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 AF...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/16/3847 |
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| author | Agustín Escobar-López Miguel Ángel Castillo-Santiago José Luis Hernández-Stefanoni Jean François Mas Jorge Omar López-Martínez |
| author_facet | Agustín Escobar-López Miguel Ángel Castillo-Santiago José Luis Hernández-Stefanoni Jean François Mas Jorge Omar López-Martínez |
| author_sort | Agustín Escobar-López |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T12:40:32Z |
| format | Article |
| id | doaj.art-20b05b9c2ddb4d849202e9ee2d6b5bd3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T12:40:32Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-20b05b9c2ddb4d849202e9ee2d6b5bd32023-11-30T22:18:34ZengMDPI AGRemote Sensing2072-42922022-08-011416384710.3390/rs14163847Identifying Coffee Agroforestry System Types Using Multitemporal Sentinel-2 Data and Auxiliary InformationAgustín Escobar-López0Miguel Ángel Castillo-Santiago1José Luis Hernández-Stefanoni2Jean François Mas3Jorge Omar López-Martínez4El Colegio de la Frontera Sur, Departamento de Observación y Estudio de la Tierra, la Atmósfera y el Océano, Carretera Panamericana y Periférico Sur s/n, San Cristóbal de las Casas 29290, Chiapas, MexicoEl Colegio de la Frontera Sur, Departamento de Observación y Estudio de la Tierra, la Atmósfera y el Océano, Carretera Panamericana y Periférico Sur s/n, San Cristóbal de las Casas 29290, Chiapas, MexicoCentro de Investigación Científica de Yucatán A. C. Unidad de Recursos Naturales, Calle 43 #130, Mérida 97205, Yucatán, MexicoCentro de Investigaciones en Geografía Ambiental, Universidad Nacional Autónoma de México, Morelia 58090, Michoacán, MexicoCátedra CONACYT, Avenida Insurgentes Sur 1582, Colonia Crédito Constructor, Benito Juárez, Mexico City 03940, Distrito Federal, MexicoCoffee 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.https://www.mdpi.com/2072-4292/14/16/3847Sierra MadreChiapasrandom forestshade coffeerecursive feature elimination |
| spellingShingle | Agustín Escobar-López Miguel Ángel Castillo-Santiago José Luis Hernández-Stefanoni Jean François Mas Jorge Omar López-Martínez Identifying Coffee Agroforestry System Types Using Multitemporal Sentinel-2 Data and Auxiliary Information Remote Sensing Sierra Madre Chiapas random forest shade coffee recursive feature elimination |
| title | Identifying Coffee Agroforestry System Types Using Multitemporal Sentinel-2 Data and Auxiliary Information |
| title_full | Identifying Coffee Agroforestry System Types Using Multitemporal Sentinel-2 Data and Auxiliary Information |
| title_fullStr | Identifying Coffee Agroforestry System Types Using Multitemporal Sentinel-2 Data and Auxiliary Information |
| title_full_unstemmed | Identifying Coffee Agroforestry System Types Using Multitemporal Sentinel-2 Data and Auxiliary Information |
| title_short | Identifying Coffee Agroforestry System Types Using Multitemporal Sentinel-2 Data and Auxiliary Information |
| title_sort | identifying coffee agroforestry system types using multitemporal sentinel 2 data and auxiliary information |
| topic | Sierra Madre Chiapas random forest shade coffee recursive feature elimination |
| url | https://www.mdpi.com/2072-4292/14/16/3847 |
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