scholarly journals Understanding the variation in wood densities of trees and its implications for carbon assessments

2019 ◽  
Author(s):  
Karthik Teegalapalli ◽  
Chandan Kumar Pandey ◽  
Anand M Osuri ◽  
Jayashree Ratnam ◽  
Mahesh Sankaran
Keyword(s):  
Intraspecific Variation ◽  
Wood Density ◽  
Aboveground Biomass ◽  
Spatial Scales ◽  
Regional Scale ◽  
Interspecific Variation ◽  
Carbon Stocks ◽  
Biomass Estimation ◽  
Continental Scale ◽  
Genus Level

AbstractWood density is a key functional trait used to estimate aboveground biomass (AGB) and carbon stocks. A common practice in forest AGB and carbon estimation is to substitute genus averages (across species with known wood densities) in cases where wood densities of particular species are unknown. However, the extent to which genus-level averages are reflective of species wood densities across tree genera is uncertain, and understanding this is critical for estimating the accuracy of carbon stock estimates. Using primary field data from India and secondary data from a published global dataset, we quantified the extent to which wood density varied among individuals within species (intraspecific variation) at the regional scale and among species within genera (interspecific variation) at regional to global scales. We used a published global database with wood density data for 7743 species belonging to 1741 genera. Linear models were used to compare the species values with the genera averages and the individual values with the species averages, respectively. To estimate the error associated with using genus-level averages for carbon stocks estimation, we compared genus values averaged at the global, old world and continental scales with species values from actually measured data. We also ran a simulation using vegetation data from a published database to calculate the estimation errors in a 1 hectare plot level when genera-averaged wood densities are used. Intraspecific variation was significantly lower than interspecific variation. Continental level genera averages led to estimates closer to the species values for the 10 genera for which most data on species was available. This was also evident from a comparison of genera averages at these three spatial scales with species values from our data. Species within certain ‘hypervarying’ genera showed relatively high levels of variation, irrespective of the spatial scale of the dataset used. The error in estimation of AGB when genera-averaged values were used for species wood densities was 0.35, 0.71 and 2.43% when 0, 10 and 25% of the girth of the trees in the simulated plot were from hypervariable genera. Our findings indicate that species values provide the most accurate estimates for individuals. Genus average wood density values at the continental scale provided more reliable estimates than those at larger spatial scales. The aboveground biomass estimation error when species wood densities were approximated to the genera-average values was 1.4 to 3.7 tonnes per ha when 10% and 25%, respectively, of the girth of trees was from species from hypervariable genera. Our findings indicate that regional or continental scale genera averages provide more reliable estimates than global data and we propose a method to identify hypervariable genera, for which species values rather than genera averages can provide better estimates of carbon stocks.

scholarly journals Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 540 ◽  
Author(s):  
Cheryl Doughty ◽  
Kyle Cavanaugh
Keyword(s):  
Salt Marsh ◽  
High Resolution ◽  
Aboveground Biomass ◽  
Vegetation Index ◽  
Coastal Wetland ◽  
Spatial Scales ◽  
Biomass Estimation ◽  
Spatial And Temporal Variability ◽  
Estimation Model ◽  
Spatial And Temporal Scales

Salt marsh productivity is an important control of resiliency to sea level rise. However, our understanding of how marsh biomass and productivity vary across fine spatial and temporal scales is limited. Remote sensing provides a means for characterizing spatial and temporal variability in marsh aboveground biomass, but most satellite and airborne sensors have limited spatial and/or temporal resolution. Imagery from unmanned aerial vehicles (UAVs) can be used to address this data gap. We combined seasonal field surveys and multispectral UAV imagery collected using a DJI Matrice 100 and Micasense Rededge sensor from the Carpinteria Salt Marsh Reserve in California, USA to develop a method for high-resolution mapping of aboveground saltmarsh biomass. UAV imagery was used to test a suite of vegetation indices in their ability to predict aboveground biomass (AGB). The normalized difference vegetation index (NDVI) provided the strongest correlation to aboveground biomass for each season and when seasonal data were pooled, though seasonal models (e.g., spring, r2 = 0.67; RMSE = 344 g m−2) were more robust than the annual model (r2 = 0.36; RMSE = 496 g m−2). The NDVI aboveground biomass estimation model (AGB = 2428.2 × NDVI + 120.1) was then used to create maps of biomass for each season. Total site-wide aboveground biomass ranged from 147 Mg to 205 Mg and was highest in the spring, with an average of 1222.9 g m−2. Analysis of spatial patterns in AGB demonstrated that AGB was highest in intermediate elevations that ranged from 1.6–1.8 m NAVD88. This UAV-based approach can be used aid the investigation of biomass dynamics in wetlands across a range of spatial scales.

