Frédéric Bretar
email: frederic.bretar@ign.fr

Adrien Chauve

Jean-Stéphane Bailly

Clément Mallet

Andres Jacome

Terrain surfaces and 3D landcover classification from small footprint full-waveform lidar data: Application to badlands

This article presents the use of new remote sensing data acquired from airborne full-waveform lidar systems. They are active sensors which record altimeter profiles. This paper introduces a set of methodologies for processing these data. These techniques are then applied to a particular landscape, the badlands, but the methodologies are designed to be applied to any other landscape. Indeed, the knowledge of an accurate topography and a landcover classification is a prior knowledge for any hydrological and erosion model. Badlands tend to be the most significant areas of erosion in the world with the highest erosion rate values. Monitoring and predicting erosion within badland mountainous catchments is highly strategic due to the arising downstream consequences and the need for natural hazard mitigation engineering. Additionaly, beyond the altimeter information, fullwaveform lidar data are processed to extract intensity and width of echoes. They are related to the target reflectance and geometry. Wa will investigate the relevancy of using lidar-derived Digital Terrain Models (DTMs) and to investigate the potentiality of the intensity and width information for 3D landcover classification. Considering the novelty and the complexity of such data, they are presented in details as well as guidelines to process them. DTMs are then validated with field measurements. The morphological validation of DTMs is then performed via the computation of hydrological indexes and photo-interpretation. Finally, a 3D landcover classification is performed using a Support Vector Machine classifier. The introduction of an ortho-rectified optical image in the classification process as well as full-waveform lidar data for hydrological purposes is then discussed.

Introduction

Remote sensing is an effective set of techniques to collect physical data from the Earth surface used as inputs in erosion/hydrological models or to monitor hydrological fields over large areas (Schultz and Engman, 2000;[START_REF] King | The application of Remote Sensing data to monitoring and modelling of soil erosion[END_REF]. Images obtained in the visible domain with optical sensors can be analyzed for generating 2D landcover and landform maps either automatically by image processing methods [START_REF] Chowdhury | Machine Extraction of Landforms from Multispectral Images Using Texture and Neural Methods[END_REF] or by photo-interpretation. The use of the infrared channel helps to detect the vegetation [START_REF] Lillesand | Remote Sensing and Image interpretation[END_REF]. In a stereoscopic configuration, images are processed to generate Digital Surface Models (DSMs) [START_REF] Kasser | Digital Photogrammetry[END_REF].

More recently, airborne lidar (LIght Detection And Ranging) systems (ALS) provide 3D point clouds of the topography by direct time measurement of a short laser pulse after reflection on the Earth surface. Moreover, such active systems, called multiple echo lidar, allow to detect several return signals for a single laser shot. It is particularly relevant in case of vegetation areas since a single lidar survey allows to acquire not only the canopy top (the only visible layer from passive sensors), but also points inside the vegetation layer and on the ground underneath. Depending on the vegetation density, some of them are likely to belong to the terrain. After a classification step in ground/offground points (also called filtering process), relevant Digital Terrain Models (DTMs) can be generated. Such DTMs are of high interest for geomorphologists to study erosion processes [START_REF] Mckean | Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry[END_REF] or to map particular landforms [START_REF] James | Using LiDAR data to map gullies and headwater streams under forest canopy: South Carolina, USA[END_REF]. Moreover, hydrologic models such as TOPOG [START_REF] O'loughlin | Prediction of surface saturation zones in natural catchments by topographic analysis[END_REF] or TOPMODEL [START_REF] Quinn | The prediction of hillslope flow paths for distributed hydrological modelling using digital terrain models[END_REF] handle topographic data either as Digital Surface/Terrain Models or as meshes. By classifying terrain under different levels and types of vegetation cover, lidar data, if suitable, could provide new land classification, i.e., terrain, cover maps. This new 3D landcover classification can even be more related to the hydrological processes that are usually modelled in hydrological production indices as the SCS runoff curve number (USD, 1986), the runoff coefficient in the rational method (Pilgrim, 1987) or the the plant cover factor in Wischmeier and Smith's Empirical Soil Loss Model (USLE) [START_REF] Wischmeier | Predicting rainfall erosion losses: a guide to conservation planning -Agriculture Handbook[END_REF]. Lidar data have also been investigated by [START_REF] Bailly | Agrarian landscapes linear features detection from LiDAR elevation profiles: application to artificial drainage network detection[END_REF] and [START_REF] Murphy | Stream network modelling using lidar and photogrammetric digital elevation models: a comparison and field verification[END_REF] for drainage networks characterization [START_REF] Cobby | Image processing of airborne scanning laser altimetry data for improved river flood modelling[END_REF][START_REF] Antonarakis | Retrieval of vegetative fluid resistance terms for rigid stems using airborne lidar[END_REF] and by [START_REF] Mason | Floodplain friction parameterization in two-dimensional river flood models using vegetation heights derived from airborne scanning laser altimetry[END_REF] as input data for flood prediction problems. For the latter, the authors use lidar data as resampled elevation grids and detect high and low vegetation areas. Vegetation heights are then converted into friction coefficients. Finally, multiple echo lidar data are typically used for the unique possibility of extracting terrain points as well as vegetation heights with high accuracy. When suitable, [START_REF] Hollaus | Airborne laser scanning and usefulness for hydrological models[END_REF] insist on the possibility to derive the roughness of the ground from lidar point clouds. However, the filtering algorithm used to process lidar data is landscape dependent and the classification result may be altered [START_REF] Sithole | Experimental Comparison of Filter Algorithms for Bare-Earth Extraction from Airborne Laser Scanning Point Clouds[END_REF]. Based on the same technology than multiple echo lidar systems, full-waveform lidar systems provide altimeter profiles of the reflected pulse. They represent the laser backscattered energy as a function of time. These profiles are processed to extract 3D points (echoes) but in addition, other interesting features that could be related to landscape characteristics. Depending on the landscape properties (geometry, reflectance) and on the laser diffraction angle (entailing small or large footprint), the recorded waveform becomes of complex shape. An analytical modelling of the profiles provides the 3D position of significant targets as well as the intensity and the width of lidar echoes (Section 3.1). A detailed stateof-the-art of such systems can be found in Mallet and Bretar (2008). This paper introduces a set of methodologies for processing full-waveform lidar data. These techniques are then applied to a particular landscape, the badlands, but the methodologies are designed to be applied to any other landscape. Indeed, badlands tend to be among the most significant areas of erosion in the world, mainly in semi-arid areas and in subhumid Mediterranean mountainous areas [START_REF] Torri | Badlands in changing environments: an introduction[END_REF]. For the latter case, more active dynamics of erosion are observed [START_REF] Regues | Seasonal patterns of runoff and erosion responses to simulated rainfall in a badland area in Mediterranean mountain conditions (Vallcebre, Southeastern Pyrenees)[END_REF]) with the highest erosion rate values in the world [START_REF] Walling | Soil erosion research methods[END_REF]. Very high concentrations of sediment during floods, up to 1000 g.l -1 , were registered [START_REF] Descroix | Processes, spatio-temporal factors and measurements of current erosion in the French Southern Alps: a review[END_REF]. Badlands are actually defined as intensely dissected natural and steeply landscapes where vegetation is sparse (Bryan and Yair, 1982). Badlands are characterized by V-shape gullies that are highly susceptible to weathering and erosion [START_REF] Antoine | Geological and geotechnical properties of the Terres Noires in southeastern France: weathering, erosion, solid transport and instability[END_REF]. These landscapes result from unconsolidated sediments or poorly consolidated bedrock, as marls, under various climatic conditions governing bedrock disinte-gration through chemical, thermal or rainfall effects [START_REF] Nadal-Romero | Badland dynamics in the Central Pyrenees: temporal and spatial patterns of weathering processes[END_REF]. The hydrological consequences of erosion processes on this type of landscapes are a major issue for economics, industry and environment: high solid transport, bringing heavily loaded downstream flood, are silting up reservoirs [START_REF] Cravero | Exploitation des retenues et transport des sdiments[END_REF] and downstream river aquatic habitats [START_REF] Edwards | Some effects of siltation upon aquatic macrophyte vegetation in rivers[END_REF]. Therefore, monitoring and predicting erosion within badland mountainous catchments is highly strategic due to the arising downstream consequences and the need for natural hazard mitigation engineering [START_REF] Mathys | Erosion quantification in the small marly experimental catchments of Draix (Alpes de Haute Provence, France). Calibration of the ETC rainfall-runoff-erosion model[END_REF]. Traditionaly, the monitoring activities in catchments are derived from heavy in situ equipments on outlets or from isolated and ponctual observations within catchments. In complement to these traditional observations, hydrologists are expecting remote sensing to help them to upscale and/or downscale erosion processes and measurements in other catchments, by providing precise and continuous spatial observations of erosion features or erosion driven factors [START_REF] Puech | Utilisation de la télédétection et des modèles numériques de terrain pour la connaissance du fonctionnement des hydrosystèmes[END_REF]. Among other inputs, erosion monitoring and modelling approaches on badlands [START_REF] Mathys | Erosion quantification in the small marly experimental catchments of Draix (Alpes de Haute Provence, France). Calibration of the ETC rainfall-runoff-erosion model[END_REF] need maps of landform features, mainly gullies [START_REF] James | Using LiDAR data to map gullies and headwater streams under forest canopy: South Carolina, USA[END_REF] that are driving the way flows and maps of important driven factors of erosion in mountainous badland catchments. These factors are soil and rocks characteristics [START_REF] Malet | Investigating the influence of soil surface characteristics on infiltration on marly hillslopes[END_REF], vegetation strata used to derive 3D landcover classes controlling rainfall erosivity, and the terrain topography [START_REF] Zhang | Modelling approaches to the prediction of soil erosion in catchments[END_REF], which allow to derive slope and aspect of marly hillslopes [START_REF] Mathys | Erosion quantification in the small marly experimental catchments of Draix (Alpes de Haute Provence, France). Calibration of the ETC rainfall-runoff-erosion model[END_REF].

