Development of allometric equations for estimating Pinus Brutia and Pinus Nigra trees biomass using regional field measurements and high-resolution crown area and height imagery

Given the interest in implementing land‐use change and forestry projects for mitigating carbon dioxide emissions, there is potentially a large demand for a system to measure carbon stocks accurately and precisely in a cost‐effective manner. As terrestrial ecosystems tend to be heterogeneous, a large number of sample plots could be needed to attain the regulatory‐required levels of precision, thus resulting in a costly process. A potential way of reducing costs of measuring the carbon stocks of forests is to collect the key data remotely. We have designed a system (a multispectral three‐dimensional aerial digital imagery system, M3DADI) that collects high‐resolution overlapping stereo imagery (≤10 cm pixels) from which we can distinguish individual trees or shrubs. In essence, we created a virtual forest that we used to measure crown area and heights of all plant groups. We used this M3DADI system to estimate the carbon stocks in aboveground biomass for the pine savanna in the Rio Bravo Carbon Sequestration Pilot Project in Belize. Seventy‐seven plots were established on the images, and using a series of nested plots we digitized the crown area and heights of pine and broadleaf trees, palmettos, and shrubs. Based on standard destructive harvest techniques, we obtained highly significant allometric regression equations between biomass carbon per individual and crown area and height. Combining the image‐plot data with the allometric equations resulted in a mean carbon stock of 13.1 Mg/ha with a 95% confidence interval of 2.2 Mg C/ha or ±16% of the mean. The coefficient of variation was high for all vegetation types (range of 31–303%), reflecting the highly heterogeneous nature of the system. We estimated that 202 plots would need to be installed to achieve a 95% confidence interval of ±10% of the mean. We compared the cost‐effectiveness of the M3DADI approach with conventional field methods based on the total person‐hours needed by both approaches to collect the same set of data for 202 plots. We found that the conventional field approach took about three times more person‐hours than the M3DADI approach.

Accurate measurement of structural traits of individual trees, such as stem diameter, crown height (H), and crown area (CA), is essential for assessing forest biomass and ecosystem functions. UAV-LiDAR technology enables detailed measurement of these traits over extensive areas for estimation of above-ground biomass (AGB). However, most previous studies using UAV-LiDAR have been conducted on planted conifer trees, with limited applications to natural forest trees. In this study, we measured H and CA for 149 canopy species encompassing 4,326 individuals across 23 natural forests in Japan, using UAV-LiDAR. We developed general, functional-type-specific, and species-specific allometric models to estimate stem diameter at breast height (DBH) and AGB based on H and CA. The models incorporating H and CA provided a substantial explanation of DBH (69%) and AGB (72%) variations across different forests and regions. The integration of functional types or species-specific information enhanced R2 by 9–12% for DBH and by 7–11% for AGB, thereby allowing the models to explain up to 81% of the variations in DBH and 83% in AGB. UAV-detectable trees accounted for more than two-thirds of the total AGB across various forest types, underscoring the reliability of UAV surveys in capturing standing AGB. The crown-based allometric models developed in this study may be useful for estimating DBH and AGB of canopy trees in multiple types of natural forests.

Estimates of aboveground woody plant biomass in hyper-arid ecosystems have predominantly relied on allometric equations developed in more mesic habitats. However, these equations do not account for local variations in plant morphology, necessitating the development of equations for the hyper-arid context. Here, we present species- and growth-form-specific allometric equations for 11 woody plant species in AlUla County, Kingdom of Saudi Arabia (KSA), based on sample sizes ranging from 8 to 50 individuals per species. Across five nature reserves in AlUla County, individuals of each selected plant species, spanning a range of size classes, were measured for height and crown area. For tree species with suitable structures (i.e. Moringa peregrina and Vachellia gerrardii), basal diameter was also recorded. All sampled plants were then destructively harvested to determine aboveground biomass. For all six shrub species, the best-fitting allometric equations included crown area and height as predictors of aboveground biomass, whereas all five tree species' equations included height (and other predictors, varying by species). The best-fitting general multi-species equations included crown area and height as predictors of aboveground biomass for both shrub and tree growth forms. The predictors in the best-fitting equations likely reflect the branched, lateral growth forms characteristic of plants in hyper-arid ecosystems, and are expected to improve the accuracy of biomass estimation compared with equations developed in mesic environments. These allometric equations provide a novel foundation for the quantitative monitoring of aboveground plant biomass and carbon stocks in the KSA and hyper-arid regions further afield.

