This web page contains the material used in the remote sensing component of the ANU course “Advanced remote sensing and GIS” (ENVS3019/ENVS6319). Each of the topics are covered by short videos and some reading material. I have posted it here for anyone who is interested in the material. ANU students can also access this through the course web site on Wattle (registered access).
- ANU students can also access this through the course web site on Wattle (registered access).
- Want to download all video slides, reading and/or tutorial materials in one step? You can, via this link.
- Topic 1: Introduction to remote sensing
- Topic 2: Optical remote sensing
- Topic 3: Other remote sensing methods
- Topic 4: Interpreting remote sensing data
- Topic 5: Vegetation remote sensing
- Topic 6: Atmosphere and water remote sensing
- Tutorials in processing and analysing remote sensing data
- Links to download data discussed in this course
Topic 1: Introduction to Remote Sensing
- RS1.1 – Remote sensing: a bit of history (5 min)
- RS1.2 – The electromagnetic spectrum (7 min)
- RS1.3 – Remote sensing: how does it work? (10 min)
- RS1.4 – Satellites and Orbits (9 min)
- RS1.5 – Space agencies and remote sensing programmes (7 min)
- Fundamentals of Remote Sensing. Canada Centre for Remote Sensing, 258 pp.
- Smith, R. (2012) Introduction to Remote Sensing. MicroImages, Inc., 26 pp.
Web sites and Resources
- For Just $250 a Week You Can Rent Your Very Own Satellite (GIZMODO)
- See the latest image loop from the geostationary Himawari-8 satellite (Bureau of Meteorology)
Topic 2: Optical Remote Sensing
- RS2.1 – Optical remote sensing: principles (8 min)
- RS2.2 – Image formation (8 min)
- RS2.3 – Interaction with the atmosphere (7 min)
- RS2.4 – Spectral response (10 min)
- RS2.5 – Image visualisation (7 min)
- RS2.6 – Image characteristics (8 min)
- Guerschman, J. P. et al. (2009) Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors. Remote Sensing of Environment 113, 928-945 (2009).
- Lewis M (2001) Discriminating vegetation with hyperspectral imagery-what is possible? Geoscience and Remote Sensing Symposium, 2001. IGARSS’01. IEEE 2001 International, (IEEE), pp 2899-2901.
- Summers D, Lewis M, Ostendorf B, & Chittleborough D (2011) Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecological Indicators 11(1):123-131.
- Shao, Y., G. N. Taff, AND R. S. Lunetta (2011) A Review of Selected MODIS Algorithms, Data Products, and Applications. Chapter 2, Q. Weng (ed.), Advances in Remote Sensing. CRC Press LLC, Boca Raton, FL, , 556.
Web sites and Resources
- NASA MODIS web site (NASA)
Topic 3: Other Remote Sensing Methods
- RS3.1 – Lidar: how does it work? (7 min)
- RS3.2 – Lidar systems (9 min)
- RS3.3 – Non-optical remote sensing methods (12 min)
- RS3.4 – Infrared remote sensing (12 min)
- RS3.5 – Passive microwave remote sensing: principles (8 min)
- RS3.6 – Passive microwave remote sensing: applications (10 min)
- RS3.7 – Radar: how does it work? (13 min)
- RS3.8 – Radar: applications (8 min)
- Yebra M, Marselis S, Van Dijk A, Cary G, & Chen Y (2015) Using LiDAR for forest and fuel structure mapping: options, benefits, requirements and costs , Bushfire & Natural Hazards CRC, Australia, 36 pp.
- Jensen, J.R. (2007) Thermal Infrared Remote Sensing. Chapter 8, Remote Sensing of the Environment. Pearson Prentice Hall, 42 pp.
- Ticehurst CJ, Bartsch A, Doubkova M, & Van Dijk AIJM (2009) Comparison of Envisat ASAR GM, AMSR-E Passive Microwave, and MODIS Optical Remote Sensing For Flood Monitoring In Australia. Proc. ‘Earth Observation and Water Cycle Science’, Frascati, Italy.
- Owe M, De Jeu R, & Walker J (2001) A methodology for surface soil moisture and vegetation opticaldepth retrieval using the microwave polarization difference index. IEEE Transactions on Geoscience and Remote Sensing 39(8):1643-1654.
- Andela N, Liu YY, van Dijk AIJM, de Jeu RAM, & McVicar TR (2013) Global changes in dryland vegetation dynamics (1988-2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data. Biogeosciences 10(10):6657-6676.
- Liu, Y. Y., A. I. J. M. van Dijk, R. A. M. de Jeu, J. G. Canadell, M. F. McCabe, J. P. Evans, and G. Wang (2015), Recent reversal in loss of global terrestrial biomass, Nature Climate Change, 5(5), 470-474,
Web sites and Resources
- Sentinel Hotspots (Geoscience Australia)
- Current radar rainfall – national composite (Bureau of Meteorology)
- Latest News from GRACE mission (NASA)
Topic 4: Interpreting remote sensing data
- RS4.1 – Satellite calibration and validation (11 min)
- RS4.2 – Satellite image classification (7 min)
- RS4.3 – Radiative transfer modelling (11 min)
- RS4.4 – Model data assimilation (8 min)
- RS4.5 – Other model-data fusion approaches (11 min)
- Asner, G. P. (1998). Biophysical and biochemical sources of variability in canopy reflectance. Remote sensing of Environment, 64(3), 234-253.
- Jensen, J.R. (2007). Remote Sensing of Vegetation. Chapter 10, Remote Sensing of the Environment. Pearson Prentice Hall, 42 pp.
- Sonnentag, O., et al. (2012). Digital repeat photography for phenological research in forest ecosystems. Agricultural and Forest Meteorology, 152, 159-177.
