Satellite and Airborne Remote Sensing Investigations in Venice, Italy:
Validation of EO1 for Urban Mapping, Lagoonal Studies, and Bathymetry

Michael Abrams

Jet Propulsion Laboratory

Pasadena, CA

Stefano Pignatti

CNR-LARA

Rome, Italy

Rosa Cavalli

CNR-LARA

Rome, Italy

Luigi Alberotanza

CNR

Venice, Italy

EO-1 Science Validation Team Final Report

Satellite and Airborne Remote Sensing Investigations in Venice, Italy

The objective of this study was to evaluate data acquired by the
spaceborne high-resolution Hyperion and Advanced Land Imager (ALI) by
integrating them with hyperspectral data from airborne sensors for
applications to urban and industrial mapping, particularly in Venice and
Porto Marghera, Italy. As well as using the sensors aboard EO-1, this
investigation used data acquired by TERRA ASTER, the Landsat Enhanced
Thematic Mapper (ETM+), the Multispectral Infrared Visible Imaging
Spectrometer (MIVIS) airborne imaging spectrometer, and IKONOS during
six satellite overpasses from March through July 2001.

**Background:**

The Landsat 7, EO-1, and TERRA satellites have been flying in formation
since the November 21, 2000, launch of EO-1. EO-1 is flying about one
minute behind Landsat in the same orbit; TERRA follows almost 30 minutes
behind EO-1 and is in a similar orbit. This formation allows the
instruments on board these satellites to view virtually identical ground
scenes so that a direct comparison of remotely senses images can be
made.

The TM+, ALI, and Hyperion each have spatial resolutions of 30 meters.
ASTER acquires images at both 15 meters and 30 meters in different parts
of the spectrum; MIVIS collects data at 8 meters resolution when at 4000
meters above sea level and at 3 meters resolution when at 1500 meters.
IKONOS satellite images are at 4 meters resolution. Figure 1 shows
Landsat ETM+, MIVIS, and EO-1 ALI and Hyperion images for a similar
area. Figure 2 shows representative images for the area under study at
the spatial resolution of each of the sensors and provides the number of
available bands.

![](media/image1.wmf){width="4.877777777777778in"
height="3.7055555555555557in"}

Figure 1 . Similar image acquired from MIVIS, ALI, ETM+, and Hyperion.

+-----------+-----------+-----------+-----------+-----------+-----------+
| ![](m     | ![](m     | ![](m     | ![](m     | ![](m     | ![](m     |
| edia/imag | edia/imag | edia/imag | edia/imag | edia/imag | edia/imag |
| e2.wmf){w | e3.wmf){w | e4.wmf){w | e5.wmf){w | e6.wmf){w | e7.wmf){w |
| idth="0.7 | idth="0.7 | idth="0.7 | idth="0.7 | idth="0.7 | idth="0.7 |
| 604166666 | 604166666 | 604166666 | 604166666 | 604166666 | 604166666 |
| 666666in" | 666666in" | 666666in" | 666666in" | 666666in" | 666666in" |
| he        | he        | he        | he        | he        | he        |
| ight="2.9 | ight="2.9 | ight="2.9 | ight="2.9 | ight="2.9 | ight="2.9 |
| 986111111 | 986111111 | 986111111 | 986111111 | 986111111 | 986111111 |
| 11111in"} | 11111in"} | 11111in"} | 11111in"} | 11111in"} | 11111in"} |
+-----------+-----------+-----------+-----------+-----------+-----------+
| ALI       | Hyperion  | TM        | ASTER     | MIVIS     | IKONOS    |
|           |           |           |           |           |           |
| 30 m      | 30 m      | 30 m      | 15 m      | 8 m       | 4 m       |
|           |           |           | VNIR;     |           |           |
| 9 bands   | 242 bands | 6 bands   |           | 102 bands | 4 bands   |
|           |           |           | 30 m SWIR |           |           |
|           |           |           |           |           |           |
|           |           |           | 9 bands   |           |           |
+-----------+-----------+-----------+-----------+-----------+-----------+

Figure 2. Spatial resolution and number of bands for each sensor.