This paper aims at investigating the potentialities of using full-waveform lidar data as relevant altimeter data, but also as a possible data source for 3D landcover classification focusing on the characterization of badland erosion features and terrain classification. If some papers have been published regarding the interpretation of full-waveform lidar data, most of them are based on large footprint lidar data acquired from satellite plateforms [START_REF] Zwally | ICESat's laser measurements of polar ice, atmosphere, ocean, and land[END_REF]. Very few researches have been carried out on the analysis of small footprint full-waveform airborne lidar data (Mallet and Bretar, 2008). Considering their novelty and their complexity, we propose to develop some new and specific guidelines related to their processing (including some physical corrections) and their management. Furthermore, we will show that DTMs generated from lidar data are of high accuracy, even over complex mountainous landscapes, which is consistent with the study of erosion processes. Finally, the extraction of the intensity and the width of each echo is investigated as potential information for landcover classification. Intensity and width are related to the target reflectance as well as to the local geometry (slope, 3D distribution of the target).

This paper begins with a background on full-waveform lidar systems (Section 2.1) as well as a brief presentation of a management system to handle the data (Section 2.2). We then present the processes to convert raw data into 3D point clouds (Section 3.1). Section 3.2 is dedicated to the development of a filtering algorithm to classify the lidar point cloud into ground/off-ground points as well as on the generation of DTMs. The echo intensity and width extracted from fullwaveform lidar data are described in Section 3.3. We focus this section on theoretical developments, basis of the introduction of intensity corrections. Section 4 presents the badland area whereon investigations have been performed as well as the data: lidar data, orthoimages and field measurements. DTMs produced by our algorithm are then validated by both field measurements (Section 5.1) and by the computation of an hydrological index (Section 5.2) compared with manually (photo-interpretation) extracted crests and thalwegs. We finally present in Section 6 the results of a 3D landcover classification using a first level of terrain vegetation cover classes and based on a supervised classifier: the Support Vector Machines (SVMs). Different features have been tested, including the three visible channels of the orthoimage. The opportunity of using full-waveform lidar data for hydrological purposes is then discussed.

2 Managing full-waveform lidar data

Background on full-waveform lidar systems

The physical principle of ALS consists in the emission of short laser pulses, with a width of 5-10 ns at Full-Width-at-Half-Maximum (FWHM), from an airborne platform with a high temporal repetition rate of up to 200 kHz in multiple echo mode. They provide a high point density and an accurate altimeter description within each laser diffraction beam. The two way runtime to the Earth surface and back to the sensor is measured. Then, the range from the lidar system to the illuminated surface is recorded [START_REF] Baltsavias | Airborne Laser Scanning: Basic relations and formulas[END_REF]. A lidar survey is composed of several parallel and overlapping strips (100 m to 1000 m width).

