Aboveground biomass

Carbon stock distribution in different tropical forest types in India is rarely studied although India is a country with mega-diversity. The present study estimates the biomass and carbon stock of major tropical forest types in India, and attempts to identify suitable interpolation techniques for mapping carbon stock. Empirically derived allometric equations and carbon conversion coefficients were used to estimate the aboveground biomass and carbon stock, respectively. The point estimates were interpolated to spatial surface using different interpolation techniques. Two main modelling approaches were implemented: deterministic modelling and stochastic modelling. Deterministic modelling was to interpolate point information using similarities between measured points (inverse distance weighted (IDW) interpolation), and fitting a smoothing curve along the measured points (polynomial interpolation). In stochastic modelling, ordinary kriging (OK) was employed using parameters derived from semivariograms. The results showed that the average carbon stock in the study area was 84 t/ha. The highest carbon stock was in evergreen forest and the lowest in thorny scrub forest. Validation of the model using the mean and RMS errors indicated that ordinary kriging performs better than IDW and polynomial interpolations.

Abstract: The disturbance of mangrove forests could affect climate regulation, hydrological cycles,
biodiversity, and many other unique ecological functions and services. Proper biomass estimation
and carbon storage potential are needed to improve forest reference on biomass accumulation. The
establishment of a site-specific allometric equation is crucial to avert destructive sampling in future
biomass estimation. This study aimed to develop a site-specific allometric equation for biomass
estimation of a mix-mature mangrove forest at Sungai Pulai Forest Reserve, Johor. A stratified line
transect was set up and a total of 1000 standing trees encompassing seven mangrove tree species
were inventoried. Destructive sampling was conducted using the selective random sampling method
on 15 standing trees. Five allometric equations were derived by using diameter at breast height
(D), stem height (H), and wood density ( ) which were then compared to the common equation.
Simulations of each allometric equation regarding species were performed on 1000 standing trees.
Results showed that the single variable (D) equation provided an accurate estimation, which was
slightly improved when incorporated with the H variable. Both D and H variables, however, gave
inconsistent results for large-scale data and imbalance of sampled species. Meanwhile, the best fit
either for small-scale or large-scale data, as well as for imbalanced sample species was achieved
following the inclusion of the   variable when developing the equation. Hence, excluding the H
variable while including the   variable should be considered as an important determinant in mixed
mangrove species and uneven-aged stand for aboveground biomass estimation. This valuation can
both improve and influence decision-making in forest development and conservation.

Keywords: mangrove; aboveground biomass; tree component; allometric equation; power function

Resumo Ecossistemas florestais são importantes no processo de estocagem de carbono da atmosfera. Áreas de reflorestamentos podem contribuir de maneira substancial nesse processo e diminuir a pressão extrativista sobre florestas nativas. Este trabalho objetivou avaliar a biomassa acima do solo viva (por meio do método não-destrutivo – equações alométricas) e morta (serapilheira) e o estoque de carbono nesses compartimentos em áreas de reflorestamento com diferentes idades (3, 5 e 7 anos) e em área de Floresta Ombrófila Densa (testemunha), localizadas no município de Cachoeiras de Macacu (RJ). Foram alocadas 30 parcelas (10 x 10 m cada) na área testemunha e 20 parcelas (20 x 30 m cada) nas áreas de reflorestamento. Os indivíduos arbóreos foram identificados, e a altura total, circunferência e diâmetro à altura do peito foram medidos. A serapilheira foi coletada com gabarito quadrado (1 x 1 m) em cinco repetições em cada área. Os valores de biomassa acima do solo e carbono estocado aumentaram à medida que a idade dos plantios de restauração florestal avançou. Os modelos alométricos utilizados foram adequados no estudo. Contudo, é necessário testar a confiabilidade dos dados com a utilização do método destrutivo nas áreas reflorestadas. Palavras-chave: Sequestro de carbono, restauração florestal, biomassa acima do solo, serapilheira. Abstract Carbon stock in forest restoration areas of the Atlantic Forest. Forest ecosystems are important in the process of storing carbon from the atmosphere. Reforestation areas can contribute substantially in this process and reduce the extractive pressure over native forests. This study aimed to assess the aboveground live biomass (by non-destructive method – allometric equations) and dead biomass (litter layer standing stock) and carbon stock in these compartments at areas of reforestation of different ages (3, 5, and 7 years old) and at a Dense Ombrophylous Forest (control) area, located in the municipality of Cachoeiras de Macacu (state of Rio de Janeiro, Brazil). We allocated 30 plots (10 x 10 m each) in the control area and 20 plots (20 x 30 m each) in the reforestation areas. Tree individuals were identified, and total height, circumference, and diameter at breast height were measured. The litter standing stock was collected by square gauge (1 x 1 m) in five replicates at each area. The values of aboveground biomass and stored carbon increased as the age of forest restoration plantings advanced. The allometric models used were adequate in the study. However, it is necessary to test the reliability of the data, using the destructive method in the reforested areas.

