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Review

A Review of Biomass Estimation Methods for Forest Ecosystems in Kenya: Techniques, Challenges, and Future Perspectives

by
Hamisi Tsama Mkuzi
1,
Caleb Melenya Ocansey
1,2,
Justin Maghanga
3,
Miklós Gulyás
1,*,
Károly Penksza
4,*,
Szilárd Szentes
5,
Erika Michéli
1,
Márta Fuchs
1 and
Norbert Boros
1
1
Department of Soil Science, Institute of Environmental Science, Hungarian University of Agriculture and Life Sciences, Páter Károly u. 1, 2100 Gödöllő, Hungary
2
CSIR—Crops Research Institute Fumesua, Kumasi GC-112-3633, Ghana
3
Department of Mathematics, Statistics and Physical Sciences, School of Science & Informatics, Taita Taveta University, Voi P.O. Box 635-80300, Kenya
4
Department of Botany, Institute of Agronomy, Hungarian University of Agriculture and Life Sciences, Páter Károly u. 1, 2100 Gödöllő, Hungary
5
Animal Breeding, Nutrition and Laboratory Animal Science Department, University of Veterinary Medicine Budapest, István u., 1078 Budapest, Hungary
*
Authors to whom correspondence should be addressed.
Land 2025, 14(9), 1873; https://doi.org/10.3390/land14091873
Submission received: 8 August 2025 / Revised: 10 September 2025 / Accepted: 11 September 2025 / Published: 13 September 2025
(This article belongs to the Special Issue Forest Soil-Water Resilience to Climate Change: Mechanisms and Management)
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Abstract

Accurate forest biomass estimation is essential for quantifying carbon stocks, guiding sustainable forest management, and informing climate change mitigation strategies. Kenya’s forests are diverse, ranging from Afromontane and mangrove ecosystems to dryland woodlands and plantations, each presenting unique challenges for biomass measurement. This review synthesizes literature on field-based, remote sensing, and machine learning approaches applied in Kenya, highlighting their effectiveness, limitations, and integration potential. A systematic search across multiple databases identified peer-reviewed studies published in the last decade, screened against defined inclusion and exclusion criteria. The main findings are (1) Field-based techniques (e.g., allometric equations, quadrat sampling) provide reliable and site-specific estimates but are labor-intensive and limited in scalability. (2) Remote sensing methods (LiDAR, UAVs, multispectral and radar imagery) enable large-scale and repeat assessments, though they require extensive calibration and investment. (3) Machine learning and hybrid approaches enhance prediction accuracy by integrating multi-source data, but their success depends on data availability and methodological harmonization. This review identifies opportunities for integrating field and remote sensing data with machine learning to strengthen biomass monitoring. Establishing a national biomass inventory, supported by robust policy frameworks, is critical to align Kenya’s forest management with global climate and biodiversity goals.

1. Introduction

Forests play a critical role in regulating the global carbon cycle, conserving biodiversity, and supporting human livelihoods [1]. Accurate estimation of forest biomass is fundamental for quantifying carbon stocks, assessing ecosystem services, and designing strategies for climate change mitigation. Globally, numerous techniques have been developed for biomass assessment, ranging from direct destructive sampling to advanced remote sensing and machine learning approaches. These methods vary in accuracy, cost, and applicability depending on forest type and management context [2,3,4].
Kenya’s forests represent a diverse ecological mosaic, including Afromontane forests, mangroves, tropical rainforests, dryland woodlands, community-managed forests, and plantations Together, they store significant carbon and provide essential ecosystem services such as watershed protection, habitat for endangered species, and resources for local communities [5,6]. However, these forests are under increasing pressure from deforestation, agricultural expansion, overgrazing, and climate variability. Although national tree cover increased to 12.1% by 2021 [7], surpassing the constitutional target of 10%, biomass loss and forest degradation remain serious concerns.
Accurate biomass estimation in Kenya is complicated by species diversity, ecological heterogeneity, and data limitations [8]. Traditional field-based methods, such as allometric equations and quadrat sampling, provide reliable estimates but are labor-intensive and difficult to scale. Remote sensing techniques, including LiDAR, Unmanned Aerial Vehicles (UAVs), and multispectral imagery, offer large-scale coverage but require calibration and technical capacity [9,10]. More recently, machine learning algorithms such as Random Forest and Support Vector Machines have been used to integrate multi-source data, enhancing predictive accuracy [11]. Despite this progress, the lack of a consolidated review of these methods in the Kenyan context limits the ability of researchers and policymakers to select the most appropriate tools for forest monitoring and carbon accounting.
This review addresses that gap by systematically synthesizing biomass estimation techniques applied in Kenya, evaluating their effectiveness, limitations, and integration potential. Specifically, the review aims to:
  • Summarize biomass estimation methods applied across Kenya’s diverse forest ecosystems.
  • Compare the strengths, limitations, and applicability of field-based, remote sensing, and machine learning approaches.
  • Identify methodological challenges and opportunities for advancing biomass estimation in Kenya.
  • Highlight implications for sustainable forest management, carbon monitoring, and alignment with global climate goals.
By consolidating existing evidence and providing comparative insights, this review contributes to both scientific understanding and practical policy design for forest conservation and climate mitigation in Kenya and beyond.

