Stock Estimates Research Articles

Research on Estimation Model of Carbon Stock Based on Airborne LiDAR and Feature Screening

The rapid and accurate estimation of forest carbon stock is important for analyzing the carbon cycle. In order to obtain forest carbon stock efficiently, this paper utilizes airborne LiDAR data to research the applicability of different feature screening methods in combination with machine learning in the carbon stock estimation model. First, Spearman’s Correlation Coefficient (SCC) and Extreme Gradient Boosting tree (XGBoost) were used to screen out the variables that were extracted via Airborne LiDAR with a higher correlation with carbon stock. Then, Bagging, K-nearest neighbor (KNN), and Random Forest (RF) were used to construct the carbon stock estimation model. The results show that the height statistical variable is more strongly correlated with carbon stocks than the density statistical variables are. RF is more suitable for the construction of the carbon stock estimation model compared to the instance-based KNN algorithm. Furthermore, the combination of the XGBoost algorithm and the RF algorithm performs best, with an R2 of 0.85 and an MSE of 10.74 on the training set and an R2 of 0.53 and an MSE of 21.81 on the testing set. This study demonstrates the effectiveness of statistical feature screening methods and Random Forest for carbon stock estimation model construction. The XGBoost algorithm has a wider applicability for feature screening.

The largest European forest carbon stocks are in the Dinaric Alps old-growth forests: comparison of direct measurements and standardised approaches

BackgroundCarbon (C) sink and stock are among the most important ecosystem services provided by forests in climate change mitigation policies. In this context, old-growth forests constitute an essential reference point for the development of close-to-nature silviculture, including C management techniques. Despite their small extent in Europe, temperate old-growth forests are assumed to be among the most prominent in terms of biomass and C stored. However, monitoring and reporting of C stocks is still poorly understood. To better understand the C stock amount and distribution in temperate old-growth forests, we estimated the C stock of two old-growth stands in the Dinaric Alps applying different assessment methods, including direct and indirect approaches (e.g., field measurements and allometric equations vs. IPCC standard methods). This paper presents the quantification and the distribution of C across the five main forest C pools (i.e., aboveground, belowground, deadwood, litter and soil) in the study areas and the differences between the applied methods.ResultsWe report a very prominent C stock in both study areas (507 Mg C ha− 1), concentrated in a few large trees (36% of C in 5% of trees). Moreover, we found significant differences in C stock estimation between direct and indirect methods. Indeed, the latter tended to underestimate or overestimate depending on the pool considered.ConclusionsComparison of our results with previous studies and data collected in European forests highlights the prominence of temperate forests, among which the Dinaric Alps old-growth forests are the largest. These findings provide an important benchmark for the development of future approaches to the management of the European temperate forests. However, further and deeper research on C stock and fluxes in old-growth stands is of prime importance to understand the potential and limits of the climate mitigation role of forests.

Modeling Forest Above-Ground Biomass of Teak (Tectona grandis L. F.) Using Field Measurement and Sentinel-2 Imagery

Over the last few decades, remote sensing has emerged as a dependable and cost-effective method for collecting precise data on forest biophysical parameters, aiding in sustainable forest management and global initiatives to combat climate change. This research aimed to develop a model for estimating the above-ground biomass (AGB) of Teak (Tectona grandis L. F.) by combining field measurements with Sentinel-2 earth observation data. The study took place in 36-year-old teak plantation areas within the Sagarnath Forest Development Project in Nepal’s Sarlahi district. Field measurements were conducted using a destructive systematic sampling method, employing 10 × 10 m2 sample plots, and the volume of logs was determined using Newton’s formula. A total of 30 sample plots were used for calibration, while 10 were utilized for validation purposes. The findings revealed that the average AGB per plot was 814 kg (equivalent to 81.4 t ha−1), with a minimum value of 716 kg (71.6 t ha−1) and a maximum value of 1,060 kg (106 t ha−1). The study utilized five independent variables, namely, the Red band, Green band, Blue band, near-infrared (NIR), and normalized difference vegetation index (NDVI) values from Sentinel-2 imagery data, to develop estimation models. Among the 12 models examined, model M10 proved to be the best fit for accurate AGB estimation (adjusted R2 = 0.9809, RMSE = 0.01269, AIC = −170, and p-value = < 8.39e−21). The equation of the best-fitted model was ln (AGB) = A + B × Red + C × Green + D × Blue2 + E × ln (NIR) + F × ln (NDVI), providing an accurate estimate of AGB. Model validation involved a t-test comparing the observed and calculated AGB values for ten sample plots, demonstrating no significant difference (p-value = 0.3662 > 0.05). This model has the potential to facilitate AGB biomass calculations and carbon stock estimates for teak plantations of similar age groups.

