A5112450585- Remote Sensing and LiDAR Applications
- Remote Sensing in Agriculture
- Forest ecology and management
- Fire effects on ecosystems
- Land Use and Ecosystem Services
- Marine and coastal ecosystems
- Explainable Artificial Intelligence (XAI)
- Big Data and Business Intelligence
- Marine Biology and Ecology Research
- Protist diversity and phylogeny
- Microbial Community Ecology and Physiology
- Geological formations and processes
- Anomaly Detection Techniques and Applications
- Coastal wetland ecosystem dynamics
- Marine and coastal plant biology
- Coastal and Marine Dynamics
Northwest Institute of Mechanical and Electrical Engineering
2023-2024
Northwest A&F University
2023-2024
Southwest Forestry University
2021-2022
Second Institute of Oceanography
2017-2020
Ministry of Natural Resources
2020
State Key Laboratory of Satellite Ocean Environment Dynamics
2017
Forest carbon sinks are vital in mitigating climate change, making it crucial to have highly accurate estimates of forest stocks. A method that accounts for the spatial characteristics inventory samples is necessary long-term estimation above-ground stocks due heterogeneity bottom-up methods. In this study, we developed a analyzing space-sensing data and predicts long time series stock changes an alpine region by considering sample’s characteristics. We employed nonlinear mixed-effects model...
Accurate estimation of forest carbon storage is essential for understanding the dynamics resources and optimizing decisions resource management. In order to explore changes in Pinus densata Shangri-La influence topography on storage, two dynamic models were developed based National Forest Inventory (NFI) Landsat TM/OLI images with a 5-year interval change annual average change. The three modelling methods used partial least squares (PLSR), random (RF) gradient boosting regression tree...
Forest above-ground biomass (AGB) is the basis of terrestrial carbon storage estimation, and making full use seasonal characteristics remote sensing imagery can improve estimation accuracy. In this study, we used multi-source time series sample plots with Random (RF) model to estimate AGB. The sources included Sentinel-1 (S-1), Sentinel-2 (S-2), S-1 S-2 combination (S-1S-2). Time single season, annual, multi-season. This study aims (1) explore optimal image acquisition season AGB; (2)...
Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it crucial to estimate AGCS accurately. In this research, we added climatic and topographic factors estimation process by a remote sensing approach explore their impact achieve more precise estimations. We hope develop accurate method for based on data climate data. The random forest (RF) has good robustness wide applicability. Therefore, modeled predicted RF sixty field sample plots Pinus...
Understanding the drivers of forest aboveground biomass (AGB) is essential to further understanding carbon cycle. In upper Yangtze River region, where ecosystems are incredibly fragile, driving factors that make AGB changes differ from other regions. This study aims investigate AGB’s spatial and temporal variation Pinus densata in Shangri-La decompose direct indirect effects attribute, climate, stand structure, agricultural activity on evaluate degree influence each factor change. The...
Abstract We examined the planktonic protistan community in Xiangshan Bay during spring 2015 using 18S rDNA sequencing. found significant spatial heterogeneity α-diversity, β-diversity (Bray–Curtis and Jaccard indices) relative abundance of dominant taxa. The was due more to variation species (operational taxonomic units) than abundance, dominated by rare biota. Salinity most important driver total abundant subcommunity, but environmental factors could not explain subcommunity. For mainly...
Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it crucial to estimate AGCS accurately. In this research, we added climatic and topographic factors estimation process by a remote sensing approach explore their impacts achieve more precise estimations. We model predict Random (RF) based on sixty field sample plots Pinus densata pure forests in southwest China extracted from Landsat 8 OLI images (Source I), Sentinel-2A II), combined III)....