Journal of Forests

Published by: Conscientia Beam
Online ISSN: 2409-3807
Print ISSN: 2413-8398
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No. 1

Seedlings Performance of Triplochiton scleroxylon (K. Schum.) under Different Light Intensities and Soil Textural Classes

Pages: 32-35
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Seedlings Performance of Triplochiton scleroxylon (K. Schum.) under Different Light Intensities and Soil Textural Classes

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DOI: 10.18488/journal.101.2020.71.32.35

Iroko, O. A. , Asinwa, I.O. , Odewale, M.A. , Wahab, W.T.

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[1]          J.-X. Liao, X.-Y. Zou, Y. Ge, and J. Chang, "Effects of light intensity on growth of four Mosla species," Botanical Studies, vol. 47, pp. 403-408, 2006.

[2]          A. Vyse, M. R. Cleary, and I. R. Cameron, "Tree species selection revisited for plantations in the Interior Cedar Hemlock zone of Southern British Columbia," The Forestry Chronicle, vol. 89, pp. 382-391, 2013.

[3]          S. S. Lawson and C. H. Michler, "Afforestation, restoration and regeneration—not all trees are created equal," Journal of Forestry Research, vol. 25, pp. 3-20, 2014.

[4]          J. Hall and S. Bada, "The distribution and ecology of Obeche (Triplochiton scleroxylon)," Journal of Ecology, vol. 67, pp. 543-564, 1979.

[5]          P. P. Bosu and E. Krampah, Triplochiton scleroxylon K. Schum: In Louppe D, Oteng Amoako AA, Brink M (Eds). Wageningen, Netherlands: Timbers / Lumber, 2005.

[6]          K. Okunomo, "Utilization of forest products in Nigeria," African Journal of General Agriculture, vol. 6, pp. 145-157, 2010.

[7]          D. O. Adelani, M. A. Aduradola, I. O. O. Aiyelaagbe, O. Akinyemi, and C. I. Agbaje, "Growth promoters of tropical forest tree seedlings: A Review," Biological and Environmental Sciences Journal for the Tropics, vol. 11, pp. 92-100, 2014a.

[8]          D. O. Adelani, M. O. Adedire, M. A. Aduradola, and R. A. Suleiman, "Enhancing seed and seedling growth of forest trees," Biological and Environmental Sciences Journal for the Tropics, vol. 11, pp. 50-56, 2014b.

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Iroko, O. A. , Asinwa, I.O. , Odewale, M.A. , Wahab, W.T. (2020). Seedlings Performance of Triplochiton scleroxylon (K. Schum.) under Different Light Intensities and Soil Textural Classes. Journal of Forests, 7(1): 32-35. DOI: 10.18488/journal.101.2020.71.32.35
Forest trees are socio-economically important but are currently threatened. This study adopted 4×4 factorial experiment in completely randomized design with ten replicates to assess effects of light intensities (100%LI, 75%LI, 50%LI and 25%LI) and soil textural classes (Sandy, Loamy, Sandy-loam and Clay) on the early growth of Triplochiton scleroxylon seedlings as a necessary step for domestication. Data collected was subjected to One-way Analysis of Variance (ANOVA). The best performance for light intensity was observed under 100%LI with plant height 14.79±1.26cm, collar diameter 2.28±0.18mm and 13.11±0.96 number of leaves while the least performance was observed in seedlings under 25%LI with seedling height 10.23±0.59cm, collar diameter 1.97±0.13mm and 6.41±0.45 number of leaves and best performance in soil textural classes was recorded under loamy soil with seedling height 14.90±1.20cm, collar diameter 2.33±0.18mm and 13.11±0.32 number of leaves while the least performance was observed in seedlings grown with sandy soil with seedling height 11.00±0.61cm, collar diameter 1.99±0.13mm and least number of leaves was recorded in seedlings with sandy-loam with 7.41±0.55 number of leaves. Overall best performance was observed in seedlings grown with sandy-loam under 100%LI with seedling height 23.80±0.75cm, collar diameter 4.69±0.34mm and 14.79±0.29 number of leaves while the least performance was observed in seedlings grown with clay under 25%LI with seedling height 10.30±0.68cm, least collar diameter 3.69±0.28mm and number of leaves 13.69±0.28 was observed in seedlings grown with sandy soil under 75%LI. Therefore, it implies that the specie require little or no shade for rapid growth and will thrive well with sandy-loam soil.
Contribution/ Originality
This study revealed that T. scleroxylon seedlings can be raised successfully under different light intensity and soil textural class which makes the species a good candidate for afforestation, enrichment planting, soil amendment, land reclamation and restoration to check climate change, land degradation and loss of biodiversity due to land encroachment.

