Estimation Of Total Aboveground Biomass Research Articles

Regression estimators for aboveground biomass and its constituent parts of trees in native southern Brazilian forests

The mathematical models used applying the Nonlinear Seemingly Unrelated Regressions (NSUR) or Weighted Nonlinear Seemingly Unrelated Regressions (WNSUR) methodologies can contribute to generate acceptable and reliable estimates of total aboveground biomass and its constituent parts, which are needed to implement forest management strategies to maintain desirable and sustainable carbon stocks. The aim of this study was: 1) to fit the sample data with independent nonlinear regression models and present the results obtained from the respective statistical estimates for total biomass aboveground and the constituent parts of trees in native forest trees. 2) To fit the sample data with regression models simultaneously, that is, whose models are composed of appropriate combinations of their coefficients, in order to obtain additivity of the estimates and present better results for the total aboveground biomass and the constituent parts of the trees. 3) To apply weighting procedures to the variances of the fitted models. 4) To evaluate the error due to the regression function on forest biomass estimation. The data came from eight sites located in the states of Parana and Rio Grande do Sul, Brazil, and information was collected on diameter at 1.30 m aboveground (DBH), total height, biomasses of the trunk components (branches and leaves) and total aboveground biomass. Non-linear functions were independently and simultaneously fitted, using DBH and total height as independent variables in the regression models. Independent fitting of equations was performed using generalized nonlinear least squares (ENGLS) and simultaneous fitting of equations was obtained by means of NSUR. Weighting, by applying a variance structure in the two procedures, was done to solve the issue of heteroscedasticity. Numerically, the equations fitted simultaneously performed better and were more efficient than the independently fitted models, which resulted in biological inconsistency, that is, non-additivity of the biomass of constituent parts of the trees and the total biomass. Simultaneous fitting generated superior statistical and biological properties to obtain tree estimates of the of constituent parts of the trees and total aboveground biomass in native forests of southern Brazil. The smaller error due to the regression function used in the forest biomass inventory was obtained by simultaneous fitting. With these results, the procedure using simultaneous and weighted fitting of equations (WNSUR) is recommended to fit biomass equations for native forests in southern Brazil.

Biomass estimation of individual trees for coppice-originated oak forests

In this study, individual tree-biomass equations are developed for the different biomass components (foliage, branch, bark, stem, and total aboveground biomass) of Quercus frainetto Ten., Quercus cerris L., and Quercus petraea (Matt.) Liebl. species, which are the main oak species in coppice-originated oak forests. This research was conducted in northern Turkish Thrace, an important coppice-oak area. A total of 474 oak trees at different development stages over different sites were destructively sampled to obtain biomass models of the major oak species in the region. Two main biomass models are established to estimate the biomass components: according to the diameter at breast height (DBH) alone and according to the d2h variable, which is the combined DBH and tree height. The goodness-of-fit statistics of the models are calculated to compare these models. The models that use the DBH alone as an independent variable explain 68–95% of the variation in the biomass of the tree components. After adding the tree height to the model, the accuracy for the bark-, stem-, and total aboveground-biomass estimates increased, while the accuracy of the foliage- and branch-biomass estimates decreased. Different oak species have different biomass components depending on their development stages. The amount of biomass components in the total aboveground biomass and foliage decreased from 20% (SDF) to 4% (LDF), the amount of bark decreased from 17% (SDF) to 12% (LDF), and the amount of stems increased from 42% (SDF) to 66% (LDF) when increasing the mean DBH of the sample plots.

Exploring the Inclusion of Small Regenerating Trees to Improve Above-Ground Forest Biomass Estimation Using Geospatial Data

