Facing the archaeological looting in Peru by local spatial autocorrelation statistics of Very high resolution satellite imagery - Maria Danese, Rosa Lasaponara, Nicola Masini

2. The increasing development of Earth Observation (EO) techniques (ground, aerial and space) and the tremendous advancement of computer science has determined an increasingly importance of remote sensing archaeological research management and preservation of cultural resources and landscape FOR protection of archaeological heritage from looting

3. Sensors : panchromatic and multispectral sensors with resolutions of 61-72cm and 2.44-2.88m. Off-nadir viewing angle (0-25 degrees). Coverage of sensor: 16.5-19km High revisit frequency of 1-3.5 days, depending on the latitudes. Bandwidth Panchromatic sensor: 450 – 900 nm; Multispectral sensor: 450-520 nm (blue); 520-600 nm (green); 630-690 nm (red); 760-900 nm (Near Infrared) Very High Resolution (VHR) Satellite imagery Sensors : panchromatic and multispectral sensors with resolutions of 61-72cm and 2.44-2.88m. Off-nadir viewing angle (0-25 degrees). Coverage of sensor: 16.5-19km High revisit frequency of 1-3.5 days, depending on the latitudes. Bandwidth Panchromatic sensor: 450 – 900 nm; Multispectral sensor: 450-520 nm (blue); 520-600 nm (green); 630-690 nm (red); 760-900 nm (Near Infrared) 860 - 1040 nm (near IR1) 760-900 nm (near IR1) 705 - 745 nm (red edge) 630-690 nm (red) 585 - 625 nm (yellow) 520-585 nm (green) 450-520 nm (blue) 400 - 450 nm (coastal) 450-780 nm Spectral range 1,84 mt 0,46 mt Spatial resolutions WorldView2 (2009) - 450-900 nm Spectral range - 0,50 mt Spatial resolutions WorldView1 (2007) 760-900 nm (near IR) 625-695 nm (red) 520-600 nm (green) 450-520 nm (blue) 450-900 nm Spectral range 1,65 mt 0,41 mt Spatial resolutions GeoEye (2008) 760-900 nm (near IR) 630-690 nm (red) 520-600 nm (green) 450-520 nm (blue) 450-900 nm Spectral range 2,44 mt 0,61 mt Spatial resolutions QuickBird (2001) 757-853 nm (near IR) 632-698 nm (red) 506-595 nm (green) 445-516 nm (blue) 450-900 nm Spectral range 4 mt 1 mt Spatial resolutions IKONOS (1999) Multispectral Panchromatic Resolutions Satellite data

4. Satellite data processing : methodological approach Detection and characterization of buried remains Panchromatic image Multispectral imagery Datafusion Datafusion products Edge detection Edge enhancement: vegetation indices, PCA, TCT, etc. Edge extraction Edge thinning Reconnaissance and Interpretation Mapping within GIS environment Evaluation of data fusion algorithms Evaluation of edge enhancement techniques Methodology Assessment of Spectral capability Paleoenvironmental studies Archaeological landscape R. Lasaponara, N. Masini, 2007. Detection of archaeological crop marks by using satellite QuickBird, Journal of Archaeological Science , 34: 214-221

6. Risposta spettrale dei crop-marks Archaeological marks spectral response: crop marks

7. Pancromatic Red band (R) NIR (near infrared) NDVI=(NIR-R)/(NIR+R) Nazca river near Cahuachi (Peru) Basament of a buried pyramid Panchromatic image Multispectral imagery Datafusion Datafusion products Edge detection Edge thresholding Edge thinning Edge extraction Line extraction NDVI PAN NIR RED NDVI

11. THE GREAT PYRAMID TEMPLO MONTICULO TEMPLO DEL ESCALONADO Historical phases: (400 B.C. – 400 A.D.) I) Sanctuary II) Ceremonial Center III-IV) Theocratic Capital V) Sacred Place Archaeological area: 25 sqkm Excavated area: 15000 sqm (6%) Adobe constructions Necropolis intrusive area Continous site evolution Proyecto Nasca (Peru): Ceremonial Centre of CAHUACHI

12. The comparative visual inspection of the available satellite dataset put in evidence that the panchromatic images are more suitable than pansharpened spectral bands to emphasize both the pitting holes and archaeological features. 2002 2005 2008 Satellite time series used to map looting in Cahuachi Looters’ holes are usually recognizable by their small and circular pits. Some parts of the holes are illuminated, others are in shade. Cahuachi study case we focused only on satellite panchromatic scenes, so it was used as INTENSITY. Consequently all these characteristics pixels with holes show very different values of reflectance, so we supposed to find a break in autocorrelated zones (soil without holes).

