Shade-grown coffee (shade coffee) is an important component of the forested tropics, and is essential to the conservation of forest-dependent biodiversity. Despite its importance, shade coffee is challenging to map using remotely sensed data given its spectral similarity to forested land. This paper addresses this challenge in three districts of northern Nicaragua, here leveraging cloud-based computing techniques within Google Earth Engine (GEE) to integrate multi-seasonal Landsat 8 satellite imagery (30 m), and physiographic variables (temperature, topography, and precipitation). Applying a random forest machine learning algorithm using reference data from two field surveys produced a 90.5% accuracy across ten classes of land cover, with an 82.1% and 80.0% user’s and producer’s accuracy respectively for shade-grown coffee. Comparing classification accuracies obtained from five datasets exploring different combinations of non-seasonal and seasonal spectral data as well as physiographic data also revealed a trend of increasing accuracy when seasonal data were included in the model and a significant improvement (7.8–20.1%) when topographical data were integrated with spectral data. These results are significant in piloting an open-access and user-friendly approach to mapping heterogeneous shade coffee landscapes with high overall accuracy, even in locations with persistent cloud cover.
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