Early warning systeM for soil dEgRadation: a statistical physics Approach to cLimate change aDaptation

Informazioni relative al progetto

Emerald

ID dell’accordo di sovvenzione: 101069222

DOI

10.3030/101069222

Progetto chiuso

Data della firma CE 3 Maggio 2022

Data di avvio 1 Giugno 2022

Data di completamento 31 Marzo 2024

Finanziato da

• European Research Council (ERC)

Costo totale

Nessun dato

Contributo UE

€ 150 000,00

Coordinato da

THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD, OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN
Ireland

Periodic Reporting for period 1 - Emerald (Early warning systeM for soil dEgRadation: a statistical physics Approach to cLimate change aDaptation)

Periodo di rendicontazione: 2022-06-01 al 2024-03-31

Sintesi del contesto e degli obiettivi generali del progetto

Climate change is an urgent challenge and impacts ecosystems, society and economic activities. Here we focus on the problem of loss of fertile soil (soil degradation), a process that is induced and accelerated by extreme weather and climate change and affects, among other sectors, agriculture, land use planning and food security. Current tools to monitor soil health for commercial purposes or policymaking are either limited in scale due to high cost, or insufficiently accurate due to the lack of underlying robust models. The EMERALD Proof of Concept aims to test and further develop a novel method to detect soil stress founded in the statistical physics of disordered systems. The use of earth observation data ensures a scalable solution to tackle the problem of desertification globally.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

The spatial distribution of vegetation has been analysed through a numerical stochastic model and through the analysis of land data from satellite images. The model is known to exhibit three phases, namely showing contiguous, fragmented, and degraded vegetation, corresponding to increasing environmental stress. Considering samples of 30km in size, we looked at the vegetation fraction and the fraction of contiguous vegetation in each sample with the aim of identifying these processes in real land data.
We have shown that the model's description of the vegetation dynamics can be qualitatively compared to the land data even with relatively low resolution data and with some necessary simplification in the model. The values of the vegetation fraction and the largest cluster size allowed us to offer a qualitative categorization of each sample between the three scenarios of contiguous, fragmented and degraded vegetation.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

The method developed in this Proof of Concept has been developed and tested on large scale Earth observation images spanning several European countries. The potential impacts involve the increased ability to monitor soil quality, which will eventually directly benefit farmers, governmental agencies for land use, food and emergency response, agricultural organizations and insurance companies, as well as indirectly having an impact on environmental aspects (land conservation) and social policies (food security and peoples’ well-being).

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