scholarly journals Above-Ground Biomass Estimation of Acacia mangium Willd. in Revegetation Area of Coal Mining

2020 ◽  
Vol 8 (1) ◽  
pp. 20
Author(s):  
Erfanda Irawan ◽  
Irdika Mansur ◽  
Iwan Hilwan
Keyword(s):  
Coal Mining ◽  
Organic Material ◽  
Acacia Mangium ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Inventory Data ◽  
Allometric Model ◽  
Allometric Models ◽  
Ground Biomass ◽  
The Mean

Acacia mangium Willd. is categorized as an invasive species in the revegetation area of coal mining. The presence of A.mangium causes a shortage the organic matter in the revegetation area. The abundance of A. mangium biomass could be used as a source of organic material for soil enhancer to improve soil fertility. The objective of this study was to develop allometric models of Acacia mangium and to estimate the potential above-ground biomass of A. mangium in PT Wahana Baratama Mining (PT WBM). This study was conducted in February-April 2019. A. mangium population and distribution were collected through vegetation inventory with 0.5% sampling intensity. The allometric models were established using a destructive method. The above-ground biomass allometric model for the four diameter classes are as follows: seedlings (B = 0,002002 - 0,02469D + 0,07322D2 with R2(adj)= 99,38%), saplings (B = 2,754 - 1,742D + 0,4093D2 with R2(adj)= 99,89%), poles (B = -9,16 - 1,153D + 0,5007D2 with R2(adj)= 99,96%), and trees (B = 0,134741D2,38 with R2(adj)= 96,94%). The allometric models were used to estimate the above-ground biomass potential total of A. mangium by using inventory data. The inventory result showed that the mean density of A.mangium is 13.187 individuals/ha with a mean diameter of 5,64 cm. The potential above-ground biomass of A. mangium in PT WBM revegetation area is estimated at up to 51.022,59 tons. The above-ground biomass of A. mangium has potential value to be utilized as a soil enhancer as well as meet the needs of organic material for the whole PT WBM revegetation areas.Keywords: above-ground biomass, coal mining, Acacia mangium, reclamation, revegetation

Studies on Acacia mangium in Kemasul Forest, Malaysia. I. Biomass and productivity

1988 ◽  
Vol 4 (3) ◽  
pp. 293-302 ◽  
Author(s):  
Lim Meng Tsai
Keyword(s):  
Leaf Litter ◽  
Large Scale ◽  
Acacia Mangium ◽  
Litter Production ◽  
Above Ground Biomass ◽  
Annual Increment ◽  
High Turnover ◽  
Litter Accumulation ◽  
Ground Biomass ◽  
The Mean

ABSTRACTMalaysia is establishing large-scale plantations for reforestation and the production of wood for pulp and paper as well as for light construction. The main species used currently is the exotic legume Acacia mangium. The above-ground biomass, litter production and litter accumulation in a four-year-old stand in Peninsular Malaysia were studied. The mean diameter at breast height (dbh) was 12 cm. The mean annual increment (MAI) in dbh of individual trees ranged from 0.9 to 5.1 cm while MAI in height of sample trees ranged from 2.9 to 5.5 m. The total above-ground biomass of the stand was 90.4 t ha−1, consisting of 57.6 t stem, 14.1 t branch and 5.4 t leaf. Litter production averaged 10.23 t ha−1 yr−1 with leaf litter making up 87.4% of the total. Leaf litter accumulation amounted to 6.64 t ha−1 and the turnover constant of leaf litter was estimated at 1.35. The high productivity is discussed in relation to the high turnover of foliage and the low turnover of litter.

scholarly journals An Allometric Model Based Approach For Estimating Biomass In Seven Indian Bamboo Species In Western Himalayan Foothills, India

2021 ◽  
Author(s):  
R. Kaushal ◽  
S. Islam ◽  
Salil Tewari ◽  
J. M.S. Tomar ◽  
S. Thapliyal ◽  
...  
Keyword(s):  
Biomass Production ◽  
Dry Weight ◽  
Allometric Equations ◽  
Bamboo Species ◽  
Above Ground Biomass ◽  
Fresh Weight ◽  
Allometric Model ◽  
Allometric Models ◽  
Ground Biomass ◽  
Himalayan Foothills

Abstract The rapid growth rate, high biomass production, and annual harvesting, makes bamboo as suitable species for commercial production. Allometric equations for many broadleaf and conifer tree species are available. However, knowledge on biomass production and allometric equations of bamboos are limited. This study aims at developing species specific allometric models for predicting biomass and synthetic height values as a proxy variable for seven bamboo species in Himalayan foothills. Two power form based allometric models were used to predict above ground and culm biomass using Diameter at breast height (D) alone and D in combination with culm height (H) as independent variable. This study also extended to establishing H-D allometric model that can be used to generate synthetic H values as proxy to missing H. In the seven bamboo species studied, among three major biomass component (culm, branch and foliage), culm is the most important component with highest share (69.56 to 78.71%).Distribution of percentage (%) share of culm, branch and foliage to above ground fresh weight varies significantly between different bamboo species. D. hamiltonii has highest productivity for above ground biomass components. Ratio of dry to fresh weight of seven bamboo species was estimated for culm, branch, foliage and above ground biomass to convert fresh weight to dry weight.