The emitted electromagnetic wave interacts with objects depending on its wavelength. The main influences on the laser light come from artificial or natural objects belonging to the illuminated surface. For ALS systems, near infra-red sensors are used (typical wavelengths from 0.8 to 1.55 µm). The Pulse Repetition Frequency (PRF) depends on the acquisition mode and on the flying altitude. Contrary to multiple echo systems which record only some high energy peaks in real time, full-waveform lidar systems record the entire signal of the backscattered laser pulse. Figure 1 shows raw full-waveform data.

Full-waveform systems sample the received waveform of the backscattered pulse at a frequency of 1 GHz. The footprint size depends on the beam divergence and on the flight altitude. Most commercial airborne systems are small footprint (typically 0.3 to 1 m diameter at 1000 m altitude).

Handling full-waveform lidar data

Initially, raw full-waveform lidar data are sets of range profiles of various lengths. Raw profiles are acquired and stored in the sensor geometry following both the scan angle of the lidar system and a chronological order along the flight track. After the georeferencing process and the pre-processing step (Section 3.1), raw profiles become vectors of attributes containing, for each 3D point, the x,y,z-coordinates, additional parameters (intensity and FWHM) and a link to the sensor geometry. Managing these data is much more complex than images: the topology (neigbhorhood system, topological queries) is designed to be as efficient as possible when accessing and storing the data. Indeed, the data volume is drastically larger than traditional laserscanning techniques: it takes 140 GB for an acquisition time of 1.6 h with a PRF of 50 kHz. Moreover, a 3D/2D visualization tool is also necessary to handle the attributes, both in the sensor and in the ortho-rectified geometries (cf. figure 2). A specific software has therefore been developed for these purposes.

3 Processing full-waveform lidar data

From 1D signals 3D to point clouds

Contrary to multiple echo lidar sensors which provide directly 3D point clouds, full-waveform sensors acquire 1D depth profiles along the line of sight for each laser shot. The derivation of 3D points from these signals is composed of two steps:

-The waveform processing step provides the signal maxima location, i.e., the range values, as well as additional parameters describing the echo shape.

-The georeferencing process turns the range value to a {x, y, z} triplet within a given geographic datum.

WAVEFORM PROCESSING: It aims at maximizing the detection rate of relevant peaks within the signal in order to foster information extraction. In the literature, a parametric approach is generally chosen to fit the waveform. Parameters of a mathematical model are estimated. The objective is twofold. A parametric decomposition gives the signal maxima, i.e., the range values of the different targets hit by the laser beam. Then, the best fit to the waveform is chosen among a class of functions. This allows to introduce new parameters for each echo and to extract additional information about the target shape and its reflectance.

Our methodology is based on a paper written by [START_REF] Chauve | Processing full-waveform lidar data: modeling raw signals[END_REF]. The authors describe an iterative waveform processing using a Non-Linear Least Squares fitting algorithm. After an initial coarse peak detection, missing peaks are found in the residuals of the difference between the modelled and initial signals. If new peaks are detected, the fit is performed again. This process is repeated until no further improvement is possible. This enhanced peak detection method is useful to model complex waveforms with overlapping echoes and also to extract weak echoes.

The Gaussian function has been shown to be suitable to model echoes within the waveforms [START_REF] Wagner | Gaussian Decomposition and calibration of a novel smallfootprint full-waveform digitising airborne laser scanner[END_REF]. Its analytical expression is:

f G (x) = I exp - |x -µ| 2 2σ 2 (1)
where µ is the maximum location, I the peak amplitude, and σ the peak width.

For each recorded waveform, the transmitted pulse is also digitized. By retrieving its maximum location, the time interval between the pulse emission and its impact on a target is known. The range value of the target ensues from the timeof-flight calculation.

In this paper, the echo amplitude will be refered to as intensity. However, in the literature, the intensity can also be associated to the total energy of the echo, product of the intensity and σ. The standard deviation σ corresponds to the half width of the peak at about 60% of the full height. In some applications, however, the Full-Width-at-Half-Maximum (FWHM) is often used instead. We have FWHM= 2σ

√ 2 ln 2.

GEOREFERENCING: Similarly to multiple echo lidar sensors, computing the {x, y, z} coordinates of each echo in a geodetic reference frame from the range value requires additional data. The scan angle is used jointly to the range to calculate the {x, y, z} position for each point in the scanner coordinate frame. Then, the GPS position of the aircraft, and the sensor attitude values (roll, pitch, heading) for each laser shot are recovered from the full-waveform data file to calculate the {x, y, z} in a given geodetic datum. Finally, the positions can be transformed in some cartographic projection (French NTF Lambert II Etendu in this paper, see Section 4 for more details).

The transformation formulas cannot be expressed because they differ from a sensor to another. Offset values are different, depending on the configuration of the laser system, GPS and Inertial Measurement Unit (IMU) devices.

After applying the advanced step of waveform modelling, full-waveform lidar data generate denser point clouds than multiple echo data. It is particularly relevant when studying the vegetation structure (Mallet and Bretar, 2008). However, we will see in Section 6 that additional parameters are also of interest for landcover or soil classification.

From point clouds to DTM

The processing of a lidar point cloud consists in classifying the data, which is generally associated to the resampling of the data on a regular grid. Due to the very fine geometry of a lidar point cloud, many algorithms have been developed to automatically separate gound points from off-ground points [START_REF] Sithole | Experimental Comparison of Filter Algorithms for Bare-Earth Extraction from Airborne Laser Scanning Point Clouds[END_REF]. Most of these approaches have good results when the topography is regular, but remain unperfect in case of mixed landscapes and slope conditions: parameters of the algorithms are often difficult to tune and do not fit over a large area. When ground points are mis-classified as off-ground points, the accuracy of the DTM may decrease (it depends on the spatial resolution and on the interpolation method). Inversely, when off-ground points (vegetation or man-made objects) are considered as ground points, the DTM becomes spiky which can be misinterpreted by hydrological models. Vegetated landscapes with sparse vegetation in a mountainous areas (alpine landscapes) are particularly interesting for the study of natural hydrology and the phenomenoms of erosion (cf. Section 4). Nevertheless, the processing of such landscapes need strong human interactions to correct the classification: typical errors are mis-classification of vegetation points in steep slopes, over-estimation of the DTM in case of dense vegetated areas, under-estimation of the DTM in open slope areas when the local slope is not explicitly introduced in the process.