Color-Based Forest Cover Type Image Segmentation using K-Means Clustering Approach

Pages: 18-31
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Color-Based Forest Cover Type Image Segmentation using K-Means Clustering Approach

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DOI: 10.18488/journal.101.2020.71.18.31

Yeong Nain Chi

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[1]          Global Forest Observations Initiative (GFOI), "Integration of remote-sensing and ground-based observations for estimation of emissions and removals of greenhouse gases in forests: Methods and Guidance (Edition 2.0). Food and Agriculture Organization, Rome. Retrieved from: https://unfccc.int/files/land_use_and_climate_change/redd/submissions/application/pdf/redd_20140218_mgd_report_gfoi.pdf," 2016.

[2]          MathWorks, MATLAB (R2019b) Image processing toolbox™ user's guide. Natick, MA: The MathWorks, Inc, 2019.

[3]          L. G. Shapiro and G. C. Stockman, Computer vision. New Jersey: Prentice-Hall, 2001.

[4]          F. Schroff, A. Criminisi, and A. Zisserman, "Single-histogram class models for image segmentation. In: Kalra, P. K. and Peleg, S. (eds.) Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science," ed Berlin, Heidelbergb: Springer, 2006, p. 4338.

[5]          T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silvermank, and A. Y. Wu, "An efficient k-means clustering algorithm: Analysis and implementation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 881-892, 2002. Available at: https://doi.org/10.1109/TPAMI.2002.1017616.

[6]          Z. Huang, "Extensions to the k-means algorithm for clustering large data sets with categorical values," Data Mining and Knowledge Discovery, vol. 2, pp. 283-304, 1998.

[7]          X. Wang, R. Hänsch, L. Ma, and O. Hellwich, "Comparison of different color spaces for image segmentation using graph-cut," in Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP-2014), 2014, pp. 301-308. Retrieved from: https://www.cv.tu-berlin.de/fileadmin/fg140/VISAPP_2014_127_CR.pdf .

[8]          P. J. Baldevbhai and R. Anand, "Color image segmentation for medical images using L* a* b* color space," IOSR Journal of Electronics and Communication Engineering, vol. 1, pp. 24-45, 2012. Available at: https://doi.org/10.9790/2834-0122445.

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Yeong Nain Chi (2020). Color-Based Forest Cover Type Image Segmentation using K-Means Clustering Approach. Journal of Forests, 7(1): 18-31. DOI: 10.18488/journal.101.2020.71.18.31
In order to understand forest composition, classifying forest cover type can help research regarding forest resilience, carbon sequestration, and climate change concerns. The purposes of this study were to develop and implement some image processing functions based on the histogram of forest cover type color image, and to classify forest cover type using its color feature sets of image pixels. Color-based image segmentation that is based on the color feature of image pixels assumes that homogeneous colors in the image correspond to separate clusters and hence meaningful objects in the image. The Image Processing Toolbox of MATLAB R2019a was used to convert the original forest cover type image to the enhance contrast image, including histogram of enhance contrast image. Furthermore, It was also used to analyze color-based forest cover type image segmentation using the enhance contrast image for this study. Using K-Means clustering analysis, a three-cluster solution was developed, labeled as Hardwoods (Yellow Color) Cover Type, Hardwoods (Gray Color) Cover Type, and Loblolly Pines Cover Type. There was a significant difference among three different forest cover type clusters in terms of histograms and L*a*b* color space features visually.
Contribution/ Originality
This study is one of very few studies which have classified forest cover type using its color feature sets of image pixels in order to understand forest composition. This study also addresses that K-Means clustering analysis can be utilized to develop and implement the classification of forest cover type image.

