Rafael Bailon-Ruiz

Arthur Bit-Monnot

Simon Lacroix

Planning to Monitor Wildfires with a Fleet of UAVs

We present an approach to plan trajectories for a fleet of fixed-wing UAVs to observe a wildfire evolving over time. Realistic models of the terrain, of the fire propagation process, and of the UAVs are exploited, together with a model of the wind. The approach tailors a generic Variable Neighborhood Search method to these models and associated constraints. Simulation results show ability to plan observation trajectories for a small fleet of UAVs, and to update the plans when new information on the fire are incorporated in the fire model.

I. INTRODUCTION

When wildfires occur, the information on the fire front is key for the responders. Its extent, strength and spreading speed are indeed essential parameters to know in order to define efficient countermeasures. Gathering such information is a difficult task: wildfires may extend over tens of square kilometers, often in remote areas, and their spread is governed by various parameters, among which some are known with large uncertainties -in particular the wind on the ground and the fuel.

Satellite imagery can bring useful information over the whole fire extent [START_REF] Martinis | Mapping burn scars, fire severity and soil erosion susceptibility in southern France using multisensoral satellite data[END_REF]. Firefighters can resort to helicopters to gather more timely and precise information such as the flame height, but such operations are costly and risky. A fleet of fixed-wing UAVs equipped with thermal infrared cameras can be more agilely deployed, and can be used to monitor the evolution of wildfires [START_REF] Ollero | Multiple eyes in the sky: Architecture and perception issues in the comets unmanned air vehicles project[END_REF], [START_REF] Casbeer | Cooperative forest fire surveillance using a team of small unmanned air vehicles[END_REF].

This article presents an approach to plan wildfire observations for a fleet of UAVs (Fig. 1), in order to provide firefighters with a map of the fire front. This problem raises numerous challenges: the scales of time and distance involved are large, the process to monitor is dynamic, the influence of the wind is predominant for both the UAV and the fire propagation, the UAV motions and observations are constrained, and the various sources of uncertainties impose the revision of the plans after the incorporation of new observations into the fire map.

Approach and contribution: Given initially known characteristics of the terrain, initial observations of the wildfire, e.g. as provided by a network of ground fire sensors or satellite imagery, and the wind, a propagation model predicts the fire front for the next hours. These predictions are exploited by a Variable Neighborhood Search (VNS) approach, that plans the trajectories of the UAVs to observe the fire front. Fire observations are integrated into a fire map, which is used to update the observation plans.

The main contribution of the paper is the tailoring of the VNS to cope with realistic models and constraints of the Fig. 1: A fleet of UAVs equipped with thermal cameras fly over a wildfire, at a leveled altitude, where the wind is considered steady and spatially homogeneous. On the ground, the terrain shape generates more complex winds, which govern the spread of the fire front. The lowest layer represents the fire map that incorporates the observations of the UAVs (in red) and the fire propagation predictions (in yellow). This map is meant to be used by the firefighters, but is also the basis upon which further observation trajectories are planned. considered application. A generic VNS approach proceeds by evaluating local modifications to a plan, which is a sequence of oriented waypoints, linked by UAV trajectories accounting for kinematic constraints and the wind. Local plan updates include slight modifications of waypoints, and waypoint insertions, which are sampled on the basis of the fire front propagation model. The impact of these plan modifications is evaluated by the predicted fire observations after having updated the UAVs trajectories they entail.

Outline: The next section reviews related state of the art, showing that little work has been devoted to planning the observations of spreading phenomena. Section III presents the UAV and fire models used in the planning problem formulation. Section IV is the heart of the paper: it depicts how the generic VNS approach is tailored to our specific use-case and associated models. Section V presents results obtained with realistic simulations.

II. RELATED WORK

Various research projects have tackled the problem of wildfire remote monitoring. For instance the COMETS European project addressed the use of a heterogeneous fleet of UAVs for cooperative fire detection [START_REF] Merino | Cooperative fire detection using unmanned aerial vehicles[END_REF]. The goal was to detect, locate and monitor fire spots with aerial vehicles equipped with infrared cameras. More recently, ASAPTERRA focused in automated information processing in the context of hazard response, exploiting in particular satellite imagery for rapid wildfire mapping [START_REF] Martinis | Mapping burn scars, fire severity and soil erosion susceptibility in southern France using multisensoral satellite data[END_REF].

