Facing the archaeological looting in Peru by local spatial autocorrelation statistics of Very high resolution satellite im...
The increasing development of Earth Observation (EO) techniques (ground, aerial and space) and the tremendous advancement ...
Sensors : panchromatic and multispectral sensors with resolutions of 61-72cm and 2.44-2.88m. Off-nadir  viewing angle (0-2...
Satellite data processing : methodological approach Detection and characterization of buried remains Panchromatic image Mu...
Tracce archeologiche: fenomenologia <ul><li>Crop marks  can appear as differences of height or color in crops which are un...
Risposta spettrale dei crop-marks Archaeological marks spectral response: crop marks
Pancromatic Red band (R) NIR (near infrared) NDVI=(NIR-R)/(NIR+R) Nazca river near Cahuachi (Peru) Basament of a buried py...
<ul><li>Direct surveillance (field survey) </li></ul><ul><li>Aerial surveillance </li></ul>How protect the archaeological ...
<ul><li>In such conditions, Very high resolution (VHR) satellite imagery offer a suitable chance to quantify looting and d...
Satellite Remote Sensing for monitoring clandestine archaeological excavations and looting This suggest to use an approach...
THE GREAT PYRAMID TEMPLO MONTICULO TEMPLO DEL ESCALONADO Historical phases: (400 B.C. – 400 A.D.) I) Sanctuary  II) Ceremo...
The comparative visual inspection of the available satellite dataset put in evidence that the panchromatic images are more...
Spatial Autocorrelation <ul><li>Tobler's First Law of Geography “ All things are related, but nearby things are more relat...
KDE: intensity and its measures First order effects (Absolute location)  Second order effects ( Relative location ) Proper...
Spatial autocorrelation : the nature of the problem Quantitative nature of dataset <ul><li>understand if events are simila...
Spatial Autocorrelation (SA)  in the context of   image processing   the spatial event is the  pixel spatial autocorrelati...
Global indicators of autocorrelation just measure if and how much the dataset is autocorrelated. Global indicators of Auto...
LISA allow us to understand where clustered pixels are, by measuring how much are homogeneous features inside the fixed ne...
Compute the lag distance Assume the rule of contiguyity Calculation  of Local indices the  queen’s contiguity  was choosen...
RGB Panchromatic image (PAN) Zoom of PAN the Geary’s C Getis and Ord’s Gi Moran’s I <ul><li>Summary of analysis procedure ...
Getis & Ord’s Gi <ul><li>Clusters that show the best results are those characterized by low reflectance intensity & corres...
Geary’s C representation  and  Getis & Ord’s Gi   (classification based on)  product  Ground truth (field survey in progre...
2002 2005 2008 2002 2005 2008 RGB composition of LISA (R:Geary; G: Moran; B: Getis) applied to panchromatic images of 2002...
Clandestine excavations is one of the biggest man-made risks which affect the archaeological heritage, especially in some ...
Upcoming SlideShare
Loading in …5
×

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

1,765 views
1,570 views

Published on

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

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,765
On SlideShare
0
From Embeds
0
Number of Embeds
11
Actions
Shares
0
Downloads
35
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

