An Inventory of the Above Ground Biomass in the Mau Forest Ecosystem, Kenya

Mwangi James Kinyanjui; Petri Latva-Käyrä; Prasad Sah Bhuwneshwar; Patrick Kariuki; Alfred Gichu; Kepha Wamichwe

doi:10.4236/oje.2014.410052

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OJE> Vol.4 No.10, July 2014

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An Inventory of the Above Ground Biomass in the Mau Forest Ecosystem, Kenya ()

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DOI: 10.4236/oje.2014.410052 3,821 Downloads 5,746 Views Citations

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Mwangi James Kinyanjui, Petri Latva-Käyrä, Prasad Sah Bhuwneshwar, Patrick Kariuki, Alfred Gichu, Kepha Wamichwe

Affiliation(s)

Arbonaut Oy Ltd., Helsinki, Finland.
Department of Resource Surveys and Remote Sensing, Nairobi, Kenya.
Kenya Forest Service, Nairobi, Kenya.
Natural Resource Information & Technology Ltd., Nairobi, Kenya.
PASCO Corporation, Tokyo, Japan.

ABSTRACT

Biomass assessment of the Mau Forest Ecosystem (MFE) was done as part of Kenya’s greenhouse gas inventory. Trans Mara and Mount Londiani forest blocks representing extremes of vegetation types in the MFE were selected for ground data. Based on canopy closure, four forest strata were identified as very dense, moderately dense, open and bamboo. In each stratum, 5 clusters each with 4 plots measuring 30 m × 30 m were located. Big trees (D_1.3 ≥ 10 cm) were measured per species for diameter at breast height (D_1.3) in the whole plot while height was measured for every 5^th tree. Poles (10 cm > D_1.3 ≤ 5) were measured for D_1.3 in a 10 × 10 m concentric sub plot. Saplings (5 cm > D_1.3; ht ≥ 1.5 m) and seedlings (ht < 1.5 m) were enumerated per species within 5 × 5 m and 2 × 2 m concentric sub plots, respectively. Data were recorded in a Personal Digital Assistant (PDA) and quality checked with Open Foris Collect software. Allometric equations that have been used for similar vegetation in Kenya were used to relate D_1.3 and height with biomass. The tree data were uploaded to ArboWebForest (AWF) cloud-service and using the AWF-SIMO calculation tool, average values of diameter, height, and biomass were calculated for each plot. The data were generalised to cover all areas for each block using the Sparse Bayesian linear regression process on the vegetation characteristics with 10 m resolution ALOS-AVNIR-2 images of the MFE. ANOVA was used to compare biomass generated from several allometric equations. Results show that the average biomass of the MFE was 236 Mg·ha¹. Degradation that converts dense forests into open and moderately dense forests contributed to a biomass loss of 228 Mg·ha¹ and 194 Mg·ha¹ respectively. Four allometric equations gave no significant difference (P < 0.05) in biomass for the 80 plots implying that costly processes of developing new equations may not improve accuracy. The study offers a learning lesson in Kenya’s forest inventory processes and the biomass values may show the estimates of stocking in similar forests of Kenya.

KEYWORDS

Forest, Biomass, Inventory

Cite this paper

Kinyanjui, M. , Latva-Käyrä, P. , Bhuwneshwar, P. , Kariuki, P. , Gichu, A. and Wamichwe, K. (2014) An Inventory of the Above Ground Biomass in the Mau Forest Ecosystem, Kenya. Open Journal of Ecology, 4, 619-627. doi: 10.4236/oje.2014.410052.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Sishir G. and Stephan, A.P. (2012) Carbon Pools of an Intact Forest in Gabon. African Journal of Ecology, 50, 414-427. http://dx.doi.org/10.1111/j.1365-2028.2012.01337.x

[2]	Adrien, N.D., Alexander, K. and Gode, G. (2011) Estimations of Total Ecosystem Carbon Pools Distribution and Carbon Biomass Current Annual Increment of a Moist Tropical Forest. Forest Ecology and Management, 261, 1448-1459. http://dx.doi.org/10.1016/j.foreco.2011.01.031

[3]	Fearnside, P.M., Graca, P.M.L., Filho, N.L., Rodrigues, F.J.A. and Robinson, J.M. (1999) Tropical Forest Burning in Brazilian Amazonia: Measurement of Biomass Loading, Burning Efficiency and Charcoal Formation at Alteamira, Pana. Journal of Forest Ecology and Management, 123, 65-79. http://dx.doi.org/10.1016/S0378-1127(99)00016-X

[4]	Losi, C.J., Siccama, T.G., Condit, R. and Morales, J.E. (2003) Analysis of Alternative Methods for Estimating Carbon Stock in Young Tropical Plantations. Journal of Forest Ecology and Management, 184, 355-368. http://dx.doi.org/10.1016/S0378-1127(03)00160-9

[5]	Pastor, J., Aber, J.D. and Melillo, J.M. (1984) Biomass Prediction Using Generalized Allometric Regressions for Some Northeast Tree Species. Journal of Forest Ecology and Management, 7, 265-274. http://dx.doi.org/10.1016/0378-1127(84)90003-3

[6]	Rao, M.N. and Mathuva, M.R. (2000) Legumes for Improving Maize Yields and Income in Semi-Arid Kenya. Agriculture, Ecosystems and Environment, 78, 123-137. http://dx.doi.org/10.1016/S0167-8809(99)00125-5

[7]	Cohen, M.J., Mark, T.B. and Keith, D.S. (2006) Estimating the Environmental Costs of Soil Erosion at Multiple Scales in Kenya Using Energy Synthesis. Agriculture, Ecosystems and Environment, 114, 249-269. http://dx.doi.org/10.1016/j.agee.2005.10.021

[8]	Kinyanjui, J.M. (2009) The Effect of Human Encroachment on Forest Cover, Structure and Composition in the Western Blocks of the Mau Forest Complex. Ph.D. Thesis, Egerton University, Njoro.

