Aboveground Biomass Stocks Research Articles

Predicting of biomass in Brazilian tropical dry forest: a statistical evaluation of generic equations.

Dry tropical forests are a key component in the global carbon cycle and their biomass estimates depend almost exclusively of fitted equations for multi-species or individual species data. Therefore, a systematic evaluation of statistical models through validation of estimates of aboveground biomass stocks is justifiable. In this study was analyzed the capacity of generic and specific equations obtained from different locations in Mexico and Brazil, to estimate aboveground biomass at multi-species levels and for four different species. Generic equations developed in Mexico and Brazil performed better in estimating tree biomass for multi-species data. For Poincianella bracteosa and Mimosa ophthalmocentra, only the Sampaio and Silva (2005) generic equation was the most recommended. These equations indicate lower tendency and lower bias, and biomass estimates for these equations are similar. For the species Mimosa tenuiflora, Aspidosperma pyrifolium and for the genus Croton the specific regional equations are more recommended, although the generic equation of Sampaio and Silva (2005) is not discarded for biomass estimates. Models considering gender, families, successional groups, climatic variables and wood specific gravity should be adjusted, tested and the resulting equations should be validated at both local and regional levels as well as on the scales of tropics with dry forest dominance.

Allometric Equations for Estimating Biomass and Carbon Stocks in the Temperate Forests of North-Western Mexico

This paper presents new equations for estimating above-ground biomass (AGB) and biomass components of seventeen forest species in the temperate forests of northwestern Mexico. A data set corresponding to 1336 destructively sampled oak and pine trees was used to fit the models. The generalized method of moments was used to simultaneously fit systems of equations for biomass components and AGB, to ensure additivity. In addition, the carbon content of each tree component was calculated by the dry combustion method, in a TOC analyser. The results of cross-validation indicated that the fitted equations accounted for on average 91%, 82%, 83% and 76% of the observed variance in stem wood and stem bark, branch and foliage biomass, respectively, whereas the total AGB equations explained on average 93% of the total observed variance in AGB. The inclusion of total height (h) or diameter at breast height2 × total height (d2h) as a predictor in the d-only based equations systems slightly improved estimates for stem wood, stem bark and total above-ground biomass, and greatly improved the estimates produced by the branch and foliage biomass equations. The predictive power of the proposed equations is higher than that of existing models for the study area. The fitted equations were used to estimate stand level AGB stocks from data on growing stock in 429 permanent sampling plots. Three machine-learning techniques were used to model the estimated stand level AGB and carbon contents; the selected models were used to map the AGB and carbon distributions in the study area, for which mean values of respectively 129.84 Mg ha−1 and 63.80 Mg ha−1 were obtained.

Erratum to: Aboveground biomass and carbon stock are related with soil humidity in a mangrove at the Piraquê-Açu River, southeastern Brazil

In Brazil, few studies have been conducted about the assimilation of carbon and biomass accumulation in mangroves despite its great role as a carbon sink. In this sense, this study aimed to estimate the aboveground biomass (AGB) and carbon stocked by mangrove species in a stretch along the Piraque-Acu River in southeastern Brazil, and to verify their relation with some soil parameters. For such, the height and diameter of all trees inside six plots of 100 m2 were measured and used to quantify AGB through an allometric equation, and soil samples were collected to calculate granulometry, humidity, time of infiltration and permeability. Multiple regressions were used to identify relations between AGB and soil parameters. A total of 296 trees were found in the area and the AGB and carbon assimilated were 2.92 t ha−1 and 1.46 t ha−1, respectively. Laguncularia racemosa was the most abundant species and contributed with 61% of these values. Only a positive relation between AGB/carbon of L. racemosa and soil humidity was found, probably due to the substrate characteristics which contain a large concentration of silt/clay, and may store more water and do not yet provide firmness to the roots. By comparison, this mangrove had a low amount of AGB and carbon stocked and has yet to develop structurally. Along with other ecological functions and its associated ecosystem services such as supplying food for the local community, these features highlight the importance to conserve the studied mangrove and the interlinked coastal ecosystems.

Aboveground biomass and carbon stock in the largest sacred grove of Manipur, Northeast India

Aboveground biomass and carbon stock in the largest sacred grove of Manipur was estimated for trees with diameter >10 cm at 1.37 m height. The aboveground biomass, carbon stock, tree density and basal area of the sacred grove ranged from 962.94 to 1130.79 Mg ha−1, 481.47 to 565.40 Mg ha−1 C, 1240 to 1320 stem ha−1 and 79.43 to 90.64 m2 ha−1, respectively. Trees in diameter class of 30–40 cm contributed the highest proportion of aboveground biomass (22.50–33.73%). The aboveground biomass and carbon stock in research area were higher than reported for many tropical and temperate forests, suggesting a role of spiritual forest conservation for carbon sink management.

