Manuel David Vargas Russo Federico Dukatz Graciela Ana Canziani

Transcription

Manuel David Vargas Russo
Federico Dukatz
Graciela Ana Canziani
Introduction
•Explanation of a methodology for Wetland Environmental
Analysis, based on Satelital Images.
•Models to optimize the classification and the calculus of
vegetation indexes.
•Model to calculate tendencies, with the capacity of monitoring
the temporal evolution of vegetation.
•Finally, combination of different techniques to detect possible
anomalies and variations in the study zone.
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Vargas Russo, M. - Dukatz, F.- Canziani, G.
Description of the Methodology
Satelital Images
Geometric Correction
Separation of Area of Interest
Method for Vegetation Indexes
TDI
Transformation
Unsupervised
Classification
Detection of Anomalies
Analysis of Anomalies
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Study Site
• Satelital data constitutes the base data layer for the landcover map.
They were provided by CONAE, from SAC-C within the AM
constellation.
• The Geometric Correction procedure involves the selection of
ground control points. Then they are assigned to a determined
geographic referencing system (UTM - zone 21).
•Path 226 Row 78.
Obtaining Satelital images
TDI
Transformation
This sector includes the
region studied, bounded by
the Paraguay River (West),
the Tebicuary River (South)
and the 100m contour line
(Northeast).
Unsupervised
Classification
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In order to make the processing
more efficient and more precise,
the study area or AOI (Area of
Interest) is defined, cut and
separated.
TDI
Transformation
Unsupervised
Classification
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NDVI (Normalised Difference Vegetation Index)
24/4/2003
10/5/2003
26/5/2003
TDI
Transformation
Unsupervised
Classification
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-1
Greeness leeves
1
Tasseled Cap Transformation
TDI
Transformation
Unsupervised
Classification
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+
Vegetation
-
+
Wetness
-
+
Soil Brightness
-
Tasseled Cap Transformation
SBI
GVI
Wetness
TDI
Transformation
Unsupervised
Classification
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TDI Transformation
GVIs
TDI
10/07/02
27/08/02
18/09/02
23/03/03
26/04/03
10/06/03
TDI
Transformation
• Temporal Difference Index (TDI). Defined by means of a linear
regression.
• This method is performed simultaneously over vegetation index
data from several different images.
Unsupervised
Classification
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Vegetation Intensity
TDI Transformation
α
Day
10/07/02
27/08/02
18/09/02
23/03/03
26/04/03
Tg α==
TDI
Transformation
Unsupervised
Classification
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10/06/03
TDI Transformation
Increase
Tg α==
Minimal change
Tg α==
Decrease
Tg α==
TDI
Transformation
Unsupervised
Classification
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Unsupervised Classification
•It is a process for sorting
pixels into a finite number of
individual classes or categories
in the image.
•If a pixel satisfies a certain set
of criteria, then the pixel is
assigned to a predetermined
class.
Classes
Water
Vegetation
TDI
Transformation
Unsupervised
Classification
Algorithm used: ISODATA
Identifies statistical patterns in the data.
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Soil
on a TDI Trasformation
• Defines categories following
the intensity of the variation.
Unsupervised
Classification
TDI
Transformation
Unsupervised
Classification
AOI
GVIs
TDI
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• In this example, the green regions show
zones without change, while the shades
red indicate that the vegetation has
suffered a major variation, in this case,
an increase in the vegetation.
TDI
Transformation
Unsupervised
Classification
Temporal Difference Index
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28-9-2002
10-7-2002
27-8-2002
11-8-2002
26-5-2003
10-5-2003
24-4-2003
TM Bands, 4(NIR), 3(red), and 2(blue).
TDI
Transformation
TDI
(7 Images)
Unsupervised
Classification
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10-7-2002
11-8-2002
27-8-2002
28-9-2002
24-4-2003
TDI
Transformation
Unsupervised
Classification
Images Layers
Red: band 4 (NIR)
Green: band 3 (R)
Blue: band 2 (G)
10-5-2003
26-5-2003
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There are reasons to believe
that this zone was devastated by
fire.
NBR transformation was
Tasseled
applied
to Cap
two(26-5-2003
images,) one
before and the other after the
observed change.
TDI
Transformation
Unsupervised
Classification
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UC on TDI
3 images
(24-04-2003)
(10-05-2003)
(26-05-2003)
-
0
+
NBR (Normalized Burn Ratio)
10/05/03
TDI
Transformation
Unsupervised
Classification
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26/05/03
TM bands 4 and 7 exhibited the greatest
reflectance change in response to fire. The
hypothesis is that the ratio of those bands
would be most discriminating for burn
effects.
Difference
(ten classes) only over the
limited NBR difference
AOI.
TDI
Transformation
Unsupervised
Classification
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Conclusion
• Wetland zones are very difficult to study because of very
particular features.
• The satelital image processing is an appropriate tool for
detecting temporal changes in vegetation, temperature and
anomalies that occur in wetlands, which are isolated,
extended and difficult to access.
• Transformations performed over a temporal sequence of
images generates an advantageous method for problem
detection through remote sensing.
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Acknowledgements
We thank CONAE for providing the images used in these
projects, and Raul Colomb and Ida Nöllmann for their
unlimited collaboration.
Florencia Castets (Personal de Apoyo CICPBA) helped
design this presentation.
While the European Commission financed the projects "The
Sustainable Management of Wetland Resources in
Mercosur" (Contract IC18 CT98 0262) and "Regional
Aspects of the SustainableManagement of Wetland
Resources " (Contract A4-DEV-ICFP-599A4 AM01).
REGWET

Manuel David Vargas Russo Federico Dukatz Graciela Ana Canziani

Transcription

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