3 down vote favorite I am doing a project on calculating the growth of urban areas in different cities located in different parts of India. I have been able to classify the area into urban, vegetation and water. Now the problem is I cannot calculate the area covered by each pixel. I searched on Google and the available methods uses band values, what I need is to find the area covered by urban/water/vegetation based on spectral end member values that is been defined. If anyone has idea on how to perform it please share and if possible suggest an idea to export it into a graph. What I need is to find the area covered by the different spectral endmembers (urban,veg,water) values which has been defined.  Here is a link to the script:    I'm fairly new to Earth Engine so my knowledge is quite limited, please excuse
120862
I am doing a project on calculating the growth of urban areas in different cities located in different parts of India.   I have been able to classify the area into urban, vegetation and water.   I cannot calculate the area covered by each pixel. I searched on Google and the available methods use band values, what I need is to find the area covered by urban/water/vegetation based on spectral end-member values that are been defined.   If anyone has the idea on how to perform it please share and if possible suggest an idea to export it into a graph.  Here is a link to the script:    EDIT: my aim to find the growth of an urban area or loss of vegetation in a region over the years. The urban area is indicated in red colour when you run the script.   I am finding how much area is covered by urban area so that I could measure the growth of the region.   I'm fairly new to Earth Engine.
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I need to calculate the share of buildable land for the 15 major german cities within a 50 kilometer radius of the city center.  Part 1 of the definition buildable is that the area must have a slope &lt;15% (Part 2 would be being no water surface or wetland, but this might be covered elsewhere). As a student I am completely new to GIS Software and have already tried to mess around with a DEM and the Raster Calculator in ArcMap and QGIS after watching/reading some tutorials but encountered several problems. As my guess/hope is that this should be quite an easy task for an experienced user, I thought I would rather just ask if someone would be very nice and could give me some instructions how to do that first before talking about specific problems in detail(file formats, z-values, ArcGiS Online map..) which may just be there due to a wrong approach in the first place.  Software available would be ArcMap 10.3.1 and QGIS 2.12.3 and the DEM Model used should be 90m resolution or better.  Please recommend a publicly available DEM to use, as I don't have a working one as of now.  In case projections, reference systems, stuff I know not deeply about yet, might lead to different distortions, the results do not need to be extremely precise. Perhaps there is a tutorial where this task is somehow covered?
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I have a single band raster image of 30m by 30m resolution. I have already created a polygon fishnet (grid) of 300m by 300m with point labels over that. How can I extract the mean pixel value of the raster image overlaying each feature in the fishnet grid? Is it possible with Arcgis?  Please inform me.
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I have vector data representing forests/trees/buildings that have been automatically extracted from LiDAR data.  I am looking for specification/guidance for LiDAR derived products, or any standard for defining these features.  For example, a forest is only a forest if it is larger than x square meters, or a tree is not a tree unless it is x meters tall, etc.  The automatic process has created detailed features, and as a result very large datasets.  I would like to find a standard definition so that I can thin the datasets.  Ideally, I would like to know if there are standards developed/adhered to by the USGS, USDA, NGA, NFS, etc.  Does anyone know of such standards/specifications?  Thanks in advance.  EDIT: Process is similar to .
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I am looking for a method to process a remote sensing image and extract the crown areas of the individual trees from the image.   I have both visual wavelength areal imagery, and lidar data from the area. The location in question is a desert area, so the tree cover isn't as dense as a forest area. The resolution of the aerial imagery is 0.5 feet by 0.5 feet. The lidar resolution is approximately 1 x 1 feet. Both the visual data and the lidar come from a Pima County, Arizona dataset. A sample of the type of aerial imagery I have is at the end of this post.   This question  seems to be the same issue, but there does not seem to be a good answer there.  I can obtain a reasonable classification of the vegetation types (and information about the overall percent cover) in the area by using the Iso Cluster classification in Arcmap, but this provides little information on individual trees. The closest I have to what I want is the results of passing the output of the isocluster classification through the Raster to Polygon feature in Arcmap. The problem is that this method merges near by trees into a single polygon.   Edit: I probably should have included some more detail about what I have. The raw datasets I have are:    Full las data, and a tiff raster generated from it. Visual imagery (like the sample image shown, but covering a much wider area)  Manual direct measurements of a subset of the trees in the area.    From these I have generated:    The ground/vegetation classifications. The DEM/DSM rasters.
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I am new to QGIS - I want to estimate the area of the reef under a polygon grid. My shapefiles are already in meters, however I am having a hard time calculating the area under the grid layer. Attached are the photos. I want to calculate the total area of the bold black polygons (reefs) under each grid square (image 2). How much reef is inside each square, for example, grid1: 25 sq.km, grid 1 sq.km, etc. Each reef polygon has its corresponding area in the layer attribute table.    ADDITIONAL PHOTO: RED IS THE REEF  THE ATTRIBUTES OF THE REEF LAYER
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I am really new to Deep Learning and, unfortunately, I can't find example codes on land cover classification other than  where the author wrote a script in R for a large dataset.   The main reason that I am asking is because recently I found a few papers on Remote Sensing Image classification using Deep Learning and I was wondering if there were any R examples on that subject.  So, is there a code example, preferably a step-by-step, which I can use?  I found several git repos like  that can do Deep Learning classification with R, BUTT, I am still looking for a simple multi-spectral image segmentation method. An example of that can be found at  and I also found a similar question in  for which the answer is "use something else other than R if you can".  If I find anything that works today I will be posting a working answer.
