scholarly journals Energy Stored in Above-Ground Biomass Fractions and Model Trees of the Main Coniferous Woody Plants

2021 ◽  
Vol 13 (22) ◽  
pp. 12686
Author(s):  
Rudolf Petráš ◽  
Julian Mecko ◽  
Ján Kukla ◽  
Margita Kuklová ◽  
Danica Krupová ◽  
...  
Keyword(s):  
Woody Plants ◽  
Abies Alba ◽  
Biomass Combustion ◽  
Sampling Point ◽  
Above Ground Biomass ◽  
Tree Biomass ◽  
Model Trees ◽  
Ground Biomass ◽  
Volume Production ◽  
Small Wood

The paper considers energy stored in above-ground biomass fractions and in model trees of the main coniferous woody plants (Picea abies (L.) H. Karst., Abies alba Mill., Pinus sylvestris (L.), Larix decidua Mill.), sampled in 22 forest stands selected in different parts of Slovakia. A total of 43 trees were felled, of which there were 12 spruces, 11 firs, 10 pines, and 10 larches. Gross and net calorific values were determined in samples of wood, bark, small-wood, twigs, and needles. Our results show that these values significantly depend on the tree species, biomass fractions, and sampling point on the tree. The energy stored in the model trees calculated on the basis of volume production taken from yield tables increases as follows: spruce < fir < pine < larch. Combustion of tree biomass releases an aliquot amount of a greenhouse gas—CO2, as well as an important plant nutrient, nitrogen—into the atmosphere. The obtained data must be taken into account in the case of the economic utilization of energy stored in the fractions of above-ground tree biomass and in whole trees. The achieved data can be used to assess forest ecosystems in terms of the flow of solar energy, its accumulation in the various components of tree biomass, and the risk of biomass combustion in relation to the release of greenhouse gases.

Using photography to estimate above-ground biomass of small trees

2020 ◽  
pp. 1-7
Author(s):  
Brandon R. Hays ◽  
Corinna Riginos ◽  
Todd M. Palmer ◽  
Benard C. Gituku ◽  
Jacob R. Goheen
Keyword(s):  
Cost Effective ◽  
Allometric Equation ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Tree Biomass ◽  
East African ◽  
Ground Biomass ◽  
Herbivore Exclusion ◽  
Significant Difference ◽  
Allometric Relation

Abstract Quantifying tree biomass is an important research and management goal across many disciplines. For species that exhibit predictable relationships between structural metrics (e.g. diameter, height, crown breadth) and total weight, allometric calculations produce accurate estimates of above-ground biomass. However, such methods may be insufficient where inter-individual variation is large relative to individual biomass and is itself of interest (for example, variation due to herbivory). In an East African savanna bushland, we analysed photographs of small (<5 m) trees from perpendicular angles and fixed distances to estimate above-ground biomass. Pixel area of trees in photos and diameter were more strongly related to measured, above-ground biomass of destructively sampled trees than biomass estimated using a published allometric relation based on diameter alone (R2 = 0.86 versus R2 = 0.68). When tested on trees in herbivore-exclusion plots versus unfenced (open) plots, our predictive equation based on photos confirmed higher above-ground biomass in the exclusion plots than in unfenced (open) plots (P < 0.001), in contrast to no significant difference based on the allometric equation (P = 0.43). As such, our new technique based on photographs offers an accurate and cost-effective complement to existing methods for tree biomass estimation at small scales with potential application across a wide variety of settings.

scholarly journals Above-ground tree biomass and allometric relationships of Cinnamomum tamala grown in the western hill regions of Nepal

2013 ◽  
Vol 21 (1) ◽  
pp. 3-12 ◽  
Author(s):  
BS Poudel ◽  
SK Gautam ◽  
DN Bhandari
Keyword(s):  
Model Validation ◽  
Regression Models ◽  
Above Ground Biomass ◽  
Tree Biomass ◽  
Stem Wood ◽  
Ground Biomass ◽  
Cinnamomum Tamala ◽  
Tree Component ◽  
Best Fit ◽  
Branch Wood

