# Airborne hyperspectral mapping of trees in an urban area

Patrick Launeau, Zeineb Kassouk, Françoise Debaine, Régis Roy, Patrice Mestayer, Christian Boulet, Jean-Marc Rouaud, Manuel Giraud

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Patrick Launeau, Zeineb Kassouk, Françoise Debaine, Régis Roy, Patrice Mestayer, et al.. Airborne hyperspectral mapping of trees in an urban area. International Journal of Remote Sensing, 2017, 38 (5), pp.1277-1311. 10.1080/01431161.2017.1285080. hal-02449201

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Airborne hyperspectral mapping of trees in an
urban area

Patrick Launeau<sup>a</sup>, Zeineb Kassouk<sup>b</sup>, Françoise Debaine<sup>c</sup>, Régis Roy<sup>d</sup>, Patrice G. Mestayer<sup>c</sup>, Christian Boulet<sup>f</sup>, Jean-Marc Rouaud<sup>g</sup> and Manuel Giraud<sup>a</sup>

<sup>a</sup>Laboratoire de Planétologie et de Géodynamique, LPG UMR CNRS 6112, Faculté Sciences et Techniques, Université de Nantes, Nantes, France

<sup>b</sup>Institut National Agronomique de Tunis, Tunis Mahrajène, Tunisia

<sup>c</sup>Laboratoire Littoral, Environnement, Télédétection, Géomatique, LETG UMR CNRS 6554, IGARUN, Campus du Tertre, Université de Nantes, Nantes, France

<sup>d</sup>AREVA Resources Canada Inc., Saskatoon, Canada

<sup>e</sup>Laboratoire d’Hydrodynamique Energétique et Environnement Atmosphérique, LHEEA UMR CNRS 6598 Ecole Centrale de Nantes and Institut de Recherche en Sciences et Techniques de la Ville, IRSTV FR CNRS 2488, Nantes, France

<sup>f</sup>Laboratoire de Biologie et Pathologie Végétales, LBPV EA 1157, Faculté Sciences et Techniques, Université de Nantes, Nantes, France

<sup>g</sup>Laboratoire Eau et Environnement, Département GERS, Institut Français des Sciences et Technologies des Transports, de l’Aménagement et des Réseaux, Route de Bouaye, Bouguenais, France

The management of trees in urban areas requires accurate maps, which are difficult to build in the dense patchwork of numerous material properties. Remote sensing is a useful technique that measures the response of all vegetation occurrences, including trees, when high spatial resolution is available. The continuous narrow spectral bands of hyperspectral images enable the detection of the oxygen and water content, which ensures a perfect correction of the atmospheric effect. When calibrated, sunlight reflectance images can be used to map surface chemical compositions by the detection of diagnostic and sharp absorption features. In the visible and near-infrared, the vegetation is detected by chlorophyll-a absorption features that are characteristic of the pigment content. The reflectance intensities due to the texture of leaves occur between 450 and 920 nm while the water content imprint is detectable beyond 920 nm. The sharp spectral feature intensities of the main asso-ciated pigments, not only chlorophylls, are well quantified by indices measuring a normalized difference of reflectance in a spectral interval between two bounding wavelengths. A regression line calculated on all bands within that interval ensures a low sensitivity of the indices to the smaller variations in reflectance intensity. Such unbiased indices may be combined, using successive index thresholds deduced from a training spectral library, to divide the spectra into subsets, minimizing the confusion between the numerous vegetation types with almost identical compositions. Therefore, for each subset of the spectra, a classic spectral angle mapping (SAM) method can be used on the corresponding sub-selection of the spectral library to measure angles at full spectral resolution and map tree types with great accuracy, grouped according to their spectral similarity. In this study, chemical and physical information is carefully separated. The tree crown physical properties are studied by comparing the local juxtaposition of
pixel sets to a characteristic texture identifiable by image segmenta-tion into objects. Instead of looking for objects in the reflectance image or any statistical compression of its information, a 25 channel co-image, built from 11 information layers of chemical sharp spectral feature indices and 14 information layers of SAM indices matching a spectral library of reference vegetation groups, was used. Tree cano-pies also present wide internal variations due to (i) a complex mixture with a background in the case of sparse foliage, or (ii) pigment content adaptation to light exposure intensity from one side to another. Both effects are minimized by using the mean spectrum of each object, assuming that less significant spectra, being at plus or minus one or two standard deviations from an object mean spectrum, would be less affected by anomalous pixel data. Thus, two overlapping hierarchic layers atthepixelscaleandtheobjectscaleareavailabletodescribe the main chemistry or pigment content that identifies the vegetation types. The final classification is given by the upper layer at the object scale but in such an organization, the pixel scale layers can be used to analyse the data further and reorder them to obtain other parameters potentially useful for management purposes.

# 1. Introduction

Complete inventories of urban vegetation, based on the field mapping of numerous properties, are expensive and time-consuming to compile. Remote sensing provides a potential solution to this problem (Ridd 1995; Pauleit and Duhme 2000). Recent advances in imagery enable intensive mapping at high spatial resolutions (e.g. Iovan, Boldo, and Cord 2008), increasingly with the assistance of lidar data (Tigges, Lakes, and Hostert 2013). However, Paul (2007) stated that accurate spatial registration, appropriate field verification, and high spatial and spectral resolutions are needed to achieve a good classification in urban areas. All of these requirements can be fulfilled by airborne hyperspectral remote sensing, as demonstrated by Heiden et al. (2012).

Until now, the main advantage of airborne hyperspectral remote sensing over satellite imagery has been the larger number of spatial and spectral details, which are essential to map small objects accurately in urban areas. In principle, almost contiguous bands enable the precise identification of sharp absorption features, which are determined by both the chemical and physical properties of specific materials (Clark and Roush 1984; Goetz et al. 1985), such as their pigment and foliar structure (Sims and Gamon 2002). Over the last decades, many studies have been conducted on natural targets such as coastal, wetland or forest vegetation (Roberts, Smith, and Adams 1993; Asner 1998; Sims and Gamon 2002; Clark, Roberts, and Clark 2005; Zomer, Trabucco, and Ustin 2007), but far less research has focused on urban areas (Small 2001; Xiao, Ustin, and McPherson 2004; Herold et al. 2004; Heiden et al. 2007, 2012).

Regarding urban tree mapping, we must consider (i) a large number of different species, (ii) a highly variable scale of spatial distribution, and (iii) a complex overlap with other materials, such as trees overhanging lawns, pavements and houses. Fortunately, with 50 cm pixels, trees appear as patches of tens to hundreds of pixels. As stated by Clark, Roberts, and Clark (2005), tree species discrimination is best accomplished by aggregating pixels into their respective crowns for object-based classification using
spectral and spatial properties. Object-based classification methods have been widely discussed for processing high spatial resolution images and many methods have been developed to select the objects (Kettig and Landgrebe 1976; Hay, Niemann, and McLean 1996; Geneletti and Gorte 2003; Heyman et al. 2003; Walter 2004; Yu et al. 2006; Mathieu and Aryal 2007; Li et al. 2010; Lian and Chen 2011). Blaschke and Strobl (2001) and Blaschke (2010) also demonstrated that object-oriented approaches are a good alternative to pixel-based image analysis. Using an object-oriented classification of IKONOS images based on both spectral and spatial information, Zhang, Feng, and Jiang (2010) have already performed an urban area classification with a success rate higher than 87%.

Phenological effects, such as leaf yellowing due to water stress or nutrient deficit, rearrange the set of pigments such that the spectral reflectance of a vegetation species may change with the illumination conditions (Lichtenthaler et al. 2007, 2013). Trying to overcome this problem, several authors have constructed their training reference spectral library with samples selected in the image or collected in the field at the same time, choosing well identified sets of pixels for each healthy and/or stressed species. They have used distances between pixel and reference spectra to identify the species (Dennison and Roberts 2003; Clark, Roberts, and Clark 2005; Hestir et al. 2008; Ustin et al. 2009; Roth, Dennison, and Roberts 2012; Somers and Asner 2013). Spectral angle mapping (SAM), defined by Kruse et al. (1993), is the most commonly used distance because it is not sensitive to lighting variations over tree crown sides.

As stated by Hatfield et al. (2008), vegetation reflectance is correlated with chlorophyll-a (chl-a) content and photosynthesis. Chl-a dominates the spectral properties of leaves in the blue and red wavelength ranges, around 450 nm and 670 nm respectively, while cell physiology determines the reflectance efficiency in the near-infrared (NIR) domain. In shaded areas, the chlorophyll content may increase to compensate for illumination deficiency, while in areas exposed to full sunlight other pigments such as carotenes may increase (Lichtenthaler et al. 2007, 2013). Consequently, weather conditions, soil background, shadows and light scattering all affect the reflectance spectra of vegetation. The canopy reflectance is also affected by the pixel fractions of soil and non-photosynthetic vegetation elements such as branches and trunks (Rahman et al. 2003; Hamayouni 2005). It follows that the spectra are different when measured in the laboratory and in situ (Chappelle, Kim, and McMurtrey 1992; Gitelson, Gritz, and Merzlyak 2003; Gitelson 2004): individual leaf reflectances obtained in well-controlled laboratory conditions may not be relevant for real site canopies. Various models presented by Jacquemoud et al. (2000) could be used to detect the amount of pigments as shown by Ustin et al. (2009) but this requires a complete spectral library of the leaves of all species under study, which is not yet available. Sims and Gamon (2002) and Brantley, Zinnert, and Young (2011) preferred to use spectral indices to highlight the amount of key pigments in order to identify vegetation species. According to Zarco-Tejada et al. (2005), spectral indices may account for the stress impact on pigment assemblage, non-photosynthetic material and background effects. Spectral slopes may also be used but Ghiyamat et al. (2013) showed that they do not provide any advantage over a direct calculation based on reflectances. Even in perfect conditions, species identification remains difficult as there is high spectral variation within one species family grown in different geographical locations (Ghiyamat et al. 2013). Conversely, different species may
display identical spectra, which led Jones, Coops, and Sharma (2010) to use lidar data to discriminate them according to their height (see also Cho et al. 2012).

The present study is part of a series of concerted efforts to enrich urban geographical databases for environmental research and management purposes. While urban Geographical Information Systems (GIS) are achieving higher geometrical precision for buildings and networks, the attributes contain rather poor information about the material properties, mostly limited to colour. At a test site in Nantes, Roy et al. (2009) explored the capabilities of hyperspectral airborne remote sensing for identifying surfaces with a view to mapping their water perviousness while Kassouk et al. (2010) presented a first attempt to extend hyperspectral methods to vegetation species. Long, Simonetto, and Bocher (2010) used a combination of visible and radar remote sensing to identify 'objects', and Furusho, Rosant, and Mestayer (2012) carried out an extraction of vegetated surfaces from a QuickBird image in conjunction with the BDTopo® urban database of the French National Institute of Geographic and Forest Information (IGN).

