Aerial photography and ground obscuration

Examination of aerial photography is one of the primary methods by which archaeologists have surveyed the landscape of England for new sites and for new information about known sites, in a process that continues to this day.  However, it is only possible to find buried archaeological features by this method under certain conditions.  One particular adverse condition that halts all aerial photographic survey work is the obscuration of the ground surface by human and natural features.  Woodlands / forests (LiDAR can see through these to some extent, but photography cannot), lakes, buildings, roads, railways, etc. can hide the ground surface and make the detection of surface and subsurface features impossible.

As a result, distributions of archaeological sites discovered through aerial prospection will inevitably be biased towards areas of open country, particularly arable and pasture lands.  If we wish to make quantitative statements about such distributions, we need a methodology by which to quantify the obscuration of the ground surface, in order to demonstrate which areas of apparent blankness on such a distribution map are, in fact, only blank due to the impossibility of aerial prospection.

When the Ordnance Survey made available some of its data under its OpenData initiative, it became possible to undertake this quantification of obscuration using some quite simple (albeit intensive) computational methods.  This is because the Vector Map product produced by the OS is organised thematically, making it quite simple to download and join together thematic map layers for the whole of the UK (as the current project is only concerned with England, the method discussed below has only been undertaken for England, however).  This forms a series of data layers that would have been very difficult to pull together prior to the OpenData initiative.

To build up a map of ground obscuration for England, the following OS OpenData layers were downloaded and joined together for several regions (European parliamentary constituencies) that together spanned the whole country*: buildings, water areas, forested areas (all polygons), roads, and railways (line data).  It would have been possible to include other layers (such as glasshouses), but it was decided that those listed above were sufficient to produce a good generalised map.  The spatial precision of these layers actually appears very good, especially for the resolution of analysis undertaken (see below).  Buffers were generated for the roads and railway lines, of varying width depending on the type of entity (based on a quick survey of a few entities of each type on Google Earth): 10m for most types of road and for narrow gauge railways; 15m for A roads and single track railways; 20m for trunk roads; and 25m for motorways and multi track railways.

The buffer layers, buildings, water areas and forest layers were then joined together using the union tool in ArcGIS to create a polygon map of ground obscuration for each region.  A 1km by 1km polygon grid square layer was generated using Geospatial Modelling Environment and then reduced down to the outline of England via a spatial overlap query.  The identity tool in ArcGIS was then used to calculate how the polygons in the obscuration layers overlapped with the grid polygons, and the area of each resulting overlap polygon was then calculated.  The attribute tables were exported for these output layers and joined together in Excel into one big table listing the ID number (CELLID) for the related grid square and the area of each obscuration polygon within that square.  A python script was written which went through this table, adding together the total area of obscuration for each CELLID (this took around seven hours to process), and outputting a new table listing CELLID and total area of obscuration.

This output table was joined to the 1km by 1km grid square layer in ArcGIS based upon the CELLID.  We now knew the total area of obscuration for each kilometre grid square of England.  The percentage obscuration was calculated and this percentage figure was then used to create a 1km resolution raster layer showing what percentage of each cell’s ground surface area was obscured by buildings, woodland, water, roads and railways:

% obscuration of ground cover for 1km grid squares in England
% obscuration of ground cover for 1km grid squares in England

Obviously, as with all models, this is not a perfect or perfected result, but I do believe that it provides a very useful quantification of the extent to which the ground surface of England is obscured to any aerial visual observer (the picture would be somewhat different for LiDAR prospection, as then I would not have included trees as a form of obscuration).  There are undoubtedly other types of obscuration feature that could also have been included (areas of alluvium or peat, perhaps) and there may be some types of included feature that can, in certain circumstances, be seen through.  It does, however, provide a good basis for quantifying the extent to which gaps in aerial prospection results for England have resulted from the impossibility of achieving results through that method.  In the context of this project, this is particularly relevant when dealing with English Heritage’s National Mapping Program data, as this was constructed on the basis of aerial survey.

– Chris Green

* This division into regions was purely to ease the processing burden on ArcGIS and my computer.

Author: Chris Green

Postdoctoral Researcher (GIS)

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