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

  1. 1. Facing the archaeological looting in Peru by local spatial autocorrelation statistics of Very high resolution satellite imagery Maria Danese (1) , Rosa Lasaponara (2), Nicola Masini (1) 1 CNR-IBAM, C/da S. Loia Zona industriale, 85050, Tito Scalo (PZ), Italy 2 CNR-IMAA, C/da S. Loia Zona industriale, 85050, Tito Scalo (PZ), Italy <ul><li>INDEX </li></ul><ul><li>Satellite Remote Sensing for Archaeological research </li></ul><ul><li>How face the clandestine excavations by satellite remote sensing (potential and limit) </li></ul><ul><li>Concepts of spatial autocorrelation </li></ul><ul><li>Improving the detection of archaeological looting by spatial autocorrelation : application in Cahuachi, discussion and conclusions </li></ul>
  2. 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. 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. 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
  5. 5. Tracce archeologiche: fenomenologia <ul><li>Crop marks can appear as differences of height or color in crops which are under stress due to lack of water or deficiencies in other nutrients. Crop-marks can be formed both as negative marks above wall foundations and as positive marks above damp and nutritious soil of buried pits and ditches </li></ul><ul><li>Soil marks are traces of archaeological features visible in ploughed or harrowed often for very restricted periods before the crops begin to grow. </li></ul><ul><li>Shadow marks can be seen in the presence of micro-topographic relief variations that can be made visible by shadowing in low sunlight angle conditions. </li></ul>Traces of archaeological features
  6. 6. Risposta spettrale dei crop-marks Archaeological marks spectral response: crop marks
  7. 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
  8. 8. <ul><li>Direct surveillance (field survey) </li></ul><ul><li>Aerial surveillance </li></ul>How protect the archaeological heritage from clandestine excavations? . <ul><li>time consuming, </li></ul><ul><li>expensive </li></ul><ul><li>not suitable ( for remote archaeological sites, characterized by difficult accessibility ) </li></ul>Are suitable? <ul><li>Direct surveillance </li></ul><ul><li>Aerial surveillance </li></ul><ul><li>not suitable for extensive areas </li></ul><ul><li>non practicable in several countries due to military or political restrictions </li></ul>
  9. 9. <ul><li>In such conditions, Very high resolution (VHR) satellite imagery offer a suitable chance to quantify looting and damage affecting the archaeological heritage thanks to their global coverage and frequent revisitation times. </li></ul><ul><li>Recent applications: </li></ul><ul><li>Iraq (Stone, 2008), other countries of Middle East (Parcak, 2007) </li></ul>Umma, Iraq. 2008 QuickBird image : note extensive looting pits.
  10. 10. Satellite Remote Sensing for monitoring clandestine archaeological excavations and looting This suggest to use an approach, based on local spatial autocorrelation statistics <ul><li>A time series of panchromatic and multispectral satellite images (2002-2008) allowed the mapping of looting over the years. </li></ul><ul><li>Looters’ holes : small and circular pits (0.7-3 m diameter) filled with sand, and by scattered remains </li></ul><ul><li>The reliability of the detection was evaluated by field surveys : </li></ul><ul><li>Rate of success high for flat areas </li></ul><ul><li>Unsatisfactory for other areas (mound) </li></ul>Investigated area
  11. 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. 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).
  13. 13. Spatial Autocorrelation <ul><li>Tobler's First Law of Geography “ All things are related, but nearby things are more related than distant things ” (1970) </li></ul>Positive Autocorrelation (or attraction) Negative Autocorrelation (or repulsion) No Autocorrelation (or random) Events : near and similar ( clustered distribution ) between events when, even if they are near, they are not similar ( uniform distribution ) no spatial effects , neither about the position of events, neither their properties <ul><li>called “event” the number of spatial occurrences in the considered variable, </li></ul><ul><li>spatial autocorrelation measures the degree of dependency among events, </li></ul><ul><li>considering at the same time their similarity and their distance relationships </li></ul>
  14. 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)
  15. 15. Spatial autocorrelation : the nature of the problem Quantitative nature of dataset <ul><li>understand if events are similar or dissimilar </li></ul><ul><li>(define the intensity of the spatial process, how strong a variable happens in the space ) </li></ul>Geometric nature of dataset <ul><li>the conceptualization of geometric relationships (.. at which distance are events that influence each other (distance band )) </li></ul>Calculation method : Euclidean distance Direction considered : or contiguity methods (tower c., bishop c., queen c.) distance Definition of spatial event 1 2 3
  16. 16. Spatial Autocorrelation (SA) in the context of image processing the spatial event is the pixel spatial autocorrelation statistics are calculated considering geographical coordinates of its centroid Geometric nature : lag distance <ul><li>lag distance : the range over which autocorrelation will be calculated or the separation distance between events </li></ul>Quantitative nature : spectral reflectance <ul><li>Pixel reflectance value for each band </li></ul><ul><li>SA measures the degree of dependency among spectral bands </li></ul>3 2 1
  17. 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. 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. 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
  20. 20. RGB Panchromatic image (PAN) Zoom of PAN the Geary’s C Getis and Ord’s Gi Moran’s I <ul><li>Summary of analysis procedure </li></ul><ul><li>1. Once lag distance is found and </li></ul><ul><li>2. Assumed the queen’s contiguity . </li></ul><ul><li>3. Local indicators of spatial association were calculated </li></ul><ul><li>Results : </li></ul><ul><li>Geary index (d), allows to best represent the rough surface, so the pitting holes due to its capability to detect dissimilarity </li></ul><ul><li>Getis and Ord Gi (e) needs a classification, before to be interpretated </li></ul>Calculation of LISA
  21. 21. Getis & Ord’s Gi <ul><li>Clusters that show the best results are those characterized by low reflectance intensity & corresponding low Gi values or high reflectance intensity & corresponding high Gi values show positive spatial autocorrelation </li></ul><ul><li>These clusters were then converted to polygons with the aim to obtain the map of the looting phenomenon </li></ul><ul><li>in Cahuachi corresponding values were found considering equal intervals as follow </li></ul><ul><li>where I is the intensity, G is the index and n is the number of classes wanted in the classification </li></ul>
  22. 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. 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. 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

×