scholarly journals Uncertainty assessment in aboveground biomass estimation at the regional scale using a new method considering both sampling error and model error

2017 ◽  
Vol 47 (8) ◽  
pp. 1095-1103 ◽  
Author(s):  
Yu Fu ◽  
Yuancai Lei ◽  
Weisheng Zeng ◽  
Ruijun Hao ◽  
Guilian Zhang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Aboveground Biomass ◽  
Regional Scale ◽  
Model Fitting ◽  
Model Error ◽  
New Method ◽  
Biomass Estimation ◽  
Calibration Data ◽  
Data Set ◽  
Sources Of Error

Uncertainty associated with multiple sources of error exists in biomass estimation over large areas. This uncertainty affects the accuracy of the resultant biomass estimates. A new method that introduces Taylor series principles into a Monte Carlo simulation procedure was proposed and developed for estimating regional-scale aboveground biomass, along with quantifying the corresponding uncertainty arising from both sampling and model predictions. Additionally, the effect of sample size on estimates during model fitting was studied based on the new method to determine whether the effect of the size of the calibration data set can be neglected when the number of simulations is sufficiently large. The results revealed that the proposed method not only produces more reliable estimates of both biomass and uncertainty but also effectively and separately quantifies the uncertainties associated with different sources of error. The new method also reduced the effect of model uncertainty on final estimates. The uncertainty that was associated with model error increased significantly with decreasing sample sizes during model fitting, and the error was not reduced by increasing the number of Monte Carlo simulations.

scholarly journals Allometric equation for aboveground biomass estimation in moist Afromontane forest in Gesha and Sayilem district in Kaffa zone, southwestern Ethiopia

2020 ◽  
Author(s):  
Admassu Merti ◽  
Teshome Soromessa ◽  
Tura Bareke
Keyword(s):  
Wood Density ◽  
Aboveground Biomass ◽  
Allometric Equation ◽  
Allometric Equations ◽  
Individual Species ◽  
Biomass Estimation ◽  
Regression Equations ◽  
Strongly Correlated ◽  
Afromontane Forest ◽  
Species Specific

Abstract Background: Allometric equations which are regressions linking the biomass to some independent variables are used to estimate tree components from the forest. The generic equation developed by many authors may not adequately reveal the tree biomass in a specific region in tropics including in Ethiopia. Therefore, the use of species specific allometric equations is important to achieve higher levels of accuracy because trees of different species may differ. The objective of the study was to develop species-specific allometric equations for Apodytes dimidiata, Ilex mitis, Sapium ellipticum and shrubs (Galiniera saxifraga and Vernonia auriculifera) using semi-destructive method for estimating the aboveground biomass (AGB). For purpose of sampling trees, individual species were categorized into trees whose Diameter at breast height (DBH) is ≥ 5 cm.Results: All the necessary biomass calculations were done, and biomass equations were developed for each species. The regression equations relate AGB with DBH, height (H), and density (ρ) were computed and the models were tested for accuracy based on observed data. The best model was selected based higher adj R2 and lower residual standard error and Akaike information criterion than rejected models. The relations for all selected models are significant (p<0.000), which showed strong correlation AGB with selected dendrometric variables. Accordingly, the AGB was strongly correlated with DBH and was not significantly correlated with wood density and height individually in Ilex mitis. In combination, AGB was strongly correlated with DBH, height; DBH and wood density; are better for carbon assessment than general equations.Conclusions: The specific allometeric equation developed for the Gesha-Sayilem Afromontane Forest which can be used in similar moist forests in Ethiopia for the implementation of Reduced Emission from Deforestation and Degradation (REDD+) activities to benefit the local communities from carbon trade.

scholarly journals Biomass and total carbon in oak forests of Southern Colombian Andes: contributions to the REDD+ project-wide approach.