Research on the contribution of understory components to the total above ground biomass (AGB) has to date received very little attention because most prior biomass estimation studies have ignored small regenerating trees beneath the main canopy with the assumption that their contribution to biomass is generally negligible. Only a few biomass studies have emphasized a considerable contribution to biomass of understory components in forest ecosystems. However, this study of native, tropical, deciduous forest biomass in the Central Highlands of Vietnam was able to explore the contribution of small regenerating trees to total biomass by exploiting a large field inventory of hundreds to thousands of individually-counted small regenerating trees per hectare. Thus, this study investigated the influence of small regenerating tree biomass on models of the relationship between total AGB and remote sensing data. These analyses were trained with and without topographic variables derived from ASTER-GDEM. Our results demonstrate that the inclusion of small regenerating understory trees (R2 = 0.42, NRMSE or %RMSE = 30.5%) provides a quantifiable improvement in total estimated AGB compared to using only large woody canopy trees (R2 = 0.21, NRMSE or %RMSE = 36.6%) when correlating field-based biomass measurements with optical image-derived variables. All analyses show that the inclusion of terrain factors made an important contribution to biomass modeling. This study suggests that for young, open forests where there are many small regenerating trees, the contribution of understory biomass should be taken into consideration to improve total AGB estimation.

Allometric Model for Predicting Aboveground Biomass and Carbon Stock of Acacia Plantations in Sarawak, Malaysia

Allometric equations estimating biomass and carbon stock for Acacia mangium in Malaysia have been developed. However, in previous studies they were obtained from small trials or experimental plots. In this study, models were proposed to quantify the aboveground biomass as well as the amounts of stored and sequestered carbon in two planting variants, namely second generation Acacia mangium and Acacia hybrid plantations that were approximately 10 years old. Linear, power, exponential, and logarithmic functions were fitted for aboveground biomass using trunk diameter at breast height (DBH) as the independent variable. The best fit model for estimation of total aboveground biomass was in the form of a power function y = aDb. Application of the developed model yielded total aboveground biomasses of Acacia hybrid and second generation Acacia mangium of 113.3 Mha-1 and 178.9 Mha-1, respectively. This study indicated that 10-year-old second generation Acacia mangium and Acacia hybrid had sequestered 30.8 Mha-1 and 19.5 Mha-1, respectively, of CO2 annually. This study showed that the Acacia mangium plantation plays a role as a carbon sink. Thus, these forest plantations benefit the economy and also mitigate carbon emissions.

Determinants of Above-Ground Biomass and Its Spatial Variability in a Temperate Forest Managed for Timber Production

The proper estimation of above-ground biomass (AGB) stocks of managed forests is a prerequisite to quantifying their role in climate change mitigation. The aim of this study was to analyze the spatial variability of AGB and its uncertainty between actively managed pine and unmanaged pine-oak reference forests in central Mexico. To investigate the determinants of AGB, we analyzed variables related to forest management, stand structure, topography, and climate. We developed linear (LM), generalized additive (GAM), and Random Forest (RF) empirical models to derive spatially explicit estimates and their uncertainty, and compared them. AGB was strongly influenced by forest management, as LiDAR-derived stand structure and stand age explained 80.9% to 89.8% of its spatial variability. The spatial heterogeneity of AGB varied positively with stand structural complexity and age in the managed forests. The type of predictive model had an impact on estimates of total AGB in our study site, which varied by as much as 19%. AGB densities varied from 0 to 492 ± 17 Mg ha−1 and the highest values were predicted by GAM. Uncertainty was not spatially homogeneously distributed and was higher with higher AGB values. Spatially explicit AGB estimates and their association with management and other variables in the study site can assist forest managers in planning thinning and harvesting schedules that would maximize carbon stocks on the landscape while continuing to provide timber and other ecosystem services. Our study represents an advancement toward the development of efficient strategies to spatially estimate AGB stocks and their uncertainty, as the GAM approach was used for the first time with improved results for such a purpose.

DEVELOPMENT OF LOCAL ALLOMETRIC EQUATION TO ESTIMATE TOTAL ABOVEGROUND BIOMASS IN PAPUA TROPICAL FOREST

Recently, pantropical allometric equations have been commonly used across the globe to estimate the aboveground biomass of the forests, including in Indonesia. However, in relation to regional differences in diameter, height and wood density, the lack of data measured, particularly from eastern part of Indonesia, may raise the question on accuracy of pantropical allometric in such area. Hence, this paper examines the differences of local allometric equations of Papua Island with equations developed by Chave and his research groups. Measurements of biomass in this study were conducted directly based on weighing and destructive samplings. Results show that the most appropriate local equation to estimate total aboveground biomass in Papua tropical forest is Log(TAGB) = -0.267 + 2.23 Log(DBH) +0.649 Log(WD) (CF=1.013; VIF=1.6; R2= 95%; R2-adj= 95.1%; RMSE= 0.149; P<0.001). This equation is also a better option in comparison to those of previously published pantropical equations with only 6.47% average deviation and 5.37 points of relative bias. This finding implies that the locally developed equation should be a better option to produce more accurate site specific total aboveground biomass estimation.

