The folder contains five subfolders, one for each of the five Landsat Thematic Mapper (TM) Level-1 Terrain-corrected images you'll be working with. The images were acquired in the years 1990, 1995, 2000, 2005, and 2010.

You've created a multidimensional raster layer in Esri's native multidimensional format and observed through visual inspection and measurement that the Chuquicamata copper mine had expanded over time. But multispectral imagery contains more than just what meets the eye. Next, you'll extract additional information using a different band combination and a band ratio.

Difference between multispectral and hyperspectral remote sensing

In this tutorial, you created a multidimensional mosaic dataset using Landsat TM imagery, converted it to Cloud Raster Format, and observed the change over time in the Chuquicamata copper mine. You also changed the band combination and generated a band ratio to further visualize the changes.

Next, you'll specify the product definition. The product definition controls how data is added to the mosaic dataset and how it is displayed by default. You'll choose a product definition that is appropriate for Landsat TM data.

Multispectralimagingskin

The Geoprocessing pane appears, showing the Add Rasters To Mosaic Dataset tool. By default, the Chuquicamata_Landsat dataset is the input dataset.

The main mine pit is now over 4 km long. Additionally, several secondary pits have appeared or expanded between 1990 and 2010.

Learn techniques to display and enhance rasters and imagery in ArcGIS. Learn to appropriately symbolize rasters based on their attributes and intended use, modify raster properties to support better visualization and interpretation, and apply out-of-the-box appearance functions to enhance the viewing experience.

Multispectralimagingarchaeology

CRF is an Esri native file format that is optimized for storing both standard and multidimensional raster data for distributed computing. You can also transpose a multidimensional CRF dataset for faster temporal profiling, especially when working with many slices. All multidimensional analysis tools in ArcGIS Pro generate CRF outputs, and CRF offers more options for data management.

When the tool finishes running, there is no apparent modification to the mosaic dataset. You'll check its properties to observe the changes.

There are five sections containing information about the data source, the rasters, the bands, the band statistics, and the spatial reference.

The map zooms out so that the large surface copper mine is visible. The image is displayed by default as a natural color composite display, where the red, green, and blue bands are displayed with the corresponding channels, so features in the image are rendered as the human eye would see them in real life.

Multi spectral imagingcamera

The StdTime drop-down menu contains the 5 dates from 1990 to 2010. Each date corresponds to a slice of the multidimensional raster. You can choose any of them and observe the map update to that different slice. In the example image, the 1990-02-15 slice is selected.

You've created your mosaic dataset, but it is not yet a multidimensional mosaic dataset. Although it has time information (the AcquisitionDate field in the attribute table), multidimensional mosaic datasets require explicit information about the variables and dimensions contained in the dataset to become fully actionable. To do this, you'll build multidimensional metadata for your mosaic dataset.

The map updates with each slice in the multidimensional raster, creating an animation. You can see how the mine has changed over time.

Next, you'll choose the coordinate system for the mosaic dataset. You'll choose the WGS 1984 UTM Zone 19S coordinate system, which is appropriate for the region of Chile where the Chuquicamata mine is.

The reflectance is highest in short-wave infrared band 1 (displayed in red) in the light pink areas in the image, while reflectance for these bands is generally low in the dark purple areas in the image.

Although the folder you added contains multiple images, only one image seems to appear on the map. The images depict the same location, but at different times, so they overlap. To learn more about the imagery that was added to the layer, you'll inspect the attribute table.

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Hyperspectral vs multispectral

Previously, you created a multidimensional mosaic dataset using five Landsat TM images. Next, you'll convert the mosaic dataset to the cloud raster format (CRF) and measure and visualize the changing Chuquicamata copper mine.

You'll apply this Iron Oxide ratio and determine whether it helps you better distinguish the changes in the Chuquicamata mine. To generate the Iron Oxide band ratio, you'll perform the calculation using a raster function, which applies calculations directly to a raster's pixel values without requiring new data to be saved.

