Karim Azouzi

Antoine Bruneau-Voisine

Laure Vendier

Jean-Baptiste Sortais
email: jean-baptiste.sortais@lcc-toulouse.fr

Stéphanie Bastin
email: stephanie.bastin@lcc-toulouse.fr

Asymmetric transfer hydrogenation of ketones promoted by manganese(I

Keywords: Manganese, chiral aminophosphine ligands, transfer hydrogenation, reduction, ketones

teaching and research institutions in France or abroad, or from public or private research centers.

Introduction

Chiral alcohols are highly valuable synthetic intermediates for the production of pharmaceutical, agrochemical and fine chemical products. As a result, intensive research work has been devoted to the development of chiral organometallic complexes and their implementation in asymmetric direct hydrogenation or transfer hydrogenation of prochiral ketones. [START_REF] Seo | Catalytic Homogeneous Asymmetric Hydrogenation: Successes and Opportunities[END_REF] Noble metal have proven to be particularly efficient for this class of transformation, and for this reason, are still widely used in this area. [START_REF] De Vries | The Handbook of Homogeneous Hydrogenation[END_REF] Quite recently, this field of research has met a significant shift towards the use of earth-abundant, inexpensive and environmentally friendly first row transition metals such as Fe, [START_REF] Zell | From ruthenium to iron and manganese -a mechanistic view on challenges and design principles of base metal hydrogenation catalysts[END_REF][START_REF] Darwish | Asymmetric catalysis using iron complexes -Ruthenium Lite'?[END_REF][START_REF] Wei | Iron Catalysis in Reduction and Hydrometalation Reactions[END_REF][START_REF] Zuo | Amine(imine)diphosphine Iron Catalysts for Asymmetric Transfer Hydrogenation of Ketones and Imines[END_REF] Co [START_REF] Junge | Cobalt-Pincer Complexes in Catalysis[END_REF][START_REF] Ai | Hydride Transfer Reactions Catalyzed by Cobalt Complexes[END_REF][START_REF] Li | Iron-, Cobalt-, and Nickel-Catalyzed Asymmetric Transfer Hydrogenation and Asymmetric Hydrogenation of Ketones[END_REF] and Mn. [START_REF] Zell | From ruthenium to iron and manganese -a mechanistic view on challenges and design principles of base metal hydrogenation catalysts[END_REF][START_REF] Kallmeier | Manganese Complexes for (De)Hydrogenation Catalysis: A Comparison to Cobalt and Iron Catalysts[END_REF][START_REF] Stanbury | Electro and photoreduction of CO2 driven by manganese-carbonyl molecular catalysts[END_REF][START_REF] Valyaev | Manganese organometallic compounds in homogeneous catalysis: Past, present, and prospects[END_REF][START_REF] Elangovan | Selective Catalytic Hydrogenations of Nitriles, Ketones and Aldehydes by Well -Defined Manganese Pincer Complexes[END_REF] However, in this context, examples of Mn-based pre-catalysts are still quite scarce both in asymmetric transfer hydrogenation (ATH) and direct hydrogenation (ADH).

In 2017, Zirakzadeh and Kirchner [START_REF] Zirakzadeh | Enantioselective Transfer Hydrogenation of Ketones Catalyzed by a Manganese Complex Containing an Unsymmetrical Chiral PNP′ Tridentate Ligand[END_REF] reported the first example of asymmetric transfer hydrogenation of aromatic ketones catalyzed by a manganese complex of a chiral ferrocenyl tridentate PNP ligand. Shortly after, Clarke [START_REF] Widegren | A Highly Active Manganese Catalyst for Enantioselective Ketone and Ester Hydrogenation[END_REF] described a Mn-based catalytic system supported by a related PNN ligand for the asymmetric direct hydrogenation of ketones. In the same vein, Zhong and coworkers developed another class of ferrocenyl PNN tridentate ligands incorporating an imidazole moiety for the asymmetric direct hydrogenation of benzophenone derivatives with ee up to 99%. [START_REF] Ling | Highly Enantioselective Synthesis of Chiral Benzhydrols via Manganese Catalyzed Asymmetric Hydrogenation of Unsymmetrical Benzophenones Using an Imidazole-Based Chiral PNN Tridentate Ligand[END_REF] The pincer type complex incorporating a PNP tridentate ligand bearing chiral phospholane groups published by Beller [START_REF] Garbe | Enantioselective Hydrogenation of Ketones using Different Metal Complexes with a Chiral PNP Pincer Ligand[END_REF][START_REF] Garbe | Manganese(I)-Catalyzed Enantioselective Hydrogenation of Ketones Using a Defined Chiral PNP Pincer Ligand[END_REF] proved to be a better catalyst for the reduction of prochiral aliphatic ketones (ee up to 99%) than for the aromatic ones. In contrast, Han and Ding reported very recently a manganese catalyst containing a NNP ligand that allowed the ADH of a large range of aromatic ketones with high activities (TON up to 9800) and enantioselectivities up to 98%. [START_REF] Zhang | Lutidine-Based Chiral Pincer Manganese Catalysts for Enantioselective Hydrogenation of Ketones[END_REF] J o u r n a l P r e -p r o o f

