Identifying coffee agroforestry system types using multitemporal sentinel-2 data and auxiliary information
Coffee is one of the most important agricultural commodities of Mexico. Mapping coffee land cover is still a challenge because it is grown mainly on small areas in agroforestry systems (AFS), which are located in hard-to-access mountainous regions. The objective of this research was to map coffee AFS types in a mountainous region using the changing spectral response patterns over the dry season as well as supplementary data. We employed Sentinel-1, Sentinel-2 and ALOS-Palsar images, a digital elevation model, soil moisture layers, and 150 field plots. First, we defined three coffee AFS types based on their structural and spectral characteristics. Then, we performed a recursive feature elimination analysis to identify the most relevant predictor variables for each land use/cover class in the region. Next, we constructed a predictor variable dataset for each AFS type and one for the remaining land use/cover classes. Afterward, four maps were generated using a random forest (RF) classifier. Finally, we combined the four maps into a unique land-cover map through a maximum likelihood algorithm. Using a validation sample of 932 sites derived from Planet images (4.5 m pixel size), we estimated a 95% map overall accuracy. Two AFS types were classified as having low error; the third, with the highest tree density, had the lowest accuracy. The results obtained show that the infrared and near-infrared bands from the Sentinel-2 scenes are particularly useful for coffee AFS discrimination. However, supplementary data are required to improve the performance of the classifier. Our findings also highlight the importance of the multi-temporal and multi-dataset approach for identifying complex production systems in areas of high topographic heterogeneity. © 2022 by the authors.
| Main Authors: | , , , , |
|---|---|
| Format: | info:eu-repo/semantics/article biblioteca |
| Language: | eng |
| Subjects: | info:eu-repo/classification/Autores/SIERRA MADRE, info:eu-repo/classification/Autores/CHIAPAS, info:eu-repo/classification/Autores/RANDOM FOREST, info:eu-repo/classification/Autores/SHADE COFFEE, info:eu-repo/classification/Autores/RECURSIVE FEATURE ELIMINATION, info:eu-repo/classification/cti/2, info:eu-repo/classification/cti/24, info:eu-repo/classification/cti/2415, info:eu-repo/classification/cti/241502, |
| Online Access: | http://cicy.repositorioinstitucional.mx/jspui/handle/1003/2816 |
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