# Conceptualization of territorial resilience potential indicators to coastal hazards through the use of unmanned aerial vehicles

Jérémy Jessin, Charlotte Heinzlef, Nathalie Long, Damien Serre

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Jérémy Jessin, Charlotte Heinzlef, Nathalie Long, Damien Serre. Conceptualization of territorial resilience potential indicators to coastal hazards through the use of unmanned aerial vehicles. Ocean and Coastal Management, 2024, 257, pp.107350. 10.1016/j.ocecoaman.2024.107350. hal-04692591

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# Conceptualization of territorial resilience potential indicators to coastal hazards through the use of Unmanned Aerial Vehicles

Jérémy Jessin¹,²*, Charlotte Heinzlef³, Nathalie Long², Damien Serre ³,⁵

¹UMR EIO, University Polynesie Francaise, Ifremer, ILM, IRD, BP 6570, Tahiti 98702, French Polynesia

2 UMR LIENSs, La Rochelle Université-CNRS, 2 Rue Olympe de Gouges, 17000 La Rochelle, France; nathalie.long@univ-lr.fr

3 CEARC, University Versailles Saint-Quentin-en Yvelines, University Paris Saclay, 11 Boulevard D'alembert, 78280 Guyancourt, France;
charlotte.heinzlef@uvsq.fr

4 Mayane France—MayaneLabs, 75011 Paris, France; damien.serre@mayane.eu

5 ARIACTION, Ecole d'urbanisme et d'architecture du Paysage, Université de Montréal, 2940 chemin de la Côte-Sainte-Catherine, Montréal
(Québec), H3C 3J7, Canada

*Corresponding author – jeremy.jessin@univ-lr.fr

## Abstract:

Major natural hazards in recent decades have led to a revisiting of the concept of resilience, particularly in order to analyze traditional models of response to an unforeseen event and post-crisis management mechanisms. For resilience to be an applicable and operational concept to guide management and inform decisions, it must ultimately be characterized for its assessment. This assessment can be done through the identification of indicators specifically contextualized to the study site and object of study. However, today, operationalizing of the concept of resilience tends to draw on annual census of population or aggregated data, providing a generalized large scale view of the territorial resilience potential. The objective of this study is to identify a table of territorial resilience potential indicators to coastal hazards applied to coastal island territories, supplied by data from UAVs to allow for rapid and site specific data acquisition for repetitive surveys. This acquisition method makes it possible to calculate a number of relevant indicators for assessing the resilience potential of coastal areas. In particular, it allows rapid updating of data following major meteorological events and identification of hot and cold spots of resilience potential of a specific study site. To demonstrate the applicability of this method to island territories, the island of Bora Bora in French Polynesia was used as a case study. Finally, these kinds of results can be fed through a spatial decision support system to help decision-makers choose an adaptation and protection strategy. This will allow the development of risk management strategies to move towards resilient territories, over a long period of time.

## 1.0 Introduction

One of the many consequences of climate change can be observed today by the increase of extreme events and natural hazards both in intensity and frequency (Collins and Sutherland, 2019). While these effects are global, some populations are more at risk than others, most notably, populations that live close to coasts due to sea level rise (SLR), flooding, intense cyclones and hurricanes, erosion, and other associated coastal hazards (Barnett and Campbell, 2010). Island territories can be made up of coastal plains, sometimes quite narrow, such as on volcanic islands,
on which are concentrated the majority of the population and infrastructures such as airports. The
spatial configuration of these island territories means they are more exposed to the hazards of the
sea and ocean and therefore more vulnerable. Small Island Developing Nations are a group of 52
global south countries in 3 different regions (Caribbean, Pacific, and Africa, Indian Ocean,
Mediterranean and South China Sea) that face unique social, economic and environmental
vulnerabilities (Betzold, 2015) and are also exceptionally vulnerable to these coastal hazards,
specifically, floods, marine submersion, and erosion (Meheux et al., 2007). Part of these islands,
are of small sizes and at low elevations (Taylor and Kumar, 2015), and their coastal zones play an
integral role in the settlement and development of the territory. For instance, about 79% of the
French Polynesian population is living less than 1 km from the sea (Andrew et al. 2019).
Additionally, the high climate sensitivity of their physical, ecological, and societal features (e.g.
reef-dependent beach and reef island systems) are driving increased exposure and vulnerability.
And finally, these Small Island Developing Nations are already regularly experiencing extreme
events, such as tsunamis and cyclones (Hoeke et al. 2013; Magnan et al. 2022). While climate
prediction averages on a global scale are accelerating at an alarming rate, the Pacific averages for
SLR are even higher, sometimes reaching even four times higher than the global mean (Gemenne
et al., 2019; Nurse et al., 2014). In conclusion, islands are amongst the most susceptible territories
to climate change specifically because of the concentration of inhabitants, infrastructure,
agriculture, recreational activities and/or tourism in their coastal areas (Oppenheimer et al., 2019).
In fact, two-thirds of the territories most impacted by climate change and experiencing the highest
rates of Gross Domestic Product (GDP) losses due to man-made disasters are island nations, with
annual average GDP losses ranging from 1 to 9%. (Giardino et al., 2018). The high stakes of these
territories in regards to these risks requires the implementation of new management strategies such
as the resilience concept.

Natural hazards can have an important impact on the population and produce significant
material damage. These losses undermine the sustainable development of these territories (Guha-
Sapir et al., 2012). Accurate forecasts and warnings in a form that is readily understood and
educating people on how to prepare against such hazards, before they become disasters, can
improve the protection of lives, property, and the environment (IPCC, 2022; Oppenheimer et al.,
2019). Until recently, the concept of risk management orbited around reacting to a hazard and
rebuilding. However, due to the increase in frequency and intensity of extreme events as well as
the amount of stakes in urban development, risk management methods have had to adjust.
Catastrophes like Hurricane Katrina showed weaknesses in forecasting and prevention, as well as
an insufficient organization of those responsible for crisis management, not only in New Orleans
but on a global scale. These disasters mobilized the world of research to analyze the traditional
models of response to an unforeseen event and the post-crisis management mechanisms which has
allowed for new concepts to be brought forward (Lhomme et al., 2010; Toubin, 2014; Serre et al.,
2016; Heinzlef et al., 2022).

