Detecting abandoned citrus crops using Sentinel-2 time series. A case study in the Comunitat Valenciana region (Spain)
Agricultural land abandonment (ALA) is becoming a growing phenomenon around the world that needs to be monitored and quantified. A massive abandonment of citrus orchards has been experienced in the last years in the Comunitat Valenciana (CV) region (Spain) driven by different socio-economic factors....
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| Formato: | article |
| Lenguaje: | Inglés |
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International Society for Photogrammetry and Remote Sensing
2023
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| Acceso en línea: | https://hdl.handle.net/20.500.11939/8643 https://www.sciencedirect.com/science/article/pii/S0924271623001181 |
| _version_ | 1855032828180299776 |
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| author | Morell-Monzó, serg Sebastiá-Frasquet, María T. Estornell, Javier Moltó, Enrique |
| author_browse | Estornell, Javier Moltó, Enrique Morell-Monzó, serg Sebastiá-Frasquet, María T. |
| author_facet | Morell-Monzó, serg Sebastiá-Frasquet, María T. Estornell, Javier Moltó, Enrique |
| author_sort | Morell-Monzó, serg |
| collection | ReDivia |
| description | Agricultural land abandonment (ALA) is becoming a growing phenomenon around the world that needs to be monitored and quantified. A massive abandonment of citrus orchards has been experienced in the last years in the Comunitat Valenciana (CV) region (Spain) driven by different socio-economic factors. Therefore, developing time and cost-efficient methods for monitoring ALA is a priority. Citrus are a perennial crop trees which make orchards have low spectral variation during the year. In the CV region, they are planted in relatively small parcels, thus creating a highly fragmented and heterogeneous landscape. This study proposes a machine learning-based classification framework that uses annual time series of spectral indices extracted from Sentinel-2 images to identify crop status at parcel level. The method is based on features extracted from the reconstructed OSAVI and NDMI time series used to train a Random Forest classifier. Then, a parcel-based classification is performed using the parcel boundaries and the probabilities of belonging to each category for the full pixels inside the boundaries. The research assessed the potential to identify three statuses of crops (non-productive, productive, and abandoned). Results on three different temporal and spatial datasets provided an overall accuracy ranging from 89 to 92 %, demonstrating the importance of multi-temporal data to identify the abandonment of perennial crops. Furthermore, we studied the ability of the model to be spatially and temporally transferred. Limitations to recall the abandoned parcels when using models trained in other areas or time periods are exposed, opening the way to model improvements. |
| format | article |
| id | ReDivia8643 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | International Society for Photogrammetry and Remote Sensing |
| publisherStr | International Society for Photogrammetry and Remote Sensing |
| record_format | dspace |
| spelling | ReDivia86432025-04-25T14:49:12Z Detecting abandoned citrus crops using Sentinel-2 time series. A case study in the Comunitat Valenciana region (Spain) Morell-Monzó, serg Sebastiá-Frasquet, María T. Estornell, Javier Moltó, Enrique Sentinel 2 Agricultural land abandonment Citrus crops N01 Agricultural engineering E90 Agrarian structure A50 Agricultural research U10 Mathematical and statistical methods Time series analysis Crop monitoring Perennial crops Agricultural land abandonment (ALA) is becoming a growing phenomenon around the world that needs to be monitored and quantified. A massive abandonment of citrus orchards has been experienced in the last years in the Comunitat Valenciana (CV) region (Spain) driven by different socio-economic factors. Therefore, developing time and cost-efficient methods for monitoring ALA is a priority. Citrus are a perennial crop trees which make orchards have low spectral variation during the year. In the CV region, they are planted in relatively small parcels, thus creating a highly fragmented and heterogeneous landscape. This study proposes a machine learning-based classification framework that uses annual time series of spectral indices extracted from Sentinel-2 images to identify crop status at parcel level. The method is based on features extracted from the reconstructed OSAVI and NDMI time series used to train a Random Forest classifier. Then, a parcel-based classification is performed using the parcel boundaries and the probabilities of belonging to each category for the full pixels inside the boundaries. The research assessed the potential to identify three statuses of crops (non-productive, productive, and abandoned). Results on three different temporal and spatial datasets provided an overall accuracy ranging from 89 to 92 %, demonstrating the importance of multi-temporal data to identify the abandonment of perennial crops. Furthermore, we studied the ability of the model to be spatially and temporally transferred. Limitations to recall the abandoned parcels when using models trained in other areas or time periods are exposed, opening the way to model improvements. 2023-06-09T07:08:01Z 2023-06-09T07:08:01Z 2023 article publishedVersion Morell-Monzó, S., Sebastiá-Frasquet, M. T., Estornell, J. & Moltó, E. (2023). Detecting abandoned citrus crops using Sentinel-2 time series. A case study in the Comunitat Valenciana region (Spain). ISPRS Journal of Photogrammetry and Remote Sensing, 201, 54-66. 1872-8235 (online) 0924-2716 (PrintISSN) https://hdl.handle.net/20.500.11939/8643 10.1016/j.isprsjprs.2023.05.003 https://www.sciencedirect.com/science/article/pii/S0924271623001181 en This research was partially funded by regional government of Spain, Generalitat Valenciana, within the framework of the research project AICO/2020/246. Funding for open access charge: CRUE-Universitat Politècnica de València Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ openAccess International Society for Photogrammetry and Remote Sensing electronico |
| spellingShingle | Sentinel 2 Agricultural land abandonment Citrus crops N01 Agricultural engineering E90 Agrarian structure A50 Agricultural research U10 Mathematical and statistical methods Time series analysis Crop monitoring Perennial crops Morell-Monzó, serg Sebastiá-Frasquet, María T. Estornell, Javier Moltó, Enrique Detecting abandoned citrus crops using Sentinel-2 time series. A case study in the Comunitat Valenciana region (Spain) |
| title | Detecting abandoned citrus crops using Sentinel-2 time series. A case study in the Comunitat Valenciana region (Spain) |
| title_full | Detecting abandoned citrus crops using Sentinel-2 time series. A case study in the Comunitat Valenciana region (Spain) |
| title_fullStr | Detecting abandoned citrus crops using Sentinel-2 time series. A case study in the Comunitat Valenciana region (Spain) |
| title_full_unstemmed | Detecting abandoned citrus crops using Sentinel-2 time series. A case study in the Comunitat Valenciana region (Spain) |
| title_short | Detecting abandoned citrus crops using Sentinel-2 time series. A case study in the Comunitat Valenciana region (Spain) |
| title_sort | detecting abandoned citrus crops using sentinel 2 time series a case study in the comunitat valenciana region spain |
| topic | Sentinel 2 Agricultural land abandonment Citrus crops N01 Agricultural engineering E90 Agrarian structure A50 Agricultural research U10 Mathematical and statistical methods Time series analysis Crop monitoring Perennial crops |
| url | https://hdl.handle.net/20.500.11939/8643 https://www.sciencedirect.com/science/article/pii/S0924271623001181 |
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