What is it about?
Remote sensing products are typically assessed using a single accuracy estimate for the entire map, despite significant variations in accuracy across different map areas or classes. Estimating per-pixel uncertainty is a major challenge for enhancing the usability and potential of remote sensing products. This paper introduces the dataDriven open access tool, a novel statistical design-based approach that specifically addresses this issue by estimating per-pixel uncertainty through a bootstrap resampling procedure.
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Why is it important?
The ability to produce error estimates for each pixel in the map is a novel aspect in the context of the current advances in remote sensing and forest monitoring and assessment. It constitutes a significant support in forest management applications and also a powerful communication tool since it informs users about areas where map estimates are unreliable, at the same time highlighting the areas where the information provided via the map is more trustworthy.
Perspectives
The current version of dataDriven exploits simple regression models to predict the attribute of interest for all pixels in the population. Still, future releases may be able to integrate further techniques, including machine learning and artificial intelligence imputation.
Piermaria Corona
CREA Research Centre for Forestry and Wood
Read the Original
This page is a summary of: Per-Pixel Forest Attribute Mapping and Error Estimation: The Google Earth Engine and R dataDriven Tool, Sensors, June 2024, MDPI AG,
DOI: 10.3390/s24123947.
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