Resilience, a long-standing notion, is a key concept within the field of risk management, that
has now become an imperative. This concept defines the capacity of a territory to return to “normal”
or even improve after a damaging event. An important distinction is made between the ability of a
society to "withstand" an event, with the ability of a society to return to its original state or "normal"
functioning (Masselink and Lazarus, 2019). While the scientific literature varies on the definition
of resilience due to the variety of types of resilience: biologic, political, territorial, occupational
and others. This study defines resilience as the ability of a system and its component parts to
anticipate, absorb, accommodate, or recover from the effects of a shock or stress in a timely and
efficient manner (Heinzlef et al., 2022; Jessin et al., 2022). The concept of resilience often implies
the occurrence of a crisis or damage, however, the measurement of resilience does not wait for a
crisis to occur. Resilience can thus be considered intrinsic to the environment and inherent to the
internal functioning of the system. This is referred to as the Territorial Resilience Potential (TRP).
In this context, a TRP is the potential capacity of the socio-ecological systems to cope with
disturbances, induced by factors, by adapting whilst maintaining their essential functions.

While resilience is widely used as a theoretical concept, it is confronted with complexities in regards to its operationalization due to its multi-disciplinary origin, the complexity and interconnections of dynamic natural systems, the multitude of definitions, as well as the lack of data availability (Cimellaro et al., 2018; Jessin et al., 2022).

There are a variety of tools useful for operationalizing the concept of resilience starting from the phases of data acquisition all the way to data valorization. Studies have been carried out to operationalize this concept by using statistical databases such censuses which can provide information such as population demographics and land use; (Cutter et al., 2008; MacAskill and Guthrie, 2014; Cai et al., 2016; Cutter, 2016; Rus et al., 2018; Serre and Heinzlef, 2018; Song et al., 2018; Zheng et al., 2018; Cariolet et al., 2019; Heinzlef et al., 2019; Assarkhaniki et al 2020; Santos et al., 2020). These databases can allow for the creation of indicators to evaluate the resilience of a territory in regards to natural hazards. These studies tend to provide a global view (spatially speaking) of the territory's resilience, without the possibility of frequent data acquisition. Dynamic environments such as islands and their coasts that are particularly vulnerable to the threats of climate change and can benefit greatly from such updated data. For this reason, this study focuses on the implementation of two spatial tools that can bridge the gap between rapid, site specific data acquisition for repetitive surveys and effective communication of this information while focusing primarily on land use indicators. Unmanned Aerial Vehicles (UAVs) and photogrammetric techniques have shown to be able to produce high spatial resolution, low cost, site specific data at high temporal frequencies (Gonzalves and Henriques, 2015). Data sources today can come in the form of satellite images or national databases and are too costly both in time and price for frequent surveying. They allow for a global overview rather than a zoom on a specific portion of the coast. This UAV data, when paired to the methodology of resilience evaluation has the potential to produce precise, up to date, and repetitive data to define the resilience and to identify key spots of vulnerability on dynamic territories such as littoral zones on islands (Jessin et al., 2023). This analysis by UAV is not exhaustive of all the factors that make up the resilience of a territory but can bring complementary information (Jessin et al., 2023).

After obtaining data from sources of information such as statistical databases and/or UAVs, it is necessary to analyze, structure and store said data to make it available in order to withdraw tangible knowledge that can aid decision-makers in their complex task of providing optimal coastal management solutions (Fabri, 1998; Kardel et al., 2011). The implementation phase is often overlooked within scientific studies today, drastically reducing the efficiency of risk management strategies. This step will help reduce the complexity of such a concept and render it more accessible outside of the scientific community. A recent study (Berrang-Ford et al., 2021) screened 1682 articles on the adaptation to climate change to assess the extent of implementation. This study found that while 62% of papers provided evidence that adaptation efforts can reduce the risk or vulnerability, only 3.4% of papers indicated that the risk reduction outcomes of adaptation responses were formally assessed after implementation (Berrang-Ford et al., 2021). The recent rise of articles on climate change adaptation is documented in review papers (Haunschild et al., 2016; Callaghan et al., 2020; Nalau & Verrall, 2021), implying that while there are increasing efforts to propose solutions to cope to climate change, these studies show there is little implementation phase of their respective projects. Access to and communication of the acquired knowledge are important
factors within this issue and can serve to close the gap between research and action. Spatial decision support systems (SDSS) are an effective approach for agglomerating data from a variety of sources (Fabri, 1998; Kardel et al., 2011). A contextualized SDSS is an effective tool to process raw data into tangible knowledge which will render raw information accessible and more comprehensible to decision-makers, so that they can make informed decisions on strategies at the local scale.

Overall, this article aims to conceptualize a table of TRP indicators specifically acquired by UAVs for the operationalization of the concept of resilience to highlight the ability of socio-ecological systems in island coastal environments to cope with disturbances.

Firstly, this study will agglomerate a list of indicators and their variables. Secondly, the interconnection between the variables themselves and their associated natural hazards will be identified. And lastly, to assess the feasibility of this method, this study will use UAV data acquired in French Polynesia (Bora Bora) as a case study and quantify two of the indicators as an example for the evaluation of the TRP.

## 2.0 Literature Review

### Defining Resilience

The scientific literature varies on the definition of resilience due to the variety of types of resilience; biologic, political, territorial, occupational and others (Holling, 1973). However, throughout the variety of definitions presented, three main essential phases are remain present; anticipation, absorption, adaptation (Holling, 1973; Cutter et al., 2008; Twigg, 2009; Peacock et al., 2010; Chelleri, 2012; Frazier et al., 2013; Cai et al., 2016; Cimellaro et al., 2016; Asadzadeh et al., 2017; Santos et al., 2020).

Before the shock (anticipate), resilience means:

- The capacity to anticipate, especially unpredictable and inconceivable scenarios, in order to ensure the operational capacities of systems and organizations, even in "damaged mode";

- The capacity to manage or preserve the essential functions, structures and organizations;

- The capacity to learn.

During the event (absorb), resilience can correspond to:

- The capacity to resist and cope with a shock;

- The capacity to absorb a shock, which involves a degree of flexibility.

After the shock (adapt), resilience reveals:

- The capacity to recover and rebuild, here or elsewhere;

- A system's capacity to maintain its integrity and return (or bounce back) to either the former state of balance or a new state;

- An individual or community's capacity to adapt, to take advantage of a negative situation and to renew and transform the system. (Twigg, 2009).

Resilience is built up before a disturbance, manifested during the disturbance, and continues to build after the disturbance is over (Cutter, 2016; Folke, 2006; Heinzlef et al., 2022; Holling, 1973; Lei et al., 2014; Lhomme et al., 2010; Meerow et al., 2016). These disturbance can be abrupt and unexpected such as tsunamis and flash floods, or continuous, for instance, erosion and SLR (Masselink and Lazarus, 2019). Resilience is based on the idea that a system requires capacities of anticipation, absorption, or reactive adaptation to shocks or disturbance, particularly in a changing environment, such as coastal environments (Lei et al., 2014).

