There is generally an inevitable spatiotemporal transformation in land use and land cover (LULC) due to anthropogenic and natural factors. Deforestation and forest degradation are two of the main human-caused factors contributing to LULC alteration, particularly in tropical regions (Debel et al., 2014; Mba, 2018; Olusogo, 2018). Because forests can store carbon as biomass, deforestation and forest degradation influences the carbon emissions into the atmosphere, which causes global warming. Also, global warming in turn, triggers climate change (Arundhati et al., 2021). Climate change occurs due to natural and anthropogenic factors for millions of years (Gowda and Padaria 2022) through the release of greenhouse gases into the atmosphere, primarily carbon dioxide (Giri and Ranjan 2017).

There has been a growing interest in how different land use systems affect the concentration of CO2 in the atmosphere (IPCC et al., 2014). Also, the significance of grasslands and cultivated lands in the global carbon cycle is noteworthy. Trees in agricultural areas and other land-use systems have a great potential for sequestering carbon dioxide due to their large expanse. According to a worldwide survey, more than ten per cent of cultivated lands across the globe have more than 45% tree cover (Agevi et al., 2017). Additionally, it has been reported that cultivated lands can sequester biomass carbon, suggesting that trees in such lands have an equal capacity to sequester carbon and contribute to mitigating climate change. However, their carbon pools are still much smaller than forests' because the annual gains in fixed CO2 tend to be quickly released back into the atmosphere. The main causes of this are the burning of crop residue or decomposition, followed by harvesting-induced removal. Thus, knowing the role of various carbon pools as sources or sinks of carbon requires accurate inventorying and monitoring of the various pools under different land uses (Dube et al., 2016). 

For a reliable assessment of a country's potential for sequestering carbon dioxide, it is necessary to understand how the country's forests-including their age, composition, and effects from various management practices-affect carbon storage at the landscape level. Therefore, estimating biomass and carbon is a crucial procedure required towards reducing the effects of climate change (Nuthammachot, Askar, Stratoulias, and Wicaksono, 2020). The net carbon dioxide flux from land use, land-use change, and forestry comprises carbon fluxes from deforestation and forest degradation, regrowth of forests after wood harvest, and shifting cultivation. To discourage deforestation and forest degradation, the United Nations Framework Convention on Climate Change (UNFCCC) promotes the carbon offset program. In this regard, reducing greenhouse gas emissions from the land-use sector is used to mitigate climate change (Awoniyi and Amos 2016; Romijn et al., 2013). The carbon offset program provides financial incentives to nations that protect large areas of rainforest, which prevent deforestation and lessen carbon emissions (Saeed, McDermott, and Boyd, 2017). In other words, a nation can sell the avoided carbon emissions as carbon credits on the global market if it can demonstrate a decrease in greenhouse gas emissions through avoided deforestation and forest degradation. Of course, this will require estimation of the carbon stocks or monitoring, reporting, and verification (MRV).

Many studies on the assessment of biomass carbon stocks is in the literature. Rajpoot, Joshi and Singh (2023) estimated the carbon stocks in dominated forest of Palash, the Malwa Plateau Region of the India using forest inventory. The result showed the carbon stocks (Kg. ha-1) ranging from 57.00 to 67.00 with the mean carbon stocks of 62.50. Also, Padder et al. (2024) measured grass biomass dynamics in Daksum Forest Range, Kashmir using three 0.1 hectare transects based on altitude. The result revealed that the grasses' biomass content varied from 18.50 q ha-1 to 35.15 q ha-1. The lower altitude show the highest biomass content at 35.15 qha-1 as a result of less biotic interference. This was followed by middle site at 27.98 q/ha while upper altitude exhibit the lowest biomass content at 18.15 q ha-1. Furthermore, Kumar, Kaur and Narzary (2023) studied the basic concepts of evaluating pollution effects with an emphasis on forest ecosystems. They analysed the combined impact of climate change and pollution on forest ecosystem services (ES) based on chosen tree physiological functions.

