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Using Standardized Time Series Land Cover Maps to Monitor the SDG Indicator “Mountain Green Cover Index” and Assess Its Sensitivity to Vegetation Dynamics

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Year of publication 2021

Place of publication Rome, Italy ;

Author De Simone, L.; Navarro, D.; Gennari, P.; Pekkarinen, A.; de Lamo, J.

Publisher Multidisciplinary Digital Publishing Institute (MDPI) ;

Product type Article

Category Journal article

Synopsis (short abstract) SDG indicators are instrumental for the monitoring of countries’ progress towards sustainability goals as set out by the UN Agenda 2030. Earth observation data can facilitate such monitoring and reporting processes, thanks to their intrinsic characteristics of spatial extensive coverage, high spatial, spectral, and temporal resolution, and low costs. EO data can hence be used to regularly assess specific SDG indicators over very large areas, and to extract statistics at any given subnational level. The Food and Agriculture Organization of the United Nations (FAO) is the custodian agency for 21 out of the 231 SDG indicators. To fulfill this responsibility, it has invested in EO data from the outset, among others, by developing a new SDG indicator directly monitored with EO data: SDG indicator 15.4.2, the Mountain Green Cover Index (MGCI), for which the FAO produced initial baseline estimates in 2017. The MGCI is a very important indicator, allowing the monitoring of the health of mountain ecosystems. The initial FAO methodology involved visual interpretation of land cover types at sample locations defined by a global regular grid that was superimposed on satellite images. While this solution allowed the FAO to establish a first global MGCI baseline and produce MGCI estimates for the large majority of countries, several reporting countries raised concerns regarding: (i) the objectivity of the method; (ii) the difficulty in validating FAO estimates; (iii) the limited involvement of countries in estimating the MGCI; and (iv) the indicator’s limited capacity to account for forest encroachment due to agricultural expansion as well as the undesired expansion of green vegetation in mountain areas, resulting from the effect of global warming. To address such concerns, in 2020, the FAO introduced a new data collection approach that directly measures the indicator through a quantitative analysis of standardized land cover maps (European Space Agency Climate Change Initiative Land Cover maps—ESA CCI-LC). In so doing, this new approach addresses the first three of the four issues, while it also provides stronger grounds to develop a solution for the fourth issue—a solution that the FAO plans to present to the Interagency and Expert Group on SDG Indicators (IAEG-SDG) at its autumn 2021 session.

This study (i) describes the new approach to estimate the MGCI indicator using ESA’s CCI-LC and products, (ii) assesses the accuracy of the new approach; (iii) reviews the limitations of the current SDG indicator definition to monitor progress towards SDG 15.4; and (iv) reflects on possible further adjustments of the indicator methodology in order to address them. This is an open access article published externally in ISPRS International Journal of Geo-Information Volume 10, Issue 7. Received: 7 April 2021 / Revised: 14 June 2021 / Accepted: 15 June 2021 / Published: 23 June 2021

Cite this content as:

De Simone, L.; Navarro, D.; Gennari, P.; Pekkarinen, A.; de Lamo, J. Using Standardized Time Series Land Cover Maps to Monitor the SDG Indicator “Mountain Green Cover Index” and Assess Its Sensitivity to Vegetation Dynamics. ISPRS Int. J. Geo-Inf. 2021, 10, 427. https://doi.org/10.3390/ijgi10070427

Language English

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