Assessment Of Biomass Research Articles

COMPARATIVE METHODS OF BIOMASS AND CARBON STOCK ASSESSMENT OF SOME SELECTED TREE SPECIES USED FOR CHARCOAL PRODUCTION IN THE GUINEA SAVANNA ECOLOGICAL ZONE OF NIGERIA

Non-destructive method of above-ground biomass (AGB) and carbon stock assessment is the most frequently used method to quantify the amount of carbon sequestered by the trees/vegetation because it involves assessing the biomass and carbon without cutting down the tree species. However, to validate the claim of the biomass and carbon stock of the non-destructive method, the destructive method must be employed to ascertain the real biomass and carbon stock by harvesting the tree species. This study incorporated both the destructive and non-destructive biomass and carbon stock assessment methods. The results of the independent t-test (t = 3.351, p = 0.00475) between the two methods revealed that the destructive method had higher biomass (M=0.425, SD=0.14313) than the non-destructive method (M=0.2338, SD=0.07463) at an alpha value of 0.05. The ANOVA among the tree species harvested for charcoal production further revealed a significant difference in the mean biomass, where Vitellaria paradoxa and Parkia biglobosa had the highest. At the same time, Pterocarpus erinaceus and Daniellia oliveri recorded the least biomass and carbon stock of the tree species used for charcoal production in the study area.

Integrating Copernicus LMS with ground measurements data for leaf area index and biomass assessment for grasslands in Poland and Norway

ABSTRACT The integration of satellite data from the Copernicus Land Monitoring Service (CLMS) with ground-based measurements represents a pioneering approach to the assessment of leaf area index (LAI) and in situ biomass at grasslands in Poland and Norway. The aim of this study was to develop a method for assessing grass growth conditions and predicting biomass yield based on Sentinel-2 data and CLMS products. LAI values derived from CLMS were compared with in-situ measurements on grassland plots in the two regions of Podlaskie (PL84) and Wielkopolskie (PL41). Small random statistical errors observed in the differences represent a significant opportunity to use Copernicus data to develop a relationship model for biomass prediction. The NDII index calculated using Sentinel2 was considered important for biomass assessment in the humid areas of Norway. This relational biomass prediction model could provide valuable information to farmers, improving their ability to manage grasslands effectively. As a result of the research, relational models were developed has been developed to predict grassland fresh biomass yield with an R 2 accuracy of 0.72 for the first cut, 0.81 for the second cut and 0.91 for the third cut. This allows farmers to effectively manage and monitor grassland throughout the growing season.

Enhancing Forest Canopy Height Mapping in Kaziranga National Park, Assam, by Integrating LISS IV and SAR data with GEDI LiDAR data Using Machine Learning

Abstract. This study investigates the integration of spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR data with optical imagery from Linear Imaging Self Scanning Sensor (LISS IV), Sentinel-1 Synthetic Aperture Radar (SAR) and PALSAR data for continuous forest canopy height mapping in Kaziranga National Park (KNP), Assam for the years 2018 and 2022. Four machine learning models were trained and evaluated to assess the predictive ability of LISS IV data in conjunction with SAR variables. The mean canopy height was measured at approximately 8.58 m in 2018, which increased to about 9.07m in 2022. Results reveal Extreme Gradient Boosting (XGB) as the top-performing model, achieving an RMSE of 5.47m and an R2 of 0.55. In comparison, Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN) achieved RMSE values of 5.49, 5.51, and 5.73, respectively. Analysis shows a prevalent occurrence of canopy heights below 5 meters in KNP (more than 35% of the area), while taller canopies beyond 20m can be found in less than 5% of the area. This finding underscores the importance of integrating satellite data and machine learning and highlights the novel application of LISS IV data in enhancing canopy height mapping. Furthermore, it represents the first comprehensive attempt to map canopy height in KNP, laying the groundwork for further research on biomass assessment and carbon sequestration in this vital biodiversity hotspot. Overall, the study highlights the potential of leveraging advanced remote sensing technologies and machine learning approaches for improved understanding and management of forest ecosystems.

