Chalk grassland mapping in Surrey County using remote sensing and citizen science data: a random forest approach

Chalk grassland is a highly biodiverse but fragmented priority habitat in Europe, and its conservation is constrained by the lack of fine-scale, up-to-date habitat maps. In the UK, this challenge is compounded by limited systematically field data, especially where access to privately owned land restricts ecological surveys. This study developed a fine-scale mapping approach for chalk grassland in Surrey, UK, by integrating structured citizen science data, multi-temporal PlanetScope imagery, topographic variables, and soil information via random forest classification. Citizen science surveys, designed around UK Habitat Classification criteria and validated by Surrey Wildlife Trust, produced 155 confirmed chalk grassland presence quadrats and 669 high-confidence absence points. The models achieved high classification performance in both years, with overall accuracy of 0.9939 in 2023 and 0.9758 in 2024, alongside strong precision, recall, and F1-scores. Soil type and elevation were the most influential predictors, indicating that chalk grassland distribution is strongly shaped by stable environmental conditions, while spectral variables improved habitat discrimination within ecologically suitable settings. Independent validation using 116 long-term chalk grassland polygons supported the ecological realism of the predictions. The result produced 3 m habitat maps providing a promising and scalable framework for conservation planning, restoration targeting, and Local Nature Recovery Strategies.