2. Materials and Methods

A systematic literature search was conducted to identify peer-reviewed studies on biomass estimation methods applied in Kenyan forest ecosystems. The key concepts and terms in this study were biomass estimation techniques, forest ecosystems, and their relevance in Kenya. The main keywords and their variants included in the study are summarized in Table 1 below:
To gather all the information on the subject, this study made use of effective and pertinent databases and search engines. Six electronic databases were selected for their breadth and relevance: Google Scholar, PubMed, IEEE Xplore, ScienceDirect, SpringerLink, and Wiley Online Library. Boolean operators like AND and OR were applied to link terms to increase the specificity of the search results. For instance, “biomass estimation in forest ecosystems in Kenya” and “UAV technology for above-ground biomass estimation” could have been among the search terms.
The review applied clear eligibility criteria, including only peer-reviewed articles published between 2009 and 2024 that focused on Kenyan forests or wooded ecosystems and explicitly reported biomass estimation methods. Exclusions covered non-peer-reviewed or outdated studies, work outside Kenya, and papers without methodological detail. Screening followed PRISMA guidelines (Figure 1): studies were recorded, duplicates removed, and studies assessed through title or abstract review and full-text evaluation. Ten eligible studies were retained for data extraction and synthesis (Table 3). From each, information was extracted on methods, ecosystem type, geographic scope, strengths, limitations, and quantitative outcomes. These data were organized into comparative tables (Table 4) to evaluate method applicability across Kenyan forests.
For the international context, Kenya spans diverse ecological zones from humid Afromontane to arid woodlands, with forests covering ~12.1% of its 569,250 km2 land area as of 2021 [7,12]. Rainfall ranges from <500 mm in arid regions to >2000 mm in highlands [13], and forest governance is guided by the Forest Conservation and Management Act (2016) [14]. This ecological and policy background provides the foundation for interpreting the scope and relevance of biomass estimation methods in Kenya.

3. Kenyan Forest Ecosystems

3.1. Forest Structure, Diversity, and Ecological Significance in Kenya

Kenya’s forest ecosystems are diverse and ecologically significant, including natural forests, plantations, community forests, farm forests, and private forests in the Arid and Semi-Arid Lands (ASALs) and agricultural regions [15]. These ecosystems vary structurally due to climate, altitude, and soil type differences, leading to distinct variations in biomass, biodiversity, and carbon sequestration potential [2]. Afromontane forests, such as the Mau Forest, Mt. Kenya, Aberdare, and Cherangani Hills, exhibit high tree diversity and biomass [16]. These forests are essential for regulating hydrological cycles, preventing soil erosion and acting as vital carbon sinks [17]. Additionally, they support numerous endangered species, making them essential for biodiversity conservation [18].
Diverse structural patterns found in Kenyan forests affect their ecological functions and ability to store carbon. Ecosystem resilience and productivity are determined by forest structure characteristics like species composition, tree density, and canopy height. Forest function is evaluated at stand and landscape scales, and metrics of structural diversity, such as biomass density and basal area, offer information about the stability of the ecosystem [2]. Afromontane and dry tropical forests, characterized by high species diversity, enhance ecosystem stability through efficient light distribution and nutrient cycling. Monoculture plantations, on the other hand, exhibit less variability, which leaves them more vulnerable to diseases, pests, and climate change [19].
According to the National Forest Resource Assessment report, Kenya’s forest cover was 8.0% in 1990, equivalent to approximately 45,426 km2 of the country’s total land area of 569,250 km2. However, between 1990 and 2000, this coverage declined to 2.3% before increasing to 8.83% (c. 5,226,192 hectares) by 2018 (Figure A1) due to improved reforestation activities [2]. By 2021, total tree cover had risen to 12.13% (c. 7,180,000 hectares), surpassing the constitutional target of 10% [7]. The coastal and Western regions recorded the highest tree cover.
Young trees, especially in degraded or recovering forests, are essential for regeneration and the preservation of structural diversity. Research suggests that mixed-species forests provide better carbon sequestration outcomes than monoculture plantations due to their diverse growth rates and ecological interactions [7]. Wooded grassland forests within dryland ecosystems present distinctive traits, with tree density and canopy cover fluctuating based on environmental factors such as rainfall patterns and soil properties [20]. Remote sensing technologies, such as LiDAR and UAV systems, have enhanced the quantification of forest structure attributes. Accurate measurements of tree height, canopy cover, species identification, and biomass estimation are provided by these techniques. For instance, spatiotemporal analysis of forest cover change in the Cherangani Hills demonstrates how land use changes affect biomass dynamics and carbon storage in Kenyan ecosystems [21].
Agriculture, land conversion, and logging have fragmented forests, negatively impacting forest structure and biodiversity [22]. Fragmentation reduces biomass density, species richness, and resilience to climate change. To lessen these effects, conservation strategies should prioritize the establishment of ecological corridors to enhance landscape connectivity, thereby protecting forest integrity and sustaining biodiversity [3]. Efficient conservation planning and forest management, aided by technological developments in remote sensing, will be essential in ensuring the long-term stability and ecological function of Kenya’s forests.

3.2. Types of Forests in Kenya

Kenya’s forest landscape is composed of multiple forest types that reflect the country’s ecological and climatic diversity (Figure 2). These include the moist Afromontane forests found in highland areas, dryland forests prevalent in arid and semi-arid zones, coastal and mangrove forests along the Indian Ocean, plantation forests established for timber production, and community and farm forests that contribute significantly to agroforestry and rural livelihoods (Table 2). Each of these forest types plays a unique role in ecological stability, carbon storage, water regulation, and socio-economic development. The following subsections provide a more detailed characterization of each type.

3.2.1. Afromontane Forests

Afromontane forests, such as the Mau Forest, Mt. Kenya, Aberdare, and Cherangani Hills, exhibit high tree diversity and biomass [2]. These forests are crucial in the hydrological cycle, preventing soil erosion and acting as vital carbon sinks [3]. Additionally, they support numerous endangered species, making them essential for biodiversity conservation [4]. Located between 1800 and 3000 m above sea level, these montane ecosystems receive 1000–3000 mm of annual rainfall and are characterized by cool, moist conditions and fertile volcanic soils. Dominant flora include Ocotea usambarensis, Podocarpus spp., and Juniperus procera, with bamboo (Yushania alpina) forming dense belts at higher elevations [23]. Important species like the African elephant, black rhino, mountain bongo, and endemic birds like the Aberdare cisticola are all part of the fauna’s diversity. These forests support both ecological integrity and human well-being by regulating water catchments and regulating the climate.