Ficus thonningii is a multipurpose browse tree in northern Ethiopia. Despite its importance, techniques for quantifying its browsable biomass have not been developed. To develop best-estimation equations, the dendrometric parameters total height (H), crown height (CH), crown diameter (CD), diameter at stump height (DSH), diameter at breast height (DBH), crown depth (CDp), crown area (CA) and crown volume (CV) were measured from 12 sampled trees comprising three age ranges. Leaves and edible twigs of the sampled trees were clipped, oven dried, weighed and recorded as dry weight (DW). Regression analysis and a multicollinearity test were employed to remove non-significant predictors of DW. Results showed that only CV, CA, CD, CDp and DSH showed a strong correlation with DW. There was high collinearity between CD and CA, CD and CV, and CA and CV. However, CV and DSH had a higher correlation with DW than their counterparts, which suggested their use in the model. Therefore, the best allometric equation was: DW = 0.8470*CV - 0.2202*DSH - 1.5315 (R 2 = 0.99). This equation estimated that F. thonningii produces a very high amount of browsable biomass at all ages compared to common fodder species. The model can be used to plan the browsing rate and understand the ecological role of the species.

Estimation of aboveground tree biomass and carbon in mixed maize/tree parklands by nondestructive means requires the development of allometric equations from readily measurable variables such as diameter at breast height and tree height. Equations of this type have not been well developed for Faidherbia albida in eastern and southern Africa. In this study, F. albida trees were characterized in block plantings and in naturally regenerating parklands at six sites in Malawi. Allometric equations were developed for block planted and parkland management regimes. Forty-five intact trees with diameters ranging from 5 to 38 cm were sampled in the block planting while in parklands thirty-eight trees with diameters ranging from 5 to 116 cm were sampled. Destructive sampling was used to measure volumes and collect wood samples. Diameter at breast height, tree height and crown areas were used as predictors for dry weight of the above-ground biomass. Comparing the estimated equations to previously published data shows that these local species-specific equations differ slightly and that both can be used in the estimation of biomass in F. albida trees. Individual trees in parklands stored more biomass and carbon while block-planted trees stored more biomass per hectare. In parklands, F. albida crown area cover per hectare was 17.8 %, but could feasibly be increased under natural regeneration to as much as 23.1 %.

Estimation of biomass, volume and growth of subtropical forests in Shitai County, China

IntroductionPlantation forest is an important component of global forest resources. The accurate estimation of tree aboveground biomass (AGB) in plantation forest is of great significance for evaluating the carbon sequestration capacity. In recent years, UAV-borne LiDAR has been increasingly applied to forest survey, but the traditional allometric model for AGB estimation cannot be directly used without the diameter at breast height (DBH) of individual trees. Therefore, it is practicable to construct a novel allometric model incorporating the crown structure parameters, which can be precisely extracted from UAV LiDAR data. Additionally, the reduction effect of adjacent trees on crown area (Ac) should be taken into account.MethodsIn this study, we proposed an allometric model depending on the predictor variables of Ac and trunk height (H). The UAV-borne LiDAR was utilized to scan the sample plot of dawn redwood (DR) trees in the test site. The raw point cloud was first normalized and segmented into individual trees, whose Acs and Hs were sequentially extracted. To mitigate the effects of adjacent trees, the initial Acs were corrected to refer to the potential maximum Acs under undisturbed growth conditions. Finally, the corrected Acs (Acc) and Hs were input into the constructed allometric model to achieve the AGBs of DR trees.Results and discussionAccording to accuracy assessment, coefficient of determination (R2) and root mean square error (RMSE) of extracted Hs were 0.9688 and 0.51 m; R2 and RMSE of calculated AGBs were 0.9432 and 10.91 kg. The unrestricted growth parts of the tree crowns at the edge of a plantation forest could be used to derive the potential maximum Ac. Compared with the allometric models for AGB estimation relying only on trunk H or on initial Ac and H, the novel allometric model demonstrated superior performance in estimating the AGBs of trees in a plantation forest.

