Climate change has already done a lot of harm to our world including forest fires. Now researchers are using drone technology to better understand the forests and measure its health.
Researchers from Harvard University have now decided to use drones in order to better understand the Amazon rainforest. Through drone-based sensors, the team hopes to figure out the unique ‘fingerprint’ of various rainforest ecosystems, helping them monitor the health of the forest and understand how it is responding to climate change, deforestation and fire.
As reported by Engadget, each plant gives out a different chemical signal, volatile organic compound (VOC) signature, or fingerprint that helps them interact with organisms around them. These signals can change depending on factors such as drought or flood. By monitoring these fingerprints, the researchers can study how forest ecosystems adapt to stressors.
The Amazon’s VOCs were previously monitored by only some towers constructed in one type of ecosystem. The data gathered was limited and biased, and the biosphere emissions models assumed nearby ecosystem had the same VOC emissions.
The drone-based system has been worked upon since the last two years in order to map the VOCs given out in various ecosystems in central Amazonia. The research has proved that various ecosystems have various VOC signatures.
Researcher Scot Martin said, “With our chemical sensors, we can better understand the current functioning of the forest and how it is changing with shifting regional climate, including a more frequent occurrence of fires in recent years in the central part of the Amazon.”
For the future, the team plans to sample more ecosystems in water-logged valleys along rivers. For this, they will use a boat as a launching platform and also hope to test a three-drone fleet, as per Harvard University.
“This research highlights how little we understood forest heterogeneity,” said Martin. “But drone-assisted technologies can help us understand and quantify VOC emissions in different, nearby ecosystems in order to better represent them in climate and air quality model simulations.”