Estimates Of Total Biomass Research Articles

Floristic composition and biomass estimate of Rubiaceae in Bangi Permanent Forest Reserve, Selangor, Peninsular Malaysia

A study on floristic composition, community structure and biomass of Rubiaceae was carried out in Bangi Permanent Forest Reserve, Selangor. In the 0.45 ha of ecological plots, a total of 17 species from three genera of Rubiaceae was recorded for trees with a diameter ≥ 1.0 cm DBH. Three genera viz. Canthium, Ixora and Urophyllum were represented by two species each, while remaining genera comprised of a single species each. Shrub group represented the largest total number of species with seven species followed by the understorey with four species. The range of tree DBH in the study plot was between 1.0 and 31.3 cm. The genus with highest density was Aidia, viz. 244 trees/ha, represented by Aidia densiflora. Total basal area for Rubiaceae was 1.69 m2/ha, with the genus Aidia, specifically the species Aidia densiflora, contributing the highest basal area of 0.57 m2/ha (33.91%). Total biomass estimate (above ground and below ground) for this family was 16.71 t/ha. At genus level, Timonius contributed the highest total biomass value, viz. 5.82 t/ha (34.85%), represented by the species Timonius wallichianus.

Computing bias and variance for Pacific sardine (Sardinops sagax) biomass estimated from aerial surveys in California nearshore waters

Pacific sardine (Sardinops sagax) stocks off the U.S. Pacific coast undergo boom and bust cycles modulated by decadal changes in oceanic and atmospheric conditions. During periods of low abundance, Pacific sardine may contract into nearshore waters, making it more challenging to be surveyed by research vessels that cannot operate in the shallowest habitats, 0 to 40.00 m. In response, a new transect-based design and methods were developed to compute bias and variance of biomass estimated from a collaborative aerial survey implemented in nearshore waters. From experimental aerial surveys conducted in 2010 and from 2018 to 2020, two professional aerial fish spotters underestimated school biomass by 10.5% and 12.6%, whereas most of the variability of aerial biomass was due to within-transect variance, with no significant contribution from the among-transect variance. Implementation of the new transect-based design to aerial surveys from 2020 to 2023 in southern and northern California showed that a substantial biomass of Pacific sardine occurred in nearshore waters, representing 69.9% of total biomass estimated from acoustic surveys in spring 2021, and 36.5% in summer 2021. Seasonal biomass estimates were variable within each region, affected by school dynamics during daytime in nearshore waters and the timing of aerial surveys as determined by weather and environmental conditions. Given that nearshore aerial biomass comprised a measurable proportion of total biomass estimates for Pacific sardine during the recent period of low abundance, it has been used to inform acoustic survey catchability, which is no longer assumed to be 1.0 in recent Pacific sardine stock assessments.

Overcoming conceptual hurdles to accurately represent trees as cohorts in forest landscape models

Forecasting the response of vegetation to climate change and human actions at global to landscape scales often relies on system models. Many such models represent the composition and demography of vegetation using age class cohorts of species or vegetation types. Forest landscape models (FLM) simulate spatially explicit forest dynamics in response to disturbance and abiotic drivers and constraints at landscape spatial and temporal scales (105 – 108 ha, hundreds of years), and many represent trees as species-age cohorts on a grid of cells to make the model tractable at landscape scales. However, it has become evident that complications arise when calculating cell-level summaries involving multiple cohorts because one cannot assume that all cohorts simultaneously occupy the entire physical space of a cell. A fundamental assumption of a widely used FLM (LANDIS-II) is that cohorts are homogeneously distributed across the grid cells that they occupy. It is only implicitly assumed that the stems (trunks) and crowns (foliage) of the individual trees that make up the cohorts physically occupy some proportion of the cell's area with a non-clustered distribution, but the model does not track those proportions. In reality, when cohort biomass is reduced by disturbance or turnover (self-thinning), gaps are created that are at least temporarily unoccupied. In current versions of LANDIS-II, such gaps are ignored. We describe a new approach for cohort-based FLMs to estimate those proportions (including open space) and apply them to compute more realistic estimates of light attenuation and cell-level estimates of cohort and total cell biomass. This approach accounts for gap dynamics in a FLM where stems are not explicitly modeled, allowing cohorts to partially fill cells. These improvements allow more accurate simulation of forest “gap” dynamics in cohort-based landscape models to produce more accurate absolute biomass estimates at species, cell and landscape scales.

