Proceedings of the Second International Symposium on Fire Economics, Planning, and Policy: A Global View
Wildland-Urban Interface Analyses for Fire
Management Planning 1
Raffaella Marzano,2 Andrea Camia,3 Giovanni Bovio4
Abstract
In Southern Europe wildfires involving settlements are becoming more and more frequent
because of the increasing number of houses and infrastructures located within and adjacent
areas prone to wildfires. This phenomenon has become a real challenge for wildland fire
protection managers and public policy makers.
The work presented describes some results of a research on fire management in wildlandurban interface (W-UI) areas that is being carried out within the WARM (Wildland-Urban
Area Fire Risk Management) Project, funded by the European Commission.
A study area of about 100 km2 has been selected, according to its relevance to the W-UI
problem, nearby Turin, the main city of Piemonte Region, situated in the Northwestern part of
Italy. The characterization of the bio-geophysical environment of the study area through
spatial analysis has been realized. Very high resolution satellite images (Quickbird) of the
study area have been acquired and image analysis procedures with object oriented image
classification approach are being realized. Landscape analysis methods derived from
Landscape Ecology are being employed, selecting specific indicators to be applied to the WUI conditions for the description of the wildfire environment and trying to obtain analytical
tools to optimize planning processes.
Introduction and objectives
Wildland-urban interface (W-UI) environments can be described as composite
systems where various structures (most notably private homes) and other human
constructions meet or are intermingled with forest, wildland and other vegetation
fuels. The wildland-urban interface presents many challenges and concerns for
natural resource managers, one of the most urgent being wildfire management. The
phenomenon of wildfires occurring in the wildland-urban interface has actually
experienced a considerable increase in the last decades.
The purpose of this work, which is carried out within the WARM (WildlandUrban Area Fire Risk Management) Project, funded by the European Commission, is
to provide new tools to enhance natural resource management, planning and policymaking at the wildland-urban interface.
1
An abbreviated version of this paper was presented at the second international symposium on fire
economics, planning and policy: a global view, 19–22 April 2004, Córdoba, Spain.
2
Forest engineer, PhD Student, Dep. Agroselviter, University of Turin, Via Leonardo da Vinci 44,
10095 Grugliasco (TO), Italy. email: raffaella.marzano@unito.it.
3
Doctor in Forestry, Dep. Agroselviter, University of Turin, Via Leonardo da Vinci 44, 10095
Grugliasco (TO), Italy. (Current affiliation: Joint Research Centre, Institute for Environment and
Sustainability - TP 261, I-21020 Ispra (VA) Italy. email: andrea.camia@jrc.it).
4
Full Professor, Dep. Agroselviter, University of Turin, Via Leonardo da Vinci 44, 10095 Grugliasco
(TO), Italy. email: giovanni.bovio@unito.it.
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GENERAL TECHNICAL REPORT PSW-GTR-208
Session Poster—Wildland-Urban Interface Management Planning—Marzano, Camia and Bovio
Among the objectives of WARM Project there is the definition of
methodologies to study and characterize specific components of wildland-urban
areas, including elements of bio-geophysical environment, socio-economic medium,
land-uses, administrative division and fuels. The characterization of these typical
elements and their mapping will provide useful supports for the elaboration of
strategic management planning of wildland-urban interface environments.
In this manuscript results are presented concerning the development of analysis
methods for W-UI environment characterization through Geographic Information
Systems (GIS) and remote sensing techniques, and the subsequent production of
meaningful derived maps. Wildland-urban interface landscape is characterized by a
number of components that have to be identified, described and mapped in order to
obtain useful information for the elaboration of integrated forest fire defense
planning. GIS tools have proved to be really functional for W-UI management,
through their capability of handling in an integrated environment multi-source and
multi-resolution spatial data (Camia and others 2003). On the other hand, the use of
very high resolution (VHR) satellite images provides the possibility of obtaining a
high degree of detail, helping in the definition and mapping of components of the WUI landscape.
