Cropland and tree cover mapping using Sentinel-2 data in an agroforestry landscape, Burkina Faso

Abstract

Sentinel-2, with high spatial resolution bands and increased number of spectral channels, has provided increased capabilities for vegetation mapping. Cropland masks within heterogeneous areas such as the Sudano-Sahel zone have become useful for monitoring landscapes. The objectives of this study were to assess the utility of Sentinel-2 data in classification of cropland for the purpose of creating a cropland mask, and estimation of tree cover. An assessment of the cloud-free, wet season satellite images from 2017 and 2018 (15 in total), from the Saponé agroforestry parkland landscape in Burkina Faso was conducted. The random forest machine learning algorithm is applied to images to perform classification with field-based data as training data, tree crown cover estimation with high resolution Pléiades image and to assess variable importance. The results reveal that due to the dynamic cropping practices, the cropland mask needed to be produced for a single year at a time, and high model accuracy was indicated for 2017 with overall accuracy of 94.7%, yet lower for 2018 (90.9%), even though similar acquisition image dates were used. The best result for 2017 was produced using multi-temporal images from October 7 and 22, while the best result for 2018 was obtained using a single image from October 22. Variable importance measures revealed that the green, NNIR, red, NIR and vegetation red edge5 bands were most important in both 2017 and 2018 analysis. The percent of tree crown cover was estimated for 2017 using Sentinel-2 images from June 29 and October 22 and a random forest regression algorithm. The R2 of the best regression equation was 0.42 with a RMSE of 15.1. The RF prediction had values ranging from 0.52% to 85% tree cover. The relationship between observed and predicted tree cover was linear, however, there was an underestimation of higher percentage tree cover values and an overestimation of very sparse tree cover. Based on the results, Sentinel-2 may be useful for monitoring cropland at landscape level and identifying tree crown cover. However, this study would have benefited from using more discriminating field-based training data (i.e.crop types and harvested fields) to identify active cropland. In conclusion, the Sentinel-2 data, with its 10 m pixels and range of spectral bands in particular the red and vegetation red edge produced good quality cropland masks. The use of high resolution supplementary image (Pléiades) is also recommended as a source of training data for producing cropland masks and tree cover data. The results presented here will contribute to an ongoing research project on the role of trees on agroforestry landscape productivity.