Tag: water level

Explore Flood Resilience in Toronto: An Interactive Mapping Tool

Author: Shantelle Miller
Geovisualization Project Assignment @TMUGeography, SA8905, Fall 2024

Introduction: Why Flood Resilience Matters

Urban flooding is a growing concern, especially in cities like Toronto, where increasing urbanization has disrupted the natural water cycle. Greenspaces, impervious surfaces, and stormwater infrastructure all play vital roles in reducing flood risks, but understanding how these factors interact can be challenging.

To address this, I created an interactive mapping tool using ArcGIS Experience Builder that visualizes flood resilience in Toronto. By combining multiple datasets, including Topographic Wetness Index (TWI), greenspaces, and stormwater infrastructure, this map highlights areas prone to flooding and identifies zones where natural mitigation occurs.

One of the tool’s standout features is the TWI-Greenspace Overlay, which pinpoints “Natural Absorption Zones.” These are areas where greenspaces overlap with high TWI values, demonstrating how natural environments help absorb runoff and reduce flooding.

Why Experience Builder?

I chose ArcGIS Experience Builder for this project because it offers a user-friendly, highly customizable platform for creating dynamic, interactive web maps. Unlike static maps, Experience Builder allows users to explore data in real-time with widgets like toggleable layers, dynamic legends, and interactive pop-ups.

  • Multi-Dataset Integration: It supports the combination of multiple datasets like TWI, greenspaces, and stormwater infrastructure.
  • Widgets and Tools: Users can filter data, view attributes, and toggle layers seamlessly.
  • No Code Required: Although customizable, the platform doesn’t require coding, making it accessible for users of all technical backgrounds.

The Importance of Data Normalization and Standardization

Before diving into the data, it’s essential to understand the critical role that data normalization and standardization played in this project:

  • Ensuring Comparability: Different datasets often come in various formats and scales. Standardizing these allows for meaningful comparisons across layers, such as correlating TWI values with greenspace coverage.
  • Improving Accuracy: Normalization adjusts values measured on different scales to a common scale, reducing potential biases and errors in data interpretation.
  • Facilitating Integration: Harmonized data enables seamless integration within the mapping tool, enhancing user experience and interaction.

Data: The Foundation of the Project

The project uses data from the Toronto Open Data Portal and Ontario Data Catalogue, processed in ArcGIS Pro, and published to ArcGIS Online.

Layers

Topographic Wetness Index (TWI):

  • Derived from DEM
  • TWI identifies areas prone to water accumulation.
  • It was categorized into four levels (low, medium, high, and very high flood risk), with only the highest-risk areas displayed for focus.

Greenspaces:

  • Includes parks, forests, and other natural areas that act as natural buffers against flooding.

Impervious Surfaces and Pervious Surfaces:

  • Pervious Surfaces: Represent natural areas like soil, grass, and forests that allow water to infiltrate.
  • Impervious Surfaces: Represent roads, buildings, and other hard surfaces that contribute to runoff.

Stormwater Infrastructure:

  • Displays critical infrastructure like catch basins and sewer drainage points, which manage water flow.

TWI-Greenspace Overlay:

  • Combines high-risk TWI zones with greenspaces to identify “Natural Absorption Zones”, where natural mitigation occurs.

Creating the Map: From Data to Visualization

Step 1: Data Preparation in ArcGIS Pro

  1. Imported raw data and clipped layers to Toronto’s boundaries.
  2. Processed TWI using terrain analysis and classified it into intuitive flood risk levels.
  3. Combined pervious and impervious surface data into a single dataset for easy comparison.
  4. Created the TWI-Greenspace Overlay, merging greenspaces and TWI data to show natural flood mitigation zones.
  5. Normalized and standardized all layers.

Step 2: Publishing to ArcGIS Online

  1. Uploaded processed layers as hosted feature layers with customized symbology.
  2. Configured pop-ups to include detailed attributes, such as TWI levels, land cover types, and drainage capacities as well as google map direct link for each point feature.

