The review, led by Annissa Muhammed Ahmedin, followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, the rigorous protocol that has become the gold standard for synthesizing scientific evidence. From an initial search, the team distilled 147 peer-reviewed studies published between 2000 and 2025, spanning a quarter century of methodological evolution. The studies covered the full spectrum of approaches used today: direct field measurements, allometric modelling, satellite and airborne remote sensing, machine learning, carbon stock models, and increasingly, integrated frameworks that combine several of these techniques at once. By treating these studies as a single body of evidence rather than isolated reports, the review offers something rare—a bird’s-eye view of an entire measurement discipline.

At the foundation of everything lies the humblest method: walking into a forest with measuring tapes and measuring the trees. Field inventories remain the bedrock of biomass science. Teams of surveyors record trunk diameters, estimate tree heights, and identify species, then convert those measurements into biomass using allometric equations—mathematical relationships, often derived from felled and weighed trees, that link easily measured dimensions to total mass. These ground-based datasets are the only source of truly direct biomass information, and the review is emphatic on this point: field measurements and locally calibrated allometric equations remain indispensable for developing and validating every other method. Without trees that have actually been cut down, weighed, and analyzed, there would be no way to check whether a satellite algorithm or a machine learning model is telling the truth.

But field inventories have an obvious limitation: they are slow, labor-intensive, and expensive, and they sample only tiny fractions of forest landscapes. A single field plot might cover a fraction of a hectare, while a national forest estate spans millions. This is where remote sensing enters the story, and where the review documents a decisive shift. Over the past two decades, the field has moved decisively away from purely conventional inventories toward multi-source approaches that fuse ground observations with optical satellite imagery, radar, and Light Detection and Ranging technology, known as LiDAR. Each of these technologies reads the forest differently. Optical sensors capture reflected sunlight and can detect changes in vegetation cover, though they struggle with clouds and dense canopies. Radar penetrates cloud cover and responds to the woody structure of forests, making it valuable in perpetually cloudy tropical regions. LiDAR, whether flown from aircraft or carried by satellites, fires laser pulses that partially penetrate the canopy and return three-dimensional profiles of forest structure—arguably the closest a remote instrument comes to directly measuring biomass.

The third pillar of the modern approach is artificial intelligence. Machine learning algorithms, trained on combinations of satellite data and field plots, can learn complex, nonlinear relationships between what sensors observe from above and what surveyors measure on the ground. The review found that these techniques have substantially enhanced spatial coverage and predictive capability, allowing researchers to extrapolate from sparse field data across entire continents. Deep learning architectures can ingest stacks of imagery from multiple sensors and produce wall-to-wall biomass maps at resolutions that would have been unimaginable when the review’s earliest studies were published in 2000. The transition the authors describe is not a replacement of old methods by new ones, but a layering: artificial intelligence and remote sensing built on top of, and continuously anchored by, ground truth.

Yet the review’s most sobering finding concerns the limits of all this technology. Estimation accuracy, the authors conclude, remains strongly influenced by vegetation structure, environmental conditions, data quality, and model transferability. Dense tropical forests with towering, structurally complex canopies are notoriously hard to measure from space because sensors saturate—once biomass passes a certain threshold, the signal from above stops increasing, no matter how much carbon the forest actually holds. Open woodlands and savannas present different problems, with sparse trees and bright soil backgrounds confusing optical algorithms. Terrain matters too: steep slopes distort radar and LiDAR returns. A model trained in one forest type often performs poorly when transferred to another, a problem the review identifies as a central challenge for global carbon accounting.

Uncertainty compounds at every step of the estimation chain. A field plot measurement carries its own error; the allometric equation used to convert diameters to biomass carries another; the model linking field plots to satellite pixels adds more; and the process of scaling from pixels to landscapes and nations adds yet another layer. These errors do not simply average out—they can accumulate, and different studies handle them in strikingly different ways, making results difficult to compare. The review emphasizes that no single approach is universally optimal across all ecosystem conditions, a conclusion that cuts against the appeal of one-size-fits-all carbon mapping products. What works in boreal Canada may fail in the Congo Basin, and what succeeds in temperate plantations may stumble in mangroves.

The practical stakes of these methodological choices are enormous. Countries reporting forest carbon under international climate frameworks must produce numbers that are accurate, consistent, and defensible, because those numbers determine everything from national emissions accounts to the financial value of forest conservation programs. Carbon markets, which channel billions of dollars toward projects that claim to avoid deforestation or restore degraded land, depend on biomass estimates to calculate credits. Overestimates can inflate the climate benefits of a project; underestimates can discourage investment in genuinely valuable conservation. The review’s message is that reliable assessments require integrated frameworks that balance accuracy, scalability, cost, and uncertainty through the complementary use of field observations, remote sensing, and advanced modelling—rather than relying on any single source of evidence.