A methodology that handles these problems has been recently developed (Bretar and Chehata, 2008) and is used in this study to compute the DTMs. It is based on a two step process:

i. The computation of an initial surface using a predictive Kalman filter: it aims at providing a robust surface containing low spatial frequencies of the terrain (main slopes). The algorithm consists in analyzing the altimeter distribution of the point cloud of a local area in the local slope frame. Points of the first altimeter mode (lowest points) belong to the terrain. A DTM value at a specific position depends on the neighboring pixels through their respective uncertainties. The predictive Kalman framework provides not only a robust terrain surface (the slopes are also integrated in the predictive filter), but also an uncertainty σ DTM for each DTM pixel as well as a map of normal vectors -→ n .

ii. The refinement of this surface using a Markovian regularization: it aims at integrating micro relieves (lidar points within the uncertainty σ DTM ) in a minimization process to refine the terrain description. Formulated in a Bayesian framework, additional prior information (crest, thalweg etc.) can also be integrated in the refinement process.

The lidar point cloud is then classified based on geometric criteria. A lidar point is labelled as GROUND if it is located within a buffer zone defined as the corresponding DTM uncertainty σ DTM . Otherwise, it is considered as OFF-GROUND. In natural landscapes, off-ground points belong mainly to vegetation, and sometimes to human-made features (e.g., electric power lines, shelters). Vegetation areas are described as non-ordered point cloud (high variance) compared to human-made structures. Vegetation points are therefore extracted by fitting a plane on the off-ground points. If the residuals are higher than a defined threshold (∼ 0.3 m), points are labelled as VEGETATION. Figure 3 summarizes the entire algorithm to calculate a DTM from a lidar point cloud. This classification is not explicitly used in the following, but for generating the validation set related to the supervised classification (Section 6). Figure 4 is a 3D bird view of the orthoimage superimposed on the DTM.

Processing intensity and width of lidar echoes

Beyond the 3D point cloud, full-waveform lidar data provide intensity and width of each echo (Section 3.1) that are potential interesting features for landcover classification. The backscattered intensity (or received power) is a function of the laser power, the distance source-target, the incidence angle, the target reflectivity, the absorption by the atmosphere etc. The use of such features in a landscape classification framework necessitates a global coherence between all strips. Correcting the recorded intensity values from some of known contributions makes possible the analysis of "physical" parameters such as the target reflectivity. We propose also to analyze the effect of the incidence angle on the Full-Widthat-Half-Maximum.

The intensity

According to [START_REF] Nicodemus | Geometrical Considerations and Nomenclature for Reflectance[END_REF], the scattered radiant flux P s in the zenith/azimuth angles (θ s , φ s ) within the cone Ω s is related to the incident flux P i in the direction (θ i , φ i ) within Ω i by (Figure 5)

P s (θ s , φ s , Ω s ) = (Ω i , Ω s )P i (θ i , φ i , Ω i ) (2)
where (Ω i , Ω s ) is the biconical reflectance. Introducing the backscattered cross section of the target σ, equation 2 can be rewritten as (Wagner et al., 2008b):

P s = D 2 r 4πR 4 β 2 t σP i (3)
where D r is the diameter of the receptor, R the range from sensor to target, β t the laser beam width. With σ = πρ m R 2 β 2 t cos θ s [START_REF] Höfle | Correction of laser scanning intensity data: Data and model-driven approaches[END_REF])

P s = D 2 r ρ m 4R 2 cos θ s P i (4)
where ρ m is the target reflectance, which depends on the material.

Since the recorded intensity is proportional to the backscatterred flux P s , correcting intensity values gives access to the target reflectance, and therefore, in case of a Lambertian surface, to the classification of the material. Since the apparent reflecting surface is smaller in case of non-zero incidence angle than in case of zenithal measurements (the cosinus dependency in equation 4), recorded intensity values are corrected from the scalar product of the emitted laser direction and the corresponding terrain local slope extracted from the DTM.

We have also remarked that emitted pulses have significant amplitude variations along the flight track which may alter the spatial homogeneity of returned waveforms. The FWHM has shown some spatial variability in our data set. Considering the badland and alpine landscape, we investigated the influence of the incidence angle on the FWHM only in case of bare soil areas. Indeed, the FWHM of under-vegetation ground points may have been modified by the complex optical medium. These investigations have been performed on simulated waveforms reflected by a tilted planar surface. We show that, in our acquisition configuration (low divergence angle, low flight altitude), the FWHM should stay constant with various incidence angles. We cannot extend this conclusion for ground points below the vegetation since the waveform has been modified through the canopy cover. The spatial variability is therefore attributed to a more complex spatial beam response of the surface due to structures and/or reflectance properties.

Materials

Lidar data have been acquired over the Draix area, France. Draix area is an experimental area on erosion processes in badlands located in the South of the French Alps. It belongs to the Euromediterranean Network of Experimental and Representative Basins (ERB). The Draix area consists in five research experimental catchments, highly equipped and monitored for more than thirty years. Thirteen research units working on erosion and hydrology processes are grouped within the GIS Draix organization [START_REF] Mathys | Information available[END_REF]. Results for the most two eroded catchments are presented here: they concern the Laval and the Moulin catchments.

Lidar data

The data acquisition was performed in April 2007 by Sintégra (Meylan, France) using a RIEGL© LMS-Q560 system. This sensor is a small footprint airborne laser scanner and its main technical characteristics are presented in [START_REF] Wagner | Gaussian Decomposition and calibration of a novel smallfootprint full-waveform digitising airborne laser scanner[END_REF]. The lidar system operated at a PRF of 111 kHz. The flight height was approximatively 600 m leading to a footprint size of about 0.25 m. The point density was about 5 pts/m 2 .

The temporal sampling of the system is 1 ns. Each return waveform is made of one or two sequences of 80 samples. For each profile, a record of the emitted laser pulse is also provided (40 samples).

For this study, three overlapping strips have been used with perpendicular direction. For each of them, a sub-part corresponding to the Moulin and the Laval catchement have been extracted. Strip footprints are presented in figure 8 and denoted S6 (blue), S7 (red), S8 (green).