Phytoplankton ecology in the early years of a boreal oil sands end pit lake

BackgroundBase Mine Lake (BML) is the first full-scale end pit lake for the oil sands mining industry in Canada. BML sequesters oil sands tailings under a freshwater cap and is intended to develop into a functional ecosystem that can be integrated into the local watershed. The first stage of successful reclamation requires the development of a phytoplankton community supporting a typical boreal lake food web. To assess the diversity and dynamics of the phytoplankton community in BML at this reclamation stage and to set a baseline for future monitoring, we examined the phytoplankton community in BML from 2016 through 2021 using molecular methods (targeting the 23S, 18S, and 16S rRNA genes) and microscopic methods. Nearby water bodies were used as controls for a freshwater environment and an active tailings pond.ResultsThe phytoplankton community was made up of diverse bacteria and eukaryotes typical of a boreal lake. Microscopy and molecular data both identified a phytoplankton community comparable at the phylum level to that of natural boreal lakes, dominated by Chlorophyta, Cryptophyta, and Cyanophyta, with some Bacillariophyta, Ochrophyta, and Euglenophyta. Although many of the same genera were prominent in both BML and the control freshwater reservoir, there were differences at the species or ASV level. Total diversity in BML was also consistently lower than the control freshwater site, but consistently higher than the control tailings pond. The phytoplankton community composition in BML changed over the 5-year study period. Some taxa present in 2016–2019 (e.g., Choricystis) were no longer detected in 2021, while some dinophytes and haptophytes became detectable in small quantities starting in 2019–2021. Different quantification methods (qPCR analysis of 23S rRNA genes, and microscopic estimates of populations and total biomass) did not show a consistent directional trend in total phytoplankton over the 5-year study, nor was there any consistent increase in phytoplankton species diversity. The 5-year period was likely an insufficient time frame for detecting community trends, as phytoplankton communities are highly variable at the genus and species level.ConclusionsBML supports a phytoplankton community composition somewhat unique from control sites (active tailings and freshwater lake) and is still changing over time. However, the most abundant genera are typical of natural boreal lakes and have the potential to support a complex aquatic food web, with many of its identified major phytoplankton constituents known to be primary producers in boreal lake environments.

Tree Biomass Modeling Based on the Exploration of Regression and Artificial Neural Networks Approaches

Understanding the dynamics of tree biomass is a significant factor in forest ecosystems, and accurate quantitative knowledge of its development provides support for the optimization of forest management. This work aimed to employ innovative practices in tree biomass modeling, artificial neural network approaches along with the least-squares regression methodology, in order to construct reliable and accurate estimation and prediction models that contribute to solving the emerging problems in the field of sustainable forest management. Based on this aim, different modeling strategies were developed and explored. The nonlinear seemingly unrelated regression (NSUR) methodology, the generalized regression (GRNN), the resilient propagation (RPNN) and the Bayesian regularization (BRNN) artificial neural network algorithms were utilized for the construction of reliable biomass models to attain the most accurate and reliable tree biomass components and total tree biomass estimations. The work showed that GRNN models provided a significantly better performance compared with the other modeling methodologies tested. Considering the non-parametric nature of the GRNN neural network algorithm, the fact that it was designed for nonlinear regression-type problems capable of dealing with small datasets, this modeling approach warrants consideration as an effective alternative to nonlinear regression or to other neural network approaches to the field of tree biomass modeling.

Using age compositions derived from spatio-temporal models and acoustic data collected by uncrewed surface vessels to estimate Pacific hake (Merluccius productus) biomass-at-age

