Joshua Gray

Bio

Josh uses geospatial analytics to understand how land use and vegetation dynamics interact with the climate system to produce changes in water and carbon cycles, particularly in managed lands like croplands and timber plantations. He develops remote sensing algorithms that blend imagery from multiple Earth-orbiting satellites to improve time series datasets for studies of phenological change. His interests also include remote sensing with Unmanned Aerial Systems (UAS) and big-data geocomputation.

Publications

• Machine learning approach for modeling daily pluvial flood dynamics in agricultural landscapes , ENVIRONMENTAL MODELLING & SOFTWARE (2023)
• Observations of Satellite Land Surface Phenology Indicate That Maximum Leaf Greenness Is More Associated With Global Vegetation Productivity Than Growing Season Length , GLOBAL BIOGEOCHEMICAL CYCLES (2023)
• Multiresolution Broad Area Search: Monitoring Spatial Characteristics of Gapless Remote Sensing Data , Journal of Data Science (2022)
• Spatial Analysis of Forest Product Manufacturers in North Carolina , FOREST SCIENCE (2022)
• Using Deep Learning and Very-High-Resolution Imagery to Map Smallholder Field Boundaries , REMOTE SENSING (2022)
• Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model , REMOTE SENSING OF ENVIRONMENT (2021)
• Longer greenup periods associated with greater wood volume growth in managed pine stands , AGRICULTURAL AND FOREST METEOROLOGY (2021)
• Multisensor fusion of remotely sensed vegetation indices using space-time dynamic linear models , JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS (2021)
• Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery , REMOTE SENSING OF ENVIRONMENT (2020)
• Mapping Understory Invasive Plants in Urban Forests with Spectral and Temporal Unmixing of Landsat Imagery , PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING (2020)