The purpose of the present study is to combine the determination of both physical and chemical parameters in a hierarchical classification in order to gradually build layers of knowledge of the urban tissue. By working on hyperspectral images, we first have access to standalone chemical information per pixel. A few components, such as pigments, can be detected by spectral indices measuring diagnostic and sharp spectral features, which are convenient to separate the data into smaller subsets that are easier to process one after the other. Once these main features have been segmented, more subtle changes can be analysed from small spectral undulations, which include phenological effects complicating the identification of trees. A chemical level of determination can be established for each pixel (i.e. a pigment). Physical properties are linked to the diffusion of light by the surface within each pixel but they are difficult to identify at this pixel scale and neighbouring pixels need to be established to deduce these (spatial) physical properties from the arrangement of pixels next to each other. One physical property is, therefore, attached to one local pixel neighbourhood that can define an object. Thus, a basic chemical composition has to be defined first at the pixel scale. Next, physical properties can be extracted to determine the object of interest from which all levels of information can be recombined to refine further the determination of the material constituting each object. In this way, once trees have been determined, even in a large group of species, all levels of information can be used at the pixel scale within each object to study, for example, the spectral effects of drought stress or possible disease.

The relevance of chemical information carried by spectra is therefore a key point of this work. Thus, the first step is the analysis of spectral separability between tree species, similar to that of Herold et al. (2004), in order to assemble them into inseparable vegetation groups. The partition of the spectra by their main spectral features is similar to that of Xiao, Ustin, and McPherson (2004) using multiple masking techniques to process the data by subsets defined on the basis of key spectral indices. Small spectral undulations specific to species groups are measured by the SAM techniques of Kruse et al. (1993). Finally, the determination of the physical properties deduced from a small pixel neighbourhood is an object-based classification at the tree crown scale like that of Clark, Roberts, and Clark (2005) but relying in our study on the information layers. At a
spatial resolution of 50 cm per pixel, tree crowns or mono-specific groves are objects of tens to hundreds of pixels with a homogeneous composition. In the hierarchical procedure described, pixel data are preserved within each object and the statistical weight of the mixed pixels of the object boundaries and the influence of canopy directional effects can be calculated (Bousquet et al. 2005; Gerber et al. 2011), such as leaf shadow or specular scatter. Based on the rationale of Heiden et al. (2007), a hierarchical classification is defined with a decision tree inspired by the flow chart of Xiao, Ustin, and McPherson (2004) to retrieve tree species groups by a sequence of requests on the information layers at the object scale.

The process of hyperspectral classification often includes a step of data reduction, or pre-classification, consisting of reducing bands by using either an appropriate feature selection, as discussed by Clark, Roberts, and Clark (2005), or the minimum noise fraction (MNF) transform of Green et al. (1988) and Boardman and Kruse (1994) to concentrate the information into a few components. According to Hestir et al. (2008), this approach also minimizes the phenological effect.

Our approach is different since we keep all the information. We use only the partial inversion of the MNF transform inter-pixel spatial noise to obtain clean spectra. Then, instead of processing each pixel, we can work at the level of the object and process only the object mean spectrum or any of its statistics, if necessary. Moreover, each layer of information is always attached to both chemical and physical properties. We are not comparing SAM with other supervised methods, such as the Support Vector Machine (SVM) well tested, for example, by Jekel et al. (2012). Neither are we competing with fast automatic classifications of urban areas; instead, we are analysing the chemical and physical properties of materials with the main focus on trees to help with their management in urban areas. Practically, we build a co-image composed of layers of chemical or physical information and its interpretation, such as biological information about trees, at the pixel and/or the object scale, to combine them into a hierarchical classification for management purposes.

## 2. Study area context and data acquisition

The airborne data acquisition was part of the experimental FluxSAP 2010 (Mestayer et al. 2011) survey conducted in the north-east of Nantes (France). The test site is located in the neighbourhood of Pin Sec (Figure 1), which is the long-term environmental observatory of the ‘Institut de Recherche en Sciences et Techniques de la Ville’ (IRSTV). One objective was to provide detailed geographical observations to the participants in the research programme on ‘the role of vegetation in urban sustainable development evaluated through the stakes related to climate, water, energy, and ambiences’ (VegDUD), (Musy, 2014). The aim of this project was to map all the trees in the city, not only in the public domain where data are accessible in the city database but also in private areas where the land occupation is unknown. In fact, most of the study area is occupied by low housing complexes with small heterogeneous private gardens that are adjacent to higher building complexes with much larger public parks. An industrial and commercial zone marks the northern edge of the city. Even though species cannot be precisely determined, the chemical and physical characteristics of the trees are key parameters for the estimation of the water flux to the atmosphere, which explains our need for a classification based on chemical and physical properties (Mestayer et al. 2011).
<figure><img src="image_2.png" /><figcaption>Figure 1. Processed HySpex® image mosaic (true colours) acquired over the test site in Nantes (France).</figcaption></figure>

The survey was performed by the 'ACTIMAR' from Brest (France) with the camera of the LPG of Nantes. The HySpex® VNIR 1600 camera is a pushbroom visible and near-infrared (VNIR) device produced by Norsk Elektro Optikk. The light is collected on a Si-wire charge-coupled device (CCD) with 1600 lines and 160 rows giving 160 spectral bands between 400 and 1000 nm with a bandwidth (i.e. full width at half maximum, FWHM) of 4.5 nm and a sampling interval of 3.7 nm. The images were acquired in 20 N-S flight lines over the northeast area of Nantes (France) on 21 May 2010 (Figure 1), between 12 noon and 4 pm local time (GMT+2). The airplane was equipped with an integrated differential global positioning system (GPS) and an inertial measurement unit (IMU). The platform flew at an altitude of 1600 m above ground at a speed of 120 ft s⁻¹ (i.e. 131.67 km h⁻¹), ensuring an average spatial resolution of 0.6 m pixel⁻¹. The atmosphere was quiet and the sky was clear during the flight.
Radiance data were processed to obtain the relative reflectance using the Airborne Atmospheric and Topographic Correction (ATCOR4) model (Richter and Schläpfer 2014) with water vapour retrieval at 820 nm. The 920–1000 nm domain containing the water vapour absorption at 940 nm was not used in the study due to its strong noise level. A set of 95 field measurements of vegetation samples was performed concurrently with the flight with an analytical spectral device (ASD) FieldSpec3 spectrometer (2151 bands from 350 to 2500 nm, 3–10 nm resolution) to provide accurate ground references; these data were resampled at the HySpex VNIR 1600 resolution. Because of defective dynamic camera signals on shiny targets, due to factory calibration degradation over time, the spectra of a 25 m² white quartz sand area were used to perform a final spectral intensity adjustment between airborne and ground reflectances to remove the weak anomalous spectral undulation that remained after the atmospheric compensation.

The spectral 'smile' effect was partially removed due to oxygen under-compensation as shown in Figure 2. This problem was solved by replacing the erroneous data with a linear interpolation of the five neighbouring channels (from 754 to 769 nm) and removing the noisy channels over 925 nm in the MNF calculation to invert the first 15 components enabling the production of clean spectra.

The images were georeferenced by 'ACTIMAR' using the IMU and differential GPS data, in the coordinate system UTM, on the World Geodetic System 1984 (WGS84) datum, zone 30 north. As the accuracy of this georeferencing was insufficient for some image rows due to wind fluctuations during the acquisition, an additional georeferencing was performed based on an image-to-image matching to an aerial orthophotography produced by the IGN and registered as a BDOrtho® product with a resolution of 0.5 m pixel⁻¹. Twenty image rows were also merged using the bilinear interpolation of the mosaic function provided by the software ENVI®. This pre-processing resulted in a single image with a spatial resolution of 0.5 m (Figure 1).

<figure><img src="image_3.png" /><figcaption>Figure 2. Atmospheric-corrected and intensity-adjusted reflectance spectrum (1) showing oxygen under-compensation at 762 nm and noise excess above 925 nm, and after the inverse minimum noise fraction (MNF) transform using the first 15 MNF bands (2) calculated on the adjusted reflectance below 925 nm (3) with a linear interpolation from 754 to 769 nm (4) erasing the oxygen artefact.</figcaption></figure>
## 3. Methodology

The first step of the method is the analysis of the chemical association of pigments characterizing trees from which the physical properties of the canopy can be deduced by spatial statistics in small pixel neighbourhoods. These are collected by training to build a spectral library (Section 3.1), which is decomposed into indices representing sharp spectral features (Section 3.2) and weak undulation spectral features highlighted by a SAM method (Section 3.3). The relevance of this spectral decomposition for the identification of the chemical composition of associated pigments is tested by an intercomparison between all the library elements (Section 3.4). Then, the tree crown physical properties can be deduced from the exploration of the homogeneous pixel clusters creating a texture characterizing an object (Section 3.5). The second step recombines all the layers of chemical and physical information to map the trees at the object scale (Section 3.6).

### 3.1. Training classes and spectral library

The study area (Figure 1) is characterized by a mixture of individual houses aligned along streets and building blocks separated by parking lots and vegetation areas. During an extensive ground survey, 24 tree species were catalogued (Table 1). Sample leaves were collected for laboratory analysis and their location was recorded with a hand-held GPS unit.

These coordinates were used to retrieve the sampled species in the HySpex image and to define a series of corresponding reference regions of interest (ROI), less than 5 × 5 pixels (2.5 m × 2.5 m) in size. The average reflectance spectrum of the 16–25 pixels of each ROI was compared to the average spectrum of the corresponding collected leaves measured with the ASD Fieldspec® (Figure 3). This comparison revealed some clear differences. The main distortion of the spectrum comes from the leaf distribution within the canopy, from the bright crown tops to the dark intermediate layers that flatten the mean spectrum. When sparse leaves allow the background to appear, the pixel reflectance is a mixture of the canopy and background reflectances. Therefore, it was decided to build a training spectral library from the HySpex image of clean ROIs in which the background is not visible rather than using laboratory leaf spectra.

### 3.2. Spectral feature selection and indices

In their recent survey, Brantley, Zinnert, and Young (2011) presented the most popular indices: the normalized difference vegetation index (NDVI) of Rouse et al. (1974), the modified chlorophyll absorption ratio index (MCARI) of Daughtry et al. (2000), the triangular vegetation index (TVI) of Broge and Leblanc (2000) and some of their variants. Zarco-Tejada et al. (2005) added the photochemical reflectance index (PRI) of Gamon, Peñuelas, and Field (1992), the optimized soil-adjusted vegetation index (OSAVI) of Rondeaux, Steven, and Baret (1996), and the curvature index (CUR) of Zarco-Tejada et al. (2000).