Carbon Stocks of Fine Woody Debris in Coppice Oak Forests at Different Development Stages

Dead woody debris is a significant component of the carbon cycle in forest ecosystems. This study was conducted in coppice-originated oak forests to determine carbon stocks of dead woody debris in addition to carbon stocks of different ecosystem compartments from the same area and forests which were formerly elucidated. Weight and carbon stocks of woody debris were determined with recent samplings and compared among development stages (diameter at breast height (DBH, D1.3m)), namely small-diameter forests (SDF) = 0–8 cm, medium diameter forests (MDF) = 8–20 cm, and large-diameter forests (LDF) = 20–36 cm). Total woody debris was collected in samplings; as bilateral diameters of all woody debris parts were less than 10 cm, all woody parts were in the “fine woody debris (FWD)” class. The carbon concentrations of FWD were about 48% for all stages. Mass (0.78–4.92 Mg·ha−1) and carbon stocks (0.38–2.39 Mg·ha−1) of FWD were significantly (p > 0.05) different among development stages. FWD carbon stocks were observed to have significant correlation with D1.3m, age, basal area, and carbon stocks of aboveground biomass (Spearman rank correlation coefficients; 0.757, 0.735, 0.709, and 0.694, respectively). The most important effects on carbon budgets of fine woody debris were determined to be coppice management and intensive utilization. Also, national forestry management, treatments of traditional former coppice, and conversion to high forest were emphasized as having substantial effects.

Post-stratified change estimation for large-area forest biomass using repeated ALS strip sampling

Post-stratified model-assisted (MA) and hybrid (HY) estimators are used with repeated airborne laser scanning (ALS) strip sampling and national forest inventory field data for stratum-wise and overall estimation of aboveground biomass (AGB) stock and change. The study area covered the southern portion of the Hedmark County in Norway. Both MA and HY estimation substantially reduced the uncertainty in AGB change when compared with estimation using the field survey only. Relative efficiencies (relative variance) of 4.15 (MA) and 3.36 (HY) for overall estimates were found. The results suggest the MA estimator for single-time estimation and the HY as more appropriate for change estimation by cover class. With the HY estimator, a nested post-stratification scheme is demonstrated, combining cover classes with change classes, which enables detailed reporting for change according to cause within each cover class, and has the potential to improve the estimation precision. Finally, parametric bootstrapping is demonstrated as an empirical alternative to estimate the model-error component in the HY estimator. The model error estimated with parametric bootstrapping converged to the analytically determined value of the HY estimator within 1000 bootstrap samples.

Allometric Models for Predicting Aboveground Biomass and Carbon Stock of Tropical Perennial C4 Grasses in Hawaii.

Biomass is a promising renewable energy option that provides a more environmentally sustainable alternative to fossil resources by reducing the net flux of greenhouse gasses to the atmosphere. Yet, allometric models that allow the prediction of aboveground biomass (AGB), biomass carbon (C) stock non-destructively have not yet been developed for tropical perennial C4 grasses currently under consideration as potential bioenergy feedstock in Hawaii and other subtropical and tropical locations. The objectives of this study were to develop optimal allometric relationships and site-specific models to predict AGB, biomass C stock of napiergrass, energycane, and sugarcane under cultivation practices for renewable energy and validate these site-specific models against independent data sets generated from sites with widely different environments. Several allometric models were developed for each species from data at a low elevation field on the island of Maui, Hawaii. A simple power model with stalk diameter (D) was best related to AGB and biomass C stock for napiergrass, energycane, and sugarcane, (R2 = 0.98, 0.96, and 0.97, respectively). The models were then tested against data collected from independent fields across an environmental gradient. For all crops, the models over-predicted AGB in plants with lower stalk D, but AGB was under-predicted in plants with higher stalk D. The models using stalk D were better for biomass prediction compared to dewlap H (Height from the base cut to most recently exposed leaf dewlap) models, which showed weak validation performance. Although stalk D model performed better, however, the mean square error (MSE)-systematic was ranged from 23 to 43 % of MSE for all crops. A strong relationship between model coefficient and rainfall was existed, although these were irrigated systems; suggesting a simple site-specific coefficient modulator for rainfall to reduce systematic errors in water-limited areas. These allometric equations provide a tool for farmers in the tropics to estimate perennial C4 grass biomass and C stock during decision-making for land management and as an environmental sustainability indicator within a renewable energy system.