Generating biomass-at-age indices for fisheries stock assessments with acoustic data collected by uncrewed surface vessels (USVs) has been hampered by the need to resolve acoustic backscatter with contemporaneous biological (e.g., age) composition data. To address this limitation, Pacific hake (Merluccius productus; “hake”) acoustic data were gathered from a USV survey (in 2019) and acoustic-trawl survey (ATS; 2019 and eight previous years), and biological data were gathered from fishery-dependent and non-target (i.e., not specifically targeting hake) fishery-independent sources (2019 and eight previous years). To overcome the lack of contemporaneous biological sampling in the USV survey, age class compositions were estimated from a generalized linear mixed spatio-temporal model (STM) fit to the fishery-dependent and non-target fishery-independent data. The validity of the STM age composition estimation procedure was assessed by comparing estimates to age compositions from the ATS in each year. Hake biomass-at-age was estimated from all combinations of acoustics (USV or ATS in 2019, ATS only in other years) and age composition information (STM or ATS in all years). Across the survey area, proportional age class compositions derived from the best STM differed from ATS observations by 0.09 on average in 2019 (median relative error (MRE): 19.45%) and 0.14 across all years (MRE: 79.03%). In data-rich areas (i.e., areas with regular fishery operations), proportional age class compositions from the STM differed from ATS observations by 0.03 on average in 2019 (MRE: 11.46%) and 0.09 across years (MRE: 54.96%). On average, total biomass estimates derived using STM age compositions differed from ATS age composition-based estimates by approximately 7% across the study period (~ 3% in 2019) given the same source of acoustic data. When biomass estimates from different sources of acoustic data (USV or ATS) were compared given the same source of age composition data, differences were nearly ten-fold greater (22% or 27%, depending on if ATS or STM age compositions were used). STMs fit to non-contemporaneous data may provide suitable information for assigning population structure to acoustic backscatter in data-rich areas, but advancements in acoustic data processing (e.g., automated echo classification) may be needed to generate viable USV-based estimates of biomass-at-age.

Rice crop growth monitoring with sentinel 1 SAR data using machine learning models in google earth engine cloud

The rainfed rice crop monitoring and yield prediction have been Herculean task with optical remote sensing systems operation under cloud cover. The free-of-cost sentinel 1 based SAR data along with machine learning models in GEE cloud was used for rainfed rice crop monitoring for 214 farm plots on a micro-scale in Hooghly, West Bengal, India. The individual plot rice parcel showed a low median backscattering signature for the SAR data during the land preparation/crop transplanting stages with VH and VV at −17.63 dB and −9.63 dB, respectively; whereas, higher median backscattering was experienced at the peak vegetation stage of VH and VV Polarization with −15.20 dB and −6.34 dB, respectively. The random forest model was found best suited with R2 of 0.87 for total crop biomass estimation. The backscatter values have a sound correlation with Heading NDVI, which validated the suitability of SAR images for crop monitoring under rainfed conditions. Further, crop yield prediction using SAR data and total biomass data through machine learning models showed positive correlation for Random forest, Extreme gradient boosting, and Decision tree models with an Area under receiver operating characteristics curve (AUROC) test accuracy of 0.99. These low-cost, high temporal SAR data based models can be used for near real-time crop monitoring even under overcast conditions in near future.

Preliminary target strength measurement of Sprattus sprattus and its influence on biomass estimates in the Adriatic Sea (Mediterranean Sea)

Post-processing data analysis has the ability to affect target strength (TS) measurement, which in turn influence the biomass estimates of acoustic surveys. In situ experiments are considered as the best method to measure species-specific acoustic reflectivity since fish are measured in their natural environment without constrains. Nevertheless, this approach poses some problems concerning the methods to be applied to analyze acoustic data, especially when only a small number of monospecific hauls are available. In this study we tested several alternative ways to compute the b20 of sprat (Sprattus sprattus) in a poor data state in the Mediterranean Sea where the TS of the species is seldom studied. The method proposed by MacLennan and Menz (1996) and a pilot approach were used in parallel with the implementation of three single target detection algorithms to analyze two monospecific sprat hauls performed in the Adriatic Sea in 2014 and 2020. Next, we applied a Monte Carlo simulation to investigate the influence of TS variability on biomass estimates. We obtained six b20 values (range, −68.8 dB to −65.5 dB), all higher than the current known value of − 71.7 dB. The resulting coefficient of variation in the overall uncertainty on absolute biomass estimates overcame the 17%. The different models provided different total biomass estimates, however, the method proposed herein applied on high-density filter single target detection data suggest a value of b20 between − 67.5 dB and − 68.8 dB for sprat in the Mediterranean Sea, which significantly reduces absolute sprat biomass values between 2014 and 2021.