14. KDE: intensity and its measures First order effects (Absolute location) Second order effects ( Relative location ) Properties of a spatial distribution* * Gatrell et al. (1996) ds = the neighbourhood each point (s) E() = expected mean Y(ds) : events number in the neighbourhood Large scale variation in the mean value of a spatial process (global trend) Small-scale variation around the gradient or Local dependence of a spatial process (local clustering)

17. Global indicators of autocorrelation just measure if and how much the dataset is autocorrelated. Global indicators of Autocorrelation Moran’s index where, N is the total pixel number, Xi and Xj are intensity in i and j points (with i≠j), Xi is the average value, wij is an element of the weight matrix I Є [-1; 1] if I Є [-1; 0) there’s negative autocorrelation; if I Є (0 ; 1] there’s positive autocorrelation; if I converges to o there’s null autocorrelation. Geary’s C where symbols have the same meaning than the Moran’s index expression C [0; 2]; if C [0; 1) there’s positive autocorrelation; if C (0 ; 2] there’s negative autocorrelation; if C converges to 1 there’s null autocorrelation (Geary, 1954), (Moran, 1948)

18. LISA allow us to understand where clustered pixels are, by measuring how much are homogeneous features inside the fixed neighbourhood Local Indicators of Spatial Autocorrelation (LISA) Local Moran’s index high value of the Local Moran’s index means positive correlation both for high values both for low values of intensity (reflectance value) (Anselin, 1995), Local Geary’s C index Detection of areas of dissimilarity of events (pixel reflectance value) (Cliff & Ord, 1981) Getis and Ord’s Gi index high value of the index means positive correlation for high values of intensity, while low value of the index means positive correlation for low values of intensity (Getis and Ord, 1992; Illian et al., 2008) ▪ N is the events number ▪ X i ed X j are the intensity values in the point i and j (with i≠j) ▪ is the intensity mean ▪ w ij is an element of the weights matrix

19. Compute the lag distance Assume the rule of contiguyity Calculation of Local indices the queen’s contiguity was choosen, because the analysis should be done in all the directions also for the curve configuration of holes. The best value is the lag that maximizes Moran’I (fig.1) and minimizes C (fig.2), allowing to captures in the best way the autocorrelation of the image. The lag choosen for all the three years is 2 . Fig. 2. Results obtained with global Geary’s C and lag distance between 1 and 10 calculated for 2002 Quickbird image. Fig. 1. Results obtained with global Moran’s I and lag distance between 1 and 10 calculated for 2002 Quickbird image. Lag distance and the rule of contiguity

22. Geary’s C representation and Getis & Ord’s Gi (classification based on) product Ground truth (field survey in progress) survey of hole pits Identification of hole pits Computation of : i) rate of success ( 75-90% in the considered test areas ), ii) false alarms; iii) rate of unsuccess In Cahuachi, the detection of looting pits on mounds has been significantly improved (75-90%) by applying local spatial autocorrelation statistics. Such improvement is still more evident if we compare the panchromatic satellite time series with the correspondent time series processed by local spatial autocorrelation statistics Cluster linked to looting pits False alarm Looting pits not detected by means of local spatial autocorrelation

23. 2002 2005 2008 2002 2005 2008 RGB composition of LISA (R:Geary; G: Moran; B: Getis) applied to panchromatic images of 2002 QB (a), 2005 QB (b) and 2008 WW1 (c). RGB composition emphasize pits enhancing their edges (circled with magenta ). The multitemporal comparison of the three RGB images clearly shows an increasing number of pits from 2002 to 2008 and, therefore, the intensification of the looting phenomenon over the years . Panchromatic time series (2002;2005;2008) The improvement obtained by LISA application is still more evident if we compare the panchromatic satellite time series with the correspondent time series processed by local spatial autocorrelation statistics

24. Clandestine excavations is one of the biggest man-made risks which affect the archaeological heritage, especially in some countries of Southern America, Asia and Middle East. To contrast and limit this phenomenon a systematic monitoring is required. In this context, VHR satellite imagery can play a fundamental role to identify and map looted areas. The Cahuachi study case herein presented put in evidence the limits of VHR satellite imagery in detecting features linked to looting activity. This suggested to experience local spatial autocorrelation statistics which allowed us to improve the reliability of satellite in mapping looted area. In Cahuachi, the detection of looting pits on mounds has been significantly improved (75-90%) by applying local spatial autocorrelation statistics. CONCLUSIONS