I am trying to research ways into obtaining landuse data for a project which calculates the visual impacts of wind parks.

I have a DEM and DOPs for a relatively large region (5000km²), but I have no landuse data.

Does anyone know how I could obtain or create such data to create a database of landuse types .i.e residential, agriculture, industry, forest, woodland etc?

Is is possible to create such a dataset from satelite imagery or would I have to think about digitizing polygons from aerial photos?

I would be very interested in hearing view or reading methodologies for similar projects.

closed as too broad by PolyGeo Mar 1 '17 at 4:29

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  • 1
    Are you interested in land use or land cover data sets (there is a difference)? – artwork21 May 10 '12 at 11:33
  • Which geographic region are you interested in? – Aaron May 30 '13 at 15:27

Creating landuse database, you need sensor technologies to detect and classify objects. i think you already know that:

There are two main types of remote sensing: passive remote sensing and active remote sensing. Passive sensors detect natural radiation that is emitted or reflected by the object or surrounding areas. Reflected sunlight is the most common source of radiation measured by passive sensors.

Active collection, on the other hand, emits energy in order to scan objects and areas whereupon a sensor then detects and measures the radiation that is reflected or backscattered from the target. RADAR and LiDAR are examples of active remote sensing where the time delay between emission and return is measured, establishing the location, height, speed and direction of an object.


if you dont have multi-band aerial photo, it is very hard to classify objects and the classification accuracy rate will be down for certain objects.

there is lots of free data(old dated) on internet which can you use for classification. you can also find some free data here.


in remote sensing world there are lots of satellite with different features for different needs. they diverses from others with lots of features as spatial, temporal and spectral resolutions. for spectral resoulutions:

The spatial resolution specifies the pixel size of satellite images covering the earth surface.

High spatial resolution: 0.6 - 4 m

» GeoEye-1

» WorldView-2

» WorldView-1

» QuickBird





» SPOT-5 Medium spatial resolution: 4 - 30 m



» CBERS-2 Low spatial resolution: 30 - > 1000 m

for temporal specification:

The temporal resolution specifies the revisiting frequency of a satellite sensor for a specific location.

High temporal resolution: < 24 hours - 3 days

Medium temporal resolution: 4 - 16 days

Low temporal resolution: > 16 days

for spectral resolution :

Spectral Resolution

In the first instance, a sensor's spectral resolution specifies the number of spectral bands in which the sensor can collect reflected radiance. But the number of bands is not the only important aspect of spectral resolution. The position of bands in the electromagnetic spectrum is important, too.

High spectral resolution: - 220 bands

Medium spectral resolution: 3 - 15 bands

Low spectral resolution: - 3 bands

you can find more information about Characterization of Satellite Remote Sensing Systems here.


you can find more tutorial about classification with remote sensing data and Image Anlaysis in envi site (they give example data too).


If your study area is within the conterminous United States, you might be interested in the National Land Cover Database (NLCD); the latest data is from 2006, and is at a 30-meter resolution.


There are several global land cover products. Globcover, from ESA, is the one (I think) with the best resolution - 300m. It has a fairly detailed nomenclature based on FAO's classification system.

enter image description here

Also, if your AOI is in Europe, you can get CORINE Land Cover - a Land Use/Land Cover vector Map for Europe with 25ha of Minimum Mapping Unit and 44 classes of thematic detail.

enter image description here

Both are free.

I'm not sure at what scale and what region you're working in, but these are very good solutions. If you're getting into deriving your own Land Use/Land Cover map, you may as well forget the rest cause you'll have much work on your hands.

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