3.2.2. Mangrove Forests

Mangrove forests are major carbon sinks due to their high biodiversity and biomass density (Figure 3). Mangroves excel in capturing carbon in both above- and below-ground biomass and are therefore efficient long-term carbon storage, highlighting the need for comprehensive carbon pool assessments [24]. According to [25], soil carbon storage in mangrove ecosystems can range from 400 to over 1000 Mg C/ha, depending on variables like species composition, sediment depth, and geomorphological setting. Mangroves flourish in intertidal zones at elevations near sea level in Lamu, Mombasa, and Kwale counties, which are important mangrove areas along the Kenyan coast. According to [26] these, forests usually grow in clay-rich, anaerobic, waterlogged soils that have a high salinity and organic content. The dominant mangrove species include Rhizophora mucronata, Avicennia marina, Ceriops tagal, and Sonneratia alba, which differ in zonation according to tidal inundation and salinity tolerance. In addition to sequester carbon, these species offer vital ecosystem services like shoreline stabilization, pollution filtration, and mollusk, fish, and crustacean breeding grounds. These habitats are also essential for avian species such as the mangrove kingfisher and different types of waders.
Table 2. Forest Types in Kenya and Spatial Coverage in Kenya (Source: [27]).
Table 2. Forest Types in Kenya and Spatial Coverage in Kenya (Source: [27]).
Forest TypeLocationMain ThreatsSpatial Coverage (ha)
Afromontane Forests (Mixed indigenous forests
Bamboo forests)		Mt. Kenya, Aberdares, Mau Complex, Cherangani HillsEncroachment, logging, climate change1,445,553
Tropical Rainforests (Mixed indigenous forests)Kakamega, Shimba Hills, South NandiAgricultural expansion, logging, habitat fragmentation144,615
Coastal Forests and KayaKwale, Mombasa, Tana Delta, KilifiLand reclamation, logging, urbanization295,871
MangrovesLamu, Kwale, Mombasa, Tana Delta, KilifiOverharvesting, aquaculture, salinity intrusion48,522
Dryland/WoodlandsBaringo, Turkana, Samburu, Kitui, Taita TavetaOvergrazing, charcoal production, desertification1,875,316
Riverine forestsAlong Tana, Athi, Ewaso NyiroWater abstraction, pollution135,231
Plantation Forests (Indigenous and exotic trees)Mau Complex, Kericho, Uasin Gishu, Mt. KenyaPoor management, illegal logging286,716
Total Area 4,231,824

3.2.3. Coastal and Kaya Forests

The Kaya forests are situated in the coastal area of Kenya, primarily within Kilifi, Kwale, and Mombasa counties, and are revered as sacred landscapes by the Mijikenda communities. Traditionally used for religious rituals and community governance, these forested sites have been designated UNESCO World Heritage Sites due to their profound cultural, spiritual, and ecological significance [28]. Ecologically, the Kaya forests are remnants of the coastal tropical dry forests, typically found at low elevations ranging from 30 to 300 m above sea level. They support a distinctive collection of plants and animals and are found on sandy or loamy soils that drain well and are formed from ancient coral limestone. In addition to a significant number of rare and endemic species such as Vitex keniensis and Zanthoxylum chalybeum, the dominant tree species are Afzelia quanzensis, Milicia excelsa, and Encephalartos hildebrandtii [29]. Numerous birds, butterflies, and primates, such as the endangered Fischer’s turaco and the coastal Sykes’ monkey, can be found in the forests. Despite their significance, illegal logging, fast urbanization, and land conversion for infrastructure and agriculture are posing a growing threat to the Kaya forests. In addition to threatening biodiversity, this degradation weakens the traditional ecological knowledge and cultural heritage ingrained in these sacred spots.

3.2.4. Dryland Forests

A vital part of Kenya’s forest ecosystem are dry forests, which are primarily found in semi-arid areas like portions of Baringo, Samburu, Kitui, and Tana River counties. These forests, which are usually found between 400 and 1200 m above sea level, grow well on sandy to loamy, shallow soils that drain well and are frequently covered by rocky substrates that are low in organic matter. These ecosystems are dominated by drought-tolerant tree species like Commiphora Africana, Acacia tortilis, and Acacia senegal [30]. They have impressive aridity adaptations, such as small, waxy leaves that prevent water loss and deep root systems. Given that species like Acacia tortilis exhibit high root-to-shoot ratios and significant carbon capture for their size, these dryland forests are crucial for storing carbon, especially through below-ground biomass [31]. In otherwise hostile settings, they also offer vital ecosystem services like water retention, microclimate regulation, and erosion control. Birds like hornbills and weavers, which rely on the thorny canopy and shrub understory for nesting, as well as fauna like dik-diks, gerenuks, and other reptile species, are all examples of biodiversity. These forests support local livelihoods by providing fuelwood, medicinal plants, honey, and gums. However, overgrazing, the production of charcoal, illicit logging, and conversion to cropland continue to put them under tremendous strain [32]. Their conservation is essential for maintaining carbon stocks in arid regions, improving resilience to climate change, and promoting biodiversity. Improved biomass modeling using altitude–carbon relationships may further enhance monitoring and management across Kenya’s forest types [33].
Land 14 01873 g003aLand 14 01873 g003b
Figure 3. (a) Acacia tortilis (Source: https://acacia-ae.com); (b) Commiphora africana (Source: https://alchetron.com); (c) Mangrove forest (Source: Authors); (d) Bamboo forest (Source: Authors).
Figure 3. (a) Acacia tortilis (Source: https://acacia-ae.com); (b) Commiphora africana (Source: https://alchetron.com); (c) Mangrove forest (Source: Authors); (d) Bamboo forest (Source: Authors).
Land 14 01873 g003aLand 14 01873 g003b