Coastal island territories are dynamic systems and with complex interconnections between a wide array of factors. That must now learn to balance previously natural environmental dynamics
that have been supplemented by human-dominated systems. Because of this, identifying a TRP requires the resilience assessment to take into account both the fluctuations and the long-term stability of this dynamic coastal environment (Gonzalves and Henriques, 2015). Thus, coastal resilience is defined in this study as the capacity of socio-ecological systems in the coastal environment to cope with disturbances, induced by factors such as SLR, extreme events and human impacts, by adapting whilst maintaining their essential functions (Horton et al., 2018; Masselink and Lazarus, 2019).

## Resilience Assessment Through Indicators

Resilience is a multi-dimensional concept, which requires tools capable of representing different kinds of social, artificial or natural geographic data on the territory. The indicator-based approach is well established for the assessment of geographic phenomena (Gallopín, 1997; Merkle and Kaupenjohann, 2000). An indicator accomplishes two main functions: to synthesize the factors that are typically required to explain a circumstance; and to simplify the understanding and interpretation of the results for the user. An indicator is a quantitative or qualitative measure derived from observed facts that simplify and communicate the reality of a complex situation (Freudenberg, 2003). Indicators reveal the relative position of the phenomena being measured and when evaluated over time, can illustrate the magnitude of change (a little or a lot) as well as direction of change (up or down; increasing or decreasing).

Evaluating the resilience of a territory using indicators will assess the socio-economic and natural capacities of the community/environment to resist, absorb and recover from disruption as well as serve as a tool for territorial decision-making (Fabbri, 1998). Finally, for resilience to be an applicable concept that helps guide management and political decision-making, it ultimately requires the quantification indicators (Pimm et al., 2019). A variety of studies are based on a systemic assessment of resilience through the use of indicators followed by a cartographic approach (Cutter et al., 2008; Cariolet et al., 2019; Lamaury et al., 2021; Rus et al., 2018; Song et al., 2018). This cartographic approach allows for the knowledge to be more visually communicated to decision makers which tends to improve comprehension. Some studies have evaluated resilience by prioritizing indicators such as critical infrastructure, however these studies tend to be technical centric (Serre, 2016). While this evaluation is evidently a crucial aspect of the resilience evaluation, it is also important to integrate other additional components in order to gain a more holistic understanding of the territory. Studies assessing resilience in a holistic manner allow for the categorization of resilience indicators into several broad dimensions: social, economic, institutional, urban, technical, community capital, infrastructural, and natural dimensions. These dimensions defined by indicators such as access to sanitation and electricity, demand of water supply, drainage of infrastructure, and shoreline evolution and surface elevation, respectively (MacAskill and Guthrie, 2014; Cutter, 2016; Cai et al., 2016; Song et al., 2017; Serre and Heinzlef, 2018; Zheng et al., 2018; Santos et al., 2020; Lamaury et al., 2021). The natural dimension is often left out (Song et al., 2018), because the data supplying this dimension generally comes from large databases which do not offer the high spatial resolution necessary for the analysis of this dimension. This higher resolution allows for a finer analysis, facilitating site specific research.

Table 1 provides examples of studies and their respective dimensions in which the indicators have been categorized as well as the data sources that supply said indicators. This table highlights the previous points that the natural/physical dimension is often left out, and most studies that do evaluate the natural dimension tend to draw on annual censuses and to use aggregated data, which does not allow for a zoom on at-risk spots (Cutter, 2008, 2016; Song et al., 2018). This study
aims to bridge the gap by integrating the natural dimension within the resilience analysis through the use of UAVs as the data acquisition source which will additionally allow for the acquisition of site specific data as well as controlling the temporality of the data (i.e. acquiring data before and after a storm) (Jessin et al., 2023).

<table><caption>Table 1: Examples of resilience dimensions and the data sources used for assessment within various studies</caption><thead><tr><td>Reference</td><td>Dimensions</td><td>Data sources</td></tr></thead><tbody><tr><td>Cutter et al., 2010</td><td>Social, economic, institutional, infrastructure, community capital</td><td>Federal census and county wide databases</td></tr><tr><td>Lamaury et al., 2022</td><td>Social, urban, technical</td><td>National census</td></tr><tr><td>Assahkahani et al., 2020</td><td>Social, economic, institutional, infrastructural, environmental</td><td>Nationwide statistical surveys</td></tr><tr><td>Rus et al., 2018</td><td>Infrastructural, socio-economic, organizational</td><td>Commune-wide statistics</td></tr><tr><td>Zheng et al., 2018</td><td>Economic, social, ecological, infrastructure</td><td>City wide statistical yearbook</td></tr><tr><td>Santos et al., 2020</td><td>Socio-economic, Technical, Environmental</td><td>National census</td></tr></tbody></table>

## 3.0 Material and Methods

### *Study Site*

French Polynesia is composed of 121 islands and atolls (71 of which inhabited), containing a wide array of geographic diversity, stretching over 2.5 million square kilometers and 5 archipelagos in the South Pacific Ocean (Figure 1). Today, the population of French Polynesia is roughly around 300,000, of which 69% live on Tahiti, the largest island (ISPF, 2017). The economy is moderately developed and dependent on tourism, imported goods, and the financial assistance of mainland France. The Pacific islands are considered the most highly exposed to natural hazards in the world, with the highest disaster-related deaths, representing 75% of the global mortality between 1970–2011 (Edmonds & Noy, 2007; Guha-Sapir et al., 2012). Coastal areas have played a decisive role in human settlement history and continue to be the main places for settlement, economic activities, and infrastructure development. Coastal tourism (transportation, accommodation, catering, and related services) accounts for 27% of the GDP (ISPF 2017), and pearl farming plays a critical and reputational role (IEOM 2020). Within the islands of French Polynesia, urban development is specifically concentrated within the narrow coastal strip and generally does not go very far up the mountains located right at the foot of the coasts. Roads and infrastructure are also mainly coastal such as the Faaa international airport on the island of Tahiti which is located only 2 m above sea level (Bessat et al. 2006). This is the only international airport of the 5 archipelagos and one of the main source of import/export locations. This role coastal systems play in French Polynesia prompted the selection of the study site. This study focuses on the island of Bora Bora as a case study, more specifically the Matira peninsula of Bora Bora, in the south of the island. This location was selected due to its urban development and high rates of tourism. This zone is the most visited location by tourists on the island, it is composed of public beaches, hotels, restaurants, and local residential homes. This island is dominated by luxury hotels and restaurants specifically along the barrier reef and on the southern tip. Additionally, the Matira peninsula has previously been listed as at risk of erosion and marine submersion by other studies and local officials (Gabrie et al., 1994;
Gairin et al., 2021). Bora Bora is the most famous island within the 5 archipelagos and was crucial in the development of the international tourism industry for French Polynesia.