After the simple NIR-to-red ratio vegetation index (SR) of Jordan (1969), the NDVI was developed by Rouse et al. (1974) and Deering and Haas (1980) to identify vegetation surfaces based on the fact that chloroplast cells strongly absorb light in the red
<table><caption>Table 1. Tree catalogue of the species identified in the Pin Sec neighbourhood in the northeast of Nantes (France).</caption><thead><tr><th>Type</th><th>Botanic name</th><th>Botanic family</th><th>Common name</th></tr></thead><tbody><tr><td>Deciduous trees with red leaves</td><td>Prunus cerasifera Ehrh<br>Acer palmatum Thunb., var. dissectum 'red'</td><td>Rosaceae<br>Sapindaceae<br>(=Aceraceae)</td><td>Purple leaf plum<br>Red leaved<br>Japanese maple</td></tr><tr><td>Deciduous trees with green leaves</td><td>Betula pendula Roth.<br>Salix babylonica L.<br>Fraxinus excelsior L.<br>Catalpa bignonioides Walter<br>Acer negundo L.</td><td>Betulaceae<br>Salicaceae<br>Oleaceae<br>Bignoniaceae<br>Sapindaceae<br>(=Aceraceae)</td><td>Silver birch<br>Weeping willow<br>European ash<br>Southern catalpa<br>Boxelder maple</td></tr><tr><td>Conifers</td><td>Liquidambar styraciflua L.<br>Platanus occidentalis L.<br>Quercus palustris Muench.<br>Quercus coccinea Wangh.<br>Quercus robur L.<br>Cupressus arizonica Greene<br>Cuprocyparis leylandi (A.B.Jacks. and Dallim.) Farjon, 2002. (hybride)<br>Cedrus atlantica (Endl.) Manetti ex Carrière</td><td>Hamamelidaceae<br>Platanaceae<br>Fagaceae<br>Fagaceae<br>Fagaceae<br>Cupressaceae<br>Cupressaceae<br>Abietaceae<br>(=Pinaceae)</td><td>Sweetgum<br>Sycamore<br>Pin oak<br>Scarlet oak<br>Truffle oak<br>Arizona cypress<br>Naylor's blue leyland<br>Atlas cedar</td></tr><tr><td></td><td>Pinus sylvestris L.</td><td>Abietaceae<br>(=Pinaceae)</td><td>Scots pine</td></tr><tr><td></td><td>Pinus pinea L.</td><td>Abietaceae<br>(=Pinaceae)</td><td>Stone pine</td></tr><tr><td></td><td>Pinus radiate D. Don</td><td>Abietaceae<br>(=Pinaceae)</td><td>Monterey pine</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>Figure 3. Examples of laboratory and image spectra for a Liquidambar sample. The inserts show: (a) a field picture of the vegetation stratification, (b) the sample leaves used for laboratory analysis, (c) the extract of the HySpex image showing the canopy shadow gradient and drop shadow over the background.</figcaption></figure>

wavelengths whereas the spongy mesophyll strongly reflects light in the NIR wavelengths. Most of the later indices tend to solve the NDVI saturation problems generated by dense and multilayered canopies (Lillesaeter 1982; Baret and Guyot 1991).
Most of the indices were constructed for forestry and agriculture studies (Gitelson, Gritz, and Merzlyak 2003; Hatfield et al. 2008). The major spectral features of vegetation are related to the chl-*a* and -*b* pigments, which are: (i) indirect indicators of leaf nitrogen content and (ii) closely related to vegetation stress and senescence. The presence of carotenoids and anthocyanin, which are more specific to the physiological state, also affects the spectra. The cellular structure and leaf water content are the main determinants of the spectral form in the visible and NIR wavelength ranges (Eitel et al. 2008; Wu et al. 2008). In addition, the NIR reflectance increases as a function of leaf biomass (Kumar et al. 2001; Gitelson, Gritz, and Merzlyak 2003; Shafri, Salleh, and Ghiyamat 2006; Eitel et al. 2008). The different influences of biomass, stress and physiological processes on the species cause the vegetation spectra to change with the season, and even at shorter timescales, and with local illumination conditions between sun and shade exposure (Lichtenthaler et al. 2007, 2013) making it difficult to identify clearly the species, general or even vegetation groups (Richardson, Duigan, and Berlyn 2002). The good contrast between trees and lawns in Figure 1 is due to the date of the image acquisition at the spring maximum of vegetation growth.

Following the spectral feature separation approach developed by Heiden et al. (2007), like in Brantley, Zinnert, and Young (2011), new indices were specifically built and adapted to the HySpex spectral resolution and to the group of trees catalogued in this study (Kassouk et al. 2011). They are defined in Table 2 and Figure 4. The refined NDVI is based on the HySpex narrow spectral bands immediately straddling the red-to-NIR spectral shoulder: 675 nm for chlorophyll and 773 nm for the NIR plateau immediately off the red edge, combining the 642–675 and 798–920 nm regression lines to obtain a noiseless NDVI (Figure 4). It must be noted that the results obtained with the different NDVI definitions cannot be compared without considering the different spectral resolutions.

To differentiate between green and red leaves (quite common in the Nantes suburb test site), two new vegetation indices were defined. On the one hand, the new normalized difference green leaves index (NDGLI) takes advantage of the strong absorption of chl-*a* in the blue domain (400–500 nm) close to the relative reflectance peak of healthy leaves in the green domain (555 nm). On the other hand, because they contain anthocyanin, red leaves absorb green wavelengths up to 555 nm and present a peak of reflectance at 602 nm, enabling the definition of a new normalized difference red leaves index (NDRLI). The new indices were optimized to the HySpex channels and to the spectral properties of the vegetation types found within the studied area rather than the blue/green (BGI<sub>2</sub>) and red/green (RGI) pigment indices defined by Zarco-Tejada et al. (2005) and the photochemical reflectance index (PRI) of Gamon, Peñuelas, and Field (1992).

To avoid mixture problems with the surrounding lawns (well maintained or not) and wasteland bushes, it was necessary to identify their spectral features (e.g. s3 in Figure 4). We did not try to optimize the NDVI, for example, Qi et al. (1994) and Rondeaux, Steven, and Baret (1996) with their modified and optimized soil-adjusted vegetation indices (MSAVI and OSAVI), but focused our method exclusively on the spectral shape. Since the NIR plateau is altered by the presence of the background, which reduces the spectral shoulder, we based our normalized difference grass index (NDGI) on the spectral slope in the NIR domain. However, in this study case, the perfectly green mixed lawns,
<table><caption>Table 2. Normalized spectral index definitions.</caption><thead><tr><th>Index</th><th>Formula</th><th>FWHM/sampling interval (nm)</th><th>Authors</th></tr></thead><tbody><tr><td>SR</td><td>R<sub>NIR</sub>/R<sub>Red</sub></td><td>70 110</td><td>Jordan (1969)</td></tr><tr><td>NDVI</td><td>(R<sub>NIR</sub> R<sub>Red</sub>)/(R<sub>NIR</sub> + R<sub>Red</sub>)</td><td>70 110</td><td>Rouse et al. (1974)</td></tr><tr><td>NDVI</td><td>(R<sub>800</sub> R<sub>670</sub>)/(R<sub>800</sub> + R<sub>670</sub>)</td><td>4.5</td><td>Brantley, Zinnert, and Young (2011)</td></tr><tr><td>NDVI</td><td>(R<sub>773</sub> R<sub>675</sub>)/(R<sub>773</sub> + R<sub>675</sub>)</td><td>4.5/3.7</td><td>Present study</td></tr><tr><td>TVI</td><td>0.5 × [120 × (R<sub>750</sub> R<sub>550</sub>)<sup>2</sup> × (R<sub>670</sub> R<sub>550</sub>)]</td><td></td><td>Broge and Leblanc (2000)</td></tr><tr><td>MSAVI</td><td>0.5 × [2 × R<sub>800</sub> + 1 ((2 × R<sub>800</sub> + 1)<sup>2</sup> + 8 × (R<sub>800</sub> R<sub>670</sub>))<sup>2.5</sup>]</td><td></td><td>Qi et al. (1994)</td></tr><tr><td>OSAVI</td><td>(1 + 0.16) × (R<sub>800</sub> R<sub>670</sub>)/(R<sub>800</sub> + R<sub>670</sub> + 0.16)]</td><td></td><td>Rondeaux, Steven, and Baret (1996)</td></tr><tr><td>PRI<sub>1</sub></td><td>(R<sub>528</sub> R<sub>576</sub>)/(R<sub>528</sub> + R<sub>576</sub>)</td><td>7 12</td><td>Gamon, Peñuelas, and Field (1992)</td></tr><tr><td>PRI<sub>3</sub></td><td>(R<sub>570</sub> R<sub>539</sub>)/(R<sub>570</sub> + R<sub>539</sub>)</td><td></td><td></td></tr><tr><td>BGI<sub>2</sub></td><td>R<sub>450</sub>/R<sub>550</sub></td><td>4.5</td><td>Zarco Tejada et al. (2005)</td></tr><tr><td>RGI</td><td>R<sub>690</sub>/R<sub>550</sub></td><td></td><td></td></tr><tr><td>NDGLI</td><td>(R<sub>555</sub> R<sub>501</sub>)/(R<sub>555</sub> + R<sub>501</sub>)</td><td>4.5/3.7</td><td>Kassouk et al. (2010)</td></tr><tr><td>NDRLI</td><td>(R<sub>602</sub> R<sub>555</sub>)/(R<sub>602</sub> + R<sub>555</sub>)</td><td>4.5/3.7</td><td>Kassouk et al. (2010)</td></tr><tr><td>NDGI</td><td>(R<sub>922</sub> R<sub>773</sub>)/(R<sub>922</sub> + R<sub>773</sub>)</td><td>4.5/3.7</td><td>Kassouk et al. (2010)</td></tr><tr><td>NDchl al</td><td>(R<sub>642</sub> R<sub>675</sub>)/(R<sub>642</sub> + R<sub>675</sub>)</td><td>4.5/3.7</td><td>Kassouk et al. (2010)</td></tr><tr><td>NDDSI</td><td>(R<sub>435</sub> R<sub>501</sub>)/(R<sub>435</sub> + R<sub>501</sub>)</td><td>4.5/3.7</td><td>Present study</td></tr><tr><td>IntDS</td><td>0.1/R<sub>798</sub></td><td>4.5/3.7</td><td>Present study</td></tr><tr><td>Soilmix</td><td>10 R<sub>435</sub></td><td>4.5/3.7</td><td>Present study</td></tr><tr><td>CUR</td><td>(R<sub>675</sub> × R<sub>690</sub>)/R<sup>2</sup><sub>683</sub></td><td>4.5</td><td>Zarco Tejada et al. (2005)</td></tr><tr><td>Chlorophyll index</td><td>(R<sub>730</sub>)/(R<sub>700</sub> R<sub>710</sub>)</td><td>&lt;10 nm</td><td>Gitelson et al. (2005)</td></tr><tr><td>MPBI</td><td>(2 × R<sub>586</sub>)/(R<sub>495</sub> R<sub>600</sub>)</td><td>1</td><td></td></tr><tr><td>Id<sub>GL</sub></td><td>(2 × R<sub>555</sub>)/(R<sub>501</sub> + R<sub>602</sub>)</td><td>1</td><td>Kazemipour, Launeau, and Méléder (2012)</td></tr><tr><td>Id<sub>RL</sub></td><td>(2 × R<sub>602</sub>)/(R<sub>555</sub> + R<sub>675</sub>)</td><td>1</td><td>Kassouk et al. (2010)</td></tr></tbody></table>

R indicates a reflectance value and subscripts the wavelength or wavelength range in nm (red: from 600 to 700 nm, NIR: from 750 to 900 nm). Only bold item in the first column are used in this study.

patchworks of grass, clover, and daisy without visible soil, may present an NIR plateau slope in spring but not in autumn. This NDGI is therefore convenient to contrast the presence of lawns and trees in spring periods of strong vegetation growth. As soil mixture problems remain at the border with areas without vegetation, the mixed pixels were identified by their anomalous reflectance at 435 nm: the so-called soil mixture index (Soilmix) defined in Table 2 is conveniently used later during the classification (see Section 4.1).

