoinformation, 120, 103348. [Full text]

Detection of Planting Distribution and Change of Rubber Plantation in Northeast Thailand Based on High Spatial and Temporal Resolution Series Remote Sensing

10/2018-06/2020

Advisor: Prof. Chong Huang

• We develped Machine Learning based forest mapping algorithm by temporal statistical characteristics and time series Sentinel-1 imagery.
• We explored the appropriate window sizes for each individual texture feature of different spatial resolutions data when distinguishing rubber plantation from natural forest.
• We detected the planting year and pre-conversion land cover types of rubber plantation by improving Shapelet algorithom.
• We assessed the impact of rubber plantation expansion on regional carbon storage.

Related publications:

1. Zhang, C., Huang, C., Li, H., Liu, Q., Li, J., Bridhikitti, A., & Liu, G. (2020). Effect of textural features in remote sensed data on rubber plantation extraction at different levels of spatial resolution. Forests, 11(4), 399. [Full text]
2. Huang, C., Zhang, C., Liu, Q., Li, H., Yang, X., & Liu, G. (2021). Multi-Feature Classification of Optical and SAR Remote Sensing Images for Typical Tropical Plantation Tree Species Classification, Scientia Silvae Sinicae, 57(7), 80-91. [Full text]
3. Huang, C., Zhang, C.¹, Li, H. (2022). Assessment of the impact of rubber plantation expansion on regional carbon storage based on time series remote sensing and the invest model. Remote Sensing, 14(24), 6234. [Full text]
4. Li, H., He, Z., Huang, C., Liu, Q., Liu, G., & Zhang, C. (2021). Spatiotemporal evolution of rubber forests in southern Myanmar during 2000-2019. Resources Science, 43(12), 2403-2415. [Full text]

Data Integration of Resource Development and Utilization in the Peripheral Area of South China Sea (Indochina Peninsula Region)

10/2018-08/2021

Advisor: Prof. Chong Huang

• We conducted the field survey of land cover in southeast Asia region.
• We generated land cover maps for Indochina Peninsula and its coastal zone using multi-source remote sensing and Machine Learning in 2020 and 1990.
• We quantified and analyzed the land cover changes in Indochina Peninsula between 1990 and 2020.
• We developed a rice planting structure (single-season rice, double-season rice) extraction algorithm by integrating Dynamic Time Warping (DTW), time series statistical features, and Random Forest.
• We developed a robust method combining phenological metrics and statistical spectral indices at different phases for stratified rice paddy mapping in rice-wetland coexistent areas.
• We monitored the rice cultivation changes in the Yellow River Delta (YRD) over the past decade.
• We analyzed the impact of rice cultivation expansion on ecological conservation in the YRD in the context of water scarcity.
• We systematically analyzed the capacities of a set of statistical indicators (SWIR1, SWIR2, NDVI, VV, and VH) of time-series optical and SAR images in impervious surface extraction, and analyzed temporal rate of urban expansion, urban land density variation, and sptial expansion patterns.

Related publications:

1. Huang, C., Zhang, C.¹ (2022). Characterizing urban growth in Vientiane from 2000 to 2019 using time-series optical and SAR-based estimates of urban land. International Journal of Applied Earth Observation and Geoinformation, 109, 102798. [Full text] (¹Equal contribution)
2. Huang, C., Zhang, C.¹ (2023). Time‐series remote sensing of rice paddy expansion in the Yellow River Delta: Towards sustainable ecological conservation in the context of water scarcity. Remote Sensing in Ecology and Conservation, 9(4), 454-468. [Full text]
3. Huang, C., Xu, Z., Zhang, C., Li, H., Liu, Q., Yang, Z., & Liu, G. (2020). Extraction of rice planting structure in tropical region based on Sentinel-1 temporal features integration. Transactions of the Chinese Society of Agricultural Engineering, 36(9), 177-184. [Full text]
4. Huang, C., Zhang, C., He, Y., Liu, Q., Li, H., Su, F., … & Bridhikitti, A. (2020). Land cover mapping in cloud-prone tropical areas using Sentinel-2 data: Integrating spectral features with Ndvi temporal dynamics. Remote Sensing, 12(7), 1163. [Full text]
5. He, Y., Huang, C., Li, H., Liu, Q., Liu, G., Zhou, Z., & Zhang, C. (2019). Land-cover Classification of Random Forest Based on Sentinel- 2A Image Feature Optimization. Resources Science, 41(5), 992-1001. [Full text]

Spatial-temporal Variability of Water and Salt and Its Impact on Agricultural Land Use in the Yellow River Delta

09/2017-10/2019

Advisor: Prof. Chong Huang

• We analyzed the temporal and spatial response of groundwater depth to hydrometeorological factors (including precipitation, evaporation and runoff).
• We explore the effects of geographical conditions (including landforms, soil texture and soil types) on the relationship between precipitation and groundwater using Geographical Detectors.
• We evaluated the land reclamation and risk assessment in the coastal zone.
• We investigated the erosion and sedimentation dynamics along the Yellow River Delta and its response to runoff-sedimentation fluxes.

Related publications:

1. Zhang, C., Huang, C., He, Y., Liu, Q., Li, H., Wu, C., &Liu, G. (2020). An analysis of the space-time patterns of precipitation-shallow groundwater depth interactions in the Yellow River Delta. Hydrogeology & Engineering Geology, 47(5), 21-30. [Full text]
2. Huang, C., Zhang, C., Liu, Q., Wang, Z., Li, H., & Liu, G. (2020). Land reclamation and risk assessment in the coastal zone of China from 2000 to 2010. Regional Studies in Marine Science, 39, 101422. [Full text]
3. Li, H., Huang, C., Zhang, C., Liu, Q., & Liu, G. (2020). Coastal Erosion and Sediment Dynamics of the Yellow River Delta and its Response to the Runoff-sediment Flux Since 1976. Resources Science, 42(3), 486-493. [Full text]