10
votes
Accepted
Global shapefile of biomes/ecoregions?
The WWF ecoregions are the original Olsen et al., (2001) classification units. If this is what you are after I would recommend using The Nature Conservancy modification to Olsen that deals with some ...
6
votes
Accepted
Converting output of crosstab() in R to raster; from classified raster images to show overall land cover change in different classes?
Suppose two LULC rasters with 6 classes each one:
library(raster)
library(rasterVis)
r <- raster()
set.seed(123)
lc1 <- setValues(r, sample(1:6, 64800, replace = T))
lc2 <- setValues(r, ...
6
votes
Accepted
Replace specific raster class by surrounding classes
You can convert your clouds and shadows to null / no data (e.g. by using r.null) and then interpolate those "null areas" based on the surrounding values using the Fill nodata tool, see the ...
5
votes
Accepted
What are the necessary correction/calibration on Landsat 8 imagery for land cover classification?
You can perform a land cover classification on a single Landsat scene without performing spectral and radiometric corrections. You will only need to do those corrections if you're trying to apply ...
5
votes
Accuracy assessment using Google Earth Engine?
Yes, you can. This page describes how to do it.
// Make a Random Forest classifier and train it.
var classifier = ee.Classifier.randomForest(10)
.train({
features: training,
...
5
votes
Global shapefile of biomes/ecoregions?
There was a new map of ecorregions, published as part of this BioScience paper from 2017.
https://academic.oup.com/biosci/article-lookup/doi/10.1093/biosci/bix014
Here there is an app to visualiza it,...
5
votes
Converting output of crosstab() in R to raster; from classified raster images to show overall land cover change in different classes?
Using sample data from @aldo_tapia :
library(raster)
library(rasterVis)
r <- raster()
set.seed(123)
lc1 <- setValues(r, sample(1:6, 64800, replace = T))
lc2 <- setValues(r, sample(1:6, ...
5
votes
Reclassify values of the land use land cover class of Copernicus Global Land Cover Layers in Google Earth Engine
The variable lulc1 is an image collection with 5 elements (images between years 2015 and 2019). If you want to reclassify it, it is preferable to map entire collection with a function. Following code ...
5
votes
Accepted
LULC Classification of Sentinel-2 Using XGBoost in R
It does not look like shp@data$class exists. In "shp" I see Classvalue and Classname but not class. Also, since your vector data are polygons extract(ras,shp) results in a list object (all ...
5
votes
Accepted
Eliminate isolated pixels in Google Earth Engine classified image
Just use a focal operator or reduceNeighborhood with a mode reducer.
image.focalMode(3)
or
image.reduceNeighborhood(ee.Reducer.mode(), ee.Kernel.circle(1))
4
votes
Processing LANDSAT 8 data in R (using packages Raster, RSToolbox, etc.) in order to calculate NDVI and land cover classification?
If you download surface reflectance data from espa you can skip steps 1, 2, 3, 5 and 6, and you will benefit from a more advanced atmospheric correction.
Mask the clouds using the QA layer
maskFun &...
4
votes
Accepted
Low Recall for Radar Image Flood Classification
Welcome to the trenches of classification, probability and statistics ;) .
Assuming you used sklearn, it has a detailed user guide on what metrics it provides to evaluate your classifications:
I'll ...
4
votes
Do I need to perform any corrections on Level-2 Landsat imagery?
You do not need to do any further atmospheric correction with Landsat OLI/TIRS Level-2 data products as they are already corrected to surface reflectance. These data will be sufficient for time-series ...
4
votes
Replace specific raster class by surrounding classes
You can also do it in one step without replacing values with NULL values. Use the SAGA provided Majority filter for that:
http://www.saga-gis.org/saga_tool_doc/2.2.0/grid_filter_6.html
http://wiki....
4
votes
Sentinel-2 images
It is always preferable to use bottom of atmosphere reflectance (BOA) products (e.g. L2A) instead of top of atmosphere (TOA) reflectance products (e.g. L1C). For example, the image below shows a L1C ...
