Identifying coffee agroforestry system types using multitemporal sentinel-2 data and auxiliary information
Escobar López, Agustín [autor] | Castillo Santiago, Miguel Ángel [autor] | Hernández Stefanoni, José Luis [autor] | Mas, Jean François [autor] | López Martínez, Jorge Omar [autor].
Tipo de material: Artículo en línea Tipo de contenido: Texto Tipo de medio: Computadora Tipo de portador: Recurso en líneaTema(s): Café | Sistemas agroforestales | Cultivos múltiples | Mapeo ambientalTema(s) en inglés: Coffee | Agroforestry systems | Multiple cropping | Environmental value mappingDescriptor(es) geográficos: Sierra Madre de Chiapas (México) Nota de acceso: Acceso en línea sin restricciones En: Remote Sensing. Volumen 14, número 16, artículo número 3847 (2022), páginas 1-15. --ISSN: 2072-4292Número de sistema: 62894Resumen:Tipo de ítem | Biblioteca actual | Colección | Signatura | Estado | Fecha de vencimiento | Código de barras |
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Artículos | Biblioteca Electrónica Recursos en línea (RE) | ECOSUR | Recurso digital | ECO40000062894 |
Acceso en línea sin restricciones
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. eng