Multidimensional raster data, or image cubes, consists of rasters or imagery that have been collected over multiple times, depths, or heights and are stacked into a single dataset. You can use this data to monitor changes and trends in environmental phenomena, urban development, natural resources, and more. In ArcGIS Pro, you'll learn how to generate a multidimensional mosaic dataset that contains Landsat multispectral imagery, showing the copper mine at different points in time. You'll convert the dataset to Esri's native Cloud Raster Format (CRF) and run a quick analysis for visualizing how this copper mine has changed over time. This will give you a general understanding of how to get started with multidimensional multispectral raster data.

Clay and carbonate minerals are often found in porphyry copper deposits around mines. These types of minerals show strong absorption around 2.2μm, which would be captured in the Short-wave Infrared 2 band of Landsat TM data, and strong reflectance in the wavelengths captured in the Short-wave Infrared 1 band. In the RGB composite you created, areas in light pink mean that there is stronger reflectance in the Short-wave Infrared 1 band, indicating the presence of clay or carbonate minerals. These brighter pixels may represent areas where copper tailings were deposited.

Some names are proposed for the six spectral bands associated with your data. To fully match the description listed for Landsat 4-5 Thematic Mapper in on the USGS site, you'll rename some of the bands.

Use a geoprocessing tool to convert the mosaic dataset to Esri's native multidimensional raster type and visualize change.

Your data includes a large number of raster images. To put these images together into a single dataset, you'll create a mosaic dataset, which displays multiple raster files together as a mosaic. When you create a mosaic dataset, it starts out as an empty container that you can add raster images to later. For now, you'll use a geoprocessing tool to create the empty mosaic dataset.

This tutorial was last tested on May 31, 2023, using ArcGIS Pro 3.1. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.

Multispectralimagingsatellites

A single Landsat TM image is made of several files, which include several surface reflectance spectral bands (sr_band) and some quality assurance files (qa). The bands correspond to the following portions of the electromagnetic spectrum, as described in Landsat 4-5 Thematic Mapper:

Although you provided the AcquisitionDate field as the dimension, it is listed as StdTime. That is because the Acquisition Date is recognized as a time and date field, and is therefore accepted as a standard time value. This prevents multiple time dimensions from being added to a single multidimensional dataset.

Although there are only five primary images in the table, each image is multispectral. This means that each image is made up of six spectral bands, and each band is actually a separate raster. The mosaic dataset template accounts for this by grouping all the bands that belong to a same image together. All of this work is done by raster functions behind the scenes.

There are four tools available in this menu. Two of them, Transpose and Manage Multidimensional Raster, are only available for CRF datasets.

The Chuquicamata mine in northern Chile is the largest open pit copper mine by excavated volume on the planet. It opened in 1882, is still operational today, and has expanded significantly over the last decades. In this tutorial, you are interested in monitoring the expansion of the Chuquicamata mining area so you can analyze the impact on surrounding ecosystems.

The folder is listed under the Input Data parameter. All of the raster images in the folder will be added to the mosaic dataset.

First, you'll use a different band combination and look at the spectral profile of different points on the map. For now, the imagery is displayed as a Natural Color composite, using the bands red, green, and blue. However, using different bands can allow you to enhance the information displayed in your imagery, as different segments of the electromagnetic spectrum can emphasize different features. You'll choose a band combination that includes the short-wave infrared bands.

Using ArcGIS Pro workflows and tools, you can visualize change over time in an area of interest. Work with multispectral Landsat satellite imagery and learn how to visualize, detect, and monitor differences in surface vegetation over time.

Depending on your web browser, you may be prompted to choose a file location before you begin the download. Most browsers download to your computer's Downloads folder by default.

For Band Indexes, a hint appears showing the equation for the Iron Oxide band ratio and the bands that are required. The tool knows the Iron Oxide ratio formula, but you need to tell it on which of your imagery's bands to apply it.

To explore further, you'll use the Image Information pane to see spectral reflectance information as you move your pointer over the image.