3.2.5. Community and Farm Forests

Community forests and farm forests have recently gained prominence for their multifaceted role in agroforestry systems, offering timber, food, fodder, and fuelwood while simultaneously enhancing carbon sequestration and promoting sustainable land use [34]. These landscapes are typically located across a broad altitudinal range—from lowland dry zones to mid- and high-elevation agro-ecological zones (approximately 800 to 2500 m above sea level)—and are often established on ferralsols and luvisols in humid regions, or on sandy loams and shallow stony soils in drylands. Species selection in these systems reflects both ecological conditions and livelihood needs. In highland Afromontane zones, Juniperus procera and Grevillea robusta are commonly grown for their timber and soil-enhancing properties, while in dryland areas, drought-tolerant species such as Acacia senegal, Acacia tortilis, and Melia volkensii dominate due to their resilience and rapid growth [18]. These species display distinct biomass allocation strategies, influenced by their physiological plasticity and functional traits [35]. Biomass estimation in these heterogeneous and dynamic environments often relies on basal diameter as a reliable predictor in young and regenerating stands where traditional diameter-at-breast-height (DBH) measurements are impractical [36]. Species-specific allometric models have also been successful in increasing the precision of biomass assessments in arid and semi-arid lands (ASALs) [37], facilitating more informed management and carbon accounting in these changing forested landscapes.

3.2.6. Plantation Forests

Species characteristics influence biomass estimation. Afromontane forests are dominated by slow-growing hardwoods such as Podocarpus latifolius, Juniperus procera, and Ocotea usambarensis, which contribute to higher AGB density due to their dense wood and long lifespans [38]. Usually found at elevations of 1800 to 3000 m, these forests thrive on volcanic soils that are rich in organic matter and have good drainage [39]. On the other hand, fast-growing exotic softwoods like Pinus patula and Cupressus lusitanica make up most plantation forests, which are typically found at lower elevations between 1500 and 2400 m [40]. These species are selected for their rapid growth rates and economic viability in timber production, serving the demands of the Kenyan construction and wood-processing industries. However, their lower wood density results in reduced biomass per unit area compared to native montane forests. Additionally, plantation forests have limited ecological resilience and support lower biodiversity, providing fewer ecosystem services like soil stability, carbon sequestration, and habitat provision for native fauna [41]. The reliance on monoculture plantations thus necessitates sustainable management practices to balance timber production with ecological integrity and to reduce degradation pressures on natural forest ecosystems.

3.3. Climate and Its Influence on Forest Types in Kenya

Kenya’s forests are strongly shaped by climatic variability, with highland regions receiving more than 2000 mm of rainfall annually, supporting dense Afromontane forests, while arid zones may receive less than 500 mm, favoring drought-adapted woodlands. Over the past 40 years, average temperatures have risen by 0.21 °C, accompanied by greater rainfall variability and recurrent droughts, which have accelerated degradation in semi-arid and coastal ecosystems [42].
Climate change has significantly reduced regeneration, altered species composition, and heightened vulnerability to wildfires and pests, particularly in semi-arid and coastal ecosystems such as mangroves, which also face threats from rising sea levels and salinity intrusion [43]. These shifts have significant implications for conservation and management, underscoring the need for climate-adaptive strategies such as drought-resistant tree planting, improved water conservation, and resilience-building within forestry policy.

3.4. Actions on Forest Management in Kenya

Kenya’s diverse forest ecosystems are significantly impacted by climate change and unsustainable human activities driven by population pressure [20,44,45]. Forest management in Kenya combines sustainable use, conservation, afforestation, and reforestation with community participation and market incentives. For example, the Forest Conservation and Management Act (2016) [14]. Ref. [3] provides a legal framework, Community Forest Associations (CFAs) involve local communities in tree planting and forest protection [22], and carbon market projects like REDD+ offer financial incentives for conservation [45]. In addition, the government has integrated forest conservation into national development plans to support climate change mitigation goals [46].
Forest restoration and afforestation are crucial for climate change mitigation and ecosystem conservation. Studies show carbon credit initiatives, especially those related to reforestation and renewable energy, significantly reduce greenhouse gas emissions and generate revenue, thereby fostering sustainable economic growth [47]. Carbon finance programs like REDD+ enhance the value of standing forests, providing communities with financial incentives to preserve and expand forests [5]. Linking Kenyan Forest initiatives to global climate frameworks (e.g., the SDGs and UNFCCC commitments) is essential for ensuring sustainable biomass monitoring and conservation.
However, Kenya’s forest management efforts still face obstacles such as insufficient funding, inadequate regulatory enforcement, and inconsistent land use policies [3]. Recent government and private initiatives have accelerated afforestation and reforestation, aiming to enhance national forest cover. Agroforestry is also promoted to enhance biodiversity and provide alternative livelihoods [32]. Strengthening law enforcement, improving land use planning, raising public awareness, and fostering collaboration among government agencies, NGOs, and local communities are critical for successful forest management [48]. The ongoing initiatives underscore the need for accurate, science-based tools to evaluate forest productivity and carbon sequestration potential. Therefore, applying precise biomass estimation methods is crucial for evaluating forest health, informing policy decisions, and improving conservation efforts.