[9]	Kinyanjui, J.M., Karachi, M. and Ondimu, K.N. (2012) Documenting the Carbon content of the Mau Forest Complex. Journal of Environment, Natural Resources Management and Society, 1, 70-81.

[10]	Baldyga, T.J., Scott, N.M., Driese, K.N.L. and Gichaba, C.M. (2007) Assessing Land Cover Change in Kenya’s Mau Forest Region Using Remotely Sensed Data. African Journal of Ecology, 46, 46-54. http://dx.doi.org/10.1111/j.1365-2028.2007.00806.x

[11]	Mutangah, J.G., Mwangangi, O.M. and Mwaura, P.K. (1993) Mau Forest Complex Vegetation Survey. KIFCON Report, Nairobi, 134.

[12]	Kinyanjui, J.M., Karachi, M. and Ondimu, K.N. (2013) Natural Regeneration and Ecological Recovery in Mau Forest Complex, Kenya. Open Journal of Ecology, 3, 417-422. http://dx.doi.org/10.4236/oje.2013.36047

[13]	Kinyanjui, J.M. (2011) NDVI Based Vegetation Monitoring in the Mau Forest Complex, Kenya. African Journal of Ecology, 49, 165-174. http://dx.doi.org/10.4236/oje.2013.36047

[14]	Beentje, H.J. (1994) Kenya Trees, Shrubs and Lianas. National Museums of Kenya, Nairobi.

[15]	Prance, G.T. (1984) The Vegetation of Africa. Brittonia, 36, 273.

[16]	Sah, B.P., Hamalainen, J.M., Sah, A.K., Honji, K., Foli, E.G. and Awudi, C. (2012) The Use of Satellite Imagery to Guide Field Plot Sampling Scheme for Biomass Estimation in Ghanaian Forest. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, I, 221-226.

[17]	Miceli, G., Pekkarinen, A. and Leppanen, M. (2011) Open ForisInitiave—Tools for Forest Monitoring and Reporting. Concept Note, FAO, Rome, 7.

[18]	Henry, M., Picard, N., Trotta, C., Manlay, R.J., Valentini, R., Bernoux, M. and Saint-André, L. (2011) Estimating Tree Biomass of Sub-Saharan African Forests: A Review of Available Allometric Equations. Silva Fennica, 45, 477-569. http://dx.doi.org/10.14214/sf.38

[19]	Bradley, P.N. (1988) Survey of Woody Biomass on Farms in Western Kenya. Ambio, 17, 40-48.

[20]	Kuyah, S., Dietz, J., Muthuri, C., Jamnadass, R., Mwangi, P., Coe, R. and Neufeldt, H. (2012) Allometric Equations for Estimating Biomass in Agricultural Landscapes: Aboveground Biomass. Agriculture and Ecosystem Environment, 158, 216-224. http://dx.doi.org/10.1016/j.agee.2012.05.011

[21]	Schnitzer, S.A., Mangan, S.A., Dalling, J.W., Baldeck, C.A., Hubbell, S.P., et al. (2012) Liana Abundance, Diversity, and Distribution on Barro Colorado Island, Panama. PLoS ONE, 7, e52114. http://dx.doi.org/10.1371/journal.pone.0052114

[22]	Kangas, A. and Rasinm?ki, J., Eds. (2008) SIMO Adaptable Simulation and Optimization for Forest Management Planning. Department of Forest Resource Management Publications, University of Helsinki, Helsinki, 40.

[23]	JAXA (2012) Japan Aerospace Exploration Agency. http://www.eorc.jaxa.jp/ALOS/en/index.htm

[24]	Junttila, V., Maltamo, M. and Kauranne, T. (2008) Sparse Bayesian Estimation of Forest Stand Characteristics from Airborne Laser Scanning. Forest Science, 54, 543-552.

[25]	Junttila, V., Kauranne, T. and Leppanen, V. (2010) Estimation of Forest Stand Parameters from Airborne Laser Scanning Using Calibrated Plot Databases. Forest Science, 56, 257-270.

[26]	Haralick, R.M., Shanmugan, K. and Its’hack, D. (1973) Textural Features for Image Classification. Transactions on Systems, Man and Cybernetics, SMC-3, 610-621. http://dx.doi.org/10.1109/TSMC.1973.4309314

[27]	Edwards, D.P., Brendan,F. and Emily, B.(2010) Protecting Degraded Rainforests: Enhancement of Forest Carbon Stocks under REDD+. Conservation Letters, 3, 313-316. http://dx.doi.org/10.1111/j.1755-263X.2010.00143.x

[28]	Kinyanjui, J.M., Karachi, M. and Ondimu, K.N. (2012) Estimating Forest Volume and Yield in the Western Blocks of the Mau Forest Complex, Kenya. Journal of Environment, Natural Resources Management and Society, 1, 59-69.

[29]	IPCC (2006) Guidelines for National Greenhouse Gas Inventories. Volume 4: Agriculture, Forestry and Other Land Use. The Intergovernmental Panel on Climate Change. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_04_Ch4_Forest_Land

[30]	Glenday, J. (2006) Carbon Storage and Emissions Offset Potential in an East African Tropical Rainforest. Forest Ecology and Management, 235, 72-83. http://dx.doi.org/10.1016/j.foreco.2006.08.014