Given the awareness of global climate change and carbon sequestration protocols for the voluntary and controlled markets, realistic methods for valuing vegetation biomass and carbon stocks are becoming increasingly important. Of course, calculating the total biomass of the forest indicates as to the likely quantity of carbon that can be released during the clearing or burning of forests as carbon dioxide. It also displays the amount of CO2 that a forest can absorb from the atmosphere. It is noteworthy that both the above-ground biomass (AGB) and below-ground biomass (BGB) makes up the forest biomass. Devi et al. (2014) suggested that forest AGB is a measure of the cumulative Net Primary Productivity of trees or shrubs. It can also be understood as the total mass of biomass in live plants above the soil, including woody plants, brush, and trees (including their stems, branches, and leaves) (Sharma et al., 2013). Since it is the largest carbon pool that is most prone to frequent changes, it needs to be regularly monitored. Globally, several instruments and methods have been employed such as forest inventories, remote sensing, and geographic information systems.

This research was conducted to study the methods for estimating the above-ground biomass and carbon stocks. The data used were gotten from a wide range of reputable digital databases including Scopus, Google Scholar, Web of Science, Science Direct, and other search engines. Numerous output sources, including journals, books, book chapters, institutional and government reports, webpages, and documents were found through the search. The important literature were chosen, read, arranged, assessed, synthesized, and used for the citation.

A. Forest inventory for the above-ground biomass estimation

Forests and woodlands, which include parklands, bush fallows, and savannahs, are replaceable natural resources. Additionally, the forest ecosystem contributes significantly to carbon reserves and mitigates the effects of global warming (Vicharnakorn et al., 2014; Addi et al., 2019; Tadese et al., 2019). This is because forests account for almost 80% of all terrestrial biomass that is alive today. Since the AGB of living trees makes up the majority of the total biomass in forests, it is the subject of most activities about assessments of forest biomass. The reason is that it is used to quantify the roles that forests play as carbon sources or sinks for supporting sustainable forest management (Temesgen et al., 2015; Pandit et al., 2018). However, forest ecosystems are complex entities that cannot be understood in the absence of methodical data and information.

Information about the state of the productivity of the forest is provided by the evaluation of forest resources concerning carbon stocks (Bohre et al., 2013) and timber volume (Onyekwelu et al., 2013). This assessment, which is typically completed through ground-based forest inventories is extremely important for the various applications. Some examples are science, decision-making, the creation of policies, and the creation of plans for sustainable management. Forest inventory is the methodical gathering of data and forest information for evaluation. It is typically used to gather the fundamental data and information needed for planning, monitoring, assessment, selling of timber, and sustainable forest management (Zerihun and Yemir 2013; Dau, 2015).

A wide range of organizations (e.g., governments, non-governmental organizations, research organizations, and logging companies) may have gathered data for the forest inventory for various reasons. Although the original purpose of the forest inventory was to supply wood, it eventually evolved into the standard method for quantifying and tracking the biomass of forests. The primary reason is that it provides the comprehensive and in-depth data needed to generate estimates of carbon stocks and change at the national level (Birdsey et al., 2013). For sustainable forest management (Tewari, 2016), they offer essential biotic and abiotic information about forest estates (Wenger, 2013). Measurements of biophysical characteristics, such as tree height and diameter at breast height (DBH, 1.3 m) are necessary for biomass estimation using allometric equations and are parts of the forest inventory process. According to Djomo et al. (2016), allometric models that depict the statistical relationships between tree biomass and the dendrometric variables work best in plantations or uniform forests with stands that are roughly the same age (Kumar et al., 2015).

Since wood is the primary product around which most temperate countries have based their political systems, these nations have robust systems in place for the assessment and management of their forest resources. This means that in the temperate region, where forest biomass stocks are a function of well-established sample-based forest inventories, nationally determined carbon contributions will be relatively consistent (Fridman et al., 2014). However, despite substantial efforts by the UN Food and Agriculture Organization (FAO) to establish such systems in several countries since the 1990s; national forest inventory systems are either very new or non-existent in many tropical or subtropical countries (Schimel et al., 2015). But, the growth of national forest inventories in the Democratic Republic of the Congo and Brazil suggests that the situation is changing (Xu et al., 2017).