Full life cycle assessment of insect biomass allows estimation of bioflows across water, air, and land

AbstractAs global environmental change continues, animals face uncertain habitat availability and quality that influences life cycle phenology and population dynamics. For decades, the population abundance and emergence patterns of burrowing mayflies have been used as a sentinel for water quality changes in large freshwater systems around the world. Despite reduced point source pollutants, evidence is mounting that the interactions among habitat loss, contaminants, and changing climate could be causing declines in mayfly production and shifts in emergence timing. We combined radar observations with traditional field measures to identify changes in mayfly populations from nymph to adult. We studied Hexagenia sp. secondary production in a large reservoir, Lake Seminole, which has contrasting water sources and land use on each arm that could contribute to differences in emergence patterns. We predicted that mayfly secondary production would be higher, and emergence would be earlier in the Chattahoochee arm versus the Flint arm because of differences in available nutrients and temperature. Benthic declines in abundance and biomass followed radar observations of emergence. Mean annual water temperature was similar, with the Flint arm having less seasonal variation. Mayfly growth was similar across the lake, but production was higher in the upper Flint arm, perhaps because of temperature stability, higher nutrient concentrations, and more lotic conditions. The natural abundance of nitrogen‐stable isotopes in mayflies showed distinct patterns between the arms and from nymph to adult. Linking benthic sampling with radar observations verified our capability to track mayfly biomass across the landscape and begin to calibrate previous measures of production with radar‐derived abundance. Coupling radar observations with stable isotope and tissue nutrient measurements allowed us to further quantify the subsidies moving from aquatic to terrestrial ecosystems, setting the framework to examine both historic and future population changes and mayfly contributions to cross‐ecosystem subsidies.

Assessment of biomass of moss Thuidium cymbifolium Dozy & Molk. for removal of Eriochrome Black T and Auramine-O dyes from aqueous solutions: Kinetics, Isotherms, Thermodynamics, and Microtoxicity studies

Assessment of biomass of moss Thuidium cymbifolium Dozy & Molk. for removal of Eriochrome Black T and Auramine-O dyes from aqueous solutions: Kinetics, Isotherms, Thermodynamics, and Microtoxicity studies

Assessment of Biomass, Carbon Stock and Sequestered CO2 in Teak (Tectona grandis L.f.) Plantation at Pt. Ravishankar Shukla University Campus, Raipur, Chhattisgarh, India

Carbon sequestration through tree plantation has significant role to mitigate the increasing level of CO2. Accurate measurement of carbon in teak plantations is required for estimating their contribution to global carbon stocks. The purpose of present work was to assess total biomass, carbon stock and sequestered CO2 in an 18 years old teak (Tectona grandis L. f.) plantation using allometric equations. Estimated total biomass, carbon stock and sequestered CO2 were 79.31, 37.28 and 136.66 t ha-1, respectively. Teak plantations in Chhattisgarh serve an important role in carbon sequestration, which contributes to climate change mitigation. It is a fast-growing species with robust, high-carbon wood, accumulates a substantial quantity of carbon over time. Expanding and sustaining these plants reduces atmospheric CO2 levels while also benefiting the region’s economy through timber production and forest-based livelihoods.

Farmers’ willingness to introduce short-rotation tree plantations on agricultural land: A case study in southern Sweden

To meet climate targets, expanding Populus spp. tree cultivation is proposed as a potential biomass feedstock, especially on agricultural land that does not come into conflict with food production. However, biomass potential assessments typically overlook landowners' perspectives, risking a gap between theoretical potentials and realisation. Here, we test empirical consequences of two hypotheses based on a survey targeting southern Swedish farmers: 1) Relying exclusively on agricultural land cover data to identify abandoned agricultural land leads to an overestimation of the total agricultural land that can be utilised for future biomass production from Populus spp. feedstocks. 2) The absence of data on farmers' intentions to cultivate fast-growing tree species on agricultural land leads to overestimation of the potential biomass supply from Populus spp. in biomass assessments. Findings suggest that less than 50 % of farmers with unsubsidised arable land, which is often assumed to be abandoned, would consider cultivating these tree species on this type of land (26 % [7–48]). Furthermore, only 11 % [6–17] would consider cultivating Populus spp. on agricultural land overall during 2021–2030, indicating a generally low level of interest among farmers. However, higher rates were observed in forested areas. The projected near-future cultivation potential of 2.0 kha [1.1–3.0] suggests an at least threefold overestimation in previous theoretical assessments. This study highlights a disparity between biophysical land data and producer perspectives, showing that neglecting farmers’ perspectives risks overestimating the biomass supply, potentially leading to misguided expectations and inefficient policies. Our findings support targeted policy recommendations.