<figure><img src="image_2.png" /><figcaption>Figure 1: Location of French Polynesia and Bora Bora. Adapted from World Atlas ©</figcaption></figure>

## Selected indicators for Resilience Potential Assessment

In the context of management and evaluation, an indicator provides information to measure characteristics, assess performance and evaluate capacities. An indicator thus provides a description of how to measure an issue. The development of resilience indicators was originally adopted from the science of vulnerability indicators, which argues that indicators are not values, but variables, that are an operational representation of an attribute (Gallopin, 1997, Schönthaler and Von Andrian-Werburg, 2010). An indicator's goal is to simplify the understanding and interpretation of results. An indicator does not, however, reflect the reality of a territory, but rather provides an image of something at a given moment (Maby, 2003). It is, however, a relevant tool for characterizing the evolutionary processes of resilience in the face of risks.
The assessment of the TRP must take into consideration the geographical and even cultural context of the study site. As mentioned previously, coastal island territories have unique specificities (insularity, dynamic, and vulnerable environments), therefore the indicators are selected in regards to the literature review on disaster resilience, as well as the context of coastal island territories and the specific study site. The selected indicators were either created based on the context of the study site or chosen from existing literature. A search was conducted on Google Scholar, ScienceDirect, and JSTOR, identifying studies that evaluated resilience through the use of indicators in order to analyze potential indicators that could be applied to this specific study. The search terms used were: "resilience indicator assessment", "resilience evaluation", "resilience to natural risks", "resilience indicators to coastal hazards" "resilience to floods" "resilience to man-made disasters", and "conceptualization of resilience". The search was limited to articles in English and did not have a date limitation. Articles were first selected on a preliminary comb through based on title and abstract that corresponded to an evaluation of resilience to natural hazards. A second effort was then conducted, which consisted of hand selecting specific indicators within these studies. The indicators selected were only those which could be potentially observed or complemented by a specific data source as well as applied to a the specific territory of coastal island territories.

The originality of this table of indicators lies in the data acquisition sources. Only indicators capable of being identified by UAV or that UAVs would bring complementary data to were selected. This study was able to construct site-specific indicators to represent the different components of resilience. This study recognizes that the agglomeration of these indicators will not be able to measure the entirety of a territory's resilience but simply bring additionally information to its assessment. The indicators are categorized first by dimension of resilience (artificial or natural environment), followed by the variables that constitute each specific indicator and the unit of measure. Other dimensions such as social, economic, and other dimensions were excluded from this study because their data sources could not be acquired by UAV. The artificial dimension deals with the building characteristics of the infrastructures and the land use of the territory (Cutter et al., 2014; Cimellaro et al., 2016; Asadzadeh et al., 2017; Serre and Heinzlef, 2018; Alberico et al., 2020), while the natural dimension deals with the environmental and physical characteristics of the territory (Cutter et al., 2014). Additionally, the contribution of impact an indicator has on the TRP is listed. This column identifies whether the indicator has a positive or a negative impact on the resilience of the territory justified through the scientific literature in regards to a specific hazard. For instance, the presence of groynes/shoreline protective structures variable within the infrastructure indicator was determined as having a positive impact on the TRP because it reduces the impact of erosion, thus protecting the urban development. Since these indicators and variables have been identified in regards to the human perspective, variables such as groynes and shoreline protective structures will consider the advantages of using these structures to combat storms and SLR rather than the natural disadvantages that accompany these structures i.e. blocking sedimentary dynamics. Finally, since disasters occur over a certain period of time, resilience assessment has to integrate this temporality (Boin, 2005). When assessing the temporality of the impact, the time scale is reduced to the disaster's timeline. Three scenarios are considered: before, during and after the disaster (Figure 2). Lamaury et al., (2021) use this methodology and state that not all variables are included in each scenario, for example; the employment situation has little influence before and during the disruption but is necessary for the aftermath, for rebuilding, as well as restarting economic activity (Lamaury et al., 2021). However, when conducting a similar analysis for these indicators and their variables, all variables were found to have an impact during all three phases. Figure 2 demonstrates how the three phases of analysis (before, during, and after)
come into play with previous and future shocks. The “after” phase of a shock is separated from the future risk’s “before” phase. After the shock constitutes of immediate efforts post disturbance such as: exiting shelters and returning home (if possible). The rebuilding efforts will occur in the “before” phase of the previous shock.

<figure><img src="image_3.png" /><figcaption>Figure 2: Conceptual schema of the temporality of a shock. Time is shown as a linear arrow with 3 shocks or disasters represented temporally. Within each shock, three phases are identified (before, during, and after), with T serving as the moment in time in which the shock occurs.</figcaption></figure>

## Data Acquisition by UAV

In order to demonstrate the application of this indicator-based methodology, this study begins the quantification process by calculating two variables from each dimension (artificial and natural environments): beach presence and presence of groynes/shoreline protective structures. This case study serves as an example of the feasibility of this method. The data was acquired by UAV imagery during a larger campaign to acquire data on a variety of different archipelagos and islands in French Polynesia, in December of 2022 (Jessin et al., 2023). The protocol for the UAV data acquisition is adopted from the Long et al., 2016 study, which consist of using commercial UAVs for image acquisition of the study site, geo-referenced to ground control points that were acquired during a GNSS survey. The UAV flew at an altitude of 70 meters, a 5.2 meters per second and captured an image every 2.5 seconds, i.e. at equal distance intervals. The camera was angled at 90 degrees (at nadir) and the percentage of image overlap consisted of 80% on the front and 70% on the side. Four flights were conducted on the Matira peninsula in order to respect the legislation and flight times, which were then all merged together for the post processing procedure. Combining both the UAV images and the GNSS data allows for the application of photogrammetry techniques to create 3D models from 2D images. As results, a digital elevation model and an ortho-mosaic are produced, through the use of photogrammetry software: Pix4D Mapper. Finally, when uploaded into GIS software (ArcGIS), the processing of the data can occur, such as cartographic efforts for spatialization and visualization which will later allow for the indicator quantification.

## Indicator Spatialization Method

Through the use of GIS software (ArcGIS pro), the selected variables were able to be spatialized. For the calculation of the beach presence, a polygon was created all along the coast of the study site. This zone was defined as the area from the water line to the first appearance of vegetation or artificial structure. As for the second indicator identifying the presence of shoreline protection: groynes and shoreline protective structures, this indicator was spatially digitalized and mapped on the orthomosaic obtained by the UAV.

