Abstract
Over the past two decades spaceborne LiDAR systems have gained momentum in the remote sensing community with their ability to accurately estimate canopy heights and aboveground biomass. This article aims at using the most recent global ecosystem dynamics investigation (GEDI) LiDAR system data to estimate the stand-scale dominant heights ( <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dom</sub> ), and stand volume (V) of Eucalyptus plantations in Brazil. These plantations provide a valuable case study due to the homogenous canopy cover and the availability of precise field measurements. Several linear and nonlinear regression models were used for the estimation of <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dom</sub> and V based on several GEDI metrics. <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dom</sub> and V estimation results showed that over low-slopped terrain the most accurate estimates of <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dom</sub> and V were obtained using the stepwise regression, with an root-mean-square error (RMSE) of 1.33 m (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.93) and 24.39 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> .ha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.90) respectively. The principal metric explaining more than 87% and 84% of the variability (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dom</sub> and V was the metric representing the height above the ground at which 90% of the waveform energy occurs. Testing the postprocessed GEDI metric values issued from six available different processing algorithms showed that the accuracy on <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dom</sub> and V estimates is algorithm dependent, with a 16% observed increase in RMSE on both variables using algorithm a5 vs. a1. Finally, the choice to select the ground return from the last detected mode or the stronger of the last two modes could also affect the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dom</sub> estimation accuracy with 12 cm RMSE decrease using the latter.
Highlights
IN the last couple of decades, global concerns on the increased atmospheric concentration of greenhouse gases such as CO2 has risen the interest in quantifying the state and change of forest resources due to the key role of forests in the global carbon cycle [1], [2]
The different tested models in this study showed that Global Ecosystem Dynamics Investigation (GEDI) waveform metrics could be used to obtain good accuracies of canopy heights and wood volumes, with a root mean squared percentage error (RMSPE) of 7.1% on canopy height estimation and 20.4% on wood volume estimation
We analyzed GEDI data in order to determine its accuracy in estimating stand-scale dominant heights (Hdom) and stand volume (V) of intensively managed Eucalyptus plantations in Brazil
Summary
IN the last couple of decades, global concerns on the increased atmospheric concentration of greenhouse gases such as CO2 has risen the interest in quantifying the state and change of forest resources due to the key role of forests in the global carbon cycle [1], [2]. The accurate estimation of forest biomass is needed to better determine its precise role in the global carbon cycle [4], [5]. The primary source of above ground biomass (AGB) estimation in tropical forests at large scales came in the last years from observations and measurements from different satellite remote sensing platforms. Radar, and LiDAR are the three main sources of remotely sensed data used in AGB estimation techniques. LiDAR systems either airborne or spaceborne have the capability to capture the horizontal and vertical structure of vegetation comprehensively [9], and can estimate biomass with better precision in comparison to the techniques using radar or optical data [10], [11]. GLAS’s ~60 m diameter footprint was larger than the ideal resolution for forest observations [13], its capability to estimate forest parameters (e.g. canopy heights and biomass) has been exploited in numerous studies during its operational and post-
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