4. Biomass Estimation Methods in Forests

The precision of biomass estimation relies on the integration of many data sources, including field data, LiDAR and UAV remote sensing, and satellite data [46,49,50]. Integrative methodologies are advised for enhancing biomass estimation by the amalgamation of field data, remote sensing, and sophisticated modeling tools. These hybrid approaches enhance accuracy and provide scalable options for monitoring biomass and carbon stocks across various forest ecosystems.
Biomass estimation involves various methodologies, each presenting distinct advantages and challenges that render them appropriate for diverse applications and research sizes (Table 4). The accuracy and reliability of biomass estimation models can be further improved by integrating supplementary data on microbial communities involved in biomass decomposition processes. The alpine systems provide insights into the role of microbial communities in litter decomposition across forest types, which can be used to improve biomass estimation, for example, in forest floors in Kenyan ecosystems [51]. Below is a summary of key biomass estimation methods that have been applied in Kenya in the last fifteen years. Milestone studies demonstrate the progression from allometric and regression models to advanced remote sensing and machine learning approaches.
Table 3. Timeline of methodological development in biomass estimation studies in Kenya (2009–2024).
Table 3. Timeline of methodological development in biomass estimation studies in Kenya (2009–2024).
StudyMethods UsedKey LimitationsKey Results
Rodríguez-Veiga et al. (2020) [52]Random Forest; CARDAMOM model-data fusionLack of national forest inventory for calibrationEO-derived AGB carbon stocks: 140 Mt C (forests), 199 Mt C (wooded grasslands); total loss 1.89 Mt C
Olale et al. (2019) [53]Tree coring; water immersion for wood densityConventional methods resource-intensive; auger cores underestimate densityWood densities 0.36–0.67 g/cm3; auger cores yielded lower values
Mutwiri et al. (2017) [37]Airborne LiDAR; ground truthingInaccurate height estimates in complex crownsVariable accuracy depending on canopy height and elevation
Eragae & Gichuhi (2017) [54]Wildlife Works regression modelLimited coverage of species in drylandsCommiphora highest biomass (241 Mg); Acacia & Vachellia sequester 5.4 kg CO2 yr−1
Broas (2015) [55]Airborne laser scanning + regressionModerate error rates; some over/under detection~50% trees correctly identified; prediction error ≈ 163 kg
Kinyanjui et al. (2014) [56]Allometric equations; Bayesian regressionDeveloping new equations costly; transferability limitedNo significant differences across four tested equations
Cohen (2014) [57]Mixed-effects allometric modelsExtrapolation to high AGB values uncertainAGB carbon in mangroves 5.4–7.2 Mt C
Githaiga (2013) [58]Regeneration sampling; below-ground coresWeak AGB–BGB correlation; human disturbance effectsSalinity strongly influenced biomass accumulation
Muturi et al. (2012) [36]Linear vs. power modelsHeight unreliable vs. DBH/basal diameterPower models better; basal diameter strongest predictor
Kairo et al. (2009) [59]Allometric equations; biomass partitioningAsian models unsuitable; site-specific variationDifferent species show varied biomass distribution in plantations

4.1. Field-Based Techniques

Field-based parameters are vital in estimating biomass in Kenyan forests. Some of the important parameters include diameter at breast height (DBH): This is the standard measurement made at about 1.3–1.4 m above the ground and is widely used in forestry surveys. Another important measurement is the diameter at stump height (DSH): This is the diameter at the beginning of the tree stump and is quite useful for the multi-stemmed or cut trees (Figure 4). Other vital measurements include the crown width, which is the widest distance across the tree’s canopy, and the tree height, which is the highest point from the base of the tree to the top of the tree. Wood density is the mass of the wood per unit volume and is usually measured in grams per cubic centimeter (g/cm3), while Wood Specific Gravity (WSG) is the ratio of the density of wood to that of water and gives information on the structural properties of the wood. Basal area is the cross-sectional area of the tree trunk at breast height, given by π × (DBH/2)2, and is another important parameter, especially when summed over an area to give an idea of the stand density. These measurements are often obtained by conventional methods like manual calipers and increment borers, which provide exact albeit laborious data and are utilized in allometric models to estimate AGB precisely. Research has demonstrated that incorporating wood density into allometric equations enhances the precision of biomass estimates, particularly in mangroves and dryland ecosystems [60,61].
Allometric equations employed in field methodologies rely on tree dimensions, such as diameter at breast height (DBH), to determine biomass. The quadrat method has effectively assessed biomass output in mangrove forest [62], while tree coring has been employed to ascertain wood density and biomass [53]. Comprehensive data collection is essential for the development and enhancement of ecosystem-specific allometric models, incorporating species-level traits and environmental variables [63]. Field-based approaches possess some restrictions, including resource constraints, expenses, and the necessity for extensive field data, which constitutes a significant drawback. The integration of conventional field data with remote sensing technology might mitigate these constraints and enhance biomass estimating methods. This hybrid methodology facilitates the verification of extensive biomass forecasts and the adjustment of remote sensing models [56].

4.2. Remote Sensing Techniques

Remote sensing technologies such as multispectral and hyperspectral imaging provide extensive assessments of forest structure and biomass, with LiDAR and UAVs offering high precision in canopy height, tree density, and ground elevation mapping [64]. UAV hyperspectral data have proven effective for species identification and AGB estimation in Kenyan forests [65], and when combined with LiDAR, provide robust frameworks for carbon stock evaluation. Field data (DBH, height, wood density) remain essential for validating models and correcting bias [66]. Non-destructive methods, including allometric models, LiDAR, and UAVs, achieve uncertainties below 5% [67,68] and are complemented by satellite platforms such as Sentinel-2 and Landsat [69,70,71]. Machine learning approaches, particularly Random Forest and Support Vector Machines, enhance analysis by integrating vegetation indices, canopy cover, and soil characteristics, with South Africa’s high-resolution SOC mapping demonstrating their scalability for Kenya’s forests [72]. Despite challenges of cost, technical capacity, and ground-truthing, integrating remote sensing with field data improves precision and scalability of biomass estimation.