## Synthesis of Procedure for TRP Analysis Through UAV Data Acquisition

Figure 3 present a schematic representation of the procedure adopted during this methodology along with the elements involved, which highlights 3 specific phases: input, data processing and outputs.
The input phase is comprised of the raw data acquisition by UAV which consists of the aerial images taken by the UAV as well as the GNSS survey conducted needed for accurate geo-referencing. Additionally, this raw data source can be complemented by census data from federal/district/local databases as have done other studies that have conducted resilience evaluations, which would thus be added within the input portion of the schematic presented below. Subsequently, these input elements require data processing which leads to the second step. In step 2, the raw data undergoes processing in GIS and/or photogrammetry software, these software provide the tools necessary to create the models and ortho-mosaics as well as the digitizing of the indicators and their variables. Step 3 uses the produced elements: the models and the spatialized indicators and applies the resilience formula and evaluation rules while aggregating the variables in order to create a map of indicators which when aggregated will lead to a cartographic representation of the TRP.

<figure><img src="image_4.png" /><figcaption>Figure 3: Synthesis of Procedure for TRP Analysis Through UAV Data Acquisition</figcaption></figure>

## 4.0 Results

### Defining a Table of Resilience Indicators

This study selected/created the following set of indicators and their variables to be evaluated by UAVs and represent the TRP in islands in regards to the context of island coastal territories around the globe, which included taking into consideration three of the most prominent coastal hazards impacting these territories: floods (fluvial), marine submersion, and erosion (coastal). There are two main resilience dimensions in which the indicators are organized into: artificial and natural environment. These four indicators are based on the existing literature and
selected by their spatialized nature (table 2). The indicators are: Community strength and well-being; Infrastructure; Land; and Ecosystem functionality, and are discussed below. Finally, each indicator is represented by a certain amount of variables.

The analysis of which hazards come into play with each variable will allow for the identification of which factors most impact specific hazards and risks. Aiding decision makers later on to adapt to the specific needs of certain zones within their community. Figure 4 highlights which variables are impacted by which of the three coastal hazards listed previously. Marine submersion is the only hazard to have an impact on all of the variables, while floods and erosion split the rest of the variables (7 and 8 variables respectively). Only the surface elevation, presence of wetlands, and the presence of natural vegetation variables are inter-connected by all three hazards. Table 2 and figure 4 are described and presented below.

## Community strength and well-being

Within the “community strength and well-being indicator,” two variables are identified: bulky debris and presence of sidewalks. Both of these variables are connected to two of the three hazards: floods and marine submersion (figure 4). The bulky debris variable consists of abandoned vehicles, household appliances, furniture, scrap metal etc. These debris contribute to the waste of a territory and are susceptible to being washed away by floods, storms, and/or landslides and potentially blocking the flow of streams and rivers, thus contributing negatively to the TRP (Serre et al., 2016). Bulky debris were assessed as having an impact on the TRP for all three phases of the shock (before, during and after). Knowing the location of these large debris beforehand allows for the identification of zones most susceptible to the movement of dangerous debris during a disaster. After the shock these debris can end up blocking the flow of rivers and streams. The presence of sidewalks variable assumes that sidewalks increase the walkability of a community, thus rendering an area more accessible to the population. This variable is also playing a role before, during, and after a shock by allowing for movement to and from shelters and safe spaces especially in the case of road infrastructure failure. This variable therefore has a positive impact on the TRP (Hu et al., 2018; Fu and Wang, 2018; Tumini et al., 2017).

## Infrastructure

The “infrastructure” indicator is represented by two variables: built and unbuilt spaces and the presence of groynes/shoreline protective structures. Both of these variable are connected to the marine submersion hazard, while the built and unbuilt spaces variable is connected to flooding and the presence of groynes/shoreline protective structures variable is connected to erosion (figure 4). The expansion of built-up areas can generate an increase in impermeable surface areas thus rendering the territory more susceptible to flooding and impacting the TRP negatively (Burton, 2015; Hung et al., 2016; Wheater and Evans, 2009; Tumini et al., 2017; Delgado-Ramos and Guibrunet, 2017). Groynes and shoreline protective structures however, provide protection from extreme events, reducing the impact of floods regarding human stakes, SLR, and storms. This variable has been deemed to positively contribute to the TRP (Burton, 2015; DasGupta & Shaw, 2015; Kammouh et al., 2019). Both of these variables play a role in the first two phases: before the shock (identifies zones less susceptible to flooding and marine submersion) and during (reduced risk in these zones). The built and unbuilt spaces variable also plays a role in the after phase as the reduced flooding causes less destruction and damages, allowing for a faster return to equilibrium. The presence of groynes/shoreline protective structures variable in regards to the “after” phase negatively impacts the TRP because the structures are blocking the sedimentary dynamics,
therefore, in cases of erosion, the beach will not be able to replenish on its own. Thus, due to the
negative impact, the after phase of this variable is not considered to play a role.

## Land

The “land” indicator is represented by seven variables: beach presence, surface elevation, beach slope, presence of coastal shoreline restoration, proximity to rivers, presence of natural vegetation, and the presence of wetlands. The surface elevation, presence of natural vegetation and presence of wetlands variables are connected to all three hazards. The beach presence, beach slope and presence of coastal shoreline restoration variables are connected to marine submersion and erosion hazards, and finally, the proximity to rivers variable is connected marine submersion and floods hazards (figure 4). The presence of a beach provides a buffer zone against coastal hazards protecting the environment and urban development behind it. Therefore the presence of beaches positively contribute to the TRP (Kim and Park, 2018; Toubes et al., 2017). On the other hand, in regards to the surface elevation variable, greater elevation change along a study site is linked to greater slope and therefore more at risk to erosion and landslides, thus the surface elevation variable has a negative contribution on the TRP (Song et al., 2018). Similarly, the greater the beach slope, the more susceptible the beach becomes to erosion and increases wave power, this variable also contributes negatively (Kim and Park, 2018; Toubes et al., 2017). Zones with shoreline restoration activity serve to protect beaches which are natural buffer zones, the presence of these zones thus provides a positive contribution to the TRP (Cutter, 2015; Dasgupta and Shaw, 2015). However, increasing the proximity to a river increases the risk of flooding during extreme events, thus this variable negatively contributes to the TRP (Hung et al., 2016; Kotzee and Ryers, 2016; Toubes et al., 2017). And finally, natural vegetation and wetlands both serves as ecological buffer zones from hazards, specifically floods and marine submersion. Therefore both of these variables positively contribute to the TRP (Beatley and Newman, 2013; Kaye-Blake et al., 2019; Brody et al. 2012; Manyena et al., 2019). These seven variables play a role in all three phases: before the shock (identifies zones less susceptible to their associated hazards), during (reduced risk in these zones), and after (less destruction and damages allows for a faster return to equilibrium).