Estimation of biomass and carbon storage of tree plantations in northern Iran

To estimate the aboveground biomass and carbon storage in multi-species plantations we used species-specific equations method and three other generic methods. Astara county is located in northern Iran. It has a total area of 1,757 ha. Based on species-specific allometric equations, total aboveground biomass was calculated andvaried between 81.13 and 98.21 t·ha&lt;sup&gt;–1 &lt;/sup&gt;for Acer velutinum, 68.36 and 83.44 t·ha&lt;sup&gt;–1&lt;/sup&gt; for Quercus castanifolia, 71.88 and 119.22 t·ha&lt;sup&gt;–1&lt;/sup&gt; for Tilia begonifolia, 56.07 and 61.98 t·ha&lt;sup&gt;–1&lt;/sup&gt; for Fraxinus excelsior and from 37.92 to 51.34 t·ha&lt;sup&gt;–1&lt;/sup&gt; for Prunus avium. There was a significant difference between the mean values of total aboveground biomass estimation obtained by species-specific equations and the three generic methods for Alnus subcordata, Pinus taeda and Pinus nigra. Results indicated that using generalized methods produced more reliable and accurate estimations for native species than for exotic species for rapid biomass and carbon estimation in order to decide on plantation development in the area.

Estimation of stem and tree level biomass models for Prosopis juliflora/pallida applicable to multi-stemmed tree species

The aim of this paper is to develop biomass models for commonly multi-stemmed Prosopis juliflora/pallida trees. The data were collected on three of the Cape Verde islands (Maio, Santiago and Santo Antao). The dataset covers 240 trees containing 1,882 stems with stem diameter at breast height over 2 cm; of that 255 individual tree stems were sampled destructively. These calibration data were used to construct stem and tree-level models for estimation of total aboveground biomass and its fine and course fractions with diameter threshold of 5 cm. A set of parameterized biomass models for multi-stemmed Prosopis spp. trees suited for biomass estimation at tree and stem levels using appropriate set of independent variables, commonly available in forest inventory programs, was created. The effect of site (island) on tree allometry was not detected. The two-phase construction of tree biomass models based on destructive sampling limited to individual stems combined with a routine field measurement of entire multi-stemmed tree specimen represents a practicable approach leading to biomass and carbon assessment that may be generally suited for tree species with complex multi-stemmed growth form similar to that of Prosopis spp.

Improving the accuracy of tree-level aboveground biomass equations with height classification at a large regional scale

A nonlinear simultaneous equations system based on diameter at breast height (DBH), used to ensure additivity for biomass of individual tree components, was fitted for Cunninghamia lanceolata. The data consisted of measurements taken from 600 sample trees in southern China. All sample trees were harvested and measured for biomass of stem wood, stem bark, branches and foliage. Tree height was classified into 2–5 levels based on the height–diameter relationship across a large geographical area. The nonlinear extra sum of squares method and the Lakkis–Jones test were used to evaluate significant differences between biomass equations with DBH only and classified equations and to assess whether height classification had a significant effect on improving the accuracy of the biomass equations. Based on the PRESS residuals, several statistical indicators, e.g. prediction determination coefficient and root mean square error, were used to evaluate model performance. The results show the height classification obviously improved model performance of the fitted equation by increasing the prediction determination coefficient, decreasing root mean square error and reducing bias and absolute bias by DBH class, especially for total aboveground biomass, stem wood biomass and stem bark biomass. Three-levels of height classification was the best for the total aboveground biomass, following by 4-levels, 5-levels and 2-levels. Although statistically significant differences between the classified equations and the equations with DBH only were found for measurements of branch biomass and foliage biomass, their proportions of total aboveground biomass was small and had only a small effect on improving the accuracy of total aboveground biomass estimates. Height classification increased the stability of parameters for the estimation of stem wood and stem bark biomass. The classified biomass equations could be applied to estimate individual tree biomass in the Chinese National Forest Inventory and the method of height classification may be used with other tree species.

Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models

In this study, the performance of nine widely used and accessible crop growth simulation models (APES-ACE, CROPSYST, DAISY, DSSAT-CERES, FASSET, HERMES, MONICA, STICS and WOFOST) was compared during 44 growing seasons of spring barley (Hordeum vulgare L.) at seven sites in Northern and Central Europe. The aims of this model comparison were to examine how different process-based crop models perform at multiple sites across Europe when applied with minimal information for model calibration of spring barley at field scale, whether individual models perform better than the multi-model mean, and what the uncertainty ranges are in simulated grain yields. The reasons for differences among the models and how results for barley compare to winter wheat are discussed.Regarding yield estimation, best performing based on the root mean square error (RMSE) were models HERMES, MONICA and WOFOST with lowest values of 1124, 1282 and 1325 (kgha−1), respectively. Applying the index of agreement (IA), models WOFOST, DAISY and HERMES scored best having highest values (0.632, 0.631 and 0.585, respectively). Most models systematically underestimated yields, whereby CROPSYST showed the highest deviation as indicated by the mean bias error (MBE) (−1159kgha−1). While the wide range of simulated yields across all sites and years shows the high uncertainties in model estimates with only restricted calibration, mean predictions from the nine models agreed well with observations. Results of this paper also show that models that were more accurate in predicting phenology were not necessarily the ones better estimating grain yields. Total above-ground biomass estimates often did not follow the patterns of grain yield estimates and, thus, harvest indices were also different. Estimates of soil moisture dynamics varied greatly.In comparison, even though the growing cycle for winter wheat is several months longer than for spring barley, using RMSE and IA as indicators, models performed slightly, but not significantly, better in predicting wheat yields. Errors in reproducing crop phenology were similar, which in conjunction with the shorter growth cycle of barley has higher effects on accuracy in yield prediction.

Sprouting productivity and allometric relationships of two oak species managed for traditional charcoal making in central Mexico

Sustainable production systems for woodfuels in developing countries require basic information on tree productivity, and particularly on their coppicing productivity under current forms of management. We report biomass equations and sprouting productivity of two oak species (Quercus castanea and Quercus laeta) subject to traditional forms of woodfuel harvesting at Cuitzeo basin in central Mexico. Biomass components analyzed were total aboveground biomass (AGB), woody biomass suitable for charcoal making (WSC) and residues (foliage and small branches). The estimation of total aboveground biomass (AGB) and woody biomass suitable for charcoal making (WSC) of individual trees, when expressed as a function of DBH in the form y = a(DBH)b, resulted in values of pseudo-R2 higher than 92%. The Mean Annual Increment (MAI) of both species increased with site age. Significant differences were found in regrowth rates of these species. Maximum charcoal potential productivity in kg ha−1 year−1 is achieved between 30 and 50 years depending on the decay rate of coppicing-shoot density over time. This roughly doubles current harvest cycles of 10–15 years followed by charcoalers. Oaks in developing countries have the potential to be used as a mid-term rotation coppice species for energy purposes. We argue that the results shown in this study are an important input for designing appropriate management strategies for traditional oak charcoal production in developing countries

Mapping biomass with remote sensing: a comparison of methods for the case study of Uganda

