GEOG2021
Environmental Remote Sensing

EO data in Environmental Models
• Introduction to Extended Practical
• Hydrological model of a tropical wetland
– motivation
– constructing a model
– Why ? How ? What is it used for ?
– Where do EO datasets fit into this ?
– Show how the data can be used, and what are the implications of the model

EO data in Environmental Models
• to gain experience in
– undertaking a (collaborative or individual) integrated science research project
– computer-based environmental modelling and methods of analysis
– presenting your findings in written and verbal form

Cat Tien

Bau Sau
Dong Nai
Bau Sau catchment
Landsat TM image (March 1992)

Why develop a hydrological model ?
understanding prediction management tool investigating “scenarios”
(sensitivity analysis)

What things “might / should” appear in such a model ?
Since we are interested in flows of water in a “system”, then think in terms of:rainfall river / catchment flows efficiencies? (of flows from catchment) - why ??
“Floods”

Why do we need to bother with “data” ?
model development model validation / verification and what sorts of data do we need ?
met data (rainfall, evapotranspiration,...) hydrological data (flow rates, wetland areas, flooded areas, catchment areas, run-off)

Can EO data help ?
spatial coverage (and / or sampling) compared with in-situ data collection temporal repeat
And if so, how ?
“detecting” and monitoring...
And if so, what sort of EO data ?
Optical, microwave, anything else,...
What are the relative advantages and disadvantages of different sorts of EO data ?

• How do we pose the model ?
– What are the model variables / parameters ?
– Are they “measurables” (and if not all, then which ones, and what do we do with the other ones) ?
• Model will be a combination of empirical and physically- based

Topography
(Daily) Precipitation throughout the year wet / dry season
Evapotranspiration
Outflow data

Basic EO dataset is a series of radar (=microwave) images acquired with the ERS satellite
SAR = Synthetic aperture radar where “radar” = microwave (i.e. microwave part of the EM spectrum) and “aperture synthesis” is a technique for getting high (i.e. good) spatial resolution
The images cover a time period from Jan 1999 to Feb 2000

Distortions as SAR is a side-looking sensor
Tiger Hill
Bau Sau wetlands

Monochrome (i.e. single wavelength in the microwave spectrum)
ERS microwave images acquired at wavelength of ~5cm
(very different to optical images which are at microns)
As they are only at a single wavelength, we often try to use multi-temporal data gives extra information colour composites, ...

The images are apparently “noisy” (variations in brightness of pixels) so sometime we have to smooth (filter) them in order to detect features
If we do smoothing, what effects does this have on the data?

A hydrological model is already constructed. You will be able to operate it on the UNIX workstations, in your home/Data directory
You will also be able to modify it
(by introducing new input and output variables) and changing other details of the model if you wish

How do we verify this ?
How do we re-run the model to get better agreement ?

EO data in Environmental Models
1 Definition of the problem: Define, in quantifiable terms, what it is you want to monitor (and if possible, to what degree of accuracy)
2 Assessment of resources: What data do you have available to achieve this? What sort of model is appropriate to the task? Is what you propose feasible in the time/other constraints available?
3 Preliminary study and sensitivity analysis: have a first pass attempts at defining the method and models and gain an understanding of the nature and sensitivities of both model and data.

EO data in Environmental Models
4 Refinement: After this initial investigation, does your model or the way you are using it need any refinement? If so, refine it and re-run the previous analysis.
5 Calibration: Assuming you are working with an empirical model, once you are happy with the basic approach, apply the model to a calibration dataset to parameterise it for the environment you are working in.
6 Validation: Apply the model with the calibrated parameterisation to an independent dataset to tests its ability to provide accurate predictions.
7 Write up: Write up and present your findings, discussing and presenting the method, model & results, providing an indication of how
(far) you have met your aims and where the work could go from this point.

Distinctions between different types of model empirical / theoretical which do you think hydroModel is ?
Testing / validating / verifying models
Understanding, and if possible quantification of “uncertainty”

Have a look at the practical read and think about the aims, explore the data get a feel for what is involved
Go back to the earlier practicals to revise and learn more about some of the techniques you think you need colour composites, smoothing, filtering, classification, … and how to do these in ‘imagine’:image statistics, area of interest (AOI), ...

Assessed Practical (2500 words) counts for 40% of the course
Project Discussions - 5th Dec 2001
Submission date: Tue 8th Jan 2002 (12:00)