2016 ◽  
Vol 64 (1) ◽  
pp. 399
Author(s):  
Adriana Yepes ◽  
Andrés Sierra ◽  
Luz Milena Niño ◽  
Manuel López ◽  
César Garay ◽  
...  
Keyword(s):  
Tropical Forests ◽  
Aboveground Biomass ◽  
Belowground Biomass ◽  
Carbon Stocks ◽  
Total Carbon ◽  
Biomass Estimation ◽  
Natural Forests ◽  
Colombian Andes ◽  
Greenhouse Emission

Carbon estimations in tropical forests are very important to understand the role of these ecosystems in the carbon cycle, and to support decisions and the formulation of mitigation and adaptive strategies to reduce the greenhouse emission gases (GHG). Nevertheless, detailed ground-based quantifications of total carbon stocks in tropical montane forests are limited, despite their high value in science and ecosystem management (e.g. REDD+). The objective was to identify the role of these ecosystems as carbon stocks, to evaluate the contribution of the pools analyzed (aboveground biomass, belowground biomass and necromass), and to make contributions to the REDD+ approach from the project scale. For this study, we established 44 plots in a heterogeneous landscape composed by old-grown forests located in the Southern Colombian Andes. In each plot, all trees, palms and ferns with diameter (D) ≥ 15 cm were measured. In the case of palms, the height was measured for 40 % of the individuals, following the Colombia National Protocol to estimate biomass and carbon in natural forests. National allometric equations were used to estimate aboveground biomass, and a global equation proposed by IPCC was used for belowground biomass estimation; besides, palms’ aboveground biomass was estimated using a local model. The necromass was estimated for dead standing trees and the gross debris. In the latter case, the length and diameters of the extremes in the pieces were measured. Samples for wood density estimations were collected in the field and analyzed in the laboratory. The mean total carbon stock was estimated as 545.9 ± 84.1 Mg/ha (± S.E.). The aboveground biomass contributed with 72.5 %, the belowground biomass with 13.6 %, and the necromass with 13.9 %. The main conclusion is that montane tropical forests store a huge amount of carbon, similar to low land tropical forests. In addition, the study found that the inclusion of other pools could contribute with more than 20 % to total carbon storage, indicating that estimates that only include the aboveground biomass, largely underestimate carbon stocks in tropical forest ecosystems. These results support the importance of including other carbon pools in REDD+ initiatives’ estimations. 

scholarly journals Allometric Equations for Aboveground Biomass Estimations of Four Dry Afromontane Tree Species

2020 ◽  
Author(s):  
Getaneh Gebeyehu ◽  
Teshome Soromessa ◽  
Tesfaye Bekele ◽  
Demel Teketay
Keyword(s):  
Wood Density ◽  
Aboveground Biomass ◽  
Tree Species ◽  
Model Fit ◽  
Allometric Equations ◽  
Biomass Estimation ◽  
Parameter Estimates ◽  
Total Biomass ◽  
Allometric Models ◽  
Species Specific

Abstract Background: Tree species based developing allometric equations are important because they contain the largest proportion of total biomass and carbon stocks of forests. Studies on developing and validating the species-specific allometric models (SSAM) remain insufficient that may result to biomass estimation errors in the forests. The purpose of this study is to determine the wood density of four tree species and develop and validate the accuracy of allometry for biomass estimations. A total of 103 sample trees representing four species were harvested semi-destructively. The species specific allometric equations (SSAM) were developed using aboveground biomass (AGB in kg) as dependent variable, and three of the predictor’s variables: diameter at beast height (DBH in cm), height (H in m) and wood density (WD in g cm-3). The relation between dependent and independent variables were tested using multiple correlations (R2). The model selection and validation was based on statistical significance of model parameter estimates, Akaike Information Criterion (AIC), adjusted coefficient of determination (R2), residual standard error (RSE) and mean relative error (MRE). Results: The results showed that the AGB correlated significantly with diameter at breast height (R2 > 0.944, P < 0.001), and tree height (R2 > 0.742, P <0.001). The species-specific allometric models, which include DBH, H and WD predicted AGB with high-model fit (R2 > 93.6%, P < 0.001). These models for biomass estimations produced small MRE (1.50–3.40%) and AIC (-7.04 –12.84) compared to a single predictor (MRE:-0.4 – 20.1%; AIC: -7.25 – 35.29). The SSAM also predicted AGB against predictors with high-model fit (R2 > 93.6%, P < 0.001) and small MRE: 1.50 – 3.40% compared to existing general allometric models (MRE: - 31.3 – 11.31%). Conclusions: The research confirmed that the inclusion of DBH, H, and WD in the SSAM predicted AGB with small bias than a single or two predictors. The wood density values of those studied species can be used as the references for biomass estimations using general allometric equations. The study contributes to species-specific allometric models for understanding the total biomass estimation of species. Therefore, the application of species-specific allometric models should be considered in biomass estimations of forests.