4
votes
Accepted
Unsupervised classifier, no data in training input
When using .sample() it drops all features that intersect masked pixels. This happened in your case as your image contains non relevant bands and some masked bands: MSK_CLASSI_OPAQUE, ...
3
votes
Accepted
Classifying raster that takes neighbours into account?
Thank to all those who made comments on my question. Based on those comments, I was able to write code that took neighbours into account in my classification, which was successful. I am sharing my ...
3
votes
Accepted
Export Google Earth Placemarks to shapefile
As for converting KML to SHP you can use an open source library GDAL. For conversion in command line tool you can type:
ogr2ogr -f 'ESRI Shapefile' output.shp input.kml
Of course you can put this in ...
3
votes
Accepted
Can you apply Maximum Likelihood classification to NDVI?
You can apply a Maxiumum Likelihood classification to a single band image. However, the results will not be very useful and could be achieved just as easily by simply reclassifying the single band ...
3
votes
Accepted
Simple land classification (urban, suburban, rural) using polygons in MapInfo?
Do you have polygons defining the rural, suburban and urban areas? If so this should be fairly straightforward with a few steps
add three float fields to your existing coverage table to hold the ...
3
votes
Accepted
Classification of buildings using USGS Landsat 8 images on Google Earth Engine
The issue is clouds. You're trying to select only less cloudy scenes, but you're not doing anything to remove the cloudy pixels within those scenes. You are going to need a way to remove cloudy pixels ...
3
votes
Array in python just full of zeros when opened using GDAL
Firstly, why do you think the dataset is being read as all 0's? If you're just looking at the output of print(lc) then you're not seeing the whole array just snippets from the edges (3 elements from ...
3
votes
Splitting up a region into training data and validation data for Landsat7 classification
Use randomColumn(columnName, seed), which adds a column of random values between 0 and 1 to the featurecollection. Then filter on <0.2 and >0.2 to get 20% for validation, 80% for testing.
// Make ...
2
votes
Performing Random Forest using EnMap Box?
With the imageRF, you can use as many variables or layers as you want, you just need to have them stacked up together in one (ENVI) file. Then, you parameterize your model using that layer stack. ...
2
votes
Accepted
Inputs for spatial autocorrelation
You need to put some thought into your experimental design and talk with somebody well versed in statistical analysis. In what you are describing, your experimental units are the transects and not the ...
2
votes
Accepted
Converting data from geodatabase to latitude and longitude format
You can't convert a geodatabase to lat long. If i have understood correctly, your feature class type in the geodatabase is polygon, the solution is same for other feature class type (point and ...
2
votes
Differentiating between Water and Built-Up pixels in ArcGIS Desktop?
Differentiating between built-up and water classes should be very achievable, especially with the nIR band available. Electromagnetic radiation (EMR) is highly absorbed in the nIR portion of the ...
2
votes
Why do some tools / scripts not open for use in the ArcGIS?
I found the answer - I just needed to find the drive and folder location for the script "IsoClusterUnsupervisedClassification.py" right click in the ArcToolbox>Add Toolbox>Scripts (where the script ...
2
votes
Accepted
Surface reflectance product - do I always need it?
Since you just want to delineate the borders between two land uses you don't need to worry about using images with atmospheric correction. In case your application really needs the surface values (for ...
2
votes
Accepted
Know of any existing quality training/validation datasets for land cover?
Global Land Survey (GLS)
At 30-meter resolution, this land cover is one of the finest available. The University of Maryland teamed up with the USGS to lace together its circa 2010 tree cover, bare ...
Only top scored, non community-wiki answers of a minimum length are eligible
Related Tags
land-classification × 192land-cover × 44
remote-sensing × 39
google-earth-engine × 37
qgis × 36
classification × 32
arcgis-desktop × 23
land-use × 19
raster × 17
image-classification × 17
r × 15
random-forest × 15
landsat × 13
python × 12
landsat-8 × 12
digital-image-processing × 9
machine-learning × 9
ndvi × 8
shapefile × 6
arcgis-10.2 × 6
google-earth-engine-javascript-api × 6
image-segmentation × 6
arcmap × 5
arcgis-pro × 5
envi × 5