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 (R2 = 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.

Highlights

  • Canopy cover information is essential for understanding spatial and temporal variability in vegetation biomass, local meteorological processes and hydrological transfers within vegetated environments [1]

  • A model rooted in Airborne Laser Scanning (ALS) data was developed to derive Geoscience Laser AltimeterSystem (GLAS) waveform ground return scaling factors to refine GLAS estimates of Gap Fraction (GF)

  • Scaling factors were predicted for unique GLAS footprints via an Random Forest (RF) model built on the identified most important five investigated predictor attributes, namely: soil phosphorus and nitrogen contents, vegetation height, Moderate Resolution Imaging Spectroradiometer (MODIS) VCF (MOD44B product) and slope

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Summary

Introduction

Canopy cover information is essential for understanding spatial and temporal variability in vegetation biomass, local meteorological processes and hydrological transfers (i.e., energy in heat variations, gas and water) within vegetated environments [1]. Canopy cover has been monitored via satellite and airborne remote sensing technologies for decades, providing information on vegetation and biomass conditions from local (100’s of km2 ) to global scales [2,3,4]. The use of Airborne Laser Scanning (ALS) for deriving canopy cover indices has been demonstrated at forest stand scales by many [7,8,9,10,11,12,13]; due to costs and sampling logistics, ALS acquisitions are typically limited in spatial extent. Satellite (ICESat), has the potential to retrieve canopy cover indices at near global scales, but has not demonstrated the same success as ALS to date. The large-footprint continuous waveform nature of Remote Sens. 2017, 9, 59; doi:10.3390/rs9010059 www.mdpi.com/journal/remotesensing

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