Global carbon balance of the forest: satellite-based L-VOD results over the last decade

Monitoring forest carbon (C) stocks is essential to better assess their role in the global carbon balance, and to better model and predict long-term trends and inter-annual variability in atmospheric CO2 concentrations. On a national scale, national forest inventories (NFIs) can provide estimates of forest carbon stocks, but these estimates are only available in certain countries, are limited by time lags due to periodic revisits, and cannot provide spatially continuous mapping of forests. In this context, remote sensing offers many advantages for monitoring above-ground biomass (AGB) on a global scale with good spatial (50–100 m) and temporal (annual) resolutions. Remote sensing has been used for several decades to monitor vegetation. However, traditional methods of monitoring AGB using optical or microwave sensors are affected by saturation effects for moderately or densely vegetated canopies, limiting their performance. Low-frequency passive microwave remote sensing is less affected by these saturation effects: saturation only occurs at AGB levels of around 400 t/ha at L-band (frequency of around 1.4 GHz). Despite its coarse spatial resolution of the order of 25 km × 25 km, this method based on the L-VOD (vegetation optical depth at L-band) index has recently established itself as an essential approach for monitoring annual variations in forest AGB on a continental scale. Thus, L-VOD has been applied to forest monitoring in many continents and biomes: in the tropics (especially in the Amazon and Congo basins), in boreal regions (Siberia, Canada), in Europe, China, Australia, etc. However, no reference study has yet been published to analyze L-VOD in detail in terms of capabilities, validation and results. This paper fills this gap by presenting the physical principles of L-VOD calculation, analyzing the performance of L-VOD for monitoring AGB and reviewing the main applications of L-VOD for tracking the carbon balance of global vegetation over the last decade (2010–2019).

Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment

With climate change and urbanization expansion, wetlands, which are some of the largest carbon stocks in the world, are facing threats such as shrinking areas and declining carbon sequestration capacities. Wetland carbon stocks are at risk of being transformed into carbon sources, especially those of wetlands with strong land use–natural resource conservation conflict. Moreover, there is a lack of well-established indicators for evaluating the health of wetland carbon stocks. To address this issue, we proposed a novel framework for the safety assessment of wetland carbon stocks using the Super Slack-Based Measure (Super-SBM), and we then conducted an empirical study on the Quanzhou Bay Estuary Wetland (QBEW). This framework integrates the unexpected output indicator (i.e., carbon emissions), the expected output indicators, including the GDP per capita and carbon stock estimates calculated via machine learning (ML)-based remote sensing inversion, and the input indicators, such as environmental governance investigations, climate conditions, socio-economic activities, and resource utilization. The results show that the annual average safety assessment for carbon pools in the QBEW was a meager 0.29 in 2015, signaling a very poor state, likely due to inadequate inputs or excessive unexpected outputs. However, there has been a substantial improvement since then, as evidenced by the fact that all the safety assessments have exceeded the threshold of 1 from 2018 onwards, reflecting a transition to a “weakly effective” status within a safe and acceptable range. Moreover, our investigation employing the Super-SBM model to calculate the “slack variables” yielded valuable insights into optimization strategies. This research advances the field by establishing a safety measurement framework for wetland carbon pools that leverages efficiency assessment methods, thereby offering a quantitative safeguard mechanism that supports the achievement of the “3060” dual-carbon target.