Estimation of tree biomass and carbon stock in the Tangkoko Nature Reserve, North Sulawesi

The objective of this study is to tree biomass and forest carbon stocks contained in the Tangkoko Nature Reserve (TNR) of North Sulawesi. Phylogenetic diversity was analysed based on tree species composition weighted by aboveground biomass. A stratified systematic sampling was used in the study across transect lines. Field measurements follow the method described in the SNI 7724:2019 on ground-based forest carbon accounting. Based on allometric approach, this study produces a quantity of 255.45 Mg.ha−1 of total biomass estimation consisting of 201.14 Mg.ha−1 AGB and 54.31 Mg.ha−1 BGB. A standard approach of C-stock calculation (i.e. 47% of tree biomass and 50% of soil organic biomass) results in quantities of 94.53 and 27.15 Mg.ha−1 respectively). The importance of species traits for biomass estimation should be considered by initiatives in reforestation for carbon offsetting.

Additive modeling systems to simultaneously predict aboveground biomass and carbon for Litsea glutinosa of agroforestry model in tropical highlands

Aim of study: To develop and cross-validate simultaneous modeling systems for estimating components and total tree aboveground biomass and carbon of Litsea glutinosa in an agroforestry model with cassava.
 Area of study: In the Central Highlands of Vietnam, the agroforestry model widely planted on fallow land of ethnic minorities is a mixture of 65% L. glutinosa in combination with 35% cassava (Manihot esculenta).
 Material and methods: Twenty-two 300-m2 circular sample plots were located, representing the range of tree age, plantation density, and a 6-7 year rotation cycle. In each sample plot, one selected tree with a diameter at breast height equal to the plot quadratic mean diameter was destructively sampled. The relationships among tree aboveground biomass and carbon (AGB/AGC) and their components with dendrometric variables diameter, height, age, and crown area were examined using factor analysis. To fit systems of equations for AGB/AGC and their components, we compared two methods: weighted nonlinear least-squares (WNLS) and weighted nonlinear seemingly unrelated regression (WNSUR).
 Main results: The results of the leave-one-out cross-validation showed that the simultaneous WNSUR approach to modeling systems of four tree components, total biomass, and carbon provided better results than independent WNLS models.
 Research highlights: The simultaneous WNSUR modeling system provided improved and reliable estimates of tree components, total biomass, and carbon for L. glutinosa in an agroforestry model with cassava compared to independently fitted WNLS models.

Comparing environmental DNA with whole pond survey to estimate the total biomass of fish species in ponds

Abstract Environmental DNA (eDNA) has recently been developed for detecting the distribution of macroorganisms in aquatic systems and for estimating biomass/abundance. Although positive relationships between eDNA concentrations of species and biomass/abundance have been observed in previous studies, estimation of total biomass in a particular ecosystem based on eDNA concentration has not been investigated. To test this, we sampled eDNA from the littoral and pelagic zones of eight small ponds in Japan. Then, we drained ponds to capture all fish inhabiting each pond. We compared the eDNA copies measured by quantitative real‐time polymerase chain reaction analysis with the captured biomass of three dominant fish species (common carp, bluegill sunfish, and largemouth bass) obtained by direct capture. Differences in eDNA concentrations were compared between the littoral and pelagic zones of each pond. We found positive relationships between the eDNA copies and the whole captured biomass of all target species using Type II logarithmic equations, and the eDNA copies appeared to be saturated at a higher total biomass. We found positive correlations between the total number of eDNA copies and the total captured biomass of three fish species. This demonstrated the first step in estimating the population biomass of target fish species under natural conditions using eDNA measurement.

Total Stem Biomass Estimation Using Sentinel-1 and -2 Data in a Dense Coniferous Forest of Complex Structure and Terrain

Accurate above-ground biomass (AGB) estimation across multiple spatial and temporal scales is essential for mitigating climate change and optimizing forest management strategies. The aim of the present study was to investigate the potential of Sentinel optical and Synthetic Aperture Radar (SAR) data in reliably estimating the plot-level total stem biomass (TSB), which constitutes the dominant material among the different tree components of AGB (stem, branches, and leaves). The study area was located in a dense coniferous forest characterized by an uneven-aged structure and intense topography. A random forest (RF) regression analysis was performed to develop TSB predictive models using Sentinel-1 and -2 images in an individual and combined manner. Consequently, three RF models were produced and evaluated for their predictive performance through the k-fold cross-validation (CV) method. The results showcased that the individual use of Sentinel-1 contributed to the production of the most accurate plot-level TSB estimates (i.e., coefficient of determination-R2 = 0.74, relative mean square error (RMSE) = 1.76 Mg/1000 m2, mean absolute error (MAE) = 1.48 Mg/1000 m2), compared to the use of Sentinel-2 data individually and the Sentinel-1 and -2 combination. In fact, the synergistic use of optical and SAR data led to the generation of an RF model that only marginally underperformed the SAR model (R2 = 0.73 and R2 = 0.72, respectively).