Uncertainty assessment of grassland aboveground biomass using quantile regression forests

Uncertainty assessment of grassland aboveground biomass using quantile regression forests

Мегабентос западного прибрежья архипелага Новая Земля в приловах донного трала

As a part of a marine expedition on the R/V «Dalnye Zelentsy» along the western coast of the Novaya Zemlya archipelago, an assessment of megabenthic species diversity and biomass in bycatch of trawl was carried out in August 2023 specimens of 29 taxa belonging to 6 phyla were identified during the survey. The highest species diversity (20 species) was recorded in the northern coastal area of the Novaya Zemlya archipelago, where the dominant forms in terms of biomass were sea urchins Strongylocentrotus pallidus – 237.1 kg/km2. A tendency for a 5-fold decrease in megabenthos species diversity and a decrease in the average biomass of organisms – from 17.2 to 0.47 kg/km2 was detected with transition from north to south. The association of sea urchins S. pallidus and brittle stars Gorgonocephalus sp. (84 % biomass share of representatives in bycatch) is identified in the north of the study area, which is replaced by the association of sea urchins S. pallidus and crustaceans Chionoecetes opilio (74.6 %) when moving to the south. According to the results of the RDA analysis, the effects of trawl fishing and type of water mass have the greatest impact on species diversity and biomass of megabenthic organisms. Trawling intensity, type of water mass, and sediment characteristics explain 75 % of the observed variability in these biological parameters.

Assessment of mangrove structures and biomass on islands along the Java Sea: a case study on Bawean Islands and Karimunjawa Islands

Mangroves on the islands along the Java Sea play a crucial role in supporting biodiversity, protecting coastlines, and sequestering carbon. However, there is a lack of information on mangrove structure and biomass in the area. This study aimed to assess the mangrove structures and biomass on Desa Daun, located on the Bawean Islands, and Kemujan Island, located on the Karimunjawa Islands, respectively. The study utilized a combination of line transect and quadrant plot methods with a size plot of 10x10 m. We established 7 line transects and 28 quadrant plots in the mangrove forest of Desa Daun and similarly set up 7 line transects and 21 quadrant plots on Kemujan Island. Within each quadrant, the mangrove species were identified, and their diameters at breast height (DBH) were recorded. To assess the relative significance of each species, the importance value index (IVI) was calculated, and allometric equations were applied to estimate biomass and carbon storage in the trees. PlanetScope imagery was also used to quantify the carbon stock of mangroves on those islands. In Desa Daun, 13 mangrove species were identified, while 12 species were recorded on Kemujan Island. The species Nypa fruticans and Excoecaria agallocha exhibited the highest IVI in Desa Daun and Kemujan Island, respectively. Notably, Rhizophora apiculata demonstrated the greatest biomass in both locations, with measurements of 43.08 Mg B/ha in Desa Daun and 102.22 Mg B/ha in Kemujan Island. Overall, mangrove stands on Desa Daun stored biomass of 113.60 Mg B/ha and carbon of 50.98 Mg C/ha, whereas mangrove stands on Kemujan Island stored biomass of 295.04 Mg B/ha and carbon of 131.95 Mg C/ha. Mangrove stands of the whole Bawean Islands sequestered 22,941 Mg C, while Karimunjawa Islands hold much higher C stocks, 79,451 Mg C. Despite being smaller in size, the Karimunjawa Islands have more extensive mangrove coverage compared to the Bawean Islands due to their geomorphological characteristics, which in turn influence their mangrove structures and carbon stocks. However, the loss of mangroves in the Karimunjawa Islands due to aquaculture expansions could increase carbon loss in the islands.

Morphological discrimination of fouling Ulva and Blidingia species on Pyropia rafts in Rudong, China: an in-field protocol for early monitoring of algae forming the Yellow Sea green tide

Abstract Fouling seaweeds on nori cultivation rafts in Subei radial sand ridges (Jiangsu, China) are believed to be the source of the Yellow Sea green tide (YSGT). Although major species are consistent across years, their biomass and proportions undergo significant annual changes, which was speculated to be related to the size of YSGT. Therefore, early detection and biomass assessment of the bloom-forming algae are crucial, increasing the need to develop a morphological identification method suitable for large sample sizes in the field. In this study, fouling seaweeds were collected from this area in spring 2017. Five major species, Ulva aragoënsis, Ulva compressa, Ulva linza, Ulva prolifera, and Blidingia sp. were molecularly identified, and confirmed as the predominant fouling seaweeds on nori rafts over the past decade. In this unique ecological niche, the morphological features of species changed significantly yet stably. Based on morphological observations, an identification key for these five species was established. Furthermore, a simplified version containing only two indices was developed for practical field use, with an average accuracy of 81 % (δ 2 = 0.003) in empirical accuracy assessments. This method can be used to monitor the community structure and succession of fouling seaweeds at the geographical source of YSGT.

