Craig Mahoney

Slope Estimation from ICESat/GLAS

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Multisensor and Multispectral LiDAR Characterization and Classification of a Forest Environment

Abstract Airborne LiDAR is increasingly used in forest carbon, ecosystem, and resource monitoring. For practical design and manufacture reasons, the 1064 nm near-infrared (NIR) wavelength has been the most commonly adopted, and most literature in this field represents sampling characteristics in this wavelength. However, due to eye-safety and application-specific needs, other common wavelengths are 1550 nm and 532 nm. All provide canopy structure reconstructions that can be integrated or compared through space and time but the consistency or complementarity of 3D airborne LiDAR data sampled at multiple wavelengths is poorly understood. Here, we report on multispectral LiDAR missions carried out in 2013 and 2015 over a managed forest research site. The 1st used 3 independent sensors, and the 2nd used a single sensor carrying 3 lasers. The experiment revealed differences in proportions of returns at ground level, vertical foliage distributions, and gap probability across wavelengths. Canopy attenuation was greatest at 532 nm, presumably due to leaf tissue absorption. Relative to 1064 nm, foliage was undersampled at midheight percentiles at 1550 nm and 532 nm. Multisensor data demonstrated differences in foliage characterization due to combined influences of wavelength and acquisition configuration. Single-sensor multispectral data were more stable but demonstrated clear wavelength-dependent variation that could be exploited in intensity-based land cover classification without the aid of 3D derivatives. This work sets the stage for improvements in land surface classification and vertical foliage partitioning through the integration of active spectral and structural laser return information.

ICESat/GLAS Canopy Height Sensitivity Inferred from Airborne Lidar

Variations in laser properties and data acquisition times introduced inconsistencies in Geoscience Laser Altimeter System (GLAS) data. The effect of data inconsistencies, on two GLAS height retrieval methods, from three study sites, are investigated and validated against airborne laser scanning (ALS) percentile heights, from three data sources: all/first return point clouds, and raster canopy height models. GLAS/ALS controls were established as a basis against which the influence of laser number, transmission energy, and seasonality were assessed through comparison statistics. The favored GLAS height method best compared with ALS 95th percentile heights from an all return point cloud. Optimal GLAS data (R2 = 0.69, RMSE = 8.10 m) were noted when GLAS acquired data during summertime from high energy, laser three transmissions. As GLAS data can be used in global biomass assessments, there is a need to understand and quantify the influence of these data inconsistencies on canopy height estimates. (Less)

Continental-Scale Canopy Height Modeling by Integrating National, Spaceborne, and Airborne LiDAR Data

Abstract Canopy height estimates are widely used in forest biomass and carbon assessment modeling applications with the goal of mitigating climate change through the modification of forest sustainability strategies. As a result, large-scale accurate estimates of contemporary forest conditions are required. The current study utilizes Random Forest (RF) algorithms to integrate land cover, vegetation, soil, and other supplementary data with Geoscience Laser Altimeter System (GLAS) data to predict a wall-to-wall canopy height model (CHM) across Australia. Multiple CHMs are predicted from RF models trained from unique permutations of 6 predictor variables. Each 250 m resolution CHM is independently validated against airborne laser scanning (ALS) heights from 18 countrywide sites; the best CHM yielding R2 = 0.72, and RMSE = 7.43 m. The best countrywide CHM is compared against 2 similar products from the literature, both of which are subject to intersecting ALS performance assessment also. The developed CHM product utilizes up-to-date data, and is tailored to Australia, complementing the National Ecosystem Surveillance Monitoring project mandated to the Terrestrial Ecosystem Research Network (TERN) by the Australian Department of Environment. Furthermore, with future altimetry-based Earth observation missions due for launch, the developed CHM will act as a baseline from which monitoring investigations can be executed.

Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems

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.

Continental Estimates of Canopy Gap Fraction by Active Remote Sensing

ABSTRACT This study estimates canopy gap fraction (GF) from Geoscience Laser Altimeter System (GLAS) waveform data retrieved from a cross section of the Australian ecosystem, represented by 4 sites. However, as the literature supports, waveform profiles from systems such as GLAS require scaling in order to retrieve more accurate estimates of GF; the degree of scaling required is not a priori known, which has hindered large-scale estimates of GF by such systems to date. We employ spatially coincident GLAS footprints and airborne laser scanning (ALS) point cloud data from each site to scale initial estimates of GLAS GF to match ALS estimates, which allows the inference of scale factors to all footprints across the Australian forest landscape via the Random Forest (RF) technique. These refined estimates of GF are submitted to a second RF model to yield regional predictions of GF across the forest landscape. These predictions are validated against ALS data from 20 sites throughout Australia (R2 = 0.56, RMSE = 25.95%) and compared to MODIS, and also compared to ALS data (R2 = 0.13, RMSE = 66.46%). The developed method for producing large-scale estimates of GF shows promise for future studies and refinement, and is particularly pertinent as future spaceborne waveform systems come online (ICESat-2 and the Global Ecosystem Dynamics Investigation LiDAR).

A Forest Attribute Mapping Framework: A Pilot Study in a Northern Boreal Forest, Northwest Territories, Canada

A methods framework is presented that utilizes field plots, airborne light detection and ranging (LiDAR), and spaceborne Geoscience Laser Altimeter System (GLAS) data to estimate forest attributes over a 20 Mha area in Northern Canada. The framework was implemented to scale up forest attribute models from field data to intersecting airborne LiDAR data, and then to GLAS footprints. GLAS data were sequentially filtered and submitted to the k-nearest neighbour (k-NN) imputation algorithm to yield regional estimates of stand height and crown closure at a 30 m resolution. Resulting outputs were assessed against independent airborne LiDAR data to evaluate regional estimates of stand height (mean difference = −1 m, RMSE = 5 m) and crown closure (mean difference = −5%, RMSE = 9%). Additional assessments were performed as a function of dominant vegetation type and ecoregion to further evaluate regional products. These attributes form the primary descriptive structure attributes that are typical of forest inventory mapping programs, and provide insight into how they can be derived in northern boreal regions where field information and physical access is often limited.