72

What is Lanczos resampling? Although the theory is described in an early paper and the Wikipedia article, a "feel" for resampling methods is best obtained by computing them on simple or standard images. This can be a vast topic, requiring extensive experimentation, but some simplifications are available: These operators work separately in each color ...


28

This is easy in QGIS too, though a little less obvious. There are a couple of ways you can do it: Raster Calculator - simply use the raster calculator and you can set the resolution and extent there and can make them match another raster by selecting the raster band you want to match in the Raster Bands list and then clicking the "Current layer extent" ...


25

Aerial photos are continuous data. Each pixel represents the response of a region of a sensor to light directed at it and as that light varies, the response varies continuously. The result is usually discretized (often into 255 or 256) categories, but that doesn't change the nature of the data. Therefore you want to interpolate rather than using ...


16

Actually it's not all that situation dependent and is all about statistical error. Any time you resample to a higher resolution, you are introducing false accuracy. Consider a set of data measured in feet at whole numbers only. Any given point may be +/- 0.5 feet from its actual location. If you resample to the nearest tenth, you are now saying any given ...


13

Use Block statistics. This works like Focal statistics by computing a statistical summary (such as the mean you desired) within a specified neighborhood of cells (such as an 8 by 8 square, where 8 = 240 m / 30 m), except it performs this only for a regular subdivision of the grid, rather than with a set of overlapping neighborhoods, one at each cell. You ...


12

The following gdal script is useful to resample an image to a smaller pixel size: import os from osgeo import gdal # Change working directory os.chdir("directory with rasters") # Open raster and get band in_ds = gdal.Open('raster') in_band = in_ds.GetRasterBand(1) # Multiply output size by 3 out_rows = in_band.YSize * 3 out_columns = in_band.XSize *...


10

When you run geoprocessing operations in ArcMap (e.g. tools from the ArcToolbox pane) they conform to two sets of parameters. First are the parameters in the window itself, e.g. input file, output file path, etc. Second are the parameters in the Environment Settings window (see below). These Environment Settings let you fine-tune your geoprocessing ...


9

What you are dealing with is interpolation techniques. That is, you have two data points, and need to estimate the value of a point in-between. There are many such available, but the three main ones for the kind of raster processing you're looking for are: Nearest Neighbour: it'll simply take the value of the closest data point, and replicate it. So, if we ...


7

I would like to add Block Statistics as another method to alter the resolution of a raster. Depending upon your specific goals, Block Statistics allows fine control of how pixels are assigned based on: A user defined neighborhood (e.g. rectangle, circle, wedge etc) The type of statistics calculated within each block (e.g. mean, majority, variety etc). In ...


7

gdal_rasterize doesn't perform any interpolation or resampling. By default it updates pixels under a point, on the line render path, or whose center point is within the polygon. But you can choose to update all pixels touched by lines or polygons with the -at flag. -at: Enables the ALL_TOUCHED rasterization option so that all pixels touched by ...


7

I found that the easiest way is to create a new raster file with dimensions equal to the reference file, SetGeoTransofrm and SetProjection of the new raster file to match the reference file then re-project the file and output, sample code shown below: from osgeo import gdal, gdalconst inputfile = #Path to input file input = gdal.Open(inputfile, gdalconst....


7

Edited answer (11-SEP-2018) The following answers and descriptions are based on QGIS 3.2. They will not touch on interpolation methods since the questioner is already familiar with them. I assume that the question specifically asked about the settings in Layer Properties -> Symbology -> Resampling (as in the figure below) Zoomed In - Nearest Neighbour, ...


6

The maximum accuracy you can get is that of the lowest cell resolution (or largest cell size), and splitting your cells won't increase the accuracy. That's why ArcGIS uses by default the largest cell size when combining rasters. Combining rasters using the lowest cell size is misleading, it lets you think your accuracy is higher than what it actually is. ...


6

All resampling methods have advantages and inconvenients. Nearest neighbours preserves the pixel values but might duplicate or remove some of them. Therefore you will probably end up with some discontinuities on your profiles (slope, etc). Cubic convolution, on the opposite will yield smooth slopes that are nice for hydrological model, but will not ...


6

All of the detailed information about MOD12 data can be found in the Algorithm Theoretical Basis Document (ATBD). On page 23, it says that the forest classes require >60% coverage of the pixel.