scholarly journals Forest above ground biomass estimation and forest/non-forest classification for Odisha, India, using L-band Synthetic Aperture Radar (SAR) data

2014 ◽  
Vol XL-8 ◽  
pp. 651-658 ◽  
Author(s):  
M. Suresh ◽  
T. R. Kiran Chand ◽  
R. Fararoda ◽  
C. S. Jha ◽  
V. K. Dadhwal
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Support Vector ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Inventory Data ◽  
Backscatter Coefficient ◽  
Ground Biomass ◽  
Forest Classification ◽  
Aperture Radar

Tropical forests contribute to approximately 40 % of the total carbon found in terrestrial biomass. In this context, forest/non-forest classification and estimation of forest above ground biomass over tropical regions are very important and relevant in understanding the contribution of tropical forests in global biogeochemical cycles, especially in terms of carbon pools and fluxes. Information on the spatio-temporal biomass distribution acts as a key input to Reducing Emissions from Deforestation and forest Degradation Plus (REDD+) action plans. This necessitates precise and reliable methods to estimate forest biomass and to reduce uncertainties in existing biomass quantification scenarios. <br><br> The use of backscatter information from a host of allweather capable Synthetic Aperture Radar (SAR) systems during the recent past has demonstrated the potential of SAR data in forest above ground biomass estimation and forest / nonforest classification. <br><br> In the present study, Advanced Land Observing Satellite (ALOS) / Phased Array L-band Synthetic Aperture Radar (PALSAR) data along with field inventory data have been used in forest above ground biomass estimation and forest / non-forest classification over Odisha state, India. The ALOSPALSAR 50 m spatial resolution orthorectified and radiometrically corrected HH/HV dual polarization data (digital numbers) for the year 2010 were converted to backscattering coefficient images (Schimada et al., 2009). <br><br> The tree level measurements collected during field inventory (2009&ndash;'10) on Girth at Breast Height (GBH at 1.3 m above ground) and height of all individual trees at plot (plot size 0.1 ha) level were converted to biomass density using species specific allometric equations and wood densities. The field inventory based biomass estimations were empirically integrated with ALOS-PALSAR backscatter coefficients to derive spatial forest above ground biomass estimates for the study area. <br><br> Further, The Support Vector Machines (SVM) based Radial Basis Function classification technique was employed to carry out binary (forest-non forest) classification using ALOSPALSAR HH and HV backscatter coefficient images and field inventory data. The textural Haralick’s Grey Level Cooccurrence Matrix (GLCM) texture measures are determined on HV backscatter image for Odisha, for the year 2010. PALSAR HH, HV backscatter coefficient images, their difference (HHHV) and HV backscatter coefficient based eight textural parameters (Mean, Variance, Dissimilarity, Contrast, Angular second moment, Homogeneity, Correlation and Contrast) are used as input parameters for Support Vector Machines (SVM) tool. Ground based inputs for forest / non-forest were taken from field inventory data and high resolution Google maps. <br><br> Results suggested significant relationship between HV backscatter coefficient and field based biomass (R<sup>2</sup> = 0.508, p = 0.55) compared to HH with biomass values ranging from 5 to 365 t/ha. The spatial variability of biomass with reference to different forest types is in good agreement. The forest / nonforest classified map suggested a total forest cover of 50214 km2 with an overall accuracy of 92.54 %. The forest / non-forest information derived from the present study showed a good spatial agreement with the standard forest cover map of Forest Survey of India (FSI) and corresponding published area of 50575 km<sup>2</sup>. Results are discussed in the paper.

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

Mortality and Annual Changes in Distribution of Above-Ground Biomass in an Aspen Sucker Stand

1971 ◽  
Vol 1 (4) ◽  
pp. 262-266 ◽  
Author(s):  
D. F. W. Pollard
Keyword(s):  
Annual Production ◽  
Above Ground Biomass ◽  
Net Production ◽  
Ground Biomass ◽  
Insufficient Time ◽  
The Mean ◽  
Annual Changes ◽  
Diameter Classes

Biomass (stems and branches) increased from 17 000 kg h−1 in the 4th year to 34 000 kg h−1 in the 7th year of development of an aspen sucker stand. The bulk of the biomass was distributed in the middle and upper diameter classes of shoots; net annual increases only occurred in the upper classes. About 80% of shoots dying in the 3 years of study were less than 2 cm dbh; the biomass lost in these amounted to 200 kg h−1 or less each year. The remaining 20% mortality occurred in the 7th year among shoots 2–5 cm dbh infected with Diplodiatumefaciens. Biomass lost in these larger shoots amounted to 4 900 kg h−1; this was close to the discrepancy between net production (stems and branches) in the 7th year (2600 kg h−1 per annum) and net production in the 5th and 6th years (about 7000 kg h−1 per annum.) Results suggest that although high rates of net annual production are obtainable in short rotations, the mean annual production is strongly influenced by disease because of insufficient time for enhanced growth of survivors.

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.

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