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.
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.
Carbon Sequestration Assessment of Selected Campus Champion Trees
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.
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.
Color-Based Forest Cover Type Image Segmentation using K-Means Clustering Approach
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.
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.