|
34 | 34 | "source": [ |
35 | 35 | "## Access Landsat imagery\n", |
36 | 36 | "\n", |
37 | | - "We've added an multispectral imagery layer item to our organization from ArcGIS Online that we'll use for this tutorial. Let's connect and get the specific item by it's ID value in our organization:" |
| 37 | + "We've added a multispectral imagery layer item to our organization from ArcGIS Online that we'll use for this tutorial. Let's connect and get the specific item by its ID value in our organization:" |
38 | 38 | ] |
39 | 39 | }, |
40 | 40 | { |
|
140 | 140 | "metadata": {}, |
141 | 141 | "source": [ |
142 | 142 | "### Access the layers available with the Landsat Imagery Layer item\n", |
143 | | - "This imagery layer item contains the imagery layer that we'll be using for this tutorial. Let's save a reference to the layer in the `landsat` variable. Querying the variable will in the Jupyter notebook will quickly render it as an image" |
| 143 | + "This imagery layer item contains the imagery layer that we'll be using for this tutorial. Let's save a reference to the layer in the `landsat` variable. Querying the variable in Jupyter Notebook will quickly render it as an image." |
144 | 144 | ] |
145 | 145 | }, |
146 | 146 | { |
|
353 | 353 | "source": [ |
354 | 354 | "## Apply built-in raster functions\n", |
355 | 355 | "\n", |
356 | | - "The multispectral imagery layer can be rendered using several different raster functions (also known as raster function templates). Each raster function template incorporates different satellite bands to highlight different land cover features. Obtain list of predefined raster function templates defined by the service backing the imagery layer:" |
| 356 | + "The multispectral imagery layer can be rendered using several different raster functions (also known as raster function templates). Each raster function template incorporates different satellite bands to highlight different land cover features. Obtain a list of predefined raster function templates defined by the service backing the imagery layer:" |
357 | 357 | ] |
358 | 358 | }, |
359 | 359 | { |
|
398 | 398 | "cell_type": "markdown", |
399 | 399 | "metadata": {}, |
400 | 400 | "source": [ |
401 | | - "Let's apply the 'Color Infrared with DRA' raster function to visualize the color infrared view. This can be done using the `apply` function in the `arcgis.raster.functions` module. This function applies a server defined raster function template, given it's name, to the Imagery layer." |
| 401 | + "Let's apply the 'Color Infrared with DRA' raster function to visualize the color infrared view. This can be done using the `apply` function in the `arcgis.raster.functions` module. This function applies a server-defined raster function template, given its name, to the Imagery layer." |
402 | 402 | ] |
403 | 403 | }, |
404 | 404 | { |
|
447 | 447 | "cell_type": "markdown", |
448 | 448 | "metadata": {}, |
449 | 449 | "source": [ |
450 | | - "A **true-color image** uses red, green and blue satellite bands to create an image that looks like a photograph. The **color infrared** view, on the other hand, uses the near infrared, red and green satellite bands to create an image. As a result, vegetation appears bright red in the image above." |
| 450 | + "A **true-color image** uses red, green, and blue satellite bands to create an image that looks like a photograph. The **color infrared** view, on the other hand, uses the near infrared, red and green satellite bands to create an image. As a result, vegetation appears bright red in the image above." |
451 | 451 | ] |
452 | 452 | }, |
453 | 453 | { |
|
491 | 491 | "source": [ |
492 | 492 | "### Setting an area of interest\n", |
493 | 493 | "\n", |
494 | | - "When working with Imagery layers, you are typically working in an area of interest. You can set the extent of the Imagery layer to that area of interest, and query it to visualize the imagery layer with that extent within the notebook:" |
| 494 | + "When working with Imagery layers, you are typically working in an area of interest. You can set the extent of the Imagery layer to that area of interest and query it to visualize the imagery layer to that extent within the notebook:" |
495 | 495 | ] |
496 | 496 | }, |
497 | 497 | { |
|
538 | 538 | "cell_type": "markdown", |
539 | 539 | "metadata": {}, |
540 | 540 | "source": [ |
541 | | - "Since we will be using the landsat layer further down in this notebook, let's set it's extent to our area of extent as well:" |
| 541 | + "Since we will be using the landsat layer further down in this notebook, let's set its extent to our area of extent as well:" |
542 | 542 | ] |
543 | 543 | }, |
544 | 544 | { |
|
569 | 569 | "source": [ |
570 | 570 | "## Exporting Images from Imagery Layer\n", |
571 | 571 | "\n", |
572 | | - "In addition to visualizing the imagery layers in the Jupyter Notebook, or using a map widge, they can be exported using the `export_image` method provided by ImageryLayers:" |
| 572 | + "In addition to visualizing the imagery layers in Jupyter Notebook, or using a map widget, they can be exported using the `export_image` method provided by ImageryLayers:" |
573 | 573 | ] |
574 | 574 | }, |
575 | 575 | { |
|
778 | 778 | "source": [ |