scholarly journals Comparative Analysis of Seasonal Landsat 8 Images for Forest Aboveground Biomass Estimation in a Subtropical Forest

Forests ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 45 ◽  
Author(s):  
Chao Li ◽  
Mingyang Li ◽  
Jie Liu ◽  
Yingchang Li ◽  
Qianshi Dai
Keyword(s):  
Aboveground Biomass ◽  
Regional Scale ◽  
Coniferous Forest ◽  
Mixed Forest ◽  
Subtropical Forest ◽  
Acquisition Time ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Forest Types ◽  
Forest Aboveground Biomass

To effectively further research the regional carbon sink, it is important to estimate forest aboveground biomass (AGB). Based on optical images, the AGB can be estimated and mapped on a regional scale. The Landsat 8 Operational Land Imager (OLI) has, therefore, been widely used for regional scale AGB estimation; however, most studies have been based solely on peak season images without performance comparison of other seasons; this may ultimately affect the accuracy of AGB estimation. To explore the effects of utilizing various seasonal images for AGB estimation, we analyzed seasonal images collected using Landsat 8 OLI for a subtropical forest in northern Hunan, China. We then performed stepwise regression to estimate AGB of different forest types (coniferous forest, broadleaf forest, mixed forest and total vegetation). The model performances using seasonal images of different forest types were then compared. The results showed that textural information played an important role in AGB estimation of each forest type. Stratification based on forest types resulted in better AGB estimation model performances than those of total vegetation. The most accurate AGB estimations were achieved using the autumn (October) image, and the least accurate AGB estimations were achieved using the peak season (August) image. In addition, the uncertainties associated with the peak season image were largest in terms of AGB values < 25 Mg/ha and >75 Mg/ha, and the quality of the AGB map depicting the peak season was poorer than the maps depicting other seasons. This study suggests that the acquisition time of forest images can affect AGB estimations in subtropical forest. Therefore, future research should consider and incorporate seasonal time-series images to improve AGB estimation.

Intra- and interspecific variation in wood density and fine-scale spatial distribution of stand-level wood density in a northern Thai tropical montane forest

2009 ◽  
Vol 25 (4) ◽  
pp. 359-370 ◽  
Author(s):  
Witchaphart Sungpalee ◽  
Akira Itoh ◽  
Mamoru Kanzaki ◽  
Kriangsak Sri-ngernyuang ◽  
Hideyuki Noguchi ◽  
...  
Keyword(s):  
Growth Rate ◽  
Intraspecific Variation ◽  
Wood Density ◽  
Spatial Association ◽  
Interspecific Variation ◽  
Maximum Diameter ◽  
Individual Species ◽  
Size Growth ◽  
Sapling Growth ◽  
Species Specific

Abstract:Tropical tree wood density is often related to other species-specific functional traits, e.g. size, growth rate and mortality. We would therefore expect significant associations within tropical forests between the spatial distributions of stand-level wood density and micro-environments when interspecific variation in wood density is larger than intraspecific variation and when habitat-based species assembly is important in the forest. In this study, we used wood cores collected from 515 trees of 72 species in a 15-ha plot in northern Thailand to analyse intra- and interspecific variation in wood density and the spatial association of stand-level wood density. Intraspecific variation was lower than interspecific variation (20% vs. 80% of the total variation), indicating that species-specific differences in wood density, rather than phenotypic plasticity, are the major source of variation in wood density at the study site. Wood density of individual species was significantly negatively related to maximum diameter, growth rate of sapling diameter and mortality of saplings. Stand-level mean wood density was significantly negatively related to elevation, slope convexity, sapling growth rate and sapling mortality, and positively related to slope inclination. East-facing slopes had significantly lower stand-level mean wood densities than west-facing slopes. We hypothesized that ridges and east-facing slopes in the study forest experience strong and frequent wind disturbance, and that this severe impact may lead to faster stand turnover, creating conditions that favour fast-growing species with low wood density.

