With the COP 20 well underway in Lima, deforestation and carbon emissions have been a key topic of discussion in terms of addressing the increasingly important issue of climate change. Rainforests have long been identified as crucial natural assets on account of their ability to sequester and store carbon, thus removing it from our atmosphere. However, the conservation of rainforests largely concerns the preservation of areas containing high quantities of stored carbon, and identifying areas most at risk to deforestation and thus large-scale CO2 release.
Until now, this identification has generally been carried out through photographic means, which make it difficult to monitor the areas in real-time, and can sometimes be affected by cloud cover that can obscure areas from view. However, this week in Lima, a Stanford University Environmental Science professor by the name of Greg Astor has presented a new method of carbon quantification that has the potential to revolutionise the field of forest conservation and consequential avoided emissions.
Astor’s method, the Airborne Taxonomic Mapping System, consists of a pair of lasers that are beamed down from his ‘Dornier 228 prop plane’ at a rate of 500,000 pulses per minute, which measure the quantity of carbon contained in the sinews of every tree, root and plant in the forest. When the beams bounce back, an on-board spectrometer uses the data to produce colour coded results; red signifying high levels of carbon (characteristic of intact forest); brown signifying reduced carbon through degradation induced by logging; and blue, characteristic of built up areas, signifies little to no carbon storage. The lasers can penetrate any cloud cover, and produce accurate results in real-time.
Using this method Astor was able to quantify the level of carbon sequestered in the entire 128 million hectares of Peru, and presented his results in the form of a high-resolution carbon map to the COP 20 during the week. This is the first time a high-resolution carbon map of an entire country has been produced. This method could potentially change the way governments and organisations, including REDD+ projects, identify areas of high risk within rainforests, allowing more efficient conservation of rainforests worldwide.
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