EO with python Flashcards
Why learn python?
for new algorithms or data formats that don’t exist yet, if files are too large for the GUI based system to hold at once (code can carefully manage memory) and it lets us manage many files (looping command line functions over input files)
what is python?
a “high level” language written for “hacking”
+ Many of the hardware controls are hidden from you, but can be called if the user wants.
* It is an “interpreted language”
* Slower to run than lower level languages.
Why are we in a data age?
More sensors generating more data than ever before, more of it is freely available
* New techniques to make use of that data, informing decisions and making new discoveries
this is why we require programming skills
benefits of using python in the data age
- Test new ideas
- Produce custom applications
- Not have to pay expensive license fees
How many NASA product levels are there?
5
Level 0
direct from the sensor, rawest data, usually encode file describing voltages (only for mission team)
couldn’t load into Arc
Level 1
Raw data
Has been encoded but still in terms of instrument voltage read out.
Can be used by the science community, not easy.
Colour distortion, clouds
Level 2
derived geophysical variables as seen by the satellite.
After algorithms have been applied to level 1 instrument data to estimate land surface properties (surface reflectance, NDVI, DTM, DSM, CHM, backscatter, elevation, etc… Derived for the swath of the satellite.
Level 3
level 2 data that has been gridded.
Swath of the satellite has been gridded onto some common raster layer.
Level 4
modelled product from level 2 or 3. This is when models estimate properties the satellite is not directly sensitive to. Ie. landcover class, biomass
How do you estimate biomass
Alometric equations using Diameter Breast Height (DBH), tree height and species. These are equations people have made for estimating biomass for a wide range of tree species.
What is needed if you want to upscale from field plots to remotely sensed data?
To upscale, we must have data covering a large area which is sensitive to our
parameter of interest
Definition of sensitivity in the context of upscaling biomass estimations
- The variable changes at a predictable rate with the variable of interest
- It can “explain the variance” of our parameter of interest
What is sensitive to biomass?
- Tree diameter (total in a plot is the “basal area”)
- Tree height
- Forest density (canopy cover)
- A combination of the two
What remote sensing method?
Passive optical - density through measuring greenness
Lidar - tree height and cover
Radar - backscatter and decoherence could be used
If you have height from remote sensing, how do you get biomass?
prelate height to biomass (measured in the field). Start by testing to see if there is a relationship by plotting two datasets together (either strong positive, strong negative or no clear relationship)
what correlations coefficient assesses strength of correlation?
pearson’s (-1 to +1)
what is a good positive correlation?
more than or equal to 0.6
List the techniques used to determine which lidar derived metrics are sensitive to biomass
- Height of Median Energy (HOME), also know as relative height 50 (RH50) - the height at which half of the returns are above and below - created for full waveform lidar (link in one note)
- mean canopy height MCH - within a pixel
for discrete return data link in one note
What is ABGD
Above Ground Biomass Density
What does ABGD corelate to well??
ABGD
equation for Canopy Height Map (CHM)
CHM=DSM-DTM
then average values over the area of interest
How did the luton Lidar metric for biomass work?
- Create a CHM
- Lay over a coarser-resolution grid
- Calculate mean CHM per coarse grid-cell
- Relate MCH to plot level biomass (ground data)
- Compare MCH to plot biomass
- Apply model to entire remotely sensed dataset to produce a map
what happens to RH50 with taller trees
it increases
what happens to RH50 with denser forest
it increases
how does cover influence MCH
lower cover, more zeroes, bring down MCH
In Liar you can download data from Scottish government in 3 forms
point cloud (raw data)
DTM and DSM
What needs to be considered when data collecting in the field (ground calibration considerations)?
cover useful lidar and biomass metrics (highs, mediums and lows), collect sufficient data to create a reliable model and estimate accuracy/ uncertainty of the product
How do you guarantee good ground calibration is achieved
We can use statistical techniques to make sure this happens - random stratified sampling - picking high medium and low forests - 5 plots in each - ideally add plots until error metric changes but that is time consuming…