BackgroundAssessing biomass is gaining increasing interest mainly for bioenergy, climate change research and mitigation activities, such as reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+). In response to these needs, a number of biomass/carbon maps have been recently produced using different approaches but the lack of comparable reference data limits their proper validation. The objectives of this study are to compare the available maps for Uganda and to understand the sources of variability in the estimation. Uganda was chosen as a case-study because it presents a reliable national biomass reference dataset.ResultsThe comparison of the biomass/carbon maps show strong disagreement between the products, with estimates of total aboveground biomass of Uganda ranging from 343 to 2201 Tg and different spatial distribution patterns. Compared to the reference map based on country-specific field data and a national Land Cover (LC) dataset (estimating 468 Tg), maps based on biome-average biomass values, such as the Intergovernmental Panel on Climate Change (IPCC) default values, and global LC datasets tend to strongly overestimate biomass availability of Uganda (ranging from 578 to 2201 Tg), while maps based on satellite data and regression models provide conservative estimates (ranging from 343 to 443 Tg). The comparison of the maps predictions with field data, upscaled to map resolution using LC data, is in accordance with the above findings. This study also demonstrates that the biomass estimates are primarily driven by the biomass reference data while the type of spatial maps used for their stratification has a smaller, but not negligible, impact. The differences in format, resolution and biomass definition used by the maps, as well as the fact that some datasets are not independent from the reference data to which they are compared, are considered in the interpretation of the results.ConclusionsThe strong disagreement between existing products and the large impact of biomass reference data on the estimates indicate that the first, critical step to improve the accuracy of the biomass maps consists of the collection of accurate biomass field data for all relevant vegetation types. However, detailed and accurate spatial datasets are crucial to obtain accurate estimates at specific locations.

Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models

We compared the performance of eight widely used, easily accessible and well-documented crop growth simulation models (APES, CROPSYST, DAISY, DSSAT, FASSET, HERMES, STICS and WOFOST) for winter wheat ( Triticum aestivum L.) during 49 growing seasons at eight sites in northwestern, Central and southeastern Europe. The aim was to examine how different process-based crop models perform at the field scale when provided with a limited set of information for model calibration and simulation, reflecting the typical use of models for large-scale applications, and to present the uncertainties related to this type of model application. Data used in the simulations consisted of daily weather statistics, information on soil properties, information on crop phenology for each cultivar, and basic crop and soil management information. Our results showed that none of the models perfectly reproduced recorded observations at all sites and in all years, and none could unequivocally be labelled robust and accurate in terms of yield prediction across different environments and crop cultivars with only minimum calibration. The best performance regarding yield estimation was for DAISY and DSSAT, for which the RMSE values were lowest (1428 and 1603 kg ha −1) and the index of agreement (0.71 and 0.74) highest. CROPSYST systematically underestimated yields (MBE – 1186 kg ha −1), whereas HERMES, STICS and WOFOST clearly overestimated them (MBE 1174, 1272 and 1213 kg ha −1, respectively). APES, DAISY, HERMES, STICS and WOFOST furnished high total above-ground biomass estimates, whereas CROPSYST, DSSAT and FASSET provided low total above-ground estimates. Consequently, DSSAT and FASSET produced very high harvest index values, followed by HERMES and WOFOST. APES and DAISY, on the other hand, returned low harvest index values. In spite of phenological observations being provided, the calibration results for wheat phenology, i.e. estimated dates of anthesis and maturity, were surprisingly variable, with the largest RMSE for anthesis being generated by APES (20.2 days) and for maturity by HERMES (12.6). The wide range of grain yield estimates provided by the models for all sites and years reflects substantial uncertainties in model estimates achieved with only minimum calibration. Mean predictions from the eight models, on the other hand, were in good agreement with measured data. This applies to both results across all sites and seasons as well as to prediction of observed yield variability at single sites – a very important finding that supports the use of multi-model estimates rather than reliance on single models.

Biomass and Stand Characteristics of a Highly Productive Mixed Douglas-Fir and Western Hemlock Plantation in Coastal Washington

Abstract Aboveground biomass predictive equations were developed for a highly productive 47-year-old mixed Douglas-fir and western hemlock stand in southwest Washington State to characterize the preharvest stand attributes for the Fall River Long-Term Site Productivity Study. The equations were developed using detailed biomass data taken from 31 Douglas-fir and 11 western hemlock trees within the original stand. The stand had an average of 615 live trees per hectare, with an average dbh of 35.6 cm (39.1 cm for Douglas-fir and 33.3 cm for western hemlock) and an average total tree height of 31.6 m (32.8 m for Douglas-fir and 30.2 m for western hemlock). Equations developed were of the form ln Y = b1 + b2 ln dbh, where Y = biomass in kg, dbh = diameter in cm at 1.3 m height, b1 = intercept, and b2 = slope of equation. Each tree part was estimated separately and also combined into total aboveground biomass. The total aboveground biomass estimation equations were ln Y = −0.9950 + 2.0765 ln dbh for Douglas-fir, and ln Y = −1.6612 + 2.2321 ln dbh for western hemlock. The estimate of the aboveground live-tree biomass was of 395 Mg ha−1 (235 Mg ha−1 for Douglas-fir and 160 Mg ha−1 for western hemlock), with 9.5, 29.3, 12.9, 308, and 32.7 Mg ha−1 in the foliage, live branches, dead branches, stem wood, and stem bark, respectively. When compared with biomass estimates from six other studies, ranging in age from 22 to 110 years and from 96.3 to 636 Mg ha−1, the biomass of the Fall River site was relatively high for its age, indicating very high productivity.

