Remote sensing augments field data and facilitates foresight required for forest management by providing spatial and temporal observations of forest characteristics at landscape and regional scales. Statistical and machine-learning models derived from plot-level field observations can be extrapolated to larger areas using remote sensing data. For example, instruments such as light detection and ranging (LiDAR) and hyperspectral sensors are frequently used to quantify forest characteristics at the stand to landscape level. Moreover, multispectral imagery and synthetic aperture radar (SAR) data sets derived from satellite platforms can be used to extrapolate forest resource models to large regions. The combination of novel remote sensing technologies, expanding computing capabilities, and emerging geospatial methods ensures a data-rich environment for effective strategic, tactical, and operational planning and monitoring in forest resource management.