4.3. Model-Based Techniques

Biomass estimation in Kenyan forests relies on diverse modeling approaches tailored to forest type and measurement objectives. Allometric models remain the most widely used, estimating AGB from measurable attributes such as DBH and tree height, with species-specific equations accounting for variation in tree density and growth forms. Wildlife Work regression models, for example, have been applied successfully in dryland habitats [19]. Additive models based on the WGLS method also support accurate carbon stock inventories by maintaining additivity across tree components [10]. Quadrat-based sampling provides valuable data for model calibration and species–biomass relationships, though it is costly and spatially limited [24]. Other approaches, including Wildlife Work regression and Gaussian process regression, have shown utility in dryland and multitrophic systems [24,68].
The application of machine learning has significantly advanced modeling capacity. Algorithms such as Random Forest and Support Vector Machines integrate multi-source data, including field measurements and remote sensing, to improve accuracy and scalability [15]. CARDAMOM, a model–data fusion framework, has further provided insights into carbon balance and biomass management in Kenyan forests [15]. Integrating hyperspectral and LiDAR data has enhanced precision in dense forests, while recent studies highlight the importance of incorporating forested grasslands into national carbon accounting. Despite progress, persistent challenges include limited data accessibility, high costs, and lack of standardization. Critically, the absence of a comprehensive national forest inventory constrains effective carbon stock assessment, underscoring the need for stronger collaboration and harmonization [73,74].

4.4. Hybrid Approaches

Integrative methodologies in biomass estimation are increasingly recognized for their capacity to amalgamate the advantages of field-based, remote-sensing, and machine-learning techniques [75]. This hybrid methodology improves the precision and thoroughness of biomass evaluations across diverse environments. Integrating methods helps overcome the limitations of using any single approach. The amalgamation of these strategies is especially advantageous in intricate and varied ecosystems, where conventional methods may prove inadequate.

4.4.1. Field-Based and Remote Sensing Integration

Field-based measurements provide essential ground truth for calibrating and validating remote sensing models but are often constrained in spatial and temporal coverage by logistical limitations [76]. Remote sensing technologies such as LiDAR, hyperspectral imaging, and satellite platforms offer comprehensive coverage across vast and challenging terrains and are highly effective for quantifying AGB in forested and wetland ecosystems [69,77]. Integrating field data with remote sensing reduces bias and noise in satellite-derived predictions, yielding more accurate biomass density maps with improved spatial resolution [20]. However, field data collection remains laborious and costly, limiting its scope. Emerging approaches, including machine learning and high-resolution digital elevation models, can address these limitations by enhancing data integration and reducing uncertainties. While this combined methodology strengthens accuracy and scalability, challenges persist, particularly in reconciling discrepancies across datasets and ensuring consistency in field measurements. Continued advances in sensor technology and integration methods are therefore critical for improving biomass monitoring systems.

4.4.2. Machine Learning and Data Integration

Machine learning algorithms such as Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Networks (ANN) are increasingly applied to process complex remote sensing datasets, managing non-linear interactions and improving biomass prediction accuracy [69,78,79]. Hybrid models that integrate multiple data sources, including Sentinel-1 and Sentinel-2, further enhance estimation performance, with the fusion of SAR and optical data using RF showing success in coastal wetlands [80]. Advanced models such as LightGBM and XGBoost refine predictions by optimizing hyperparameters and employing innovative optimization techniques [81]. The combination of hyperspectral and LiDAR data with machine learning has yielded highly accurate AGB estimates in tropical forests, with R2 values reaching 0.997 [82], while integrated space–air–ground approaches have effectively estimated shrub biomass in encroached grasslands [83]. Similarly, integrating Landsat and ALOS-2 PALSAR data with machine learning has improved biomass estimation in artificial coniferous forests, mitigating saturation effects in dense stands [84]. Despite these advances, challenges remain: data fusion is resource-intensive, requires specialized expertise, and precision is highly dependent on input data quality and resolution. Nevertheless, continued innovation in integrative machine learning approaches offers strong potential for more accurate and scalable biomass evaluations across diverse ecosystems.

4.5. Comparative Insights

A synthesis of Kenyan studies (Table 3) reveals that:
  • Field methods provide baseline accuracy but lack scalability.
  • Remote sensing enables broad coverage but depends on calibration and financial investment.
  • Models and ML approaches enhance integration and predictive capacity but are data-intensive.
  • Hybrid approaches offer the most robust outcomes, aligning with global trends in biomass monitoring.
Kenya’s biomass estimation methods broadly mirror those applied globally, but challenges remain in species-specific modeling, cost barriers, and institutional coordination. The absence of a national biomass inventory is a key limitation compared to countries with standardized monitoring systems.