The Landsat 4-5 TM data was downloaded from the USGS EarthExplorer website.World Topographic Map sources: Esri, TomTom, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community World Hillshade layer sources: Esri, Maxar, Airbus DS, USGS, NGA, NASA, CGIAR, N Robinson, NCEAS, NLS, OS, NMA, Geodatastyrelsen, Rijkswaterstaat, GSA, Geoland, FEMA, Intermap, and the GIS user community

The parameter to process the data as multidimensional is checked by default. The Build Multidimensional Transpose parameter, if checked, modifies the storage structure in the CRF for faster processing when working with many slices. In this case, you have a small number of slices, so transposing it is not necessary. You'll leave the parameter unchecked.

You've created a multidimensional mosaic dataset. The mosaic dataset is a data management solution for managing many rasters over space and time. Next, you'll convert the mosaic dataset to the powerful CRF format.

Now that your CRF multidimensional raster dataset is ready, you'll measure how the Chuquicamata mine has grown between 1990 and 2010.

Multispectralimagingin agriculture

Before you can visualize change using multidimensional raster data, you need to convert the individual image files into a multidimensional stack. You'll create a multidimensional mosaic dataset from Landsat images collected over the Chuquicamata copper mine in Chile, from 1990 to 2010.

Next, you'll use the multispectral bands of your dataset to better visualize the change in the Chuquicamata copper mine.

The Transpose tool allows you to build a multidimensional transpose, which improves the performance of the dataset when analyzing pixel values over a dimension. The Manage Multidimensional Raster tool allows you to append or delete variables and dimensions in an existing multidimensional raster.

The mosaic dataset is now flagged as multidimensional and can be used in multidimensional analysis and management tools. Surface Reflectance (StdTime = 5) means that this multidimensional raster allows you to follow the evolution of the variable Surface Reflectance through 5 different time points.

The Iron Oxide ratio has been used to identify hydrothermal alteration minerals associated with copper mineralization (Pour & Hashim, 2014). The Iron Oxide band ratio uses spectral reflectance information from the red and blue portions of the electromagnetic spectrum because iron oxides or hydroxides have high reflectance at 0.63-0.69μm (Red band) and high absorption at 0.45-0.52μm (Blue band).

Multispectral images in Remote sensing

There are five images with a category of Primary and four with a category of Overview. The five primary images are the actual Landsat imagery. Overviews are like raster pyramids for a mosaic dataset: they are reduced resolution overview images that are generated to improve the speed at which the mosaic is displayed.

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The Thematic Mapper instrument was onboard Landsats 4 and 5 starting in 1982 and decommissioned in 2013. Landsat Level-1 Terrain products (L1TP) contain surface reflectance values. They include radiometric, geometric, and precision correction, and use a Digital Elevation Model (DEM) to correct for topographic relief. For more information, see Landsat 4-5 Thematic Mapper Collection 1 products.

The Image Information pane also provides information about the pixel row and column value (Image (X, Y)), the coordinate of the pixel (Decimal), and the source information (Source). The bands that are currently displayed with the red, green, and blue channels are also indicated in the spectral chart.

The Image Information pane appears. The default setting in the Point of Interest section is the Track Cursor option, which allows you to move your pointer over the image in your map to see the spectral reflectance for each band for the pixel under your pointer. This is also called the spectral profile for the pixel.

The ProductName field lists Surface Reflectance as the type of imagery information. If you scroll to the right on the attribute table, you'll also see a field called Acquisition Date, which lists the date and time that the images were captured.

DRA stands for dynamic range adjustment. It makes the layer rendering stretch dynamically to improve the contrast and allow you to better visualize the results.

Next, you'll generate an Iron Oxide band ratio to visualize additional information. A band ratio (or index) combines different spectral bands through a mathematical formula. The resulting output is a new raster. Different band ratios are meant to highlight different types of features and phenomena.

An empty mosaic dataset is created and added to the map. The map zooms to the area that is covered by the coordinate system you chose, but no data appears.