A national inventory may survey not only trees but also other carbon pools such as downed dead wood, standing dead trees, the forest floor, soil organic carbon, understory vegetation, and carbon in harvested wood. If not, it is possible to estimate these variables using empirical models that link them to standard inventory estimates of volume, biomass, age of the forest, and other categorical variables like climate zone or ecoregion. They can also be estimated using models that estimate changes based on disturbance history and biomass dynamics. The literature contains a large number of studies on AGB estimation using forest inventory (e.g., Shamaki and Akindele 2013; Cohen et al., 2013; Mavouroulou et al., 2018). Field measurement using destructive or non-destructive sampling was used for these investigations as well as numerous others (see the following subsections). Furthermore, these field-based approaches have shown to be the most accurate methods when compared to others; however, they are typically very resource-intensive and area-limited (Deb et al., 2017; Urbazaev et al., 2018).

(i) Destructive sampling method. Of all the methods used to estimate AGB and carbon stocks in the forest biomes, the destructive method is the most straightforward and accurate. Destructive sampling was often used to create allometric equations that estimated the AGB (Zaki et al., 2018). With this direct method, all plants are destructively harvested, and divided into their parts (e.g., stem, branches, leaves, flowers, fruits, and roots), and the carbon content of each part is then calculated as a percentage of the measured biomass (indirect method) or determined analytically. First, certain tree parts such as the trunk, leaves, and branches, are harvested using the destructive biomass estimation method.  Then, their biomass is weighed and dried (Thomas et al., 2020). In other words, it consists of two types of measurement operations: laboratory (drying of biomass, density, and volume) and field (site preparation, measurement of felled trees, weighing of logs, and sample taking).

Biomass measurements can be performed on a plot area (Sharma and Chaudhry 2015) by measuring the total biomass of a particular area, or on a single-tree regression model (Henry et al., 2013) by measuring the biomass of each tree. Alternatively, a sample of trees grown under various growth conditions can be used. However, there are significant disparities in global estimates of the above-ground biomass and carbon storage due to limitations in single-tree biomass estimates (Mitchard et al., 2014). Of course, gathering measurement data for every tree is nearly impossible, particularly when doing so on a large scale (Liu et al., 2019). This is because more labour and resources are needed, which adds to the time and expense involved (Tomppo et al., 2014; Maunro et al., 2017). On the other hand, area-based approaches indicate a section or sections of a stand where all or a portion of the trees are measured (Rice et al., 2014). This method typically makes use of fixed area plots with varying configurations and extents, primarily for research and Continuous Forest Inventory [CFI] purposes.

(ii) Non-destructive sampling method. When estimating a tree's biomass without cutting it down, the non-destructive method is used. This approach works well in ecosystems that contain rare or protected tree species because it makes harvesting these species either impractical or impossible. The non-destructive approach uses allometric equations to only affect small tree branches as opposed to the entire tree being chopped down (Feng et al., 2017). Pragasan (2014) employed it to evaluate the overall carbon stocks of trees in the Eastern Ghats of India's Chitteri Reserve Forest. It is also possible to estimate the above-ground biomass using the non-destructive approach by scaling the tree and measuring the elements required to compute the biomass using allometric models. The drawback of this approach is that it is difficult to validate because it does not involve the felling of any particular species of tree. Also, climbing can be difficult; and it takes a lot of labour and time.

