Geospatial Technologies Lab Solutions and Services
At the Geospatial Technologies Laboratory we are committed to providing geospatial solutions for our graduate students, faculty and partners in the areas of geographic information systems, global positioning systems, remote sensing, landscape ecology, and home range applications for wildlife and habitat management. When starting a project please consider the following:
Before you start a research project that requires spatial data and analysis we suggest the following solutions via our ERSI web courses or GIS courses provided by the Department of Geosciences:
- Basics of map projections
- Finding Geographic data in ArcGIS
- Referencing data to real-world locations using ArcGIS
- Getting started with GIS
Once you are ready to start your research project the following spatial solutions are available:
1. Geographic information systems
a. Software: ESRI ArcGIS, Quantum GIS, and Open Jump
b. Solutions:
i. Feature creation (points, lines, polygons)
ii. Raster and vector data fusion
iii. Distance estimation between features
iv. Data import and export
v. Geoprocessing tools
vi. Random point generation
vii. Mapping and cartography
viii. Spatial data quality assurance and quality control
ix. Creating and editing metadata
x. ArcGIS online services
xi. Solving spatial problems using ArcGIS
2. Global positioning systems
a. Software: ArcPAD, Collector.
b. Solutions:
i. Field data collection
ii. Data integration in GIS
iii. GPS training
3. Remote sensing
a. Software: ERDAS imagine, Terrset
b. Solutions:
i. Data acquisition
ii. Data pre-processing
iii. Image classification
iv. Principal component analysis
v. Water extraction models
vi. Indices estimation (e.g. normalized difference vegetation index [NDVI])
vii. Data post-processing
viii. Data fusion
ix. Time series analysis
x. Remote sensing data integration with ArcGIS
xi. Unmanned aerial vehicle data collection
xii. Unmanned aerial vehicle data analysis
xiii. 3D models in remote sensing
4. Landscape Analysis
a. Software: Fragstats, CONEFOR, ArcGIS, R
b. Solutions:
i. Point pattern analysis
ii. Graph theory analysis
iii. Landscape pattern analysis
iv. Spatial autocorrelation analysis
v. Random point analysis
vi. Focal statistics
vii. Spatial aggregation
viii. Basics of animal movement
ix. Connectivity analysis
x. Fragmentation analysis
xi. Landscape spatial and temporal dynamics
xii. Landscape conservation design
5. Home Range analysis
a. Software: BIOTAS, HRT, Animal Space use, Open Jump, R
b. Solutions:
i. Home range estimation in BIOTAS
ii. Home range estimation in HRT
iii. Home range estimation in Animal Space use
iv. Home Range Estimation in Open Jump
v. Home range estimation in R (click here to view "Analyzing Wildlife Telemetry Data in R" by John Leonard)
6. Additional solutions:
i. Habitat suitability modelling
ii. Location calculation from telemetry data