Development of a novel approach based on Earth Observations to measure and monitor the Mountain Green Cover Index (SDG 15.4.2).
A novel approach based on Earth Observations to measure the Mountain Green Cover Index (SDG 15.4.2) was developed by FAO in 2020. The development took place in consultation with SDF focal points from and National Statistics Offices in countries to address their concerns raised regarding a precedent methodology that FAO developed in 2017.
The final aim of the work was to provide countries with a standardized solution to measure and monitor SDG 15.4.2 that is robust, transparent, and cost-efficient. Tests proved that the method as very a high accuracy (>90%).
The new methodology allows calculating the 15.4.2 indicator globally at a national and subnational level, for the years 1992 through 2020, e for every new reporting year. The unprecedented dense and extensive time series can be used to calculate MGCI trends. This added value information allows for identifying countries where the indicator has improved, and to support the analysis of existing policies which had a positive impact on the indicator.
The development of the methodology was implemented by the Office of the Chief Statistician in FAO in consultation with the Forestry Division and with national SDG focal points in several countries. Geospatial global datasets were initially gathered from public sources including land cover time series (ESA- Climate Change Initiative Land Cover, Global Copernicus Land Service Land Cover 100), ground truth land cover validation (from the work of Li et al 2010), mountain elevation range (UNEP). Such data was pre-processed, resampled, and reclassified. Python scripts were developed to calculate MGCI from the land cover map time series and compiled into SDG reporting templates in excel. The full description of the methodological development is provided through a Map Story published by FAO at https://hqfao.maps.arcgis.com/apps/MapJournal/index.html?appid=d5f2b3da… A paper is under submission to the International Journal of Remote Sensing.
MGCI estimates were calculated by FAO and submitted to countries for validation as of the SDG reporting cycle 2020. Results from the validation process, confirm the positive impact of the introduction of the methodology: 39% of countries contacted responded to the validation request; 21% of countries approved the validation request, as opposed to 12% in 2017; and 13% provided their own national data, as opposed to zero such cases in 2017.
Various factors have enabled the development, testing and validation of the methodology. The availability of free and open data has indeed greatly facilitated the development of the methodology. This jointly with the extent of standardized land cover time series provided by the European Space Agency over 1992 through 2018. FAO's role of custodian agency has been instrumental in interacting with reporting countries and gathering feedback about the methodology. Findings have also indicated some constraints, these in relation to the intrinsic definition of the indicator mainly. In fact, tests showed that the MGCI has low sensitivity to forestland encroachment as a result of agriculture expansion. Furthermore, green vegetation appearing in mountains as a result of the melting of perennial ice is also another feature that the MGCI current definition does not account for. FAO has committed to working on the improvement of the indicator, and the adjustment of the EO based methodology in 2021.
Present any plans for extending the practice more widely or encouraging its adoption in other contexts. The methodology relies on free and open global data that is updated and maintained on a yearly basis by the European Space Agency. This ensures the sustainability of the solution and the possibility for any country to use it as it is out-of-the-box. In alternative countries can use their own national land cover datasets. The algorithms can be run using FOSS technology like QGIS, or can be run on EO platforms such as Google Earth Engine: these widen the potential for uptake by countries as there is no upfront investment in technology. FAO will continue to support countries in the uptake of Earth Observations for monitoring MGCI as well as all other SDG indicators where EO data is relevant.
The EO based solution highly mitigates the impacts of Covid 19 on the MGCI reporting under the SDG framework. In fact, the solution drastically reduces the need for fieldwork, which would instead be very much limited by the restrictions to movement imposed by anti-Covid19 measures in almost all countries.
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Global, at national, and subnational level