Tidal Impoundment and Mangrove Dieback at Cabbage Tree Basin, NSW: Drivers of Change and Tailored Management for the Future

Major storms can cause significant changes to coastal and wetland environments. A series of storm events in 2020 resulted in closure of the historically open estuary at Cabbage Tree Basin, Port Hacking, New South Wales (NSW), Australia. Prolonged tidal impoundment (3 months) led to substantial changes in hydrological and sedimentological processes, resulting in widespread mangrove dieback. This study aimed to quantify the degree of impact and recovery for mangroves, identify factors contributing to dieback, and consider the implications for carbon sequestration. This was achieved using remotely piloted aircraft structure-from-motion approaches, aerial photography, and field-based assessments of vegetation health and above-ground biomass (AGB). Mangroves were classified as ‘dead’, ‘partially dead’, and ‘live’. In October 2019, there was 10.8 ha of live mangroves, with this reduced to 6.6 ha by August 2022. Digital surface models (DSMs) were intersected with classified mangroves to assess the vertical distribution of each zone. All mangroves classified as ‘dead’ were distributed at elevations < 0.4 m with respect to the Australian Height Datum (AHD), suggesting these regions were persistently inundated, which was confirmed by water level loggers (inundated during logger deployment). Field data confirmed substrate elevation related to dieback with the proportion of ‘live’ mangroves greatest at elevations > 0.6 m AHD. Substrate elevation and distance to the estuary mouth were significantly correlated with species, with Avicennia marina located at lower tidal positions and closer to the entrance compared to Aegiceras corniculatum. The dieback event equated to a loss of 81.5 ± 48 Mg of above-ground biomass, 38.1 ± 22.5 Mg C, or 140 ± 82 Mg CO2 equivalence (CO2e). This study provides an important baseline for monitoring dieback events. Continued monitoring is crucial to assess recovery and to tailor management strategies.

Drivers of microbial carbon biomass variability in two oceanic regions of the Gulf of Mexico

The microbial plankton community is an integral part of the pelagic ecosystem. It hosts essential functional groups that play a vital role in organic carbon production, release, uptake, and degradation within open-ocean ecosystems. Given its significance, carbon biomass estimates are urgently needed, especially in oligotrophic regions, to provide and enhance our knowledge of biogenic carbon pools. They also aid in validating biogeochemical models that characterize the functioning of these extensive marine ecosystems within the global carbon cycle. This study addresses the temporal variability of microbial community biomass in two oceanic zones: the west-central (Perdido) and southern (Coatzacoalcos) areas of the Gulf of Mexico. During three seasonally contrasting periods (nortes, rainy, and dry seasons), seawater samples were collected from the euphotic zone in both regions to estimate the carbon biomass of different pico- (<2–3 µm), nano-, and microplankton groups (>3–200 µm). Carbon biomass assessments for the microbial groups were based on their abundance and carbon conversion factors. Overall, we found a significant contribution of pico-prokaryotic components (heterotrophic bacteria, Prochloroccocus, and Synechoccocus) to the total microbial carbon stock of the euphotic zone (84–89 % global estimates). The finding suggests these microorganisms are key functional groups that drive carbon production and fate in the Gulf of Mexico ecosystem. Pico-cyanobacteria, especially Prochloroccocus, were the dominant primary producers (68–82 % total autotrophic carbon), mainly in the upper layer of the oligotrophic euphotic zone. This vertical pattern implies that the deep chlorophyll-a maximum (DCM) depth level was unrelated to a net increase in phytoplankton biomass in the three study periods. The distribution of microbial carbon biomass exhibited striking differences associated with winter mixing (the nortes season), high river discharge accompanied by cross-shelf transport (the rainy season), and the dynamics of mesoscale structures. Ecological aspects, such as the habitat preference of the organisms and the seasonal complementary development of mixotrophic and heterotrophic grazers and their prey, were also essential drivers in regulating the microbial carbon pool of both oceanic regions. The microbial carbon assessments conducted in this study contribute to identifying and quantifying key planktonic functional groups involved in the biogeochemical carbon cycle in the Gulf of Mexico open-ocean ecosystem.

