研究目的
To review how remote sensing technologies improve the assessment of microclimate over space and time, advancing the study of organism responses to climate variation and climate-change impacts on biodiversity and ecosystems.
研究成果
Remote sensing technologies enable detailed mapping of microclimate at fine spatiotemporal resolutions, offering new opportunities to study organism-environment interactions and improve predictions of species and ecosystem responses to environmental changes, with implications for conservation, forestry, and agriculture.
研究不足
Field measurements from sensor networks are crucial but vary in design, making synthesis difficult. Remote sensing tools work best in tall habitats like forests; in short-stature vegetation, structural detail may be insufficient. Thermal imaging does not directly measure air temperature and is affected by factors like emissivity and weather conditions. Spatial coverage of remote sensing data is not yet global, and long-term microclimate data are needed.
1：Experimental Design and Method Selection:
The paper is a review, not an experimental study, so it does not describe a specific experimental design. It discusses the use of remote sensing technologies like LiDAR, photogrammetry, hyperspectral imaging, and thermal imaging for microclimate modeling and mapping, based on existing literature.
2：Sample Selection and Data Sources:
References various studies that use sensor networks, airborne and terrestrial LiDAR, UAVs, and satellite data for microclimate measurements and mapping.
3：List of Experimental Equipment and Materials:
Mentions equipment such as LiDAR sensors, thermal infrared cameras, UAVs (drones), and sensor networks for microclimate measurement, but no specific models or brands are detailed.
4：Experimental Procedures and Operational Workflow:
Describes general approaches like interpolating microclimate measurements with LiDAR data, downscaling macroclimate, and using statistical or mechanistic models, but no step-by-step procedures are provided.
5：Data Analysis Methods:
Refers to statistical modeling, regression approaches, and mechanistic models like Niche MapperTM for data analysis.
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