Detailed maps of tropical forest types are within reach: forest tree communities for Trinidad and Tobago mapped with multiseason Landsat and multiseason fine-resolution imagery

Abstract Tropical forest managers need detailed maps of forest types for REDD+, but spectral similarity among forest types; cloud and scan-line gaps; and scarce vegetation ground plots make producing such maps with satellite imagery difficult. How can managers map tropical forest tree communities with satellite imagery given these challenges? Here we describe a case study of mapping tropical forests to floristic classes with gap-filled Landsat imagery by judicious combination of field and remote sensing work. For managers, we include background on current and forthcoming solutions to the problems of mapping detailed tropical forest types with Landsat imagery. In the study area, Trinidad and Tobago, class characteristics like deciduousness allowed discrimination of floristic classes. We also discovered that we could identify most of the tree communities in (1) imagery with fine spatial resolution of ⩽1 m; (2) multiseason fine resolution imagery (viewable with Google Earth); or (3) Landsat imagery from different dates, particularly imagery from drought years, even if decades old, allowing us to collect the extensive training data needed for mapping tropical forest types with “noisy” gap-filled imagery. Further, we show that gap-filled, synthetic multiseason Landsat imagery significantly improves class-specific accuracy for several seasonal forest associations. The class-specific improvements were better for comparing classification results; for in some cases increases in overall accuracy were small. These detailed mapping efforts can lead to new views of tropical forest landscapes. Here we learned that the xerophytic rain forest of Tobago is closely associated with ultramafic geology, helping to explain its unique physiognomy.