Allometric models are commonly used to estimate biomass, nutrients and carbon stocks in trees, and contribute to an understanding of forest status and resource dynamics. The selection of appropriate and robust models, therefore, have considerable influence on the accuracy of estimates obtained. Allometric models can be developed for individual species or to represent a community or bioregion. In Bangladesh, the nation forest inventory classifies tree and forest resources into five zones (Sal, Hill, Coastal, Sundarbans and Village), based on their floristic composition and soil type. This study has developed allometric biomass models for multi-species of the Sal zone. The forest of Sal zone is dominated by Shorea robusta Roth. The study also investigates the concentrations of Nitrogen, Phosphorus, Potassium and Carbon in different tree components. A total of 161 individual trees from 20 different species were harvested across a range of tree size classes. Diameter at breast height (DBH), total height (H) and wood density (WD) were considered as predictor variables, while total above-ground biomass (TAGB), stem, bark, branch and leaf biomass were the output variables of the allometric models. The best fit allometric biomass model for TAGB, stem, bark, branch and leaf were: ln (TAGB) = -2.460 + 2.171 ln (DBH) + 0.367 ln (H) + 0.161 ln (WD); ln (Stem) = -3.373 + 1.934 ln (DBH) + 0.833 ln (H) + 0.452 ln (WD); ln (Bark) = -5.87 + 2.103 ln (DBH) + 0.926 ln (H) + 0.587 ln (WD); ln (Branch) = -3.154 + 2.798 ln (DBH) - 0.729 ln (H) - 0.355 ln (WD); and ln (Leaf) = -4.713 + 2.066 ln (DBH), respectively. Nutrients and carbon concentration in tree components varied according to tree species and component. A comparison to frequently used regional and pan-tropical biomass models showed a wide range of model prediction error (35.48 to 85.51%) when the observed TAGB of sampled trees were compared with the estimated TAGB of the models developed in this study. The improved accuracy of the best fit model obtained in this study can therefore be used for more accurate estimation of TAGB and carbon and nutrients in TAGB for the Sal zone of Bangladesh.

The aboveground biomass (AGB) of shrubs and small trees is the main component for the productivity and carbon storage of understory vegetation in subtropical secondary forests. However, few allometric models exist to accurately evaluate understory biomass. To estimate the AGB of five common shrub (diameter at base < 5 cm, < 5 m high) and one small tree species (< 8 m high, trees’s seedling), 206 individuals were harvested and species-specific and multi-species allometric models developed based on four predictors, height (H), stem diameter (D), crown area (Ca), and wood density (ρ). As expected, the six species possessed greater biomass in their stems compared with branches, with the lowest biomass in the leaves. Species-specific allometric models that employed stem diameter and the combined variables of D2H and ρDH as predictors accurately estimated the components and total AGB, with R2 values from 0.602 and 0.971. A multi-species shrub allometric model revealed that wood density × diameter × height (ρDH) was the best predictor, with R2 values ranging from between 0.81 and 0.89 for the components and total AGB, respectively. These results indicated that height (H) and diameter (D) were effective predictors for the models to estimate the AGB of the six species, and the introduction of wood density (ρ) improved their accuracy. The optimal models selected in this study could be applied to estimate the biomass of shrubs and small trees in subtropical regions.

