Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots
In the Valencian Community, the applications of precision agriculture in multiannual woody crops with high added value (fruit trees, olive trees, almond trees, vineyards, etc.) are of priority interest. In these plots, canopies do not fully cover the soil and the planting frames are incompatible wit...
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| Formato: | article |
| Lenguaje: | Inglés |
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MDPI
2022
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| Acceso en línea: | http://hdl.handle.net/20.500.11939/8349 https://www.mdpi.com/1424-8220/22/17/6642 |
| _version_ | 1855032774408273920 |
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| author | Moltó, Enrique |
| author_browse | Moltó, Enrique |
| author_facet | Moltó, Enrique |
| author_sort | Moltó, Enrique |
| collection | ReDivia |
| description | In the Valencian Community, the applications of precision agriculture in multiannual woody crops with high added value (fruit trees, olive trees, almond trees, vineyards, etc.) are of priority interest. In these plots, canopies do not fully cover the soil and the planting frames are incompatible with the Resolution of Sentinel 2. The present work proposes a procedure for the fusion of images with different temporal and spatial resolutions and with different degrees of spectral quality. It uses images from the Sentinel 2 mission (low resolution, high spectral quality, high temporal resolution), orthophotos (high resolution, low temporal resolution) and images obtained with drones (very high spatial resolution, low temporal resolution). The procedure is applied to generate time series of synthetic RGI images (red, green, infrared) with the same high resolution of orthophotos and drone images, in which gray levels are reassigned from the combination of their own RGI bands and the values of the B3, B4 and B8 bands of Sentinel 2. Two practical examples of application are also described. The first shows the NDVI images that can be generated after the process of merging two RGI Sentinel 2 images obtained on two specific dates. It is observed how, after the merging, different NDVI values can be assigned to the soil and vegetation, which allows them to be distinguished (contrary to the original Sentinel 2 images). The second example shows how graphs can be generated to describe the evolution throughout the vegetative cycle of the estimated values of three spectral indices (NDVI, GNDVI, GCI) on a point in the image corresponding to soil and on another assigned to vegetation. The robustness of the proposed algorithm has been validated by using image similarity metrics. |
| format | article |
| id | ReDivia8349 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | ReDivia83492025-04-25T14:48:56Z Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots Moltó, Enrique Multispectral analysis Sentinel 2 Orthophoto Spectral indices Google earth engine Image similarity Hyperspectral imaging N01 Agricultural engineering U30 Research methods N20 Agricultural machinery and equipment Image processing Drones precision agriculture Multispectral imagery In the Valencian Community, the applications of precision agriculture in multiannual woody crops with high added value (fruit trees, olive trees, almond trees, vineyards, etc.) are of priority interest. In these plots, canopies do not fully cover the soil and the planting frames are incompatible with the Resolution of Sentinel 2. The present work proposes a procedure for the fusion of images with different temporal and spatial resolutions and with different degrees of spectral quality. It uses images from the Sentinel 2 mission (low resolution, high spectral quality, high temporal resolution), orthophotos (high resolution, low temporal resolution) and images obtained with drones (very high spatial resolution, low temporal resolution). The procedure is applied to generate time series of synthetic RGI images (red, green, infrared) with the same high resolution of orthophotos and drone images, in which gray levels are reassigned from the combination of their own RGI bands and the values of the B3, B4 and B8 bands of Sentinel 2. Two practical examples of application are also described. The first shows the NDVI images that can be generated after the process of merging two RGI Sentinel 2 images obtained on two specific dates. It is observed how, after the merging, different NDVI values can be assigned to the soil and vegetation, which allows them to be distinguished (contrary to the original Sentinel 2 images). The second example shows how graphs can be generated to describe the evolution throughout the vegetative cycle of the estimated values of three spectral indices (NDVI, GNDVI, GCI) on a point in the image corresponding to soil and on another assigned to vegetation. The robustness of the proposed algorithm has been validated by using image similarity metrics. 2022-09-21T12:13:59Z 2022-09-21T12:13:59Z 2022 article publishedVersion Moltó, E. (2022). Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots. Sensors, 22(17), 6642. 1424-8220 http://hdl.handle.net/20.500.11939/8349 10.3390/s22176642 https://www.mdpi.com/1424-8220/22/17/6642 en info:eu-repo/grantAgreement/ERDF/POCV 2014-2020/51918 This work has been partially financed by the project “Engineering developments for the assurance of a profitable, sustainable and competitive agriculture from field to table” (internal reference number 51918) co-financed by the IVIA and the European Regional Development Fund (ERDF). Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ openAccess MDPI electronico |
| spellingShingle | Multispectral analysis Sentinel 2 Orthophoto Spectral indices Google earth engine Image similarity Hyperspectral imaging N01 Agricultural engineering U30 Research methods N20 Agricultural machinery and equipment Image processing Drones precision agriculture Multispectral imagery Moltó, Enrique Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots |
| title | Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots |
| title_full | Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots |
| title_fullStr | Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots |
| title_full_unstemmed | Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots |
| title_short | Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots |
| title_sort | fusion of different image sources for improved monitoring of agricultural plots |
| topic | Multispectral analysis Sentinel 2 Orthophoto Spectral indices Google earth engine Image similarity Hyperspectral imaging N01 Agricultural engineering U30 Research methods N20 Agricultural machinery and equipment Image processing Drones precision agriculture Multispectral imagery |
| url | http://hdl.handle.net/20.500.11939/8349 https://www.mdpi.com/1424-8220/22/17/6642 |
| work_keys_str_mv | AT moltoenrique fusionofdifferentimagesourcesforimprovedmonitoringofagriculturalplots |