The "Manual for Building Tree Volume and Biomass Allometric Equations" (Lu et al., 2016) contains protocols from FAO (Food and Agricultural Organization) for evaluating the AGB using field-based inventories. Understanding the productivity, carbon fluxes, carbon sequestration, and tree site condition of forests depends on these estimations. On the other hand, measuring DBH and height across the terrain may be expensive, time-consuming, and challenging to repeat. Uncertainties in biomass estimation from allometric models can also result from errors in height and DBH measurements (Hunter et al., 2013). Also, care must be taken when extrapolating biomass from allometric models across a landscape because growing conditions can change depending on factors such as past land use, the stage at which forests are in their succession, as well as environmental and climatic restrictions (Lu et al., 2016). In summary, the non-destructive approach provides only the tree volume. One must rely on density values of tree components from literature, which are already the result of destructive processes to estimate the biomass of trees. Furthermore, the trees must be weighted and harvested to verify the estimated biomass.  As a result, there isn't a non-destructive biomass sample in reality.

(iii) Allometric equation for the above–ground biomass estimation. A large destructive sample is needed for allometric equations. Yet, the equations can be applied in the future as a non-destructive technique to calculate the amount of the AGB and carbon stocks, and then to calculate the nutrient pools, economic returns, and rotation span. The creation of new allometric models can enhance our knowledge of the architectural limitations on plant development and increase the precision of biomass assessment protocols (Chave et al., 2014). The foundation of allometric models is the relationship between biomass and morphological traits like height, canopy diameter, canopy volume, or basal diameter (Kuyah et al., 2016). One can use each of these parameters separately or in combination to create an allometric model.

The creation of allometric models for the AGB and carbon stocks estimation has attracted much research interest. The subject of these studies have been on various vegetation types (Dimobe et al., 2019), plantations (Traoré et al., 2018), major species of trees, and woods (Bayen et al., 2015), and others. The term "allometric equation" describes a statistical model that was created and is used to estimate the biomass of trees based on their non-destructive, easily measured biometrical characteristics. Based on one or more tree variables and dendrometric measurements, the models associate the biomass of a whole tree or specific tree components (like stems, branches, leaves, or roots) with one or more tree-related variables. In this instance, the ratios of the constituents—height and diameter, biomass and diameter, and crown height and diameter—follow standard guidelines that apply to all trees grown in identical conditions. These guidelines become more valuable in consistent forests or plantations with comparable ages of stands (Kumar and Mutanga 2017). The primary goal of developing an allometric model is to avoid destroying forests during the biomass estimation process while simultaneously providing a practical and environmentally acceptable method. However, before the equations can be used widely, they must first be validated, which necessitates chopping and weighing some tree components. 

.The diameter at breast height, the height of the tree, tree volume, and the density of the wood can all be measured when estimating the AGB non-destructively by climbing the tree and measuring its various parts. Next, an ecosystem-wide allometric model (Chave et al., 2014) or a species-specific allometric model (Djomo and Chimi 2017; Daba and Soromessa 2019) are developed using the reference biomass. For a given species, type of forest, and location; allometric models are the most effective and well-established. On the other hand, general allometric equations are accessible for individual countries, taxonomic groupings, and pan-tropical trees (Chave et al., 2014). Allometric models can generally be created to cover specific sites, regional or pan-tropical scales, and can be created for a single species or multiple species (mixture of species) to represent a community or bioregion. For instance, Ganamé et al. (2021) used five woody species with high socioeconomic significance to develop allometric models for estimating the AGB in Burkina Faso at the tree component, species, and site levels. The five species (Anogeissus leiocarpa, Pterocarpus erinaceus, Mitragyna inermis, Combretum nigricans, and Isoberlinia doka) were chosen at two forest sites according to their dominance in the savanna ecosystem. The generalized pan-tropical equation model for African tropical forests was used to validate the allometric models' performance. The results showed that the five species had different biomass predictors and that the developed allometric models outperform the pan-tropical model in terms of accuracy when it comes to estimating AGB. It is nearly impossible to create allometric equations for every species present in the ecosystem, so creating multi-species equations is essential. For instance, research by Chave et al. (2014) suggests that up to 300 distinct tree species may be found within a single hectare of tropical forest. Therefore, when compared to models created for individual species at particular locations, multi-species allometric models offer methodological efficiencies for biomass estimation. 