Each sensor observes a ground swath of a different size. The ALI ground
swath is 37 km, Hyperion 7.5 km, ETM+ 185 km, MIVIS 5.6 km when flying
at 4000 meters above sea level, and ASTER 60 km.

**Investigation**

Venice consists of a low-lying land mass intersected by a number of
canals. It is situated in a lagoon and also borders the ocean. Several
islands near the largest land mass also are considered part of the urban
area. In total, the study area encompassed a dense urban environment, a
polluted lagoon, and the offshore waters of the Adriatic Sea.

Six study sites were used. The Malamocco Golf Club on the island of Lido
was used to inter-calibrate the instruments used in the study by means
of a group of balloon-borne instruments. The industrial area of Porto
Marghera, an area equipped with several meteorological stations, also
provided measurements for calibration purposes. The Marco Polo
International Airport provided easily identifiable features such as
runways and parking lots that were helpful during the data calibration
phase. The remaining area was a crop area a short distance north of the
airport that was characterized primarily by cultivated fields and very
small groves of trees. The Acqua Alta ocean platform in the Adriatic Sea
was equipped with a cluster of permanent instruments for measuring wind,
direct solar radiation, the tide, and sea waves. An additional land site
was located at Pellestrina to the south of the golf club.

Data acquired by the different sensors was subject to various
pre-processing (Table 1). In addition, a variety of image processing
techniques was applied to the data, including using the pixel purity
index (PPI), mixture tuned matched filter (MTMF), minimum noise fraction
(MNF), and spectral angle mapper (SAM).

Table 1. Venice data pre-processing.

  -------------------------- ------------ ----------- ----------- --------- -------------- ----------
                             **IKONOS**   **MIVIS**   **ASTER**   **ALI**   **Hyperion**   **ETM+**

  Adjustment of VNIR and                                                    X              
  SWIR geometry                                                                            

  Conversion to radiance at               X           X           X         X              X
  sensor                                                                                   

  ACORN atmospheric                       X Modtran   X           X         X              
  correction                                                                               

  Registration to IKONOS                  X GPX/INS   X           X         X              X
  with NN                                                                                  
  -------------------------- ------------ ----------- ----------- --------- -------------- ----------

In a validation area, each of the instruments successfully
differentiated a number of materials present. The level of each material
was given as a percentage of the total number of pixels observed in an
area. The number of pixels varied among each instrument although the
relative number of pixels was consistent in most cases. Table 2 shows
the percent of pixels identified as either vegetation, tile roof, or
pavement for each of the sensors.

Table 2. Percent of pixels identified in three classes.

  ----------------- ----------------- ----------------- -----------------
                    Vegetation        Tile Roof         Pavement

  ALI               6.1               10.6              2.3

  ETM+              3.7               13.3              2.3

  Hyperion          3.0               12.8              9.5

  ASTER             5.1               8.0               14.6

  MIVIS             4.7               6.7               7.5

  IKONOS            2.9               7.3               4.3
  ----------------- ----------------- ----------------- -----------------

As mentioned above, various processing techniques were applied to sensor
data. For a MIVIS scene, the entire roof dataset was spectrally
investigated using a PPI procedure. Two different spectra were
recognized as corresponding to brick coverings of new and old buildings.
The spatial recognition of such covering materials was obtained by means
of a SAM classification procedure. This procedure also stressed the
orthogonality of the two selected spectral classes (new and old
tiles---Figure 3).

![](media/image8.wmf){width="5.997222222222222in"
height="2.5840277777777776in"}

Figure 3. Mapping of old and new tiles using MIVIS.

Metallic coverings, asphalt, and trachyte spectral classes were
classified using a MTMF procedure to derive the abundance of each input
spectral class (Figure 4). (Trachyte is a type of igneous rock.)

![](media/image9.wmf){width="6.002083333333333in"
height="4.791666666666667in"}

Figure 4. MIVIS Mapping of metallic roofing, trachyte, and asphalt with
8-m pixels, 102 bands.

Figure 5 shows mapping of the same area using the SAM image processing
technique for each of the sensors.