Looking forward, the authors identify three priorities. First, the world needs more biomass reference datasets in underrepresented regions. The field plots and destructively harvested trees that anchor allometric equations are heavily concentrated in North America and Europe, while tropical forests—the planet’s most carbon-dense ecosystems—remain comparatively undersampled. Every new allometric equation calibrated from local trees in Africa, Southeast Asia, or Amazonia ripples outward, improving estimates far beyond the plot where it was derived. Second, model validation and uncertainty quantification need strengthening, so that biomass maps come with honest error bars rather than deceptively precise single numbers. Third, the field should develop transparent, ecosystem-specific estimation frameworks—methods tailored to particular forest types, with documented assumptions that other scientists can scrutinize and reproduce.

The review arrives at a moment when the demand for forest carbon data is accelerating. As governments tighten reporting requirements and carbon markets come under scrutiny for the integrity of their credits, the science of weighing forests remotely has never mattered more. What this systematic review makes clear is that the technology is racing ahead—satellites with unprecedented resolution, algorithms that learn from oceans of data—but the discipline’s credibility still rests on the slow, unglamorous work of measuring trees on the ground and being honest about uncertainty. The forests, and the climate they help stabilize, deserve nothing less than estimates that can withstand that scrutiny.

Subject of Research: Methods for estimating forest biomass and carbon stocks

Article Title: A systematic review of biomass and carbon stock estimation approaches: Methods, uncertainties, and emerging opportunities

Article References: Ahmedin, A. M. (2026). A systematic review of biomass and carbon stock estimation approaches: Methods, uncertainties, and emerging opportunities. PLOS Climate, 5(9), e0000935. https://doi.org/10.1371/journal.pclm.0000935

Image Credits: AI Generated

DOI: 10.1371/journal.pclm.0000935

Keywords: forest biomass, carbon stocks, systematic review, remote sensing, LiDAR, machine learning, allometric equations, carbon accounting, climate mitigation, uncertainty, field inventories, PLOS Climate

The scale of the fieldwork alone sets the study apart. Using a stratified random quadrat method, the team laid out a total of 1,050 quadrats of different sizes across the forest, a sampling intensity that allowed them to capture the fine-grained structure of the tree community rather than relying on a handful of broad transects. Within each quadrat they recorded tree species identity, counted individual stems, measured trunk diameters at breast height, the standard forestry metric abbreviated as DBH, and calculated total basal cover, a measure of the ground area occupied by tree trunks that serves as a proxy for the dominance and developmental stage of a stand. From these raw measurements they derived a battery of classical ecological indices, including the Shannon-Weiner diversity index, Margalef’s species richness index, Simpson’s index of dominance, and Pielou’s species evenness index, each of which captures a different facet of how biological variety is organized in a community.

The contrast between the two habitats was stark. The interior habitat harbored 38 tree species, compared with only 22 in the edge habitat, and its stem density reached 1,460 stems per hectare against 1,093.33 stems per hectare at the edge. Total basal cover followed the same pattern, standing at 84.41 square meters per hectare in the interior versus 50.05 square meters per hectare at the edge, indicating that interior trees were not merely more numerous but also larger and structurally more dominant. The Shannon-Weiner diversity index, which blends species richness with the relative abundance of each species, scored 3.49 in the interior against 2.90 at the edge, while Margalef’s richness index registered 5.29 versus 3.13. In every measure of diversity and structural complexity, the deep forest outperformed its own margin.

Yet the edge habitat was not simply an impoverished version of the interior. Two indices actually favored the boundary zone: Simpson’s index, which measures the probability that two randomly drawn individuals belong to the same species, was higher at the edge at 0.07 compared with 0.04 in the interior, and Pielou’s evenness index reached 1.07 at the edge against 1.04 inside. These values suggest that although fewer species live at the edge, the individuals that do survive there are distributed more evenly among those species, a pattern often observed in disturbed environments where hardy, generalist species proliferate without any single one dominating. When the researchers subjected the tree communities of the two habitats to statistical comparison, the difference in species diversity proved highly significant at the p less than or equal to 0.01 level, confirming that the edge-interior gradient represents a genuine ecological discontinuity rather than random variation.

Disturbance emerged as the most likely driver of the divide. By applying a disturbance index to each habitat, the team found that the edge habitat had suffered 38.69 percent disturbance, nearly four times the 9.25 percent recorded in the interior. Fatasil Reserve Forest sits within the Kamrup Metropolitan District, pressed against the expanding city of Guwahati, and its margins absorb the brunt of human activity, from fuelwood collection and grazing to encroachment and the microclimatic changes that follow when a closed canopy is opened to the sky. Edge effects of this kind are among the most extensively documented phenomena in conservation biology. Fragmented habitats expose interior species to altered light, temperature, wind, and humidity regimes, and the landmark long-term studies of Amazonian forest fragments cited in the paper have shown that such changes can trigger cascading losses of biodiversity that deepen over decades.