Building on a small sample spanning different diameters and heights, biomass equations are developed for a population of trees. The parameters of a mathematical model that connects biomass to the variables under observation are estimated. Alternatively, biomass can be estimated based on volume using the "Biomass Expansion Factor" (BEF) that was previously determined using tree diameter and bole length (Birdsey et al., 2013). Growing stocks can be switched to non-inventory portions of the tree using BEFs. Utilizing BEFs requires estimating stem and volume, and applying them. Thus, allometric equations are preferable to BEFs because they only need one computational step and thereby minimize error propagation (Rodríguez–Veiga et al., 2017).

Compared to other tropical continents, fewer allometric equations are reported to be available in Africa. As a consequence, the question of whether to estimate forest biomass using the pan-tropical generalized equation has arisen. Accordingly, it was determined that creating an allometric equation that is species- and site-specific, and accurate enough to estimate the AGB was crucial. This usually entails regression, where the physical characteristics of the trees (e.g., DBH, H, canopy spread, and wood density) determine the reference biomass. Based on different combinations of these parameters, many linear and non-linear models have been developed (e.g., McRoberts and Westfall 2014). Generally, Lu et al. (2016) suggested the formula for the determination of the AGB as: 

                AGB = f (DBH, H, S) 

This formula takes into account the tree height (H), and wood density (S), etc. Using non-destructive procedures, the AGB can be ascertained once the allometric equations have been derived. To do this, field sample plots will be randomly distributed and height, and DBH measured. For instance, Nunes and Camarg (2017) examined the relationship between biomass and various morphological variables to ascertain which variable is simpler to obtain for the aquatic macrophytes that are emerging, such as Spartina alterniflora and Crinum americanum. They measured the height and above-ground area of individuals from both species, varying in size and going through different developmental phases.  

B. Remote sensing of the above–ground biomass and carbon stocks assessment

The field measurements are generally the most dependable technique for obtaining accurate data on biomass and carbon values. But, it becomes less effective and more costly when applied to large area. Additionally, the field method requires a substantial amount of time and labour. As a result, it is only feasible in smaller areas (Du et al., 2014; Deb et al., 2017). Thus, remote sensing are used for estimating biomass and carbon stocks under different circumstances (Kumar et al., 2015; Yao et al., 2015; Ko et al., 2017; Jos et al., 2021). With remote sensing, it is possible to monitor the alterations in natural landscapes (Sohal and Kaushal 2023). Remote sensing generally refers to the use of electromagnetic radiation from a platform located far from an object, area, or phenomenon to make non-intrusive observations of the object, area, or phenomenon. This technology has been created and used for many years to gather information about various biomass kinds (Kumar et al., 2015). Of course, estimating carbon stocks derived from spatiotemporal geographical and global dimensions is made easy with the use of satellite data (Xiao et al., 2015; Yao et al., 2015; Ko et al., 2017). Numerous researchers have used a variety of remotely sensed data in this regard. Vastaranta et al. (2018), for instance, made use of WorldView–2 data. ALOS advanced visible and near-infrared radiometer type 2 (AVNIR-2) data was used by Wicaksono et al. (2016). Mangrove AGB was retrieved at the local scale by Fatoyinbo et al. (2018); Pereira et al. (2018) using airborne LiDAR data combined with a small number of field sampling plots.

Rapid advancements in airborne and spaceborne remote sensing technologies are being utilized more often to gather accurate and timely data over wide regions (Saukola et al., 2019). In the tropics, this supplement less frequent and spatially constrained field inventories to understand the function, structure, and productivity of forest ecosystems and to document changes in the forests' attributes at various spatial and temporal scales (Aubry–Kientz et al., 2019). Remote sensing data is suitable for biomass estimation due to its large spatial extent, high frequency of data acquisition, and higher spectral resolution (Barka et al., 2019). Besides, their usage increased significantly with open access to moderate- or high-resolution satellite data. Furthermore, the synoptic view, high spatiotemporal resolution, and digital format of remotely sensed data enable quick processing of large amounts of data and the availability of data for that specific forest area that is not accessible through field surveys (Sharma et al., 2013).

