Tag: Remote sensing

Paint by Raster: Watercolour Cartography Illustrating Landform Expansion at Leslie Street Spit, Toronto (1972 – 2025)

Emma Hauser

Geovis Project Assignment, TMU Geography, SA8905, Fall 2025

Hi everyone, welcome to my final Geovisualization Project tutorial. With this project, I wanted to combine my love of watercolour painting with cartography. I used Catalyst Professional, ArcGIS Pro, and watercolours to transform Landsat imagery spanning the years 1972 to 2025 into blocks of colour representing periods of landform expansion at Leslie Street Spit. I also made an animated GIF to help illustrate the process.

Study Area

Just to give you a bit of background to the site, Leslie Spit is a manmade peninsula on the Toronto waterfront, made up of brick and concrete rubble from construction sites in Toronto starting in 1959. It was intended to be a port-related facility, but by the early 1970s, this use case was no longer relevant, and natural succession of vegetation had begun. The landform continued to expand through lakefilling, as did the vegetation and wildlife, and by 1995 the Toronto and Region Conservation Authority started enhancing natural habitats, founding Tommy Thompson Park.

Post Classification Change Detection

The Landsat program has been providing remotely sensed imagery since 1972, at which time the Baselands and “Spine Road” had been constructed. Pairs of Landsat images can be compared by classifying the pixels as land or water in Catalyst Professional using an unsupervised classification algorithm, and performing “delta” or post classification change detection in ArcGIS Pro using the Raster Calculator to determine areas that have undergone landform expansion in that time period. The tool literally subtracts the pixel values denoting land or water of a raster at an earlier date from a raster at a later date in order to compare them and detect change. If we perform this process seven times, up until 2025, we can get a near complete picture of the land base formation of the Spit and can visualize these changes.

Let’s begin!

Step 1: Data Collection from USGS EarthExplorer

The first step is to collect 9 images forming 7 image pairs from USGS EarthExplorer. I searched for images that had minimal cloud cover covering the extent of Toronto.

For the year 1985, we need to double up on images in order to transition from the Multispectral Scanner sensor with 60m resolution to the Thematic Mapper sensor with 30m resolution. 1980 MSS and 1985 MSS will form a pair, and 1985 TM and 1990 TM will form a pair.

Step 2: Data Processing in Catalyst Professional

Now we can begin processing our images. All images must be data merged either manually (using the Translate and Transfer Layers Utilities) or using the metadata MTL.txt files (using the Data Merge tool) to join each image band together and subset (using the Clipping/Subsetting tool) to the same extent. The geocoded extent is:

Upper left: 630217.500 P / 4836247.500 L
Lower right: 637717.500 P / 4828747.500 L

Using the 2025 image as an example, my window looked like this:

I started a new session of Unsupervised Classification and added two 8 bit channels.

I specified the K-Means algorithm with 20 maximum classes and 20 maximum iterations.

I used Post-Classification Analysis (Aggregation) to assign each of the 20 classes to an information class. These classes are Water and Land. I made sure all classes were assigned and I applied the result to the Output Channel.

I got this result:

I repeated this process for all images. For example, 1972 looked like this:

I saved all of the aggregation results as .pix files using the Clipping/Subsetting tool.

Step 3: Data Processing, Visualization, and GIF-making in ArcGIS Pro

We are ready to move onto our processing and visualization in ArcGIS Pro. Here, we will be performing the post classification or “delta” change detection.

I added the aggregation result .pix files to ArcGIS Pro. I exported the rasters to GRID format. The rasters now had values of 0 (No Data), 21 (Water), and 22 (Land). I used the Raster Calculator (Spatial Analyst) to subtract each earlier dated image from the next image in the sequence. So, 1974 minus 1972, 1976 minus 1974, and so on.

I got this result (with masking polygon included, explanation to follow):

The green (0) represents no change, the red (1) represents change from Water to Land (22 – 21), and the grey (-1) represents change from Land to Water (21 – 22).

I drew a polygon (shown in white) around the Spit so we can perform Extract by Mask (Spatial Analyst). This will clip the raster to a more specific extent.

I symbolized the extracted raster’s values of 0 and -1 with no colour and value 1 as red. We now have the first land area change raster for 1972 to 1974.

I repeated this for all time periods, symbolizing the portions of the raster with value 1 as orange, yellow, green, blue, indigo, and purple.

We can now begin our animation. I assigned each change raster its appropriate time period in the Layer Properties. A time slider appeared at the top of my map.

I added a keyframe for each time period to my animation by sliding to the correct time and pressing the green “+” button on the timeline. I used Fixed transitions of 1.5 seconds for each Key Length and extra time (3.0 seconds) at beginning and end to showcase the base raster and the finished product.

