facing the archaeological looting in peru by local spatial autocorrelation statistics of very high...
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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), Italy2 CNR-IMAA, C/da S. Loia Zona industriale, 85050, Tito Scalo (PZ), Italy
INDEX
Satellite Remote Sensing for Archaeological research
How face the clandestine excavations by satellite remote sensing (potential and limit)
Concepts of spatial autocorrelation
Improving the detection of archaeological looting by spatial autocorrelation : application in Cahuachi, discussion and conclusions
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
Very High Resolution (VHR) Satellite imagery
Satellite data Resolutions Panchromatic Multispectral
IKONOS (1999)
Spatial resolutions 1 mt 4 mt
Spectral range 450-900 nm
445-516 nm (blue)
506-595 nm (green)
632-698 nm (red)
757-853 nm (near IR)
QuickBird (2001)
Spatial resolutions 0,61 mt 2,44 mt
Spectral range 450-900 nm
450-520 nm (blue)
520-600 nm (green)
630-690 nm (red)
760-900 nm (near IR)
GeoEye (2008)
Spatial resolutions 0,41 mt 1,65 mt
Spectral range 450-900 nm
450-520 nm (blue)
520-600 nm (green)
625-695 nm (red)
760-900 nm (near IR)
WorldView1(2007)
Spatial resolutions 0,50 mt -
Spectral range 450-900 nm -
WorldView2 (2009)
Spatial resolutions 0,46 mt 1,84 mt
Spectral range 450-780 nm
400 - 450 nm (coastal)
450-520 nm (blue)
520-585 nm (green)
585 - 625 nm (yellow)
630-690 nm (red)
705 - 745 nm (red edge)
760-900 nm (near IR1)
860 - 1040 nm (near IR1)
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.
Edg
e ex
trac
tion
Edge thinning
Reconnaissance and Interpretation
Mapping within GIS environment
Evaluation of data fusion algorithms
Evaluation of edge enhancement techniquesM
etho
dolo
gy
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
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
Soil marks are traces of archaeological features visible in ploughed or harrowed often for very restricted periods before the crops begin to grow.
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.
Traces of archaeological features
buried masonry
crop-marks related to buried structures (positive presences)
stressed vegetationhealthy veget. healthy veget. healthy vegetation very healthy veget. healthy veget.
buried ditch
crop-marks related to ditches (negative presences)
sandy (or permeable) soil
clayey soil
buried masonry
soil-marks related to buried structures (positive presences)
buried ditchsandy (or permeable) soil
clayey soil
damper soilless damp soil less damp soil
soil-marks related to ditches (negative presences)
soil-marks and shadows marks related to buried structures
sunbeams
buried masonry
less damp s.damper soil damper s.shaded anddamper s.
Archaeological marks spectral response: crop marks
Panchromatic image
Multispectral imagery Datafusion
Datafusion products
Edge detection
Edge thresholding
Edge thinningEdg
e ex
trac
tion
Line extraction
NDVI
Pancromatic
Red band (R)
NIR (near infrared)NDVI=(NIR-R)/(NIR+R)
PANNIRREDNDVI
Nazca river near Cahuachi (Peru)
Basament of a buried pyramid
Direct surveillance (field survey)
Aerial surveillance
How protect the archaeological heritage from clandestine excavations?
.
time consuming,
expensive
not suitable (for remote archaeological sites,
characterized by difficult accessibility)
Are suitable?
Direct surveillance
Aerial surveillance
not suitable for extensive areas
non practicable in several countries due to military or political restrictions
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.
Recent applications:Iraq (Stone, 2008), other countries of Middle East (Parcak, 2007)
Umma, Iraq. 2008 QuickBird image : note extensive looting pits.
Satellite Remote Sensing for monitoring clandestine
archaeological excavations and looting
This suggest to use an approach, based on local spatial autocorrelation statistics
A time series of panchromatic and multispectral satellite images (2002-2008) allowed the mapping of looting over the years.
Looters’ holes : small and circular pits (0.7-3 m diameter) filled with sand, and by scattered remains
The reliability of the detection was evaluated by field surveys :
Rate of success high for flat areas
Unsatisfactory for other areas (mound)
Investigated area
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 sqkmExcavated area: 15000 sqm (6%)
Adobe constructionsNecropolis intrusive areaContinous site evolution
Proyecto Nasca (Peru):
Ceremonial Centre of CAHUACHI
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).
Spatial AutocorrelationTobler's First Law of Geography “All things are related, but nearby things are more related than distant things” (1970)
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
called “event” the number of spatial occurrences in the considered variable,
spatial autocorrelation measures the degree of dependency among events,
considering at the same time their similarity and their distance relationships
KDE: intensity and its measures
First order effects(Absolute location)
Second order effects
(Relative location)
ji
ji
dsdsji dsds
dsYdsYEss
ji
))()((lim),(
0,
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)
Spatial autocorrelation : the nature of the problem
Quantitative nature of dataset
•understand if events are similar or dissimilar
(define the intensity of the spatial process, how strong a variable happens in the space )
Geometric nature of dataset
• the conceptualization of geometric relationships (..at which distance are events that influence each other (distance band))
Calculation method : Euclidean distance . 2)(2)(),( jyiyjxixjsisEd
Direction considered : or contiguity methods (tower c., bishop c., queen c.)
dis
tan
ce
Definition of spatial event 1
2
3
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
• lag distance : the range over which autocorrelation will be calculated or the separation distance between events
Quantitative nature : spectral reflectance
• Pixel reflectance value for each band•SA measures the degree of dependency among spectral bands
3
2
1
Global indicators of autocorrelation just measure if and how much the dataset is autocorrelated.
Global indicators of Autocorrelation
Moran’s index
i j i iij
i j jiij
XXw
XXXXwNI
2)()(
))((
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
i iij
i j jiij
XXw
XXwNC
2
2
)((2
)()1( (Geary, 1954),
(Moran, 1948)
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)
N
jjij
X
ii XXw
S
XXI
12
))(()(
(Anselin, 1995),
Local Geary’s C index
Detection of areas of dissimilarity of events (pixel reflectance value)
n
i
n
jij
n
iji
n
jij
n
ii w
XXw
XX
nC
1 1
1
2
1
1
2 2
)(
)(
1
(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)
2
)()(1
)(
)()()(
2
1 1
11
N
dwdwN
iS
dwxxdwdG
n
i
n
iii
n
iiii
n
ii
i
▪ N is the events number▪ Xi ed Xj are the intensity values in the point i and j (with i≠j)▪ is the intensity mean
▪ wij is an element of the weights matrix
X
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
RGB
Panchromatic image(PAN) Zoom of PAN the Geary’s C Getis and Ord’s Gi Moran’s I
Summary of analysis procedure
1. Once lag distance is found and 2. Assumed the queen’s contiguity. 3. Local indicators of spatial association were
calculated
Results :
Geary index (d), allows to best represent the rough surface, so the pitting holes due to its capability to detect dissimilarity Getis and Ord Gi (e) needs a classification, before to be interpretated
Calculation of LISA
Getis & Ord’s Gi
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 These clusters were then converted to polygons with the aim to obtain the map of the looting phenomenon
n
inax ImIm n
GG minmax
in Cahuachi corresponding values were found considering equal intervals as follow
where I is the intensity, G is the index and n is the number of classes wanted in the classification
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
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
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
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