5. Challenges and Opportunities in Biomass Estimation

Accurate biomass estimation in Kenya’s forests faces several challenges that reflect both ecological complexity and methodological limitations. First, species diversity and heterogeneity complicate the transferability of allometric equations, as models calibrated for one forest type or species often perform poorly elsewhere [85]. This is particularly evident in dryland woodlands and mangroves, where below-ground biomass remains difficult to quantify with accuracy [57]. Second, limited national inventories and datasets restrict the calibration of remote sensing and machine learning models, creating gaps in large-scale monitoring [86]. Third, technical and financial constraints, including the high cost of LiDAR and UAV platforms, and the expertise required for advanced modeling, hinder widespread adoption of cutting-edge techniques [87].
Despite these barriers, several opportunities are emerging. Integrative approaches that combine field data with remote sensing and machine learning have shown strong potential to improve precision and scalability. Advances in open-access satellite datasets (e.g., Sentinel-2, Landsat) and the development of low-cost UAVs provide affordable options for biomass monitoring [88]. Furthermore, Kenya’s policy frameworks such as the Forest Conservation and Management Act (2016) [14] and participation in REDD+ and carbon markets offer incentives to standardize biomass monitoring as part of national and global climate reporting. Regionally, experiences from countries such as Tanzania, Uganda, and the DRC provide opportunities for methodological harmonization across Africa. To build a robust biomass monitoring system, Kenya must prioritize the development of a national biomass inventory, promote data sharing across institutions, and strengthen collaboration between researchers, government agencies, and local communities. These steps will align forest monitoring with both domestic policy needs and global climate mitigation commitments.
Table 4. Challenges and opportunities for biomass estimation methods applied in Kenya.
Table 4. Challenges and opportunities for biomass estimation methods applied in Kenya.
Method CategoryStrengths/Emerging OpportunitiesLimitations/Key ChallengesApplicability
Field-based (Allometric, Quadrat, Coring)Accurate; non-destructive; cost-effective for small-scale studies; baseline accuracy essential for calibration; enables sustainable forest management and carbon stock estimation; supports development of region-specific allometric modelsLabor-intensive; species-specific; poor scalability; excludes below-ground biomass (BGB) [36]Local inventories; calibration of Remote Sensing models; baseline studies
Volume-based methodsEasy to apply; uses standard forestry dataLess precise in heterogeneous forests; conversion factors oversimplify variabilityManaged forests; operational forestry
Remote sensing—Passive (Multispectral/Hyperspectral)Large-area coverage; enables temporal monitoring; open-access imagery (e.g., Sentinel-2, Landsat) [38]; supports change detectionIndirect estimates; affected by atmosphere and sensor noise; requires calibrationRegional to national monitoring; change detection
Remote sensing—Active (LiDAR, Radar, UAVs)Provides 3D canopy detail; high accuracy; Radar works in all weather; combining LiDAR with photogrammetry and multispectral imagery improves resolution [50,54]; low-cost UAVs enhance accessibilityHigh cost and logistical complexity [79,89]; limited spatial coverage; need for calibration; low-density LiDAR reduces cost but lowers accuracy [41]; requires technical expertiseDetailed mapping in heterogeneous forests; calibration datasets; national coverage
Model-based (Empirical, Process-based)Integrates field and RS data; supports scenario analysis and carbon accounting; more accurate when combining DBH, height, crown traits with RS inputs [90]; supports policy and planningData-intensive; extrapolation errors [91,92,93]; time-consuming [41,49]; complex to develop and calibrate [56,71,78,94]Regional assessments; long-term planning; carbon accounting
Machine Learning (RF, SVM, ANN, LightGBM, XGBoost)Handles large, complex datasets; high predictive power; scalable; globally successful applications adaptable to Kenya; integration of multi-source data enhances accuracy [61,62,63]Requires large training datasets; limited Kenyan applications to date; performance depends on high-quality training dataNational monitoring; integration with field + RS data; scalable applications
Hybrid ApproachesCombines strengths of multiple methods; robust accuracy; cross-validation; supports national inventories, policy, and carbon markets; enables harmonization of RS and field data [53,60]Technically complex; requires harmonization of multi-source data; resource-demandingComprehensive assessments; decision support; supports national inventories

6. Conclusions and Recommendations

This review synthesized field-based, remote sensing, and machine learning methods for biomass estimation in Kenya, highlighting their complementary strengths and limitations.

7. Key Findings

Current Methods and Strengths/Limitations
  • Field-based approaches, including allometric equations, quadrat sampling, and coring, provide accurate site-specific estimates but are resource-intensive and difficult to scale.
  • Remote sensing techniques such as LiDAR, UAVs, multispectral, and radar imagery enable large-scale assessments but require calibration and technical investment.
  • Machine learning and hybrid frameworks enhance predictive accuracy by integrating multi-source data, though their effectiveness depends on high-quality training datasets and standardized protocols.
Gaps and Challenges
4.
Biomass monitoring in Kenya remains fragmented due to the absence of a centralized national inventory and limited methodological harmonization.
5.
Despite the Forest Conservation and Management Act (2016) [14], weak enforcement, limited investment in modern technologies, and insufficient institutional collaboration constrain effective biomass monitoring.
6.
Regional harmonization, as demonstrated in neighboring African countries, is needed to improve comparability and support transboundary climate commitments.
Policy and Management Implications
7.
Strengthening biomass monitoring is critical for sustainable forest management, national carbon accounting, and Kenya’s participation in global climate mechanisms such as REDD+ and the carbon market.
Future Research and Opportunities
8.
Species- and region-specific allometric equations, particularly for mangrove and dryland ecosystems, should be prioritized.
9.
Expanding hybrid frameworks that combine field data, remote sensing, and machine learning will improve both accuracy and scalability.
10.
Establishing a centralized national biomass inventory, supported by capacity building and data-sharing platforms, will standardize monitoring and reporting.
11.
Low-cost UAVs, open-access satellite datasets, and advanced machine learning algorithms present promising opportunities for scalable biomass monitoring.
12.
Moving from isolated studies to coordinated monitoring systems will strengthen ecological knowledge and position Kenya to align with global biodiversity conservation and climate change mitigation goals.

Author Contributions

Conceptualization, H.T.M. and C.M.O.; data curation, H.T.M., C.M.O. and N.B.; formal analysis, M.G., H.T.M., K.P., S.S. and N.B.; funding acquisition, N.B. and E.M.; writing—original draft, H.T.M. and E.M.; writing—review and editing, H.T.M., C.M.O., M.F., M.G., N.B. and J.M. All authors have read and agreed to the published version of the manuscript.

