The Understory Herbaceous Vegetation at Tropical Mountain Forest of Mount Bawakaraeng, South Sulawesi

Abstract The study about the diversity and structure of treelet vegetation in three slope areas on the mountainous forest of mount Bawakaraeng, Regency of Gowa, Province of South Sulawesi had been done. There were 17 species of treelets throughout the study area, from 13 families and 16 genera. The Asteraceae family has the highest number of species with 3 species, followed by Fabaceae and Solanaceae which each has 2 species. Pteridium aquilinum, Chromolaena odorata, Breynia oblongifolia, and Pogostemon cablin are the species with the highest important value index. The number of treelet species ranges from 15-16. Treelet species were mostly found in the flat slope area. The Shannon-Wiener diversity index (H’) ranges from 2,033-2,161 and was found to be highest on steep slopes and lowest on flat slopes. The H’ has the same tendency as Simpson’s Diversity Index and Evenness Index of Pielou (E) and reverse relationship with Simpson’s Dominance Index.

Download Full-text

Tree growth and aboveground biomass in a tropical mountain forest thirty years after selective logging in Sarawak, Borneo

Global Ecology and Conservation ◽

10.1016/j.gecco.2021.e01461 ◽

2021 ◽

Vol 26 ◽

pp. e01461

Author(s):

Renee Sherna Laing ◽

Kian Huat Ong ◽

Roland Jui Heng Kueh ◽

Nixon Girang Mang ◽

Patricia Jie Hung King

Keyword(s):

Tree Growth ◽

Aboveground Biomass ◽

Selective Logging ◽

Mountain Forest ◽

Tropical Mountain

Download Full-text

In situ 15N and 13C labelling of indigenous and plantation tree species in a tropical mountain forest (Munessa, Ethiopia) for subsequent litter and soil organic matter turnover studies

Organic Geochemistry ◽

10.1016/j.orggeochem.2011.06.010 ◽

2012 ◽

Vol 42 (12) ◽

pp. 1461-1469 ◽

Cited By ~ 11

Author(s):

Bruno Glaser ◽

Marianne Benesch ◽

Michaela Dippold ◽

Wolfgang Zech

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Tree Species ◽

Mountain Forest ◽

Tropical Mountain ◽

Organic Matter Turnover ◽

Soil Organic Matter Turnover ◽

13C Labelling

Download Full-text

Contrasting performance of Lidar and optical texture models in predicting avian diversity in a tropical mountain forest

Remote Sensing of Environment ◽

10.1016/j.rse.2015.12.019 ◽

2016 ◽

Vol 174 ◽

pp. 223-232 ◽

Cited By ~ 11

Author(s):

Christine I.B. Wallis ◽

Detlev Paulsch ◽

Jörg Zeilinger ◽

Brenner Silva ◽

Giulia F. Curatola Fernández ◽

...

Keyword(s):

Mountain Forest ◽

Avian Diversity ◽

Optical Texture ◽

Tropical Mountain

Download Full-text

AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV)

Remote Sensing ◽

10.3390/rs11121413 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1413 ◽

Cited By ~ 8

Author(s):

Víctor González-Jaramillo ◽

Andreas Fries ◽

Jörg Bendix

Keyword(s):

High Resolution ◽

Unmanned Aerial Vehicle ◽

Tree Level ◽

Mountain Forest ◽

Multispectral Images ◽

Individual Tree ◽

Tropical Mountain ◽

Aerial Vehicle ◽

Rgb Images ◽

Very High

The present investigation evaluates the accuracy of estimating above-ground biomass (AGB) by means of two different sensors installed onboard an unmanned aerial vehicle (UAV) platform (DJI Inspire I) because the high costs of very high-resolution imagery provided by satellites or light detection and ranging (LiDAR) sensors often impede AGB estimation and the determination of other vegetation parameters. The sensors utilized included an RGB camera (ZENMUSE X3) and a multispectral camera (Parrot Sequoia), whose images were used for AGB estimation in a natural tropical mountain forest (TMF) in Southern Ecuador. The total area covered by the sensors included 80 ha at lower elevations characterized by a fast-changing topography and different vegetation covers. From the total area, a core study site of 24 ha was selected for AGB calculation, applying two different methods. The first method used the RGB images and applied the structure for motion (SfM) process to generate point clouds for a subsequent individual tree classification. Per the classification at tree level, tree height (H) and diameter at breast height (DBH) could be determined, which are necessary input parameters to calculate AGB (Mg ha−1) by means of a specific allometric equation for wet forests. The second method used the multispectral images to calculate the normalized difference vegetation index (NDVI), which is the basis for AGB estimation applying an equation for tropical evergreen forests. The obtained results were validated against a previous AGB estimation for the same area using LiDAR data. The study found two major results: (i) The NDVI-based AGB estimates obtained by multispectral drone imagery were less accurate due to the saturation effect in dense tropical forests, (ii) the photogrammetric approach using RGB images provided reliable AGB estimates comparable to expensive LiDAR surveys (R2: 0.85). However, the latter is only possible if an auxiliary digital terrain model (DTM) in very high resolution is available because in dense natural forests the terrain surface (DTM) is hardly detectable by passive sensors due to the canopy layer, which impedes ground detection.

Download Full-text