Methods
Study Area
The area selected for the study is situated in Piemonte Region and located 15 km
North-West from Turin, the main city of the Region (fig. 1).
Figure 1— Boundary map and general location map of the study area.
This area corresponds to the Consortium of Communes in the mountain area of
Ceronda and Casternone Valley and consists of 5 municipalities with a total
extension of about 90 Km2. It is located at the beginning of Lanzo Valley and it
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Proceedings of the Second International Symposium on Fire Economics, Planning, and Policy: A Global View
Session Poster.—Wildland-Urban Interface Management Planning—Marzano, Camia and Bovio
comprises two valleys: Ceronda Valley and Casternone Valley. The topography is
quite complex and characterized by a range of altitude that goes from 300 m to about
1600 m a.s.l.. The highest elevation belt presents mainly grazing land with forests
typically concentrated on slopes, while the lower and flatter part presents mainly
agriculture. Urban areas are frequently concentrated in the lower part or at the
beginning of slopes. Forest vegetation cover is mainly composed by mixed
broadleaved stands, with a predominance of Quercus spp. Some coniferous artificial
stands are also present, covering a small percentage of the area.
From the analysis of the demographic database of Piemonte Region, the resident
population has increased in the last six years of about 6,7 percent, reaching about
8500 people. This phenomenon is mostly evident in the municipalities of La Cassa
and Val della Torre, since, as a consequence of their position next to Turin, many
people working in the chief town decided to move and live here. Moreover the area is
characterized by an increasing of population during holiday periods and week-ends,
because of the presence of many holiday houses.
As to the W-UI areas, the selected test site presents both situations of classical
interface and intermix. There are in fact villages with a more or less well defined
boundary towards natural vegetation but also clusters of houses and isolated
structures, the latter often completely surrounded by woodland or brushes. There are
a total of 5 small towns (Givoletto, La Cassa, Val della Torre, Vallo Torinese and
Varisella), one for each municipality, and many little villages and hamlets. These
settlements, as previously underlined, are mainly situated in the lower and flatter
parts of the study area, following the development of the valleys.
The fire season in the study area and in Piemonte Region in general is a WinterSpring one, just in agreement with the driest period of the year (Bovio and Camia
1998). Wildfires are mainly concentrated in the months from January to April and
they develop mostly under strong wind condition due mainly to Phön Wind (Bovio
and Camia 1997). An analysis of the historical wildfire series from 1980 to 2001
showed that the study area was interested by 129 wildfires and that 98 of them
involved W-UI conditions (Bovio and others 2002; Camia and others 2002).
Remote sensing applications
Remote sensing applications have been experimented in wildland-urban interface
areas. In particular, considering the nature and characteristics of the phenomenon
under investigation, it has been decided to work with very high resolution (VHR)
satellite images. The possibility offered by these kinds of images to obtain a high
degree of detail can surely help in the definition and mapping of components of the
W-UI landscape. QuickBird is currently the world's highest resolution commercial
satellite providing images at 0.61 m (panchromatic) and 2.44 m (multispectral)
resolution as well as 70 cm pan-sharpened composite products in natural and infrared
colors. We have thus acquired Quickbird images of the study area; both panchromatic
imagery, collected in 11-bit format (2048 gray levels) and delivered in 16 bit format,
and multi-spectral imagery were purchased. This latter consists of four discrete, nonoverlapping bands (Blue: 450 - 520 nm; Green: 520 - 600 nm; Red: 630 - 690 nm;
Near-IR: 760 - 900 nm) at 11 bit depth, delivered in 16 bit.
