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Recent advances in the application of spectral bands from satellite observations and machine learning algorithms (MLA) in the Google Earth Engine (GEE) cloud-computing platform have been demonstrated to enhance the accuracy of mapping forest resources. This study presents a novel method for mapping the natural distribution of highland bamboo (Oldeania alpina) using spectral bands and three machine learning algorithms, namely random forest, gradient tree boosting, and classification and regression tree. First, spectral bands, vegetation indices and textural features including contrast, entropy and inverse difference moment were derived from the Sentinel-2 elevation data. Second, observations were categorized as the dry season (December-February), short rainy season (March-May), main (long) rainy season (June-August), wet season (September-November) and annual composite. Third, the machine learning algorithms were tested using 1882 ground control points collected from field and high spatial resolution images. Finally, the best-performing machine learning algorithm was used to classify and map bamboo forests. The five land cover categories identified were bamboo stands, natural forest, other vegetation, non-vegetated areas and water bodies. The result shows that the random forest classifier is a robust algorithm with an overall accuracy of 94% considering all the annual composite's spectral, vegetation and textural variables. The highland bamboo coverage (91 km2) in the Andracha district provided valuable insights. Bamboo stands were mostly distributed in the southern and southeastern parts of the district. Here, we show that image compositing and multiple input parameters using machine learning techniques can overcome challenges facing land cover classification, which can hinder accurate mapping of Afromontane forests. We conclude that the incorporation of vegetation indices and textural features in land cover classification increases accuracy of mapping natural highland bamboo coverage and distribution. The application of this new method is a promising prospect not only in Ethiopia but also in Afromontane forests elsewhere in Africa. [ABSTRACT FROM AUTHOR]

Agarwood (Aquilaria malaccensis Lam.) (Thymelaeaceae) is valued in many cultures because of its distinctive fragrance and its use in incense, perfumery, and traditional medicine. Large-scale harvesting from natural populations caused rapid depletion of the species in the wild, and the species is now listed as "Critically Endangered" and almost extinct in the wild. The promotion of this species in agroforestry systems may pave the way for its conservation. The present study aimed to quantify the growth and coppicing ability of agarwood under monoculture and polyculture stands. Monoculture stands aged more than 20 years, and stem girth (1.37 m above ground) of 60–70 cm had the highest growth increment of 3.73 cm yr−1. Monoculture stands (>10 years old) also recorded higher number of coppicing stocks than polyculture stands. The highest coppice growth increment (4.07 cm yr−1) was recorded in the 70–80 cm stem girth in monoculture stands. Based on the results, we recommend the promotion of agarwood trees with good coppice management in forestry programs to conserve the species while also meeting the economic needs of rural communities. [ABSTRACT FROM AUTHOR]