In the field of ATH, only a handful of examples, mainly based on tri-and tetradentate ligands, have been reported so far. Morris evidenced the ability of well-defined Mn complexes supported by tridentate PNN ligands to promote the transfer hydrogenation of ketones. [START_REF] Demmans | Asymmetric Transfer Hydrogenation of Ketones with Well-Defined Manganese(I) PNN and PNNP Complexes[END_REF] Recently, efficient catalytic systems based on tetradentate ligands were developed by Beller [START_REF] Schneekönig | Manganese Catalyzed Asymmetric Transfer Hydrogenation of Ketones Using Chiral Oxamide Ligands[END_REF] and Mezzetti. [START_REF] Passera | The Manganese(I)-Catalyzed Asymmetric Transfer Hydrogenation of Ketones: Disclosing the Macrocylic Privilege[END_REF][START_REF] Passera | Mn(I) and Fe(II)/PN(H)P Catalysts for the Hydrogenation of Ketones: A Comparison by Experiment and Calculation[END_REF] While Beller showed that easily accessible and potentially tetradentate chiral oxamide ligands in combination with Mn(CO) 5 Br are highly effective in the ATH of aliphatic ketones with ee up to 93%, Mezzetti obtained excellent enantioselectivities (90 to 99%) for the asymmetric transfer hydrogenation of a large range of aryl/heteroaromatic alkyl ketones with Mn complexes supported by a tetradentate macrocyclic ligand. [START_REF] Passera | The Manganese(I)-Catalyzed Asymmetric Transfer Hydrogenation of Ketones: Disclosing the Macrocylic Privilege[END_REF] In contrast, our group showed that simple chiral diamine mixed with Mn(CO) 5 Br are capable to hydrogenate enantioselectively acetophenone and related ketones. [START_REF] Wang | Practical (asymmetric) transfer hydrogenation of ketones catalyzed by manganese with (chiral) diamines ligands[END_REF] Shortly after, this work was complemented by mechanistic studies performed by Pidko [START_REF] Van Putten | Mechanistic Complexity of Asymmetric Transfer Hydrogenation with Simple Mn-Diamine Catalysts[END_REF] on closely related catalytic systems involving well-defined manganese catalyst supported by a chiral diamine ligand.

In the continuation of our work on diamines and more generally on bidendate ligands in reduction reactions with Mn-catalysts, [START_REF] Bruneau-Voisine | Transfer Hydrogenation of Carbonyl Derivatives Catalyzed by an Inexpensive Phosphine -Free Manganese Precatalyst[END_REF][START_REF] Wei | Hydrogenation of Carbonyl Derivatives Catalysed by Manganese Complexes Bearing Bidentat e Pyridinyl-Phosphine Ligands[END_REF][START_REF] Wei | Manganese Catalyzed Reductive Amination of Aldehydes using Hydrogen as Reductant[END_REF][START_REF] Buhaibeh | Phosphine-NHC Manganese Hydrogenation Catalyst Exhibiting a Non-Classical Metal-Ligand Cooperative H2 Activation Mode[END_REF][START_REF] Wei | Manganese-Catalyzed Transfer Hydrogenation of Aldimines[END_REF] we were interested in investigating the effect of the electronic and steric properties of commercially available chiral aminophosphines onto their activity and selectivity.

It should be noticed that in the course of this study, Pidko [START_REF] Van Putten | Mechanistic Complexity of Asymmetric Transfer Hydrogenation with Simple Mn-Diamine Catalysts[END_REF] and Lacy [START_REF] Vigneswaran | β-Amino Phosphine Mn Catalysts for 1,4-Transfer Hydrogenation of Chalcones and Allylic Alcohol Isomerization[END_REF] independently reported the ability of aminophosphine ligands to promote the Mn-catalyzed transfer hydrogenation of ketones with good activities but the selectivities remained modest.

J o u r n a l P r e -p r o o f

Chart 1: Manganese based catalysts for asymmetric transfer hydrogenation

Experimental

Synthesis of complex 1

In an argon-filled glovebox, a 100 mL Schlenk flask was charged with a solution of (1S,2S)-1-

((4R,11bS)-3H-Dinaphtho[2,1-c:1′,2′-e]phosphepin-4(5H)-yl)-1-phenyl-2-propanamine L8
(100 mg, 0.224 mmol) in degassed toluene (5 mL), and pentacarbonyl manganese bromide (Mn(CO) 5 Br) (62 mg, 0.225 mmol) was added in one portion. The reaction mixture was then heated at 110°C for 4 h. Toluene was evaporated under reduced pressure and the yellow solid was washed three times with dry pentane. Complex [L8Mn(CO) 3 Br] (1) was obtained as a yellow powder (131 mg, 87%). Single crystals suitable for X-ray diffraction analysis were grown by layering a solution of the complex in CDCl 3 with pentane at 5°C.