To prevent lawns in the drop shadow of buildings and trees being wrongly classified into conifer classes presenting similar spectral shapes, we also defined a normalized difference drop shadow index (NDDSI). Since the areas in drop shadows receive only the diffused light from the blue sky, they are bluish and can be detected by combining (i) the NDDSI measuring the spectral slope in the 435–501 nm range and (ii) a darkness index at 798 nm, labelled IntDS in Table 2. Finally, we defined a normalized difference chl-a index (NDchl-al) to quantify the relative spectral trough adjacent to the red-to-NIR reflectance shoulder, due to chl-a absorption. This index is particularly effective for detecting artificial lawns and separating healthy grass from degraded lawns where the soil is visible between the leaves.

Note that none of our new indices can be computed with broadband multispectral sensor data such as from SPOT ('Satellites Pour l'Observation de la Terre') or Landsat (Land Satellite) sensors. As an example, the index values of the lawns of Figure 3
<figure><img src="image_5.png" /><figcaption>Figure 4. Examples of vegetation reflectance spectra (dotted lines) with their regression lines (solid lines) in the following wavelength intervals: 435–501 nm, 501–555 nm, 577–602 nm, 642–675 nm and 798–920 nm, yielding unbiased noise-free normalized difference drop shadow index (NDDSI), normalized difference red leaves index (NDRLI), normalized difference green leaves index (NDGLI), normalized difference chl-a index (NDchl-a), and normalized difference grass index (NDGI), respectively (s1 *Acer palmate* red, s2 *Acer negundo*, s3 lawn, s4 *Quercus palustris*, s5 *Cupressus arizonica*, s6 *Pinus sylvestris*). Normalized difference vegetation index (NDVI) (1), Id<sub>GL</sub> (2), and Id<sub>RL</sub> (3) are calculated with the regression line slope bounding values. IntDS and Soilmix indices are also based on the regression line values at 798 and 435 nm, respectively.</figcaption></figure>

computed from HySpex and Landsat 7 Enhanced Thematic Mapper Plus (+ETM) data are reported in Table 3, demonstrating the upper discriminating power of hyperspectral data or at least multispectral data using a narrow bandwidth less than 10 nm.

Following Zarco-Tejada et al. (2005) and Gitelson et al. (2005), we also explored the discriminating power of spectral curvature indices. To improve the identification of red and green leaves, two indices were built. They measure the spectral curvature around the reflectance peaks of green and red leaves, Id<sub>GL</sub> and Id<sub>RL</sub>, respectively (Table 2 and Figure 4). Both indices are close to the microphytobenthos index (MPBI) of Kazemipour, Launeau, and Méléder (2012), which was optimized to map microalgae, but are finely adapted to the observed peaks in the spectra of green and red leaves. These two indices are strongly correlated with the NDGLI and NDRLI, respectively, which is useful to eliminate noisy pixels and facilitate the object segmentation (see Section 3.4).

<table><caption>Table 3. NDVI and NDchl-al values for synthetic, natural and degraded natural lawns obtained with HySpex® and Landsat sensors.</caption><thead><tr><th>Sensor</th><th>Index</th><th>Lower band (nm)</th><th>Upper band (nm)</th><th>Synthetic lawn</th><th>Healthy natural lawn</th><th>Degraded natural lawn</th></tr></thead><tbody><tr><td>Landsat 7 ETM+</td><td>NDVI</td><td>630 690</td><td>770 900</td><td>0.437</td><td>0.758</td><td>0.625</td></tr><tr><td rowspan="2">HySpex</td><td>NDVI</td><td>673 677</td><td>771 775</td><td>0.198</td><td>0.762</td><td>0.607</td></tr><tr><td>NDchl al</td><td>673 677</td><td>640 644</td><td>0.115</td><td>0.053</td><td>0.024</td></tr></tbody></table>
In fact, these indices represent the slope of part of the reflectance spectra. In order to evaluate their relevance, a linear regression was computed for each of the spectral intervals corresponding to the NDGLI, NDRLI, NDchl-al, NDGI, and NDDSI and their coefficients of determination, denoted R², are reported in Table 4. As most of them are greater than 0.9, each spectral interval can be replaced by a straight line yielding more stable indices independent of other tiny small undulations due to other pigments, which could alter both the minimum and maximum intensities used for the calculation. By doing so, we take advantage of the hyperspectral high resolution to obtain more homogeneous index maps based on the values of about 10 continuous channels qualified by R² calculation instead of two isolated channels without any quality estimation. For consistency between indices, we also computed the other indices, NDVI, IdRL and IdGL, with the results of the corresponding linear regressions (solid lines in Figure 4). We prefer the normalized difference indices rather than the slopes themselves because the normalization compensates for the variations in solar illumination (e.g. Sabins 1997).

### 3.3. SAM indices

Greiwe and Ehlers (2005) proposed using the SAM method (Kruse et al. 1993; Kruse 2005) in urban areas due to its low sensitivity to illumination conditions (see the discussion in Clark, Roberts, and Clark 2005). In this method, the reflectance spectrum of each individual pixel is expressed as a vector in an n-dimensional space, where n is the number of spectral bands. The angle between two spectral vectors quantifies the shape difference between the corresponding spectra regardless of the light intensity. Well exposed and partially shaded areas appear identical, which is particularly useful in urban areas. The spectral angle ranges from 0 (for parallel vectors indicating identical spectra) to π/2 (for perpendicular vectors indicating inconsistent spectra).

A classification may be performed by computing the spectral angles to class reference spectra. A pixel is usually assigned to a class when its spectral angle to that class is lower than 0.100. Thus, a SAM angle image shows large variations in grey levels for insignificant pixels whereas subtle variations around the critical angle of 0.100 are hardly visible. Instead of the SAM angle, it is useful to define a SAM index as follows:

$$ \text{SAM index} = (\text{SAM angle} + 0.001)^{1}, \qquad (1) $$

where the factor 0.001 is included to avoid a division by zero in the case of a perfect match. The resulting SAM index image ranges from 2/π (i.e. 0.636) to 5 for pixels that are not consistent with the selected class and from 5 to 1000 when the SAM angle image ranges from 0.200 to 0.000, respectively, for the pixels whose spectrum is close to the

<table><caption>Table 4. Coefficient of determination R² of the linear regression computed for each of the spectral intervals corresponding to five index spectral intervals of the image sample spectra of Figure 4.</caption><thead><tr><th>Index</th><th>Sample interval (nm)</th><th>s1 <i>Acer palmate</i> red</th><th>s2 <i>Acer negundo</i></th><th>s3 lawn</th><th>s4 <i>Quercus palustris</i></th><th>s5 <i>Cupressus Arizona</i></th><th>s6 <i>Pinus sylvestris</i></th></tr></thead><tbody><tr><td>NDDSI</td><td>435 501</td><td>0.864</td><td>0.144</td><td>0.27</td><td>0.404</td><td>0.461</td><td>0.752</td></tr><tr><td>NDGLI</td><td>555 602</td><td>0.888</td><td>0.980</td><td>0.974</td><td>0.984</td><td>0.974</td><td>0.977</td></tr><tr><td>NDRLI</td><td>501 556</td><td>0.967</td><td>0.965</td><td>0.929</td><td>0.977</td><td>0.974</td><td>0.972</td></tr><tr><td>NDchl al</td><td>642 675</td><td>0.967</td><td>0.992</td><td>0.982</td><td>0.991</td><td>0.991</td><td>0.985</td></tr><tr><td>NDGI</td><td>798 922</td><td>0.874</td><td>0.732</td><td>0.996</td><td>0.914</td><td>0.843</td><td>0.930</td></tr></tbody></table>
class reference spectrum. In Figures 5(a–d), the *Liquidambar* SAM angles and SAM indices range from 0.04 to 0.91 and from 6 to 28, respectively, showing that this inversion allows a better delineation of the *Liquidambar* (1) crowns over the background and other tree crowns such as *Pinus pinea* (2) by enhancing the image contrast. This step is fundamental for the good segmentation of objects on SAM index information layers or co-images (see Section 3.5).

### 3.4. Spectral separability of chemical tree compositions

The training step (Section 3.1) collects the main tree types of the study area plus a few samples of vegetation assumed to present similar chemical pigment compositions. Since the combination of spectral features (Section 3.2) and SAM indices (Section 3.3) is the main tool for mapping such compositions, the separability must be tested for all the elements of the training spectral library. Many different vegetation types can have identical pigment associations and chemical compositions. It is thus difficult to find the closest spectrum in a spectral library containing too many spectra very close to each other. The intercomparison of library spectra is therefore a critical step in the method, which leads to spectrally inseparable tree types and other

<figure><img src="image_6.png" /><figcaption>Figure 5. *Liquidambar styraciflua* spectral angle mapping (SAM): (a) SAM angle image; (b) SAM angle grey scale bar; (c) SAM index image; (d) SAM index grey scale bar; location of (1) *Liquidambar styraciflua* and (2) *Pinus pinea* tree crowns in both images and grey scale bars.</figcaption></figure>
vegetation types being grouped together. The SAM analysis is an efficient tool to
reveal almost perfect spectral matches. Metrics other than angles can be used;
however, they would only find differences coming from parameters other than pig-
ment and chemical contents, which are not wanted in a pure layer of chemical
information.

3.5. Spatial segmentation into textural objects

In urban areas, vegetation is scattered in patches of various compositions ranging from small plants in lawns to trees aligned along streets or gathered in large groves. Each patch of homogeneous composition can be extracted as one object delineated by a boundary. This boundary may be defined by a change to either another vegetation type or another material, but it can also be due to a change in the self-organization of the vegetation pattern or texture without any change in physicochemical composition. Thus, plants and trees may be segmented on the basis of spatial or physical organization within object boundaries.

The segmentation algorithm of Baatz and Schäpe (2000), available in the Trimble® eCognition® software, is based on both spectral and textural properties, and also on the object size and changes at various scales of observation. Spectral properties may be represented by the full spectra or, as proposed in the present study, by spectral features and SAM indices allowing the information to be concentrated into fewer channels of information layers or co-images. Textural properties rely on the spatial resolution defining the pixel size and the number of pixels needed to define homogeneous/heterogeneous spatial pixel distributions. The scale parameter is therefore equivalent to the size of the window used to calculate local spatial statistics. This size can be increased to test how the local statistics can change from one scale to another. For instance, one of the statistical parameters is the co-occurrence matrix, which is limited by the number of rows and columns. A combination of all these parameters is finally used in a discriminating analysis to group pixels into objects, according to (i) the object spectral homogeneity, (ii) the object compactness, and (iii) the object boundary smoothness. For more details, please refer to Blaschke and Strobl (2001) and Benz et al. (2004). According to Wang, Sousa, and Gong (2004), the optimum scale parameter may be best determined by the visual inspection of the objects by a photo-interpreter rather than by quantitative criteria.

