LiDAR is a technology based on a laser mounted on a plane or satellite, which can yield detailed scans of forest ecosystems. This valuable information allows detailed analyses of the ecology and dynamics of tree populations, and it is commonly used for estimating forest carbon stocks. LiDAR has thus become very relevant in the context of climate change mitigation, since its accurate estimations can be used in the global market of carbon credits to pay for those who plant, grow and preserve forests, which can help in the protection of natural habitats. Researchers have recently developed universal carbon models that can be employed for global consistency in LiDAR estimations.
These models are based on tree allometry, which relates the biomass of a tree to its diameter. The tree allometry employed is tailored to tropical biomes, where efforts for protecting forests are nowadays focused. There is however a need to adapt these methods to other bioregions, to obtain LiDAR carbon models that can be employed for global carbon assessments.
The research in this PhD project will consist of modelling and mathematical development of LIDAR models from various allometric models commonly employed at bioregions other than tropical: temperate, Mediterranean, boreal and alpine. The PhD candidate will adapt current tree allometry to this new LiDAR context, deducing population level models from individual tree allometry and reverting the formulation of tree-diameter models.
The work will also involve coordination and consistent processing of data shared by many collaborators around the world, in a cooperative effort to derive global carbon models. The outcome would be a framework for further universalising the method for carbon accounting from LiDAR, which would have a strong and timely impact now that a satellite LiDAR system has just started being operational and entire countries are been scanned with airborne LiDAR technologies.
• Applicants should hold the equivalent of a minimum of a UK Honours Degree at 2:1 level in numerate subjects related to Environmental Science, Geography, Natural Sciences, Applied Mathematics/Statistics, Modelling, Computer Science or related disciplines.
• Enthusiasm for helping conserving forests and / or an interest in remote sensing / Earth observation technologies.
• Strong modelling and mathematical skills and experience working with allometric models would be an advantage.
• Skills in Geographic Information Systems
• Applicants who additionally have a Masters degree, or relevant work experience, will be particularly competitive.
• Knowledge of programming can also be an asset.
Dr Rubén Valbuena ([email protected]