## Ecosystem functionality

Finally, the “ecosystem functionality indicator” is represented by the presence of windbreaks and environmental plantings variable. This variable is connected to both the marine submersion and erosion hazards (figure 4). Windbreaks and environmental plantings serve as ecological buffers from weather conditions and reduce soil erosion and landslides. Therefore this variable is listed as a positive contribution to the TRP (Burton, 2015; Cutter et al 2008). This variable play a role in all three phases: before the shock (identifies zones less susceptible to harsh conditions and erosion), during (reduced risk in these zones), and after (allows for a faster return to equilibrium).
<table><caption>Table 2: Selected indicators for territorial resilience potential assessment by UAV</caption><thead><tr><th>Dimension</th><th>Indicator</th><th>Variable</th><th>Unit</th><th>Contribution of Impact</th><th>Justification</th><th>Temporality of Impact</th></tr></thead><tbody><tr><td rowspan="4">Artificial Environment</td><td rowspan="2">Community Strength and Well Being</td><td>Bulky Debris</td><td>number</td><td>-</td><td>Serre et al., 2016</td><td>Before, During, and After</td></tr><tr><td>Presence of sidewalks</td><td>:</td><td>+</td><td>Hu et al., 2018; Fu and Wang, 2018; Tumini et al., 2017</td><td>Before, During, and After</td></tr><tr><td rowspan="2">Infrastructure</td><td>Built and unbuilt spaces</td><td>:</td><td>-</td><td>Burton, 2015; Hung et al., 2016; Wheater and Evans, 2009; Tumini et al., 2017; Delgado-Ramos and Guibrunet, 2017</td><td>Before and During</td></tr><tr><td>Presence of groynes/shoreline protective structures</td><td>%</td><td>+</td><td>Burton, 2015; DasGupta & Shaw, 2015; Kammouh et al., 2019</td><td>Before, During, and After</td></tr><tr><td rowspan="6">Natural environment</td><td rowspan="6">Land</td><td>Beach presence</td><td>%</td><td>+</td><td>Kim and Park, 2018; Toubes et al., 2017</td><td>Before, During, and After</td></tr><tr><td>Surface elevation</td><td>m</td><td>-</td><td>Song et al., 2018</td><td>Before, During, and After</td></tr><tr><td>Beach slope</td><td>degree</td><td>-</td><td>Kim and Park, 2018; Toubes et al., 2017</td><td>Before, During, and After</td></tr><tr><td>Presence of coastal shoreline restoration</td><td>km²</td><td>+</td><td>Cutter, 2015; Dasgupta and Shaw, 2015</td><td>Before, During, and After</td></tr><tr><td>Proximity to rivers</td><td>m</td><td>-</td><td>Hung et al., 2016; Kotzee and Ryers, 2016; Toubes et al., 2017</td><td>Before, During, and After</td></tr><tr><td>Presence of natural vegetation</td><td>%</td><td>+</td><td>Kaye-Blake et al., 2019; Manyena et al., 2019</td><td>Before, During, and After</td></tr><tr><td rowspan="2">Ecosystem Functionality</td><td rowspan="2"></td><td>Presence of wetlands</td><td>%</td><td>+</td><td>Beatley and Newman (2013) and Brody et al. (2012)</td><td>Before, During, and After</td></tr><tr><td>Presence of windbreaks and environmental plantings</td><td>%</td><td>+</td><td>Burton, 2015; Cutter et al 2008</td><td>Before, During, and After</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>Figure 4: Inter-connections of Floods, Marine Submersion and Erosion on the 12 variables</figcaption></figure>

In addition to classifying which variables are associated with which of the three most prominent hazards, the inter-connection between the variables themselves has been identified. Natural systems are complex, and risks often result from a combination of multiple factors. Identifying inter-connections between different indicators and their variables allows for a more comprehensive and holistic understanding of the risks involved. Additionally, inter-connected variables can act as early warning signals for potential disasters or adverse events as well as prevent cascading effects where one hazard event sets off a chain reaction of other events. Understanding how hazards can propagate and impact different areas, helps work towards building resilience across systems. This includes designing infrastructures and policies that can absorb shocks and recover more quickly from disruptive events. Hazards and their associated risks often evolve over time, and their inter-connections may change as well. Regularly assessing and identifying new connections helps in adapting long-term risk management plans to stay relevant and effective. In conclusion, understanding the inter-connections of variables in risk management enhances our ability to anticipate, prepare for, and respond to potential hazards. It enables a more integrated and comprehensive approach to risk reduction and builds greater resilience in the face of disasters.

Figure 5 demonstrates these inter-connections between the selected variables for each hazard. In figure 5, each circle represents one specific hazard containing all the associated indicators identified. The direction of the arrow indicates the course of impact, for instance, in figure 5a, the proximity to rivers variable has an arrow pointing to the bulky debris variable, meaning the proximity to rivers impacts the bulky debris variable because the debris closest to rivers are most the susceptible to being carried off in the event of flooding. This analysis was conducted for every variable of every hazard. Within the flooding hazard, the most impacting variable is the proximity to rivers variable. This is most likely due to the fact that rivers tend to be at the origin of floods and thus the proximity to the source can prove to be impactful. Figure 5b demonstrates the inter-connections within the coastal erosion hazard. The most impacted variable is the beach presence. The presence of a beach is impacted by the surface elevation,
beach slope, the presence of natural vegetation, wetlands, windbreaks and environmental plantings as well as the presence of coastal shoreline restoration and groynes/shoreline protective structures. Occasionally, the arrows will go in both directions which indicates that both variables are impacting and impacted by each other. For instance, natural vegetation can serve as a buffer zone for beaches from weather conditions and maintain the integrity of dunes as well as relying on beaches for their own protection. Finally, figure 5c highlights interconnections of the variables in regards to the marine submersion hazard. Beach presence was additionally a prominent variable within this hazard. This variable is impacted by the presence of wetlands, natural vegetation, windbreaks and environmental plantings as well as the presence of groynes/shoreline protective structures. Beaches will be one of the first land elements to be affected or even submerged in the event of rising sea levels and storms, thus explaining why it is the most impacted variable. Surface elevation was also more heavily impacted and impacting than other variables because topography and slope play a major role in SLR, both over the long term and during extreme events.

<figure><img src="image_6.png" /><figcaption>Figure 5: Inter-connections of variables within each risk: Floods (a), Erosion (b), and Marine Submersion (c). The direction of the arrow identifies the course of impact, indicating which variable is impacting and which is being impacted.</figcaption></figure>
Geo-visualization for TRP resilience mapping

This portion serves as an example of application of the aforementioned methodology to assess its feasibility. The following figures cartographically present the spatialization of two variables: the beach presence and the presence of groynes and shoreline protectives structure within the case study of Bora Bora. The UAV imagery and post-processing obtained an orthomosaic and a digital surface model with a root-mean-square error of 10.45 centimeters on the z axis. These models allowed for an updated digitalization of the shoreline, the status of the beach, groynes and other shoreline protective structures along the study zone.