Ecosystem type affects how Amazonian tree species invest in stem and twig wood

Abstract Wood density (WD) is a key functional trait for its importance in tree performance and in biomass calculations of forests. Yet, the variation of WD among different woody tree parts, how this varies across ecosystems, and how this influences estimates of forest carbon stocks remains little understood, particularly for diverse tropical forests such as the Amazon. We assembled a dataset on stem and twig wood density from 119 tree species in three different Amazonian ecosystem types that differ considerably in soil nutrition and flooding: non‐flooded forest (Terra Firme), white‐water floodplain forest (Várzea) and black‐water floodplain forest (Igapó) to investigate (i) variation of stem and twig wood density across ecosystems, (ii) the relationships between stem and twig wood density and how these relationships vary across ecosystems. Wood density varied substantially across ecosystems. Várzea species showed lower mean WD for stems compared to Terra firme, while Igapó species showed higher WD for twigs compared to the other ecosystems. Twig and stem wood density were positively related (R2adj = 0.47) with similarly increasing rates across ecosystems, although average WD values differed between Terra firme and Igapó. For any given twig density, stem density tends to be lower in floodplain environments but higher in Terra firme, a habitat‐specific pattern of wood density variation within trees that may emerge from differences in the function of stem and twig wood for growth and survival in ecologically differentiated environments. Our results show how ecosystem has strong impacts on how trees allocate resources to different woody tissues, suggesting contrasting ecological strategies linked to ecosystem constraints. Our results suggest that greater consideration of the variation of WD within trees and how these changes across ecosystems might lead to more accurate estimates of above‐ground biomass in Amazonia. Read the free Plain Language Summary for this article on the Journal blog.

Urban trees' potential for regulatory services in the urban environment: an exploration of carbon sequestration.

Urbanisation has emerged as a formidable challenge for urban policymakers, reaching unparalleled heights and unsettling the ecological equilibrium of the cities. Urban areas now grapple with many issues encompassing climate change, resource depletion, population surges and increased pollution levels. Many planned cities have planted trees and other vegetation within the urban sectors to enhance air quality, mitigate climate effects and provide valuable ecosystem services. This study assessed tree species diversity and their potential for carbon sequestration in Panjab University Campus, Chandigarh. We established 188 plots, each comprising randomly selected quadrats measuring 10 m × 10 m, encompassing areas with varying levels of vegetation, ranging from low to moderate and high density. We used four different allometric equations to estimate tree biomass and carbon stock. Our findings revealed that 92 tree species belong to 72 genera and 35 families, with a total tree density of 975 ha-1. The total CO2 sequestration in form of carbon stock was 18,769.46 Mg C ha-1, with Manilkara hexandra (1239.20 Mg C ha-1), Ficus benghalensis (1072.24 Mg C ha-1), Kigelia pinnata (989.89 Mg C ha-1) and Lagerstroemia floribunda (716.88 Mg C ha-1) being the top contributors. Specifically, the equation of Chave et al. (2005) without tree height yielded the highest biomass and carbon stock estimates than other equations. The present study underscores the vital role of trees on the campus as potent carbon reservoirs meet to maintain an aesthetic sense for biotic components and alleviate rising levels of CO2 in the atmospheric environment. By emphasising the role of urban trees as potent carbon reservoirs, the study underscores the importance of integrating green infrastructure into urban planning strategies. Furthermore, it offers valuable guidance for urban planners. It suggests that strategic tree planting and maintenance can enhance green spaces, regulate temperatures and ultimately support regional and global climate change mitigation goals. Incorporating these findings into urban planning processes can aid policymakers in developing resilient, ecologically sustainable cities worldwide.

Top-down characterization of resource use in LCA: from problem definition of resource use to operational characterization factors for resource inaccessibility of elements in a short-term time perspective

PurposeWhen resources are extracted and used by society, they are not necessarily lost for future generations. Therefore, recent publications on impact assessment of abiotic resource use in life cycle assessment focus on a decreased accessibility of resources due to dissipation, rather than depletion. In a previous study, dissipation was defined as a function of the global change in accessible stock due to human actions, and the global amount of the accessible stock, assuming a very long-term time perspective (more than 500 years). In this paper, a short-term time perspective (25 years) is adopted.MethodsThe same generic characterization model is used, but different choices are outlined to derive characterization factors for a short-term perspective (25 years). To illustrate how the short term might be approached, a preliminary set of characterization factors is developed, based on assumptions and estimates.ResultsThe problem of resource use is defined as follows: the decrease of accessibility on a global level of primary and/or secondary elements over the short term due to the net result of compromising actions (i.e., emissions, dissipation in the technosphere, occupation in use, and exploration for new stocks). Characterization factors are derived based on assumptions, like the following: the accessible stock is based on present estimates of accessible stocks in the environment and the technosphere; estimates of accessible stocks in the technosphere are based on past extractions and generic recycling rates; all flows that are presently not recycled are assumed to be inaccessible. Finally, weighting between elements and the functions they have for the present society is based on the added value of the economic sector that is affected due to the decreased accessibility.Discussion and conclusionA preliminary set of characterization factors is proposed for 55 elements. They assess the impact of the present use of resources on the decreased accessibility in the short term due to emissions and dissipation in the technosphere. However, calculation of impact category scores is still hampered by a lack of appropriate data for dissipative flows in life cycle inventory databases. The presented calculations are based on several simplifications and proxies. A more detailed distinction of dissipative flows and estimates of stocks in the technosphere may be possible based on (dynamic) SFA modelling of elements in different applications. To derive a more mature set of characterization factors, it is recommended to use the presented model as a basis and further elaborate or replace the proxies.

