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Protected areas, or national parks, are established to preserve natural ecosystems; their effectiveness on the territory needs to be evaluated. We propose considering a time series of the SDG 15.1.2 indicator, "Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type", to quantify the presence over time of grassland ecosystem in Murgia Alta (southern Italy), within the Natura 2000 and national park boundaries. Time series of remote sensing imagery, freely available, were considered for extracting, by Support Vector Machine classifiers, a time series of grassland cover mappings from 1990 to 2021. This latter was, then, used for computing a time series of the SDG 15.1.2 indicator. A high reduction (about 15,000 ha) of grassland presence from 1990 to 2004, the foundation years of the national park, followed by the increasing stability up to nowadays, was evaluated. Furthermore, grassland presence was evaluated in a 5-km buffer area, surrounding Natura 2000 boundary, revealing a continuous loss from 1990 up to now (about 500 ha) in the absence of protection actions. This study represents the first long-term analysis for the grassland ecosystem in Murgia Alta and the first effort to analyze a time series of the SDG 15.1.2 indicator. The findings can provide inputs to governments in monitoring the effectiveness of protection actions. [ABSTRACT FROM AUTHOR]

This study focused on implementation of the Sustainable Development Goal (SDG) 11 indicators, at local scale, useful in monitoring urban social resilience. For this purpose, the study focused on updating the distribution map of the migrant population regularly residing in Bari and a neighboring town in Southern Italy. The area is exposed to increasing migration fluxes. The method implemented was based on the integration of Sentinel-2 imagery and updated census information dated 1 January 2019. The study explored a vector-based variant of the dasymetric mapping approach previously used by the Joint Research Center (JRC) within the Data for Integration initiative (D4I). The dasymetric variant implemented can disaggregate data from census areas into a uniform spatial grid by preserving the information complexity of each output grid cell and ensure lower computational costs. The spatial distribution map of regular migrant population obtained, along with other updated ancillary data, were used to quantify, at local level, SDG 11 indicators. In particular, the map of regular migrant population living in inadequate housing (SDG 11.1.1) and the ratio of land consumption rate to regular migrant population growth rate (SDG 11.3.1) were implemented as specific categories of SDG 11 in 2018. At the local level, the regular migrant population density map and the SDG 11 indicator values were provided for each 100 × 100 m cell of an output grid. Obtained for 2018, the spatial distribution map revealed in Bari a high increase of regular migrant population in the same two zones of the city already evidenced in 2011. These zones are located in central parts of the city characterized by urban decay and abandoned buildings. In all remaining city zones, only a slight generalized increase was evidenced. Thus, these findings stress the need for adequate policies to reduce the ongoing process of residential urban segregation. The total of disaggregated values of migrant population evidenced an increase of 44.5% in regular migrant population. The indicators obtained could support urban planners and decision makers not only in the increasing migration pressure management, but also in the local level monitoring of Agenda 2030 progress related to SDG 11. [ABSTRACT FROM AUTHOR]

Local and Regional Authorities require indicators at the intra-urban scale to design adequate policies to foster the achievement of the objectives of Sustainable Development Goal (SDG) 11. Updated high-resolution population density and settlement maps are the basic input products for such indicators and their sub-indicators. When provided at the intra-urban scale, these essential variables can facilitate the extraction of population flows, including both local and regular migrant components. This paper discusses a modification of the dasymetric method implemented in our previous work, aimed at improving the population density estimation. The novelties of our paper include the introduction of building height information and site-specific weight values for population density correction. Based on the proposed improvements, selected indicators/sub-indicators of four SDG 11 targets were updated or newly implemented. The output density map error values are provided in terms of the mean absolute error, root mean square error and mean absolute percentage indicators. The values obtained (i.e., 2.3 and 4.1 people, and 8.6%, respectively) were lower than those of the previous dasymetric method. The findings suggest that the new methodology can provide updated information about population fluxes and processes occurring over the period 2011–2020 in the study site—Bari city in southern Italy. [ABSTRACT FROM AUTHOR]