General procedure for hydrogen transfer reduction of acetophenone with the in situ generated catalytic system

To a solution of acetophenone (2 mmol, 232 µL) in 2-propanol (18 ml) were added a stock solution of manganese pentacarbonyl bromide in 2-propanol (0.5 mL, 0.02 mol.L -1 ) and a

J o u r n a l P r e -p r o o f

Journal Pre-proof solution of ligand (0.01 mmol) in 2-propanol (1 mL) in this order. After 10 min of heating at 80°C, the solution was then cooled to 30°C and a stock solution of tBuOK in 2-propanol (0.5 mL, 0.04 mol.L -1 ) was added. The mixture was stirred for 19 h in an oil bath at 30°C. The solution was then filtered through a small pad of silica (4 cm high in a column with a diameter of about 1cm). The silica was washed with ethyl acetate, volatiles were removed under reduced pressure and the conversion and enantiomeric excesses were determined by GC analyses performed on a Shimadzu GC-2010 apparatus equipped with a Supelco betaDEX 120 column (30 m x 0.25 mm) using Helium as the vector gas.

General procedure for hydrogen transfer reduction of ketones with well-defined complex [L8Mn(CO) 3 Br] 1

To a solution of ketone (2 mmol) in 2-propanol (19 ml) were added in this order a stock solution of complex 1 in 2-propanol (0.5 mL, 0.02 mol.L -1 ), and a stock solution of tBuOK in 2-propanol (0.5 mL, 0.04 mol.L -1 ) at 30°C. The mixture was stirred for 14 h in an oil bath at 30°C. The solution was then filtered through a small pad of silica (4 cm high in a column with a diameter of about 1 cm). The silica was washed with ethyl acetate, volatiles were removed under reduced pressure and the conversion was determined by 1 H NMR. The crude reaction mixture was purified by column chromatography (SiO 2 , a mixture of petroleum ether/ ethyl acetate as eluent) to afford the corresponding alcohol. This latter was analyzed by chiral GC.

Enantiomeric excesses were determined by GC analyses performed on a Shimadzu GC-2010 apparatus equipped with a Supelco betaDEX 120 column (30 m x 0.25 mm) using Helium as the vector gas.

Results and discussion

We performed the initial catalytic reactions with ligand L1 following the typical conditions of reaction optimized with diamine ligands [START_REF] Wang | Practical (asymmetric) transfer hydrogenation of ketones catalyzed by manganese with (chiral) diamines ligands[END_REF] ie in the presence of Mn(CO) 5 Br (0.5 mol%), ligands (0.5 mol%) and tBuOK as a base (1 mol%) at 80°C in isopropanol (c = 0.25 mol.L -1 ).