779 | 779 | "## Custom Bands\n", |
780 | 780 | "\n", |
781 | | - "You can also create your own indexes and band combinations, as well as specify stretch and gamma values to adjust the image contrast.\n", |
| 781 | + "You can also create your own indexes and band combinations and specify stretch and gamma values to adjust the image contrast.\n", |
782 | 782 | "\n", |
783 | | - "The code below first extracts the [3 (Red), 2 (Green), 1 (Blue)] bands using the `extract_bands` function and passes it's output to the `stretch` function to enhance the image:" |
| 783 | + "The code below first extracts the [3 (Red), 2 (Green), 1 (Blue)] bands using the `extract_bands` function and passes its output to the `stretch` function to enhance the image:" |
784 | 784 | ] |
785 | 785 | }, |
786 | 786 | { |
|
827 | 827 | "cell_type": "markdown", |
828 | 828 | "metadata": {}, |
829 | 829 | "source": [ |
830 | | - "This is a true-color image (similar to a photograph), created using the red, green, and blue satellite bands. Notice how much easier it was to find healthy vegetation using the NDVI vegetation index, compared to the true-color image here." |
| 830 | + "This is a true-color image (similar to a photograph) created using the red, green, and blue satellite bands. Notice how much easier it was to find healthy vegetation using the NDVI vegetation index compared to the true-color image." |
831 | 831 | ] |
832 | 832 | }, |
833 | 833 | { |
|
1425 | 1425 | "cell_type": "markdown", |
1426 | 1426 | "metadata": {}, |
1427 | 1427 | "source": [ |
1428 | | - "Equivalently we can use a constant `Enum`, e.g. `LengthUnits.METER`, or the integer `9001` to specify the same requirement.\n", |
| 1428 | + "Equivalently we can use a constant `Enum`, e.g., `LengthUnits.METER`, or the integer `9001`, to specify the same requirement.\n", |
1429 | 1429 | "\n", |
1430 | 1430 | "```\n", |
1431 | 1431 | "from arcgis.geometry import LengthUnits\n", |
|
1519 | 1519 | "collapsed": true |
1520 | 1520 | }, |
1521 | 1521 | "source": [ |
1522 | | - "You can select images by attributes using a where clause as well as using spatial and temporal filters, using the `filter_by` method.\n", |
| 1522 | + "You can select images by attributes using a where clause as well as using spatial and temporal filters using the `filter_by` method.\n", |
1523 | 1523 | "\n", |
1524 | 1524 | "The code snippet below limits the images available to those that have less than 10% cloud cover and which intersect with our area of interest:" |
1525 | 1525 | ] |
|
1769 | 1769 | "\n", |
1770 | 1770 | "When a set of rasters are selected by filtering an Imagery layer, they may have overlapping pixels.\n", |
1771 | 1771 | "\n", |
1772 | | - "The Imagery layer has methods like first(), last(), min(), max(), mean(), blend() and sum() to resolve overlap pixel values from first or last raster, use the min, max or mean of the pixel values, or blend them:" |
| 1772 | + "The Imagery layer has methods like first(), last(), min(), max(), mean(), blend(), and sum() to resolve overlap pixel values from the first or last raster, use the min, max or mean of the pixel values, or blend them:" |
1773 | 1773 | ] |
1774 | 1774 | }, |
1775 | 1775 | { |
|
1867 | 1867 | "source": [ |
1868 | 1868 | "## Difference Image\n", |
1869 | 1869 | "\n", |
1870 | | - "Difference Image mode illustrates all the changes in NDVI (vegeration index) between the two dates:\n", |
| 1870 | + "Difference Image mode illustrates all the changes in NDVI (vegetation index) between the two dates:\n", |
1871 | 1871 | "\n", |
1872 | | - "increases are shown in green, and decreases are shown in magenta. " |
| 1872 | + "Increases are shown in green, and decreases are shown in magenta. " |
1873 | 1873 | ] |
1874 | 1874 | }, |
1875 | 1875 | { |
|
2047 | 2047 | "source": [ |
2048 | 2048 | "### Persisting your analysis for visualization or analysis\n", |
2049 | 2049 | "\n", |
2050 | | - "The `save()` method on ImageryLayer class persists this imagery layer to the GIS as an Imagery Layer item. If `for_viz` parameter is True, a new Item is created that uses the applied raster functions for visualization at display resolution using on-the-fly image processing. If `for_viz` is False, distributed raster analysis is used for generating a new raster information product by applying raster functions at source resolution across the extent of the output imagery layer.\n", |
| 2050 | + "The `save()` method on ImageryLayer class persists this imagery layer to the GIS as an Imagery Layer item. If the `for_viz` parameter is True, a new Item is created that uses the applied raster functions for visualization at display resolution using on-the-fly image processing. If `for_viz` is False, distributed raster analysis is used for generating a new raster information product by applying raster functions at source resolution across the extent of the output imagery layer.\n", |
2051 | 2051 | "\n", |
2052 | 2052 | "\n", |
2053 | | - "In the example below, the threshold mask is being saved as item for visualization:" |
| 2053 | + "In the example below, the threshold mask is being saved as an item for visualization:" |
2054 | 2054 | ] |
2055 | 2055 | }, |
2056 | 2056 | { |
|
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