R Code and input data associated with: An operational land cover and land cover change toolbox; processing open-source data with open-source software, Gatis, N., Carless, D., Luscombe, D.J., Brazier, R.E. and Anderson, K (2002) Ecological Solutions and Evidence. Accurate and up-to-date land cover maps are vital for underpinning evidence-based landscape management decision-making. However, the technical skills required to extract tailored information about land cover dynamics from these open-access geospatial data often limits their use by those making landscape management decisions. Using Dartmoor National Park as an example we demonstrate an open-source toolkit which uses open-source software (QGIS and RStudio) to process freely available Sentinel-2 and public LiDAR datasets to produce fine scale (10m2 grain size) land cover maps. The toolbox has been designed for use by staff within the national park (for example), enabling land cover maps to be updated as required in the future. An area of 945 km2 was mapped using a trained random forest classifier following a classification scheme tailored to the needs of the national park. A 2019 land cover map had an overall user’s accuracy of 79 %, with 13 out of 17 land cover classes achieving greater than 70 % accuracy. Spatially, accuracy was related via logistical regression to blue band surface reflectance in the spring and topographic slope derived from LiDAR (1 m resolution), with greater accuracy in steeper terrain and areas exhibiting higher blue reflectance. Between an earlier (2017-2019) and later (2019-2021) timeframe 8 % of pixels changed, most of the change by area occurred in the most common classes. However, the largest proportional increase occurred in Upland Meadows, Lowland Meadows and Blanket Bog, all habitats subject to restoration efforts. Identifying areas of change enables future field work to be better targeted. We discuss the application of this mapping to land management within the Dartmoor national park and of the potential of tailored land cover and land cover change mapping, via this toolbox, to evidence-based environmental decision making more widely.

The code is written in RStudio and bundled, with input data, using packrat.