Although being highly active (80 % conversion after 15 min, Table 1, entry 1), ligand L1 induced a very poor selectivity (3% ee) compared to its diamino counterpart, the (1R,2R)-(+)-1,2-diphenyl-1,2-ethanediamine (DPEN) ligand (36% ee) [START_REF] Wang | Practical (asymmetric) transfer hydrogenation of ketones catalyzed by manganese with (chiral) diamines ligands[END_REF]. Operating in a more diluted medium (c = 0.1 mol.L -1 ) and decreasing the reaction temperature to 30°C improved slightly the enantioselectivity to 12% ee after 19h (entry 2). Under these conditions, we tested the aminophosphine L2 which can be considered as the analogue of the (1R,2R)-(-)-1,2diaminocyclohexane ligand L'2. Despite its more rigid structure, 1-phenylethanol was J o u r n a l P r e -p r o o f obtained in only 15% ee (vs 43% for L'2 [START_REF] Wang | Practical (asymmetric) transfer hydrogenation of ketones catalyzed by manganese with (chiral) diamines ligands[END_REF]) (entry 3). Decreasing the steric hindrance in alpha position to the amino function by replacing the phenyl group by a methyl (L3) significantly improved the enantioselectivity to 46% ee (entry 4) without any loss of activity whereas the introduction of an isopropyl (L4) resulted in a drop of the activity without any beneficial effect on the enantioselectivity (entry 5). We then investigated the use of the Nmethylated ligand L5 for the reduction of acetophenone which yielded the 1-phenylethanol with an encouraging 65% ee (entry 6), which is in line with what we observed with the diamine ligands. We were therefore interested in testing ligand L6, whose the 5-membered cyclic structure combined rigidity and alkylation of the nitrogen atom but the enantioselectivity disappointingly dropped to 5% (entry 7). We then envisaged the tuning of the phosphorus moiety. Introduction of a chiral bulky binepine moiety on the phosphorus atom led to an increase of the enantiomeric excess from 12% (L1, entry 2) to 57% (L7, entry 8). As ligand L3 induced a better selectivity than ligand L1, we therefore conducted the reduction with ligand L8 with a methyl in alpha position to the amino function, which further improved the enantioselectivity to 71 % ee (entry 10,vs 46 % ee, entry 4). As expected, due to the reversibility of the transfer hydrogenation reaction, a monitoring of the reaction evidenced a racemisation of the alcohol, the enantiomeric excess decreased from 77% ee after 14h (entry 9) to 71% after 19h (entry 10). Considering the coordination chemistry of the aminophosphines, it is noteworthy that aminophosphines L1-L8 form 5-membered chelate rings upon coordination with manganese, we wondered to which extent the coordination mode of the ligand could affect the enantioselectivity. For this purpose, we submitted a range of aminophosphines able to afford 6-membered chelate rings on coordination to the metal center for the reduction of acetophenone. Since ferrocene-based ligands have proven their efficiency in asymmetric hydrogenation reaction, [START_REF] Zirakzadeh | Enantioselective Transfer Hydrogenation of Ketones Catalyzed by a Manganese Complex Containing an Unsymmetrical Chiral PNP′ Tridentate Ligand[END_REF][START_REF] Widegren | A Highly Active Manganese Catalyst for Enantioselective Ketone and Ester Hydrogenation[END_REF][START_REF] Ling | Highly Enantioselective Synthesis of Chiral Benzhydrols via Manganese Catalyzed Asymmetric Hydrogenation of Unsymmetrical Benzophenones Using an Imidazole-Based Chiral PNN Tridentate Ligand[END_REF][START_REF] Chen | Ferrocene-based aminophosphine ligands in the Ru(II)-catalysed asymmetric hydrogenation of ketones: assessment of the relative importance of planar versus carbon-centred chirality[END_REF] we tested ligands L9 and L10 displaying opposite planar chirality. Disappointingly, the enantiomeric excesses recorded did not exceed 20% (entries 11 and 12). Similarly, ligands L11-L13 led to modest enantioselectivity not greater than 25% (entries 13-15). As ligand L8 has proven to be the most selective one for the ATH of acetophenone, we performed the synthesis of complex [L8Mn(CO) [START_REF] Zell | From ruthenium to iron and manganese -a mechanistic view on challenges and design principles of base metal hydrogenation catalysts[END_REF] Br] (1) by reacting the chiral amino phosphine ligand L8 with the manganese precursor Mn(CO) [START_REF] Wei | Iron Catalysis in Reduction and Hydrometalation Reactions[END_REF] Br in toluene at 110°C for 4h. The NMR analyses revealed the formation of two diastereomers differing only in the configuration of the metal center, [START_REF] Bauer | Chiral-at-metal complexes and their catalytic applications in organic synthesis[END_REF] one of which could be crystallized as monocrystals suitable for XRay analysis (Figure 1). The exact absolute configuration of each diastereomer was not assigned as the base is likely to cause epimerization at the metal by deprotonation of the amine function and elimination of KBr. [START_REF] Bruneau-Voisine | Transfer Hydrogenation of Carbonyl Derivatives Catalyzed by an Inexpensive Phosphine -Free Manganese Precatalyst[END_REF] J o u r n a l P r e -p r o o f Scheme 1. Chiral amino phosphine ligands screened in Mn-catalyzed asymmetric transfer hydrogenation of acetophenone.

J o u r n a l P r e -p r o o f

Journal Pre-proof The mixture of the two diastereomers was tested in the asymmetric transfer hydrogenation of acetophenone. 1-phenylethanol b1 was obtained with 75% ee and 96% isolated yield at 30°C for 14 hours (Scheme 2) which compares well with the result obtained with the in situ catalytic system after 14h (entry 9, Table 1). For ease of handling, we performed the scope of the asymmetric transfer hydrogenation of a range of ketones with the well-defined [L8Mn(CO) 3 Br] pre-catalyst 1 (Scheme 2). Ortho substitution of acetophenone with electron donating (a2-3) or electron withdrawing groups (a4) had a beneficial effect on the selectivity affording the corresponding alcohols with enantiomeric excesses ranging from 72 to 85%.

Substrates displaying bulky substituents at the alpha position to the ketone (a5-8) were hydrogenated with good to excellent enantioselectivities up to 99% ee. Although the steric hindrance renders these substrates more reluctant to reduction, increasing the reaction time to 24h for substrate a5 or raising the temperature from 30°C to 80°C for substrates a6-8 allowed the formation of the corresponding alcohols in moderate to good yields. In contrast to what was observed with diamine-based catalysts, para substitution of acetophenone with electron donating group did not alter the enantioselectivity, 1-(4-methylphenyl)ethanol b9 and 1-(4methoxyphenyl)ethanol b10 were obtained in 77% ee and 76 % ee respectively compared to 72% ee for 1-phenylethanol a1. On the opposite, substrates bearing electron withdrawing