  ----------------------------------------------------- ----------------------------------------------------- ---------------------------------------------------- ----------------------------------------------------
  ![](media/image10.wmf){width="2.998611111111111in"                                                          ![](media/image11.wmf){width="2.998611111111111in"   
  height="1.9375in"}                                                                                          height="1.9375in"}                                   

  ALI                                                                                                         ETM+                                                 

  ![](media/image12.wmf){width="2.998611111111111in"                                                          ![](media/image13.wmf){width="2.998611111111111in"   
  height="1.9375in"}                                                                                          height="1.9375in"}                                   

  Hyperion                                                                                                    IKONOS                                               

  ![](media/image14.wmf){width="2.998611111111111in"                                                          ![](media/image15.wmf){width="2.998611111111111in"   
  height="1.9375in"}                                                                                          height="1.9375in"}                                   

  MIVIS                                                                                                       ASTER                                                

  ![](media/image16.wmf){width="2.0006944444444446in"   ![](media/image17.wmf){width="1.8229166666666667in"                                                        ![](media/image18.wmf){width="1.895138888888889in"
  height="0.6145833333333334in"}                        height="0.59375in"}                                                                                        height="0.6041666666666666in"}
  ----------------------------------------------------- ----------------------------------------------------- ---------------------------------------------------- ----------------------------------------------------

Figure 5. Spectral Angle Mapper classifications.

Hyperion data was unmixed by using end-members derived from MIVIS data.
Both MIVIS and Hyperion data were converted to radiance at the sensor.
Atmospheric correction was applied, and Hyperion resampled to the MIVIS
spectral bands. Datasets were co-registered, and the empirical line
method used to normalized Hyperion to MIVIS, using MIVIS image spectra.

**Conclusion**

Overall, the ALI, ETM+, and Hyperion datasets, all at 30-meters
resolution, provided generally similar results. ALI allowed better
mapping than ETM+ because of its larger number of bands and higher
signal-to-noise ratio. Hyperion mapping was similar to that of ETM+
because of its low signal-to-noise ratio, even though its larger number
of spectral bands should improve mapping. ASTER 15-meter data was better
than any of the 30-meter data, due to the smaller pixel size. MIVIS
data, with its 8-meter pixels, were better yet because of its
hyperspectral coverage and high spatial resolution. IKONOS data provided
the greatest detail, the least amount of mixed pixels, and could
separate all of the urban classes.

**Bathymetric Analyses**

ALI has 2 characteristics that potentially improve water penetration in
clear waters compared to Landsat ETM+: higher signal-to-noise; and the
addition of a far blue band at shorter wavelengths than ETM+ Band 1. We
obtained simultaneous ALI, ETM+ and ASTER data over Lake Tahoe, CA to
examine the abilities of these data sets to penetrate water and see the
bottom. Lake Tahoe is an attractive test site because of the exceptional
clarity of the water (60+ feet), thin atmosphere, and infrequent cloud
cover. An area of the lake was selected to extract depth vs radiance
profiles for ALI data, ASTER data and ETM+ data (Figure 6)

![](media/image19.png){width="2.6381889763779527in"
height="4.332677165354331in"}![](media/image20.png){width="2.8811023622047243in"
height="4.3in"}

Figure 6. (Left) ALI bands 2, 1' and 1 in RGB for Lake Tahoe. The land
area has been masked off. (Right) Sub-area of the ALI data used to
produce profiles of depth vs. radiance for ALI, ASTER and ETM+ data.

Plots were created for ALI Band 2, ETM+ Band 2, and ASTER Band 1 (Figure
7). All 3 instruments have similar bandpasses of 0.52-0.60 microns, ALI
and ETM+ are 30m pixel resolution, and ASTER is at 15 m resolution
(resampled to 30m for this analysis). All 3 data sets were converted to
radiance using provided calibration coefficients.

![](media/image21.png){width="4.373622047244094in"
height="4.624409448818898in"}

Figure 7 Depth vs. radiance plots for a profile from the shore of Lake
Tahoe. The 3 similar 0.52-0.60 micron bands for ALI, ETM+ and ASTER are
plotted.

Both ALI and ASTER suggest that the bottom can be seen to a depth of
30'; The ETM+ data are noisier, and maybe allow 20' penetration before
the radiance values become uniform. Looking at the same area with ETM+
Band 1 and the equivalent ALI band shows a similar effect (Figure 8).