Wood Basic Density in Large Trees: Impacts on Biomass Estimates in the Southwestern Brazilian Amazon

Wood basic density (WD) plays a crucial role in estimating forest biomass; moreover, improving wood-density estimates is needed to reduce uncertainties in the estimates of tropical forest biomass and carbon stocks. Understanding variations in this density along the tree trunk and its impact on biomass estimates is underexplored in the literature. In this study, the vertical variability of WD was assessed along the stems of large trees that had a diameter at breast height (DBH) ≥ 50 cm from a dense ombrophilous forest on terra firme (unflooded uplands) in Acre, Brazil. A total of 224 trees were sampled, including 20 species, classified by wood type. The average WD along the stem was determined by the ratio of oven-dry mass to saturated volume. Five models were tested, including linear and nonlinear ones, to fit equations for WD, selecting the best model. The variation among species was notable, ranging from 0.288 g cm−3 (Ceiba pentandra, L., Gaertn) to 0.825 g cm−3 (Handroanthus serratifolius, Vahl., S. Grose), with an average of 0.560 g cm−3 (±0.164, standard deviation). Significant variation was observed among individuals, such as in Schizolobium parahyba var. amazonicum (H. ex D.), which ranged from 0.305 to 0.655 g cm−3. WD was classified as low (≤0.40 g cm−3), medium (0.41–0.60 g cm−3), and high (≥0.61 g cm−3). The variability in WD along the stem differs by wood type. In trees with low-density wood, density shows irregular variation but tends to increase along the stem, whereas it decreases in species with medium- and high-density wood. The variation in WD along the stem can lead to underestimations or overestimations, not only in individual trees and species but also in total stocks when estimating forest biomass. Not considering this systematic bias results in significant errors, especially in extrapolations to vast areas, such as the Amazon.

Development of building stock model for an emerging city in India: Learnings for enabling circular economy in the built environment

AbstractThis study explores the prospects of urban mining for a steadily developing city “Thane,” located in the Mumbai Metropolitan Region of India, through building material stock accounting (BMSA). A novel bottom‐up approach for stock accounting was developed for the cities with limited data availability, through the integration of geodata spatial analysis, archetype identification through statistical modeling, and regional building design assessment for material intensity calculation. This study is the first of its kind in the Indian context and focuses on the stock estimation of the most common building materials: concrete, reinforcement steel, and masonry bricks within the boundaries of a typical Indian urban area. The results showed that the total built‐up stock in Thane city in 2018 amounts to 84.7 million metric ton (MT) by weight; concrete 57.7%, bricks 40.4%, steel 1.9%. Such reservoirs of raw material can be harnessed to fulfill the future material requirements of the construction industry through proposed circular economy interventions such as material bank creation, local recycling facilities, and development of a secondary material market. The material stock estimated in this study was 46.1 MT/capita for permanent building dwellers and with potential infrastructural developments underway, urban mining application can be ensured by redefining design and demolition strategies and multi‐level collaboration for generating alternative material sources for the construction sector. This work of BMSA intends to provide meaningful insights into exploring the potentials of urban mining and help chart the future roadmaps of sustainable construction in India.