Conservation and restoration of fens and fen meadows often aim to reduce soil nutrients, mainly nitrogen (N) and phosphorus (P). The biogeochemistry of P has received much attention as P-enrichment is expected to negatively impact on species diversity in wetlands. It is known that N, sulphur (S) and hydrological conditions affect the biogeochemistry of P, yet their interactive effects on P-dynamics are largely unknown. Additionally, in Europe, climate change has been predicted to lead to increases in summer drought. We performed a greenhouse experiment to elucidate the interactive effects of N, S and a single drought event on the P-availability for Phalaris arundinacea. Additionally, the response of plant phosphatase activity to these factors was measured over the two year experimental period. In contrast to results from earlier experiments, our treatments hardly affected soil P-availability. This may be explained by the higher pH in our soils, hampering the formation of Fe-P or Fe-Al complexes. Addition of S, however, decreased the plants N:P ratio, indicating an effect of S on the N:P stoichiometry and an effect on the plant's P-demand. Phosphatase activity increased significantly after addition of S, but was not affected by the addition of N or a single drought event. Root phosphatase activity was also positively related to plant tissue N and P concentrations, plant N and P uptake, and plant aboveground biomass, suggesting that the phosphatase enzyme influences P-biogeochemistry. Our results demonstrated that it is difficult to predict the effects of wetland restoration, since the involved mechanisms are not fully understood. Short-term and long-term effects on root phosphatase activity may differ considerably. Additionally, the addition of S can lead to unexpected effects on the biogeochemistry of P. Our results showed that natural resource managers should be careful when restoring degraded fens or preventing desiccation of fen ecosystems.

Reliable and prompt information on forest above-ground biomass (AGB) and tree diameter at breast height (DBH) are crucial for sustainable forest management. Remote sensing technology, especially the Light Detection and Ranging (LiDAR) technology, has been proven to estimate important tree variables effectively. This study proposes predicting DBH and AGB from tree height and other LiDAR data extracted metrics. In the suggested DBH prediction, we developed a nonlinear estimation equation using the total tree height. As for the AGB prediction approach, we used regression methods such as multiple linear regression (MLR), random forest (RF), and support vector machine for regression (SVR). We conducted the study for the Gudao forest area dominated by Robinia Pseudoacacia trees, located in the Yellow River Delta (YRD), China. For our developed approaches, we used Unmanned Aerial Vehicle (UAV) and Backpack LiDAR point cloud datasets obtained in June 2017, and three field data measurements gathered in June 2017 and 2019, and October 2019, all from the same study area. The results demonstrate that: 1) the LiDAR data individual tree segmentation (ITS) from which we extracted individual tree information like tree location and tree height, was carried out with an overall accuracy F = 0.91. 2) We used the ITS height data from the field stand in 2019 as a fit and developed a nonlinear DBH estimation equation with Root Mean Square Error (RMSE) = 3.61cm, later validated by the 2017 dataset. 3) Forest AGB at stand level was estimated with the MLR, RF, and also SVR regression methods, and results show that the SVR method gave higher accuracy with R2 = 0.82 compared to the R2 = 0.72 of RF and the R2 = 0.70 of the MLR. Calculated AGB at plot level using the 2017 LiDAR data were used to validate both models' accuracy. The combining of the UAV LiDAR data and the Backpack LiDAR significantly improved the overall ITS. The UAV Li-DAR ability to provide high accuracy tree height abstraction, the DBH of the regression equation, and other extracted LiDAR metrics showed high accuracy in estimating the forest AGB. This study shows that being cost-free is not the only advantage of free available software. In the performance of ITS and the LiDAR, metrics extraction proved to be as good as the commercially available software.