The wildfire spread phenomenon is typically described as a front or boundary propagation, and the robotics literature about tracking such phenomena is large. In the case of wildfire monitoring, most approaches are based on distributed boundary tracking, resorting to automatic control solutions. [START_REF] Casbeer | Cooperative forest fire surveillance using a team of small unmanned air vehicles[END_REF] deals with communication constraints of a fleet of UAVs flying along a supposed circular fire front. [START_REF] Pham | A Distributed Control Framework for a Team of Unmanned Aerial Vehicles for Dynamic Wildfire Tracking[END_REF] depicts a wildfire monitoring system using rotarywing UAVs, focusing both on coverage and tracking. A model-free boundary tracking algorithm have been proposed in [START_REF] Saldaña | Cooperative prediction of time-varying boundaries with a team of robots[END_REF]. Automatic control approaches are reactive solutions: since wildfires can last up to several days, it is also necessary to plan solutions that drive the monitoring resources over the long term.

The observation problem we consider resembles the Orienteering Problem (OP) [START_REF] Chao | A fast and effective heuristic for the orienteering problem[END_REF]. The solution of the OP is a path visiting vertices of a graph, such that the duration of the path is less than some time budget and a collected score is maximized. There are however some essential differences for the case of wildfire monitoring. First, every location traversed by the fire is a vertex that can be visited: even when the area of interest is discrete, the number of locations is huge, and so are all the possible trajectory combinations. Also, the utility function is not a linear combination of individual rewards, because the value of observing one particular location is time-dependent and highly correlated with nearby observations. While many extensions to the OP and associated solvers have been devised (as surveyed in [START_REF] Vansteenwegen | The orienteering problem: A survey[END_REF]), none consistently handle all requirements for wildfire monitoring. In addition, fixed-wing UAVs are subject to complex non-linear motion equations due to aerodynamics, atmospheric conditions and actuator performance bounds ( [START_REF] Penicka | Dubins orienteering problem[END_REF] tackles the OP with such constraints).

III. MODELS A. Fixed-wing UAV motion

We consider a UAV v flying on a horizontal plane (x, y), at some constant altitude z, in which there is constant horizontal wind field (V wx , V wy ). The UAV flies at a constant airspeed V a , and its heading ψ is controlled with a bounded turning rate |u| ≤ ψmax . The kinematic model of the UAV is:

ẋ = V a cos(ψ) + V wx ẏ = V a sin(ψ) + V wy ψ = u
In the absence of wind, the shortest path between two oriented points for such a vehicle is given by Dubins trajectories, composed of maximum curvature sections (arcs of circle) and straight segments [START_REF] Dubins | On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents[END_REF]. However, this result does not apply when the vehicle is subject to wind. An iterative optimal motion planning algorithm that accounts for a constant wind is proposed in [START_REF] Mcgee | Optimal path planning in a constant wind with a bounded turning rate[END_REF]. To compensate the wind-induced drift, the problem is reformulated as follows: The UAV is commanded to perform the dashed trajectory to reach the virtual target. Because of the effect of the wind, the real trajectory will be the solid line and hence attain the desired end point. a ground frame, subject to wind, and an air frame, windindependent, are introduced. The fixed end point in the ground frame then becomes a moving virtual target in the air frame, with a velocity equal to the wind speed in the opposite direction. The goal of the reformulated problem is to find a Dubins path that reaches the moving virtual target (Fig. 2). This path results in the optimal trajectory to the original target in the ground frame. We use this result to find the shortest path between two oriented points during the planning phase.

B. Wildfire propagation

A wildfire starts from one or more ignition points and then spreads through the surrounding terrain. The direction and speed of spread depend on numerous factors linked to the physics of the fire, the vegetation (fuel), the terrain shape and the wind at the terrain level. Wildfire propagation is a too complex phenomenon to be modeled in exact terms based on thermodynamic and combustion laws. Instead, scientists have defined empirical models, that relate the fire propagation speed and direction to the terrain, fuel and wind. A very common propagation model is the Rothermel model [START_REF] Rothermel | A mathematical model for predicting fire spread in wildland fuels[END_REF], used in most of the support software tools for firefighters.

The Rothermel model exploits information about terrain slope, fuel parameters, and wind speed and direction. Terrain slope is static and known, fuel parameters are defined by the vegetation type and humidity, and can be considered static [START_REF] Scott | Standard fire behavior fuel models: a comprehensive set for use with Rothermel's surface fire spread model[END_REF]. The wind at the terrain level is estimated on the basis of a steady wind at a given altitude using WindNinja 1 that exploits the models presented in [START_REF] Forthofer | A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. part I. model formulation and comparison against measurements[END_REF] and the digital elevation map of the terrain.