Object oriented classification
In order to map in detail fuels and land uses in the wildland-urban interface we have
tested object-oriented classification techniques using eCognition software. In contrast
to traditional image processing methods, the basic processing units of object oriented
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image analysis are image objects or segments, and not single pixels. This kind of
classification is based on the consideration that important semantic information,
useful to facilitate classification process, can be derived by image objects and their
relationship, not by single pixels. The software does not classify single pixels, but
image objects that are previously extracted with a process of image segmentation,
called multiresolution segmentation. This procedure permits to extract image objects,
working at different resolutions and creating a hierarchical network. Classification is
conducted in the software by fuzzy logic. Class description are performed with a
fuzzy approach of nearest neighbor or using combinations of fuzzy sets on object
features, applying membership functions (Baatz and others 2001). These two
different approach support respectively different classification procedures, one based
on the selection of typical objects marked as representative samples, the other
applying features for classification based on image objects, addressing a wide variety
of information.
Working with Quickbird images we have at first tried various procedures of
multiresolution segmentation, to extract image objects at different resolutions. Then
we have applied supervised classification techniques to obtain a preliminary fuel type
(according to Prometheus system) and land use map. We have performed the first
classification using only a nearest neighbor fuzzy approach, marking typical objects
as representative samples. These samples correspond to training areas typically
representing a particular land use or fuel type class. The initial results, obtained using
nearest neighbor as unique classifier, were successively improved correcting
inaccuracies in iterative steps by assigning typically wrongly classified image objects
as samples into the right class, after the declaration of sample objects. The resulting
map, an example of which can be seen in the following figure (fig. 2), was checked
through test areas corresponding to ground truth and obtained with a specific field
work realized in the study area.
The map here presented can be considered as a very preliminary result that is
expected to be improved in the following phases of the work. We are now working
on the definition of membership functions to be added to the previous approach in
order to refine the maps. These membership functions are based on the additional
information (intrinsic, topological and context features) which can be derived based
on image objects.
Figure 2—(a) Quickbird multispectral image. (b) Preliminary land use and fuel map
obtained through a VHR satellite image object-oriented classification.
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Proceedings of the Second International Symposium on Fire Economics, Planning, and Policy: A Global View
Session Poster.—Wildland-Urban Interface Management Planning—Marzano, Camia and Bovio
Landscape analysis applications
Landscape analysis methods derived from Landscape Ecology have been tested,
aiming at selecting specific indicators to be applied to the W-UI conditions for the
description of the wildfire environment and trying to obtain analytical tools to
identify common denominators leading to W-UI fire events and to optimize planning
processes for the W-UI fire defense. The methodologies that are being set up will
provide the fire managers with the opportunity to define the W-UI zones and study in
a comprehensive way the spatial relationships among W-UI elements and their
interaction with wildland fires.
There are many different definitions for the term “landscape” depending on the
research or management context. These different interpretations usually include an
area of land containing a mosaic of patches (Urban and others 1987) or landscape
elements, whose boundaries are artificially imposed. They represent the basis (or
building blocks) for the elaboration of categorical maps. Size is not necessarily
important to define a landscape, what must be taken into account is the patches
interacting together and creating a mosaic, whose dimension is depending upon the
phenomenon taken into account.
A disturbance, like a wildfire, has the possibility to spread across a landscape
according to the abundance and arrangement of habitats being susceptible to that
particular kind of disturbance (Lloret and others 2002). The key point of the analysis
that is being carried on is to study landscape heterogeneity and the fragmentation
introduced by the presence of settlements in relation to wildfires, assessing the effect
of a complex landscape pattern, like those characterizing wildland-urban interface
environments, and of its structural composition on fire regime. A basic assumption is
represented by the fact that the fuel arrangement in a territory has a strong influence
on fire spread and that the type of land cover and fuels characteristics are closely
related (Turner and Romme 1994). Working at settlement scale, the effects of
different patch configurations within a reference W-UI landscape in determining
different levels of vulnerability to wildfire are being studied.
