Diversity, endemism, and composition of tropical mountain forest communities in Sulawesi, Indonesia, in relation to elevation and soil properties

Author(s):  
Fabian Brambach ◽  
Christoph Leuschner ◽  
Aiyen Tjoa ◽  
Heike Culmsee
2011 ◽  
Vol 8 (6) ◽  
pp. 11631-11660 ◽  
Author(s):  
N. Gharahi Ghehi ◽  
C. Werner ◽  
L. Cizungu Ntaboba ◽  
J. J. Mbonigaba Muhinda ◽  
E. Van Ranst ◽  
...  

Abstract. Globally, tropical forest soils represent the second largest source of N2O and NO. However, there is still considerable uncertainty on the spatial variability and soil properties controlling N trace gas emission. To investigate how soil properties affect N2O and NO emission, we carried out an incubation experiment with soils from 31 locations in the Nyungwe tropical mountain forest in southwestern Rwanda. All soils were incubated at three different moisture levels (50, 70 and 90% water filled pore space (WFPS)) at 17 °C. Nitrous oxide emission varied between 4.5 and 400 μg N m−2 h−1, while NO emission varied from 6.6 to 265 μg N m−2 h−1. Mean N2O emission at different moisture levels was 46.5 ± 11.1 (50% WFPS), 71.7 ± 11.5 (70% WFPS) and 98.8 ± 16.4 (90% WFPS) μg N m−2 h−1, while mean NO emission was 69.3 ± 9.3 (50% WFPS), 47.1 ± 5.8 (70% WFPS) and 36.1 ± 4.2 (90% WFPS) μg N m−2 h−1. The latter suggests that climate (i.e. dry vs. wet season) controls N2O and NO emissions. Positive correlations with soil carbon and nitrogen indicate a biological control over N2O and NO production. But interestingly N2O and NO emissions also showed a negative correlation (only N2O) with soil pH and a positive correlation with free iron. The latter suggest that chemo-denitrification might, at least for N2O, be an important production pathway. In conclusion improved understanding and process based modeling of N trace gas emission from tropical forests will not only benefit from better spatial explicit trace gas emission and basic soil property monitoring, but also by differentiating between biological and chemical pathways for N trace gas formation.


2021 ◽  
Vol 26 ◽  
pp. e01461
Author(s):  
Renee Sherna Laing ◽  
Kian Huat Ong ◽  
Roland Jui Heng Kueh ◽  
Nixon Girang Mang ◽  
Patricia Jie Hung King

2016 ◽  
Vol 174 ◽  
pp. 223-232 ◽  
Author(s):  
Christine I.B. Wallis ◽  
Detlev Paulsch ◽  
Jörg Zeilinger ◽  
Brenner Silva ◽  
Giulia F. Curatola Fernández ◽  
...  

2019 ◽  
Vol 11 (12) ◽  
pp. 1413 ◽  
Author(s):  
Víctor González-Jaramillo ◽  
Andreas Fries ◽  
Jörg Bendix

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.


Trees ◽  
2010 ◽  
Vol 25 (1) ◽  
pp. 59-70 ◽  
Author(s):  
Julia Krepkowski ◽  
Achim Bräuning ◽  
Aster Gebrekirstos ◽  
Simone Strobl

2019 ◽  
Vol 16 (10) ◽  
pp. 2335-2347 ◽  
Author(s):  
Chalthleng Lalnunzira ◽  
Francis Q. Brearley ◽  
Shri Kant Tripathi

2016 ◽  
Vol 178 ◽  
pp. 223
Author(s):  
C.I.B. Wallis ◽  
D. Paulsch ◽  
J. Zeilinger ◽  
B. Silva ◽  
G.F. Curatola Fernández ◽  
...  

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