Viable cell estimation of mammalian cells using off-gas-based oxygen uptake rate and aging-specific functional

This study developed an estimation routine for counting the viable cells in an in vitro fed-batch Chinese hamster ovary cultivation that relies on off-gas information and inlet gas mixture knowledge. We computed the oxygen uptake rate bound to the bioreactor exhaust gas outlet when the inlet gas mixture was stationary. Our mammalian biosynthesis analysis determined the stoichiometric parameters as a function of the average population age. We cross-validated an identical algorithm for mammalian and microbial cultivations and found that the’ 99% confidence band of the model generally overlapped with the error bars defined from observations. The resulting RMSE and MAE averages were 0.188 and 0.14e9cells L−1, respectively, when estimating the viable mammalian cell count. The validation for the estimation of total bacterial biomass yielded an MAE and RMSE of 1.78 g L−1 and 2.53 g L−1, respectively. Moreover, our proposed approach provides an online estimation of the average population age for both aerobically cultivated microorganisms.

Developing biomass estimation models of young trees in typical plantation on the Qinghai-Tibet Plateau, China.

Calculation of forest biomass is the basis for global carbon stock estimation, which has been included in national forest inventory projects. The volume-derived biomass method is generally used for trees with diameter at breast height (DBH) larger than 5 cm in most forest carbon sink measurement, which omits young trees (diameter at breast height <6 cm, height >0.3 m) and thus may underestimate ecosystem carbon sink capacity. Based on the biomass data of 137 young trees in five typical plantations on the Tibetan Plateau, independent biomass models were developed using the weighted generalized least squares method, with basic diameter as the predictor instead of DBH. Additive biomass models of controlling directly by proportion functions and controlling by the sum of equations were selected. Additive biomass models for the whole plant and each component were developed by applying weighted nonlinear seemingly uncorrelated regression. The results showed that the binary additive biomass model (R2 reached 0.90-0.99) performed better than the monadic biomass models and independent biomass models for the estimation of total biomass. For different tree species, two forms of the additive models had their own advantages, with neglectable difference in accuracy. From the perspective of forestry production, models of controlling directly by proportion functions were more practical. From the perspective of predictors extraction by remote sensing technology, suitable young tree biomass models were developed for remote sensing estimation. In this study, the additive model had high overall fitting accuracy and could accurately estimate the whole plant and component biomass of young trees in similar climatic environments.

Time-varying predation as a modifier of constant natural mortality for Gulf of Alaska walleye pollock

Although highly variable in time and space, predation remains the greatest source of mortality for juvenile and lower trophic-level fishes. As such, predation can have substantial and long-term effects on the dynamics of these prey. Gulf of Alaska walleye pollock (Gadus chalcogrammus) has shown considerable variability in biomass over the past four decades. During this same time, the demersal fish community transitioned from being dominated by pollock to a system comprised primarily of upper trophic-level predators. We estimated time-varying predation mortality to better understand its effects on the population dynamics of pollock in this currently “top heavy” system. Our index of predation accounted for spatiotemporal variation in predator biomass, bioenergetics-based rations, and age-specific proportions of pollock consumed (1990–2019). To evaluate population-level impacts of predation, we included an index of removals as part of the stock assessment model. This formulation allowed for non-annual data inputs and included a proportionality constant with which to scale predation. Age-specific natural mortality was allowed to vary according to a penalized random walk. We found that natural mortality ranged from 37% higher to 17% lower than the long-term mean. Resulting estimates of total pollock biomass differed by as much as 37% relative to a model without time-varying natural mortality, though the maximum difference for exploitable biomass was only 14%. Using an empirically-derived predation index to modify constant natural mortality allows stock assessment scientists to evaluate impacts of time-varying predation on assessed species. This approach provides a relatively simple way of incorporating ecological information into single-species stock assessments and may reduce bias compared to conventional models that do not account for changes in predation mortality. Notably, including predation mortality in single-species assessments may help identify inconsistencies in biomass estimates that warrant further consideration.

