Forest inventory plot sampling

Knowledge graph centered on Forest inventory plot sampling with 76 nodes and 151 connections. Top connected: Rocky Mountain Biological Laboratory, Gothic, Plant functional trait change across a warming tun, Forest responses to increasing aridity and warmth , Beetles.

Method synopsis

Uses full-waveform LiDAR returns processed through adaptive deconvolution and individual tree detection algorithms to map forest structure metrics including tree density, height distributions, and basal area at landscape scales. Validation performed through field inventory matching.

Synthesized from method descriptions across 19 papers using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — Abiotic influences on continuous conifer forest structure across a subalpine watershed (2025), Remote Sensing of Environment. The protocol was originally introduced by Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High-Resolution Remote Sensing and Surface Geophysical Data (2019), Journal of Geophysical Research: Biogeosciences. Implementations in other papers (listed below) may differ.

1. LiDAR data collection
   The National Science Foundation National Ecological Observatory Network (NEON) Airborne Observation Platform surveyed approximately 330 km² of the watershed using an Optech Gemini discrete LiDAR sensor and waveform digitizer with pulse repetition frequency between 33 and 100 kHz. Discrete-return point density ranged between 1 and 9 returns m⁻², which was insufficient for characterizing subcanopy structure.
   Quantities: 330 km² surveyed area, 1-9 returns per square meter point density, 33-100 kHz pulse frequencyDuration: Between June 12 and 26, 2018Conditions: Summer conditions during leaf-on period
   Equipment: Optech Gemini discrete LiDAR sensor, waveform digitizer
2. Waveform deconvolution and processing
   Full-waveform returns were deconvolved using adaptive deconvolution to fit a sum of Gaussian models to return pulse components. The output approximates the true distribution of scattering phenomena along the laser pulse's path. Waveforms were processed on the University of California Berkeley's high-performance computing cluster using the R package minpack.lm.
   Quantities: 190,909 waveform returns dropped (0.014% of total acquisition), 5.72 × 10³ points with 15.3 points m⁻² density obtainedDuration: Not specifiedConditions: High-performance computing environment

Typical Equipment

• Optech Gemini discrete LiDAR sensor
• waveform digitizer
• University of California Berkeley computing cluster
• R package minpack.lm

Output Measurements

• tree locations
• tree heights
• forest density
• basal area
• height percentiles
• quadratic mean diameter

Papers Using This Protocol (19)

Species Studied With This Protocol

Places Where Used