The carbon story proved equally dramatic, if statistically subtler. Total biomass in the interior habitat reached 1,023.62 megagrams per hectare, nearly double the 535.86 megagrams per hectare found at the edge. Converting biomass to carbon using standard allometric formulas and the widely applied assumption that roughly half of dry biomass is carbon, the researchers estimated a total carbon stock of 511.81 megagrams of carbon per hectare in the interior versus 267.93 megagrams of carbon per hectare at the edge. Expressed in terms of climate impact, the carbon dioxide equivalent came to 1,878.34 megagrams per hectare in the interior and 983.30 megagrams per hectare at the edge. In other words, the protected heart of this forest holds almost twice the climate-warming carbon of its exposed rim, and the difference between the two zones amounts to hundreds of megagrams of carbon dioxide equivalent per hectare.

Interestingly, when the researchers ran their statistical tests, the differences in total biomass, total carbon stock, and carbon dioxide equivalent between the two habitats did not reach significance at the p less than or equal to 0.01 threshold, even though the raw values differed substantially. The authors attribute the pattern to the enormous contribution of large-diameter trees, whose individual biomass can outweigh that of dozens of smaller stems, introducing high variability into the estimates. What did emerge as highly significant was the distribution of carbon across diameter classes. Carbon stock differed significantly among DBH classes in both habitats, peaking in the 20.1 to 30 centimeter class. This finding carries practical weight: mid-sized trees, large enough to accumulate serious woody biomass but still in their vigorous growth phase, are the workhorses of carbon sequestration in this forest, and any management action that removes or suppresses this size class would disproportionately undermine the forest’s climate function.

The biomass estimates themselves rest on allometric equations, the mathematical models that translate easily measured variables such as trunk diameter into harder-to-measure quantities like total aboveground mass. The study draws on the pantropical allometric frameworks refined by Chave and colleagues, which were calibrated through destructive sampling of trees across the tropics, alongside root biomass allocation factors developed for upland forests to account for the belowground component. This methodological lineage matters because carbon accounting for forests underpins everything from national greenhouse gas inventories to REDD+ programs that pay developing countries to keep their forests standing. When a study demonstrates that a single reserve forest can vary by nearly 250 megagrams of carbon per hectare between its edge and its interior, it underscores how coarse national averages can mask the fine-scale heterogeneity that determines where conservation dollars actually buy the most carbon.

The context of Fatasil Reserve Forest makes these findings especially urgent. Guwahati is one of the most rapidly urbanizing cities in northeastern India, and its green fragments serve double duty as biodiversity refuges and as buffers against urban heat, air pollution, and flooding. The reserve also features in documented human-wildlife interactions, including leopards moving through the urban matrix, a reminder that these fragments are living ecosystems rather than ornamental parks. Previous work by the same research group has examined tree diversity in Assam’s tropical moist deciduous forests, biomass in urban campus trees, and carbon stored by roadside plantings in Guwahati, building a picture of how vegetation across the urban-rural gradient of the region stores and cycles carbon. The new edge-interior analysis adds a critical dimension to that picture by showing that the value of a forest fragment is not uniform across its area.

The management implications are clear and the authors state them directly: the interior habitat’s ecological significance and carbon sequestration potential deserve protection, while the edge habitat requires targeted interventions to arrest its degradation. Buffer plantings, restrictions on biomass extraction, and restoration of degraded margins could shrink the effective edge zone and allow interior conditions to expand, a strategy consistent with global evidence that edge effects diminish as fragments grow or as their boundaries are softened. For a forest holding over 1,800 megagrams of carbon dioxide equivalent per hectare in its interior, the stakes of such interventions extend from the neighborhoods of Guwahati to the global climate ledger. As tropical forests worldwide continue to be carved into ever smaller pieces, studies like this one provide the quantitative grounding needed to argue that what happens at the edge of a forest is never confined to its edge.

Subject of Research: Edge-interior variation in tree diversity, community structure, and carbon stock in a tropical reserve forest in Assam, India

Article Title: Disparities in species diversity, community characteristics, and carbon stock of trees along the edge-interior gradient of Fatasil Reserve Forest, Assam, India

Article References: Kalita, P., & Yumnam, J. Y. (2026). Disparities in species diversity, community characteristics, and carbon stock of trees along the edge-interior gradient of Fatasil Reserve Forest, Assam, India. Environmental Monitoring and Assessment, 198(10), Article 1120. https://doi.org/10.1007/s10661-026-15966-y

Image Credits: AI Generated

DOI: 10.1007/s10661-026-15966-y

Keywords: forest ecology, edge effects, carbon stock, biodiversity, tree biomass, Fatasil Reserve Forest, Assam, habitat fragmentation, disturbance, allometric equations, tropical forest, carbon sequestration