ESTIMATES OF CONSTRUCTION INFRASTRUCTURE STOCK FOR CAPE VERDE: 1980–2019

Building and other construction assets constitute a significant part of a country’s physical and economic infrastructure. According to several writers, the knowledge of reliable data of building and other construction assets of a specific country or region is a crucial element for the long-term management of these assets. Built capital stock statistics at the national or international levels have been available for most countries of the world, both developed and less developed ones, for some time, but construction infrastructure stock statistics at the disaggregated level are very scarce, even for most developed countries. Furthermore, the methodologies to produce the estimates of built capital stock, at the international level, do not consider countries’ specificities. This paper discusses the methodologic issues for producing construction infrastructure stock statistics for Cape Verde, and makes estimates for the period 1980–2019. The paper outlines the Perpetual Inventory Method (PIM) used to produce capital estimation, data employed, and the assumptions made to estimate missing data. The paper analyses the level of the construction infrastructure stock estimates for Cape Verde, as well as their impact on the development pattern of the country’s construction industry, and suggests how further studies can enhance our comprehension of the relationship between construction investment and economic growth and development.

Remote Sensing Estimation of Forest Carbon Stock Based on Machine Learning Algorithms

Improving the precision of remote sensing estimation and implementing the fusion and analysis of multi-source data are crucial for accurately estimating the aboveground carbon storage in forests. Using the Google Earth Engine (GEE) platform in conjunction with national forest resource inventory data and Landsat 8 multispectral remote sensing imagery, this research applies four machine learning algorithms available on the GEE platform: Random Forest (RF), Classification and Regression Trees (CART), Gradient Boosting Trees (GBT), and Support Vector Machine (SVM). Using these algorithms, the entire Yunnan Province is classified into seven categories, including broadleaf forest, coniferous forest, mixed broadleaf-coniferous forest, water bodies, built-up areas, cultivated land, and other types. After a thorough comparison, the research reveals that the RF algorithm surpasses others in terms of accuracy and reliability, making it the most suitable choice for estimating aboveground carbon storage in forests using remote sensing data. Therefore, the study used the RF algorithm for both forest classification and the estimation of carbon storage. By extracting remote sensing factors; by using the Pearson correlation coefficient to select the most relevant factors; and by utilizing multiple linear regression, RF regression, and decision tree regression, a model for estimating aboveground carbon stocks in forests was developed. The results indicate that among the four classification algorithms, the RF classifier demonstrates superior performance, with an overall accuracy of 84.96% and a Kappa coefficient of 76.46%. In the RF regression models, the R2 values for the coniferous forest, broadleaf forest, and mixed needle-broadleaf forest models are 0.636, 0.663, and 0.638, respectively. In both RF and CART, the R2 values for the three forest-type models are greater than 0.6, indicating satisfactory model fitting performance. This study aims to explore the possibility of improving the estimation of forest carbon stocks in large-scale areas through fine land use classification. Additionally, the data sources used are completely free, and medium to low resolution can provide a better reference value for practical applications, thereby reducing the cost of utilization.

Carbon stock projection for four major forest plantation species in Japan

Carbon sequestration via afforestation and forest growth is effective for mitigating global warming. Accurate and robust information on forest growth characteristics by tree species, region, and large-scale land-use change is vital and future prediction of forest carbon stocks based on this information is of great significance. These predictions allow exploring forestry practices that maximize carbon sequestration by forests, including wood production. Forest inventories based on field measurements are considered the most accurate method for estimating forest carbon stocks. Japan's national forest inventories (NFIs) provide stand volumes for all Japanese forests, and estimates from direct field observations (m-NFIs) are the most reliable. Therefore, using the m-NFI from 2009 to 2013, we selected four major forest plantation species in Japan: Cryptomeria japonica, Chamaecyparis obtusa, Pinus spp., and Larix kaempferi and presented their forest age–carbon density function. We then estimated changes in forest carbon stocks from the past to the present using the functions. Next, we investigated the differences in the carbon sequestration potential of forests, including wood production, between five forestry practice scenarios with varying harvesting and afforestation rates, until 2061. Our results indicate that, for all four forest types, the estimates of growth rates and past forest carbon stocks in this study were higher than those considered until now. The predicted carbon sequestration from 2011 to 2061, assuming that 100 % of harvested carbon is retained for a long time, twice the rate of harvesting compared to the current rate, and a 100 % afforestation rate in harvested area, was three to four times higher than that in a scenario with no harvesting or replanting. Our results suggest that planted Japanese forests can exhibit a high carbon sequestration potential under the premise of active management, harvesting, afforestation, and prolonging the residence time of stored carbon in wood products with technology development.