Forest Total and Component Above-Ground Biomass (AGB) Estimation through C- and L-band Polarimetric SAR Data

Forest biomass plays an essential role in forest carbon reservoir studies, biodiversity protection, forest management, and climate change mitigation actions. Synthetic Aperture Radar (SAR), especially the polarimetric SAR with the capability of identifying different aspects of forest structure, shows great potential in the accurate estimation of total and component forest above-ground biomass (AGB), including stem, bark, branch, and leaf biomass. This study aims to fully explore the potential of polarimetric parameters at the C- and L-bands to achieve high estimation accuracy and improve the estimation of AGB saturation levels. In this study, the backscattering coefficients at different polarimetric channels and polarimetric parameters extracted from Freeman2, Yamaguchi3, H-A-Alpha, and Target Scattering Vector Model (TSVM) decomposition methods were optimized by a random forest algorithm, first, and then inputted into linear regression models to estimate the total forest AGB and biomass components of two test sites in China. The results showed that polarimetric observations had great potential in total and component AGB estimation in the two test sites; the best performances were for leaves at test site I, with R2 = 0.637 and RMSE = 1.27 t/hm2. The estimation of biomass components at both test sites showed obvious saturation phenomenon estimation according to their scatter plots. The results obtained at both test sites demonstrated the potential of polarimetric parameters in total and component biomass estimation.

Estimation and Spatial Mapping of Residue Biomass following CTL Harvesting in Pinus radiata Plantations: An Application of Harvester Data Analytics

The utilization of forest harvest residues for renewable bioenergy production and bioproducts has increasingly become an integrated part of forestry that helps to meet the needs of climate change mitigation and a future carbon neutral economy. An essential element in the planning of any harvesting residue recovery operation is a reliable assessment of the quantity and quality of residue biomass and its composition over a harvest area. With the now widely adopted cut-to-length (CTL) at the stump harvesting system in Pinus radiata plantations in Australia, harvesting residues left on site are significantly larger in quantity and spatially more dispersed over a harvest area in comparison to the more traditional whole-tree harvesting. The conventional approach of assessing forest harvest residues through sample plots, transects, or small study blocks has provided site-specific estimates of residue biomass. However, these estimates cannot be readily extrapolated over the plantation landscape, which varies in silviculture, site, and stand conditions. To overcome this limitation, this study relied on harvester data analytics to obtain spatially explicit estimates of residue biomass using an example data set from harvested plantations of three stand types: unthinned (T0), thinned once (T1), and thinned twice (T2). Three methods of integrating biomass equations with harvester data were compared for residue biomass estimation: (1) applying individual tree biomass equations to harvested stems, (2) applying stand-level biomass equations to gridded harvester data, and (3) integrating estimates from the first approach with recorded and estimated waste volumes of harvested stems. The estimates of total residue biomass obtained using the three methods through harvester data analytics varied between 56.2 and 156.4 t/ha in green weight across the three stand types. These estimates were validated indirectly through ex situ sample plots and proved to be comparable to the quantities of residue biomass assessed using conventional sample plots, transects, or small blocks following CTL harvesting of rotation age P. radiata plantations elsewhere in Australia. Among the three methods, the third method made the most intensive use of the harvester data and provided the most realistic estimates of residue biomass. Spatial mapping of the estimated total and component residue biomass will assist the operational planning of residue recovery and site-specific nutrient management for the long-term sustainability of P. radiata plantations.