Carbon Sequestration Assessment of Selected Campus Champion Trees

Pages: 9-17
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Carbon Sequestration Assessment of Selected Campus Champion Trees

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DOI: 10.18488/journal.101.2020.71.9.17

Yeong Nain Chi , James S. Bardsley , Tracie J. Bishop

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[1]          K. R. Kirby and C. Potvin, "Variation in carbon storage among tree species: Implications for the management of a small-scale carbon sink project," Forest Ecology and Management, vol. 246, pp. 208-221, 2007.

[2]          H. M. Cox, "A sustainability initiative to quantify carbon sequestration by campus trees," Journal of Geography, vol. 111, pp. 173-183, 2012.

[3]          C. D. Villiers, S. Chen, and Y. Zhu, "Carbon sequestered in the trees on a university campus: A case study," Sustainability Accounting, Management and Policy Journal, vol. 5, pp. 149-171, 2014.

[4]          A. Arya, S. S. Negi, J. C. Kathota, A. N. Patel, M. H. Kalubarme, and J. Garg, "Carbon sequestration analysis of dominant tree species using Geo-informatics technology in Gujarat State (INDIA)," International Journal of Environment and Geoinformatics, vol. 4, pp. 79-93, 2017.

[5]          B. Leverett and D. Bertolette, "Measuring guidelines handbook," American Forest. american forests. org/wp-content/uploads/2014/12/AF-Tree-Measuring-Guidelines_LR, 2015.

[6]          N. L. Stephenson, A. Das, R. Condit, S. Russo, P. Baker, N. G. Beckman, D. Coomes, E. Lines, W. Morris, and N. Rüger, "Rate of tree carbon accumulation increases continuously with tree size," Nature, vol. 507, pp. 90-93, 2014.

[7]          A. Van Laar and A. Akça, Forest mensuration, 2nd ed. New York: Springer-Verlag Inc, 2007.

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Yeong Nain Chi , James S. Bardsley , Tracie J. Bishop (2020). Carbon Sequestration Assessment of Selected Campus Champion Trees. Journal of Forests, 7(1): 9-17. DOI: 10.18488/journal.101.2020.71.9.17
This study aims to quantify the unique score of selected champion trees on the University of Maryland Eastern Shore (UMES) main campus. Individual trees that are evaluated as “champions” of their species are measured for their trunk’s diameter at breast-height, overall tree height, and average crown spread to calculate their total score. Based on each specimen’s total score, selecting the top 20 champion trees provides data that is beneficial in developing a UMES campus tree map using geospatial technology. Furthermore, finding the carbon sequestered value and emissions-storage amount per tree is a targeted topic for the next step in this study. This study also provides a pilot direction for UMES to value its forested regions, to maintain these colossal trees, and to help the campus reach its sustainability goals. Given the relationship between tree size and efficiency of carbon sequestration demonstrated here, additional studies to test the hypothesis that some open grown tree species gain greater girth in a shorter period of time when compared to forest-grown specimens are encouraged to aid in tree selection.
Contribution/ Originality
This study is one of very few studies which have quantified the total score and carbon sequestration of the selected champion trees on UMES main campus. This study also addresses that the conservation and protection of large girth trees, whether as components in an old growth forest or as large individual specimens such as those of UMES's campus community, should be encouraged.

Evaluating Variation in Seed Quality Attributes in Pinus Patula Clonal Orchards using Cone Cluster Analysis

Pages: 1-8
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Evaluating Variation in Seed Quality Attributes in Pinus Patula Clonal Orchards using Cone Cluster Analysis

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DOI: 10.18488/journal.101.2020.71.1.8

Jesse Omondi Owino , Peter Murithi Angaine , Alice Adongo Onyango , Samson Okoth Ojunga , John Otuoma

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[1]          A. Sivacioğlu and S. Ayan, "Evaluation of seed production of scots pine (Pinus sylvestris L.) clonal seed orchard with cone analysis method," African Journal of Biotechnology, vol. 7, pp. 4393-4399, 2008.

