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

Hamisi Tsama Mkuzi, Caleb Melenya Ocansey, Justin Maghanga, Miklós Gulyás (), Károly Penksza (), Szilárd Szentes, Erika Michéli, Márta Fuchs and Norbert Boros
Additional contact information
Hamisi Tsama Mkuzi: Department of Soil Science, Institute of Environmental Science, Hungarian University of Agriculture and Life Sciences, Páter Károly u. 1, 2100 Gödöllő, Hungary
Caleb Melenya Ocansey: Department of Soil Science, Institute of Environmental Science, Hungarian University of Agriculture and Life Sciences, Páter Károly u. 1, 2100 Gödöllő, Hungary
Justin Maghanga: Department of Mathematics, Statistics and Physical Sciences, School of Science & Informatics, Taita Taveta University, Voi P.O. Box 635-80300, Kenya
Miklós Gulyás: Department of Soil Science, Institute of Environmental Science, Hungarian University of Agriculture and Life Sciences, Páter Károly u. 1, 2100 Gödöllő, Hungary
Károly Penksza: Department of Botany, Institute of Agronomy, Hungarian University of Agriculture and Life Sciences, Páter Károly u. 1, 2100 Gödöllő, Hungary
Szilárd Szentes: Animal Breeding, Nutrition and Laboratory Animal Science Department, University of Veterinary Medicine Budapest, István u., 1078 Budapest, Hungary
Erika Michéli: Department of Soil Science, Institute of Environmental Science, Hungarian University of Agriculture and Life Sciences, Páter Károly u. 1, 2100 Gödöllő, Hungary
Márta Fuchs: Department of Soil Science, Institute of Environmental Science, Hungarian University of Agriculture and Life Sciences, Páter Károly u. 1, 2100 Gödöllő, Hungary
Norbert Boros: Department of Soil Science, Institute of Environmental Science, Hungarian University of Agriculture and Life Sciences, Páter Károly u. 1, 2100 Gödöllő, Hungary

Land, 2025, vol. 14, issue 9, 1-22

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.

Keywords: biomass estimation; remote sensing; machine learning; carbon sequestration; Kenya forests; forest cover (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/14/9/1873/pdf (application/pdf)
https://www.mdpi.com/2073-445X/14/9/1873/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:9:p:1873-:d:1748840

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().