Assessing the spatial coherence of forest cover indicators from different data sources: A contribution to sustainable development reporting

The accuracy of forest area estimates has improved over time as a result of field/cadastral surveys, enhanced remote sensing techniques, and the effectiveness of algorithms for automatical recognition of land cover types. However, forest statistics seem to be less accurate in disaggregated spatial domains such as small administrative units. To evaluate the contribution of different data sources to small-area forest cover estimation in Europe, we compared seven indicators with different spatial coverage and resolution. The analysis considered multiple information sources from innovative initiatives, such as the Copernicus Land monitoring scheme, and traditional (national) surveys. More specifically, the study examined the spatial coherence of these indicators at the municipal scale in Italy to achieve two objectives: (i) assessing the overall precision of forest cover rates and (ii) identifying spatial variations in forest cover rates associated with the technical characteristics of each data source. A spatial econometric approach was used to identify the sources of spatial divergence in forest cover rates and determine the data providers best suited to meet the information requirements of environmental reporting at the desired spatial scale. The results reveal that the selected indicators show varying degrees of internal coherence, with some indices displaying strong correlations and others delineating heterogeneous spatial patterns. Our study highlights the importance of choosing the right information source assessing forest area at the municipal level and provides a valuable approach quantifying the coherence and reliability of environmental indicators in monitoring key aspects of sustainable development.