A revised above-ground maximum biomass layer for the Australian continent

Roxburgh, Stephen H.; Karunaratne, Senani; Paul, Keryn I.; Lucas, Richard; Armston, John; Sun, Jingyi

doi:10.1016/j.foreco.2018.09.011

Cited by 28 publications

(21 citation statements)

References 24 publications

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“…A8). Similarly to Berry and Roderick (2006), Montreal Process Implementation Group for Australian and National Forest Inventory Steering Committee (2013), Commonwealth of Australia (2018) and Roxburgh et al (2019), our maps illustrate regions of high biomass in the south‐west, south‐east and east coast of the continent, with lower biomass in the north and the inner‐east, and negligible biomass in the centre. A side‐by‐side comparison reveals regions of greatest difference between our estimate and previous estimates in the tropical north where our predictions are around 100 Mg ha −1 lower.…”

Section: Discussionsupporting

confidence: 62%

“…We thus excluded plots that were significantly impacted by anthropogenic disturbance and/or by a major natural disturbance such as a cyclone or high‐severity (i.e. crown‐consuming) wildfire after 1972 as described in Roxburgh et al (2019). Plots that were burned by low to moderate‐severity prescribed fire or wildfire (i.e.…”

Section: Methodsmentioning

confidence: 99%

“…Random Forest is a machine learning model that handles numerous variables and is robust to over‐fitting (Breiman 2001) thus being well suited to the complex data set used in this study. Random Forest has been used to predict above‐ground carbon stocks in Madagascar (Vieilledent et al 2016) and Australia (Roxburgh et al 2019), map carbon stocks in the Western Amazon (Mascaro et al 2014), and predict changes to forest canopy cover with climate change in south east Australia (Williamson et al 2014). The method outperformed simple regression analysis for predicting above‐ground forest biomass at broad spatial scales (Corona‐Nunez et al 2017), and similarly has outperformed other machine learning techniques for forest biomass estimation (boosted regression trees, support vector machine, multiple regression splines (Safari et al 2017), and support vector regression (Liu et al 2017).…”

Section: Methodsmentioning

confidence: 99%

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Climate more important than soils for predicting forest biomass at the continental scale

et al. 2020

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Above‐ground biomass in forests is critical to the global carbon cycle as it stores and sequesters carbon from the atmosphere. Climate change will disrupt the carbon cycle hence understanding how climate and other abiotic variables determine forest biomass at broad spatial scales is important for validating and constraining Earth System models and predicting the impacts of climate change on forest carbon stores. We examined the importance of climate and soil variables to explaining above‐ground biomass distribution across the Australian continent using publicly available biomass data from 3130 mature forest sites, in 6 broad ecoregions, encompassing tropical, subtropical and temperate biomes. We used the Random Forest algorithm to test the explanatory power of 14 abiotic variables (8 climate, 6 soil) and to identify the best‐performing models based on climate‐only, soil‐only and climate plus soil. The best performing models explained ~50% of the variation (climate‐only: R2 = 0.47 ± 0.04, and climate plus soils: R2 = 0.49 ± 0.04). Mean temperature of the driest quarter was the most important climate variable, and bulk density was the most important soil variable. Climate variables were consistently more important than soil variables in combined models, and model predictive performance was not substantively improved by the inclusion of soil variables. This result was also achieved when the analysis was repeated at the ecoregion scale. Predicted forest above‐ground biomass ranged from 18 to 1066 Mg ha−1, often under‐predicting measured above‐ground biomass, which ranged from 7 to 1500 Mg ha−1. This suggested that other non‐climate, non‐edaphic variables impose a substantial influence on forest above‐ground biomass, particularly in the high biomass range. We conclude that climate is a strong predictor of above‐ground biomass at broad spatial scales and across large environmental gradients, yet to predict forest above‐ground biomass distribution under future climates, other non‐climatic factors must also be identified.

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Section: Discussionsupporting

confidence: 62%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Climate more important than soils for predicting forest biomass at the continental scale

et al. 2020

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“…Understory vegetation is an important part of the forested ecosystem. It contributes greatly to nutrient cycling [1, 2], wildlife habitat [3–5], fire behaviour [6–8], microclimate [2] and carbon accounting [9]. Understory vegetation communities are therefore often considered a good indicator of forest ecological integrity [10, 11].…”

Section: Introductionmentioning

confidence: 99%

Modelling vegetation understory cover using LiDAR metrics

et al. 2019

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Forest understory vegetation is an important characteristic of the forest. Predicting and mapping understory is a critical need for forest management and conservation planning, but it has proved difficult with available methods to date. LiDAR has the potential to generate remotely sensed forest understory structure data, but this potential has yet to be fully validated. Our objective was to examine the capacity of LiDAR point cloud data to predict forest understory cover. We modeled ground-based observations of understory structure in three vertical strata (0.5 m to < 1.5 m, 1.5 m to < 2.5 m, 2.5 m to < 3.5 m) as a function of a variety of LiDAR metrics using both mixed-effects and Random Forest models. We compared four understory LiDAR metrics designed to control for the spatial heterogeneity of sampling density. The four metrics were highly correlated and they all produced high values of variance explained in mixed-effects models. The top-ranked model used a voxel-based understory metric along with vertical stratum (Akaike weight = 1, explained variance = 87%, cross-validation error = 15.6%). We found evidence of occlusion of LiDAR pulses in the lowest stratum but no evidence that the occlusion influenced the predictability of understory structure. The Random Forest model results were consistent with those of the mixed-effects models, in that all four understory LiDAR metrics were identified as important, along with vertical stratum. The Random Forest model explained 74.4% of the variance, but had a lower cross-validation error of 12.9%. We conclude that the best approach to predict understory structure is using the mixed-effects model with the voxel-based understory LiDAR metric along with vertical stratum, because it yielded the highest explained variance with the fewest number of variables. However, results show that other understory LiDAR metrics (fractional cover, normalized cover and leaf area density) would still be effective in mixed-effects and Random Forest modelling approaches.

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“…Understory vegetation is an important part of the forested ecosystem. It contributes greatly to nutrient cycling (1, 2), wildlife habitat (3–5), fire behaviour (6–8), microclimate (2) and carbon accounting (9). Understory vegetation communities are therefore often considered a good indicator of forest ecological integrity (10, 11).…”

Section: Introductionmentioning

confidence: 99%

Modelling vegetation understory cover using LiDAR metrics

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et al. 2019

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21 42 model with the voxel-based understory LiDAR metric along with vertical stratum, but that other 43 understory LiDAR metrics (fractional cover, normalized cover and leaf area density) would still 44 be effective in mixed-effects and Random Forest modelling approaches. 3 45 Supporting information 572 S1Table. Definitions of all variables included in at least one of the mixed-effects or Random 573 Forest models. 574 S2 Table. Frequency of explanatory variables among the 18 random forest models run with 575 341 to 7 variables. 576 S3 Data. Data used in analyses for manuscript. Variable definitions are found in S1.

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A revised above-ground maximum biomass layer for the Australian continent

Cited by 28 publications

References 24 publications

Climate more important than soils for predicting forest biomass at the continental scale

Climate more important than soils for predicting forest biomass at the continental scale

Modelling vegetation understory cover using LiDAR metrics

Modelling vegetation understory cover using LiDAR metrics

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