OEMC project use case: Tool to estimate local temperatures changes following an increase in forest cover
Description
Changing how we use and manage the land can have some profound consequences on the climate. While biogeochemical effects are well studied and are incorporated into climate treaties, the biophysical effects of land use change are still overlooked. These relate to the changes in properties of the surface (albedo, latent heat flux, etc), and can affect the local climate, notably by changing the local land surface temperature (LST). Data-driven estimations of the changes in LST due to potential changes in forest cover have been demonstrated using a combination of satellite remote sensing LST dataset, land cover maps, and space-for-time substitution methodologies. In this use case, this methodology is being improved by: (1) making it reproducible in the Julia language; (2) adapting it so that it can take into consideration topographical effects; (3) applying it to sub-daily geostationary satellite data (from SEVIRI on-board of Meteosat Second Generation); and (4) deploying it in the JRC Big Data Analytics Platform in order to make the results part of the EU Forest Observatory. The target output will be a dataset providing a fine-scale estimation of how much surface temperature would change locally following potential changes in the forest cover. This dataset will thus provide diurnal cycles of temperature changes at a ~5 km spatial resolution for every month of the year over most of Europe and Africa. The dataset will serve to inform the JRC Forest Observatory and advise on thermal consequences of land-based mitigation strategies such a tree planting.
Stakeholder needs:
- That the deployed solution work on their internal infrastructure, namely the JRC Big Data Analytics Platform, to thus to be able to be part of the EU Forest Observatory.
- The possibility to analyse the diurnal scale.
- The extension from Europe to Africa, given the interest on the Regulation on Deforestation-free Products (EUDR), many of which come from Africa.
Implementation:
The methodology has successfully been implemented both in the Julia language and in Python. A new approach to incorporate topography has been proposed, but our test have not conclusively demonstrated that the proposed solution is mature, so we have returned to the fall-back situation of masking areas with important topography. The workflow was adapted and tested on geostationary data from SEVIRI and this was realized within the JRC BDAP platform for a whole year over Europe and Africa. A dedicated viewer based on XCUBE technology was also developed to explore the data.
Recorded talks (ordered chronologically):
- Towards a large-scale tool for estimating potential land-cover impacts from RS by Daniel Pabon-Moreno, Open-Earth-Monitor - Global Workshop 2023;
- Thinking land cover change beyond carbon: estimating biophysical effects from satellite observations by Gregory Duveiller, Open-Earth-Monitor science webinars;
- Exploring the biophysical impacts of potential changes in tree cover in Africa by Gregory Duveiller, Open-Earth-Monitor - Global Workshop 2024;
Technical info
Visit the use-case page on the OEMC website to learn more: https://earthmonitor.org/tool-to-estimate-local-temperatures-changes-following-an-increase-in-forest-cover/