Biomass regression models are presented describing total above-ground biomass, stem wood, branch wood, foliage and bark production for Tejpat (Cinnamomum tamala), a multipurpose tree which is found abundantly distributed and grown in western hill districts of Nepal. A total of 56 Tejpat trees between 6.2 and 16.5 cm diameter at breast height (DBH) from farmers’ farmland and marginal land in Arghakhanchi, Gulmi and Palpa districts were sampled and harvested. Mean fresh weight of total above-ground biomass, stem wood, branch wood, foliage and bark was 77.03, 36.39, 15.16, 17.53 and 8.2 kg tree -1, respectively. Allocation of biomass was more in stem (47.24% tree-1) than in foliage (22.75% tree-1), branch (19.69% tree-1) and bark (10.31% tree-1). Weight of tree component was estimated as a function of DBH. After removal of the outliers, data were randomly divided into two datasets: 70% for model calibration and another 30% for model validation. Correlation analysis showed positive stronger linear relationship between DBH and biomass. Five regression models (linear, logarithmic, quadratic, power and exponential) were developed. All models were statistically significant, with R2 ranging from 0.64 to 0.83. Model validation was based on root mean square error (RMSE). RMSE percentage for the best-fit equation varied between 16.64% and 44.82%. Linear model resulted in the least error and was selected as the best-fit model for prediction of biomass of bark, foliage, branch, stem and total above ground tree biomass. Biomass models developed could be applied to obtain biomass of different tree components of Tejpat grown in the study area and could even be applied to other areas which have similar conditions; but it should be validated before using them in new sites and conditions. DOI: http://dx.doi.org/10.3126/banko.v21i1.9058 Banko Janakari, Vol. 21, No. 1 2011; 3-12

scholarly journals ENERGY POTENTIAL IN PRODUCTION OF FAST-GROWING POPLAR CLONES IN SLOVAK REGIONS

2013 ◽  
Vol 10 (2) ◽  
pp. 51-54
Author(s):  
Rudolf Petráš ◽  
Julian Mecko ◽  
Viera Petrášová
Keyword(s):  
Calorific Value ◽  
Annual Production ◽  
Above Ground Biomass ◽  
Energy Potential ◽  
Lowland Forest ◽  
Ground Biomass ◽  
Poplar Clones ◽  
Density Values ◽  
Small Wood ◽  
High Level

Abstract Calorific value production from the above-ground biomass of stands was derived from its volume production. The mathematical models of growth tables of I-214 and Robusta poplar clones, biomass density values and calorific values of biomass dry matter were used for its calculation. At the stands aged 35 years and site indices of 20, 30 and 40, the calorific value has approximately 2.700, 6.000 and 9.300 GJ.ha-1 respectively. The I-214 clone has higher production than Robusta in the first half of its growth, albeit with minimum differences. The annual increments of calorific value culminate about the age of 9-13 years with values of 450-115 GJ.ha-1. Mean annual production of both clones culminates at the age of 17-26 years with values of 320-80 GJ.ha-1. Lowland forest locations with high level of ground water in Slovakia with the total area of 25.600 ha are most suitable for poplars production. On this area, we can assume the mean annual production of 3.566 TJ of gross calorific value obtained from above-ground biomass in the future. From that, about 64% is in wood, 14% in bark and 22% in small-wood. Up to 85% of this production potential is situated in the area of The Danube Lowland and the rest is mainly in southern areas of the Central and Eastern Slovakia.

