Archaeological Prospecting using Remote Sensing Techniques in Quiechapa, Oaxaca, Mexico

June 5, 2017 | Autor: David Massey | Categoria: Archaeology, Remote Sensing, Mesoamerican Archaeology, Oaxaca (Archaeology)
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Archaeological Prospecting using Remote Sensing Techniques in Quiechapa, Oaxaca, Mexico David P. Massey1, Alex Elvis Badillo2 1Department

of Geography, Indiana University – Bloomington, 47405

2Department

of Anthropology, Indiana University – Bloomington, 47405

Figure 4. ‘Brightness’, ‘Greenness’, and ‘Wetness’ of the Landsat 5 image.

Results/Future Work

A

B

C

D

E

F

Figure 2. Landsat 5 imagery with architectural feature shapefile. Figure 1. Location of Quiechapa in Oaxaca, Mexico.

Introduction Over the past fifteen years, archaeologists have increasingly adopted and adapted emerging geospatial technologies into field methodologies for the investigation of the archaeological record. Improvement in Global Positing Systems (GPS) units, mobile computing power, and digital cameras along with increased sophistication in Geographic Information Systems (GIS), image analysis, and 3D visualization software has transformed how archaeological data is analyzed, interpreted, and managed (Kvamme 2012). In addition, the digital documentation of archaeological surveys and excavations now facilitates the incorporation of increasingly accessible digital datasets from remote sensing and laser scanning (Roosevelt et al. 2015). Quiechapa (Figure 1) is a rural mountainous region located southeast of the Oaxaca Valley situated between two larger cultural spheres, the Nejapa Valley (King 2010; 2012) and the Miahuatlan Valley (Brockington 1973; Markman 1981). During the Postclassic period (AD 800 – 1521), Quiechapa was likely affected by Zapotec and Aztec conquests which triggered the moment of people into more rural and mountainous regions. Although ethnographic accounts document the presence of a garrison established during the Zapotec conquest of Tehuantepec (Burgoa 1989 [1674]:236), the Quiechapa region itself has never been archaeologically surveyed to corroborate this claim. A regional survey project is currently underway to begin to examine this 168 km2 region for the first time which will employ a combination of pedestrian and remote sensing survey methodologies. Remote sensing is a critical tool for archaeological prospection because of its distinct cost and time-saving advantages over traditional pedestrian surveys. It also allows archaeologists to non-invasively survey challenging or inaccessible terrain and contextualize sites through a wider landscape perspective (Kantner 2008; Parcek 2009; Schindling and Gibbes 2014). Remote sensing can reveal otherwise invisible archaeological sites through image or spectral analyses (Verhoeven and Doneus 2011) and other visualization techniques (Challis et al. 2011).

Goal / Methodology This preliminary investigation was designed to explore the utility of Landsat imagery for archaeological prospection in Quiechapa, Oaxaca, Mexico using a surveyed architectural feature shapefile as a control (Figure 2).

Landsat 5 imagery from 25 January 2011was downloaded from USGS EarthExplorer and processed using ERDAS Imagine. Following the lead of recent successful archaeological prospection investigations (Doneus et al. 2014; Lasaponara and Masini 2011), Principal Component Analysis (PCA) and Tasseled Cap Transformation (TC) were performed on the imagery. PCA is a standard multivariate statistical method used to reduce the dimensionality of data which results in the reduction of “noise” within an image (Table 1, Figure 3). PCA1 PCA2 PCA3 PCA4 PCA5 PCA6 Sum

Eigen Value 2625.166664 180.675325 42.55383475 9.853038556 5.700564414 1.498367741 2865.447794

Percent 0.916145 0.063053 0.014851 0.003439 0.001989 0.000523 1

Table 1. Eigen Values from PCA analysis of Landsat image. Nearly 98% of the variance in the Landsat image can be explained by the first two PCA components.

Index Band 1 Band 2 Band 3 Band 4 Band 5 Band 7

Brightness 0.048129169 0.106529022 0.191324159 0.45962006 0.795550188 0.324930016

Greenness -0.380585288 -0.137143788 -0.274065686 0.78118602 -0.21066788 -0.326499412

Wetness -0.721643283 -0.316770619 -0.311397826 -0.388437628 0.353439287 0.078202433

4th 0.478539909 -0.15808388 -0.509010754 -0.099678019 0.401682315 -0.561811984

5th -0.271622921 0.210488692 0.605168164 -0.117340373 0.189335748 -0.682693345

6th -0.171143033 0.894540955 -0.40742362 -0.062526326 0.024554127 0.000297585

Table 2. TC values with eigenvectors and eigenvalues.

Figure 5. PCA and TC overlaid on architectural features. A: PCA1; B: PCA2, C: PCA3; D: ‘Brightness’; E: ‘Greenness’; F: ‘Wetness’.

As demonstrated in Figure 5, when the results of the PCA and TC analyses are overlaid onto a section of architectural remains in ArcGIS it becomes clear that neither analyses seem to elucidate the architectural remains as clearly as we might have hoped. While there may be small locational errors with the architectural features themselves, a more likely reason for the poor performance of these analyses is due to the low resolution of the Landsat imagery. Landsat has a resolution of 30 meters which means that any architectural feature which might appear in a PCA or through a ‘Brightness’, ‘Greenness’, or ‘Wetness’ index would need to be larger/wider than 30 meters. Any potential identification of architectural features in Quiechapa, and perhaps on numerous other archaeological projects, thus requires the acquisition of much higher resolution aerial through the use of a UAV/Drone equipped a multispectral sensor by purchasing much higher resolution imagery. Future work will explore both of these options.

Bibliography

Figure 3. PCA1, PCA2, PCA 3.

TC is a global vegetation index which disaggregates the amount of soil “brightness”, vegetation “greenness” cover and moisture “wetness” context on individual pixels of a Landsat MSS or ETM image (Table 2, Figure 4). Together PCA and TC can highlight features on Earth’s surface which may be of archaeological interest, requiring ground-trothing and verification (Aqdus et al. 2012; Pope and Dahlin 1989).

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