Erratum to: Allometric Models for Estimating Aboveground Biomass, Carbon and Nitrogen Stocks in Temperate Avicennia marina Forests

Erratum to: Allometric Models for Estimating Aboveground Biomass, Carbon and Nitrogen Stocks in Temperate Avicennia marina Forests

Methods for biomass stock estimation in Mediterranean maquis systems

Abstract: As a result of Kyoto Protocol agreements, the scientific community increased its efforts to enhance the availability of biomass and organic carbon stock data in forest ecosystems. Nevertheless, a considerable data shortage has been recognized in estimating the stock of above-ground biomass (AGB) in Mediterranean maquis systems. This work aims at contributing in addressing such shortage by testing quick and non-disruptive methods to estimate the AGB stock in maquis species. Two methodologies were tested in three widespread sclerophyllous evergreen species (Pistacia lentiscus, Euphorbia dendroides, and Cystus monspeliensis). Both methodologies were based on the estimation of the apparent volume (AV): the first one assumed the shrub shape (or canopy) to be similar to a regular tridimensional solid, while the second method was based on plant digital images analysis. Results showed some differences in AV values estimated through the two methodologies, although a high correlation was found between them (R2 = 0.92-0.98) and with the AGB weight obtained from plant samples (R2 = 0.89-0.96). As a consequence, the shrubs apparent density values (i.e., weight/AV) vary depending on the method used for AV estimation. This should be taken into account when AV is used for AGB estimation. Besides, measurements of above-ground biomass were carried out to characterize the studied area. Results showed high variability in AGB values, ranging from 7.04 to 48.05 Mg ha-1 of dry matter.

Aboveground biomass and carbon stock assessment in Indian tropical deciduous forest and relationship with stand structural attributes

Indian tropical deciduous forest due to higher species richness, irregular stem densities and stem cover, multistoried canopy and microclimate, offers dynamism to the understanding of the spatial patters in aboveground biomass (AGB) and carbon (AGBC) within different plant functional types (PFTs). Field-based AGB and AGBC estimates are vital for carbon cycle study and provide inputs for data-deficit regional and global carbon and climate models.In this paper we generated the primary baseline data on tree composition, species richness (SR), AGB and AGBC gathered from nine 10,000m2 long-term ecological research (LTER) plots in three distinct PFTs {dry mixed (DM), sal mixed (SM) and teak plantation (TP)} within an Indian tropical deciduous forest along Himalayan foothills during 2009–2012. The functional relationship of PFT structural attributes i.e. SR, density, basal cover, leaf area index and litter fall on AGB was investigated. PCA was applied to study the relationship between PFT microclimate, structural attributes and diversity vs. AGB.A total of 22, 22 and 6 tree species were found in DM, SM and TP PFT, respectively. Shannon's diversity index was highest (2.46) in DM, whereas Simpson's dominance index was maximum (0.85) in TP. AGB (Mgha−1) ranged from 290.82–455.99 among all PFTs. AGBC (Mgha−1) ranged between 207.52–220.34, 215.58–228.87, and 125.94–141.18 in DM, SM and TP respectively. AGB observed strong positive correlation with tree density and cover. PCA revealed humidity and air temperature as the major microclimatic variables, leading to higher AGB.

Aboveground biomass and carbon stock are related with soil humidity in a mangrove at the Piraquê-Açu River, Southeastern Brazil

Aboveground biomass and carbon stock are related with soil humidity in a mangrove at the Piraquê-Açu River, Southeastern Brazil

Allometric models and aboveground biomass stocks of a West African Sudan Savannah watershed in Benin.

