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http://cicy.repositorioinstitucional.mx/jspui/handle/1003/2816| Identifying coffee agroforestry system types using multitemporal sentinel-2 data and auxiliary information | |
| AGUSTIN ESCOBAR LOPEZ Miguel Castillo_Santiago JOSE LUIS HERNANDEZ STEFANONI Jean Francois Mas Jorge Omar López_Martínez | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.3390/rs14163847 | |
| SIERRA MADRE CHIAPAS RANDOM FOREST SHADE COFFEE RECURSIVE FEATURE ELIMINATION | |
| 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. | |
| 2022 | |
| Artículo | |
| Remote Sensing, 14(16), 3847, 2022. | |
| Inglés | |
| Escobar-López, A.; Castillo-Santiago, M.Á.; Hernández-Stefanoni, J.L.; Mas, J.-F.; López-Martínez, J.O. Identifying Coffee Agroforestry System Types Using Multitemporal Sentinel-2 Data and Auxiliary Information. Remote Sens. 2022, 14, 3847.https://doi.org/ 10.3390/rs14163847 | |
| BIOLOGÍA MOLECULAR DE PLANTAS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Artículos de Investigación Arbitrados |
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| 2022_Agustin_Escobar.pdf | 13.95 MB | Adobe PDF | Visualizar/Abrir |