Our wildfire propagation simulation relies on building a propagation graph over a discrete environment, as proposed by [START_REF] Finney | Fire growth using minimum travel time methods[END_REF]. The fire map is modeled by a 25m resolution Cartesian grid, matching the digital elevation map resolution.

The shape of the fire front depends on the main propagation direction and the rate of spread from one cell to another [START_REF] Anderson | Predicting Wind-Driven Wild Land Fire Size and Shape[END_REF], both computed using Rothermel's method. Propagation is initialized by setting the ignition time of one or multiple cells. A cell ignition time is computed as: ignition(x, y) = min (xn,yn)∈N (x,y) {ignition(x n , y n ) + travel-time((x n , y n ), (x, y))} where N (x,y) are the neighbor cells of (x, y). This process can be seen as constructing a propagation graph, built using Dijkstra's shortest path algorithm.

Once all the neighbor cells are set on fire, the fire front moves away from the current cell. As a simplification, we consider that a cell ceases to be on fire when the fire front moves forward: ignition end (x, y) = max (xn,yn)∈N (x,y) {ignition(x n , y n )}. This implies that fire at cell (x, y) is observable in the time range [ignition, ignition end ]. In other terms, for any given time t there is a set of cells forming a level curve (isochrone) of the fire propagation manifold (Fig. 3).

C. Fire observation model

UAVs are equipped with a downward looking thermal infrared camera used to gather geo-tagged images of the fire. Using a mapping algorithm, the pixels of an image labeled as on fire can be projected to a digital elevation map to create an observed fire map. As for most UAV mapping processes, only images acquired when the camera is pointing close to the nadir are processed: the size of the area seen by the camera depends on the field of view, the UAV position, and the angle at which the camera is pointing. Fig. 4 shows an example of the observations provided by a flight over a simulated fire propagation, in which the camera footprint at nadir is 100 × 75m (4 × 3 grid cells).

The ignition time range depicted in Section III-B defines a time slack for the UAV to observe a cell on fire. Incidentally, the level curve geometry of the fire front combined with the size of the camera footprint makes the observation model robust with respect to errors in the trajectory tracking.

IV. PLANNING OBSERVATIONS

Our approach to plan the wildfire observations builds on the VNS metaheuristic that has been applied to numerous combinatorial optimization problems in Operations Research [START_REF] Hansen | Variable Neighbourhood search: Methods and applications[END_REF]. VNS algorithms are built on a sequence of neighborhoods, where each neighborhood defines a local modification to the plan allowing the generation of closely related plans (neighbors). A simple neighborhood is for instance the swap of two sequenced places to visit.

The principle of VNS algorithms is to repeatedly apply: 1) A descent phase that exploits all neighborhoods to find and apply local improvements to the current plan until no improvement is found. 2) A perturbation phase aiming at escaping local optimums reached during the descent phase. In our case, we define the plan as a sequence of waypoints to reach for each UAV. The fire observations are derived by an analysis of the Dubins trajectories that link consecutive waypoints.

One of the key benefits of VNS is its genericity and adaptable definition. In particular, the observation plans are iteratively built by the VNS process for the whole set of UAVs: the problem of allocating UAVs to areas to observe is implicitly solved, and does not require any specific process. Also, as a VNS algorithm works by applying small incremental improvements to a plan, it can be stopped at any time or restarted from an existing plan.

The challenge of a VNS approach to solve a given problem resides in its formulation and in the definition of a good set of neighborhoods for solving it in reasonable time. This section depicts the way the VNS is tailored to our problem2 .

A. Problem formulation Definition 1 (Waypoint): A waypoint w is an intermediate point of the trajectory that a UAV has to reach. A waypoint is represented by a tuple (x, y, ψ) where x, y, correspond to East/North coordinates with respect to a reference frame 3 , and ψ is the course angle.

Definition 2 (Trajectory): A trajectory T is defined as a tuple (v, t 0 , W ) where v is a UAV model as depicted in Section III-A, t 0 is the start time and W = w 0 , . . . , w n an ordered sequence of waypoints.

Considering the motion constraints of v and the given t 0 , every waypoint w in the trajectory has an associated time t(w). This is calculated by accumulating the travel time between waypoints with Dubins paths. The start, w 0 , and end, w n , waypoints of a trajectory are located at the same position to denote a round trip. Given C the set of cells ignited during the flight windows of π, our objective is to maximize the total information gathered over all cells in C. We denote utility(π) as c∈C 1 /min o∈π dist(c, o). The utility brought by an observed cell, o, depends on observations already in the plan: if there is already a nearby observation in the plan, its utility will be low. This formulation captures the important spatial correlation of ignition times in the context of wildfire monitoring.