Evaluating impacts of environmental stress and bioactive chemicals on the North Carolina blue crab population: An individual‐based model

AbstractObjectiveRecent estimates of the North Carolina blue crab Callinectes sapidus stock found that the stock is overfished and overfishing is occurring. Threats outlined in the 2018 stock assessment include climate change and estrogenic endocrine‐disrupting chemicals (EEDCs). The objective of this study was to use an individual‐based modeling approach to simulate the long‐term effects of climate change and EEDCs on the North Carolina blue crab stock.MethodsTo do this, we built an individual‐based model that simulated the life history of individual blue crabs, including but not limited to growth, reproduction, and mortality. We then tested our population of blue crabs against 30 different combinations of temperature and EEDC scenarios over 50 years to determine the long‐term effects on the population.ResultOur simulations suggested that the North Carolina blue crab population may be relatively resilient to climate change‐related temperature shifts but that there may be significant impacts at the population level as summer temperatures become more extreme. Endocrine‐disrupting chemical effects resulted in an alternative stable state of lower catch or the total extinction of the population.ConclusionThese results suggest that management strategy changes may be necessary as temperatures become more extreme in the region. In addition, more research is necessary to fully understand the effects of EEDCs on blue crabs and other crustaceans at the individual and population level.

The estimation of above-ground oil palm carbon stocks of different ages through vegetation index

Above-ground carbon stocks play an important role to support the sustainability of oil palm plantation, and vary with soil characteristics, climate conditions, and agronomic practices. The common direct approach to assess aboveground carbon stocks may be time-consuming, and laborious, so may not be suitable especially for large scale observation. Therefore, the use of remote sensing may overcome some limitations coming up for conventional methods, yet requires a considerable data validation to obtain a reliable model. In this study, the normalized difference vegetation index (NDVI) based on Sentinel-2 satellite imagery, acquired in September 2023, was used in combination with field measurements, i.e. biomass estimation, investigated in three separated of oil palm plantations differing in ages, i.e. young (< 10 years), middle (10 – 20 years) and old (> 20 years), subjected with similar soil characteristics, and climate condition, in Bojong Datar, Cibungur, and Cikasungka, West Java. Results show that above-ground carbon stocks increase with oil palm ages, i.e. from young to middle ages, with value ranging from 3.40 – 65.70 ton/ha, yet decreases down to 17 ton/ha with ages > 20 years. Similarly, it also occurred for NDVI index, i.e. increase from 0.50 – 0.59 for young to middle of oil palms, and down to 0.51 for oil palm > 20 years old. This study suggests a strong correlation between carbon stocks and NDVI values (R2 = 0.878), pointing out the potential use of vegetation index to estimate above-ground oil palm carbon stocks. In the future, with further validation, the use of NDVI to estimate plant biomass may provide a potential substitute for the field measurement methods with a considerable higher efficiency and accuracy.

Acoustic-based estimation of fish stocks in Widas Reservoir, East Java, Indonesia

Acoustic-based estimation of fish stocks in Widas Reservoir, East Java, Indonesia

Forest carbon stock-based bioeconomy: Mixed models improve accuracy of tree biomass estimates

Tree biomass and carbon stock continue to be the most important contributors to a forest-based bioeconomy. Accurate estimation of tree biomass and carbon stock therefore is crucial for planning a sustainable bioeconomy based on sustained forest bioresource production and harvest. Precise carbon accounting is also of fundamental importance for a green carbon market as well as for an improved model of quantitative global carbon cycle. However, estimating biomass and carbon at a large spatial scale without causing significant environmental disruption has been posing a methodological challenge necessitating to evolve a robust yet accurate estimation method. Although forest-specific generalized allometric models are the most widely used method to estimate forest tree biomass and carbon stock, these either under- or over-estimate the values, particularly in mixed-species forests. To address this challenge, we employed mixed equations that included species-specific models or available models for a sister species that provided a more conservative estimate of stand-level tree biomass and carbon compared to the generalized equations. We gathered primary data on tree girth and height at the stand level from sub-tropical and temperate forests of Tawang river basin, Arunachal Pradesh, India for validation. We compiled 32 equations comprising 27 species-specific and 5 forest-specific generalized models from a global dataset of allometric models. The outputs from the mixed models were comparable with the results of the harvest studies conducted by other researchers in similar forest types validating the efficacy of mixed models at a landscape scale.