- Van Dijk A & Renzullo L (2011) Water resource monitoring systems and the role of satellite observations. Hydrology and Earth System Science 15:39-55.
- Van Dijk AIJM (2011) Model-data fusion: Using observations to understand and reduce uncertainty in hydrological models. Proc. MODSIM 2011, 12-16 December 2011.
Websites and Resources
- National Arboretum Canberra Sensor Array: a multi-scale remote sensing and field-measurement system (ANU)
- Australian Landscape Water Balance: outputs from the Australian Water Resources Assessment Model System (Bureau of Meteorology)
Topic 5: Vegetation remote sensing
- RS5.1 – Introduction to vegetation remote sensing (7 min)
- RS5.2 – Forest cover change mapping (6 min)
- RS5.3 – Mapping land cover and biodiversity (12 min)
- RS5.4 – Ground cover and vegetation water use efficiency (9 min)
- RS5.5 – Fire management. pre-fire conditions (12 min)
- RS5.6 – Fire management: detecting burning and damage ( 4 min)
- RS5.7 – Fire management: post-fire assessment (8 min)
- Andrew, M. E., Wulder, M. A., & Nelson, T. A. (2014). Potential contributions of remote sensing to ecosystem service assessments. Progress in Physical Geography, 38(3), 328-353.
- Asner GP & Martin RE (2008) Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests. Frontiers in Ecology and the Environment 7(5):269-276.
- Hansen, M. C., et al. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160), 850-853.
- Yebra M, Chuvieco E, & Riaño D (2008) Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agricultural and Forest Meteorology 148(4):523-536.
- Yebra M, et al. (2013) A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products. Remote Sensing of Environment 136:455-468.
Websites and Resources
- Carnegie Spectranomics (Carnegie Institution)
- National Dynamic Land Cover Dataset of Australia (Geoscience Australia)
- Global Forest Watch, including the mapping by Hansen et al. (2013) and several other data sets
Topic 6: Atmosphere and water remote sensing
- RS6.1 – Remote sensing for weather forecasting (10 min)
- RS6.2 – Air pollution remote sensing (5 min)
- RS6.3 – Remote sensing of the global climate system (9 min)
- RS6.4 – Water remote sensing : overview (7 min)
- RS6.5 – Water quality remote sensing (8 min)
- RS6.6 – Water and ice altimetry (5 min)
- RS6.7 – Soil moisture remote sensing (10 min)
- RS6.8 – Water use remote sensing (9 min)
- Thies, B., & Bendix, J. (2011). Satellite based remote sensing of weather and climate: recent achievements and future perspectives. Meteorological Applications, 18(3), 262-295.
- Yang, J., et al. (2013). The role of satellite remote sensing in climate change studies. Nature climate change, 3(10), 875-883.
- Liu YY, et al. (2012) Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sensing of Environment 123:280-297.
- Guerschman JP, et al. (2009) Scaling of potential evapotranspiration with MODIS data reproduces flux observations and catchment water balance observations across Australia. Journal of Hydrology 369(1-2):107-119.
- Peña-Arancibia JL, et al. (2014) Dynamic identification of summer cropping irrigated areas in a large basin experiencing extreme climatic variability. Remote Sensing of Environment 154:139-152.
Websites and Resources
- EarthWindMap – an amazing visualisation of reanalysis weather data (nullschool)
The tutorials are available for (1) Matlab or (2) Python Jupyter.
(1) MatLab is commercial software for technical computing. It offers a stable, visual and interactive analysis environment and has well-developed documentation of its functions. It is recommended you choose these tutorials if you have had no previous experience with programming or if you prefer to program in a more interactive environment. You can download all Matlab tutorials here. There are also some introductory videos:
- Working with Matlab and Imagery (11 min)
- Spatio-temporal analysis (7 min)
(2) Python is an open-source, general-purpose programming language which is increasingly popular for scientific data analysis. As well as in science, Python is widely used in areas from web development, to desktop utilities, to cloud- or super- computing. If you have never worked with Python you will need to invest some additional time and effort into mastering the basics. If you have never programmed in any language you may find the learning curve too steep. You can find all the workshop materials, a free textbook and practice exercises, along with instructions on how to install Python at home in our Github repository. They have been produced as so-called Jupyter notebooks. Here is a nice introduction on youtube on how to run and write notebooks.
Getting started with Python tutorials
Step 1: Download tutorials and data as zip from github (https://github.com/ANU-WALD/remote-sensing-workshops )
Step 2: Unzip into your desired directory, e.g., E://my_directory. (You can use the 7zip app to unzip, for example)
Step 3. Find “Anaconda” in your apps & choose “Anaconda prompt” (This assumes you are using an ANU computer lab PC, which all have Anaconda installed. If you use your own PC we can provide instructions on how to install Anacxonda yourself)
Step 4. After the >> prompt type (replace E: and my_directory below with the actual ones)
>> cd my_directory
>> jupyter notebook
Step 5. A browser should open with a file explorer. Double click the first tutorial.
Step 6. Start using the notebook.
Data download links
NOTE: some of these data use FTP, other use THREDDS (What is THREDDS?)
- Landsat satellite imagery data cube for Australia (Geoscience Australia) THREDDS
- MODIS AQUA/TERRA data pool (USGS)
- Photosynthetic/Non-Photosynthetic/Bare Soil Fractional Cover Data for Australia (CSIRO) FTP
- Water, carbon and ecosystem dynamics for Australia from the OzWALD data assimilation system (ANU-WALD) THREDDS
- ANU-WALD national-scale Landsat tree cover mapping (ANU-WALD) THREDDS
- Data Products available from the Water and Landscape Dynamics group (ANU-WALD)