ESTIMATION OF ABOVE GROUND BIOMASS OF THE THREE SITES (GRAZED SITE, PROTECTED SITE AND SEED SOWN SITE) FOR COMPARING THEIR PRODUCTIVITY IN KASHMIR VALLEY

2017 ◽  
Vol 23 (2) ◽  
Author(s):  
AFSHAN ANJUM BABA ◽  
SYED NASEEM UL-ZAFAR GEELANI ◽  
ISHRAT SALEEM ◽  
MOHIT HUSAIN ◽  
PERVEZ AHMAD KHAN ◽  
...  
Keyword(s):  
Protected Areas ◽  
Aboveground Biomass ◽  
Plant Biomass ◽  
Summer Season ◽  
Above Ground Biomass ◽  
Spring Season ◽  
Kashmir Valley ◽  
Ground Biomass ◽  
Maximum Value ◽  
Protected Site

The plant biomass for protected areas was maximum in summer (1221.56 g/m2) and minimum in winter (290.62 g/m2) as against grazed areas having maximum value 590.81 g/m2 in autumn and minimum 183.75 g/m2 in winter. Study revealed that at Protected site (Kanidajan) the above ground biomass ranged was from a minimum (1.11 t ha-1) in the spring season to a maximum (4.58 t ha-1) in the summer season while at Grazed site (Yousmarag), the aboveground biomass varied from a minimum (0.54 t ha-1) in the spring season to a maximum of 1.48 t ha-1 in summer seasonandat Seed sown site (Badipora), the lowest value of aboveground biomass obtained was 4.46 t ha-1 in spring while as the highest (7.98 t ha-1) was obtained in summer.

scholarly journals The climatic imprint of bimodal distributions in vegetation cover for western Africa

2016 ◽  
Vol 13 (11) ◽  
pp. 3343-3357 ◽  
Author(s):  
Zun Yin ◽  
Stefan C. Dekker ◽  
Bart J. J. M. van den Hurk ◽  
Henk A. Dijkstra
Keyword(s):  
Land Cover ◽  
Congo Basin ◽  
Shortwave Radiation ◽  
Mean Annual Precipitation ◽  
Above Ground Biomass ◽  
Precipitation Regime ◽  
Ground Biomass ◽  
Western Africa ◽  
Precipitation Regimes ◽  
Woody Cover

Abstract. Observed bimodal distributions of woody cover in western Africa provide evidence that alternative ecosystem states may exist under the same precipitation regimes. In this study, we show that bimodality can also be observed in mean annual shortwave radiation and above-ground biomass, which might closely relate to woody cover due to vegetation–climate interactions. Thus we expect that use of radiation and above-ground biomass enables us to distinguish the two modes of woody cover. However, through conditional histogram analysis, we find that the bimodality of woody cover still can exist under conditions of low mean annual shortwave radiation and low above-ground biomass. It suggests that this specific condition might play a key role in critical transitions between the two modes, while under other conditions no bimodality was found. Based on a land cover map in which anthropogenic land use was removed, six climatic indicators that represent water, energy, climate seasonality and water–radiation coupling are analysed to investigate the coexistence of these indicators with specific land cover types. From this analysis we find that the mean annual precipitation is not sufficient to predict potential land cover change. Indicators of climate seasonality are strongly related to the observed land cover type. However, these indicators cannot predict a stable forest state under the observed climatic conditions, in contrast to observed forest states. A new indicator (the normalized difference of precipitation) successfully expresses the stability of the precipitation regime and can improve the prediction accuracy of forest states. Next we evaluate land cover predictions based on different combinations of climatic indicators. Regions with high potential of land cover transitions are revealed. The results suggest that the tropical forest in the Congo basin may be unstable and shows the possibility of decreasing significantly. An increase in the area covered by savanna and grass is possible, which coincides with the observed regreening of the Sahara.

Above-ground biomass estimation of Indian tropical forests using X band Pol-InSAR and Random Forest

2021 ◽  
Vol 21 ◽  
pp. 100462
Author(s):  
Sadhana Yadav ◽  
Hitendra Padalia ◽  
Sanjiv K. Sinha ◽  
Ritika Srinet ◽  
Prakash Chauhan
Keyword(s):  
Random Forest ◽  
Tropical Forests ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
X Band

scholarly journals Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Negar Tavasoli ◽  
Hossein Arefi
Keyword(s):  
Machine Learning ◽  
Carbon Stock ◽  
Vegetation Indices ◽  
Forest Biomass ◽  
Principal Component ◽  
Optical Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Texture Characteristics ◽  
Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

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