J o u r n a l P r e -p r o o f

Journal Pre-proof substituents at the para position led to higher activities (almost full conversions were observed at 30°C in 3h for substrates (a11-15)) but lower enantioselectivities were recorded ranging from 41% to 69 % ee. In the case of 2,2,2-trifluoroacetophenone a16, a reversed sense of asymmetric induction was observed suggesting that a different transition state is involved for this substrate compared to the other aryl/alkyl ketones, which has already been reported with Ru-based catalytic systems. [START_REF] Hodgkinson | Synthesis and Catalytic Applications of an Extended Range of Tethered Ruthenium(II)/η6-Arene/Diamine Complexes[END_REF][START_REF] Slungård | Investigation into the enantioselection mechanism of ruthenium-arene-diamine transfer hydrogenation catalysts using fluorinated substrates[END_REF] The catalytic system is compatible with heteroaromatic ketones like 4′-acetylpyridine a17 and polyaromatic ketones such as 1-acetonaphthone a18 and 2-acetonaphthone a19. Regarding alkyl alkyl ketones, cyclohexyl methyl ketone a20 was reduced to the corresponding alcohol in 90% yield, but with a low enantiomeric excess (18%).

Conclusions

In conclusion, we have screened a range of commercially available chiral aminophosphines as ligands in the manganese catalyzed asymmetric transfer hydrogenation of acetophenone. The in situ generated catalytic system involving Mn(CO) 5 Br (0.5 mol%) as metal precursor and the aminophosphine L8, bearing a bulky binepine moiety on the phosphorus atom, afforded the best performances in terms of enantioselectivity. The well-defined manganese pre-catalyst 1 supported by this latter ligand was then synthesized, fully characterized and evaluated in the enantioselective reduction of carbonyl derivatives affording the corresponding alcohols in high yields and enantioselectivities with ee up to 99%.

Figure 1 .

 1 Figure 1. Molecular structure of complex 1 (ellipsoids drawn at the 50% probability level). H atoms have been omitted for clarity. Selected bond lengths (Å) and bond angles (deg): P1-Mn1 2.2977(17), N1-Mn1 2.094(5), Br1-Mn1 2.5383(10), C1-Mn1 1.841(8), C2-Mn1 1.843(5), C3-Mn1 1.800(6), P1-Mn1-N1 83.47(14), P1-Mn1-C3 92.02(19), C3-Mn1-C2 94.4(3), C2-Mn1-N1 90.0(2).

J o u r n a l P r e -p r o o f Journal Pre-proof Scheme 2 .

 2 Scheme 2. Scope of asymmetric transfer hydrogenation of carbonyl derivatives in the presence of catalyst 1.

Table 1 .

 1 Screening of chiral amino phosphine ligands in asymmetric transfer hydrogenation of acetophenone.

	Entry	Ligand	Temp.	V iPrOH	Time	Ee (%) c Conv. (%) d
				(mL)
	1	L1	80 a	8	15 min.	3 (R)	80
		L1	80 a	8	45 min.	2 (R)	90
	2	L1	80-30 b	20	19h	12 (R)	90
	3	L2	80-30 b	20	19h	15 (S)	97
	4	L3	80-30 b	20	19h	46 (R)	96
	5	L4	80-30 b	20	19h	11 (R)	24
	6	L5	80-30 b	20	19h	65 (R)	90
	7	L6	80-30 b	20	19h	5 (S)	97
	8	L7	80-30 b	20	19h	57 (R)	96
	9	L8	80-30 b	20	14h	77 (R)	95
	10	L8	80-30 b	20	19h	71 (R)	95
	11	L9	80-30 b	20	19h	20 (S)	8
	12	L10	80-30 b	20	19h	18 (R)	10
	13	L11	80-30 b	20	19h	25 (S)	97
	14	L12	80-30 b	20	19h	1 (R)	97
	15	L13	80-30 b	20	19h	2 (S)	97

J o u r n a l P r e -p r o o fJournal Pre-proof

Acknowledgements

We thank the Centre National de la Recherche Scientifique (CNRS), the Université de Rennes 1, the Institut Universitaire de France (IUF) and the Agence National de la Recherche (ANR agency, program JCJC ANR-15-CE07-0001 « Ferracycles ») for financial support. KA thanks the ''Région Occitanie'' for a PhD fellowship.

Funding

This work was supported by the Agence Nationale de la Recherche (ANR agency, program JCJC ANR-15-CE07-0001 "Ferracycles"), l'Université de Rennes 1, the Institut Universitaire de France (IUF), the Région Occitanie and the Centre National de la Recherche Scientifique (CNRS).

CRediT author statement

For the article entitled: Asymmetric transfer hydrogenation of ketones promoted by manganese(I) pre-catalysts supported by bidentate aminophosphines Karim Azouzi, a Antoine Bruneau-Voisine, a,b Laure Vendier, a Jean-Baptiste Sortais J o u r n a l P r e -p r o o f

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:


Julie Betbeder
email: julie.betbeder@univ-rennes2.fr

Jean Nabucet
email: jean.nabucet@univ-rennes2.fr

Eric Pottier
email: eric.pottier@univ-rennes1.fr

Jacques Baudry
email: jacques.baudry@rennes.inra.fr

Samuel Corgne
email: samuel.corgne@uhb.fr

Laurence Hubert-Moy
email: laurence.moy@univ-rennes2.fr

Detection and Characterization of Hedgerows Using TerraSAR-X Imagery

Keywords: linear hedgerow network, canopy structure, Radar-SAR imagery, very high resolution, Shannon entropy index, object-oriented classification