Step 3: Building the Experience in ArcGIS Experience Builder

  1. Imported the web map into Experience Builder to design the user interface.
  2. Added widgets like the Map, Interactive Layer List, Filters, Legend, Search etc., for user interaction.
  3. Customized layouts and legends to emphasize the relationship between TWI, greenspaces, and surface types.

Interactive Features

The map offers several interactive features to make flood resilience data accessible:

Layer List:

  • Users can toggle between TWI, pervious surfaces, impervious surfaces, greenspaces, and infrastructure layers.

Dynamic Legend:

  • Updates automatically to reflect visible layers, helping users interpret the map.

Pop-Ups:

  • Provide detailed information for each feature, such as:
  • TWI levels and their implications for flood risk.
  • Land cover types, distinguishing between pervious and impervious surfaces.
  • Greenspace types and their flood mitigation potential.

TWI-Greenspace Overlay Layer:

  • Highlights areas where greenspaces naturally mitigate flooding, called “Natural Absorption Zones.”

Filters:

Enable users to focus on specific attributes, such as high-risk TWI areas or zones dominated by impervious surfaces.

Applications and Insights

  • The interactive map provides actionable insights for multiple audiences:

Urban Planners:

  • Identify areas lacking greenspace or dominated by impervious surfaces where flooding risks are highest.
  • Plan infrastructure improvements to mitigate runoff, such as adding bioswales or permeable pavement.

Planners:

  • Assess development sites to ensure they align with flood mitigation goals and avoid high-risk areas.

Homeowners:

  • Evaluate flood risks and identify natural mitigation features in their neighborhoods.
  • For example, the map can reveal neighborhoods with high TWI and limited greenspace, showing where additional stormwater infrastructure might be necessary.

Limitations and Future Work

Limitations

  1. Incomplete Data: Some areas lack detailed data on stormwater infrastructure or land cover, leading to gaps in analysis.
  2. Dynamic Changes: The static nature of the datasets means the map doesn’t reflect recent urban development or climate events.

Future Work

  1. Add real-time data on precipitation and runoff to make the tool more dynamic.
  2. Expand the analysis to include socioeconomic factors, highlighting vulnerable populations.
  3. Enhance accessibility features to ensure compliance with AODA standards for users with disabilities.

Conclusion: A Tool for Flood Resilience

Flood resilience is a complex issue requiring a nuanced understanding of natural and built environments. This interactive mapping tool simplifies these relationships by visualizing critical datasets like TWI, greenspaces, and pervious versus impervious surfaces.

By highlighting areas of natural flood mitigation and zones at risk, the map provides actionable insights for planners, developers, and homeowners. The TWI-Greenspace Overlay layer, in particular, underscores the importance of greenspaces in managing stormwater and reducing flood risks in Toronto.

I hope this project inspires further exploration of flood resilience strategies and serves as a resource for building a more sustainable and resilient city.

Thank you for reading, and feel free to explore the map experience using the link below!

Project Link: Explore Flood Resilience in Toronto
Data Source: Toronto Open Data Portal, Ontario Open Data Catalogue
Built Using: ArcGIS Pro, ArcGIS Online, and ArcGIS Experience Builder

Monitoring Water Level Changes Using High Spatial and High Temporal Resolution Satellite Imagery

Author: Menglu Wang

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2019

Introduction

The disappearing of the once world’s fourth largest lake, Aral Sea, was a shocking tragedy to the world, not only just the shrinkage of lake volume from 1,093.0 km3 in 1960 to 98.1 km3 in 2010 ( Gaybullaev et al., 2012), but also, the rate of shrinkage. Impacts on environment, local climate, citizen’s health, and agriculture are irreversible. This human made disaster could have been prevented in some degree if close monitoring of the lake was made and people are more educated about the importance of ecosystem. One efficient approach to monitor lake water level changes is the utilizing of satellite imagery .The spreading of free high spatial and high temporal resolution satellite imagery provides excellent opportunity to study water level changes through time. In this study, spatial resolution in 3  and 5 meters and temporal resolution as high as 3 days per visit PlanetScope Scene Satellite Imagery are obtained from Planet website. Iso-Cluster Unsupervised Classification in ArcGIS Desktop and Animation Timeline in ArcGIS Pro are used. Study area is set to Claireville Reservoir and 10 dates of imagery starting from April to late June are used to study water level changes.