Azov sea fishes stock assessment by swept area method through bootstrap

Recently driven changes in habitat conditions in the Azov Sea make classic applications of swept area method inconsistent. Swept area stock assessment method modification for stock estimation of aquatic biological resources through nonparametric bootstrap is presented. New approach is tested against classic calculated results of swept area method for next fish species: Azov anchovy, tulka, turbot and Black-Azov sea shad according to data collected during 2022. Results comparison of classic and modified through bootstrap stock assessment methods leads no significant deviations in mean value estimation. Maximum difference between mean estimates of stock size was 3,75%. Confidential intervals width difference between reviewed methods, in general, was not significant. Most valuable difference in confidential intervals was detected in BCa approach against classic method with quantile approach (due to methodological features of BCa in bias correction procedure). The new stock assessment approach for swept area method though bootstrap permit to calculate statistically valid estimates, their confidential intervals and perform input data validation. The absence of significant differences in the results by the classical and proposed methods indicates the further applicable possibility of classic method even in normality violation in source samples. The normality requirements in methodology of classic approach, probably, is redundant.

Carapace Length-weight, Carapace Width-weight Relationships, Condition Factor and Sex Ratio of Freshwater Crab Sartoriana spinigera (Wood-Mason, 1971) from the River Burhi Gandak, North Bihar, India

Background: The Burhi Gandak river origins in the Chautarwa Chaur region near Bisambharpur in the West Champaran district of Bihar and flows through the East Champaran, Muzafarpur, Samastipur and Begusarai districts. It falls into the river Ganga near Khagaria district. Its coordinates are 25°51'40''N latitudes and 85°48'36''E longitudes. The total area of catchment is 12500 sq. km in size overall, of which 9601 sq. km are in Bihar and the rest in Nepal. The study will be helpful for stock estimation, developing conservation and choosing appropriate species for aquaculture. The present data can be used to develop conservation plans while providing biological data needed for an effective management of this fishery. Methods: The carapace length-weight, carapace width-weight relationship, condition factor and sex ratio of freshwater crab Sartoriana spinigera (n=663) collected from River Burhi Gandak, North Bihar, between July 2020 to June 2021. In the present investigation, freshwater crabs were collected from different types of techniques, including hand-picking, digging, setting traps and taking them from their nest. Result: Total of 663 specimens of Sartoriana spinigera (Wood-Mason, 1971) of both sexes (male and female) were collected in which 363 female and 300 male. The carapace length-weight of Sartoriana spinigera females were calculated as W = 0.9122.521 with R2 = 0.875, male W = 0.43102.505 with R2 = 0.853 and length-weight of combined sex (male and female) were calculated as W = 0.6612.701 with R2 = 0.954. The carapace width-weight of female were calculated as W = 0.5542.331 with R2 = 0.831, male W = 0.7912.421 with R2 = 0.802 and carapace length-weight of combined sex (male and female) were calculated as W = 0.18122.601 with R2 = 0.900. From this study showed it could be stated that crabs have allometric growth pattern. The Fulton’s condition factor (Kn) value were calculated as 1.10 and sex ratio calculated was 1:1.49. This paper provides information.

Carbon stock estimation based on remote sensing in the northern coast of Java

Mangroves are one of the land covers on the earth’s surface that being the largest storage of carbon reserves compared to other land covers. On the other hand, quick and accurate monitoring of carbon stocks on the earth’s surface is needed. This research was conducted on the northern coast of Java and used a flexible and efficient cloud computing-based remote sensing approach by using satellite imagery data. We identify land cover classification, especially mangrove, uses the Support Vector Machine (SVM) algorithm through the GEE (Google Earth Engine) platform. The estimated value of mangrove carbon was obtained from the NDVI index (Normalized Difference Vegetation Index) analysis on sentinel-2 images. The results showed that the estimated carbon value was 1,232,311.496 tones. Strong relationship is found between NDVI and carbon stocks with R2 of 98%. The study, therefore, strongly suggests the further use of NDVI to assess and monitor carbon stocks from mangroves in the future.