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Please note that the information provided is only a partial requirement the Graduate Certificate in Remote Sensing Program at the Centre of Geographic Sciences, NSCC, Lawrencetown, Nova Scotia. The data presented is unedited, unverified, and intended solely for educational purposes. The information includes data provided by Internal Services and is subject to © 2024 COGS.

Projects Completed as part of Graduate Certificate in Remote Sensing

This page will be updated as more projects are completed and organized. 

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Image Classification and Enhancements

We used various tools and software to study how to classify images using different techniques. We looked at data from satellites and drones. Our goals included learning about supervised and unsupervised classification, as well as Object Based Image Analysis (OBIA). We also explored techniques to improve images, such as correcting atmospheric effects, removing haze, and using different methods like Principal Component Analysis (PCA) and Tasseled Cap Transformation (TCT). We also experimented with band ratios and manual enhancements.

Software:

  • Catalyst Professional 2223

    • Python 3.8​

  • ERDAS Imagine 2022

  • ArcGIS Pro 3.X

    • Python 3.9​

  • Google Earth Engine​​

    • Jupyter Notebooks​

Skills:

  • Developed understanding of the different styles and use cases for each classification and enhancement

  • Applied basic and advanced python skills to unique tasks and interfaces

  • Developed critical thinking skills associated with image classifications. Moreover, how different bands interact under various constraints.

Supervised_Classification
TCT_Halifax
ImageEnhancement
OriginalScene_Halifax
PCA_Halifax
OBIA_DabroFarms

Deep Learning

In January 2021, scientists gathered detailed information about an island called Sable Island, located south of Halifax. This island is home to the largest population of grey seals in the world. The researchers aimed to find a way to accurately count the seals more efficiently. Through manual counting, they estimated that there are around 72,000 seal pups on the island.

To help with the counting, the scientists used a technology called Deep Learning, which is similar to machine learning. This technology creates a model to count objects, in this case, seal pups, and provides accurate results. Deep learning relies on good quality data and advanced tools for its development.

The researchers used a software called ArcGIS Pro to analyze the data and see if they could get a count of the seals. If you want to see the results in a visual format, you can click here.

By further training the model, the scientists hope to improve its performance, making it a reliable method for obtaining accurate counts of objects in a short amount of time.

However, it's important to note that deep learning algorithms require significant computing power. Therefore, it's crucial to understand your device's capabilities to successfully create a model within a reasonable timeframe.

Special thanks to Nova Scotia Department of Fisheries and Agriculture, and Parks Canada for providing us with the sample dataset to explore and process the data. By exploring with this data with Deep learning there is hope of new applications and strategies for management within Sable Island Reserve, and other sites nationally and internationally

pup_zoom2.jpg

Sample image of Adult and pup seals. Over 72000 pups were counted in 2021; Deep learning algorithms should aid in the speed and accuracy
 

Lidar and Photogrammetry

Various tools and techniques were used to better understand LiDAR and Photogrammetry data processing. For LiDAR, we scanned the school and made sure that the data collected from drones and aerial sources was accurate. We also assessed the accuracy of the Lidar data. For Photogrammetry, we created 3D models using images from drones and processed different data using advanced techniques.

Software:

  • Terra Solid packages within Bentley Microstation

  • ERDAS Imagine 2022

  • Agisoft Metashape

  • ArcGIS Pro

  • Global Mapper

  • Faro Scene 

Skills:

  • Data Management

  • Macro development

  • CAD interfaces

Geoprocessing and Data Management

Data can be organized in different ways, from when it's first collected to when it's processed and delivered. Thankfully, there are tools and methods that allow people to handle different aspects of the data in a way that makes it easier to get the results they want, both now and in the future.

Some of the things I learned about included: matching up data with specific locations, creating basic maps, bringing in text or spreadsheet files, adjusting the way data is displayed, and tracing over digital maps. For the final results, I used some pretty good map-making skills in a bunch of different projects.

Software:

  • ArcGIS Pro

  • ArcGIS Online

  • Story Maps 

  • Global Mapper 24

  • GeoNova (Nova Scotia Government data portal)

Skills:

  • Data Management

  • Cartography

Capstone Project

The goal of this project was to gain experience in managing and preparing data for use in a Carbon Sequestration model and software. Nova Scotia, known for its crown forest, comprises 4.2 million hectares of forest, with 35.2% being provincially managed. Crown forests are valuable economic and ecological resources, requiring continuous management and implementation of plans. Calculating forest volume is crucial for assessing forest health and economic value, with essential variables including species and yields. These variables can be used to calculate the amount of Carbon sequestered on a designated land base, management regime, or rotation time. The result is an area file, a shape file containing key information for a Carbon model to run. The final deliverable has been formatted for Remsoft’s Woodstock (2023).

Software used:

  • ArcGIS Pro Python API

  • Visual studio code

  • R

  • Python Libraries​

Skills developed

  • Data and project management

  • Python and R Scripting

Future work:​

  • The data has been formatted to be read by Remsoft Woodstock. 

  • Future work includes creating and running the Woodstock models

  • Applying and deriving different management strategies to see the impact on the landscape

  • Replicate the models over other areas, including but not limited to:

    • Other crown forest blocks​

    • Conservation and management areas

Capstone_Minielly_2024.jpg

Field Camp

A requirement for Nova Scotia Community College (NSCC) is that students take part in a 5 week semester to develop skills. During this term, I participated in a Field camp where "mock clients" assigned projects for students to complete. "Mock clients," in this context, is the school managing client relations and modifying the deliverables as needed to meet any limitations on campus. Each student served as a Project Manager for their site or project.