BackgroundThe estimation of forest biomass changes due to land-use change is of significant importance for estimates of the global carbon budget. The accuracy of biomass density maps depends on the availability of reliable allometric models used in combination with data derived from satellites images and forest inventory data. To reduce the uncertainty in estimates of carbon emissions resulting from deforestation and forest degradation, better information on allometric equations and the spatial distribution of aboveground biomass stocks in each land use/land cover (LULC) class is needed for the different ecological zones. Such information has been sparse for the West African Sudan Savannah zone. This paper provides new data and results for this important zone. The analysis combines satellite images and locally derived allometric models based on non-destructive measurements to estimate aboveground biomass stocks at the watershed level in the Sudan Savannah zone in Benin.ResultsWe compared three types of empirically fitted allometric models of varying model complexity with respect to the number of input parameters that are easy to measure at the ground: model type I based only on the diameter at breast height (DBH), type II which used DBH and tree height and model type III which used DBH, tree height and wood density as predictors. While for most LULC classes model III outperformed the other models even the simple model I showed a good performance. The estimated mean dry biomass density values and attached standard error for the different LULC class were 3.28 ± 0.31 (for cropland and fallow), 3.62 ± 0.36 (for Savanna grassland), 4.86 ± 1.03 (for Settlements), 14.05 ± 0.72 (for Shrub savanna), 45.29 ± 2.51 (for Savanna Woodland), 46.06 ± 14.40 (for Agroforestry), 94.58 ± 4.98 (for riparian forest and woodland), 162 ± 64.88 (for Tectona grandis plantations), 179.62 ± 57.61 (for Azadirachta indica plantations), 25.17 ± 7.46 (for Gmelina arborea plantations), to 204.92 ± 57.69 (for Eucalyptus grandis plantations) Mg ha−1. The higher uncertainty of agroforestry system and plantations is due to the variance in age which affects biomass stocks.ConclusionThe results from this study help to close the existing knowledge gap with respect to biomass allometric models at the watershed level and the estimation of aboveground biomass stocks in each LULC in the Sudan Savannah in West Africa. The use of model type I, which relies only on the easy to measure DBH, seems justified since it performed almost as good as the more complex model types II and III. The work provided useful data on wood density of the main species of the Sudan Savannah zone, the related local derived biomass expansion factor and the biomass density in each LULC class that would be an indispensable information tool for carbon accounting programme related to the implementation of the Kyoto Protocol and REDD+ (reducing emissions from deforestation and forest degradation, and forests conservation, sustainable management of forests, and enhancement of forest carbon stocks) initiatives.Electronic supplementary materialThe online version of this article (doi:10.1186/s13021-016-0058-5) contains supplementary material, which is available to authorized users.

Lianas and soil nutrients predict fine‐scale distribution of above‐ground biomass in a tropical moist forest

Summary Prediction of carbon dynamics in response to global climate change requires an understanding of the processes that govern the distribution of carbon stocks. Above‐ground biomass (AGB) in tropical forests is regulated by variation in soil fertility, climate, species composition and topography at regional scales, but the drivers of fine‐scale variation in tropical forest AGB are poorly understood. The factors that control the growth and mortality of individual trees may be obscured by the low resolution of studies at regional scales. In this paper, we evaluated the effects of soil nutrients, topography and liana abundance on the fine‐scale spatial distribution of AGB and density of trees for a lowland tropical moist forest in Panama using additive regression models. Areas with larger values of AGB were negatively associated with the presence of lianas, which may reflect competition with lianas and/or the association of lianas with disturbed or open‐canopy patches within forests. AGB was positively associated with soils possessing higher pH and K concentrations, reflecting the importance of below‐ground resource availability on AGB independently of stem density. Synthesis. Our results shed new light on the factors that influence fine‐scale tree AGB and carbon stocks in tropical forests: liana abundance is the strongest predictor, having a negative impact on tree AGB. The availability of soil nutrients was also revealed as an important driver of fine‐scale spatial variation in tree AGB.

Medicago sativa improves soil carbon sequestration following revegetation of degraded arable land in a semi-arid environment on the Loess Plateau, China

Revegetation of degraded arable land with legume pasture has been proposed as an effective approach to improve vegetation conditions and control soil erosion on the Loess Plateau, China. This paper investigated the effects of revegetation on soil carbon pool and its fractions. Three treatments (natural revegetation, Medicago sativa L. introduction and Melilotus suaveolens L. introduction) were applied over 11 years from 2003 to 2013 on the semi-arid Loess Plateau; both aboveground biomass and soil organic carbon (SOC) stocks as well as its fractions (i.e., the light-fraction organic carbon (LFOC) and heavy-fraction organic carbon (HFOC)) were measured. We found that plant aboveground biomass was greater under Melilotus treatment than under Medicago treatment and natural revegetation in the first two years. Afterward, the aboveground biomass was greatest under Medicago treatment. Medicago treatment yielded higher SOC and HFOC stocks than the other two treatments during the experimental period. After revegetation for 10 years, the SOC stocks at the 0–1.5m soil layer under Medicago treatment were 52.8 and 44.8 Mg ha−1 higher than those under natural revegetation and Melilotus treatments, respectively. The mean rates of SOC and HFOC sequestration in the top 20cm of the soil layer under Medicago treatment were 0.27 and 0.23 Mg ha−1year−1 in year 11, which were greater than the other two treatments (0.22, 0.15 and −0.07, −0.03 under the two treatments, respectively). Revegetation through introduction of Medicago to degraded arable land can potentially promote SOC accumulation by enhancing the transformation from LFOC to HFOC on the semi-arid Loess Plateau.

