Primary Archaeology data for non-archaeologists?
This post is part of the May 2012 Technology Week, a quarterly topical discussion about…
The literature surrounding the use of LiDAR, light detection and ranging, imagery can often be disjointed, vague, and impractical for its application in archaeological investigation. Wanting to utilize the available data, I became frustrated with the lack of literature that described a basic methodological approach to using LiDAR. The most common usage for LiDAR in archaeological contexts continues to be identification of sites and associated features. Recent interest in LiDAR’s ability to aid in the monitoring of conditions on archaeological sites offers another opportunity to employ the available datasets (Challis et al. 2008).
LiDAR, light detection and ranging, is the constant transmittal of high-resolution laser light to the ground surface, with the time differential of each pulse recorded at the receiving station attached to a low-altitude aircraft (Fennell 2010:6-7). The accuracy of the method varies dependent on location and how the data was gathered; essentially, a micro-topographic map of the bare surface of the site and surrounding lands can be produced for archaeological analysis. LiDAR has been used in multiple case studies including both prehistoric and historic archaeological surveys with and without vegetation cover (Fennell 2010; Harmon et al. 2006; Petzold et al. 1999).
While the usage of LiDAR in archaeological contexts remains limited, the ways in which it is manipulated and more thoroughly realized continue to expand (Challis et al. 2008; Chase et al. 2011; Devereux et al. 2005; Devereux et al. 2008; Fennell 2010; Harmon et al. 2006; Rowlands and Sarris 2007). The various techniques to extrapolate information include, among others, the application of hill-shading algorithms, the manipulation of illumination sources by direction and elevation, the alteration of contour intervals through arbitrary and relational settings, the creation of local relief models, the application of statistics in analysis to include nearest neighbor, quadrat, and chi-square, the variance of resolution between micro and macro glimpses of the landscape, and even the use of multiple color gradients (Challis et al. 2008; Chase et al. 2011; Devereux et al. 2005; Devereux et al. 2008; Fennell 2010; Harmon et al. 2006; Jaillet 2011; Rowlands and Sarris 2007).
Of course, where there is potential…there is also pitfall. Some of the more common issues with LiDAR that deter it from a more widespread usage include the potential for data overload, inconsistency in its interpretive value, human error or unfamiliarity with LiDAR, present surface imagery’s inability to cope with temporal and/or cultural association, and resolution issues (Harmon et al. 2006; Jaillet 2011). Another point worth noting is that while it is without doubt a useful tool in the archaeological toolbox, it continues to be a method that works best in conjunction with other archaeological methods to include other remote sensing techniques, historic documentation and field investigation (Fennell 2010; Harmon et al. 2006; Jaillet 2011; Kvamme et al. 2006).
At this point, we come to the crux of the matter: what are we doing with LiDAR? In order to get at this question, we could go back to the algorithm. The algorithm most commonly discussed in the literature of LiDAR deals with the language of computers and programming. The meaning, in most instances, is in reference to the computer science behind its analysis and the GIS, geographic information systems, functions used to analyze it. While a great deal has been learned and a great deal more will be learned using this standard definition, I would ask that we apply the most basic ideas behind mathematical induction and recursive relations to our methodological approach to LiDAR analysis.
One solution would be to apply a back-to-the-basics approach involving the basic recursive algorithm of Divide-and-Conquer. Using the Divide and Conquer Algorithm, one would break the larger problem down into two more manageable questions. What can we do with LiDAR, in addition to we have already done? How do we go about doing it, in the most basic sense? It is the second question that appears to be the one plaguing the archaeological community most, as we have excellent examples worldwide of what can be done with LiDAR and archaeologists are continuing to apply it in innovative ways.
We need to come to a consensus on the variables that we are trying to measure using the LiDAR dataset. One way to go about this would be quantification of the variables using archaeological signatures that essentially typify features common to historic and prehistoric site types.
Essential to the idea of the Divide-and-Conquer algorithm is its parallelism, its ability to be used for multiple purposes, just as we know LiDAR can be. The same set of variables can be combined in differing ways to represent the different archaeological signatures expected of different archaeological resources. For example, a historic agricultural settlement might include linear features such as field lines, roadways, and waterways, as well as, polygon features such as structures and specific forms of vegetation. A prehistoric quarry site might include polygon features such as borrow pits and distinctive topographic features advantageous to the process of quarrying for lithic resources. The limits to the use of this technology are as of yet unmapped.
Essentially, what we need is a solution that is both mathematical and manual, a more efficient way to standardize LiDAR analysis. One potential solution would be to compute a coding system to manage the variables and allow for the ability to analyze LiDAR datasets with reference to the individual and combined variables, which would, in turn, limit the number of possible outcomes to a manageable number that could be reviewed and manually analyzed by the archaeologist.
In closing, I ask the archaeological community to rethink the algorithm in LiDAR and continue to expand upon the ways in which we use this valuable tool. Where to from here then… continue to push the bounds of this technology or begin to utilize what we have effectively? Must we make this choice or can we begin to apply consistent methodological standards to our use of LiDAR, while pushing the bounds of possibility?