The upscaling and parameterization of field/plot level inventories of forest structure, ecosystem processes, and models depend heavily on remotely sensed data. This results from the extrapolation of the relationship across the landscape's range based on the association of field- and plot-based attributes with land cover appearances, forest structure, or spectral response (Masek et al., 2015). The literature (Xiao et al., 2019) details the developments and significant achievements made in biomass estimations from remote sensing since the late 1970s. Additionally, Lu et al. (2016) provide a thorough overview of a few algorithms and techniques for modelling and estimating biomass using a variety of remote sensing data with emphasis on the importance of multi-sensor data integration in biomass modelling studies.

Generally, there are two familiar ways to estimate biomass: either directly (using multiple regression analysis, k-nearest neighbour, or neural networks), or indirectly (based on attributes estimated from remotely sensed data) like Leaf Area Index (LAI), structure (crown closure and height), Vegetation Index (VI), or image objects like shadow fraction. Biomass estimation and long-term biomass monitoring can be achieved using optical remote sensing, which obtains signals reflected from the forest canopy to extract vegetation parameters that have a significant response to biomass (Tian et al., 2016; Vaglio Laurin et al., 2016). 

Vegetation indices, which are derived from mathematical operations between particular spectral bands of satellite data, can be mathematically associated with the red, green, blue, and near-infrared (NIR) bands of vegetation (Yao et al., 2015; Ayanlade, 2017). Furthermore, vegetation indices are typically used in the field to confirm the carbon stocks in many urban carbon stocks studies (Issa et al., 2020). Using various modelling algorithms and sensors including optical, microwave, light detection and ranging (LiDAR) data, or combinations of various data sources; numerous studies on the estimation of forest AGB have been carried out globally (Nguyen et al., 2020). Various vegetation indices generated from optical satellite images have been used to determine vegetation chlorophyll and canopy structure parameters (Zhu et al., 2015). Nevertheless, cloud coverage and the structural heterogeneity of the vegetation canopy remains a significant limitations (Kumar and Mutanga 2017). Also, the sensitivity of vegetation indices to change in biomass is low as a result of the limitations of optical remote sensing in detecting vertical distributions and saturation phenomena. These issues can be ameliorated using the Synthetic Aperture Radar (SAR), which is an active remote sensing technology that has developed rapidly in recent years. Many studies have shown that SAR data provide unique and valuable information on biophysical parameters by exploiting the particular sensitivity of radar backscatter signals to AGB. Also, L-band SAR is sensitive to the vertical structure of vegetation due to its ability to penetrate through the forest. The estimation of AGB and carbon stocks based on the SAR data normally involves a regression analysis between polarized microwave backscatter data and the ground-based data obtained from field plots. Nonetheless, saturation at high biomass levels and sensitivity to soil conditions are problems in estimating AGB using the relationship between observed biomass and SAR backscattering. Some approaches have been tested to overcome saturation issues, including SAR polarization ratios to detect the contribution of the volume of scattering from various polarizations. Another operative manner to estimate AGB is to apply light detection and ranging (LiDAR). The LiDAR system can deliver detailed measurements of vegetation structure as a point cloud, providing accurate AGB estimates without saturation at high biomass levels (Wang et al., 2019; Zhang et al., 2019)

Since labour costs are frequently lower ground-based inventories than those associated with installing and maintaining sophisticated remote sensing equipment and expertise, it may be more practical in many countries to estimate AGB and carbon stocks using ground-based inventories rather than remotely sensed data. Yet, as new technologies emerge and technical capacities are strengthened over the next few years, satellite-based estimates of forest carbon stocks will probably become more accessible. 

The use of digital remote sensing data of different spatial and spectral resolutions should form an essential part of large-area forest inventories. Of course, the use of good quality high-resolution satellite imagery will improve the accuracy of AGB and carbon stocks estimation. In recent years, the scientific community and policymakers have become increasingly interested in the use of remote sensing for estimating AGB in forest environments. There is a need to critically review the accuracy and precision of various remote sensing techniques against ground observation and among methods, and their applicability in geographically varied regions.