Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems

Abstract

Spaceborne laser altimetry waveform estimates of canopy Gap Fraction (GF) vary with respect to discrete return airborne equivalents due to their greater sensitivity to reflectance differences between canopy and ground surfaces resulting from differences in footprint size, energy thresholding, noise characteristics and sampling geometry. Applying scaling factors to either the ground or canopy portions of waveforms has successfully circumvented this issue, but not at large scales. This study develops a method to scale spaceborne altimeter waveforms by identifying which remotely-sensed vegetation, terrain and environmental attributes are best suited to predicting scaling factors based on an independent measure of importance. The most important attributes were identified as: soil phosphorus and nitrogen contents, vegetation height, MODIS vegetation continuous fields product and terrain slope. Unscaled and scaled estimates of GF are compared to corresponding ALS data for all available data and an optimized subset, where the latter produced most encouraging results (R 2 = 0.89, RMSE = 0.10). This methodology shows potential for successfully refining estimates of GF at large scales and identifies the most suitable attributes for deriving appropriate scaling factors. Large-scale active sensor estimates of GF can establish a baseline from which future monitoring investigations can be initiated via upcoming Earth Observation missions.

A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data

“…However, both terrestrial and airborne LiDAR data fail to accurately obtain the relevant information about forest ecosystems at large areas due to the limited spatial coverage and high acquisition costs [13][14][15]. In contrast, monitoring forest ecosystems at larger spatial scales can be best achieved by space laser altimetry [16][17][18].…”

The Laser Vegetation Detecting Sensor: A Full Waveform, Large-Footprint, Airborne Laser Altimeter for Monitoring Forest Resources

“…This sensor provided full waveform LiDAR data for a set of well-distributed, 70-m diameter footprints for much of the globe [32,33]. GLAS data were used in various ways to calibrate models of vegetation structure parameters [34,35,36,37,38,39,40,41]. The Global Ecosystem Dynamics Investigation (GEDI), carried by the SpaceX Commercial Resupply Mission 16, launched successfully on December 5th, 2018 from Cape Canaveral in Florida.…”

Analysis of the vertical canopy structure in native forest fragments and <i>Eucalyptus</i> plantations to detect edge effects

“…The predictors of the remaining 1/3 of the training data used to build single decision trees were evaluated through the decision tree to provide a so-called out-of-bag estimate of model performance. The importance of each predictor variable as evaluated by the random forest model is subject to change based on the subset of data sampled from the training set, the order of which may change with model retraining (Mahoney et al 2017). Therefore, to evaluate variable importance and its stability among a sequence of training repetitions, we performed seven random forest iterations and calculated the average using the variable importance (Gini importance) for each iteration (Strobl et al, 2008).…”