[2]          F. Colas and M. Lamhamedi, "Production of a new generation of seeds through the use of somatic clones in controlled crosses of black spruce (Picea mariana)," New Forests, vol. 45, pp. 1-20, 2014. Available at: https://doi.org/10.1007/s11056-013-9388-2.

[3]          B. T. Styies and P. S. Mccarter, "The botany, ecology, distribution and conservation status of Pinus patula ssp. tecunumanii in the Republic of Honduras," Ceiba, vol. 29, pp. 3–30, 1988.

[4]          A. Nel, "Factors influencing controlled pollination of Pinus patula," Doctoral Dissertation, University of Natal, 2002.

[5]          R. Ennos, C. Joan, H. Jeanette, and O. B. David, "Is the introduction of novel exotic forest tree species a rational response to rapid environmental change? – A British perspective," Forest Ecology and Management. Elsevier, vol. 432, pp. 718–728, 2019. Available at: 10.1016/j.foreco.2018.10.018.

[6]          M. G. Iwaizumi, M. Ubukata, and H. Yamada, "Within-crown cone production patterns dependent on cone productivities in Pinus densiflora: Effects of vertically differential, pollination-related, cone-growing conditions," Botany, vol. 86, pp. 576-586, 2008. Available at: https://doi.org/10.1139/b08-024.

[7]          J. Burley, "Methodology for provenance trials in the tropics. Retrieved from: http://www.fao.org/docrep/93269e/93269e05.htm . [Accessed 24 May 2016]," 1969.

[8]          A. Skordilis and C. A. Thanos, "Seed stratification and germination strategy in the Mediterranean pines Pinus brutia and P. halepensis," Seed Science Research, vol. 5, pp. 151-160, 1995. Available at: https://doi.org/10.1017/s0960258500002774.

[9]          C. Fredrick, M. Catherine, N. Kamau, and S. Fergus, "Provenance and pretreatment effect on seed germination of six provenances of Faidherbia albida (Delile) A. Chev," Agroforestry Systems. Springer Netherlands, vol. 91, pp. 1007–1017, 2017. Available at: 10.1007/s10457-016-9974-3.

[10]        M. Martin, S. Newman, J. Aber, and R. Congalton, "Determining forest species composition using high spectral resolution remote sensing data," Remote Sensing of Environment, vol. 65, pp. 249-254, 1998. Available at: https://doi.org/10.1016/s0034-4257(98)00035-2.

[11]        K. Y. Peerbhay, O. Mutanga, and R. Ismail, "Does simultaneous variable selection and dimension reduction improve the classification of Pinus forest species?," Journal of Applied Remote Sensing, vol. 8, p. 085194, 2014. Available at: https://doi.org/10.1117/1.jrs.8.085194.

[12]        J. G. Martins, L. S. Oliveira, J. A. S. Britto, and R. Sabourin, "Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation," Machine Vision and Applications, vol. 26, pp. 279–293, 2015. Available at: 10.1007/s00138-015-0659-0.

[13]        N. N. Besschetnova, V. P. Besschetnov, N. A. Babich, and V. A. Bryntcev, "Physiological Differentiation of the Plus Trees of Scots Pine: Seasonal Status of Xylem," 2018.

[14]        A. Eitzinger, "Climate change adaptation: From science knowledge to local implementation. LMU München. Retrieved from: https://edoc.ub.uni-muenchen.de/23620/1/Eitzinger_Anton.pdf," 2018.

[15]        T. Adeyemo, P. Amaza, V. Okoruwa, V. Akinyosoye, K. Salman, and A. Abass, "Determinants of intensity of biomass utilization: Evidence from cassava smallholders in Nigeria," Sustainability, vol. 11, p. 2516, 2019. Available at: https://doi.org/10.3390/su11092516.

[16]        A. Ayari, D. Moya, M. Rejeb, A. B. Mansoura, A. Albouchi, J. De Las Heras, T. Fezzani, and B. Henchi, "Geographical variation on cone and seed production of natural Pinus halepensis Mill. forests in Tunisia," Journal of Arid Environments, vol. 75, pp. 403-410, 2011. Available at: https://doi.org/10.1016/j.jaridenv.2011.01.001.