6

The resampling method 'near' or 'nearest' is generally to be considered only for succinct/classified data, it attempts to assign a cell value based on the closest source pixel: This is most commonly integer (int8, int32, int64) types but can be of type float (float32, float64) where each cell represnts classified values and generally values appear more than ...


5

I know that this question is rather old, but I wanted to add my 2 cents, in case others come across this thread trying to answer the same question... The previous answers are correct when you truly wish to RESAMPLE your data, such as if you are aggregating your data from a 30 m pixel size to a 90m pixel size. In this case you are attempting to create a new ...


5

I lack the "reputation" to Comment so... If radiometric analysis is going to be performed on the aerial photos then it should be done prior to resampling/projecting. Otherwise you will almost certainly introduce unintended bias into the final product. As per blord-castillo's helpful comment above. If the proximate and final uses of the aerials are for ...


5

Before answering your ultimate question, there are a couple of other points in your statement worth looking at. It shows harsh boundaries where one CT has great access to transit and right beside that CT is one that has extremely poor access In your comment, you elaborated that these vector boundaries are Census Tracts. What I am taking from your ...


5

It's not something I've tried, but I think you can do it with gdalwarp: gdalwarp -tr 10.0 -10.0 -r bilinear src.tif dst.asc You would choose a -tr value based on the smallest value of the source's resolution, remembering to negate the y scale if it's a top-down image. gdal_translate has an -outsize parameter that might do the job, but it may just truncate ...


5

As soon as you change the spatial resolution of a raster, you are resampling. If you want to keep exactly the same values as in the input data, you can use the "nearest neighbour" method.


5

Michael Miles-Stimson is correct in his comment above; nearest-neighbour (NN) and majority resampling methods should only be applied to categorical data, i.e. nominal and ordinal level data. Elevation, even when it is presented as integer values (which is a practice that I wish we could make illegal and punishable by lengthy jail terms), is not categorical. ...


5

These two comparable tools exist because ESRI has multiple license levels. Point to Raster (Conversion) is only available with the Advanced license. Feature to Raster (Conversion) is available with all of the license levels. Point to Raster allows you much more control over how multiple points are handled when multiple points fall within a raster cell. ...


5

No, if your bands all have the same resolution, no resampling will occur when using gdal_merge.py. So it's perfectly fine to use it. As for "Pansharpening": Since the process aims to turn a low-res color image into a high-res color image with the help of a high-res panchromatic image, naturally resampling is involved. In a common implementation this would ...


4

Oh, I so disagree with @Aaron's assertion that nearest neighbor is the best method. The common ASTER products are in radiance values and as such, are 32 bit floating point. Nearest Neighbor applied to float values will produce bias and artifacts. This is the common bias effect that results in the blocky appearance of DEM's that were reprojected using nearest ...


4

By default GeoServer picks the closest resolution to the one you're asking for. Programmatically one can set three different policies, but I don't believe the setting is exposed anywhere in the GeoServer GUI. You may want to open an improvement request at jira.codehaus.org.


4

The cubic mode is incorporated in QGIS Master. Looks like noone had thought of adding it to the dropdownbox earlier. You can use gisinternals' GDAL standalone version to do all things that current QGIS Lisboa does not offer yet. Or simply try to insert -r cubic in the command box of QGIS.


4

In ArcGIS whne you resample data using bilinear resampling it only looks at the values of the centre four cells (resample documentation). As such using this method you'll still lose data if you don't compensate for the data loss. Given that we know that for the resampled cells you're after a proportion of the cells that are forested, we can think of it as ...


4

The task could feel trivial by reading the gdalwarp documentation http://www.gdal.org/gdalwarp.html and GDAL AAIGrid -- Arc/Info ASCII Grid driver documentation http://www.gdal.org/frmt_various.html. The target pixel size is three times bigger than the native resolution 0.008333333333 degrees/pixel (not 1000 m/pixel, see the comments). gdalwarp -of AAIGrid -...


4

As I messed this up in the comments and cannot edit it now, I'll try again here. You have two options: Split your roads no finer than the resolution of your DEM. As it is now, you have multiple samples (10m apart) for a single pixel (30m x 30m). The more samples per pixel, the greater the relative jump between pixels. Take 2 pixels, 30m x 30m, 1m height ...


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