The tropical forest biomass plays an important role in global carbon cycle. Estimation of above-ground biomass (AGB) is an essential aspect of carbon stocks. Estimated C pools in different forest types can be used to in making decisions about C management within forests. The present study was carried out in ten tropical forests of Cachar district to estimate AGB, carbon stocks and their relationship with density, basal area and diversity indices. The AGB was ranged from 32.47 Mg ha-1 to 261.64 Mg ha-1 and C-stocks ranged from 16.24 Mg ha-1 to 130.82 Mg ha-1. The small to medium trees contributed more AGB and C-stocks than the large trees. Cynometra polyandra, Artocarpus chama, Sapium baccatum, Ficus bengalensis, Trewia nudiflora, Xerospermum glabratum, Pterygota alata and Semecarpus anacardium were top contributor of AGB and C-stocks in different tropical forests of Cachar district of Assam. AGB showed significant relationship with basal area and diversity indices. The AGB, however, did not show significant relationship with tree densities.

Many spatial vegetative physiological and environmental impact studies demand consistent as well as fine resolution daily meteorological data, which is often unavailable. This problem is evident when studies and applications focus at the regional and local scale. Meteorological stations cannot provide the needed data due to their irregular locations and several associated shortcomings such as missing data. Climate data from global sources like the National Centers for Environmental Prediction (NCEP) and WorldClim are provided at a coarse resolution which makes them inappropriate to use directly at regional and local scales. In order to circumvent this problem, we downscaled daily WorldClim and NCEP global climate grids to produce weather data sets using a delta method to 0.0083 (approximately 1 × 1 km) spatial resolution for the Amhara region of northwestern Ethiopia. The downscaled data set includes daily precipitation, and minimum and maximum temperature from 1979 to 2010. The drizzle effect in the downscaled data set was first eliminated by using a rain threshold that removed values of <1 mm per day. We compared the downscaled values with our calibration data from 56 meteorological stations in the Amhara region. The comparison between the downscaled and the calibrated data from weather stations showed biases in the downscaled values. A delta-change bias correction technique on a 10-day average basis was used to correct the biases associated with the downscaled data. Simple additive and multiplicative bias correction methods were used for temperature and precipitation, respectively. We then validated the corrected downscaled daily weather data using ten independent weather stations from the region. We found that the downscaling and the bias correction methods have improved the NCEP values. The validation exhibited no bias. The full data set can be accessed freely under the following link: ftp://palantir.boku.ac.at/Public/ClimateDataEthiopia/.

Methods of estimating the total amount of above-ground biomass (AGB) in crop fields are generally based on labourious, random, and destructive in situ sampling. This study proposes a methodology for estimating herbaceous crop biomass using conventional optical cameras and structure from motion (SfM) photogrammetry. The proposed method is based on the determination of volumes according to the difference between a digital terrain model (DTM) and digital surface model (DSM) of vegetative cover. A density factor was calibrated based on a subset of destructive random samples to relate the volume and biomass and efficiently quantify the total AGB. In all cases, RMSE Z values less than 0.23 m were obtained for the DTM-DSM coupling. Biomass field data confirmed the goodness of fit of the yield-biomass estimation (R2 = 0,88 and 1,12 kg/ha) mainly in plots with uniform vegetation coverage. Furthermore, the method was demonstrated to be scalable to multiple platform types and sensors.