3.6. Classification scheme

The classification method proposed in this study is based on successive queries made on
the information layers constituted at the pixel scale and the object scale. Fundamental
chemical information is given by the pixel spectra in Section 3.1. As demonstrated in Section
3.2, they can be sorted by their sharp spectral features and SAM indices, which were used to
differentiate more subtle spectral shape undulations to define narrower classes in Section
3.3. However, a tree canopy is an association of pixels connected to each other and forming
an object with a particular patchwork texture of characteristic chemical compositions. Once
such objects have been defined (Section 3.5), the main spectra can be extracted from each
tree. We chose to use the mean spectrum as *a priori* the most representative spectrum of each tree since low statistical pixels are assumed to be rejected over one or two standard deviations; this will be discussed in the application section (Section 4).

To summarize, pixel chemical information layers are combined to define objects from which a mean spectrum is extracted to calculate new sharp spectral features (i.e. main chemistry) and SAM indices (i.e. angular distances to the training spectral library references) at the object scale (forming a tree crown). All these new information layers can now be combined to define a hierarchical classification of tree types. First, indices are used to separate the object mean spectrum into subsets of chemical similarities. In each subset defined by a combination of spectral feature indices, a sub-subset of the training spectral library can be defined and tested by SAM indices to identify finally each vegetation type or tree group. For example, the isolation of red leaf objects enables us to compare their mean spectra exclusively with a subset of effectively red leaf classes. The set of spectral feature indices avoids the possible confusion by SAM between spectra identical over 123 channels and differing only by a narrow sharp but weak absorption feature over 20 channels, like for red *Prunus cerasifera* and green *Prunus domestica*, for instance.

## 4. Application to the HySpex image of N–E Nantes

The methodology was defined on a training spectral library presented in Section 3.1. Its application to the study area starts with the intercomparison of the training spectral library (Section 4.1) showing that ambiguities between spectral shapes can be removed by a combination of sharp spectral feature indices prior to SAM analysis. The further application at the object size is justified by the need to remove local heterogeneities within tree crowns (Section 4.2). Section 4.3 details the classification sequence while its accuracy assessment is presented in Section 4.4.

### 4.1. Spectral library analysis

The classification method defined in this study is based on the successive use of spectral shape indices and SAM indices that are based on the indices obtained with a collection of 17 catalogued reference image ROI mean spectra (Table 5) constituting a training spectral library. Lawns and other plants, including roof sedum, are also present in this catalogue in order to contrast the tree crowns, thus increasing the collection from 17 to 21 spectra.

From the analysis of the comparisons between our 21 reference spectra (Table 5), we establish the following subdivision of the training spectral library by a set of sharp spectral feature index thresholds minimizing the SAM confusion between spectra.

(i) The NDVI threshold of 0.40 proposed by Groesz and Kastdalen (2007) may be used with the HySpex data to separate the vegetation from other urban materials, such as artificial lawns, roofs, and all types of stained asphalt.

(ii) All red and purple leaves may be identified by NDRLI >0.

(iii) Lawns are the only vegetation types with NDGI >0.08 (in spring).
<table><caption>Table 5. (a) inter-species SAM indices. (b) Species spectra shape indices of the 21 RO mean spectra of the Pin Sec neighbourhood image. (c) Principa component analysis of NDV and NDGL used to separate better conifers from deciduous trees. (d) Vegetation groups.</caption><thead><tr><th colspan="15">Species samples and other samples required for classification</th></tr><tr><th></th><th>Sedum</th><th>Prunus cesarifera</th><th>Acer Palmatum red</th><th>Betula Pendula</th><th>Salix</th><th>Frachinus Exelsior</th><th>Catalpa</th><th>Acer negundo</th><th>Liquidambar</th><th>Platanus occidentali</th><th>Quercus Palustris</th><th>Quercus coccinea</th><th>Quercus Robur</th><th>Cupressus arizonica</th><th>Cedrus Atlantica</th><th>Syriurus Syriurus</th><th>Pinus pinea</th><th>Pinus griffithii</th><th>Lawn</th><th>Degraded lawn</th><th>Drop shadow on lawn</th></tr></thead><tbody><tr><td colspan="15">(a) SAM indices</td></tr><tr><td>Sedum</td><td>7</td><td>5</td><td>7</td><td>10</td><td>19</td><td>14</td><td>13</td><td>7</td><td>18</td><td>15</td><td>8</td><td>19</td><td>13</td><td>13</td><td>7</td><td>11</td><td>9</td><td>7</td><td>15</td><td>8</td><td>12</td><td>5</td></tr><tr><td>Prunus cesarifera</td><td>5</td><td>1000</td><td>22</td><td>11</td><td>6</td><td>1000</td><td>56</td><td>14</td><td>5</td><td>10</td><td>6</td><td>19</td><td>13</td><td>13</td><td>8</td><td>5</td><td>4</td><td>4</td><td>7</td><td>4</td><td>8</td><td>5</td></tr><tr><td>Acer Palmatum red</td><td>7</td><td>22</td><td>1000</td><td>19</td><td>9</td><td>8</td><td>6</td><td>6</td><td>6</td><td>17</td><td>8</td><td>5</td><td>11</td><td>36</td><td>6</td><td>5</td><td>4</td><td>9</td><td>4</td><td>11</td><td>6</td><td>3</td></tr><tr><td>Betula Pendula</td><td>10</td><td>11</td><td>19</td><td>1000</td><td>14</td><td>13</td><td>7</td><td>20</td><td>11</td><td>10</td><td>6</td><td>12</td><td>6</td><td>19</td><td>20</td><td>8</td><td>7</td><td>5</td><td>15</td><td>5</td><td>23</td><td>9</td></tr><tr><td>Salix</td><td>19</td><td>6</td><td>9</td><td>14</td><td>1000</td><td>15</td><td>15</td><td>11</td><td>11</td><td>12</td><td>12</td><td>39</td><td>11</td><td>27</td><td>9</td><td>13</td><td>10</td><td>7</td><td>35</td><td>7</td><td>20</td><td>13</td></tr><tr><td>Babylonica</td><td>19</td><td>6</td><td>8</td><td>13</td><td>56</td><td>1000</td><td>15</td><td>16</td><td>12</td><td>12</td><td>30</td><td>12</td><td>30</td><td>9</td><td>12</td><td>9</td><td>6</td><td>24</td><td>7</td><td>17</td><td>12</td><td>5</td></tr><tr><td>Frachinus Exelsior</td><td>18</td><td>5</td><td>6</td><td>7</td><td>14</td><td>15</td><td>1000</td><td>23</td><td>7</td><td>16</td><td>30</td><td>10</td><td>5</td><td>15</td><td>11</td><td>9</td><td>12</td><td>11</td><td>9</td><td>11</td><td>5</td></tr><tr><td>Acer negundo</td><td>15</td><td>5</td><td>6</td><td>7</td><td>15</td><td>16</td><td>23</td><td>1000</td><td>7</td><td>14</td><td>25</td><td>11</td><td>6</td><td>12</td><td>12</td><td>9</td><td>7</td><td>11</td><td>9</td><td>9</td><td>10</td></tr><tr><td>Liquidambar</td><td>8</td><td>10</td><td>17</td><td>20</td><td>11</td><td>12</td><td>7</td><td>1000</td><td>9</td><td>14</td><td>6</td><td>18</td><td>19</td><td>6</td><td>5</td><td>4</td><td>10</td><td>4</td><td>12</td><td>7</td><td>3</td></tr><tr><td>Platanus occidentali</td><td>19</td><td>6</td><td>8</td><td>12</td><td>39</td><td>30</td><td>16</td><td>14</td><td>9</td><td>1000</td><td>12</td><td>18</td><td>8</td><td>17</td><td>12</td><td>8</td><td>31</td><td>8</td><td>15</td><td>12</td><td>5</td></tr><tr><td>Quercus Palustris</td><td>13</td><td>4</td><td>5</td><td>6</td><td>11</td><td>12</td><td>30</td><td>25</td><td>6</td><td>12</td><td>1000</td><td>9</td><td>5</td><td>14</td><td>11</td><td>9</td><td>10</td><td>6</td><td>13</td><td>8</td><td>10</td></tr><tr><td>Quercus coccinea</td><td>13</td><td>8</td><td>11</td><td>19</td><td>27</td><td>30</td><td>10</td><td>11</td><td>18</td><td>18</td><td>9</td><td>1000</td><td>11</td><td>9</td><td>7</td><td>5</td><td>19</td><td>6</td><td>19</td><td>9</td><td>4</td></tr><tr><td>Quercus Robur</td><td>7</td><td>20</td><td>36</td><td>20</td><td>9</td><td>9</td><td>5</td><td>6</td><td>19</td><td>8</td><td>5</td><td>11</td><td>6</td><td>5</td><td>4</td><td>9</td><td>4</td><td>11</td><td>6</td><td>3</td><td>7</td></tr><tr><td>Cupressus arizonica</td><td>11</td><td>5</td><td>6</td><td>8</td><td>13</td><td>12</td><td>15</td><td>12</td><td>6</td><td>17</td><td>14</td><td>9</td><td>5</td><td>13</td><td>14</td><td>13</td><td>10</td><td>13</td><td>13</td><td>10</td><td>7</td></tr><tr><td>Cedrus Atlantica</td><td>9</td><td>4</td><td>5</td><td>7</td><td>10</td><td>9</td><td>11</td><td>9</td><td>5</td><td>12</td><td>11</td><td>7</td><td>6</td><td>35</td><td>1000</td><td>18</td><td>11</td><td>16</td><td>8</td><td>12</td><td>8</td></tr><tr><td>Pinus Sylvestris</td><td>7</td><td>4</td><td>4</td><td>5</td><td>7</td><td>6</td><td>9</td><td>7</td><td>4</td><td>8</td><td>9</td><td>5</td><td>4</td><td>13</td><td>18</td><td>1000</td><td>7</td><td>28</td><td>6</td><td>8</td><td>10</td></tr><tr><td>Pinus pinea</td><td>15</td><td>7</td><td>9</td><td>15</td><td>35</td><td>24</td><td>12</td><td>11</td><td>10</td><td>31</td><td>10</td><td>19</td><td>9</td><td>14</td><td>11</td><td>7</td><td>1000</td><td>7</td><td>25</td><td>15</td><td>5</td></tr><tr><td>Pinus griffithii</td><td>8</td><td>4</td><td>4</td><td>5</td><td>7</td><td>7</td><td>11</td><td>9</td><td>4</td><td>8</td><td>13</td><td>6</td><td>4</td><td>13</td><td>16</td><td>28</td><td>7</td><td>1000</td><td>6</td><td>9</td><td>8</td></tr><tr><td>Lawn</td><td>12</td><td>8</td><td>11</td><td>23</td><td>20</td><td>17</td><td>9</td><td>9</td><td>12</td><td>15</td><td>8</td><td>19</td><td>11</td><td>10</td><td>8</td><td>6</td><td>25</td><td>6</td><td>1000</td><td>14</td><td>4</td></tr><tr><td>Degraded lawn</td><td>12</td><td>5</td><td>6</td><td>9</td><td>13</td><td>12</td><td>11</td><td>10</td><td>7</td><td>12</td><td>10</td><td>9</td><td>6</td><td>13</td><td>12</td><td>8</td><td>15</td><td>9</td><td>1000</td><td>14</td><td>5</td></tr><tr><td>Drop shadow on lawn</td><td>5</td><td>3</td><td>3</td><td>4</td><td>5</td><td>5</td><td>5</td><td>5</td><td>3</td><td>5</td><td>5</td><td>4</td><td>3</td><td>7</td><td>8</td><td>10</td><td>5</td><td>8</td><td>1000</td><td>5</td><td>2</td></tr><tr><td colspan="15">(b) Vegetation indices</td></tr><tr><td>NDVI</td><td>0.72</td><td>0.9</td><td>0.86</td><td>0.82</td><td>0.77</td><td>0.79</td><td>0.74</td><td>0.79</td><td>0.91</td><td>0.73</td><td>0.74</td><td>0.85</td><td>0.89</td><td>0.7</td><td>0.65</td><td>0.57</td><td>0.72</td><td>0.6</td><td>0.77</td><td>0.66</td><td>0.63</td></tr><tr><td>NDGLI</td><td>0.22</td><td>0.01</td><td>0.07</td><td>0.25</td><td>0.33</td><td>0.38</td><td>0.3</td><td>0.47</td><td>0.5</td><td>0.26</td><td>0.37</td><td>0.42</td><td>0.33</td><td>0.17</td><td>0.14</td><td>0.13</td><td>0.24</td><td>0.21</td><td>0.31</td><td>0.23</td><td>0.08</td></tr><tr><td>NDRLI</td><td><strong>0.05</strong></td><td><strong>0.05</strong></td><td><strong>0.02</strong></td><td><strong>-0.12</strong></td><td><strong>-0.1</strong></td><td><strong>-0.16</strong></td><td><strong>-0.09</strong></td><td><strong>-0.13</strong></td><td><strong>-0.23</strong></td><td><strong>-0.11</strong></td><td><strong>-0.1</strong></td><td><strong>-0.15</strong></td><td><strong>-0.19</strong></td><td><strong>-0.09</strong></td><td><strong>-0.08</strong></td><td><strong>-0.06</strong></td><td><strong>-0.09</strong></td><td><strong>-0.08</strong></td><td><strong>-0.11</strong></td><td><strong>-0.05</strong></td><td><strong>-0.1</strong></td></tr><tr><td>NDGI</td><td>0.02</td><td>0.03</td><td>0.04</td><td>0.03</td><td>0.03</td><td>0.02</td><td>0.02</td><td>0.02</td><td>0.03</td><td>0.02</td><td>0.02</td><td>0.03</td><td>0.03</td><td>0.03</td><td>0.04</td><td>0.04</td><td>0.03</td><td>0.03</td><td>0.03</td><td>0.07</td><td>0.09</td></tr><tr><td>NDChl-al</td><td>0.13</td><td>0.12</td><td>0.08</td><td>0.11</td><td>0.14</td><td>0.15</td><td>0.17</td><td>0.27</td><td>0.27</td><td>0.08</td><td>0.2</td><td>0.22</td><td>0.14</td><td>0.1</td><td>0.07</td><td>0.04</td><td>0.06</td><td>0.08</td><td>0.09</td><td>0.06</td><td>0.09</td></tr><tr><td>IdGL</td><td>0.14</td><td>-0.04</td><td>0.05</td><td>0.44</td><td>0.52</td><td>0.71</td><td>0.45</td><td>0.78</td><td>1.06</td><td>0.44</td><td>0.57</td><td>0.75</td><td>0.68</td><td>0.29</td><td>0.24</td><td>0.21</td><td>0.38</td><td>0.32</td><td>0.51</td><td>0.31</td><td>0.2</td></tr></tbody></table>

