scholarly journals Machine Learning Classification of Endangered Tree Species in a Tropical Submontane Forest Using WorldView-2 Multispectral Satellite Imagery and Imbalanced Dataset

2021 ◽  
Vol 13 (24) ◽  
pp. 4970
Author(s):  
Colbert M. Jackson ◽  
Elhadi Adam

Accurate maps of the spatial distribution of tropical tree species provide valuable insights for ecologists and forest management. The discrimination of tree species for economic, ecological, and technical reasons is usually necessary for achieving promising results in tree species mapping. Most of the data used in tree species mapping normally have some degree of imbalance. This study aimed to assess the effects of imbalanced data in identifying and mapping trees species under threat in a selectively logged sub-montane heterogeneous tropical forest using random forest (RF) and support vector machine with radial basis function (RBF-SVM) kernel classifiers and WorldView-2 multispectral imagery. For comparison purposes, the original imbalanced dataset was standardized using three data sampling techniques: oversampling, undersampling, and combined oversampling and undersampling techniques in R. The combined oversampling and undersampling technique produced the best results: F1-scores of 68.56 ± 2.6% for RF and 64.64 ± 3.4% for SVM. The balanced dataset recorded improved classification accuracy compared to the original imbalanced dataset. This research observed that more separable classes recorded higher F1-scores. Among the species, Syzygium guineense and Zanthoxylum gilletii were the most accurately mapped whereas Newtonia buchananii was the least accurately mapped. The most important spectral bands with the ability to detect and distinguish between tree species as measured by random forest classifier, were the Red, Red Edge, Near Infrared 1, and Near Infrared 2.

2020 ◽  
Vol 14 (1) ◽  
pp. 34
Author(s):  
Faezah Pardi

This study was conducted at Pulau Jerejak, Penang to determine the floristic variation of its tree communities. A 0.5-hectare study plot was established and divided into 11 subplots. A total of 587 trees with diameter at breast height (DBH) of 5 cm and above were measured, identified and recorded. The tree communities comprised of 84 species, 63 genera and 32 families. The Myrtaceae was the most speciose family with 10 recorded species while Syzgium glaucum (Myrtaceae) was the most frequent species. The Myrtaceae recorded the highest density of 306 individuals while Syzgium glaucum (Myrtaceae) had the highest species density of 182 individuals. Total tree basal area (BA) was 21.47 m2/ha and family with the highest BA was Myrtaceae with 5.81 m2/ha while at species level, Syzgium glaucum (Myrtaceae) was the species with the highest total BA in the plot with value of 4.95 m2/ha. The Shannon˗Weiner Diversity Index of tree communities showed a value of 3.60 (H'max = 4.43) and Evenness Index of 0.81 which indicates high uniformity of tree species. The Margalef Richness Index (R') revealed that the tree species richness was 13.02. Myrtaceae had the highest Importance Value of 20.4%. The Canonical Correspondence Analysis (CCA) showed that Diospyros buxifolia (Ebenaceae) and Pouteria malaccensis (Sapotaceae) were strongly correlated to low pH. Dysoxylum cauliflorum (Meliaceae) and Eriobotrya bengalensis (Rosaceae) were correlated to phosphorus (P) and calcium ion (Ca2+), respectively. Therefore, the trees species composition at Pulau Jerejak showed that the biodiversity is high and conservation action should be implemented to protect endangered tree species. Keywords: Floristic variation; Tree communities; Trees composition; Pulau Jerejak; Species diversity


2006 ◽  
Vol 49 (4) ◽  
pp. 320-325 ◽  
Author(s):  
Heung Kyu Moon ◽  
Ji Ah Kim ◽  
So Young Park ◽  
Yong Wook Kim ◽  
Ho Duck Kang

2020 ◽  
Vol 14 ◽  

Breast Cancer (BC) is amongst the most common and leading causes of deaths in women throughout the world. Recently, classification and data analysis tools are being widely used in the medical field for diagnosis, prognosis and decision making to help lower down the risks of people dying or suffering from diseases. Advanced machine learning methods have proven to give hope for patients as this has helped the doctors in early detection of diseases like Breast Cancer that can be fatal, in support with providing accurate outcomes. However, the results highly depend on the techniques used for feature selection and classification which will produce a strong machine learning model. In this paper, a performance comparison is conducted using four classifiers which are Multilayer Perceptron (MLP), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest on the Wisconsin Breast Cancer dataset to spot the most effective predictors. The main goal is to apply best machine learning classification methods to predict the Breast Cancer as benign or malignant using terms such as accuracy, f-measure, precision and recall. Experimental results show that Random forest is proven to achieve the highest accuracy of 99.26% on this dataset and features, while SVM and KNN show 97.78% and 97.04% accuracy respectively. MLP shows the least accuracy of 94.07%. All the experiments are conducted using RStudio as the data mining tool platform.


2014 ◽  
Vol 37 (1) ◽  
pp. 69-72
Author(s):  
Giriraj Panwar ◽  
Kumar Ambrish ◽  
S. Srivastava

Indopiptadenia oudhensis (Brandis) Brenan is an endangered tree species of family Mimosaceae. Species is mainly distributed at Indo-Nepal border and facing threats such as anthropogenic pressure, habitat destruction, over exploitation, low seed viability and poor seed germination.


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