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RSS FeedsRemote Sensing, Vol. 10, Pages 810: Terrestrial Laser Scanning to Detect Liana Impact on Forest Structure (Remote Sensing)

 
 

24 may 2018 18:00:07

 
Remote Sensing, Vol. 10, Pages 810: Terrestrial Laser Scanning to Detect Liana Impact on Forest Structure (Remote Sensing)
 


Tropical forests are currently experiencing large-scale structural changes, including an increase in liana abundance and biomass. Higher liana abundance results in reduced tree growth and increased tree mortality, possibly playing an important role in the global carbon cycle. Despite the large amount of data currently available on lianas, there are not many quantitative studies on the influence of lianas on the vertical structure of the forest. We study the potential of terrestrial laser scanning (TLS) in detecting and quantifying changes in forest structure after liana cutting using a small scale removal experiment in two plots (removal plot and non-manipulated control plot) in a secondary forest in Panama. We assess the structural changes by comparing the vertical plant profiles and Canopy Height Models (CHMs) between pre-cut and post-cut scans in the removal plot. We show that TLS is able to detect the local structural changes in all the vertical strata of the plot caused by liana removal. Our study demonstrates the reproducibility of the TLS derived metrics for the same location confirming the applicability of TLS for continuous monitoring of liana removal plots to study the long-term impacts of lianas on forest structure. We therefore recommend to use TLS when implementing new large scale liana removal experiments, as the impact of lianas on forest structure will determine the aboveground competition for light between trees and lianas, which has important implications for the global carbon cycle.


 
47 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 10, Pages 811: Rain Microstructure Parameters Vary with Large-Scale Weather Conditions in Lausanne, Switzerland (Remote Sensing)
Remote Sensing, Vol. 10, Pages 809: Time-Series Multispectral Indices from Unmanned Aerial Vehicle Imagery Reveal Senescence Rate in Bread Wheat (Remote Sensing)
 
 
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