Data Acquisition

To download the satellite imagery, a statement of research interest needed to be submitted to Planet Sales personal on their website (https://www.planet.com/). After getting access, go on typing in the study area and select a drawing tool to determine an area of interest. All available imagery will load up after setting a time range, cloud cover percentage, area coverage, and imagery source. To download a imagery, go select a imagery and click “ORDER ITEM” and items will be ready to download on the “Orders” tab when you click on your account profile. When downloading a item, noticing that there is a option to select between “Analytic”, “Visual”, and “Basic”. Always select “Analytic” if analysis will be made on the data. “Analytic” indicating geometric and radiometric calibration are already been made to imagery.

Methodology

ArcGIS desktop is used to implement classification and data conversion. Following after, ArcGIS Pro is used to create a animated time slide. Steps are list below:

  1. After creating a file geodatabase and opening a map, drag imagery labeled letter ending with “SR” (surface reflectance) into the map .
  2. Find or search “Mosaic To New Raster” and use it to merge multiple raster into one to get a full study area (if needed).
  3. Create a new polygon feature class and use it to cut the imagery into much smaller dataset by using “Clip”. This will speed up processing of the software.
  4. Grab “Image Classification” tool from Customize tab on top after selecting “Toolbars”.
  5. On “Image Classification” toolbar, select desired raster layer and click on “Classification”. Choose Iso Cluster Unsupervised classification. Please see Figure 1. for classified result.
  6.  Identify classes that belong to water body. Search and use “Reclassify” tool to set a new value (for example: 1) for classes belong to water body, leave new value fields empty for all the rest of classes. Check “ Change missing values to NoData” and run the tool. You will get a new raster layer contain only 1 class: water body as result (Figure 2. and Figure 3.).
  7. Use “Raster to Polygon” tool to convert resulted raster layer to polygons and clean up misclassified data by utilizing editor tool bar. After select “Start editing” from Editor drop down menu, select and delete unwanted polygons (noises).
  8. Use resulted polygons to cut imagery in order to get a imagery contain water bodies only.
  9. Do the above process for all the dates.
  10. Open ArcGIS Pro and connect to the geodatabase that has been using in ArcGIS Desktop.
  11. Search and use “Create Mosaic Dataset” tool to combine all water body raster into one dataset. Notes: Select “Build Raster Pyramids” and “Calculate Statistics” in Advanced Options.
  12. After creating a mosaic dataset, find “Footprint” under the created layer and right click to open attribute table.
  13. Add a new field, set data type as “text” and type in dates for these water body entries. Save edited table.
  14. Right click on the layer and go to properties. Under time tab, select “each feature has a single time field” for “Layer Time”, select the time field that just has been created for “Time Field”, and specify the time format same as the time field format.
  15. A new tab named “Time” will show up on first line of tabs in the software interface.
  16. Click on the “Time” tab and specify “Span”. In my case, the highest temporal resolution for my dataset is 3 days, so I used 3 days as my “Span”.
  17. Click the Run symbol in the “Play Back” section of tabs and one should see animated maps.
  18. If editing each frame is needed, go to “Animation” tab on the top and select “Import” from tabs choose “Time Slider Step”. A series will be added to the bottom and waiting to be edited.
  19. To export animated maps as videos, go to “Movie” in “Export” section of Animation tabs. Choose desired output format and resolution.  
Figure 1. Classified Satellite Imagery
Figure 2. Reclassify tool example.
Figure 3. Reclassified satellite imagery

Conclusion

A set of high temporal and high spatial resolution imagery can effectively capture the water level changes for Claireville Reservoir. The time range is 10 dates from April to June, and as expected, water level changes as time pass by. This is possibly due to heavy rains and flood event which normally happens during summer time. Please see below for animated map .

Reference

Gaybullaev, B., Chen, S., & Gaybullaev, D. (2012). Changes in water volume of the Aral Sea after 1960. Applied Water Science, 2(4), 285–291. doi: 10.1007/s13201-012-0048-z