Funding

The authors acknowledge the financial support of the Stipendium Hungaricum Scholarship, Number 22_473819. This work was supported by the Doctoral School of Environmental Sciences (Gödöllő), Hungarian University of Agriculture and Life Sciences, MATE, and the Flagship Research Groups Program 2024 and the Research Excellence Program 2025 of the Hungarian University of Agriculture and Life Sciences.

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

AGB Above-Ground Biomass
ASALs Arid and Semi-Arid Lands
BGBBelow-Ground Biomass
CARDAMOM CARbon DAta–MOdel fraMework
CFAs Community Forest Associations
DBH Diameter at Breast Height
DSH Diameter at Stump Height
EO Earth Observation
LiDAR Light Detection and Ranging
REDD+Reducing Emissions from Deforestation and Forest Degradation
RF Random Forest
RGB Red, Green, Blue
SARSynthetic Aperture Radar
SDGsSustainable Development Goals
UAV Unmanned Aerial Vehicles
UNESCOUnited Nations Educational, Scientific, and Cultural Organization
UNFCCC United Nations Framework Convention on Climate Change
WGLS Weighted Generalized Least Squares Method
WSG Wood-Specific Gravity

Appendix A

Land 14 01873 g0a1
Figure A1. Trends in forest cover in Kenya, 1990–2021.
Figure A1. Trends in forest cover in Kenya, 1990–2021.
Land 14 01873 g0a1

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Land 14 01873 g001
Figure 1. PRISMA flow diagram showing the systematic literature review process of biomass estimation methods in Kenya. The diagram summarizes the identification, screening, eligibility, and inclusion process. Note: The double asterisk (**) refers to records excluded by automation tools versus manual screening. In this review, no automation tools were used; all exclusions were performed manually.
Figure 1. PRISMA flow diagram showing the systematic literature review process of biomass estimation methods in Kenya. The diagram summarizes the identification, screening, eligibility, and inclusion process. Note: The double asterisk (**) refers to records excluded by automation tools versus manual screening. In this review, no automation tools were used; all exclusions were performed manually.
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Figure 2. Natural and Planted Forests and Spatial Coverage in Kenya (Source: [13]).
Figure 2. Natural and Planted Forests and Spatial Coverage in Kenya (Source: [13]).
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Figure 4. Standard points of stump height measurement. Note: The white line indicates the correct position of the diameter tape (Source: https://www.epa.nsw.gov.au). Subfigures: (A) flat and level stump measured at right angle; (B) leaning stump with diameter measured at right angle; (C) stump on a slope with measurement taken on the uphill side; (D) leaning stump on sloping ground; (E) stump on sloping ground with irregular base; (F) forked stump measured below the fork; (G) buttressed stump measured above the buttress.
Figure 4. Standard points of stump height measurement. Note: The white line indicates the correct position of the diameter tape (Source: https://www.epa.nsw.gov.au). Subfigures: (A) flat and level stump measured at right angle; (B) leaning stump with diameter measured at right angle; (C) stump on a slope with measurement taken on the uphill side; (D) leaning stump on sloping ground; (E) stump on sloping ground with irregular base; (F) forked stump measured below the fork; (G) buttressed stump measured above the buttress.
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Table 1. Keywords used in the literature search.
Table 1. Keywords used in the literature search.
ThemeKeywords/Terms
Biomass estimation“biomass estimation”, “above-ground biomass”, “below-ground biomass”, “carbon stock”
Methods“allometric models”, “field-based methods”, “remote sensing”, “LiDAR”, “UAV”, “machine learning”, “additive models”, “volume-based models”
Ecosystem context“forest ecosystems”, “Afromontane”, “mangroves”, “dryland woodlands”, “plantations”, “community forests”, “agroforestry”
Relevance“Kenya”, “East Africa”, “sustainable forest management”, “climate change”, “carbon sequestration”, “future perspectives”
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MDPI and ACS Style

Mkuzi, H.T.; Ocansey, C.M.; Maghanga, J.; Gulyás, M.; Penksza, K.; Szentes, S.; Michéli, E.; Fuchs, M.; Boros, N. A Review of Biomass Estimation Methods for Forest Ecosystems in Kenya: Techniques, Challenges, and Future Perspectives. Land 2025, 14, 1873. https://doi.org/10.3390/land14091873

AMA Style

Mkuzi HT, Ocansey CM, Maghanga J, Gulyás M, Penksza K, Szentes S, Michéli E, Fuchs M, Boros N. A Review of Biomass Estimation Methods for Forest Ecosystems in Kenya: Techniques, Challenges, and Future Perspectives. Land. 2025; 14(9):1873. https://doi.org/10.3390/land14091873

Chicago/Turabian Style

Mkuzi, Hamisi Tsama, Caleb Melenya Ocansey, Justin Maghanga, Miklós Gulyás, Károly Penksza, Szilárd Szentes, Erika Michéli, Márta Fuchs, and Norbert Boros. 2025. "A Review of Biomass Estimation Methods for Forest Ecosystems in Kenya: Techniques, Challenges, and Future Perspectives" Land 14, no. 9: 1873. https://doi.org/10.3390/land14091873

APA Style

Mkuzi, H. T., Ocansey, C. M., Maghanga, J., Gulyás, M., Penksza, K., Szentes, S., Michéli, E., Fuchs, M., & Boros, N. (2025). A Review of Biomass Estimation Methods for Forest Ecosystems in Kenya: Techniques, Challenges, and Future Perspectives. Land, 14(9), 1873. https://doi.org/10.3390/land14091873

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