Complementary allometric model of understory tree biomass in the semi-deciduous rainforest of Cameroon

Forest understorey contains remarkable plant diversity, contributing to the heterogeneous environments for other biotic gatherings and soil supplement aggregations. Nonetheless, the biomass of the understorey vegetation is neglected because of the lack of appropriate allometric equations without which there are uncertainties in biomass estimation. This study was aimed at developing multispecies allometric equations that will be used to estimate the aboveground biomass of different compartments (trunk, crown, and leaves) of understorey trees in the semi-deciduous rainforest of Cameroon. Understorey tree diameter (1.0-10.0 cm), height, and crown diameter were measured on 1023 trees as biomass predictors. The results showed that the fit of the model improves with more predictive variables, four of which were considered in all studied compartments (trunk, crown, and leaves). Existing specific and pantropic allometries based on diameter tend to overestimate the aboveground biomass of understorey trees when compared to the allometry developed in this study. This study highlighted the importance of a specific aboveground biomass allometric equation for understorey trees. Furthermore, the multispecies allometric equation developed for understorey trees complements those recently developed for overstorey trees, thereby contributing to the total aboveground woody biomass estimation in semi-deciduous forest in the Congo Basin.

ANALISIS POTENSI SIMPANAN KARBON AGROFORESTRI PERKEBUNAN KOPI ROBUSTA (COFFEA CANEPHORA) DI PEGUNUNGAN ARGOPURO, KABUPATEN BONDOWOSO

Coffee plantation areas have the potential to absorb carbon dioxide in the atmosphere to reduce the greenhouse gas (GHG) emissions. Especially if coffee plantations are developed with forest plants in agroforestry area within forest management patterns. On the other hand, some coffee agroforestry now, are planted with horticultural crops that can reduce carbon sequestration ability to reduce climate change impact. The objectives of the study are to identify the parameters of the abiotic environment and the potential for carbon storage in robusta coffee agroforestry at Argopuro mountains, Bondowoso Regency. Through the calculation of plant biomass and carbon stock, it is potential to approach the amount of carbon uptake in plants to reduce carbon emissions in the atmosphere. Coffee plantation is one area that can increase carbon sequestration in the atmosphere. The results showed that microclimate parameters at robusta coffeeagroforestry at Argopuro mountains in Bondowoso regency i.e. temperature, air humidity, light intensity has average values of 29.2 oC; 54%; and 2166 lux respectively, then an average of soil pH is 6.00. There were some commonly plants founds in robusta coffee plantation i.e mango trees, avocado trees, dadap trees, pine trees, and more banana plants. Total biomass estimation in robusta coffee plantation area is 144,834 tonnes/ha. The identification of carbon stock show that the robusta coffee agroforestry area with ??2000 m2 can contribute to reduce atmospheric carbon emissions by 72.417 tonnes/ha in Argopuro mountains, Maesan District, Bondowoso Regency.&#x0D; Keywords: Argopuro Mountains; Bondowoso; Carbon stock; Coffee agroforestry; Climate Change.

Estimation of Individual Tree Stem Biomass in an Uneven-Aged Structured Coniferous Forest Using Multispectral LiDAR Data

Stem biomass is a fundamental component of the global carbon cycle that is essential for forest productivity estimation. Over the last few decades, Light Detection and Ranging (LiDAR) has proven to be a useful tool for accurate carbon stock and biomass estimation in various biomes. The aim of this study was to investigate the potential of multispectral LiDAR data for the reliable estimation of single-tree total and barkless stem biomass (TSB and BSB) in an uneven-aged structured forest with complex topography. Destructive and non-destructive field measurements were collected for a total of 67 dominant and co-dominant Abies borisii-regis trees located in a mountainous area in Greece. Subsequently, two allometric equations were constructed to enrich the reference data with non-destructively sampled trees. Five different regression algorithms were tested for single-tree BSB and TSB estimation using height (height percentiles and bicentiles, max and average height) and intensity (skewness, standard deviation and average intensity) LiDAR-derived metrics: Generalized Linear Models (GLMs), Gaussian Process (GP), Random Forest (RF), Support Vector Regression (SVR) and Extreme Gradient Boosting (XGBoost). The results showcased that the RF algorithm provided the best overall predictive performance in both BSB (i.e., RMSE = 175.76 kg and R2 = 0.78) and TSB (i.e., RMSE = 211.16 kg and R2 = 0.65) cases. Our work demonstrates that BSB can be estimated with moderate to high accuracy using all the tested algorithms, contrary to the TSB, where only three algorithms (RF, SVR and GP) can adequately provide accurate TSB predictions due to bark irregularities along the stems. Overall, the multispectral LiDAR data provide accurate stem biomass estimates, the general applicability of which should be further tested in different biomes and ecosystems.