Estimating species-specific wood density from the genus average in Indonesian trees

2006 ◽  
Vol 22 (4) ◽  
pp. 481-482 ◽  
Author(s):  
J. W. F. Slik
Keyword(s):  
Tropical Forests ◽  
Spatial Scales ◽  
Carbon Stocks ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Tree Biomass ◽  
Allometric Model ◽  
Species Specific ◽  
Amazonian Forests ◽  
Source Of Error

To be able to make accurate estimates of the carbon stocks present in the world's tropical forests, there is a growing need for accurate tree biomass estimations on large spatial scales (Chave et al. 2004, Cummings et al. 2002, Nascimento & Laurance 2004). Wood-specific gravity forms an important component of these biomass estimations (Baker et al. 2004, Magcale-Macandog 2004, Nogueira et al. 2005). Even though Chave et al. (2004) found that the most important source of error in above-ground biomass estimation (AGB) is currently related to the choice of allometric model, Baker et al. (2004) show that for two estimates of AGB in Amazonian forests derived using different allometric equations, stand-level specific wood gravity still explained 45.4% and 29.7% of the total variation in AGB.

The Determinants of Entrepreneurship Development in Spatial Systems

2017 ◽  
Vol 13 ◽  
pp. 8-24
Author(s):  
Zbigniew Zioło
Keyword(s):  
Spatial Scales ◽  
Institutional Barriers ◽  
Entrepreneurship Development ◽  
Continental Scale ◽  
Spatial Systems ◽  
Business Conditions ◽  
Mutual Relations ◽  
Growth Shape ◽  
Strategic Goal

The processes of technological  progress create new opportunities for economic, social and cultural growth, shape new relations between economic  entities and their environment,  and influence changes in the determinants  of entrepreneurship development.  These processes vary significantly in certain geographic locations, characterised by an enormous  diversity of natural, social, economic and cultural structures. As a consequence, this creates different opportunities  and different conditions for the development of entrepreneurship in certain spatial scales, from the continental scale, through national and regional to local scales. The article presents complex conditions  for the development of entrepreneurship, highlights its limitations resulting from institutional  barriers, and the importance of knowing the mechanisms of mutual relations between spatial systems and the influence of control instruments. The quality of central and local government authorities is of particular significance here, which do not always properly use the mechanisms of rational business support. A serious barrier to the development of entrepreneurship is the low quality of social capital, manifested in a lack of trust in institutional authorities and reluctance to engage in entrepreneurship and business development. The conclusions point out that further research should be developed that will take into account changing business conditions, with a defined strategic goal of raising the quality and standard of living, international competitiveness of the country and products in different market categories.

Biomass of understory species of Pinus halepensis Mill. forests in the Kassandra Peninsula - Chalkidiki

1998 ◽  
Vol 63 ◽  
Author(s):  
P. Smiris ◽  
F. Maris ◽  
K. Vitoris ◽  
N. Stamou ◽  
P. Ganatsas
Keyword(s):  
Moisture Content ◽  
Aboveground Biomass ◽  
Quercus Ilex ◽  
Pinus Halepensis ◽  
Biomass Estimation ◽  
Regression Equations ◽  
Pistacia Lentiscus ◽  
Quercus Coccifera ◽  
Understory Species ◽  
Cistus Incanus

This  study deals with the biomass estimation of the understory species of Pinus halepensis    forests in the Kassandra peninsula, Chalkidiki (North Greece). These  species are: Quercus    coccifera, Quercus ilex, Phillyrea media, Pistacia lentiscus, Arbutus  unedo, Erica arborea, Erica    manipuliflora, Smilax aspera, Cistus incanus, Cistus monspeliensis,  Fraxinus ornus. A sample of    30 shrubs per species was taken and the dry and fresh weights and the  moisture content of    every component of each species were measured, all of which were processed  for aboveground    biomass data. Then several regression equations were examined to determine  the key words.

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