Abstract

BackgroundForests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools. Airborne laser scanning (ALS) produces detailed 3D maps of forest canopy structure from which aboveground carbon density can be estimated. Working with a ALS dataset collected over the 8049-km2 Wellington Region of New Zealand we create maps of indigenous forest carbon and evaluate the influence of wind by examining how carbon storage varies with aspect. Storms flowing from the west are a common cause of disturbance in this region, and we hypothesised that west-facing forests exposed to these winds would be shorter than those in sheltered east-facing sites.MethodsThe aboveground carbon density of 31 forest inventory plots located within the ALS survey region were used to develop estimation models relating carbon density to ALS information. Power-law models using rasters of top-of-the-canopy height were compared with models using tree-level information extracted from the ALS dataset. A forest carbon map with spatial resolution of 25 m was generated from ALS maps of forest height and the estimation models. The map was used to evaluate the influences of wind on forests.ResultsPower-law models were slightly less accurate than tree-centric models (RMSE 35% vs 32%) but were selected for map generation for computational efficiency. The carbon map comprised 4.5 million natural forest pixels within which canopy height had been measured by ALS, providing an unprecedented dataset with which to examine drivers of carbon density. Forests facing in the direction of westerly storms stored less carbon, as hypothesised. They had much greater above-ground carbon density for a given height than any of 14 tropical forests previously analysed by the same approach, and had exceptionally high basal areas for their height. We speculate that strong winds have kept forests short without impeding basal area growth.ConclusionSimple estimation models based on top-of-the canopy height are almost as accurate as state-of-the-art tree-centric approaches, which require more computing power. High-resolution carbon maps produced by ALS provide powerful datasets for evaluating the environmental drivers of forest structure, such as wind.

Highlights

  • Forests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools

  • We have recently shown that model accuracy can be improved by including gap fraction alongside the-Canopy Height (TCH) in regression models developed for tropical regions (Coomes et al 2017; Jucker et al 2017), because it improves the prediction of basal area, but we have not explored whether this is true in temperate regions

  • We recently showed that tree-centric approaches to carbon mapping perform well in Alpine coniferous forests (r2 = 0.98 when field and Airborne laser scanning (ALS) estimates of carbon stocks are compared) and that a correction factor can be applied to account for the small obscured trees that were invisible from the air (Dalponte and Coomes 2016)

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Summary

Introduction

Forests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools. Working with a ALS dataset collected over the 8049-km Wellington Region of New Zealand we create maps of indigenous forest carbon and evaluate the influence of wind by examining how carbon storage varies with aspect. Forests are a key component of the global carbon cycle (Pan et al 2013), storing and sequestering more carbon than any other ecosystem (Gibbs et al 2007). Accurate monitoring of forest carbon stocks underpins these climate change mitigation programmes (Agrawal et al 2011) and most developed nations have reporting systems as part of international treaty commitments (e.g. New Zealand, Coomes et al (2002)). Disturbance of forest by wind and fire has enduring influences on regional carbon stocks and fluxes (e.g. Bradford et al (2008), Coomes et al (2012), Holdaway et al (2014)) and appear to be increasing in response to changing climate

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