Assessment of Post-Harvest Rice Crop Biomass and Carbon Stock using Remote Sensing Data in Google Earth Engine

Assessment of Post-Harvest Rice Crop Biomass and Carbon Stock using Remote Sensing Data in Google Earth Engine

A Case Study on Biomass Assessment in a Semi-Arid Olive Rainfed System in Morocco

Thanks to its potential life span of hundreds of years, olive can develop great biomass and sequester large amounts of carbon (C). It may therefore play a critical role in reducing atmospheric C and improving farmer livelihoods by providing additional revenue from C credits. Predicting olive’s impacts on C cycling relies on allometric models. In our specific conditions, such models reliably predicted biomass accumulation with a minimum of three dendrometric measurements: tree age, height, and diameter at 10 cm.

Stereo matching and 3D reconstruction with NeRF supervision for accurate weight estimation in free-swimming fish

Non-invasive methods of accurate estimation of fish weight are essential for biomass assessment, precision feeding, and effective management in aquaculture. However, the lack of large-scale stereo matching datasets and costly depth sensing equipment in underwater scenarios present significant challenges to accurately measure the size of free-swimming fish. This study introduces a novel NeRF-supervised stereo matching network for precise depth estimation and 3D reconstruction of the fish body. The state-of-the-art neural rendering methods are employed to generate stereo training data from image sequences. In this study, the residual network with deformable convolutions was proposed to extract contextual texture features, significantly enhancing disparity estimation accuracy at the edge of the fish body. Additionally, multiscale spatial features are fused using a feature pyramid network (FPN). To enhance the adaptability of disparity search, we introduce a lightweight Triplet Attention module to capture cross-dimensional dependencies prior to constructing the correlation volume. Experimental results demonstrate the effectiveness of the proposed depth estimation method, achieving an impressive result of 92.35% in the similarity metric, compared to the ground truth. Moreover, we developed a weight estimation model based on bass perimeter using instance segmentation and 3D reconstruction techniques. Notably, a robust correlation (correlation coefficient of 0.98) between fish perimeter and weight is observed, highlighting the potential application of our methods to enhance the accuracy of weight estimation for free-swimming fish.

Monitoring the Vertical Variations in Chlorophyll-a Concentration in Lake Chaohu Using the Geostationary Ocean Color Imager

Due to the external environment and the buoyancy of cyanobacteria, the inhomogeneous vertical distribution of phytoplankton in eutrophic lakes affects remote sensing reflectance (Rrs) and the inversion of surface chlorophyll-a concentration (Chla). In this study, vertical profiles of Chla(z) (where z is the water depth) and field Rrs (Rrs_F) were collected and utilized to retrieve the vertical profiles of Chla in Lake Chaohu in China. Chla(z) was categorized into vertically uniform (Type 1: N = 166) and vertically non-uniform (Type 2: N = 58) types. Based on the validation of the atmospheric correction performance of the Geostationary Ocean Color Imager (GOCI), a Chla(z) inversion model was developed for Lake Chaohu from 2011 to 2020 using GOCI Rrs data (Rrs_G). (1) Five functions of non-uniform Chla(z) were compared, and the best result was found for Chla(z) = a × exp(b × z) + c (R2 = 0.98, RMSE = 38.15 μg/L). (2) A decision tree of Chla(z) was established with the alternative floating algae index (AFAIRrs), the fluorescence line height (FLH), and wind speed (WIN), where the overall accuracy was 89% and the Kappa coefficient was 0.79. The Chla(z) inversion model for Type 1 was established using the empirical relationship between Chla (z = surface) and AFAIRrs (R2 = 0.58, RMSE = 10.17 μg/L). For Type 2, multivariate regression models were established to estimate the structural parameters of Chla(z) combined with Rrs_G and environmental parameters (R2 = 0.75, RMSE = 72.80 μg/L). (3) There are obvious spatial variations in Chla(z), especially from the water surface to a depth of 0.1 m; the largest diurnal variations were observed at 12:16 and 13:16 local time. The Chla(z) inversion method can determine Chla in different layers of each pixel, which is important for the scientific assessment of phytoplankton biomass and lake carbon and can provide vertical information for the short-term prediction of algal blooms (and the generation of corresponding warnings) in lake management.

