Ran Meng Email and Phone Number

Smart agriculture; Deep learning; Multi-scale remote sensing

Unmanned Aerial System (UAS) remote sensing of terrestrial ecosystems -Assemble, program, and operate UAS platforms -Integrate payloads (e.g., thermal-infrared cameras, DLSR camera for 3D constructions, spectrometer) and navigation equipment on UAS platforms -Measure and scale key plant processes and traits to link with UAS observations -Spectroscopic analysis of biochemical traits of Arctic and greenhouse plants -Studies of forest disturbance ecology using a fusion of very high spatial resolution imagery, airborne thermal-infrared , LiDAR, and imaging spectroscopy data

-Mapped and analyzed spatial-temporal patterns of tamarisk defoliation by beetles using two algorithms (Random Forests and Forest Disturbance Index) -Spectroscopic analysis of green, desiccated and dead tamarisk canopies by the combination of spectral feature selection (ISI), spectral matching (CCSM), and machine learning (Random Forests) techniques -Explored the effects of short-interval fires with multiple environmental factors on vegetation regrowth in southern California shrublands by multiple regression analysis -Mapped southern California shrublands using object-oriented classification method -Analyzed and modeled post-fire vegetation regrowth in Sierra Nevadan forests by spatial modeling techniques based on time series remote sensing anlaysis

-Analyzed vegetation dynamics by wavelet-transform-based method-Mapped land use and land cover using time-series MODIS imagery by decision tree method-Retrieved temperature as the real surface temperature using Landsat imagery -Analyzed the temporal and spatial characteristics of recent 100a climate in China using CRU05 climate data