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Trees outside forests in global drylands

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 cl... see more

31/10/2025

UCPH

2M€

Project Budget: 2M€

Project leader

KOBENHAVNS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

PARTICIPATION DEPralty Sin fecha límite de participación.

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Financing granted El organismo H2020 notifico la concesión del proyecto The day 2020-08-20

ERC-2020-STG: ERC STARTING GRANTS Scope:Objectives

I+D

Cerrada does 5 years

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characteristics of the participant

Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Private Information

No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

UCPH

1.71M€ | Leader

ASERPAL

267.16K€ | participant

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Project duration: 62 months Date Start: 2020-08-20
End date: 2025-10-31

Project leader

KOBENHAVNS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Project Budget 2M€

participation deadline Sin fecha límite de participación.

Project description 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.

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