Descripción del proyecto
Desvelar la importancia ecológica y económica de los árboles no forestales
Los investigadores nunca han prestado demasiada atención a los árboles no forestales. De hecho, apenas disponemos de datos sobre su densidad y tamaño, aunque desempeñan una función crucial para la biodiversidad y proporcionan una gran variedad de servicios ecosistémicos. El proyecto financiado con fondos europeos TOFDRY se centrará en los árboles de las tierras secas del mundo con el fin de arrojar luz sobre cómo afectan la intervención humana y el cambio climático a los árboles de las tierras secas, así como el modo en que estos árboles pueden contribuir a mitigar la degradación, el cambio climático y la pobreza. Para encontrar respuestas, el proyecto estudiará los árboles de forma individual, registrando no solo su cobertura sino también su densidad, el tamaño de las copas, los servicios ecológicos fundamentales y los factores socioambientales determinantes. Las imágenes obtenidas por satélite y los abundantes datos de campo se combinarán con técnicas de aprendizaje profundo para identificar objetos en las imágenes.
Objetivo
Drylands cover approximately 65 million km² of the Earth’s land surface but their tree and shrub cover is a major unknown in terrestrial research. This is because a large proportion of dryland trees grow isolated without canopy closure and most scientific and non-scientific interest is devoted to forests, while the density and size of trees outside of forests is not well documented. However, these non-forest trees play a crucial role for biodiversity and provide ecosystem services such as carbon storage, food resources, livelihoods and shelter for humans and animals. The limited attention devoted to the quantification of dryland trees leads to an underrepresentation of non-forest trees in development strategies and climate\vegetation models, and the economic and ecological importance of non-forest trees is largely unknown at large scale.
Through this project I will work towards a wall-to-wall identification of trees in global drylands, and study their ecological services and socio-environmental determinants. The breakthrough is that trees are not assessed as canopy fraction of an area, but as individuals, allowing to identify not only their coverage but also their density, crown size, and key ecological services. I will apply a new generation of satellite imagery at sub-meter resolution and extensive field data in conjunction with fully convolutional neural networks, a deep learning technique being able to identify objects within imagery at an unprecedented accuracy. In doing so, I will lay the groundwork for new insights into the contribution of human agency and climate change to the distribution of dryland trees and their role in mitigating degradation, climate change and poverty.
Ámbito científico
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringsatellite technology
- natural sciencesbiological sciencesecologyecosystems
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic changes
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Programa(s)
Régimen de financiación
ERC-STG - Starting GrantInstitución de acogida
1165 Kobenhavn
Dinamarca