One of the most valuable aspects of the field camp was the hands-on experience it provided. We were given the opportunity to plan, collect data, process it, and finally, report on our findings. This practical approach was instrumental in developing our skills and understanding of project management. 

A summary of the workflow and critical aspects follows

Site Selection:

  • Two forested sites that have had different treatments were selected.​​

    1. Four Mile Stillwater Creek

      • 2019 Treated for Hemlock Wooly Aphid (HWA)​​

    2. MCFC Block AP178002

      • Patchwork harvest strategy used to reduce canopy to 30 % of Baseline

Data Collection:​​

  • Drone data

    • ​​LiDAR:

      • Matrice 300 RTK with L1 payload​
      • Terrain Follow enabled with a 30m DEM correcting elevations
    • Multispectral:​
      • Matrice 210 with Altum payload​
      • 6 band configuration (R, G, B, NIR, Red Edge and Thermal)
  • RTK points
    • Leica G16 collecting various control points​
  • Aeropoints
    • Collected targets for photogrammetry calibration

Data Processing

  • LiDAR:

    • DJI Terra used to convert the L1 data to LAS format

    • RTK points exported and formatted via Leica Infinity

    • LAS data was classified using Terra Solid packages within the Microstation

    • LAS file control reports are generated

    • Deliverables generated within Microstation and Global Mapper

  • Multispectral:

    • Raw images imported in Agisoft Metashape

    • An alignment and georeferencing were completed using Aeropoint data​​

    • Orthomosaic was created
    • Note: A different workflow was used to process the MCFC data, as there existed a projection issue that could not be resolved within the time limit 

Deliverables:

  • Data products created:

    • DSM and DTM

    • Canopy Height Model (CHM)

    • Very High-Resolution Orthomosaic​

Skills:

  • Project Management

  • Data collection

  • Data processing

  • Critical Thinking

Altum_Ortho_FourMile
L1_Ortho_RonsLot
DTM_RonsLot
DTM_FourMile

Other Exposures

Through various instructor lead lectures and tutorials, exposure to various applications within specific software was explored.

Flight Line Planning

  • For Aerial and Drone data collections

  • ArcGIS Pro for map

  • Nav Canada used for flight limit and logging

Google Earth Studio and Blender 

  • Fly through of area​

  • 2 minute video of an area of interest with 3D buildings

ERDAS Imagine 2022​

  • Thermal image processing​

  • Hyperspectral data processing

  • Interferometry and 

  • Google Earth Engine​

    • Exploring the interface with coding​

Flood Modelling

  • ArcGIS Model Builder

RADAR​

  • ​Theory and applications

  • Alaska Satellite Facility

  • SNAP

GNSS Survey​

  • Theory and applications​

  • Collected data using:

    • Static GNSS​

    • Real Time Kinematics (RTK)

    • Post Processed Kinematics (PPK)

3D Modelling​​

  • ContextCapture​

  • ArcGIS Pro

  • Faro Scene

  • DJI Terra

📋Skills Inventory

🛰️ Remote Sensing & LiDAR Tools:

  • ArcGIS Pro 3.1: Advanced geospatial analysis and cartography.

  • Open-source GIS for spatial data visualization and analysis.

  • Global Mapper: Terrain analysis and 3D data visualization.

  • Bentley MicroStation: CAD platform integrating geospatial data for infrastructure projects.

  • 3D model creation from photographs for spatial representations.

  • TerraSolid (TerraMatch, TerraScan): LiDAR data processing for precise surface modeling.

  • ERDAS Imagine 2022: Remote sensing applications, including image classification and spatial modeling.

  • DJI Terra: Drone mapping and real-time 3D reconstructions.

  • Faro Scene: Processing and managing 3D laser scan data.

  • Creation of LAS/LAZ files: Generating and managing LiDAR data formats.

  • Orthoimage creation: Producing geometrically corrected images for accurate mapping.

🧩 Scripting & Automation:

  • Python (3.8 - 3.12): Automating geospatial data processing and analysis.

  • Managing and querying spatial databases for data-driven decisions.

  • Jupyter Notebooks: Interactive data analysis and reproducible geospatial workflows.

  • Visual Studio Code: Primary code editor for developing and debugging scripts.

  • Automation within Catalyst: Streamlining image processing tasks to enhance efficiency.

  • ATCOR (Atmospheric Correction): Improving satellite image accuracy through atmospheric correction.

  • HAZEREM (Haze Removal): Eliminating atmospheric haze from imagery for clarity.

  • Principal Component Analysis (PCA): Reducing dimensionality in spatial datasets to identify key features.

  • Tassel Cap Transformation (TCT): Enhancing vegetation and soil information in multispectral images.

  • Mosaic Datasets: Managing large collections of raster data for seamless analysis.

📡 Data Collection Tools:

  • GNSS Survey RTK (Leica and Carlson): High-precision geospatial data collection using Real-Time Kinematic GPS.

  • Static GPS: Accurate point data acquisition over extended periods.

  • Total Station (Leica): Precise land surveying and topographic measurements.

🚁 Drone Operations:

  • DJI Matrice 300 RTK (L1 Payload): Advanced UAV operations equipped with LiDAR sensors for detailed terrain mapping.

  • Drone Experience: Planning and executing UAV flights for data collection in various environments.

🎥 Other Exposures:

  • Video Editing: Proficiency in Adobe Premiere Pro and DaVinci Resolve for multimedia content creation.

  • 3D Modeling & Animation: Experience with Blender for creating 3D models and animations.

  • Cloud Processing: Utilizing Google Earth Engine for large-scale geospatial data analysis.

  • Creating dynamic visual content with Google Earth Studio.

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