Orthoimages

Two orthoimages were available for the study. The first one is extracted from the French IGN data basis BDOrtho©. Acquired in fairly good conditions (almost no shadowed zones) by the IGN digital camera, a physical-based radiometric equalization process has been applied [START_REF] Paparoditis | High-end aerial digital cameras and their impact on the automation and quality of the production workflow[END_REF]. The ground resolution is 0.5 m. The triplet of {red, green, blue} channels of the IGN image will be referred in this article to as RGB IGN . The second orthoimage has been calculated from aerial images acquired during the lidar survey by an embedded digital camera. Since the survey has been performed early in the morning, numerous shadowed areas appear. Moreover, no radiometric equalization has been performed entailing a rather poor radiometric quality (see figure 13). The ground resolution is 0.2 m. The triplet of {red, green, blue} channels of this image will be referred in this article to as RGB RAW .

Field measurements

Quality control points (or ground truth data) were surveyed by a mixed campaign DGPS, and a Total Station (coordinate reference system NTF Lambert II Etendu). The accuracy was 0.025 m in planimetry, and 0.03 m in altimeter. These points are chosen mainly on thalwegs (bottom of gullies) and crests. Some gullies are so deep that the GPS system was not able to work at the same accuracy. Those points were then surveyed with the help of a Total Station. 3D crossed validation between DGPS and Total Station surveys performed in six points were never greater than one centimeter. A total of 2886 quality control points have been used (cf. figure 8).

DTMs analysis

Qualification of DTMs with field measurements

We present in table 2 a comparison between field measurements and three DTMs generated from each single strip (S6, S7 and S8). One can observe that there is a bias in each DTM w.r.t field measurements as well as a significant standard deviation and RMS.

Looking carefully throught the lidar data and the statistics, a strip adjustment problem was diagnosticated. In order to validate the DTMs, the West part of the field measurements has been used to adjust the DTM while the East part to validate the terrain surface (denoted West East in table 1). We also took the dual configuration (East West) to test the relevancy of the proposed correction. Here, the adjustement consists in finding the best 3D translation T opt that minimizes a RMS between field measurements and their projection on the DTM. We used a brute force method to explore the entire parameter space. A x, y (resp. z) search step of 0.4 m (resp. 0.1 m) was chosen in relation to the planimetric (resp. altimeter) accuracy of lidar points. The symmetrical validation gives some hints on the real deformations of the DTM, which are generally much more complex than a 3D translation. Table 1 gathers the results of both the optimal correction applied to each DTM and the mean and standard deviation of respectively West East and East West configurations.

Table 1 shows that the adjustement improves the final accuracy of DTMs both by decreasing the bias and the standard deviation. However, one can notice that the optimal 3D translation varies depending on the West East and East West configuration. A 0.4 m difference in the y-direction of T opt for DTMs S7 and S6 doubles the standard deviation, while a 0.8 m difference in the y-direction of T opt for DTM S8 has no effect on the final accuracy. These observations tend to show that the deformations between DTMs is not purely a 3D translation, but is of different nature such as polynomial (surface tilt) or non linear (rotation). As a conclusion, we can say that, after a simple geometric adjustement, the calculated DTMs at 1 m resolution have an absolute altimeter accuracy of some decimeters. A better geometric adjustement should improve this accuracy.

Qualification of DTMs with hydrological indices and photo-interpretation

The quality assessment of a DTM for hydrological purposes is not completely satisfying when considering only the altimeter error distribution. Other DTM quality criteria directly connected to the usual hydrological information extracted from DTM may be used: drainage networks, drainage areas, slopes like presented in (Charleux-Demargne, 2001). These criteria are mainly based on the basic landform information related to the first and the second derivative of a DTM. However, these criteria are not easy to use in a qualification process since (1) they are conditioned by both the algorithms and the parameters used to produce the information (e.g., a drainage area threshold in the D8 flow accumulation algorithm), (2) reference data are not easily available (how to survey drainage networks?) and finally (3) the quantification of quality is often not properly defined (how to compare dissimilarities of drainage networks?). Moreover, criteria are usually not generic: it is related to a specific hydrological index.

In order to overcome these problems, a single criteria is proposed for a quantified auto-evaluation of DTMs at a given resolution in erosion areas with an hydological and morphological point of view. This criteria is the rate of crests and thalwegs observed from an orthoimage that are detected from the convergence index (CI) built on a DTM [START_REF] Köthe | SARA System -System zur Automatischen Relief-Analyse[END_REF]. The convergence index corresponds, for each DTM cell, to the mean difference between angle deviations. These angle deviations are calculated in each of the eight adjacent pixels. For an adjacent pixel, the angle deviation is the absolute difference, in degrees, modulo 180, between its aspect and the azimuth to the central pixel [START_REF] Zevenbergen | Quantitative Analysis of Land Surface Topography[END_REF]. The convergence index is a symetric and continuous index ranging from -90°up to 90°. This index highlights crests when highly positive and thalwegs when highly negative. Figure 9 shows the convergence indexes computed on S6. Main thalwegs and crests appear with respectively highly negative (blue) and positive (red) values. At a given location, a thalweg (resp. a crest) is considered to be detected in the DTM) if CI values belong to [-90°, -η] (resp. to [η, 90°], η ∈ R). On a "perfect" DTM without noise, only CI=0 (i.e., η=0) indicates a plane terrain without any crests and thalwegs, whatever the slope is. When dealing with noisy DTM, thresholding the CI with η to retrieve significant crests and thalwegs becomes a challenging task. We therefore simulated a distribution of CI from a set of 1000 virtual noisy DTMs. They were generated with a trend corresponding to a plane of constant slope (e.g., 33°is the mean slope of Draix area). The simulation consists in generating Gaussian random fields [START_REF] Lantuejoul | Geostatistical Simulation: Models and Algorithms[END_REF] using the LU method [START_REF] Journel | Mining Geostatistics[END_REF] following noise spatial distribution models with parameters: range, nugget and sill (variance) for spatial covariance.

Since the simulated CI distribution is of Gaussian shape, we set η to two times the standard deviation. We accept that five percents of CI values due to hazard on noise can be classified in significant crest and thalweg.

