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13-agriculture.qmd

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@@ -39,9 +39,9 @@ Mke.activate!(type = "png")
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We will use an image from the
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[Harmonized Sentinel-2 MSI](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED)
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dataset. The image is the result of cloud masking and averaging between
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dates 2025-07-01 and 2025-07-30, followed by a download within a specified
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box of latitude and longitude values:
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dataset, downloaded within a box of latitude and
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longitude values after cloud removal and averaging
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between dates 2025-07-01 and 2025-07-30:
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```{julia}
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image = GeoIO.load("data/sentinel.tif")
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of the agricultural field into a polygonal area for measurements of area and
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perimeter.
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The ability to convert geospatial data between these two representations (i.e.,
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"raster" vs. "vector") based on a categorical variable is key for advanced
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geospatial data science workflows.
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## Methodology
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In order to assign meaning to the pixels of the image, we first need to visualize
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color = rgb |> Map(["R", "G", "B"] => ascolor => "RGB")
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```
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The `viewer` displays the colors that are familiar to us:
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The `viewer` displays the colors and reveals the features of the land:
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```{julia}
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color |> viewer
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### Image segmentation
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From visual inspection, we select the `"MGRVI"` index for image segmentation.
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It creates a strong contrast between pixels that are inside and outside the
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It produces a strong contrast between pixels that are inside and outside the
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agricultural field. We use the `Map` transform again to create a binary
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variable in terms of the 30th percentile of the spectral index:
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```
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Additionally, we use the `ModeFilter` transform to eliminate small artifacts
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that are not relevant for the stated objectives:
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that are not relevant for our stated objectives:
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```{julia}
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mask = binary |> ModeFilter()

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