What is it about?
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.
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Why is it important?
Current research addresses the urgent need to quantify forest cover and monitor its changes, evaluating the coherence of indicators derived from various geo-spatial data sources and their redundancy when applied to local administrative units. Quantitative techniques derived from sequential econometric methods can be used to assess the reliability of a panel of forest cover indicators grounded on a multidimensional notion of coherence. The proposed framework – which includes various techniques such as correlation analysis, regression models and principal component analysis - has demonstrated its flexibility to different environmental conditions and socioeconomic contexts. Furthermore, it assessed the precision of a wide range of indicators that quantify a target phenomenon from multiple data sources .
Perspectives
The variations observed between different forest cover indicators derived from various sources stress the need for meticulous consideration when choosing data for administrative-scale assessment; even seemingly similar definitions of forests can produce significantly different results due to mapping features, influencing statistical results and, consequently, policy recommendations. For large-scale monitoring programs that rely on indicator dashboards, it is imperative to not only ensure internal and external coherence, statistical reliability and redundancy, but also to quantify the informative power of the individual components and the stability of results to any change in the composing dimensions, basically time and space.
Piermaria Corona
CREA Research Centre for Forestry and Wood
Read the Original
This page is a summary of: Assessing the spatial coherence of forest cover indicators from different data sources: A contribution to sustainable development reporting, Ecological Indicators, January 2024, Elsevier,
DOI: 10.1016/j.ecolind.2023.111498.
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