(Continued)
<table><caption>Table5. (Continued).</caption><thead><tr><th>Species samples and other samples required for classification</th><th>Sedum</th><th>Prunus cerasifera</th><th>Acer Palmatum red</th><th>Betula Pendula</th><th>Salix</th><th>Babylonica</th><th>Fraxinus Exellior</th><th>Catalpa</th><th>Acer negundo</th><th>Liquidambar</th><th>Platamus occidentali</th><th>Quercus Paliostri</th><th>Quercus coccinea</th><th>Quercus Robur</th><th>Cupressus anizonica</th><th>Cedrus Atlantica</th><th>Pinus Sylvestris</th><th>Pinus pinea</th><th>Pinus griffithii</th><th>Pinus griffithii</th><th>Lawn</th><th>Degraded lawn</th><th>Drop shadow on lawn</th></tr></thead><tbody><tr><td>Id4</td><td>0.23</td><td>0.27</td><td>0.19</td><td>0.01</td><td>0.08</td><td>0.01</td><td>0.12</td><td>0.15</td><td>0.01</td><td>0.01</td><td>0.00</td><td>0.14</td><td>0.09</td><td>-0.02</td><td>0.06</td><td>0.02</td><td>0.01</td><td>0.01</td><td>0.03</td><td>0.01</td><td>0.03</td><td>0.02</td></tr><tr><td>NDDSI</td><td>-0.01</td><td>0.12</td><td>0.04</td><td>0.03</td><td>-0.01</td><td>0.00</td><td>0.00</td><td>-0.04</td><td>0.05</td><td>0.01</td><td>0.01</td><td>0.00</td><td>0.00</td><td>0.06</td><td>0.00</td><td>0.01</td><td>0.02</td><td>0.00</td><td>0.01</td><td>-0.01</td><td>-0.03</td><td>0.12</td></tr><tr><td>IntDS</td><td>0.26</td><td>0.28</td><td>0.19</td><td>0.28</td><td>0.23</td><td>0.23</td><td>0.3</td><td>0.22</td><td>0.23</td><td>0.28</td><td>0.3</td><td>0.2</td><td>0.22</td><td>0.37</td><td>0.36</td><td>0.51</td><td>0.28</td><td>0.37</td><td>0.26</td><td>0.28</td><td>1.56</td><td>0.3</td></tr><tr><td>Soilmix</td><td>0.48</td><td>0.36</td><td>0.52</td><td>0.42</td><td>0.54</td><td>0.53</td><td>0.56</td><td>0.53</td><td>0.32</td><td>0.58</td><td>0.55</td><td>0.5</td><td>0.41</td><td>0.57</td><td>0.68</td><td>0.58</td><td>0.5</td><td>0.41</td><td>0.46</td><td>0.56</td><td>0.3</td></tr><tr><td>(c) PCA of NDGL and NDVI for non-red leaves</td><td>0.61</td><td>0.56</td><td>0.58</td><td>0.7</td><td>0.73</td><td>0.79</td><td>0.69</td><td>0.86</td><td>0.95</td><td>0.65</td><td>0.74</td><td>0.85</td><td>0.8</td><td>0.56</td><td>0.5</td><td>0.45</td><td>0.63</td><td>0.53</td><td>0.71</td><td>0.58</td><td>0.45</td></tr><tr><td>(d) Vegetation groups</td><td>1</td><td>2</td><td>3</td><td>4</td><td>4</td><td>5</td><td>5</td><td>6</td><td>4</td><td>5</td><td>4</td><td>7</td><td>9</td><td>8</td><td>9</td><td>10</td><td>9</td><td>11</td><td>12</td><td>13</td></tr></tbody></table>

Large box bounding boxes enclose species characterized by red eaves, green eaves conifers, and awns within which grey boxes indicate vegetation groups. Thin bounding boxes indicate confusing SAM indices that require the addition of shape indices.
(iv) Conifers have a lower NDVI than deciduous trees, due to the small intercellular air space of their mesophyll and the needle shape of their leaves (Xiao, Ustin, and McPherson 2004). Because of their pigment content, conifers also have a lower NDGLI than deciduous trees. When it is spring in the study area, both indices increase together from conifer to deciduous trees. Consequently, a principal component (PC) analysis was used to quantify the correlation between NDGLI and NDVI (Figure 6). A discriminating function based on a rotation of the data along the main component (i.e. PC1) of this principal component analysis was defined. Deciduous trees have a PC1 value larger than 0.65 (Figure 6) and can, therefore, be separated from the conifers with PC1 ≤0.65.

Thus, the set of sharp spectral feature indices enables a separation of the spectral library into four large vegetation types: deciduous trees with red or green leaves, conifers and lawns. SAM indices can be used safely in each subset. This step of spectral library screening is essential to avoid confusion between species with too close SAM indices (underlined in grey in Table 5). However, this is not sufficient to separate all the species and so some that were spectrally and chemically similar (with SAM pigment analysis in VNIR) had to be merged. They are identified by their number in the last line of Table 5:

* Ten SAM index vegetation groups of trees (G1–G10), some of which cannot be linked to any botanical convention; for instance, the three different species of oaks (*Quercus*) fall into three different vegetation groups (G4, 5, 7).

* Three lawn vegetation groups (G11, 12, 13), which correspond to different collections of plants with grass, identified to minimize confusion with trees, especially conifers in lawn areas.

* One group for the sedum on roofs (G14), also identified to prevent confusion with red-leafed trees.

<figure><img src="image_7.png" /><figcaption>Figure 6. Differentiation of vegetation types based on the spectral shape indices: NDGLI vs. NDVI plot with the ellipse at 2σ of their covariance calculated with the green deciduous and conifer data only.</figcaption></figure>
Consequently, for the classification and mapping of vegetation in the study area, the spectral library was reduced to 14 vegetation groups: 10 for trees, several of them including two or three species, and four for other vegetation types (Figure 7).

## 4.2. Image segmentation into objects

Instead of segmenting the objects directly on the hyperspectral image (Figures 8(a,b)), a new pseudo-image with all the information layers was created (subsequently called the 'co-image' to avoid confusion with the original image), with the same geographical extent and resolution but in which each pixel is composed of 25 indices: the eight sharp spectral feature indices (see Figure 8(d₁)), the 14 SAM indices of the vegetation groups (Figure 8(d₂)), and the three accessory indices of drop shadows and other soil mixtures (Figure 8(d₃)). This method reduces the number of data to process from 143 to 25 bands, but its main interest is in reducing the sensitivity to shading effects other than pigment variations described by Lichtenthaler et al. (2007, 2013). The darkest pixels in the shade display anomalous bluish spectra, well demonstrated by the trend normalization of Roy et al. (2008) in Figure 8(c). The boundary between the canopy shadow gradient and drop shadow on a background is detected by the combination of the NDDSI and IntDS indices (Figure 8(d₄)). As each tree crown (e.g. object (1) in Figure 8(f)) is homogeneous, displaying one colour characteristic in the combination of either the sharp spectral feature indices (Figure 8(d₁)) or the vegetation group SAM indices (Figure 8(d₂)), it is successfully delineated by this segmentation method (Figure 8(e)).