Key messageCrown area, sapling height, and biovolume extracted from UAV-acquired RGB images provided accurate estimates of aboveground biomass and carbon stocks in a 5-year-old holm oak (Quercus ilex L.) plantation. Our models regressing UAV-derived sapling variables against ground-based measurements exhibited high R2 values (0.78–0.89), thereby reflecting that RGB data can be used as an effective tool for measuring young individuals.ContextThe monitoring of tree sapling performance from the early stages of reforestation is of particular importance in the context of the global efforts to restore forests. Yet, most models to estimate carbon sequestration are developed for adult trees. Thus, the few models specifically developed for young trees rely on ground-based field sampling of tree growth parameters, which is time-consuming and difficult to implement at large spatial scales.AimsOur objectives were as follows: (1) to study the potential of UAV-based RGB imagery to detect and extract sapling variables (e.g., crown area, height, and biovolume) by comparing ground-based sapling measurements with UAV-derived data and (2) to compare the accuracy of the data estimated from RGB imagery with existing traditional field-based allometric equations.MethodsWe used a 5-year-old holm oak (Quercus ilex L. subsp. ballota (Desf.) Samp.) plantation (N = 617 plants), and their crown area, height, and biovolume were estimated from RGB imagery. Subsequently, the plants were harvested and the UAV-derived data were compared with field-measured sapling height and aboveground biomass values. Carbon content in leaves and stems was measured in a subsample of the saplings to estimate carbon stocks.ResultsThe models fitted with UAV-derived variables displayed high performance, with R2 values from 0.78 to 0.89 for height, leaf and stem biomass, total aboveground biomass, and carbon stocks. Moreover, aboveground biomass outputs calculated with field height and UAV-derived height using allometric equations exhibited R2 values from 0.65 to 0.68.ConclusionsGiven the affordable cost of RGB cameras and the versatility of drones, we suggest that UAV-based models may be a cost-effective method to estimate the biomass and carbon stocks of young plantations. However, further studies conducting drone flights in different conditions are needed to make this approach more scalable.

Knowledge of the biomass allometry and partitioning is essential for understanding shrub adaptive strategies to degraded habitats as well as for estimating organic carbon storage. We studied biomass accumulation, allocation patterns, and allometric models of Salsola passerina shrub in the Alxa Desert steppe, Northwestern China. We measured aboveground and belowground biomass accumulation across different ages (0–50 years) by destructive sampling. The biomass allocation patterns between aboveground biomass, leaves, branches, and roots were studied by fitting allometric functions for both pooled and age‐classed data. Allometric biomass models were developed by regressing on single‐input variable of basal diameter, crown area, height, and age alone or on the pairwise variables of above four parameters. Biomass accumulation increased with age, aboveground components represented 86–89% of the total biomass, root to shoot biomass ratios increased with shrub age. Allometry patterns of S. passerina were relatively constant, and the growth rate of root was faster than that of aboveground components. Allometric models with two‐input variables were obviously better than single variable models. Crown area and basal diameter were the best predictors for biomass of S. passerina shrub.

ABSTRACTThe development of shrub allometric models is crucial for accurate biomass assessment, as well as for scientific studies of carbon storage and carbon cycling of desert ecosystems. The aim of the present study was to construct allometric models to predict biomass using easily measured variables for xerophytic shrubs. The 12 most widespread shrub species of northern China were selected and a total of 385 individuals were harvested to obtain the weight of its components (leaves, twigs, branches, and roots), the crown area (CA) and plant height (H). Based on a high coefficient of determination (R2), a low standard error of estimate (SEE), and low Akaike information criterion (AIC) values, 72 species-specific and 24 multispecies models with CA and H as independent variables were developed. The function lnW (biomass of different components) = a + b × lnX (predictor variable) was selected as optimal model. CA was revealed as the best independent variable for the biomass of leaves and twigs, and V (CA × H) was the best predictor variable for branches, aboveground, belowground, and total biomass. In conclusion, for the first time species-specific and multispecies models were constructed with a high goodness of fit of leaves, twigs, branches, aboveground, belowground, and total biomass for 12 shrub species in northern China. Compared to multispecies models, species-specific models had improved accuracy. Since biomass quantification is the basis of carbon stocks estimation, the models presented here can be considered as alternative tool for assessing carbon storage and carbon cycling of desert ecosystems.