I added overlays (a legend and title) to my map. I ensured the Start Key was 1 (first) and the End Key was 9 (last) so that the overlays were visible throughout the entire 13.5 second animation.

I exported the animation as a GIF – voila!

Step 4: Watercolour Map Painting

To begin my watercolour painting, I used these materials:

  • Pencil and eraser
  • Drafting scale (or ruler)
  • Watercolour paper (Fabriano, cold press, 25% cotton, 12” x 15.75”)
  • Watercolour brushes (Cotman and Deserres)
  • Watercolour palettes (plastic and ceramic)
  • Watercolour drawing pad for test colour swatches
  • Water container
  • Lightbox (Artograph LightTracer)
  • Leslie Spit colour-printed reference image
  • Black India ink artist pen (Faber-Castell, not pictured)
  • Masking tape (not pictured)
  • Lots of natural light
  • JAZZ FM 91.1 playing on radio (optional)

I first sketched out in pencil some necessary map elements on the watercolour paper: title, subtitle, neatline, legend, etc. I then taped the reference image down onto the lightbox, and then taped the watercolour paper overtop.

I mixed colour and water until I achieved the desired hues and saturations.

From red to purple, I painted colours one by one, using the reference illuminated through the lightbox. When the last colour (purple) was complete, I added the Baselands and Spine Road in grey as well as all colours for the legend.

To achieve the final product, I added light grey paint for the surrounding land and used a black artist pen to go over my pencil lines and add a scale bar and north arrow.

The painting is complete – I hope you enjoyed this tutorial!

Visualizing Urban Land Use Growth in Greater Sào Paulo

By: Kevin Miudo

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2018

https://www.youtube.com/watch?v=Il6nINBqNYw&feature=youtu.be

Introduction

In this online development blog for my created map animation, I intend to discuss the steps involved in producing my final geovisualization product, which can be viewed above in the embedded youtube link. It is my hope that you, the reader, learn something new about GIS technologies and can apply any of the knowledge contained within this blog towards your own projects. Prior to discussing the technical aspects of the map animations development, I would like to provide some context behind the creation of my map animation.

Cities within developing nations are experiencing urban growth at a rapid rate. Both population and sprawl are increasing at unpredictable rates, with consequences for environmental health and sustainability. In order to explore this topic, I have chosen to create a time series map animation visualizing the growth of urban land use in a developing city within the Global South. The City which I have chosen is Sào Paulo, Brazil. Sào Paulo has been undergoing rapid urban growth over the last 20 years. This increase in population and urban sprawl has significant consequences to climate change, and such it is important to understand the spatial trend of growth in developing cities that do not yet have the same level of control and policies in regards to environmental sustainability and urban planning. A map animation visualizing not only the extent of urban growth, but when and where sprawl occurs, can help the general public get an idea of how developing cities grow.

Data Collection

In-depth searches of online open data catalogues for vector based land use data cultivated little results. In the absence of detailed, well collected and precise land use data for Sào Paulo, I chose to analyze urban growth through the use of remote sensing. Imagery from Landsat satellites were collected, and further processed in PCI Geomatica and ArcGIS Pro for land use classification.

Data collection involved the use of open data repositories. In particular, free remotely sensed imagery from Landsat 4, 5, 7 and 8 can be publicly accessed through the United States Geological Survey Earth Explorer web page. This open data portal allows the public to collect imagery from a variety of satellite platforms, at varying data levels. As this project aims to view land use change over time, imagery was selected at data type level-1 for Landsat 4-5 Thematic Mapper and Landsat 8 OLI/TIRS. Imagery selected had to have at least less than 10% cloud cover, and had to be images taken during the daytime so that spectral values would remain consistent across each unsupervised image classification.

Landsat 4-5 imagery at 30m spectral resolution was used for the years between 2004 and 2010. Landsat-7 Imagery at 15m panchromatic resolution was excluded from search criteria, as in 2003 the scan-line corrector of Landsat-7 failed, making many of its images obsolete for precise land use analysis. Landsat 8 imagery was collected for the year 2014 and 2017. All images downloaded were done so at the Level-1 GeoTIFF Data Product level. In total, six images were collected for years 2004, 2006, 2007, 2008, 2010, 2014, 2017.

Data Processing

Imagery at the Level-1 GeoTIFF Data Product Level contains a .tif file for each image band produced by Landsat 4-5 and Landsat-8. In order to analyze land use, the image data must be processed as a single .tiff. PCI Geomatica remote sensing software was employed for this process. By using the File->Utility->Translate command within the software, the user can create a new image based on one of the image bands from the Landsat imagery.