![](media/image22.png){width="4.625in" height="4.125in"}

Figure 8. Depth vs. radiance plots for a profile from the shore of Lake
Tahoe. The 2 similar 0.45-0.52 micron bands for ALI and ETM+ are
plotted.

ALI data suggest penetration to a depth of 50', and ETM+ Band 1 data
show no penetration, probably due to the very low signal-to-noise. A
final plot shows the penetration of the ALI far blue band (Figure 9)

![](media/image23.png){width="4.2492125984251965in"
height="4.624409448818898in"}

Figure 9. Depth vs. radiance for ALI far blue band.

The curve of depth vs. radiance becomes asymptotic at about 6140 feet.
This is a depth of 60 feet. The analysis and comparison of ETM+ and ALI
for depth penetration confirms the initial hypothesis that ALI, with its
higher signal-to-noise, and additional far blue band, achieves greater
depth penetration for bands equivalent to ETM+, and the far blue band
gives greater penetration than the traditional blue band.

**Vegetation mapping in the Venice Lagoon**

In the Venice Lagoon, submerged aquatic vegetation maps are used to plan
selective harvesting of benthic macro-algae and all activities of sea
phanerogam plantations. Mapping techniques currently used are based on
in situ observations and aerial photo-interpretations. Results obtained
with multispectral data were limited to the detection of entire
submerged vegetation cover. In this study we compared the mapping
capability of ALI data and ETM+ data to map submerged vegetation and
separate different types of vegetation. Sea truth data was obtained from
boat surveys at the time of the satellite overpasses.

ETM+ data were of limited use in mapping vegetation. Image spectra
derived from the data (Figure 10) show very little distinctions between
macroalgae's and phanerogam's spectral signatures. Only discrimination
of vegetation cover could be achieved.

![](media/image24.png){width="2.746456692913386in"
height="3.356299212598425in"}![](media/image25.wmf){width="3.6263779527559055in"
height="3.297244094488189in"}

Figure 10. (Left) False color ETM+ composite image of Southern Basin in
Venice Lagoon. (Right) ETM+ image spectra of macroalgae and phanerogam;
very little discrimination between species types is seen.

The same areas were extracted from ALI data, and spectral signatures
were plotted (Figure 11). The higher signal-to-noise of ALI compared to
ETM+, and the presence of a far blue band produces signatures that are
distinct and well separated.

![](media/image26.wmf)

Figure 11. Spectral signatures derived from ALI data for lagoon
vegetation types.

Sub-pixel mapping of vegetation types using ALI data was performed using
SSAP method. The submerged vegetation species mixtures generate, for
each pixel, a composite spectral signature. The analysis assumes that
every pixel contains a fraction of the material of interest, and the
remainder contains the background materials. The analysis detects the
material of interest by subtracting fractions of candidate background
spectra. The output is presented in the form of fraction planes (maps)
for each material of interest. Analysis for benthic macro-algae and sea
phanerogams is shown in Figure 12. The analysis used ALI bands 1-5 for
spectral classification. Band 9 was used to mask out the land portion of
the image.

![](media/image27.jpeg){width="2.9866141732283467in"
height="5.624409448818898in"}![](media/image28.jpeg){width="3.1318897637795278in"
height="5.624409448818898in"}

Figure 12. (Left) SSAP classification of ALI data for benthic
macro-algae. (Right) SSAP classification of ALI data for sea
Phanerogams.

The SSAP method also permits mixture maps to be produced. For the 2
types of vegetation, a 3-part classification was created: dominantly sea
phanerogams, dominantly benthis macroalgae, and a mixture of the two
types. This was compared with our sea-truth maps, and the results are
quite satisfactory (Figure 13).

![](media/image29.png){width="5.995833333333334in" height="4.73125in"}

Figure 13. SSAP mixture classification of benthc macroalgae and sea
phanerogam in Venice Lagoon using ALI data.

The comparison of ALI and ETM+ data for vegetation mapping in Venice
Lagoon indicates that the improved signal-to-noise and additional blue
band of ALI compared to ETM+ allow significantly better recognition and
mapping of vegetation types.