Spatial and Spectral features for Horticulture mapping
Remote sensing data allows the continuous mapping of horticultural crops in periurban areas. These are very important for their functions in the provision of local food and for other ecosystem services they provide. This work presents a methodological development and the results of a hierarchical cl...
| Autores principales: | , , , , |
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| Formato: | Conferencia |
| Lenguaje: | Inglés |
| Publicado: |
Universidad Técnica Federico Santa María, Chile
2024
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/19388 |
| _version_ | 1855486299861942272 |
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| author | Marinelli, María Victoria Mari, Nicolás Alejandro Pons, Diego Hernan Giobellina, Beatriz Liliana Scavuzzo, Carlos Marcelo |
| author_browse | Giobellina, Beatriz Liliana Mari, Nicolás Alejandro Marinelli, María Victoria Pons, Diego Hernan Scavuzzo, Carlos Marcelo |
| author_facet | Marinelli, María Victoria Mari, Nicolás Alejandro Pons, Diego Hernan Giobellina, Beatriz Liliana Scavuzzo, Carlos Marcelo |
| author_sort | Marinelli, María Victoria |
| collection | INTA Digital |
| description | Remote sensing data allows the continuous mapping of horticultural crops in periurban areas. These are very important for their functions in the provision of local food and for other ecosystem services they provide. This work presents a methodological development and the results of a hierarchical classification of the horticultural periurban area of Cordoba city, based on the spectral and spatial properties of satellite imagery. The methodology present is automatable, making it suitable for continuous monitoring. The classification obtained with the RF algorithm yields a global kappa of 0.77 and in particular for the horticultural class a precision of 0.82. With a hierarchical classification only of the horticultural area result in an amount of 1860 ha. With spectral information taken in radiometer fields campaigns evaluated by spectral angle mapper, we can observe as using Sentinel 2 spectra and parrot camera produce better separability of horticultural crops that the hyperspectral one. |
| format | Conferencia |
| id | INTA19388 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Universidad Técnica Federico Santa María, Chile |
| publisherStr | Universidad Técnica Federico Santa María, Chile |
| record_format | dspace |
| spelling | INTA193882024-09-13T13:12:15Z Spatial and Spectral features for Horticulture mapping Marinelli, María Victoria Mari, Nicolás Alejandro Pons, Diego Hernan Giobellina, Beatriz Liliana Scavuzzo, Carlos Marcelo Áreas Periurbanas Cultivo de Hortalizas Alimentación Humana Periurban Areas Vegetable Growing Human Feeding Remote sensing data allows the continuous mapping of horticultural crops in periurban areas. These are very important for their functions in the provision of local food and for other ecosystem services they provide. This work presents a methodological development and the results of a hierarchical classification of the horticultural periurban area of Cordoba city, based on the spectral and spatial properties of satellite imagery. The methodology present is automatable, making it suitable for continuous monitoring. The classification obtained with the RF algorithm yields a global kappa of 0.77 and in particular for the horticultural class a precision of 0.82. With a hierarchical classification only of the horticultural area result in an amount of 1860 ha. With spectral information taken in radiometer fields campaigns evaluated by spectral angle mapper, we can observe as using Sentinel 2 spectra and parrot camera produce better separability of horticultural crops that the hyperspectral one. EEA Manfredi Fil: Marinelli, María Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Córdoba; Argentina Fil: Marinelli, María Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Marinelli, María Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Observatorio de Agricultura Urbana, Periurbana y Agroecología (O-AUPA); Argentina Fil: Mari, Nicolás Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Cruz del Eje; Argentina Fil: Mari, Nicolás Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Observatorio de Agricultura Urbana, Periurbana y Agroecología (O-AUPA); Argentina Fil: Pons, Diego Hernan. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina Fil: Giobellina, Beatriz Liliana. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Córdoba; Argentina Fil: Giobellina, Beatriz Liliana. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Observatorio de Agricultura Urbana, Periurbana y Agroecología (O-AUPA); Argentina Fil: Scavuzzo, Carlos Marcelo. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); Argentina 2024-09-13T13:04:31Z 2024-09-13T13:04:31Z 2019-09-25 info:ar-repo/semantics/documento de conferencia info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/19388 978-956-356-095-4 (Online) eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf Universidad Técnica Federico Santa María, Chile Proceedings of BigDSSAgro 2019. III International Conference on Agro BigData and Decision Support Systems in Agriculture. 25-27 September 2019, Valparaíso, Chile. p. 37-40 |
| spellingShingle | Áreas Periurbanas Cultivo de Hortalizas Alimentación Humana Periurban Areas Vegetable Growing Human Feeding Marinelli, María Victoria Mari, Nicolás Alejandro Pons, Diego Hernan Giobellina, Beatriz Liliana Scavuzzo, Carlos Marcelo Spatial and Spectral features for Horticulture mapping |
| title | Spatial and Spectral features for Horticulture mapping |
| title_full | Spatial and Spectral features for Horticulture mapping |
| title_fullStr | Spatial and Spectral features for Horticulture mapping |
| title_full_unstemmed | Spatial and Spectral features for Horticulture mapping |
| title_short | Spatial and Spectral features for Horticulture mapping |
| title_sort | spatial and spectral features for horticulture mapping |
| topic | Áreas Periurbanas Cultivo de Hortalizas Alimentación Humana Periurban Areas Vegetable Growing Human Feeding |
| url | http://hdl.handle.net/20.500.12123/19388 |
| work_keys_str_mv | AT marinellimariavictoria spatialandspectralfeaturesforhorticulturemapping AT marinicolasalejandro spatialandspectralfeaturesforhorticulturemapping AT ponsdiegohernan spatialandspectralfeaturesforhorticulturemapping AT giobellinabeatrizliliana spatialandspectralfeaturesforhorticulturemapping AT scavuzzocarlosmarcelo spatialandspectralfeaturesforhorticulturemapping |