A plan π is valid only if for every T i and F i the following conditions are respected:

• w 0 , . . . , w n is a feasible ordered sequence of waypoints for v, meaning that each pair of consecutive waypoints is connected by a valid Dubins trajectory. In order to focus the search to trajectories close to the fire front, we use an iterative process projectff ("project on fire front") that exploits the fire propagation graph depicted in Section III-B. Given a waypoint w i of a trajectory and a waypoint w to project, projectff(w i , w) returns a waypoint w such that t(w ) ∈ [ignition w , ignition w end ], that is, a waypoint w which is on the fire front when arriving from w i .

Given a random waypoint w and a trajectory T with waypoints w 0 , . . . , w n , we construct a neighbor for each i ∈ [0, n-1] by (i) inserting the w from projectff(w i , w); and (ii) for each j ∈ [i, n-2], replacing w j+1 by projectff(w j , w j+1 ). In a nutshell, this inserts a new waypoint in the trajectory and then updates all subsequent waypoints to make sure they are still on the fire front. This procedure is illustrated in Fig. 5.

2) Shuffling: Typical VNS implementations include some neighborhoods that remove, replace or exchange waypoints between two trajectories. However, we found those to be inefficient in our setting, due to the mostly continuous trajectories in which a waypoint is best considered together with the preceding and following one. Instead, we do shuffling to provide similar benefits at a larger scale.

Definition 6 (Shuffling): A shuffling function f (π, k) : Π × N → Π produces a new plan by introducing a perturbation into the plan π. This perturbation is dependent on the current iteration k of the search.

The shuffling function used here removes a random fraction of each trajectory in the current plan, with the objective of escaping local extrema.

3) VNS Algorithm: The VNS algorithm takes as inputs a sequence of neighborhoods, a shuffling function, a maximum run time, and an initial plan.

The initial plan π init is built by taking an empty trajectory (v, t min , w 0 , w n = w 0 ) for each flight window. It is easy to see that such a plan is valid as the start time and the round trip conditions are respected.

Given π init , the descent phase of VNS tries to generate plan improvements by systematically and sequentially trying all neighborhoods [N 1 , . . . , N n ] until a neighborhood N i provides an improvement. Particular neighbor plans of N i are computed by the gen-neighbor function.

Definition 7 (gen-neighbor): Given a plan π and a neighborhood N , the function gen-neighbor N (π) returns either (i) a valid plan π ∈ N (π) such that utility N (π ) > utility N (π), or (ii) nil if the neighborhood failed to generate an improving neighbor.

When the neighbor plan computed by gen-neighbor gives an improvement, the current plan is updated and the process restarts from the first neighborhood N 1 . When gen-neighbor is not able to generate an improvement for any N i , the best plan found so far is perturbed by the shuffling function and the descent phase restarts from the first N 1 . This process is repeated until a maximum runtime is reached, at which point the best plan found is returned.

V. RESULTS

Fig. 2 :

 2 Fig. 2: Dubins trajectory under the presence of constant wind.The UAV is commanded to perform the dashed trajectory to reach the virtual target. Because of the effect of the wind, the real trajectory will be the solid line and hence attain the desired end point.

1Fig.

  Fig. Result of a fire propagation in a mountainous area. The background color indicates the heights of the digital terrain map, level curves indicate ignition times labeled in minutes relatively to the fire start, denoted as a red dot. Arrows represent the local wind speed and direction. The time required to compute this propagation is about 11s on an Intel Core i7 PC running at 2.70GHz.

Fig. 4 :

 4 Fig. 4: Observations provided when the UAV is following the trajectory in blue over a fire propagation model. Green patches are the cells that have been observed while on fire, the gray patches are the other observed cells. Observations are only made when the UAV is flying along straight lines.

Definition 3 (

 3 Flight Window): A flight window (v, T, d max , [t min , t max ]) represents the opportunity for the UAV v to make a trajectory T and whose duration is at most d max . The trajectory should start and end within the time window [t min , t max ]. Definition 4 (Plan): Given a set of flight windows {F 0 , . . . , F m }, A plan π is a set of trajectories T = {T 0 , . . . T m }, in which each trajectory T i fits within the flight window F i .