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I am new in remote sensing field. I sometimes got confused when I read about image pre-processing. Can someone please elaborate to me the difference and some example methods to do radiometric correction and radiometric calibration ?  Is converting DN values to Top Of Atmospheric (TOA) reflectance values from 2 sets of images (e.g Landsat 7) from different time counted as correcting or calibrating ? Are DOS (Dark Object Subtraction) and Sun-angle correction parts of radiometric correction ?
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I am searching for building/property data which include building height, building area, lot size, and building type classifications(preferably as complete as possible). I am working on an Earthquake Model for highly urbanized locations, especially those around fault lines.
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My question is, how would I make the visible HDRI/Enviroment Map be image 1 and then have reflections as Image 2? If someone can post a node setup, or a .py file that would be great &lt;3 I was thinking maybe using the Is Camera Ray node but I'm not confident it would work, and have no idea where to start.
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I have a large number of rasters with 3 bands (R,G,B). What I want to do is add all 3 bands of each raster together to end up with a single band image with the summed R,G,B values in each pixel but I'm not sure how to do this in ModelBuilder. Do I have to somehow split each raster first, then add each one?  How would I iterate through each band of every raster?  Edit:Basically this is what I have so far. Right now it will take 3 bands of 1 raster and add the bands then go on to create a polygon of a certain reclassed area. I want to do this on a large number of rasters but am unsure of how to make it work.
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I am working on a computational model of the abundance of wild pollinators across a landscape.  The model itself is complete, and I am now struggling with a post-processing step.  I have my GDAL pollinator supply raster that looks something like this (lighter colors mean higher pollinator visitation to a pixel):    And I have an OGR shapefile of points representing sample locations on the landscape:    I'm trying to run some analysis on the pixels under these points, but to do so, I need to be able to extract the value of a pixel under a point.  Is it possible to extract the value of a pixel under a point using only OGR and GDAL through Python?  I would prefer to avoid reading the entire raster into memory through ReadAsArray(), as my output rasters are very, very large (too large to fit into memory).  I noticed , which is similar, but requires a command-line call.
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I'm 90% sure this basic question has been asked before but looking through the other posts I simply do not see a useful answer.  I need to calculate polygonal(vector) catchment areas from a raster based on minimum driving distance/cost from a set of points. I've already got the raster, and for that matter, the nodes(intersections with cost attribute) used to interpolate it. What I need now are polygons for each point describing the area nearer it than any other point. This is basically like a voronoi diagram but based on network distance rather than euclidean space.   How exactly can I do this using only open-source tools? I'm comfortable with the command line if I just know what I'm looking for. I feel like GDAL is going to be the answer, but if so, what exactly is the command I'm looking for?  Thanks!
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I'm working with two data sets:   NHD polyline stream layer for my entire state HUC Watershed boundary Polygons for the entire state   I am looking for a way to calculate the total length of streams within each watershed boundary polygon, and would ideally like to have each polygon then attributed with the sum of stream miles within its boundary.  Can anyone help me?
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Though EVI coefficients have not been developed for Sentinel 2 imagery, I am using the same coefficients used for MODIS to calculate EVI for Sentinel imagery.  I know that Sentinel 2 imagery is TOA reflectance and has a scaling factor of 10000. Hence, can I use the following formula to calculate EVI  (2.5* (NIR/10000-red/10000))/(NIR/10000+(6*red/10000)- (7.5*blue/10000)+1)   I did the above calculation in Arcmap and QGIS but got completely different results- QGIS gave me a plausible range of -0.048 to 0.3915,while ArcMap included a range of -31.68 to 45.17. The mean and std in ArcMap are 0.1718 and 0.0887 leading me to believe that these extreme values are a few pixels only. How do I clip these extra pixels of in ArcMap?
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I am planning to create a NetCDF file from a script (Github). My shapefile is in GCS_WGS_1984 (this is one of the required files for the script). Now, the script requires to define the cell/pixel height and width in degrees. Furthermore, my purpose is to create a grid pixel/cell equivalent to a 500 m by 500 m pixel/cell. I am also posting the lines (degree values don't represent 500 m) where it's asking to define these values for further reference. Moreover, the project area is located in the Pacific North West (US). ###Cell height and width in degree, Edit accordingly cell_h=0.05 cell_w=0.05  Update Based on Mr. Stimsons' comment I have updated my question so that it can make more sense: How can I approximate geographic coordinates (WGS 84) to 500 m? That way I will be able to determine the pixel height and width and replace those values in the code with appropriate values approximating a 500 m by 500 m pixel in degree.
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I have a single band raster file as drawn in the labeled image attached.  The size of raster is around 4k pixel in each x and y axis. Large part of this raster is empty. Only in small portions of the raster contains data as labeled. Is there any efficient way I can extract rectangular patches around the data portions of the large tif raster file? Any pythonic way using gdal or rasterio or some other package. What I have done so far: I have followed from sklearn.feature_extraction.imageway to extract 2D patches where there is data as follows. from sklearn.feature_extraction.image import extract_patches_2d im = imageio.imread(largeFilePath) # reading the large image x, y = np.nonzero(im) # taking xy locations with non zero values  # determining xleft, xright, yleft and yright of the non zero area in  raster  xl,xr = x.min(),x.max()  yl,yr = y.min(),y.max()   #Extraing the non zero area from my original raster only_desired_area = im[xl:xr+1, yl:yr+1]  # my desired window to extract  window_shape = (480, 480)  # 2D Ptaches extraction B = extract_patches_2d(only_desired_area, window_shape, max_patches=100)  Issue: This doesn't have georeference information because I haven't used gdal or rasterio