[17]        J. Albrecht, Tree seed handbook of Kenya. Nairobi, Kenya: GTZ Forestry Seed Centre Muguga. Edited by W. Omondi, J. O. Maua, and F. N. Gachathi, 2nd ed. Nairobi: Kenya Forestry Research Institute, 1993.

[18]        P. M. Angaine, A. A. Onyango, and J. O. Owino, "Morphometrics of Pinus patula crown and its effect on cone characteristics and seed yield in Kenya," Journal of Horticulture and Forestry, 2020.

[19]        A. A. Onyango, P. M. Angaine, and J. O. Owino, "Patula pine (Pinus patula) cone opening under different treatments for rapid seed extraction in Londiani, Kenya," Journal of Horticulture and Forestry, 2020.

[20]        M. J. Crawley, "‘Multivariate statistics’ in The R Book," 2nd ed Chichester, UK: John Wiley & Sons, Ltd, 2013, pp. 809–824.

[21]        P. Bartlein, "Principal components and factor analysis, geographic data analysis. Retrieved from: http://geog.uoregon.edu/bartlein/courses/geog495/lec16.html . [Accessed 2 May 2020]," 2018.

[22]        R. B. Darlington, "Factor analysis. Retrieved from: http://node101.psych.cornell.edu/Darlington/factor.htm . [Accessed 2 May 2020]," 2020.

[23]        N. Gooroochurn and G. Sugiyarto, "Competitiveness indicators in the travel and tourism industry," Tourism Economics, vol. 11, pp. 25–43, 2005. Available at: 10.5367/0000000053297130.

[24]        A. Reusova, "Hierarchical clustering on categorical data in R, towards data science. Retrieved from: https://towardsdatascience.com/hierarchical-clustering-on-categorical-data-in-r-a27e578f2995. [Accessed 2 May 2020]," 2018.

[25]        D. Ortiz-Gonzalo, V. Philippe, O. Myles, N. Andreasde, A. Alain, and S. R. Todd, "Farm-scale greenhouse gas balances, hotspots and uncertainties in smallholder crop-livestock systems in Central Kenya," Agriculture, Ecosystems and Environment. Elsevier, vol. 248, pp. 58–70, 2017. Available at: 10.1016/j.agee.2017.06.002.

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Jesse Omondi Owino , Peter Murithi Angaine , Alice Adongo Onyango , Samson Okoth Ojunga , John Otuoma (2020). Evaluating Variation in Seed Quality Attributes in Pinus Patula Clonal Orchards using Cone Cluster Analysis. Journal of Forests, 7(1): 1-8. DOI: 10.18488/journal.101.2020.71.1.8
Clonal seed orchards are majorly established for the production of seed of known quality attributes. However, these seed sources often cross-pollinate over the years, forming new varieties of unknown seed quality traits. Given the long period that it takes forestry tree species to naturalize through provenance trials, it is desirable to develop rapid techniques for assessing seed quality traits to support the expansion of clonal seed sources. We evaluated the variability in seed quality among Pinus patula clonal seed orchards based on three physical cone characteristics (length, diameter, and weight) using cluster analysis and Principal Component Analysis. The results indicated that cone length was the significant component controlling for the groupings, with width and weight having almost similar influencing power as factors. Cluster analysis identified five optimal natural groupings out of a possible 14 clusters. The optimal groups had values that could easily be used in the grading of cones. The results suggest that cluster analysis holds promise for tree improvement specialists as a rapid, unbiased, and novel technique for assessing clonal seed material at a reasonably affordable cost. It is expected that future seed harvests in Pinus patula seed orchards will target cone length as an indicator of superior seed quality.
Contribution/ Originality
This study uses a new estimation methodology based on P. patula cone morphometric characteristics. The length of cone, the diameter of cone, the weight of cone and seed yield per cone from clonal seed orchards of in Londiani, Kenya are assessed using principal component analysis and cluster analysis methodologies.