We show some results on a sub-aera of Draix. The simulated CI distribution (performed on 33°slope, Gaussian noise of zero mean and 2.66 standard deviation) provides a threshold value η = 8.46. We show the results of the thalweg and crest detection on figure 10. Figure 10(a) is a manual delineation of apparent crests and thalwegs. The photo-interpretation process is applied on main structures, but very close linear elements as well as the elements near sporadic vegetated elements are not considered. Table 3 presents the quality criteria for S6 and S8. The overall accuracy for S6 (resp. S8) is 62.8 % (resp. 45.8 %). These relatively low values can be explained by the following grounds. Firstly, the threshold η has been automatically calculated: the parameters of the simulation may be refined to reach better results. Secondly, the photo-interpreted thalwegs and crests have been extracted form a 0.2 m -resolution image and then compared to a 1 m -resolution DTM: relieves smaller that the resolution cell of the DTM are smoothed. Thirdly, if large crests and thalwegs are well defined in the DTM, they may not appear in the photo-interpreted features since they may either be located in shadowed areas (thalwegs) or in saturated bright areas (crests). The comparison is therefore biased. Moreover, when comparing results for S6 and S8, results show that (1) S6 is better representing landforms than S8 probably due to georeferencing problems and (2) crests are more precisely detected in DTMs than thalwegs, which has to be more deeply investigated.

Discussion

Regarding the altimeter quality of full-waveform LiDAR DTMs, we obtain a rather precise and accurate relief restitution of a catchment of several square kilometers (about the same as the one obtained with multi-echo LiDAR). However, we showed that altimetric criteria are not sufficient since some differences in the restitution of eroded terrain features are observed between DTMs (coming from different strips). In addition, morphological criteria has necessary to be considered. The observation of local erosion processes requires a more detailed relief restitution. Other techniques like terrestrial LiDAR or photogrammetry by unmanned aerial vehicles [START_REF] Jacome | Extraction d'un modèle numrique de terrain par photographies par drone[END_REF] are more accurate and precise, but, are not well adapted to survey large areas. However, considering the altimeter accuracy of DTMs (approximately 0.9 m for 2 standard deviation on the altimeter random error), and that the local ablation speed over Draix area is of 1.5 cm per year [START_REF] Oostwoud | Erosion and sediment transport on steep marly hillslopes, Draix, Haute-Provence, France: an experimental field study[END_REF], change detection and monitoring of erosion effects would require a delay between surveys of several decades. Nevertheless, the loss of sediment volume within catchments are not homogeneous and are temporary stored on hill-slope gully networks: ∼ 200 tons/km 2 are trapped in the gully network), which corresponds to an approximate of 150 m 3 [START_REF] Mathys | L'érosion des Terres Noires dans les Alpes du Sud : contribution l'estimation des valeurs annuelles moyennes (bassins versants expérimentaux de Draix, Alpes-de-Haute-Provence, France)[END_REF]. These volumes are significant enought to shorter time lag for a multidate analysisfull-waveform Li-DAR DTM (lower than a decade), even with an accuracy of some decimeters. Only full-waveform LiDAR survey which gives an adequate compromise between precision, accuracy and extent makes possible the monitoring of sediment volume displacement in the gully network at a catchments scale 6 3D landcover classification

Methodology

Lidar data have been used so far as accurate altimeter data to extract ground points and generate DTMs. The challenges were to automatically process the data in a mountainous landscape with steep slopes and vegetation, the whole with the highest accuracy. We mentionned in the introduction that a landcover map is an important input of hydrological models, especially for the parameterization of the hydrological production function. We therefore propose in this section to describe the inputs and outputs of a classification framework wherein lidar width and intensity values can be integrated and their benefit evaluated. Indeed, the interpretation of additional lidar parameters has been barely studied and reveals to be of interest for landcover classification. Wagner et al. (2008a) proposed classification rules based on a decision tree for vegetation/non-vegetation areas in a urban landscape using solely the width and the amplitude: a point is considered as VEGETATION if (1) it is not the last pulse of a profile containing multiple returns (2) it is a single return with low amplitude (≤75) and large width (≥1.9 ns). Focusing on the study of the vegetation, [START_REF] Reitberger | Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees[END_REF] have integrated different features to segment individual trees in a graph-cut framework. Among them, the authors show that the feature corresponding to the average intensity on the entire tree plays the most important role in leaf-on conditions, while the ratio between the number of single reflections and the number of multiple reflections is the most important in leaf-off conditions.

Here, we would like to answer the question: do lidar width and intensity values improve a classification pattern in badlands? An efficient supervised classification algorithm called Support Vector Machines (SVM) has been used [START_REF] Chang | LIBSVM: a library for support vector machines[END_REF]. In recent years, SVM was shown to be relevant technique for remote sensing data analysis [START_REF] Huang | An assessment of support vector machines for land cover classification[END_REF]: ability to mix data from different sources, robustness to dimensionality, good generalization ability and a nonlinear decision function (contrary to decision trees for instance). In this paper, the 3D lidar point cloud is labelled, thus providing a 3D landcover classification. (Mallet et al., 2008) applied this technique with success for classifying urban areas from full-waveform lidar data.

Four classes have been identified focusing on a first and simple hierarchical level of 3D land cover classification, relevant for badlands landscapes with anthropogenic elements: 1-LAND, 2-ROAD, 3-ROCK and 4-VEGETATION. The three first classes can be ordered on an increasing erosion sensitivity criteria. The first class LAND is taking into account terrain under natural vegetation cover and cultivated areas in grassland. The second one, ROADs, are linear elements with natural (marls), bared but compacted material. These elements are known to impact runoff production within catchments. The third one contains areas with bared black marls in gullies, the main source of sediment production. The latter, vegetation, could be used further to describe the 3D vegetation structure, useful for a more detailed hierarchical level of 3D land cover classification. SVM algorithm requires its own feature vector for each 3D lidar point to be classified. Only three lidar features have been retained. Indeed, it appears that the larger the number of features, the more difficult to make an interpretation of the results. They are:

-d DTM , the distance between the 3D point and the DTM, -Int, the echo intensity, -FWHM, the echo width (see section 3.1).

Additionally, the RGB IGN and RGB RAW features have been added in the classifier, providing three radiometric attributes (figure 12). Their introduction allows a discrimination between road and land impossible with the lidar features and improve the classification results. The training set over each of the four classes has been defined as follow:

-ROAD and ROCK: 200 lidar points are selected in a road and rock mask defined on the orthoimage.

-VEGETATION: 200 lidar points are selected within a vegetation mask (lidar points classified as vegetation in section 3.2)

-LAND: 200 lidar points are selected (1) in a land mask on the orthoimage (2) in the intersection of the vegetation mask and a ground mask (lidar points classified as ground in section 3.2).

We have implemented the SVM algorithm with the LIB-SVM software [START_REF] Hsu | LIBSVM: a library for Support Vector Machine[END_REF], selecting the generic Gaussian kernel. For more theoretical explanations, please see [START_REF] Pontil | Properties of support vector machines[END_REF].