Figure 6 presents the spatialization of the beach presence variable on the Bora Bora study site. This study site was selected due to its strong urban development, thus accompanied by anthropic pressures. This figure shows the presence of a beach (more or less wide) along the entirety of the study site, except at the very tip of the peninsula where the beach has been covered by a shoreline protective structure. Because of the high rates of erosion this coast was reinforced by rockfills and shoreline protective structures. Along the rest of the study site, the beach is generally less than 10m wide, with occasional areas reaching up to 15m. These larger beaches are generally found in front of hotels and restaurants that are most likely actively invested in maintaining the beach for tourism practices. The presence of a beach contributes positively to the TRP (Kim and Park, 2018; Toubes et al., 2017). These zones of wider beaches therefore more positively impact the TRP of this study site.

<figure><img src="image_7.png" /><figcaption>Figure 6: Geo-visualization of the beach presence variable</figcaption></figure>
In continuation of attempting to attest the feasibility of this methodology, this study selected a second variable to spatialize: the presence of groynes and shoreline protective structures. This variable was chosen for two reasons, first, while the beach presence variable was of the natural dimension, this second variable represents the second dimension: the artificial dimension. Secondly, because beach presence was assessed to have an interconnection with the presence of groynes and shoreline protective structures as identified in figure 7. Two main types of shoreline protective structures were identified; perpendicular to the waterline, also known as groynes, (Figure 7a) or parallel (Figure 7b). Figure 8 represents the geo-visualization approach to spatializing the presence of groynes and shoreline and other protective structures on the study site. These structures were most likely put in place in an attempt to protect the shoreline from waves, flooding, and limit sand displacement. While both types of structures, parallel and perpendicular serve to protect the shoreline, they are purposefully identified apart from one another because groynes tend to be used to reduce sand displacement, while the parallel shoreline protective structures are used to absorb wave impact and limit the impacts of SLR (Williams et al., 2016). Figure 8 shows the presence of these structures on the entirety of study site, with a zoom on the southern end because of the concentration of such structures in this specific area and a lack thereof within the rest of the study site. The end of this peninsula is the most exposed as well as where most of the tourism on the island is concentrated. In fact, most of the coast of the very tip of this peninsula has been entirely rebuilt by a substantial shoreline protective structure. The literature (Burton, 2015; DasGupta & Shaw, 2015; Kammouh et al., 2019) associates zones in the presence of such structures with a higher territorial resilience potential because of the protection it provides to the urban development. The southern tip of the study site is the most dense in terms of presence of groynes and shoreline protective structures, thus this area could be considered to have a higher TRP.

<figure><img src="image_8.png" /><figcaption>Figure 7: Examples of the two main types of shoreline protective structures found on the Bora Bora study site. Perpendicular (a) and parallel (b) to the water line.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Figure 8: Resilience potential for presence of dykes and embankments indicator on Bora Bora</figcaption></figure>

Figure 5 identified interconnections between the variables, including a link between beach presence and groynes/shoreline protective structures. More specifically, that groynes and shoreline protective structures have an impact on beach presence. When combining the spatialization of both variables this becomes more evident. Figure 9 presents the same southern portion of the study site as in figure 8 where these variables come into play with each other. The beach of the western coast is much more eroded when compared to the eastern coast. As mentioned before, there are two types of structures, perpendicular groynes and parallel structures (in this case) made of rockfill, which tend to accomplish different goals (sand displacement vs. limiting waves and SLR impacts). The groynes on the east coast are in good condition made up of solid cement while the ones on west coast are in worse conditions, most likely withered away by weather conditions. The groynes are made up of loose rocks that are often partially or entirely submerged underwater (as evident by their irregular shapes and sizes
in figure 9). The structural condition of the groynes plays a large role in their functionality as they tend to no longer serve their purpose when not properly maintained (Williams et al., 2016). Additionally the western coast is more parallel protective structures, which prevents beaches being replenished by sedimentary deposits, which most likely also contributes to the lack of beach on this coast. The western coast is almost entirely composed of residential houses while the eastern coast is (separated by a street and 2 meter stone wall) is home to a luxury hotel. This dichotomy most likely explains the well maintained groynes and therefore a more present beach. The tourism industry relies on the beach and has the financial capacities to put forward resources to protect it, as well as even possibly recharging the beach with foreign sand.

<figure><img src="image_10.png" /><figcaption>Figure 9: Spatialization of interconnections between beach presence variable and groynes/shoreline protective structures variable</figcaption></figure>
## 5.0 Discussion

This study has produced a table of contextualized indicators for the assessment of territorial resilience potential (TRP) of oceanic island coastal territories through the use of UAVs. This table not only identifies the indicators but the dimensions, variables, units of measurement, contribution of impact to the resilience potential, references to the justification by the scientific literature and lastly, the phase of resilience temporality in which it occurs. Additionally, this study attempted to demonstrate the feasibility of using UAVs as a data source for resilience assessment by geo-visualizing two different variables, one for each dimension.

### *Benefits and Advantages*

The objective of this study was to introduce an original data source to a risk management strategy that would allow for the acquisition of high spatial resolution data, at high temporal frequencies. Obtaining site specific data would help decision makers choose appropriate policies for specific locations within their community, especially when faced with the ever increasing frequency and intensity of coastal hazards due to climate change. With the evolution of the resilience concept and technologic advancements in the field of UAVs, the link between the two has become feasible however relatively undocumented within the scientific literature. While further efforts are required to test this method to the full extent, this study serves as a baseline study in order to demonstrate the feasibility of using UAVs for the assessment of the territorial resilience potential.

There are a variety of advantages when adopting this method. Firstly, this assessment allows for a holistic approach by incorporating different dimensions which enables the previously technical-centric efforts to extend their analyses from critical infrastructure (ex. communication, energy, transportation infrastructure). This multi-dimensional assessment approach allows for the development of other types of resilience such as the natural dimension which is often left out in resilience evaluation studies. Secondly, as mentioned previously, the resilience assessment method can prove to be a complex concept due to its many definitions, multifaceted nature, and weighted indicators. This complexity can also prove difficult to integrate local actors outside of the scientific domain. The simplified methodology (i.e. non-weighted indicators) proposed within this study can facilitate the comprehension of the resilience concept for local stakeholders and users of the evaluation model which would allow for more collaborative measures. Subsequently, a handful of studies (Cutter et al., 2008; Jessin et al., 2021; Song et al., 2018) have argued that one of the major limits of the indicator based method is the availability of input data in a given territory, specifically in global south countries where annual census and large national databases are not as complete or accessible. UAVs would provide additional data that could be renewed more quickly than the institutions in charge of to do it, specifically in regards to land use. Additional benefits from using UAVs are: relatively cheap data acquisition, site specific data, allows for repetitive surveys, adaptability, rapid deployment, and high resolution data (Jessin et al., 2023). These factors allow UAVs to serve as efficient tools and have the potential to bring useful data for a resilience assessment that will supply local authorities and decision-making bodies with a more specific knowledge of their territory.

