Remote sensing and geospatial analysis research is a fundamental component of modern-day forest monitoring and analyses. Using satellites or instruments on aircraft to capture data on physical characteristics from a distance allows information to be collected consistently at large scales across landscapes. When merged with ground data, such as that from the Forest Inventory and Analysis program’s plots, the status and trends of natural resource conditions can be identified and used to improve management. It also allows for the cost-effective detection of impacts and threats from fire, insects, disease, and other natural processes.

The Forest Service Monitoring, Remote Sensing and Geospatial Analysis Research Program supports forest inventory monitoring and analysis by collecting, analyzing, and sharing data to track land use/land cover change and disturbance and apply an array of geospatial datasets. Additionally, the program provides tools to support small area estimates for localized analysis to increase understanding about the effects of change on important resources such as wildlife, biodiversity, water, outdoor recreation, wilderness, and ecosystem services.

The Forest Service invests in this work because:

• Monitoring land use and land cover change with remote sensing allows information across vast landscapes to be collected in a highly integrated, efficient manner to support the monitoring of all lands. Collecting, evaluating, and publishing data about U.S. forests and grasslands is one of the core functions of the Forest Service, and the data derived from remote sensing is essential for these analyses.

• Monitoring disturbance using historical Landsat imagery allows analysts to characterize active disturbance regimes and monitor recovery.

• Though Forest Inventory and Analysis program data tracks broad-scale, long-term changes to forest conditions, finer scale and faster detection of forest changes is possible through remote sensing. This supports land management policy and decision-making and provides additional support to sustainability reporting, EPA and USDA Greenhouse Gas reporting, and the congressionally-mandated Resources Planning Act (RPA) Assessment reports.

• Understanding and combatting the impacts of climate change requires harnessing remote sensing and geospatial technologies to collect high resolution data that better characterizes the complex structure of forest resources. Increasing the measurement of the forest’s vertical structure through cutting-edge 3D detection technologies helps scientists better estimate carbon, improve characterization of wildlife habitat, and understand the inherent stress and mortality risks triggered by various agents including fire, insect, diseases, and competition.

Featured Work

Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory offers FIA's experience with national forest inventory and remote sensing integration as a case study for other countries wishing to improve the efficiency of their NFI programs.

In the publication Mapping forest change using stacked generalization: An ensemble approach, researchers describe their work testing the performance of several leading forest disturbance detection algorithms against ensembles of the outputs of those same algorithms based upon stacking using both parametric and Random Forests-based fusion rules.

Pushing boundaries: new directions in inventory techniques and applications: Forest Inventory and Analysis (FIA) symposium 2015 reports invited presentations and contributions to the 2015 Forest Inventory and Analysis (FIA) Symposium, which was hosted by the Research and Development branch of the U.S. Forest Service.

Innovation in the Interior: How state-of-the-art remote sensing is helping to inventory Alaska s last frontier: A team composed of researchers with the U.S. Forest Service Pacific Northwest Research Station, NASA’s Goddard Space Flight Center, American University, and Michigan State University developed a remote-sensing and groundbased solution to inventory interior Alaska. 

Highly local model calibration with a new GEDI LiDAR asset on Google Earth engine reduces Landsat forest height signal saturation describes how scientists tested Landsat-based relative height predictions using a new Global Ecosystem Dynamics Ivestigation asset on Google Earth Engine.

Large area forest yield estimation with pushbroom Digital Aerial Photogrammetry describes how low-cost pushbroom Digital Aerial Photography (DAP) is used to aid in the estimation of forest volume over large areas in Washington State (USA).