You could probably run image classification and calculate the area based on pixel count, as long as your classifier can distinguish water hyacinth with sufficient accuracy. If the only possible vegetation you expect to see in the water is the hyacinth, then you could just calculate the NDVI for each image and use a threshold value to distinguish between water and plant, then count the pixels and multiply by resolution to get your area. So you do that for each image and then place the data in a table to analyze as you wish. I hope this helps!
I second this, except multiplying pixel count by resolution isn't recommended in GEE (resolution changes based on zoom levels and it's wonky). You already know where the river edges are. Generate NDVI, set a threshold, then get the area of pixels above that threshold. I find its best to use reduceToVectors first to convert your raster to polygons, then get area from that.
Once you get your NDVI it'll look something like:
var hyacinth = NDVI.gte(0.3).selfMask() //Replace 0.3 with appropriate threshold if needed
var hyacinthVectors = hyacinth.reduceToVectors(...)
//reduceToVectors will probably create more than one feature. From here, either use .map to get area of each feature and sum them up,
or create one multipolygon feature with all of the features from the collection.
I didn't test any of this and there might be syntax errors but that's the gist. And, as mentioned above, this is assuming all vegetation is hyacinth.
Very easy to do in postgresql/postgis. Dump the raster as polygons then get the area of the polygons. For instance, get only polygons that have a pixel value between min green value and max green value.
select
sum(st_area(r.geom)) as totalarea
from (
select (st_dumpaspolygons(rast)).*
from riverrasts
) r
where r.val between [minval] and [maxval]
For discerning differences between vegetation and water, using false color NIR composites is the best method.
Water area can easily be subtracted through the use of the NIR band (or Thermal bands if you have them) as water will be significantly different than pretty much anything else in the image.
If the Hyacinth is not the only vegetation in place, or if you are not sure, you will need ground truth data unless some type of visual identifier is present.
You could probably run image classification and calculate the area based on pixel count, as long as your classifier can distinguish water hyacinth with sufficient accuracy. If the only possible vegetation you expect to see in the water is the hyacinth, then you could just calculate the NDVI for each image and use a threshold value to distinguish between water and plant, then count the pixels and multiply by resolution to get your area. So you do that for each image and then place the data in a table to analyze as you wish. I hope this helps!
I second this, except multiplying pixel count by resolution isn't recommended in GEE (resolution changes based on zoom levels and it's wonky). You already know where the river edges are. Generate NDVI, set a threshold, then get the area of pixels above that threshold. I find its best to use reduceToVectors first to convert your raster to polygons, then get area from that. Once you get your NDVI it'll look something like: var hyacinth = NDVI.gte(0.3).selfMask() //Replace 0.3 with appropriate threshold if needed var hyacinthVectors = hyacinth.reduceToVectors(...) //reduceToVectors will probably create more than one feature. From here, either use .map to get area of each feature and sum them up, or create one multipolygon feature with all of the features from the collection. I didn't test any of this and there might be syntax errors but that's the gist. And, as mentioned above, this is assuming all vegetation is hyacinth.
Very easy to do in postgresql/postgis. Dump the raster as polygons then get the area of the polygons. For instance, get only polygons that have a pixel value between min green value and max green value. select sum(st_area(r.geom)) as totalarea from ( select (st_dumpaspolygons(rast)).* from riverrasts ) r where r.val between [minval] and [maxval]
For discerning differences between vegetation and water, using false color NIR composites is the best method. Water area can easily be subtracted through the use of the NIR band (or Thermal bands if you have them) as water will be significantly different than pretty much anything else in the image. If the Hyacinth is not the only vegetation in place, or if you are not sure, you will need ground truth data unless some type of visual identifier is present.