The impact of integrating WorldView-2 sensor and environmental variables in estimating plantation forest species aboveground biomass and carbon stocks in uMgeni Catchment, South Africa

Reliable and accurate mapping and extraction of key forest indicators of ecosystem development and health, such as aboveground biomass (AGB) and aboveground carbon stocks (AGCS) is critical in understanding forests contribution to the local, regional and global carbon cycle. This information is critical in assessing forest contribution towards ecosystem functioning and services, as well as their conservation status. This work aimed at assessing the applicability of the high resolution 8-band WorldView-2 multispectral dataset together with environmental variables in quantifying AGB and aboveground carbon stocks for three forest plantation species i.e. Eucalyptus dunii (ED), Eucalyptus grandis (EG) and Pinus taeda (PT) in uMgeni Catchment, South Africa. Specifically, the strength of the Worldview-2 sensor in terms of its improved imaging agilities is examined as an independent dataset and in conjunction with selected environmental variables. The results have demonstrated that the integration of high resolution 8-band Worldview-2 multispectral data with environmental variables provide improved AGB and AGCS estimates, when compared to the use of spectral data as an independent dataset. The use of integrated datasets yielded a high R2 value of 0.88 and RMSEs of 10.05tha−1 and 5.03tCha−1 for E. dunii AGB and carbon stocks; whereas the use of spectral data as an independent dataset yielded slightly weaker results, producing an R2 value of 0.73 and an RMSE of 18.57tha−1 and 09.29tCha−1. Similarly, high accurate results (R2 value of 0.73 and RMSE values of 27.30tha−1 and 13.65tCha−1) were observed from the estimation of inter-species AGB and carbon stocks. Overall, the findings of this work have shown that the integration of new generation multispectral datasets with environmental variables provide a robust toolset required for the accurate and reliable retrieval of forest aboveground biomass and carbon stocks in densely forested terrestrial ecosystems.

Informed consent utilizing satellite imagery in forestry carbon trading with North Korea

Informed consent can assist in mitigating investment risks associated with forestry carbon trading in North Korea by supporting corporate responsibility. Scientific developments for collecting information necessary to informed consent far outpace the development of inter-Korean regulatory frameworks. This paper seeks to identify how the intersection of legal and technological disciplines can foster informed consent, through the responsible application of satellite data. The permanent record of standard satellite remote-sensing systems demonstrates its capability of presenting area-wide visual evidence of forest conditions. Such data are essential to initiating informed consent, establishing a carbon stock baseline, detecting and quantifying rates of land-cover change, and quantifying above-ground biomass stocks as specified in the Kyoto Protocol. This research could be a valuable reference for utilizing the capabilities of satellites with informed consent obligations in North Korea by suggesting the provision of realistic informed consent mechanisms.

Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery

Periurban forests are key to offsetting anthropogenic carbon emissions, but they are under constant threat from urbanization. In particular, secondary Neotropical forest types in Andean periurban areas have a high potential to store carbon, but are currently poorly characterized. To address this lack of information, we developed a method to estimate periurban aboveground biomass (AGB)—a proxy for multiple ecosystem services—of secondary Andean forests near Bogotá, Colombia, based on very high resolution (VHR) GeoEye-1, Pleiades-1A imagery and field-measured plot data. Specifically, we tested a series of different pre-processing workflows to derive six vegetation indices that were regressed against in situ estimates of AGB. Overall, the coupling of linear models and the Ratio Vegetation Index produced the most satisfactory results. Atmospheric and topographic correction proved to be key in improving model fit, especially in high aerosol and rugged terrain such as the Andes. Methods and findings provide baseline AGB and carbon stock information for little studied periurban Andean secondary forests. The methodological approach can also be used for integrating limited forest monitoring plot AGB data with very high resolution imagery for cost-effective modelling of ecosystem service provision from forests, monitoring reforestation and forest cover change, and for carbon offset assessments.