For this project, I selected the first spectral band from Landsat 4-5 Thematic Mapper images, and then sequentially added bands 2,3,4,5, and band 7 to complete the final .tiff image for that year. Band 6 is skipped as it is the thermal band at 120m spatial resolution, and is not necessary for land use classification. This process was repeated for each landsat4-5 image.Similarly for the 2014 and 2017 Landsat-8 images, bands 2-7 were included in the same manner, and a combined image was produced for years 2014 and 2017.

Each combined raster image contained a lot of data, more than required to analyze the urban extent of Sào Paulo and as a result the full extent of each image was clipped. When doing your own map animation project, you may also wish to clip data to your study area as it is very common for raw imagery to contain sections of no data or clouds that you do not wish to analyze. Using the clipping/subsetting option found under tools in the main panel of PCI Geomatica Focus, you can clip any image to a subset of your choosing. For this project, I selected the coordinate type ‘lat/long’ extents and input data for my selected 3000×3000 pixel subset. The input coordinates for my project were: Upper left: 46d59’38.30″ W, Upper right: 23d02’44.98″ S, Lower right: 46d07’21.44″ W, Lower Left: 23d52’02.18″ S.

Land Use Classification

The 7 processed images were then imported into a new project in ArcPro. During importation, raster pyramids were created for each image in order to increase processing speeds.  Within ArcPro, the Spatial Analyst extension was activated. The spatial analyst extension allows the user to perform analytical techniques such as unsupervised land use classification using iso-clusters. The unsupervised iso-clusters tool was used on each image layer as a raster input.

The tool generates a new raster that assigns all pixels with the same or similar spectral reluctance value a class. The number of classes is selected by the user. 20 classes were selected as the unsupervised output classes for each raster. It is important to note that the more classes selected, the more precise your classification results will be. After this output was generated for each image, the 20 spectral classes were narrowed down further into three simple land use classes. These classes were: vegetated land, urban land cover, and water. As the project primarily seeks to visualize urban growth, and not all types of varying land use, only three classes were necessary. Furthermore, it is often difficult to discern between agricultural land use and regular vegetated land cover, or industrial land use from residential land use, and so forth. Such precision is out of scope for this exercise.

The 20 classes were manually assigned, using the true colour .tiff image created from the image processing step as a reference. In cases where the spectral resolution was too low to precisely determine what land use class a spectral class belong to, google maps was earth imagery referenced. This process was repeated for each of the 7 images.

After the 20 classes were assigned, the reclassify tool under raster processing in ArcPro was used to aggregate all of the similar classes together. This outputs a final, reclassified raster with a gridcode attribute that assigns respective pixel values to a land use class. This step was repeated for each of the 7 images. With the reclassify tool, you can assign each of the output spectral classes to new classes that you define. For this project, the three classes were urban land use, vegetated land, and water.

Cartographic Element Choices:

 It was at this point within ArcPro that I had decided to implement my cartographic design choices prior to creating my final map animation.

For each layer, urban land use given a different shade of red. The later the year, the darker and more opaque the colour of red. Saturation and light used in this manner helps assist the viewer to indicate where urban growth is occurring. The darker the shade of red, the more recent the growth of urban land use in the greater Sào Paulo region. In the final map animation, this will be visualized through the progression of colour as time moves on in the video.

ArcPro Map Animation:

Creating an animation in ArcPro is very simple. First, locate the animation tab through the ‘View’ panel in ArcPro, then select ‘Add animation’. Doing so will open a new window below your work space that will allow the user to insert keyframes. The animation tab contains plenty of options for creating your animation, such as the time frame between key frames, and effects such as transitions, text, and image overlays.

For the creation of my map animation, I started with zoomed-out view of South America in order to provide the viewer with some context for the study area, as the audience may not be very familiar with the geography of Sào Paulo. Then, using the pan tool, I zoomed into select areas of choice within my study area, ensuring to create new keyframes every so often such that the animation tool creates a fly-by effect. The end result explores the very same mapping extents as I viewed while navigating through my data.

While making your own map animation, ensure to play through your animation frequently in order to determine that the fly-by camera is navigating in the direction you want it to. The time between each keyframe can be adjusted in the animation panel, and effects such as text overlays can be added. Each time I activated another layer for display to show the growth of urban land use from year to year, I created a new keyframe and added a text overlay indicating to the user the date of the processed image.

Once you are satisfied with your results, you can export your final animation in a variety of formats, such as .avi, .mov, .gif and more. You can even select the type of resolution, or use a preset that automatically configures your video format for particular purposes. I chose the youtube export format for a final .mpeg4 file at 720p resolution.

I hope this blog was useful in creating your very own map animation on remotely sensed and classified raster data. Good luck!