Whilst most hedgerow functions depend upon hedgerow structure and hedgerow network patterns, in many ecological studies information on the fragmentation of hedgerows network and canopy structure is often retrieved in the field in small areas using accurate ground surveys and estimated over landscapes in a semi-quantitative manner. This paper explores the use of radar SAR imagery to (i) detect hedgerow networks; and (ii) describe the hedgerow canopy heterogeneity using TerraSAR-X imagery. The extraction of hedgerow networks was achieved using an object-oriented method using two polarimetric parameters: the Single Bounce and the Shannon Entropy derived from one TerraSAR-X image. The hedgerow canopy heterogeneity estimated from field measurements was compared with two backscattering coefficients and three polarimetric parameters derived from the same image. The results show that the hedgerow network and its fragmentation can be identified with a very good accuracy (Kappa index: 0.92). This study also reveals the high correlation between one polarimetric parameter, the Shannon entropy, and the canopy fragmentation measured in the field. Therefore, VHSR radar images can OPEN ACCESS Remote Sens. 2014, 6 3753 both precisely detect the presence of wooded hedgerow networks and characterize their structure, which cannot be achieved with optical images.

Introduction

Hedgerows are important features in many landscapes of the world [START_REF] Forman | Hedgerows and hedgerow networks in landscape ecology[END_REF]. They are linear features composed of trees and/or shrubs of various species, managed by people as fences or for the provision of wood. Therefore, they have very different structures at tree and hedgerow scales in different landscapes [START_REF] Larcher | Landscape grammar: A method to analyse and design hedgerows and networks[END_REF]. They fulfill ecological, as well as cultural functions, such as control of soil erosion, microclimatic effects, landscape beautification, wood production, and water quality [START_REF] Baudry | Hedgerows: An international perspective on their origin, function and management[END_REF]. A dense hedgerow network plays a key role in habitat connectivity for some species and thus influences the degree of fragmentation of the landscape [START_REF] Burel | Effect of landscape structure and dynamics on species diversity in hedgerow networks[END_REF].

However, hedgerows have different structures according to their species composition, vegetation density and management regime. Most hedgerow functions depend upon hedgerow structure [START_REF] Brandle | Windbreaks in North American Agricultural Systems[END_REF] and hedgerow network patterns. The windbreak function is one of the most analyzed with physical models based on a qualitative characterization of hedgerows. The windbreak effect is the most straightforward process to relate to hedgerow or shelterbelt structure. [START_REF] Torita | Relationship between shelterbelt structure and mean wind reduction[END_REF] [START_REF] Torita | Relationship between shelterbelt structure and mean wind reduction[END_REF] showed that the product of the width and the total area density (the projected area of leaf, branch and stem per unit ground area divided by the crown length) is a good predictor of the windbreak effect. Hedgerows also provide habitat and landscape connectivity for species. Hedgerows with dense tree and shrub layers harbor more forest dwelling species than hedgerows with scattered trees [START_REF] Mccollin | Hedgerows as habitat for woodland plants[END_REF]. For [START_REF] Hinsley | The influence of hedge structure, management and landscape context on the value of hedgerows to birds: A review[END_REF] [START_REF] Hinsley | The influence of hedge structure, management and landscape context on the value of hedgerows to birds: A review[END_REF], "The two most important factors positively associated with species richness and abundance of breeding birds in hedgerows are hedge size (height/width/volume) and the presence/abundance of trees". Thus, the linear fragmentation of hedgerow networks and canopy fragmentation (Figure 1) influence landscape properties such as landscape connectivity and the degree of landscape fragmentation.

In many ecological studies, information on the fragmentation of hedgerow networks and canopies is often retrieved in the field using accurate ground surveys [START_REF] Baudry | A holistic landscape ecological study of the interactions between farming activities and ecological patterns in Brittany, France[END_REF]. Most of the time, hedgerow structure is only described for small areas from field measurements because this process is too time-consuming; hedgerow structure is estimated over landscapes in a semi-quantitative manner (e.g., [START_REF] Defra | Hedgerow Survey Handbook. A Standard Procedure for Local Surveys in the UK[END_REF]), which is approximate.