Results and discussion

The data set S6 has been analyzed. Figure 11 shows the histograms of lidar derived features corresponding to the four selected classes. d DTM and Int have bounded values which describe the vegetation (resp. > 1 m and between 0 and 20), whereas the width values tend to be uniform between 3 ns and 4.5 ns. ROAD and LAND have similar distributions for lidar derived features, which explains the high confusion values in table 4. The distributions of ROCK is flattened for d DTM since many points are choosen in very steep slopes, and are therefore more sensitive to the DTM quality. The intensity of ROCK is slighty different from the other classes. Figures 12 and13 shows the histograms of RGB IGN . 6 are the confusion matrices corresponding to the classification results with respectively {d DTM , RGB RAW } and {d DTM , RGB IGN }. One can observe a significant discrepancy between both radiometric features with an average accuray of 82.1 % using {d DTM , RGB RAW } and 92.2 % using {d DTM , RGB IGN }. The true positive values of ROCK (resp. ROAD) increase from 73 % (resp. 74 %) to 89.3 % (resp. 93.5 %) when using {d DTM , RGB IGN } instead of {d DTM , RGB RAW }. Moreover, the confusion between several classes decreases significantly: ROAD with ROCK decreases from 10.4 % to 3.7 %, ROAD with LAND from 13.8 % to 1.2 %, ROCK with ROAD from 19.8 % to 8 %, LAND with ROAD from 4.2 % to 1.5 %. In other words, the use of {d DTM , RGB IGN } instead of {d DTM , RGB RAW } gives better classification results.

True positive values are higher when using image-based features {d DTM , RGB IGN } than {d DTM , Int, FWHM} and the confusion between classes most of the time decreases: LAND with ROAD decreases from 19.4 % to 1.5 %, ROAD with ROAD decreases from 22.4 % to 8 %. Nevertheless, the comparison is more mitigated with {d DTM , RGB IGN }. Indeed, true positive values of ROAD decrease from 84.2 % to 74 % and the confusion between the other classes increases significantly. However, LAND is better classified with less confusion with ROAD (19.4 % to 4.2 %). As a result, it appears that even if the average accuracy of a classification using imagebased features is better, intensity and width of lidar echoes have interesting discriminative properties.

The results of the introduction of lidar intensity and width in the classification process are shown in tables 7 and 8. There are minor effects on the results when using 4), the improvement is particularly consistent for ROCK, LAND and VEGETATION, but true positive values of ROAD decrease from 84.2 % to 77.1 % and the confusion with ROCK increases from 7.5 % to 13.4 %. In fact, the radiometry of roads are sensitive to tree shadows. The combination of the very high resolution of RGB RAW and the time of the survey (early in the morning) feeds the training set with bright and dark (shadow) radiometric values. On the contrary, lidar intensity and width do not depend on the sun configuration. Superimposed on the orthoimage of figure 15, a 3D landcover classification obtained with {RGB IGN , Int, FWHM, d DTM } is presented in figure 16 and figure 17.

Finally, the quality of the classification depends mainly on the DTM accuracy (represented here as d DTM ). Moreover, within the framework of the methodology, it appears that a classification based on {Int, FWHM, d DTM } is suitable, but gives a worse accuracy than a classification based on {d DTM , RGB RAW } or {d DTM , RGB IGN }. Used on their own, fullwaveform lidar data are relevant to discriminate vegetation from non vegetation points, but the confusion between other classes remains not negligible. The intensity and the width do not improve the classification accuracy if the radiometric features have a good separation between classes. Otherwise, the benefit is rather small, but in case of artefacts in a class (like shadow) for which lidar measurements are not sensitive. Inversely, the use of poor radiometric features may alter the classification result of specific landscapes (here ROAD) where intensity and width are well bounded. Even if intensites and widths appear poorly discriminant for the first level of 3D landcover classification we used in addition to usual RGB images, we are quite convinced that it could be more useful for lower hierarchical levels of 3D landcover classification. For instance, these waveform parameters would probably give information on vegetation density and type as well as local bared soil properties impacting laser reflectances. These investigations will be the next steps of our research.

Conclusions

The different points treated in this paper entail some conclusions. Firstly, the accuracy of the full-waveform lidar data we worked on (badlands) was proven decimetric. Even if erosion dynamics on these landscapes would require a centimetric accuracy to be studied yearly, DTMs generated from lidar survey are consistent for hydrological sciences at the catchment level. Moreover, we showed that these data permit to identify most of gullies and crests of badland landscapes through geomorphological indices. We focused this paper on generating and qualifying DTMs, but also on the automatic computation of a 3D landcover classification. We showed that lidar intensity and width contain enough discriminative information on badlands to be classified in LAND, ROAD, ROCK and VEGETATION with ∼ 80 % accuracy. Compared to usual landcover classification from aerial or satellite images, 3D landcover classification is a new and interesting approach for hydrologists since it allows to parametrize in a much direct way hydrological or erosion production parameters as, for instance, the plant cover C factor [START_REF] Wischmeier | Predicting rainfall erosion losses: a guide to conservation planning -Agriculture Handbook[END_REF]. However, the introduction of image-based radiometric features combined to lidar ones in the classifier improved the accuracy of the classification (∼ 92 %). They bring relevant discrimination between classes but cancelled most part of the value added from full-waveform data. This is mainly due to the generality of the landcover classes we chose, but it would probably be more discriminant for more detailed landcover classes.

Fig. 1 .

 1 Fig. 1. Raw full-waveform lidar data: five emitted pulses and their respective backscattered signals.

Fig. 2 .

 2 Fig. 2. 3D representation of georeferenced waveforms on a crest. A tree is lying on the crest.

Fig. 3 .

 3 Fig. 3. Flowchart of the geometrical processes of a lidar point cloud.

Fig. 4 .

 4 Fig. 4. 3D bird view of the orthoimage superimposed on the DTM. ©IGN

Fig. 5 .

