XXII ISPRS Congress, 25 August —
1.4 Effectiveness of the work
The urban environment is a very complex and dynamic context,
since it involves a large number of factors which evolve
continuously. On the other hand, most of the processes which
develop in the framework of urban areas are connected with the
physical space. Therefore, measure, analyze and understand the
dynamic processes and their changes along the time, it is
critical to generate accurate spatial and temporal information. It
is necessary that the spatial information could allow the more
precise estimation of the developing phenomena of the urban
areas over a territory. It is important the estimation the rate of
growth in terms of consumption of natural resources, but it is
also required more detailed information on the morphological
characteristics of the urban fabric in order to outline different
patterns of spatial growth and to make it possible to estimate
which kind of urban settlement is moving towards a sustainable
model of development. Even more important is to use
automated tools that allow rapid and detailed analysis over huge
areas and in different geographical contexts. In this framework
the investigation aims to suggest possibilities to improve
analytical tools for analysis and management of the urban and
natural landscape, also supporting the processes of planning
with data continuously updating.
2. LAND COVER CLASSIFICATION THROUGH
REMOTE SENSING TECHNIQUES APPLIED ON
LANDSAT 4-5 TM IMAGERY
2.1 Analyzed data
Data source is provided through the use of USGS Glovis
webpage, and based on Landsat 4-5 TM collection for the year
2011". In general, Landsat satellite provides multi-spectral
images, at 30m of resolution and at different wavelengths,
thermal images at 60m of resolution and panchromatic images
at 15m of resolution. The satellite uses three primary sensors
that have evolved over more than thirty years: MSS (Multi-
spectral Scanner), TM (Thematic Mapper) and ETM+
(Enhanced Thematic Mapper Plus). Table 1 shows with more
details the main characteristics for all the sensors of Landsat.
ETM+ multi-
spectral
Panchromatic] ETM-* thermal TT 2-0.
Table 1. Main characteristics for Landsat satellite imagery.
GLCF Global Land Cover Facility
The images are downloaded in GeoTIF format and the pixel is
identified with a Digital Number (DN) on a scale of 0 to 255.
We have calibrated the images in order to convert the DN in a
value of reflectance which provides values on a scale of 0 to 1.
After calibration process, it was proceed to mosaic all the
necessary imagery apt to cover each of the analyzed areas.
* © LANDSAT Image Copyright 2011, USGS
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
01 September 2012, Melbourne, Australia
Together with the multispectral images based on bands 1, 2, 3,
4, 5, and 7 of Landsat TM 4-5, it was used for this study the
Digital Elevation Models (DEM) with a resolution of 30m, to
provide the physical characteristics of the territory.
2.2 Premise
Spatial resolution, spectral information and advanced
processing techniques are important in order to get the best
results from satellite imagery analysis. One of the main parts of
this investigation is focused on enhance the spectral
information through the generation of additional layers (in
addition to the original information provided by Landsat
sensors) in order to minimize the mistakes of classification
processes. Actually if we work with the six bands of Landsat,
we will get lot of problems in the results of classification,
mostly due to mixing characteristics of land cover classes, and
in particular between soils, and impervious classes. It is because
the spectral characteristics of these classes seem to be quite
similar in certain wavelengths. While vegetation results the
most obvious information in the remote imagery. The leaf of
plant exhibits a strong absorption property in the red band and a
strong reflectance in the NIR. The reflection reduces slightly
from green band to red band and then a reflection valley is
generated. The reflection rose sharply in the NIR and a
reflection peak is formed; a valley again in the SWIR for the
reflection weakens rapidly (Lin et al. 2010). Water shows the
highest reflectance values at the band 1, i.e. the blue band,
whereas gradually decreasing in successive bands to reach the
lowest values at the SWIR bands (Figure 1).
7 N ~~. Soil
eu 2 U impervious
Refiectance
Vegetation
0.45-0.515 0.525-0.605 0.63-0.69 0.75-0.9 1.55-1.75 2.09-2.35
Blue Green Red NIR SWIR SWIR
Wavelength
Figure 1. Trend of Spectral characteristics for four land cover
classes in the case of Landsat 4-5 sensor
Based on the study of the spectral characteristics of the
material, a lot of techniques for specific material abundance
detection and indices of defined characters have been
developed until now. We have taken advantage of these
instruments to generate a multi-indices image, based on 28
indices to reduce the mistakes due to the most common
classification techniques.
2.3 Methodology
2.3.1 Building a Multi-index image: Previous treatments have
been applied such as calibration, to get reflectance values from
the digital numbers (DN) of the GeoTIF images, and
atmospheric correction by using Quick Atmospheric method.
Several images were “mosaicked” together, in order to cover
the areas under investigation which, in our case, refer to the
administrative boundaries of the Autonomous Communities
along the Spanish Mediterranean coast. After that it has been
used several band transformation procedures to extract single