Models for predicting above-ground biomass of Betula pubescens spp. czerepanóvii in mountain areas of southern Norway

The objectives of this study were (1) to develop models for estimation of total above-ground biomass, tree crown biomass and stem biomass of mountain birch (Betula pubescens spp. czerepanóvii), and (2) to test the stability of the relationships between biomass and biophysical tree properties across geographical regions and tree size ranges. The models were developed using a mixed modelling approach accounting for the hierarchical structure of the data that originated from sample plots. Diameter at breast height, tree height, and the ratio between height and diameter were candidate explanatory variables, but only diameter was statistically significant (p<0.05). The model fit values (pseudo-R 2) were 0.91, 0.60 and 0.85 for the three respective models. A substantial part of the model random errors could be attributed to between-plot variations. The conclusion related to objective (1) was that the models are well suited for biomass prediction of mountain birch in the mountain areas of southern Norway. Furthermore, models reported in previous research that had been calibrated on data from other regions were applied on the current data set. The results indicate that models calibrated for small trees produced predictions diverging from the observed values of the current data set. The differences between predicted and observed values also seem to vary along a site productivity gradient. Still, even though the differences between predicted and observed values using the different models varied quite a lot, the relationships were relatively stable within certain limits. The conclusion related to objective (2) was that biomass models can be applied outside the region for which they were developed, which in many cases is necessary because local models do not exist. However, the properties of the model development data related to tree size range and site productivity should be similar to those of the area for which predictions are being made.

Evaluation of non-destructive methods for estimating biomass in marshes of the upper Texas, USA coast

The estimation of aboveground biomass is important in the management of natural resources. Direct measurements by clipping, drying, and weighing of herbaceous vegetation are time-consuming and costly. Therefore, non-destructive methods for efficiently and accurately estimating biomass are of interest. We compared two non-destructive methods, visual obstruction and light penetration, for estimating aboveground biomass in marshes of the upper Texas, USA coast. Visual obstruction was estimated using the Robel pole method, which primarily measures the density and height of the canopy. Light penetration through the canopy was measured using a Decagon light wand, with readings taken above the vegetation and at the ground surface. Clip plots were also taken to provide direct estimates of total aboveground biomass. Regression relationships between estimated and clipped biomass were significant using both methods. However, the light penetration method was much more strongly correlated with clipped biomass under these conditions (R2 value 0.65 compared to 0.35 for the visual obstruction approach). The primary difference between the two methods in this situation was the ability of the light-penetration method to account for variations in plant litter. These results indicate that light-penetration measurements may be better for estimating biomass in marshes when plant litter is an important component. We advise that, in all cases, investigators should calibrate their methods against clip plots to evaluate applicability to their situation.

Radiometric slope correction for forest biomass estimation from SAR data in the Western Sayani Mountains, Siberia

We investigated the possibility of using multiple polarization (SIR-C) L-band data to map forest biomass in a mountainous area in Siberia. The use of a digital elevation model (DEM) and a model-based method for reducing terrain effects was evaluated. We found that the available DEM data were not suitable to correct the topographic effects on the SIR-C radar images. A model-based slope correction was applied to an L-band cross-polarized (hv) backscattering image and found to reduce the topographic effect. A map of aboveground biomass was produced from the corrected image. The results indicated that multipolarization L-band synthetic aperture radar (SAR) data can be useful for estimation of total aboveground biomass of forest stands in mountainous areas.

