Remote Sensing Tools for Monitoring Forests and Tracking Their Dynamics

Richard Massey, Logan T. Berner, Adrianna C. Foster, Scott J. Goetz, Udayalakshmi Vepakomma

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationAdvances in Global Change Research
PublisherSpringer Science and Business Media B.V.
Number of pages19
StatePublished - 2023
Externally publishedYes

Publication series

NameAdvances in Global Change Research
ISSN (Print)1574-0919
ISSN (Electronic)2215-1621

ASJC Scopus subject areas

  • Global and Planetary Change
  • Atmospheric Science
  • Management, Monitoring, Policy and Law


Dive into the research topics of 'Remote Sensing Tools for Monitoring Forests and Tracking Their Dynamics'. Together they form a unique fingerprint.

Cite this