A5081480445- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Fire effects on ecosystems
- Land Use and Ecosystem Services
- Remote Sensing and Land Use
- Species Distribution and Climate Change
- Smart Agriculture and AI
- Rangeland and Wildlife Management
- Urban Heat Island Mitigation
- Environmental Changes in China
- Fire Detection and Safety Systems
- Climate change and permafrost
- Spectroscopy and Chemometric Analyses
- Leaf Properties and Growth Measurement
- Plant Water Relations and Carbon Dynamics
- Cryospheric studies and observations
- Geology and Paleoclimatology Research
- Forest Insect Ecology and Management
- Agriculture, Land Use, Rural Development
- Ecology and Vegetation Dynamics Studies
- Soil and Land Suitability Analysis
- 3D Surveying and Cultural Heritage
- Innovative Educational Techniques
- Plant responses to elevated CO2
- Sustainability in Higher Education
Harbin Institute of Technology
2023-2025
Shaanxi Polytechnic Institute
2024
Huazhong Agricultural University
2019-2024
Beijing Academy of Artificial Intelligence
2023
Brookhaven National Laboratory
2015-2020
University of Utah
2012-2015
University of Chinese Academy of Sciences
2009
Institute of Geographic Sciences and Natural Resources Research
2009
Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial temporal variability in LMA has been long-standing goal of ecological research an essential component for advancing Earth system models. Despite the substantial variation within across Earth's biomes, efficient, globally generalizable approach to predict still lacking. We explored capacity from spectra much global trait space, with...
Summary Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models ( RTM s) with field forest leaf canopy characteristics to test three hypotheses for satellite‐observed reflectance seasonality: changes in area index, canopy‐surface leafless crown fraction and/or demography. Canopy s PROSAIL FL i ES ), driven by these factors combined, simulated patterns well, explaining...
Spatial-explicit weed information is critical for controlling infestation and reducing corn yield losses. The development of unmanned aerial vehicle (UAV)-based remote sensing presents an unprecedented opportunity efficient, timely mapping. Spectral, textural, structural measurements have been used mapping, whereas thermal measurements-for example, canopy temperature (CT)-were seldom considered used. In this study, we quantified the optimal combination spectral, structural, CT based on...
Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture. However, the unique agronomic practice (i.e., varied stubble height treatment) ratooning could lead to inconsistent phenology, which had a significant impact on rice. Multi-temporal unmanned aerial vehicle (UAV)-based remote sensing can likely monitor productivity and reflect maximum potential across growing seasons improving compared with previous methods. Thus, this study, we...
Increased fire frequency has been shown to promote alien plant invasions in the western United States, resulting persistent vegetation type change. Short interval fires are widely considered be detrimental reestablishment of shrub species southern California chaparral, facilitating invasion exotic annuals and producing "type conversion". However, supporting evidence for conversion largely at local, site scales over short post-fire time scales. Type not or widespread past range improvement...
Abstract Leaf quantity (i.e., canopy leaf area index, LAI), quality per‐area photosynthetic capacity), and longevity all influence the seasonality of tropical evergreen forests. However, these components phenology are poorly represented in most terrestrial biosphere models (TBMs). Here, we explored alternative options for representation effects TBMs that employ Farquahar, von Caemmerer & Berry (FvCB) CO 2 assimilation. We developed a two‐fraction (sun shade), two‐layer (upper lower)...
Southern Corn Rust (SCR) is one of the most destructive diseases in corn production, significantly affecting quality and yields globally. Field-based fast, nondestructive diagnosis SCR critical for smart agriculture applications to reduce pesticide use ensure food safety. The development spectral disease indices (SDIs), based on situ leaf reflectance spectra, has proven be an effective method detecting plant field. However, little known about signatures that can assist accurate SCR, no...
Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional global climate feedbacks. In Arctic, is warming twice as fast compared to average (known ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. order quantify these changes assess their ecosystem structure function, methods are needed accurately characterize types. However, commonly used ground-based...
Unmanned aerial vehicles-collected (UAVs) digital red–green–blue (RGB) images provided a cost-effective method for precision agriculture applications regarding yield prediction. This study aims to fully explore the potential of UAV-collected RGB in prediction winter wheat by comparing it multi-source observations, including thermal, structure, volumetric metrics, and ground-observed leaf area index (LAI) chlorophyll content under same level or across different levels nitrogen fertilization....
Accurate mapping of tree species is highly desired in the management and research plantation forests, whose ecosystem services are currently under threats. Time-series multispectral satellite images, e.g., from Landsat-8 (L8) Sentinel-2 (S2), have been proven useful general forest types, yet we do not know quantitatively how their spectral features (e.g., red-edge) temporal frequency data acquisitions 16-day vs. 5-day) contribute to level. Moreover, it unclear what extent fusion L8 S2 will...
Forest recovery from past disturbance is an integral process of ecosystem carbon cycles, and remote sensing provides effective tool for tracking forest over large areas. Although the products (tracking conversion to non-forest type) derived using Landsat Time Series Stack-Vegetation Change Tracker (LTSS-VCT) algorithm have been validated extensively mapping disturbances across United States, ability this approach characterize long-term post-disturbance (the forest) has yet be assessed. In...