Nowadays, remotely sensed data offer a unique opportunity to map and characterize hedgerow networks at a landscape scale. Some studies conducted in different disciplines as geology [START_REF] Karnieli | Automatic extraction and evaluation of geological linear features from digital remote sensing data using a hough transform[END_REF] or medical imaging [START_REF] Li | Wavelet Transform for Directional Feature Extraction in Medical Imaging[END_REF] aimed to develop methodologies to extract linear features. Though remote sensing images are widely used to characterize landscapes [START_REF] Mulder | The use of remote sensing in soil and terrain mapping-A review[END_REF], only a few articles address the detection and characterization of linear features. Regarding hedgerow network detection, most studies use aerial photograph interpretation for hedgerow mapping [START_REF] Burel | Structural dynamic of a hedgerow network landscape in Brittany France[END_REF]. Accurate mapping of linear hedgerows can be problematic due to their small areal extent and fragmented nature [START_REF] Lechner | Remote sensing of small and linear features: Quantifying the effects of patch size and length, grid position and detectability on land cover mapping[END_REF]. Due to the appearance of very high spatial resolution (VHSR) sensors, remotely sensed data can now be used to automatically map hedgerows in agricultural landscapes [START_REF] Vannier | Wooded Hedgerows Characterization in Rural Landscape Using Very High Spatial Resolution Satellite Images[END_REF][START_REF] Aksoy | Automatic Mapping of Linearwoody Vegetation Features in Agricultural Landscapes[END_REF][START_REF] Tansey | Object-oriented classification of very high resolution airborne imagery for the extraction of hedgerows and field margin cover in agricultural areas[END_REF]. [START_REF] Lausch | Applicability of landscape metrics for the monitoring of landscape change: Issues of scale, resolution and interpretability[END_REF] [START_REF] Lausch | Applicability of landscape metrics for the monitoring of landscape change: Issues of scale, resolution and interpretability[END_REF] suggested that the spatial resolution should be below 5 m to capture linear features in a landscape. [START_REF] Fauvel | Hedges Detection Using Local Directional Features and Support Vector Data Description[END_REF] [START_REF] Fauvel | Hedges Detection Using Local Directional Features and Support Vector Data Description[END_REF] developed a method based on the waveform recognition theory applied to VHSR optical images to detect hedgerows as linear landscape elements composed of trees, distinct from forest patches. [START_REF] Deng | Recognition of shelterbelt continuity using remote sensing and waveform recognition[END_REF] [START_REF] Deng | Recognition of shelterbelt continuity using remote sensing and waveform recognition[END_REF] conducted belt continuity recognition and determined that belt gaps less than 10 m were not reliably detected using SPOT 5 images. Although the resolution of VHSR allows for the extraction of small elements and this device does not depend on weather conditions, there is only one study, to our knowledge, that has evaluated radar images to extract the linear hedgerow network; [START_REF] Bargiel | Capabilities of high resolution satellite radar for the detection of semi-natural habitat structures and grasslands in agricultural landscapes[END_REF] [START_REF] Bargiel | Capabilities of high resolution satellite radar for the detection of semi-natural habitat structures and grasslands in agricultural landscapes[END_REF] used textural features computed from the images of the backscattering coefficients derived from a high resolution satellite radar image to detect semi-natural habitats including hedgerows. However, in this case, only the intensity of the backscattering signal was considered, whereas it could be interesting to consider polarimetric indicators which take into account the number and nature of backscattering mechanisms for hedgerow extraction. Although some studies have aimed at extracting hedgerows automatically, few studies address the issue of hedgerow characterization. [START_REF] Wiseman | Quantification of shelterbelt characteristics using high-resolution imagery[END_REF] [START_REF] Wiseman | Quantification of shelterbelt characteristics using high-resolution imagery[END_REF] applied an object-oriented method to detect shelterbelt in a Canadian prairie, and studied the spectral features of the species composition of hedgerows using SPOT 5 imagery. [START_REF] Czerepowicz | Using satellite image data to estimate aboveground shelterbelt carbon stocks across an agricultural landscape[END_REF] [START_REF] Czerepowicz | Using satellite image data to estimate aboveground shelterbelt carbon stocks across an agricultural landscape[END_REF] explained over 70% of the variance in aboveground shelterbelt biomass using spatial and spectral attributes extracted from Quickbird imagery. Until now, there have been no studies aimed at characterizing canopy fragmentation. Such a study could help to improve our knowledge of different hedgerow functions such as ecological functions, considering hedgerows as a landscape connector for species dispersion. The hedgerow structure is difficult to describe using passive optical sensors. Although many models have been developed to describe canopy structure using VIS-NIR imagery, varied vegetation types such as grasses, shrubs, trees reflect similarly, especially when leaf-on, and some reflectance based indices saturate above LAI of about 4 [START_REF] Jacquemoud | PROSPECT + SAIL models: A review of use for vegetation characterization[END_REF]. Other remotely sensed data, such as SAR (Synthetic Aperture Radar) and LIDAR (Light Detection and Ranging) data, offer a new opportunity to characterize hedgerows in a whole landscape. Indeed, LIDAR remote sensing has the ability to acquire three dimensional measurements of a study site, at a fine scale, which is useful for estimating a variety of tree features (tree height, volume, biomass) [START_REF] Popescu | A voxel-based lidar method for estimating crown base height for deciduous and pine trees[END_REF][START_REF] Vé Ga | Multi-level filtering segmentation to measure individual tree parameters based on Lidar data: Application to a mountainous forest with heterogeneous stands[END_REF][START_REF] Heinzel | Exploring full-waveform LiDAR parameters for tree species classification[END_REF]. However, LIDAR data are generally acquired in one shot because each data acquisition is very costly. Radar SAR data are easier to acquire and allow a priori to access to the inner structure of the hedgerows when the radar signal interacts with the tree structure. To our knowledge, radar images have not been evaluated to characterize canopy fragmentation. In this study, we therefore investigated whether the spatial resolution of VHSR radar data like TerraSAR-X images allows the canopy structure to be taken into consideration.