To apply landscape concepts and metrics to the research, it has been necessary
to choose a reference landscape and to identify the single patches within this
landscape. It has been decided to focus on settlements and their immediate
surrounding and to concentrate the efforts on them; for this reason it has been
selected a settlement within the study area to experiment procedures based on
landscape ecology. Starting from the boundaries of the selected settlement, a buffer
of 500 m has been realized with GIS techniques. An area of 253 ha was obtained with
this elaboration (fig. 3a). The idea was to obtain polygons (vector format) or grid
cells (raster format) classified into discrete land use classes. Patches were delineated
interpreting aerial photographs and Quickbird satellite images. A divisive approach
was applied (fig. 3b). Each patch was digitized starting from a single patch (the entire
landscape), corresponding to the boundaries of the above mentioned buffer, and then
successively partitioning this into homogeneous patches, a vector map constructed
from digitized lines was obtained.
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Figure 3—(a) The selected “landscape” (the red line identify the settlement
boundaries, while the blue line corresponds to the 500 m buffer). (b) Patch
digitization.
For the initial analyses, eight land use types were selected to identify the
different patches, according to the phenomenon under consideration and the area
characteristics: brushes; fields; grass; grass – trees; house; non combustible;
shingle; trees. The final patch mosaic was then converted from a vector (fig. 4a) to a
raster format (fig. 4b) obtaining a 1 m pixel raster map, in order to apply software
algorithms for landscape metric calculation.
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Proceedings of the Second International Symposium on Fire Economics, Planning, and Policy: A Global View
Session Poster.—Wildland-Urban Interface Management Planning—Marzano, Camia and Bovio
Figure 4—(a) The final patch mosaic (vector format) with its fuel and land use
legend. (b) The final patch mosaic (raster format) its fuel and land use legend.
Selection of vulnerability indicators
Landscape metrics are algorithms that are used to quantify specific spatial
characteristics of patches, classes of patches, or entire landscape mosaics (McGarigal
and Marks 1995). Landscape metrics quantify the pattern of the landscape within a
designated landscape boundary, focusing on the spatial character and distribution of
patches. There are three different analysis levels and consequently three different
metric levels:
ƒ
Patch metrics
ƒ
Class metrics
ƒ
Landscape metrics.
Landscape metrics are usually divided into two general categories: one includes
metrics that are able to quantify the composition of the map without taking into
account spatial attributes, and the other includes metrics that quantify the spatial
configuration of the map, requiring spatial information to be implemented
(McGarigal and Marks 1995, Gustafson 1998).
Once realized a categorical map of different land uses, the goal was to
characterize the composition and spatial configuration of the patch mosaic, through
landscape metrics. Attention was focused mostly on structural metrics, able to
measure the physical composition or configuration of the patch mosaic without
explicit reference to an ecological process (McGarigal and Marks 1995); just in a
following step the functional relevance of the metric value will be interpreted. To
calculate landscape metrics, we made use of FRAGSTATS (McGarigal and Marks
1995) that was applied to the 1 m pixel raster map. For a given landscape mosaic, the
software can compute several metrics for each patch in the mosaic, each patch type
(class) in the mosaic and the landscape mosaic as a whole. Almost all metrics
available with FRAGSTATS have been at first computed for the settlement selected
as study case. The selection of metrics that could provide indication about settlement
vulnerability is right now being realized, in order to find a relationship between
metrics and fire risk. To accomplish this task metrics obtained from real landscapes,
corresponding to settlements located on the territory, are being compared with
simulated landscapes, representing different levels of wildfire risk.
Conclusions
The paper aims at presenting the conceptual and methodological approach that is
being applied for the analysis of W-UI areas in relation to wildfire management. The
final goal of the research is actually the elaboration of useful recommendations about
wildland-urban interface environments and their relation to wildfire risk, in order to
enhance management decision making processes. Some preliminary results were
presented, but the work is still in progress.
Remote sensing and geographical information system techniques, together with
landscape analyses applying concepts and indicators derived from Landscape
Ecology, can be usefully applied to the study of W-UI environments, providing
crucial information for land-use planning and policymaking.
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Acknowledgments
This research was funded by the European Commission - Energy, Environment
and Sustainable Development Research Programme; Project WARM - WildlandUrban Area Fire Risk Management (EVG1-CT-2001-00044).
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