Fig. 5 :

 5 Fig.5: Waypoint insertion process. A random chosen waypoint w (dashed, light blue) is inserted in a trajectory between w i and w i+1 (dark blue). w is re-projected into a previous isochrone (light blue) whose time corresponds to the time needed to reach it from w i . Finally, due to the increment in travel time between w i and w i+1 , w i+1 is moved to a later isochrone.

•

  The trajectory is fully contained in the allowed temporal interval, i.e., t min ≤ t 0 ≤ t n ≤ t max where t n is the arrival time at w n .• The trajectory does not exceed the maximum duration, i.e., t n -t 0 ≤ d max .• The trajectory starts and ends at the UAV base, i.e., w 0 = w n .

LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France

University of Sassari, Italy

A more detailed description, reasoning and statistical analysis of the approach can be found in[START_REF] Bit-Monnot | A local search approach to observation planning with multiple uavs[END_REF]

The flight altitude z being kept constant at any time, we omit it on the waypoint definition

Acknowledgments: This work was partially supported by the European Interreg Sudoe program, through the funding of the FIRE-RS project

A. Initial plan

We consider a mountainous region of 5km×5km where multiple fire ignitions occur. We let the algorithm run for 1 minute on an Intel Core i7 PC at 2.70GHz. Fig. 6 shows a scenario with two fire fronts spreading north (the elevation map is only used to compute the fire propagation, and is not shown). Two UAVs starting at different positions are available to observe both wildfires, with a flight duration limited to 10 minutes. The trajectories given by the VNS algorithm follow the fire front to maximize the utility of the plan. Fig. 7 shows three wildfires being observed by two UAVs taking off from the same base with larger allowed flight duration. The planning algorithm does task distribution implicitly in such a way that the UAVs do not observe the same locations concurrently. Even though they start at the same time, the different paths are planned such that both UAVs arrive at each fire at different times.

B. Replanning

Due to model uncertainty and changing environmental conditions, the actual fire may diverge from the predicted one. Researchers from the wildfire community recently developed data assimilation approaches that integrate realtime fire front information into the propagation models [START_REF] Rochoux | Regional-scale simulations of wildland fire spread informed by real-time flame front observations[END_REF], which in turn allows correcting and improving the parameters that govern the predicted wildfire. We suppose that the observations made by the UAVs can be used by such data assimilation techniques to provide an updated fire map. The monitoring plan should be revised to react to this new situation.

The VNS approach is able to start from any valid plan. In a replanning stage, we use as π init the previously computed plan π prev , and the VNS algorithm is constrained to improve only future parts of it. First, projectff translates the future waypoints to ignited locations (waypoints that can not be translated are removed from the plan). Then, the current plan is refined following the procedure described in IV-B.3 for initial plans.

A replanning scenario is shown in Fig. 8. First, the planner generates the trajectory in blue based on the basis of the predicted fire map in purple. Five minutes later, an update of the predicted fire map is made (e.g. by incorporating data gathered so far by the UAV). This update is shown in orange: the fire actually propagates faster than initially predicted. The planner adapts the remaining of the original plan from minute six, yielding the red trajectory.

VI. CONCLUSION

We have presented a wildfire monitoring system based on fleets of fixed-wing UAVs: we have modeled the problem by introducing the dynamics of fire propagation and UAV motion in the presence of wind. Then, we have introduced a VNS-based planning algorithm capable of generating plans using this realistic models. Finally, we have shown a small selection of typical wildfire scenarios for which the planning algorithm was able to generate a sound monitoring plan.

In the near future, the monitoring system will be integrated with a real command and control software for UAVs [START_REF] Pinto | The LSTS toolchain for networked vehicle systems[END_REF], enabling improved UAV simulation and field experiments.

Future work on the algorithmic side will consider exploiting 3D UAV motion, to overcome terrain constraints in mountainous areas and to improve observations. We will also explore approaches that apply an economy of means principle: instead of allowing all the UAVs to exploit their whole flight duration, the number of UAVs to deploy as well as their take-off time should be defined in order to optimize the ratio between resource usage and quality of the observed fire map in the long term. Finally, integrating fire front tracking capabilities to react to discrepancies between the propagation prediction and observations will be considered.

The planning algorithm code and wildfire propagation model are available online at https://github.com/ laas/fire-rs-saop.