### *Limitations and challenges*

There are additionally some shortcomings to this method. First, when considering the impact a variable or indicator has on the resilience, there can be several answers depending on the perspective, the type of island, and the hazard being considered. More concretely, the indicators simply identify a negative or positive contribution to the TRP. When going through the literature to justify an indicator, at times the interpretations of the indicator/variable's
characteristics lead to different or even contradictory interpretations depending on the spatial,
political and/or cultural context. For instance, in regards to the presence of groynes and
shoreline protective structures, this study chose to assign this variable a positive contribution
of impact on the TRP. This is due to the shoreline protection and short term limitation of sand
displacement that these structures provide, serving as a buffer zone, protecting the urban
development behind them (Burton, 2015; Kammouh et al., 2019). However, these structures
can also have a negative impact on the natural environment of a territory by increasing erosion
(Muthusankar et a., 2017)). Some of these structures can modify hydro-sedimentary dynamics,
which in some cases can lead to erosion. UAVs, while providing numerous advantages, have
their shortcomings as well, such as; reduced spatial coverage (compared to manned aircrafts or
satellites), various laws and regulations involving UAV flights (depending on the country), and
certain geo-referencing complexities (outlined in Jessin et al., 2022).

Another challenge to consider is that some of the variables are going to be more or less
present on certain islands due to their economic status, local risk perception, or legislation. For
instance, the bulky debris variable, while very present on islands in French Polynesia will not
be a factor on certain islands that have the resources to implement services and systems that
collect and sort bulky debris in allocated locations.

Assarkhaniki et al., 2020 argued that the initial challenge to comprehensively incorporating
resilience measurement is the lack of a common understanding about resilience. The problem
is the existence of several overlapping definitions and dimensions. This complexity can
additionally cause a lack of involvement from the local community. The inclusion of local
actors allows for the acquisition of cultural and applicable knowledge only available through
the association of these local actors. Therefore, a collaborative approach, prioritizing open
access data and communication with local actors would provide a solution to these issues. These
factors do however require a long term approach and the constant updating of data and result,
one of which can use UAVs to remedy the issue, the other however can benefit from the
implementation of a spatial decision support system. UAVs and spatial decision support
systems are both tools capable of filling the gap of lack of data and lack of effective
communication within the scientific domain. While certain limitations are present within this
resilience assessment method, these tools have the potential to serve as solutions and fully
operationalize the concept of resilience.

## Perspectives and Link to Resilience Observatory

As mentioned before, this study serves as a foundation for the introduction of UAVs in the
TRP assessment method. The next step is to pair this spatialization effort with resilience
evaluation rules. These rules paired with the quantification of the variables would assign a
numerical score of the TRP. Additionally allowing for the cartographic representation of the
contribution of impact on the TRP. Essentially creating a map that combines all the variables
to physically show which zones have a more positive or negative resilience potential.

In order to promote territorial collaboration, this methodology is anticipated to be
connected to a localized resilience observatory. This study serves as a foundation for this project
as it would finalize the cycle of data collection by providing up to date, accessible,
agglomerated knowledge in order to assist decision makers. The next step will be to provide
tools that are capable of linking the previously acquired data to such an observatory. Tools such
as GIS approaches are useful for the geo-visualization aspects but also as concepts such as the
participative GIS. The utilization of the Resilience Observatory as a spatial decision support
system in French Polynesia is still in the embryonic phase. Some studies have been conducted
on the operationalization of the concept of resilience and its application into an observatory
(Bourlier et al., 2021; Heinzlef et al., 2022; Heinzlef & Serre, 2020; Heinzlef & Serre, 2022;
Jessin et al., 2022; Jessin et al., 2023; Lamaury et al., 2021; Serre & Heinzlef, 2021). These
studies have begun identifying the role that the Resilience Observatory will play, its internal
functions, existing strategies and tools aiming at facilitating the operationalization of the
concept and how it will valorize data to support said observatory.

**6.0 Conclusion**

The operationalization of the concept of resilience is a complex endeavor that requires a wide array of data in order to represent a variety of factors at play. The concept of resilience often implies the occurrence of a crisis or damage, however, the measurement of resilience does not wait for a crisis to occur. Thus, this study analyses the territorial resilience potential which is defined as the potential capacity of the socio-ecological systems to cope with disturbances, induced by factors, by adapting whilst maintaining their essential functions. The use of indicators to categorize and assess a territory's resilience has been documented, however tends to utilize large databases that do not allow for site specific data, giving a rather global view (spatially speaking) of a territory's resilience. The use of contextualized, site specific indicators would allow for a zoom on hot and cold spots within a territory facilitating the decision making process for local decision makers.

Acquiring data with this level of precision however is not commonly accessible or in some cases does not even exist. UAVs have shown to be a data acquisition source that provide high resolution data capable of repeating frequent surveys at a relatively low cost. This study has attempted to bridge the gap between this data source and territorial resilience assessment methods, by conceptualizing resilience indicators capable of being acquired by UAV. By using Bora Bora as a case study, this study provided a table of territorial resilience potential indicators applicable to coastal island territories, composed of four indicators each with its own variables in order to test the feasibility of this method. These variables underwent an analysis which combed through the inter-connections in regards to the coastal hazards at play as well as the inter-connections between the variables themselves. This revealed that a couple of variables play an important role in a territory's resilience potential to coastal risks. Beach presence, surface elevation, and the proximity to rivers, are notably the variables most impacting of, or, impacted by other variables. In order to further the demonstration of feasibility of using UAVs for resilience assessment, ortho-mosaics and digital surface models were produced from the acquired UAV imagery. Two variables are spatially identified: beach presence, and the presence of groynes/shoreline protective structures (one from each dimension).

This method comes with its limitations: complexity within the concept causing a difficulty
in comprehension especially when communicating with actors outside the scientific world as
well as varying factors which impact the resilience of a territory differently causing the
contribution of impact to be based on perspective. A spatial decision support system would help
agglomerate the acquired knowledge and render this data more accessible to local actors as well
as providing collaborative approaches, reducing some of the limitations of this methodology.
While this method does not permit for a complete assessment of a territory's resilience
potential, it does provide data that has been previously inaccessible and adds complementary
information to the resilience assessment, ultimately aiding decision makers in rendering our
communities stronger when faced with disaster.
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