In this study, we analyzed the above-ground biomass data for 631 trees with a diameter P10 cm from different biogeographical regions in Colombia. The aims of this research were (1) to evaluate the accuracy of the most commonly employed pantropical allometric models for the estimation of above-ground bio- mass of natural forests in different sites located along a complex environmental gradient, (2) to develop new models that enable more precise estimations of current carbon stores in the above-ground biomass of natural forest ecosystems in Colombia, and (3) to evaluate the effect on allometric models of forest type classifications as determinants of above-ground biomass variation. The Brown et al. (1989) model for moist forests, which includes diameter, height, and wood density, showed the overall best perfor- mance in Colombian sites. The Type II models of Chave et al. (2005; hereafter Chave II), which include diameter and wood density but not height, tended to strikingly overestimate the above-ground biomass (54.7 ± 135.7%) in the studied Colombian sites. The use of forest classification based on the life zone sys- tem systematically led to better statistical models to estimate AGB at the individual scale and site scale than the use of Chave’s classification. Our results propose that Chave II models should be evaluated prior to their use for a given ecosystem. For Colombia, the new allometric models developed, which employed diameter, wood density, and height, could help improving our understanding of the carbon cycle. Forest type classification was found to be an important determinant of the above-ground biomass estimation when altitudinal and other complex environmental gradients are included. The new models presented here can be considered as an alternative option for assessing carbon stocks in the above-ground biomass of natural forests in neotropical countries.

In an area where urbanization is rapidly growing, carbon is slowly sequestered which clogs the ozone layer. With forest biomass, carbon is easily sequestered and stored by trees. This research focuses on the potential carbon storage of the Arroceros Forest Park, one of the last lungs of the metropolis located in the heart of the National Capital Region, Manila, Philippines. Trees with ≥10 cm diameter at breast height (DBH) were inventoried, from two (2) hectare area of site. These trees were used in the estimation of the biomass and carbon stock. The Power-Fit Equation from Banaticla (insert year), = 0.342 (DBH (exp (0.73))) was used in the study. Results showed that Swietenia macrophylla dominated the park. Species with highest contribution of biomass and carbon is the Swietenia macrophylla with value of 149.55t/ha. The carbon formed from this was 45%, and estimated carbon stock present is 30.59Ct/ha. Total aboveground biomass and carbon stock in the forest park is estimated at 640.21t/ha, and 130.95Ct/ha, respectively. Provided the carbon stock estimate, this could give more importance to Arroceros Forest Park in carbon sequestration. Site must be protected and enhanced to promote the important role of green spaces in Metro Manila.

Estimates of national-scale forest carbon (C) stocks and fluxes are typically based on allometric regression equations developed using dimensional analysis techniques. However, the literature is inconsistent and incomplete with respect to large-scale forest C estimation. We compiled all available diameter-based allometric regression equations for estimating total aboveground and component biomass, defined in dry weight terms, for trees in the United

Dual-polarised ALOS/PALSAR mosaic data were utilised in this study to provide information on forest cover change, activity data, and spatially explicit aboveground biomass estimates in support of REDD+ readiness processes implemented in Victoria-Anepahan mountain range, Palawan, Philippines. Ground-truthed data was used for forest/non-forest classification. Forest inventory plots of variable sizes were used to investigate the relationship of radar backscatter and ground-estimated aboveground biomass. Overall classification accuracies at 87.28% and 91.60% were achieved using the support vector machine classifier for the 2007 and 2010 PALSAR data, respectively. Results showed that a total of 4,864.24 ha of forests were converted to other land uses within the study area in a span of four years. R-squared correlation of radar backscatter to biomass was observed to be higher at 1.0 ha plot sizes for the combination of radar channels consisting of HV polarisation, HH/HV ratio, and contras...