J.-P Malet

G Ferhat

P Ulrich

P Boetzlé

J Travelletti

The French National Landslide Observatory OMIV -Monitoring surface displacement using permanent GNSS, photogrammetric cameras and terrestrial LiDAR for understanding the landslide mechanisms

Keywords: Landslide monitoring, GNSS, camera, LiDAR

The French Landslide Observatory (OMIV -Observatoire Multi-disciplinaire des Instabilités de Versants) is a French-research initiative clustering five research institutes in earth sciences (e.g. OSUG/ISTerre in Grenoble, EOST-IPGS in Strasbourg, OCA/GéoAzur in Nice; THETA-Chrono-Environnement in Besançon, and EMMAH in Avignon) under the auspices of INSU (Institut National des Sciences de l'Univers) since 2007. The objectives of OMIV are (1) to conduct geo(morpho)logic, hydrologic, geodetic and seismic observations for the long-term understanding of landslide processes, and (2) to maintain robust, long-lasting multi-parameter datasets freely available for the international geoscience community. Four large and continuously active landslides in France are monitored by the OMIV research group; they have been chosen according to their past history of monitoring, to the hazard/risk they may create and to the scientific challenges they raise up. The four studied landslides are: (1) the Avignonet landslide (30 km South of Grenoble) and the Super-Sauze landslide (5 km South to Barcelonnette) which are soft-rock slides developed in clays for which the susceptibility to rainfalls and earthquake is the main open question; (2) the La Clapière (100 km North of Nice) and the Séchilienne landslide (25 km East of Grenoble) which are typical mature and immature large scale rock mass gravitational instabilities. On these four pilot sites, the OMIV research group at EOST/IPGS is in charge of coordinating the continuous observations of landslide deformation, in terms of sensors installation, data transfer and management, and the development of processing strategies. Different instruments are exiting on the landslides: GNSS receivers, photogrammetric cameras and terrestrial LiDAR campaigns. In this paper, we present the comparisons of DD (Double Differencing) and PPP (Precise Point Positioning) solutions of GNSS data processing. Moreover, we show stereo-photogrammetric processing of time series of terrestrial optical images, and image-based processing of the LiDAR point cloud time series.

Introduction

The objective of the French National Landslide Observatory OMIV is to provide long-term monitoring of three categories of landslide parameters: (1) kinematic observations (surface and in-depth displacements), (2) hydro-meteorological observations and (3) seismological observations. Monitoring is essential to understand the mechanics of landslides, and predict their behavior in time and space. Scientists at the OMIV Observatory monitor selected landslides in order to learn more about the physical processes that trigger landslides or control their movement, develop original landslide investigation methods and hydromechanical models of slope deformation. For the survey, a multi-technique monitoring strategy is proposed (Figure1).

Structure of the service and objectives

Given the wide style of techniques used at OMIV, several French laboratories are involved (Figure 2). Four major monitoring methods are employed (seismology, meteorology and hydro-chemistry, event catalogues, kinematics and geodesy).

Seismological measurements are coordinated and planned by the Grenoble team (OSUG/ISTerre). Three types of seismological information related to landslide dynamics: (1) continuous seismological records of the landslide; (2) the landslide response to regional earthquakes (e.g. the signal of regional earthquakes recorded on the landslide); (3) the seismic records of events occurring within (e.g. slidequakes, rockfalls) or very close (e.g. small local earthquakes) to the landslide.

Concerning environmental parameters such as meteorological parameters (rain precipitations, wind force and direction), hydrological and chemical measurements are performed by the team at Nice (OCA/Géoazur) and by the Strasbourg team (EOST/LHyGes). The task consists in collecting information on the local and regional meteorology through the setup of weather stations; the slope hydrology in the critical zone (unsaturated and saturated zone) using hydrogeological (piezometers), hydrochemical (ion/cation analysis, isotope analysis, water quality) and hydrogeophysical (soil resistivity, soil temperature) techniques.

Event catalogues are established by the Strasbourg team (EOST/IPGS). These records consist of landslide catalogues (landslide type, date and intensity) and landslide controlling factors (relief, lithology, local geodynamics, landcover).

The last component of this large set of measurements concerns the surface displacement monitoring performed at Strasbourg (EOST/IPGS) which is the main topic of this article.

Monitored landslides

Four large and continuously active landslides in France are monitored by the OMIV research group. They have been chosen according to their past history of monitoring, to the hazard/risk they may create and to the scientific challenges they raise up. We will focus on the Ubaye valley located in the French Southern Alps, because this region is affected by several large and active landslides (Figure 4) and in particular the Super-Sauze landslide which is a rapidly deforming zone (0.5 cm.d -1 to few dm.d -1 , Malet et al., 2005). This landslide is located on the north-facing slope of the Barcelonnette Basin. This landslide has developed in a torrential basin located in the upper part of the Sauze torrent (Figure 4).