We developed species specific equations to predict aboveground biomass (AGB) of ten woody species in Borana rangelands of southern Ethiopia. A total of 150 plants 15 for each species were measured for biometric variables including the diameter at stump height (DSH), diameter at breast height (DBH), tree height (TH) and crown diameters were destructively harvested to obtain dry biomass. Many equations that related three biomass components: total aboveground, stem and branches to single or combination of predicator variables: DSH, DBH, TH, crown area (CA) and crown volume (CV) fit the data well to predict total AGB and by components for each of the species (adj.R2 > 0.80; P 0.80; P 0.93; P < 0.0001. A generalized mixed-species allometric model developed from the pooled data of seven species was most accurately predicted by the combination of three predicators (DSH-TH-CA models), with adj. R2 between 0.84 and 0.90 for all AGB categories. Hence, our species-specific allometric models could be adopted for the indirect biomass estimation in semi-arid savanna ecosystem of southern Ethiopia. The mixed species allometric models will give a good opportunity when species-specific equations are not available and contribute to estimate the biomass and carbon stock in woody vegetations of East African rangelands.

Forests play a significant role in climate change mitigation by sequestering and storing more carbon from the atmosphere than any other terrestrial ecosystem. Although a number of studies have been done on carbon stock estimations, the influence of environmental factors on forest carbon stocks has not been properly addressed. This study was conducted to estimate the carbon stock and its variation along the altitudinal gradients in Egdu dry afromontane forest found in Oromia Regional State of Ethiopia. The carbon stock in the different carbon pools and analysis of the influence of the environmental variables were studied by collecting data in sixty-nine quadrat plots of 10 x 20 m distributed along transect lines. To estimate carbon in above and below ground biomass; each tree in the study site having diameter at breast height (DBH) of ≥ 5 cm were measured for DBH and height. Above ground biomass was estimated by using allometric model while below ground biomass was determined based on the ratio of below ground biomass to above ground biomass factors. The mean total carbon stock density of Egdu Forest was found to be 614.72 ± 35.79 t ha-1 (ranging from 182.6 to1416 t ha-1), of which 45.24% of carbon was contained in the above ground biomass, 9.05% in below ground biomass, 0.56% in litter carbon and 45.15% was stored in soil organic carbon (0-30 cm depth). The carbon stocks in above ground biomass, below ground biomass, litter biomass and soil organic carbon exhibited distinct patterns along environmental gradients (slope gradient and slope aspect). The analysis of carbon stock variation of different carbon pools on eight different aspects of the forest area showed a significant variation with exception of soil organic carbon stock. The amount of carbon stock in above and below ground biomass, soil organic carbon and the total carbon stock was higher on the northern aspect as compared to other aspects. On the other hand, the carbon density of the forest carbon pool components showed a negative correlation with slope gradient; with increasing % slope, the above and below ground carbon, soil organic carbon and the total carbon stock decreased. This study concluded that the carbon stock value of Egdu Forest is large, and the carbon storage in different carbon pools of the forest area varies with slope aspect and slope aspect.

This study aimed to establish an allometric model for estimation of aboveground biomass, and carbon sequestration in A. lanata mangrove forest growing in Muna Regency, Southeast Sulawesi. Research methods were done by transect and 5 quadrats with size of 100 m2 each. A total of thirteen individual trees with different sizes were harvested. While DBH and D30 were measured. The samples were separated into stems, branches, and leaves and then weighted. The sample from each fresh organs were taken and brought to the Laboratory and then oven dried at 80°C for 7 days. The allometric equations were established by using independent variables (DBH and D30), and dependent variables (Ws, Wb, Wl). The partial and overall aboveground biomasses were calculated from allometric model, while carbon stock and CO2 sequestration were estimated. The results showed that the independent variable of DBH was more applicable for estimation of Ws, Wb, Wl, and total biomasses (Mg ha-1) of A. lanata forest, which were estimated as 28.28±3.48, 6.40±0.79, 5.00±0.66, and 40.08±4.97 respectively. The carbon stock in stems (13.24±1.63 Mg ha-1) was higher than in branches (3.01±0.37 Mg C ha-1) as well as in leaves (2.35±0.31 Mg C ha-1). The total of carbon stock were estimated at about 18.83±2.33 Mg C ha-1. Meanwhile, the total of CO2 absorption by A. lanata mangrove was 43.95±5.45 Mg CO2 ha-1. Therefore a regenerated A. lanata mangrove in this in-active pond area had potentiality on carbon stock and sequestrations, although these vegetation condition was still in the growth stage.