<figure><img src="image_8.png" /><figcaption>Figure 7. Spectra of tree groups (G1–G10) obtained by averaging the spectra of species grouped according to Table 4. Other plants (G11–G14) are only used for contrast to delineate tree crowns better and avoid confusion.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Figure 8. Example of the transformations of an image sample: (a) the image in true colour displayed with a standard ENVI® Gaussian look up table (wavelength in nm) where the black arrows point to drop shadows, white arrows to pure soil mixtures, and blue arrows to the erroneous identification of conifers; (b) the same image with the channels shifted towards the infrared; (c) the trend-normalized true colour image showing drop shadow colour distortion in blue; (d) the colour composite image of vegetation indices; (dii) the colour composite image of drop shadow and soil mixture detection; (diii) the colour composite image of SAM indices of vegetation groups G11 (lawn), G4 (Salix, Franix, Platanus, Quercus) and G8 (Cupressus, Cedrus); (e) the object image in mean true colours; (f) an illustration of object mean spectrum vegetation group SAM indices G11, G4, and G8 (compare with (diii)); (g) pixel-based; and (h) object-based classifications of vegetation groups (see Figure 10 for colour code).</figcaption></figure>

The optimum set of eCognition software setup values for the Pin Sec neighbourhood was as follows: (i) the scale factor (size of the gliding window used to calculate the local statistics) was set to 10 pixels in order to capture objects with a size comparable to that
of most tree crowns, the larger ones being divided into smaller sub-objects (see Figures 8(a–h)); (ii) the pseudo ‘colour’ factor (20 channels) was given a weight of 80% as proposed by Wang (2008); (iii) the smoothness and compactness factor values of 10% were found to fine-tune the segmentation of the tree canopies.

The segmentation performed with the co-image was then transferred to the hyperspectral image as illustrated in Figures 8(a–h) for a small area. The method identified 48,088 vegetation objects in the study area of the Pin Sec neighbourhood, of which 28,906 were larger than 25 pixels, ranging from isolated trees or bushes to relatively large tree groves.

Figures 9(a,b) illustrate some limitations encountered with the method. The large spectral variations of the pixels included in the tree crown (1) of Figure 8(e) (Figure 9(a)) are due to spectral distortions from the shadowing bluish effect (min spectrum in Figure 9(a)) or from the light solar specular effect of intensity shift (max spectrum in Figure 9(a)). This is the main reason for choosing to minimize the influence of such distortions by using the mean spectrum of each object. Note that small objects appear at the transition between contrasted materials (e.g. objects (2)–(6) identified in Figure 8(e)) for which the number of pixels is too small to make a good estimation of the mean spectrum (Figure 9(e)). This may result in an erroneous classification. The pixel heterogeneity at the boundaries has a stronger influence when the objects are small and elongated, such as for objects (4) and (5) in Figure 8(e) and Figure 9(b).

<figure><img src="image_10.png" /><figcaption>Figure 9. Examples of spectra of objects identified in Figure 8(e): (a) statistics of the pixel spectra in object (1), showing the reflectance variations within a tree canopy; (b) distorted spectra of small lawn objects due to the presence of pure soil mixed at the object scale: (5) was well detected, (6) was confused with a conifer group; (2) degraded lawn, (3) healthy lawn and (4) lawn in drop shadows.</figcaption></figure>
### 4.3. Classification sequence

The classification method described in this study is illustrated by the decision tree presented in Figure 10 using the threshold values of sharp spectral feature indices of the information layer or co-image determined in Section 4.1. It was applied to the mean spectra of the 28,906 vegetation objects larger than 5 × 5 pixels identified in the 25-channel co-image. It was also applied to the spectra of the hyperspectral image pixels to check the advantage of the object-based classification.

The first step verifies if the object NDVI is larger than 0.40, to select only vegetation. The second step tests the presence of drop shadow to identify lawns that would otherwise be classified as conifers (see object (3) in Figure 8(f)). Dropped shadows are detected using both the NDDSI and IntDS while a SAM analysis distinguishes the lawns (G13) from other unidentified materials grouped in G15. The third step checks the presence of red pigments; the roof sedum (G14) and the red-leafed trees (G1 and G2) are further separated by the SAM analysis. The fourth step uses the NDGI reflecting the spectral slope in the infrared range to isolate most of the lawns from the green- and blue-leafed trees. The identification of the lawn types (G11, G12) by the SAM analysis is

<figure><img src="image_11.png" /><figcaption>Figure 10. Decision tree used for object-based or pixel-based classification. The ellipses are the resulting vegetation groups with the colour code used in Figures 11 and 12. G15 and G16 contain the unclassified objects/pixels. The index selections use the information layers of 11 chemical sharp spectral features whereas the SAM selection of each spectra subset uses the information layers of 14 SAM indices calculated at full spectral resolution between pixels/objects and the training spectral library of Table 5.</figcaption></figure>
also necessary to avoid tree misclassifications. Step number five uses the principal component analysis to separate deciduous trees from conifers. Some confusion between trees close to the threshold limit of the first principal component axis PC1 (Figure 6) is avoided by checking the spectral curvature in the green range with the IdGL. Deciduous trees can now be classified by SAM analysis into five vegetation groups (G3–G7). In step number six, the Soilmix index is used to separate into an additional accessory group (G16) the confusions due to the mixture of lawn, conifers and bare soil from the 3 conifer vegetation groups (G7–G9). Finally, a SAM analysis is used once again to identify the potential presence of lawns (G10–G11) that would have been missed in step 4 because of their spectral proximity to some conifer classes (see Figure 9(b)). The difference between pixel-based and object-based classifications is illustrated in Figures 8(g,h) and discussed during the presentation of the results in Section 5.

The final map of the Pin Sec and surrounding neighbourhoods is shown in Figure 11. More than 34% of the study area is covered by an upper layer of vegetation objects larger than 25 pixels (6.25 m²) while 2% are smaller. Lawns and deciduous trees are the dominant types (Table 5). The trees cover various kinds of surfaces, such as pavements, buildings and other vegetation types, which are not detected by the airborne remote sensing because the tree canopy masks its background as discussed in Section 4.5. This is why it was decided to identify trees exclusively as an independent upper layer, which may eventually be combined with other surface information available in urban databases such as the boundaries of properties, roads, buildings, and ground soil occupation (including lawns).

## 4.4. Accuracy assessment

Two independent ground-truth datasets were used to assess the accuracy of the method: (1) a series of isolated trees identified in the field with the help of a botanist, and (2) some tree rows in the public domain monitored in the field after the final classification.

In the sample of Figures 12(a–g), various lawns, ranging from a healthy green lawn to a light brown degraded lawn, are identified without confusion with trees. Conifers are darker than deciduous trees with the noticeable exception of *Betula* trees characterized by a small foliage density (G3 in Figures 12(a–g)). *Betula* trees often seem darker than expected because of the asphalt background that appears in the airborne remote-sensing image. The mixture of spectra may result in a poor classification with some confusion between conifers and deciduous trees (see G3 location in Figure 12(b)). Some *Liquidambar* trees are well classified in class G6, e.g. in the tree grove at the eastern side of Figures 12(a–g) (G6 location in Figure 12(a)), while some of them are wrongly classified in classes G3 and G4 in the north-eastern corner (Figures 12(a,b)) because the phenological adaptation of leaves to a shaded environment modified the pigment content (Lichtenthaler et al. 2007, 2013) and distorted their reflectance spectra. *Quercus robur* (G7), *Pinus pinea* (G10), and *Cedrus atlantica* (G8) are less often misclassified. However, there is no perfect classification and in tree groves or other compact tree associations our method often identifies only the main tree groups.

Some better results are obtained for the trees in alignments that are subject to fewer multiple light reflections. The isolated crowns in open areas provide the best results. For instance, the tree alignment of Jules Verne Boulevard (from point a to b in the upper left
<figure><img src="image_12.png" /><figcaption>Figure 11. Map of the 14 vegetation types found in the Pin Sec and surrounding neighbourhoods (see Figure 2) using the colour code of Figure 10. Note the detection of sedum (G14) on the roof of a building in the southeast of the image. Jules Verne Boulevard and Koufra Street are identified by a–b and b–c marks.</figcaption></figure>

corner of Figure 11) is composed of 118 *Carpinus betulus* L. whose spectrum is not in the initial library (Table 1). Seventy-six per cent of them were classified in the same class G4 and only 24% in other classes (15% G5 and 8% G3). The tree alignment on Koufra Street (from point b to c in Figure 11) is composed of 54 *Acer saccharinum* and 36 *Prunus serrulata*: 91% of the *Acer saccharinum* trees with dense foliage were successfully classified in the class G5, which contains the close species *Acer negundo*. Because of their sparse foliage, only 69% of the *Prunus serrulata* trees were classified in the class G4 while various mixtures plus the background caused the others to fall into three other classes: 11% in G3, 11% in G5, and 8% in G6.

Table 6 shows that the successful scores of the method are not very different when applied to the objects or to the pixels. However, the differences are important since they correspond
<figure><img src="image_13.png" /><figcaption>Figure 12. Example of a ground-truth dataset: (a) the true colour image with a vegetation mask enhancing the colour contrast between tree crown foliages; (b) the object classification with the colour code of Figure 10; (c) photographs of a *Quercus robur* (G7), (d) a *Cedrus atlantica* (G8), (e) a *Liquidambar* (G6), (f) a *Pinus pinea* (G10), and (g) a *Betula pendula* (G3).</figcaption></figure>

to an improved removal of the confusions between tree groups and other vegetation groups,
with the total percentage of identified trees increasing from 18.8% to 20.8%.

**5. Discussion and conclusion**

The classification of trees in urban areas is a difficult task as different types of vegetation
overlap with buildings and other artificial materials. However, airborne hyperspectral
images may improve their identification because they give simultaneous access to many
types of vegetation in both public and private areas.