We will also discuss some GNSS observations acquired at the La Clapière landslide in order to illustrate the performance of different processing techniques (PPP precise point positioning and DD double difference).

Instruments for surface displacement monitoring

The surface displacement monitoring performed by the Strasbourg team is based on geodetic measurements such as tacheometry, GPS, LiDAR and also terrestrial optical images.

GPS and total station measurements

Permanent GPS networks are installed on or closed to the landslide (Figure 5). The requested accuracy of the estimated planimetric and vertical coordinates has to be better than 1 cm.

Expensive GNSS receiver and antenna were classically installed on the deforming zone, but given the possible loss of the instruments, low-cost GNSS receivers have been recently installed (Figure 6, Boetzlé, 2015;Ferhat et al. 2015). The Infra-Survey company has developed the Geomon lowcost receiver which enables a precision of 1 to 3 cm.

Moreover, permanent surveys of benchmarks installed at the landslide surface using automatic total stations or GNSS-RTK (Global Navigation Satellite System Real Time Kinematic) are performed regularly from a reference station outside the deforming zone (Figure 7). The requested accuracy of the estimated planimetric and vertical coordinates is 2 cm.

LiDAR campaigns

Campaigns of airborne and terrestrial LiDAR are setup on the most active parts of the landslide to obtain distributed information on the topography. The requested accuracy of the estimated planimetric and vertical coordinates is better than 4 cm (Travelletti et al. 2014).

Terrestrial optical images

Permanent monitoring using terrestrial optical cameras and image correlation techniques are also performed in routine at the Super-Sauze landslide (Travelletti et al., 2012;Gance et al., 2014). The requested accuracy of the estimated planimetric and vertical coordinates is better 4 cm.

Surface displacement obtained with geodetic and optical observations 4.1 Geomon data processing

In order to evaluate the precision of the Geomon low-cost receivers, we have installed 5 devices on the Super-Sauze landslide and one reference on a stable zone (Figure 8).

The Geomon observations were processed using the RTKLib software. Positions of Geomon-1 to 5 are daily computed relatively to a closed reference station SAZ4 and to a more distant one BACT (~5 km, in the city of Barcelonnette) (see locations of stations in Figure 8). The Geomon-6 station should have been used as the reference station but it encountered a failure during the period of acquisition (June 18 to July 16, 2015).

Time series of baseline lengths Geomon-4 to the reference site of SAZ4 or BACT vary daily for a 1 to 2 cm in horizontal component East and North, for up to 5 cm in the vertical component (Figure 9). The increase of the scatter in the position estimated is clearly linked to the distance to the reference.

DD vs PPP processing at La Clapière landslide

La Clapière landslide is monitored by a set of 3 GPS antennas located on top and center of the sliding zone. A reference station named CLAP has been installed the bottom and outside of the deforming zone (Figure 10).

Historically, the positions of the moving antennas (CLP1 to 3) have been computed relative to the reference station CLAP using the GAMIT/GLOBK software. This approach used the classical Double Difference technique (DD) to eliminate most common errors at nearby sites (atmospheric and clock errors). In order to evaluate an alternative strategy of processing the data, we use the Precise Point Positioning technique (PPP) through two softwares. GINS software is developed by the GRGS/CNES (France) and the SCR-PPP is developed at the NRCAN (Canada). Moreover relative positioning using the DD technique was evaluated using RTKLib software.

As an example, Figure 11 shows the differences for the baseline CLAP-CLP2 for 2011 between PPP approaches (GINS and SCR-PPP) and the DD GAMIT/GLOBK, which serves as a reference solution. SCRS-PPP solution is really close (less than 1 cm in North or East component) to the GAMIT/GLOBK solution. Offset of 12 cm is observed with RTKLIB in the vertical component (Figure 11). The GPS and GNSS observations located on the major landslides are currently reprocessed using the SCRS-PPP software. Results in terms of time series of position and baseline length variation will be provided on OMIV website: http://omiv.unistra.fr

Correlation of optical images

The kinematics of the Super-Sauze landslide is monitored by several remote terrestrial optical cameras. This instrumentation consists in a low-cost D70 Nikon reflex digital camera installed on a concrete pillar located on a stable crest in front of the landslide at a distance of 300 m from the lower part and 900 m from the main scarp (Figures 12A, B, andC). Every four days, a series of images is acquired at 11:00, 12:00, 13:00 and 14:00 GMT in order to increase the probability of acquiring at least one image with good meteorological and illumination conditions.