 5 Fig. 5. Coordinate system of the scattered and incident radiant flux.

  Figure 6(a) represents the ratio between the intensity values of the emitted laser pulse and the average intensity values over the whole strip along the flight track (x-axis). Considering the high PRF of the laser, intensity values are constant along the scan line. The effects of such variations are visible in the returned waveforms as vertical lines (figure 6(b)). We therefore normalized the returned waveforms by the average intensity value of all emitted pulses. The effects of the correction are presented in figure 6(c). One can notice that vertical lines have disappeared. (a) Ratio between the intensity value of the emitted laser pulse and the average intensity values over the whole strip. Values are represented in grey level scale and stretched between 0.72 and 1.35 (b) Raw return intensity of the first echo. Values are represented in grey level scale and stretched between 0 and 150. (c) Corrected return intensity of the first echo from the laser fluctucations. Values are represented in grey level scale and stretched between 0 and 150.

Fig. 6 .

 6 Fig. 6. Effect of the correction from the laser fluctuations. Images are presented in the sensor geometry.

  3.3.2 The Full-Width-at-Half-Maximum

  (a) Orthoimage acquired during the lidar survey (RGBRAW). (b) Orthoimage extracted from the IGN BDOrtho© (RGBIGN).

Fig. 7 .

 7 Fig. 7. Two orthoimages showing RGB RAW and RGB IGN over the Draix area.

Fig. 8 .

 8 Fig. 8. Lidar strips superimposed on the orthoimage. The blue (resp. red and green) strip is denoted S6 (resp. S7 and S8). Field measurements are also plotted: blue points represent the West subset, red points the East subset.

Fig. 9 .

 9 Fig. 9. CI computed on S6 superimposed to the orthoimage.

Fig. 10 .

 10 Fig. 10. (a) Test area (85 * 85 m) with photo-interpreted thalwegs (blue) and crests (yellow). (b) Detection of significant crests (red) and thalwegs (blue).

Fig. 14 .

 14 Fig. 14. Ground truth classification of S6 for each class LAND (dark brown and green), ROAD (red), ROCK (orange) and VEGETATION (dark green).

Fig. 15 .

 15 Fig. 15. Orthoimage of the Draix area.

Fig. 16 .

 16 Fig. 16. Classification results: LAND (dark brown), ROAD (red), ROCK (orange).

Fig. 17 .

 17 Fig. 17. Classification results: LAND (dark brown), ROAD (red), ROCK (orange) and VEGETATION (dark green).

  {RGB IGN , Int, FWHM, d DTM } instead of {RGB IGN , d DTM }. The classification is bettered when using {RGB RAW , Int, FWHM, d DTM } instead of {RGB RAW , d DTM }, true positive values of ROCK increase from 73 % to 76.2 %, ROAD increase from 74 % to 77.1 %, VEGETATION are similar and LAND increase from 85.8 % to 87.1 %. When comparing {RGB RAW , Int, FWHM, d DTM } with {Int, FWHM, d DTM } (table

Table 2 .

 2 Field comparison of the DTM before adjustement. DTM # Pts Mean ± Stdd (m) RMS (m)

	S6	1749	0.18 ± 1.05	1.07
	S7	2797	-0.20 ± 0.79	0.82
	S8	2886	0.41 ± 0.65	0.77

Table 3 .

 3 Morphological quality criteria results

	DTM	S6	S8
	Detected crests (%)	72.6 47.1
	Detected thalwegs (%) 53.5 44.7
	Overall (%)	62.8 45.8

Table 4 .

 4 Confusion matrix corresponding to the classification with {Int, FWHM, dDTM}.

	# points	ROCK	ROAD	VEGETº	LAND
	71216 ROCK	69.6	22.4	0.4	7.3
	13244 ROAD	7.5	84.2	0.1	6.6
	402995 VEGETº	0.9	0	94.2	4.7
	279321 LAND	9.1	19.4	2.7	68.6
	AA	79.1 %

Table 5 .

 5 Confusion matrix corresponding to the classification with {dDTM, RGB RAW }.

	# points	ROCK	ROAD	VEGETº	LAND
	71216 ROCK	73	19.8	0.9	5.9
	13244 ROAD	10.4	74	0.2	13.8
	402995 VEGETº	0.9	0.4	95.8	2.8
	279321 LAND	7	4.2	2.9	85.8
	AA	82.1 %

Table 6 .

 6 Confusion matrix corresponding to the classification with {dDTM, RGB IGN }.

	# points	ROCK	ROAD	VEGETº	LAND
	71216 ROCK	89.3	8	0.2	2.2
	13244 ROAD	3.7	93.5	0	1.2
	402995 VEGETº	0.8	0.2	95.7	3.2
	279321 LAND	4	1.5	4.3	90
	AA	92.2 %

Table 7 .

 7 Confusion matrix corresponding to the classification with {dDTM, Int, FWHM, RGB IGN }.

	# points	ROCK	ROAD	VEGETº	LAND
	71216 ROCK	88.2	10.4	0.1	1
	13244 ROAD	3.7	93.8	0	1
	402995 VEGETº	0.1	0.2	96.5	3.1
	279321 LAND	3.5	1.6	4	90.7
	AA	92.3 %

Table 8 .

 8 Confusion matrix corresponding to the classification with {dDTM, Int, FWHM, RGB RAW }.

	# points	ROCK	ROAD	VEGETº	LAND
	71216 ROCK	76.2	19.5	0	4
	13244 ROAD	13.4	77.1	0.1	7.8
	402995 VEGETº	0.3	0.1	95.3	4.2
	279321 LAND	5	5	2.7	87.1
	AA	83.9 %

Acknowledgements. The authors would like to deeply thank the GIS Draix for providing the full-waveform lidar data and for helping in ground truth surveys. They are grateful to INSU for its support to GIS Draix through the ORE program.

The classification is validated with lidar points belonging to the masks defined in the training step, but the training points. ∼ 40 % of the total number of points have been validated. Figure 14 shows the four validation sets for each class. A confusion matrix is then calculated for each configuration. True positive values correspond to the diagonal values of the confusion matrix. The accuracy of the classification results are quantified by the average accuracy AA, mean of the diagonal values of the confusion matrix. AA does not depends on the number of points in each validation set.

When using solely lidar derived features {d DTM , Int , FWHM}, table 4 indicates that the confusion between classes is not negligible particularly for some of them: ROCK with ROAD reaches 22.4 %, while LAND with ROAD reaches 19.4 %, what was predictable looking through the statistics of the training set (Figure 11). The vegetation has a high percentage of true positive (94.2 %) and is well detected. With an average accuracy of 79.1 %, it appears that a classification based only on lidar derived features is consistent. Before testing the effects of introducing lidar intensity and width, we investigated the impact of the radiometric quality of the orthoimages on the classification results. Tables 5 and