The atmospheric correction is an important preliminary step for the quality of the
final results. In this study, ATCOR4 was successfully used; however, it was not able to
<table><caption>Table 6. Vegetation group statistics with pixel-based and object-based analyses (classes G15 and G16 contain the unclassified objects/pixels).</caption><thead><tr><th rowspan="2"></th><th colspan="2">Pixel</th><th colspan="2">Area</th><th colspan="2">Cover</th></tr><tr><th>Class</th><th>(number)</th><th>(m²)</th><th>(%)</th><th>Cover</th><th>(%)</th></tr></thead><tbody><tr><td>Pixel based analysis</td><td>Total</td><td>5,267,888</td><td>1,316,972</td><td>63.90</td><td></td><td>63.90</td></tr><tr><td>Red dec.</td><td>1</td><td>1584</td><td>396</td><td>0.02</td><td>0.31</td><td></td></tr><tr><td></td><td>2</td><td>24,311</td><td>6,078</td><td>0.29</td><td></td><td></td></tr><tr><td>Green deciduous</td><td>3</td><td>333,639</td><td>83,410</td><td>4.05</td><td></td><td></td></tr><tr><td></td><td>4</td><td>440,509</td><td>110,127</td><td>5.34</td><td></td><td></td></tr><tr><td></td><td>5</td><td>107,372</td><td>26,843</td><td>1.30</td><td>14.42</td><td>18.83</td></tr><tr><td></td><td>6</td><td>163,527</td><td>40,882</td><td>1.98</td><td></td><td></td></tr><tr><td></td><td>7</td><td>143,769</td><td>35,942</td><td>1.74</td><td></td><td></td></tr><tr><td>Conifers</td><td>8</td><td>128,337</td><td>32,084</td><td>1.56</td><td></td><td></td></tr><tr><td></td><td>9</td><td>93,894</td><td>23,474</td><td>1.14</td><td>4.10</td><td></td></tr><tr><td></td><td>10</td><td>115,619</td><td>28,905</td><td>1.40</td><td></td><td></td></tr><tr><td>Lawns</td><td>11</td><td>370,664</td><td>92,666</td><td>4.50</td><td></td><td></td></tr><tr><td></td><td>12</td><td>604,182</td><td>151,046</td><td>7.33</td><td>15.51</td><td>15.51</td></tr><tr><td></td><td>13</td><td>303,951</td><td>75,988</td><td>3.69</td><td></td><td></td></tr><tr><td>Sedum</td><td>14</td><td>29,295</td><td>7,324</td><td>0.36</td><td>0.36</td><td>0.36</td></tr><tr><td>Mix</td><td>15</td><td>94,558</td><td>23,640</td><td>1.15</td><td>1.41</td><td>1.41</td></tr><tr><td></td><td>16</td><td>21,501</td><td>5375</td><td>0.26</td><td></td><td></td></tr><tr><td>Object based analysis</td><td>Total</td><td>541,0690</td><td>135,2673</td><td>65.63</td><td></td><td>65.63</td></tr><tr><td>Red dec.</td><td>1</td><td>685</td><td>171</td><td>0.01</td><td>0.31</td><td></td></tr><tr><td></td><td>2</td><td>25,003</td><td>6,251</td><td>0.30</td><td></td><td></td></tr><tr><td>Green deciduous</td><td>3</td><td>390,216</td><td>97,554</td><td>4.73</td><td></td><td></td></tr><tr><td></td><td>4</td><td>557,982</td><td>139,496</td><td>6.77</td><td></td><td></td></tr><tr><td></td><td>5</td><td>103,287</td><td>25,822</td><td>1.25</td><td>15.33</td><td>20.77</td></tr><tr><td></td><td>6</td><td>115,688</td><td>28,922</td><td>1.40</td><td></td><td></td></tr><tr><td></td><td>7</td><td>96,391</td><td>24,098</td><td>1.17</td><td></td><td></td></tr><tr><td>Conifers</td><td>8</td><td>201,407</td><td>50,352</td><td>2.44</td><td></td><td></td></tr><tr><td></td><td>9</td><td>74,355</td><td>18,589</td><td>0.90</td><td>5.14</td><td></td></tr><tr><td></td><td>10</td><td>147,599</td><td>36,900</td><td>1.79</td><td></td><td></td></tr><tr><td>Lawns</td><td>11</td><td>301,053</td><td>75,263</td><td>3.65</td><td></td><td></td></tr><tr><td></td><td>12</td><td>614,130</td><td>153,533</td><td>7.45</td><td>13.13</td><td>13.13</td></tr><tr><td></td><td>13</td><td>167,036</td><td>41,759</td><td>2.03</td><td></td><td></td></tr><tr><td>Sedum</td><td>14</td><td>13,576</td><td>3,394</td><td>0.16</td><td>0.16</td><td>0.16</td></tr><tr><td>Mix</td><td>15</td><td>24,593</td><td>6,148</td><td>0.30</td><td>0.31</td><td>0.31</td></tr><tr><td></td><td>16</td><td>909</td><td>227</td><td>0.01</td><td></td><td></td></tr></tbody></table>

correct all the instrumental effects such as, in particular, an insufficient dynamic of the camera signal increasing over time from the factory calibration. However, a single bright reference could correct for the gain distortions between the stressed airborne HySpex camera and the ASD® spectrometer measuring on-field reflectances for all the flight lines acquired during the same session. Finally, the oxygen narrow band at 762 nm (due to an irregular 'smile' effect of the camera) and the noisy water vapour range above 925 nm were removed to calculate the best MNF correction (see Figure 2).

In urban areas, the presence of bright targets on many building surfaces imposes the use of a short integration time to prevent pixel saturation. Consequently, darker pixels in shadow areas are coded with a small digital range. This is quite problematic for identifying vegetation that has a strong pigment absorption (and channels with strong water vapour absorption) whose pixels are almost set to zero. It is therefore useless to remove the shade effect in such dark pixels with crushed absorption features and sharp signal to noise increases.

The high resolution of hyperspectral channels reveals subtle changes in the reflectance spectra and enables numerous pigments to be detected. However, as the trees are
saturated in pigments, they display quite flat reflectance spectra on both sides of a red edge, well identified by a simple NDVI. Therefore, pigments other than chl-*a* must be analysed below the red edge using the slope of short characteristic straight segments, obtained by a linear regression, quantified as a normalized difference index. They are mainly the pigment associations driving the green and red intensities of the foliage, yielding the NDGLI and NDRLI, respectively. Above the red edge, where the close interaction between grass and soil contrasts with the efficiency of the light reflectance by tree leaves, at least in the spring season, the analysis of the reflectance slope defining the grass index NDGI can separate them from the trees.

Starting with the NDVI, our method progressively splits the image between (0) non-vegetation and (1) vegetation, which is successively split, according to a five-step classification flow chart, into (2) drop shadows (combining NDDSI and IntDS), (3) red leaves (using NDRLI), (4) lawns (using NDGI), (5) deciduous trees (using $Id_{GL}$ and a combination of NDGLI and NDVI), and (6) conifers (using Soilmix), facilitating a more accurate examination of vegetation groups with less confusion.

The rather flat shape of the reflectance spectra on both sides of the red edge also induces close resemblances between vegetation species. For instance, it was shown in Table 5 that individuals of the *Quercus* species, which are in principle spectrally separable using the SAM analysis, may be confused with *Franix* and *Salix* or *Catalpa* and *Acer* species. It is therefore misleading to classify species with identical spectral signatures suggesting grouping them in separable vegetation groups. Moreover, vegetation groups might be used as image descriptors: instead of looking for all possible vegetation spectra in the image, it is proposed to look for only the 14 vegetation groups identified in this study, from which a 14 channel co-image measuring the similarity to each vegetation group can be built. By combining them with 11 maps of indices, a 25 channel co-image is obtained, which describes from channel 1 to 11 the morphology of the reflectance spectra and from channel 12 to 25 their similarity to the other key vegetation groups, preventing their confusion.

Tree crown foliage always displays lighting variations that may generate a misclassification at the pixel scale. The object segmentation of the eCognition software reduces this effect. Instead of applying this object detection algorithm to the original spectral image, it was applied to the 25 channel co-image. This enabled the detection of vegetation objects corresponding to homogeneous patches or textures of, for example, a subset of branches within a large tree crown, an isolated tree crown, and an association of small trees or other vegetation such as lawns, which must be considered to prevent a false determination of trees. As shown in the isolated tree crown object example of Figure 9(a), the mean, maximum, minimum and standard deviation highlight the high spectral variability within the objects. To prevent this effect, we chose to process the mean spectrum of each object for its classification, using the flow chart selection of Figure 10. A comparison between pixel-based and object-based classifications is illustrated in Figures 8(a–h).

The originality of the method presented in this study is that it prioritizes information layers in co-images without losing the full resolution of the spectra for final SAM analysis. For instance, sharp spectral feature index information layers can describe a chemical composition at the pixel scale and at the object scale. An object is a particular association of pixels forming a local physical texture delineating its boundaries. In this case, we chose
to characterize each object by its mean spectrum. It was then easy to use a combination
of information layers, at the pixel or object scale, to combine diagnostic and sharp spectral
features to identify the subset of the training spectral library that must be compared with
the pixel spectrum or the object mean spectrum at a full spectral resolution. Practically, we
associated with the original 143 channel image a co-image of 25 information layers,
calculated at pixel and object size, to test any new combination of information quickly,
which is particularly interesting for tree management.

The method has two drawbacks that are inherent to living trees: (1) the trees with very sparse foliage exposing a large amount of background (e.g. *Betula pendula* and *Prunus serrulata*) cannot be properly identified; (2) the phenological adaptation that potentially changes the pigment association from both sides of a tree crown leads to local misclassifications. The first drawback could be overcome using additional data measuring the length of the light interaction in the tree crown, from a lidar, for example. The second could be managed by using multi-temporal images to monitor the phenological evolution over time (we only processed spring data) but this is beyond the scope of this work.

Our best results were obtained for the alignments of trees with dense foliage where
the crowns are isolated from other vegetation. The identification of trees appears more
difficult in garden groves where multiple interactions are possible with other vegetation
types, lawns or other plant layers constituting the background.

Many other classifications could have been tested; however, they were not within the
scope of this study. We focused here on the chemical analysis of trees to emphasize that
species classification should be reconsidered from a spectral separability approach. The
misidentification of some species by hyperspectral angle measurements is not due to a
lack of hyperspectral quality but to the fact that some different species can have the
same chemical composition and thus identical spectra. Instead of combining many
information types, we also focused on the chemical composition given by spectral
shape analysis and only tested the physical properties of the tree by object identification
based on the texture formed by juxtaposed pixel chemical compositions. Further work in
preparation will combine independent measurements of the physical properties to
improve the final classification.

Acknowledgements

We would like to thank botanist Romaric Perrocheau from the Nantes Botanic Garden ('Jardin des Plantes') and the Actimar Company. This research was supported by the French national research agency (ANR) programme 'Villes Durables' (Sustainable Cities) within the VegDUD Project (ANR-09-VILL-0007-01), by the European fund FEDER and by the Pays de la Loire Region. Trade names and companies are given for the benefit of the reader and do not imply any endorsement of the product or company by the authors.

The main software used in this work is:

* ATCOR® 4: http://www.rese.ch/products/atcor/atcor4/

• eCognition: http://www.ecognition.com/

* ENVI: http://www.harrisgeospatial.com/ProductsandSolutions/GeospatialProducts/ENVI.aspx
Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This research was supported by the French National Research Agency (ANR) programme ‘Villes Durables’ (Sustainable Cities) within the VegDUD Project [ANR-09-VILL-0007-01]; the European fund FEDER; and the Pays de la Loire Region.

ORCID

Patrick Launeau ID http://orcid.org/0000-0001-7465-9041

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