Testing allometric equations for prediction of above-ground biomass of mallee eucalypts in southern Australia

Paul, Keryn I.; Roxburgh, Stephen H.; Ritson, Peter; Brooksbank, Kim; England, Jacqueline R.; Larmour, John S.; Raison, R. J.; Peck, Adam; Wildy, Dan T.; Sudmeyer, R. A.; Giles, R. C.; Carter, Jennifer; Bennett, Richard G.; Mendham, Daniel; Huxtable, Dan; Bartle, John R.

doi:10.1016/j.foreco.2013.09.040

Cited by 38 publications

(32 citation statements)

References 36 publications

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“…Similarly to [17], a strong, negative relationship was found between brightness reflectance and vegetation coverage (r = 0.81); this relationship represents the bare soil spots that result from the mining activities in the study site. The TCW is a highly weighted (positively) spectral variable with CHM (r =0.78); this relationship supported the modeling approach to estimate the biomass according to the canopy heights [13,27]. Fourth, Zhao et al [28] stated that the multiple linear models with height-related metrics estimate the biomass variation better than the profile-related metrics because the biomass is directly related to the height and/or DBH, whereas the canopy-profile-based metrics provide very limited information.…”

Section: Major Findingssupporting

confidence: 54%

“…The Jenkins allometric equations model are suitable for predicting the biomass based on DBH for different coniferous species [26][27][28], Jenkins et al [22] estimated the biomass as follow:…”

Section: Allometric Equations and Ground Lidar Metrics From Individuamentioning

confidence: 99%

“…Equations that are suitable for a large number of species have been developed [24,25] to correct the allometric equations previously published, such as in [26][27][28][29]. Additionally, many scientists have adopted various allometric equations in their case studies because they can be used to address the spatial variability [28,30,31].…”

Section: Introductionmentioning

confidence: 99%

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Estimating Forest Biomass Dynamics by Integrating Multi-Temporal Landsat Satellite Images with Ground and Airborne LiDAR Data in the Coal Valley Mine, Alberta, Canada

Badreldin

Sánchez‐Azofeifa

2015

Remote Sensing

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Assessing biomass dynamics is highly critical for monitoring ecosystem balance and its response to climate change and anthropogenic activities. In this study, we introduced a direct link between Landsat vegetation spectral indices and ground/airborne LiDAR data; this integration was established to estimate the biomass dynamics over various years using multi-temporal Landsat satellite images. Our case study is located in an area highly affected by coal mining activity. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI and EVI2), chlorophyll vegetation index (CVI), and tasseled cap transformations were used as vegetation spectral indices to estimate canopy height. In turn, canopy height was used to predict a coniferous forest's biomass using Jenkins allometric and Lambert and Ung allometric equations. The biophysical properties of 700 individual trees at eight different scan stations in the study area were obtained using high-resolution ground LiDAR. Nine models (Hi) were established to discover the best relationship between the canopy height model (CHM) from the airborne LiDAR and the vegetation spectral indices (VSIs) from Landsat images for the year 2005, and HB9 (Jenkins allometric equation) and HY9 (Lambert and Ung allometric equation) proved to be the best models (r 2 = 0.78; root mean square error (RMSE) = 44 Mg/H, r 2 = 0.67; RMSE = 58.01 Mg/H, respectively; p < 0.001) for estimating the canopy height and the biomass. This model accurately captured the most affected areas (deforested) and the reclaimed areas (forested) in the study area. Five years were chosen for studying the biomass change : 1988, 1990, 2001, 2005, and 2011. OPEN ACCESSRemote Sens. 2015, 7 2833Additionally, four pixel-based image comparisons were analyzed (i.e., 1988-1990, 1990-2005, 2005-2009, and 2009-2011), and Mann-Kendall statistics for the subsets of years were obtained. The detected change showed that, in general, the environment in the study area was recovering and regaining its initial biomass after the dramatic decrease that occurred in 2005 as a result of intensive mining activities and disturbance.