Pattern and dynamics of biomass stock in old growth forests: The role of habitat and tree size

Forest ecosystems play a fundamental role in the global carbon cycle. However, how stand-level changes in tree age and structure influence biomass stock and dynamics in old-growth forests is a question that remains unclear.In this study, we quantified the aboveground biomass (AGB) standing stock, the coarse woody productivity (CWP), and the change in biomass over ten years (2004–2014) in a 25 ha unmanaged broad-leaved Korean pine mixed forest in northeastern China. In addition, we quantified how AGB stock and change (tree growth, recruitment and mortality) estimations are influenced by the variation in habitat heterogeneity, tree size structure and subplot size.Our analysis indicated that Changbai forest had AGB of 265.4 Mg ha−1 in 2004, and gained1.36 Mg ha−1 y−1 between 2004 and 2014. Despite recruitment having better performance in nutrient rich habitat, we found that there is a directional tree growth trend independent of habitat heterogeneity for available nutrients in this old growth forest. The observed increases in AGB stock (∼70%) are mainly attributed to the growth of intermediate size trees (30–70 cm DBH), indicating that this forest is still reaching its mature stage. Meanwhile, we indicated that biomass loss due to mortality reduces living biomass, not increment, may be the primary factor to affect forest biomass dynamics in this area.Also, spatial variation in forest dynamics is large for small sizes (i.e. coefficient of variation in 20 × 20 m subplots is 53.2%), and more than 90 percent of the inherent variability of these coefficients was predicted by a simple model including plot size. Our result provides a mean by which to estimate within-plot variability at a local scale before inferring any directional change in forest dynamics at a regional scale, and information about the variability of forest structure and dynamics are fundamental to design effective sampling strategies in future study.

Abiotic Controls on Macroscale Variations of Humid Tropical Forest Height

Spatial variation of tropical forest tree height is a key indicator of ecological processes associated with forest growth and carbon dynamics. Here we examine the macroscale variations of tree height of humid tropical forests across three continents and quantify the climate and edaphic controls on these variations. Forest tree heights are systematically sampled across global humid tropical forests with more than 2.5 million measurements from Geoscience Laser Altimeter System (GLAS) satellite observations (2004–2008). We used top canopy height (TCH) of GLAS footprints to grid the statistical mean and variance and the 90 percentile height of samples at 0.5 degrees to capture the regional variability of average and large trees globally. We used the spatial regression method (spatial eigenvector mapping-SEVM) to evaluate the contributions of climate, soil and topography in explaining and predicting the regional variations of forest height. Statistical models suggest that climate, soil, topography, and spatial contextual information together can explain more than 60% of the observed forest height variation, while climate and soil jointly explain 30% of the height variations. Soil basics, including physical compositions such as clay and sand contents, chemical properties such as PH values and cation-exchange capacity, as well as biological variables such as the depth of organic matter, all present independent but statistically significant relationships to forest height across three continents. We found significant relations between the precipitation and tree height with shorter trees on the average in areas of higher annual water stress, and large trees occurring in areas with low stress and higher annual precipitation but with significant differences across the continents. Our results confirm other landscape and regional studies by showing that soil fertility, topography and climate may jointly control a significant variation of forest height and influencing patterns of aboveground biomass stocks and dynamics. Other factors such as biotic and disturbance regimes, not included in this study, may have less influence on regional variations but strongly mediate landscape and small-scale forest structure and dynamics.

Aboveground biomass and carbon stocks modelling using non-linear regression model

Aboveground biomass (AGB) is an important source of uncertainty in the carbon estimation for the tropical forest due to the variation biodiversity of species and the complex structure of tropical rain forest. Nevertheless, the tropical rainforest holds the most extensive forest in the world with the vast diversity of tree with layered canopies. With the usage of optical sensor integrate with empirical models is a common way to assess the AGB. Using the regression, the linkage between remote sensing and a biophysical parameter of the forest may be made. Therefore, this paper exemplifies the accuracy of non-linear regression equation of quadratic function to estimate the AGB and carbon stocks for the tropical lowland Dipterocarp forest of Ayer Hitam forest reserve, Selangor. The main aim of this investigation is to obtain the relationship between biophysical parameter field plots with the remotely-sensed data using nonlinear regression model. The result showed that there is a good relationship between crown projection area (CPA) and carbon stocks (CS) with Pearson Correlation (p < 0.01), the coefficient of correlation (r) is 0.671. The study concluded that the integration of Worldview-3 imagery with the canopy height model (CHM) raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the lowland Dipterocarp forest.