SURVEYS OF OIL SPILL INCIDENTS AFFECTING MANGROVE HABITAT IN AUSTRALIA: A PRELIMINARY ASSESSMENT OF INCIDENTS, IMPACTS ON MANGROVES, AND RECOVERY OF DEFORESTED AREAS

Over the past three decades, nine notable oil spills affected mangrove habitat in Australia. Six were the result of shipping incidents, and three others were land-based incidents. There were two incidents in the 1970s, three in the 1980s, and four in the 1990s. Types of oil released changed from crude oils in the 1970s to fuel and diesel oils in the 1990s. The total volume of oil released into Australian waters in mangrove-related incidents since 1970 was around 5,000 tonnes. Dispersants were used in the four major incidents involving shipping. Hydrocarbon in sediments generally decreased rapidly after spillage, leaving unrecognisable trace residual fractions in most locations after several years.At least 220 ha of mangroves were oiled in Australia since 1970, and around 13 ha were killed. The extent of oiling depended largely on the amount of oil released. The area of deforestation however, depended on the type of oil with the greatest areas killed by crude oils and diesel, and lesser damage caused by fuel oils. Recovery of deforested sites depended chiefly on site exposure. Sites of greatest exposure experienced both rapid and slow recovery, and sometimes rapid decline. In exposed locations, sediment flushing appeared important in stimulating recovery, while greater exposure also allowed premature destruction of growing plants by strong seas and storms. Based on estimates of total above-ground biomass, mean rates of recovery matched those observed in Panama, and full biomass recovery of areas deforested by oil spills might take around 36 years on average, with 50 per cent recovery after 20 years.

Biomass, structure, and trophic environment of peatland vegetation in Minnesota

The structure of peatland vegetation in Minnesota is affected by trophic conditions, the soil-water regime, and the disturbance history. Trophic conditions have a major effect on the species composition. Structurally, this results in the exclusion of most tall shrubs, hardwood trees, and forbs from ombrotrophic sites and a greater biomass of mosses and ericad shrubs on those sites than on minerotrophic sites. Classification of peatland vegetation into physiognomic groups provides reasonable estimates of total aboveground biomass. Trophic conditionsper se, however, have a minor effect on aboveground biomass; on both ombrotrophic and minerotrophic peatlands, biomass increases from less than 10 Mg ha−1 on the wettest sites to over 100 Mg ha−1 on the driest, undisturbed sites. Peatland sites that have comparatively dry, well-aerated soils support dense forests if they have not recently been disturbed; shrubs dominate on those sites after disturbance. Excessively wet sites are dominated by mosses (particularly on ombrotrophic peatlands) or graminoids (on minerotrophic peatlands). On ombrotrophic sites, the relatively dry conditions that lead to higher tree and total biomass also lead to lower pH resulting in an inverse relationship between biomass and pH.

Biomass and net annual primary production regressions for Populusgrandidentata on three sites in northern lower Michigan

Dimension analysis techniques were used in the harvest of 31 largetooth aspen (Populusgrandidentata Michx.) from three mature stands (55 ± 7 years) representing a wide range of soil quality and clonal variation among aspen in northern lower Michigan, U.S.A. Regression equations were derived to predict component biomass and net annual production from tree dbh. Evaluation by analysis of covariance indicated significant differences (P &lt; 0.05) in regression models among the sites.Total aboveground biomass of P. grandidentata was 171 565, 128 765, and 38 530 kg/ha at the good, intermediate, and poor soil sites where largetooth aspen constituted 81.5, 79.0, and 48.3% of the stand basal area, respectively. Corresponding aboveground net annual production values were 11 038, 7259, and 2925 kg/ha. Component percentages of total biomass were generally similar among sites, except for leaves. Variations in production percentages showed a production per unit leaf weight gradient parallel to the site quality gradient (i.e., poor site production per unit leaf weight was 33% less than the good site value). The errors inherent in the substitution of regressions derived from data from other sites were examined. Total biomass estimates ranged from −27 to +40% of accepted values. Errors for individual components ranged from −33 to +51%. Total aboveground biomass estimates from regressions for the combined data from all sites were acceptable within a standard error of the mean on the good and intermediate sites and with an allowance of 19% error on the poor site.