Cannabinoid residuals in tissues of lambs fed spent hemp biomass and consumer's exposure assessment

Spent hemp biomass (SHB) contains trace amounts of cannabinoids, including Δ9-tetrahydrocannabinol (Δ9-THC), that may accumulate in the tissues of animals consuming SHB. We measured cannabinoid residues in the liver, adipose tissue, and muscle of finishing lambs fed either 10% or 20% SHB for 8 weeks, or 4 weeks followed by 4 weeks SHB withdrawal. We detected multiple cannabinoids in the liver at a similar proportion to the SHB. However, CBD and Δ9-THC were enriched >20-fold in the adipose and muscle, compared to their proportion in SHB. The highest concentration of Δ9-THC was detected in adipose tissue and was 7.4-times higher than in muscle. Most cannabinoids were undetectable in tissues after 4 weeks of clearance. The consumers’ exposure assessment on Δ9-THC revealed tissue levels of total THC (THCA+Δ9-THC) that exceed the acute reference dose of 1 μg/kg BW across population groups. When consuming meat from the lambs fed 10% and 20% SHB, the maximum total THC exposure was 2.03 and 7.32 μg/kg BW, respectively, equal to or below the Lowest Observed Adverse Effect Level of 36 μg/kg BW, the No Observed Adverse Effect Level of 12 μg/kg BW or a tolerable dose intake of 7 μg/kg BW.

Weakly supervised classification of acoustic echo-traces in a multispecific pelagic environment

Abstract In trawl-acoustic methods, machine learning can objectively assign species composition to echo-traces, providing a reproducible approach for improving biomass assessments and the study of schooling behaviour. However, the automatic classification of schools in multispecies environments is challenging due to the difficulty of obtaining ground truth information for training. We propose a weakly supervised approach to classify schools into seven classes using catch proportions as probabilities. A balancing strategy was used to address high dominance of some species while preserving species mixtures. As the composition of schools from multispecific catches was unknown, model performance was evaluated at the school and haul level. Accuracy was 63.5% for schools from single-species catches or those identified by experts, and a 20.1% error was observed when comparing predicted and actual species proportions at the haul level. Positional and energetic descriptors were highly relevant, while morphological characteristics showed low discriminative power. The highest accuracies were obtained for juvenile anchovy and Muller’s pearslide, while sardine was the most challenging to classify. Our multioutput approach allowed the introduction of a metric to assess the confidence of the model in classifying each school. As a result, we introduced a method to classify echo-traces considering prediction reliability.

Loss of carbon stock in the forests of Uttarakhand due to unprecedented seasonal forest fires

Unprecedented seasonal forest fires pose a significant threat to the carbon stocks of diverse ecosystems, particularly in regions like Uttarakhand, west Himalaya. Understanding the impact of varying fire frequencies on different forest types is crucial for effective conservation and management strategies. This study aims to assess the loss of carbon stock in three distinct forest types—Sal, Pine, and Mixed across an elevation gradient in Uttarakhand, facing unprecedented seasonal forest fires. By investigating pre- and post-fire conditions, analyzing biomass dynamics, and mapping fire frequencies, the research aims to provide insights into the complex interplay of fire regimes and forest resilience. The investigation covers vegetation analysis, biomass assessment, and fire frequency mapping. Biomass and carbon stock calculations were carried out using a non-destructive sampling method. Fire frequency maps were generated using Landsat satellite imagery spanning a decade, integrating MODIS hotspot data for classification. The study reveals distinct patterns in biomass changes across Sal, Pine, and Mixed forests in response to varying fire frequencies. Sal forests exhibit resilience to low-intensity fires, while Pine forests show higher sensitivity. Carbon stock contributions of dominant species varied significantly, with Sal and Chir-Pine forests emerging as crucial contributors. High fire frequencies lead to substantial carbon stock reduction in all forest types. The findings emphasize the sensitivity of aboveground biomass to fire frequency, with significant carbon stock loss observed in higher fire frequency classes. The study underscores the importance of nuanced conservation strategies tailored to distinct forest types and species characteristics. This research provides valuable insights for policymakers, forest managers, and conservationists in formulating targeted conservation and management approaches.