Comparison of temporal Lidar DEM.

Four to five terrestrial laser scans are acquired at the Super-Sauze landslide (toe, main scarp) since 2007. The dense point clouds have been acquired with a terrestrial long-range Optech ILRIS-3D laser scanning device from the same base stations (Figure 14).

The time series are analyzed using two approaches: 1) a method of correlation of gradient images, and 2) a method of feature tracking in the raw 3D point clouds. The estimated surface displacements are then compared with GPS surveys on reference targets. The Figure 15 presents an example of results (Travelletti et al., 2014).

Conclusions

The French Landslide Observatory (OMIV -Observatoire Multi-disciplinaire des Instabilités de Versants) is a French-research initiative clustering

Fig. 1

 1 Fig. 1 Multi-technique monitoring strategy at OMIV Landslide Observatory (http://omiv.unistra.fr).

Fig. 2

 2 Fig. 2 Structure partners at the OMIV Observatory (http://omiv.unistra.fr).

Fig. 3

 3 Fig.3The four main landslides monitored by OMIV in the French Alps.

Fig. 4

 4 Fig. 4 Ubaye valley and major landslides around the city of Barcelonnette (French Southern Alps): Poche, Super Sauze and La Valette landslides (Stumpf et al. 2014).

Fig. 5

 5 Fig. 5 Permanent GNSS Trimble antenna on the Super Sauze landslide (Boetzlé, 2015).

Fig. 6

 6 Fig. 6 Geomon low-cost GNSS receiver and its solar panel on the Super-Sauze landslide (Boetzlé, 2015).

Fig. 7

 7 Fig. 7 Reference benchmark observed by GNSS GR10 Leica receiver close to the Super-Sauze landslide but outside the sliding zone (Boetzlé, 2015).

Fig. 8

 8 Fig. 8 Location of the five low-cost receivers (Geomon 1 to 5) at the Super-Sauze landslide. The reference and stable station is Geomon 6 outside the deforming zone (Boetzlé, 2015).

Fig. 9

 9 Fig. 9 Time series of relative positions of Geomon-4 referred to the permanent GNSS station SAZ4 or BACT during June 18 to July 16, 2015. Location of Geomon-4 is indicated in Figure 8. (Boetzlé, 2015).

Fig. 10

 10 Fig. 10 Location of the GPS antennas at La Clapière landslide. CLAP is the reference station, CLP1 to 4 are on the deforming zone.

Fig. 11

 11 Fig. 11 Differences for the baseline CLAP-CLP2 in 2011 for several processing approaches (GINS-PPP, SCRS-PPP, DD-RTKLib). The differences are indicated relative to the reference solution provided by GAMIT/GLOBK software.

Fig. 12

 12 Fig. 12 Overview of the Super-Sauze landslide (South French Alps). (A) Monitoring system by terrestrial optical photography. (B) Image acquired by the monitoring system presenting the different parts of the landslide from the camera location. (C) View of the landslide morphology in the local coordinate system on a shaded relief computed with a 0.25 m resolution DEM interpolated from an airborne LiDAR survey (ALS) acquired in July 2009. The invisible areas from the camera location are indicated (Travelletti et al., 2012).

Fig. 13

 13 Fig. 13 Displacement rate amplitude (color) and direction (arrow) in the image plane and cumulated displacements along eight profiles crossing the landslide over the period 20 May-25 June 2008. In order to highlight the displacement direction, the arrow length is normalized for each image (Travelletti et al. 2012).

Fig. 14

 14 Fig. 14 Schematic presentation of the projective projection applied on the TLS point clouds with the different coordinate systems involved in the procedure (Travelletti et al. 2014).

Fig. 15

 15 Fig. 15 2D displacement fields obtained from the correlation of the grey-intensity images related to the acquisition periods of (A) July-October of the years 2008 and 2009, and (B) October-May of the years 2007 and 2008. The displacement maps are draped on their corresponding grey-intensity images (Travelletti et al. 2014).

five research institutes in earth sciences. The four studied landslides are studied (Avignonet, Super-Sauze, La Clapière and Séchilienne).

Super-Sauze is monitored by several techniques developed by the OMIV research group at EOST/IPGS. We are using geodetic (GPS, tacheometry), terrestrial LiDAR (TLS) and optical images to provide deformation field of the surface of the landslide.

Concerning GNSS processing technique, we evaluate the difference between DD (Double Differencing) and PPP (Precise Point Positioning). We also evaluate the RTKLib software for relative positioning.