Classification on Landsat 8

Dear Florence,

So the limitations are that as you point out you are using the information collected in the whole plot (70x70 meters) as the training data of the classifier while the geometry used is the central point (which actually represents one single pixel (30x30 meters)

We are going to add an extra column to the Fusion Table export in the coming version of Collect Earth in which you will also get a KML Polygon. This means that in your Fusion table you will be able to choose what type of geometry you want to use in the classification.

My guess is that the classification will improve a bit with that, but clearly the biggest improvement will be achieved by filtering the rows of the Fusion table before using them as training set. For instance, you should remove plots where there is a mixs of Land Covers ( so where there is both trees and water, infrastructure and trees and so on). Also, if you are using the confidence indicators, also use this info to filter out plots where the user has signal that he is not confident of the assessment.

Classifying using Land Use is clearly more challenging than using Land Cover. Good results are usually achieved by using the tree cover percentage. Remember to create classes that are aggregations ( so don't use 2%,4%,6% and so on as classes, instead use classes like 0-6%, 8-20%, 20-40%, 50-70% and 70-100% ). This will give you better results.

We have no paper on this, but you can surely find other sources for that...if you need a GEE script to start with I can find one tomorrow, let me know!

So yes, the spectral value would be that of the pixel that corresponds to the central point (the red point) as this is the geometry used in the FeatureCollection (the fusion table)

As you guess the best solution is to use plots where there are no mixes of land use/land cover. You could record the percentage cover of different land covers in the Collect form and then "clean" out the plots with mixed covers.

Land Use by definition will be more challenging to classify (using the GEE scripts for supervised classification using Collect Earth dat) than Land Cover.

If you use tree cover to classify you get a tree cover map of course. So if you want Land Use you need to use Land Use. My point is that using Land Use the results will not be as good as Tree Cover.

If you come out with a good script or good solution to classify please share the URL in the forum!!!

So a follow up question here:

If the country's min. area threshold to qualify as forest is 0.50ha and our Collect Earth survey for that country has plot of 0.49ha (0.50ha) size

Does it mean the only plots that would qualify as Forest landuse category are those with 100% tree cover, only? Because the plot size is equal to the threshold.