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How to Build a Better Map of Tree Biomass

Photo of Aboveground biomass map created with LIDAR and FIA plots for Anne Arundel and Howard Counties, Maryland. Kristofer Johnson, USDA Forest ServiceAboveground biomass map created with LIDAR and FIA plots for Anne Arundel and Howard Counties, Maryland. Kristofer Johnson, USDA Forest ServiceSnapshot : A logical way to validate biomass maps derived from remotely sensed data is to validate them with independent ground inventory estimates, but integration of the two systems is not without challenges. Forest Service scientists compared maps derived from Light Detection and Ranging (LIDAR) to Forest Inventory and Analysis (FIA) estimates in the fragmented forests of Maryland, leading to recommendations about how to improve their agreement.

Principal Investigators(s) :
Johnson, KristoferBirdsey, Richard
Research Location : Maryland
Research Station : Northern Research Station (NRS)
Year : 2014
Highlight ID : 607

Summary

Accurate, high-resolution Light Detection and Ranging (LIDAR) biomass maps improve decision making for carbon sequestration by identifying areas for the growth, or protection, of tree biomass. Forest Inventory and Analysis (FIA) program data are valuable for evaluating the accuracy of LIDAR-based maps because: (1) the systematic arrangement of plots provides unbiased estimates, (2) it follows well-documented measurement protocols, and (3) data are quality controlled. Still, integration of the two observation systems is not without challenges. Forest Service scientists compared wall-to-wall LIDAR-derived biomass maps to FIA data in two Maryland counties and also investigated allometric model-related errors. In areas of medium to dense biomass, FIA data were valuable for evaluating map accuracy by comparing plot biomass to pixel values and allometric model errors were relatively small. Comparison results at the county level were confounded because trees on nonforest plots were not measured. Agreement between the two systems improved when the FIA data were combined with a previous inventory of nonforest plots. To be successfully integrated with LIDAR, FIA sampling should include measurements of all trees in a landscape when possible. Other helpful enhancements include improving GPS accuracy of plot locations and intensifying data collection in small counties with few FIA plots.

Forest Service Partners

External Partners

  • Craig Wayson, International Programs, and Rachel Riemann, Northern Research Station
  • Andrew O Finley, Michigan State University
  • Anu Swantaran and Ralph Dubayah, University of Maryland

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