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Section: Major Findingssupporting

confidence: 54%

Section: Allometric Equations and Ground Lidar Metrics From Individuamentioning

confidence: 99%

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Estimating Forest Biomass Dynamics by Integrating Multi-Temporal Landsat Satellite Images with Ground and Airborne LiDAR Data in the Coal Valley Mine, Alberta, Canada

Badreldin

Sánchez‐Azofeifa

2015

Remote Sensing

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“…The notable exception was the Generic mallee-form Eucalyptus allometric model which yielded relatively precise predictions when individuals were selected evenly on the natural scale. This is because mallee plantings are typically established by tube stock rather than by seed, resulting in relatively low spatial variability in stand density (Paul et al 2013c), and relatively low variability in individual stem diameters.…”

Section: Discussionmentioning

confidence: 99%

Guidelines for constructing allometric models for the prediction of woody biomass: How many individuals to harvest?

et al. 2015

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Abstract. The recent development of biomass markets and carbon trading has led to increasing interest in obtaining accurate estimates of woody biomass production. Aboveground woody biomass (B) is often estimated indirectly using allometric models, where representative individuals are harvested and weighed, and regression analyses used to generalise the relationship between individual mass and more readily measured non-destructive attributes such as plant height and stem diameter (D). To satisfy regulatory requirements and/or to provide market confidence, allometric models must be based on sufficient data to ensure predictions are accurate, whilst at the same time being practically and financially achievable.Using computer resampling experiments and allometric models of the form B ¼ aD b the trade-off between increasing the sample size of individuals to construct an allometric model and the accuracy of the resulting biomass predictions was assessed. A range of algorithms for selecting individuals across the stem diameter size-class range were also explored. The results showed marked variability across allometric models in the required number of individuals to satisfy a given level of precision. A range of 17-95 individuals were required to achieve biomass predictions with a standard deviation within 5% of the mean for the best performing stem diameter selection algorithm, while 25-166 individuals were required for the poorest. This variability arises from (a) inherent uncertainty in the relationship between diameter and biomass across allometric models, and (b) differences between the diameter size-class distribution of individuals used to construct a model, and the diameter size-class distribution of the population to which the model is applied. Allometric models are a key component of quantifying land-based sequestration activities, but despite their importance little attention has been given to ensuring the methods used in their development will yield sufficiently accurate biomass predictions. The results from this study address this gap and will be of use in guiding the development of new allometric models; in assessing the suitability of existing allometric models; and in facilitating the estimation of uncertainty in biomass predictions.

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“…Allometric models are useful for assessment of aboveground-biomass (AGB) and net primary productivity (ANPP) of forests [1][2][3] and shrubs [4][5][6]. They are widely utilized for forest inventory and management because they offer a non-destructive, relatively accurate, and labor-efficient method [7,8].…”

Section: Introductionmentioning

confidence: 99%

Habitat Effect on Allometry of a Xeric Shrub (Artemisia ordosica Krasch) in the Mu Us Desert of Northern China

She

Zhang

Qin

et al. 2015

Forests

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Allometric models are useful for assessment of aboveground net primary productivity (ANPP) and aboveground biomass (AGB) of forests and shrubs, and are widely implemented in forest inventory and management. Multiple forms of allometric models have been used to estimate vegetation carbon storage for desert shrubland, but their validity for biomass estimation has not been tested at a region scale with different habitats. To verify the validity of habitat-specific models, general models (combining data from all habitats/sites), and previously developed models for biomass prediction, we developed both general models and habitat-specific models for aboveground biomass and ANPP of Artemisia ordosica Krasch, a dominant shrub of the Mu Us Desert. Our results showed that models based on crown area or canopy volume consistently explained large parts of the variations in aboveground biomass and ANPP. Model fitting highlighted that general allometric models were inadequate across different habitats, and habitat-specific models were useful for that specific habitat. Previous models might be inappropriate for other sites because of site quality differences. There was a strong habitat effect on the allometric relationships of A. ordosica. Although our study is a case in point, the results indicate that allometric models for desert shrubs should be used with caution and require robust validation if adopted from other studies or applied to different sites/habitats.

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Testing allometric equations for prediction of above-ground biomass of mallee eucalypts in southern Australia

Cited by 38 publications

References 36 publications

Estimating Forest Biomass Dynamics by Integrating Multi-Temporal Landsat Satellite Images with Ground and Airborne LiDAR Data in the Coal Valley Mine, Alberta, Canada

Estimating Forest Biomass Dynamics by Integrating Multi-Temporal Landsat Satellite Images with Ground and Airborne LiDAR Data in the Coal Valley Mine, Alberta, Canada

Guidelines for constructing allometric models for the prediction of woody biomass: How many individuals to harvest?

Habitat Effect on Allometry of a Xeric Shrub (Artemisia ordosica Krasch) in the Mu Us Desert of Northern China

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