camila m
tiohtià:ke | montréal

This map presents the land cover classification of the Annapolis Valley, Nova Scotia, using USGS Landsat 8 imagery. The classification highlights key land cover types, including urban areas, herbaceous zones, forests, barren lands, and water bodies, offering insights into the region’s agricultural landscapes and ecological diversity.

The project utilized advanced image processing in Catalyst Professional (formerly PCI Geomatica) and spatial analysis in ArcGIS Pro. The layout includes a study area map, a satellite imagery inset, and a detailed legend, emphasizing the region's diverse land use patterns and enhancing interpretability.

This project explores the use of aerial photogrammetry to generate precise digital elevation models (DEMs) for the Bridgewater area. Through meticulous block adjustments of 45 aerial photographs, internal and external orientations were established to align images. Tie points and control points were generated and assessed to refine spatial relationships between overlapping images.

DEM production employed both automated photogrammetric techniques and mass point interpolation, with results compared to existing provincial datasets for accuracy validation. The final mosaic integrates elevation and color data, producing a seamless representation of Bridgewater's topography—a fusion of spatial science and visual clarity.

This project utilizes GIS and remote sensing techniques to identify optimal locations for solar panel installation in the Mile-End neighborhood of Montreal. High-resolution LiDAR data and building footprint datasets were analyzed to assess solar radiation potential, incorporating factors such as building elevation, roof angle, and seasonal solar variation.

The solar analysis was conducted using ArcGIS Pro's Solar Radiation tool, which calculates the total insolation on building rooftops. Parameters like latitude, seasonal sunlight variation, and shadow effects were integrated into the analysis, allowing for precise identification of areas with the highest solar energy potential. This detailed modeling supports energy efficiency planning by highlighting rooftops best suited for solar panel installation.

The workflow, developed with ArcGIS Pro's ModelBuilder, streamlines the analysis by automating processes like extracting building footprints and calculating solar insolation. The study provides valuable insights for urban planning, promoting sustainable energy initiatives in industrial and residential areas.

Project Title: Locating the Optimal Locations for Solar Panels in Mile-End, Montreal, Quebec

• Software: ArcGIS Pro
• Centre of Geographic Sciences 2021
  
• Lawrencetown, Nova Scotia, Canada

This project investigates the impact of drought on California's agricultural heartland using remote sensing techniques. By analyzing Landsat 8 imagery, false-color composites and the Normalized Difference Moisture Index (NDMI) were employed to assess vegetation health and water stress in the San Joaquin and Salinas Valleys.

Band ratios highlight areas where vegetation exhibits drought stress, aiding in distinguishing between healthy crops and those requiring irrigation. The NDMI further quantifies moisture levels in vegetation, revealing patterns of water scarcity. This study underscores the vulnerability of water-intensive crops and the need for sustainable agricultural practices in a changing climate

Advanced Digital Image Processing: Monitoring California’s Water-Intensive Crops

• Software:  Catalyst Professional + ArcGIS Pro
• Centre of Geographic Sciences, 2021
  
• Lawrencetown, Nova Scotia Canada

Supervised Classification: Halifax Land Cover Classification

• Software: Catalyst Professional + ArcGIS Pro
• Centre of Geographic Sciences, 2021
  
• Lawrencetown, Nova Scotia Canada

This project applied supervised classification techniques to analyze land cover in the Halifax region. Using satellite imagery and digital surface models (DSM), the classification identified key land cover types, including buildings, herbaceous areas, residential zones, roads, tree cover, and water. NDVI (Normalized Difference Vegetation Index) and surface elevation data were incorporated to assess vegetation health and topographic features.

The project demonstrates the use of supervised remote sensing methods to create a comprehensive overview of urban and natural landscapes. The results support informed decision-making for urban planning and environmental management. Completed as part of the Remote Sensing program at the Centre of Geographic Sciences (COGS).

Field Work: Middleton LiDAR Survey Check Points

• Software: Catalyst Professional + ArcGIS Pro
• Centre of Geographic Sciences, 2021
  
• Lawrencetown, Nova Scotia Canada

This reference map complements a survey report, visualizing the locations of vertical accuracy validation (VVA) and non-vertical accuracy (NVA) check points for a LiDAR survey conducted in Middleton. The map provides essential spatial context for the survey, overlaying check points on high-resolution imagery to ensure clarity and alignment with geographic features.

Designed for precision, the map supports the analysis and validation of LiDAR data accuracy by identifying specific sites where ground truth measurements were taken. This reference is a critical tool for ensuring data quality and validating the effectiveness of the survey methods.