- Canadian Space Agency (CSA) Earth-observation (EO) satellites
- European Space Agency (ESA) Earth-observation missions
- NASA EO missions: current fleet
- Japanese Space Agency JAXA: Research
- Italian Space Agency (ASI)
- German Aerospace Centre (DLR)
- Jaxa data products
- ESA/JAXA/NASA dashboard
- GoC Firework: Daily smoke forecast maps
- Firesmoke.ca
- Hourly pm2.5 obs
- College of DuPage NEXLAB & satellite radar
- click here to download ESA SNAP!
- building SNAP from source
- SNAP developers
- Setting default Java version on Ubuntu (SNAP requires v8)
- STEP forum
- SNAP developer guide
- Flood monitoring with S1
- Landslide detection with S1
- How to create interferogram using SNAP
- How to create mosaic of two S1 products in adjacent paths
- Change detection using S1 in QGIS
- Data prep for Stamps permanent scatterer interferometry
- 6th ESA radar polarimetry training
- StaMPS
- Info on calibration / noise floor: S. R. Cloude, H. Chen, D. G. Goodenough and W. Hong, "A Pauli-Sylvester Approach for Calibration and Validation of ALOS2 Quad-Pol Data", Proc. of CEOS SAR Calval Workshop , Tokyo Denki University, 7-9 September, 2016
- Burned area mapping using S2
- Deforestation monitoring with Sentinel-2
- Veg monitoring for Agri w Sentinel2
- Active fire monitoring with S3
- Sentinel2 Users Manual
- SentinelHub Playground
- ALOS-1 L1.5 Terrain Correction
- ASF Mapready software
- Big data for geoscience
- An open source Geographic Data Science (ENVS363/563)
- The course was designed by Prof. Arribas-Bel from the University of Liverpool. Please refer to the accompanying GitHub repository to the course as well.
- GOES satellite data
- National snow and ice data centre
- NOAA snow and ice products
- Canadian National Fire (Polygon) Database
- USGS earth explorer
- SPOT data
- Sentinel2 data from GCP
- EODMS python api
- Active Floods in Canada
- matplotlib animation
- matplotlib figure
- 3d plotting in matplotlib
- geemap: Python package for interactive mapping w Google Earth Engine (GEE)
- geemap in Google Co-lab
- landsat on GCS
- 2022 "A Computationally Efficient Method for Updating Fuel Inputs for Wildfire Behavior Models Using Sentinel Imagery and Random Forest Classification "
- Dynamic World, Near real-time global 10 m land use land cover mapping
- Judi Beck
- Marty Alexander
- Brian Stocks
- Machine Learning of Wildfire Fuel Types
- Canada wildfire: Webinars and Training
- Rob Skakun, National Burned Areas Composite(NBAC) Feb 18 2022
- Mike Wotton, Lightning Fire Occurrence Preduction Modelling, July 8 2022
- U. Toronto: Michael Wotton
- Prometheus simulation software
- how does fire think and act: Dana Hicks
- Drones in wildfire mgmt
- Wildland fire evacuations in Canada from 1980 to 2021
- A comparison of plume rise algorithms to stack plume measurements in the Athabasca oil sands
- Use of MODIS data to assess atmospheric aerosol before, during, and after community evacuations related to wildfire smoke
- Tracking Pyrometeors With Meteorological Radar Using Unsupervised Machine Learning
- The 2018 fire season in North America as seen by TROPOMI: aerosol layer height intercomparisons and evaluation of model-derived plume heights
- Wotton & Martell, A lightning fire occurrence model for Ontario
- Woolford et al, The development and implementation of a human-caused wildland FOP system for Ontario
- Phelps & Woolford, Comparing calibrated Stat and ML methods for wildland fire occurrence prediction
- Phelps & Woolford, Guidelines for effective evaluation and comparison of wildland FOP models
- https://weather.gc.ca/astro/clds_vis_animation_e.html?id=nw&utc=00
- "change the last two digits from 00 to 12 to get the 12z model run. Usually ready 0915PDT ish each day"
- https://kkraoj.users.earthengine.app/view/live-fuel-moisture
- https://code.earthengine.google.com/e27240a92ecf64bbadf8a082b91c711c?hideCode=true
- https://omegazhangpzh.users.earthengine.app/view/wildfire-monitor-v7
- NumPy Documentation
- Scikit-learn: machine learning in Python
- NVIDIA: even easier introduction to CUDA
- Research software engineering with Python
- Data Science at command line
- K-prototypes algorithm (categorical K-means)
- tSNE embedding
- isotonic regression
- Overview of clustering methods: scikit-learn
- Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
- Geographic coordinates conversion
- Local tangent plane coordinates
- Geodetic datum
- Geodetic coordinates
- Axes conventions: world ref frames
- ENU coordinates
- Convert ENU to ECEF
- ESA: transforms between ECEF and ENU coordinates
- Wiley: coordinate xforms
- ODM
- Gimbal
- Principal Axes
- Pinhole camera model
- Depth of field
- Adding location tags to images
- ODM image geoloc files
- ODM high quality orthophotos
- Geoloc w DEM
- Image rect proj
- Yaw, pitch, roll, omega, phi, kappa