4 Classifying the Forest Surfaces in Metropolitan Areas …
53
to contribute according to all its capacities and capabilities in reducing the risk of
natural disasters (Nishani 2017). But at the local level the case is not promising, and
will be discussed further in this chapter during the interpretation of the results.
Finally, this chapter is aiming to contribute to the understanding of disaster
risk which is being strongly marked as the Priority 1 under Sendai Framework for
Disaster Risk Reduction 2015–2030 (UNISDR 2015). Similarly, it is aligned with
the Objective 1 of the Science Plan for Integrated Research on Disaster Risk (IRDR)
by contributing to the characterization of wildfire hazard. More specifically, this
work presents a framework for identifying the hazards, and vulnerabilities leading to
forest fire risk (Objective 1.1), contributing to the forecasting of forest fire hazards
and assessing their risks (Objective 1.2), and developing a dynamic modeling of
wildfire risk (Objective 1.3) (ICSU 2008).
4.2 Methods
This study puts forward a cost-free and rapid method for categorizing the forest
surfaces being closely related to metropolitan areas based on their Wildfire Ignition Probability Index (WIPI) and Wildfire Spreading Capacity Index (WSCI). The
original method applies a multi-criteria (social, environmental, and geophysical)
framework and utilizes commercial software such as ArcGIS for spatial analysis and
Meteonorm for collecting environmental data (Hysa and Ba¸ skaya 2019).
Instead of relying on commercial software, which may not be accessible to a
wider DRR stakeholder, this study utilizes QGIS as an open-source software during
all geospatial analytical phases. Furthermore, data used are mainly gathered from
various free and open-sourced geospatial databases; e.g., E-OBS for environmental
raster data; Copernicus Portal for Urban Atlas-UA, DEM, and Vegetation density;
Open Street Map for transportation network.
Furthermore, in previous studies the method was tested on rural forested lands,
quite remote from the urbanized territories. But in this work, the context of the
metropolitan zone as the selected study area is specific and very sensitive in relation
to the wildfire risk and forest fires regimes as mentioned in the introduction of this
chapter.
4.2.1 Study Area
At this stage, the method is tested on “Durana Metropolitan Area”, covering Tirana
as the capital of Albania and Durres as the largest national port city (Extent coordinates; 19.39–20.24 E, 41.12–41.59 N). Physically the area can be defined as a valley
opening toward the west-facing Adriatic Sea, while its landscape is characterized as
canvas of disconnected ecologies: the terraced hills, foothill villages, a linear urban
53
to contribute according to all its capacities and capabilities in reducing the risk of
natural disasters (Nishani 2017). But at the local level the case is not promising, and
will be discussed further in this chapter during the interpretation of the results.
Finally, this chapter is aiming to contribute to the understanding of disaster
risk which is being strongly marked as the Priority 1 under Sendai Framework for
Disaster Risk Reduction 2015–2030 (UNISDR 2015). Similarly, it is aligned with
the Objective 1 of the Science Plan for Integrated Research on Disaster Risk (IRDR)
by contributing to the characterization of wildfire hazard. More specifically, this
work presents a framework for identifying the hazards, and vulnerabilities leading to
forest fire risk (Objective 1.1), contributing to the forecasting of forest fire hazards
and assessing their risks (Objective 1.2), and developing a dynamic modeling of
wildfire risk (Objective 1.3) (ICSU 2008).
4.2 Methods
This study puts forward a cost-free and rapid method for categorizing the forest
surfaces being closely related to metropolitan areas based on their Wildfire Ignition Probability Index (WIPI) and Wildfire Spreading Capacity Index (WSCI). The
original method applies a multi-criteria (social, environmental, and geophysical)
framework and utilizes commercial software such as ArcGIS for spatial analysis and
Meteonorm for collecting environmental data (Hysa and Ba¸ skaya 2019).
Instead of relying on commercial software, which may not be accessible to a
wider DRR stakeholder, this study utilizes QGIS as an open-source software during
all geospatial analytical phases. Furthermore, data used are mainly gathered from
various free and open-sourced geospatial databases; e.g., E-OBS for environmental
raster data; Copernicus Portal for Urban Atlas-UA, DEM, and Vegetation density;
Open Street Map for transportation network.
Furthermore, in previous studies the method was tested on rural forested lands,
quite remote from the urbanized territories. But in this work, the context of the
metropolitan zone as the selected study area is specific and very sensitive in relation
to the wildfire risk and forest fires regimes as mentioned in the introduction of this
chapter.
4.2.1 Study Area
At this stage, the method is tested on “Durana Metropolitan Area”, covering Tirana
as the capital of Albania and Durres as the largest national port city (Extent coordinates; 19.39–20.24 E, 41.12–41.59 N). Physically the area can be defined as a valley
opening toward the west-facing Adriatic Sea, while its landscape is characterized as
canvas of disconnected ecologies: the terraced hills, foothill villages, a linear urban