The aim of this study was to address the issue of evaluating VHSR radar SAR images to (i) detect hedgerows in order to characterize hedgerow networks; and (ii) characterize the canopy structure at the hedgerow scale.

Material and Methods

Study Site

The investigated area is a Long Term Ecological Research site named "Pleine Fougè res" (130 km 2 ), located on the southern part of the Bay of the Mont-Saint-Michel, France (http://osur.univ-rennes1.fr/ za-armorique/) (Figure 2). It is referenced in the LTER-Europe (lterEurope.net) and the ILTER networks. This study focuses on the Sougeal marsh which is part of the Couesnon river floodplain. It is a flat area, mostly composed of agricultural plots intended for growing grassland and maize without wooded areas. Grazing and mowing are the two main grassland management practices. The agricultural plots are surrounded by a dense wooded hedgerow network. The hedgerows offer contrasted structures while being dominated by Salix sp.

Field Data

Field Data Acquisition

Ground surveys were conducted during the image acquisition to estimate the canopy structure of hedgerows under different amounts of canopy branch cover and clumping. At the date of image acquisition, the trees were leafless as shown by the hemispherical photographs taken the same day (Figure 3). A series of 55 hemispherical photographs distributed over 15 hedgerows were taken in April 2012 using a Canon EOS 7D camera with a Circular Fisheye for APS-C sensor (with a focal length of 10 mm, a diagonal angle of view approximately 180° and a minimum opening of F22). Each hemispherical photograph was geolocated using differential GPS (GeoXH Trimble, accuracy 0.10 m). Figure 3 presents different canopy fragmentation patterns encountered on the study site with more or less dense cover, showing different branch layouts, since at this period of the year the trees had no leaves.

Field Data Processing

The hemispherical photographs were classified into two classes, "Branches" and "Sky" (Figure 3), using the Contrast Split Segmentation algorithm implemented in eCognition. This algorithm segments the image into dark and bright image objects based on a threshold value that maximizes the contrast between them [START_REF] Definiens | Ecognition Professional: User Guide 4[END_REF]. Initially it executes a chessboard segmentation at different scales and then performs the split on each square. The parameters used here were as follows: "Chessboard Tile Size" of 100,000, a threshold ranging from 150 to 200 applied on the "Green layer" and a step size of 1.

The number of pixels is strictly identical from one picture to another, since the size of the hemispherical photographs is the same. At the end of the process, the heterogeneity structure index developed by [START_REF] Burel | Landscape Ecology: Concepts, Methods, and Applications[END_REF] [START_REF] Burel | Landscape Ecology: Concepts, Methods, and Applications[END_REF] was calculated (Figure 3) in order to quantify the canopy structure heterogeneity of the field plots using the following equtation:

(1)

With

the occurrence proportion of the couples c of adjacent pixels of different classes ("branches/sky", branch/branch, sky/sky) and nnh the number of types of couples.

This metric has high values for complex canopy structures, when the density of branches is high and they are interwoven. Conversely, a simple structure of straight branches for example, yields a low value. HS index values increase with the proportion of "branch/sky" interfaces. Thus, the increase of HS values highlights an increase of the canopy fragmentation and also of its canopy cover rate relative to sky background. In addition to the HS index we tested others metrics (preliminary tests not included in this article) as the number of interfaces "branch/sky" or the grain (adapted from Vannier et al., 2011 [31]) which highlight the size of the canopy gap. The HS index was selected because it showed the best correlation with the parameters derived from the radar image (σ° HH, σ° VV, Single Bounce, Double Bounce, and Shannon entropy).

Satellite Data

One dual-polarization TerraSAR-X image was acquired 19 April 2012 in High Resolution Spotlight mode with HH and VV polarizations, a ground spatial resolution of 1.5 m and an azimuth resolution of 2.2 m. An incidence angle of 37 degrees was chosen to maximize the vegetation penetration. Generally we consider that small angles are more sensitive to ground surfaces [START_REF] Ulaby | Radar Polarimetry for Geoscience Applications[END_REF][START_REF] Corgne | An integrated approach to hydro-geological lineament mapping of a semi-arid region of West Africa using Radarsat-1 and GIS[END_REF] and higher/medium angles are more sensitive to vegetation roughness [START_REF] Baghdadi | Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island[END_REF].

The TerraSAR-X image was acquired during leaf-off period. Indeed, it should be not possible to characterize the hedgerow structure during summer as X band cannot penetrate the vegetation foliage to access the inner structure of hedgerows. A Lee refined filter [START_REF] Lee | Speckle analysis and smoothing of synthetic aperture radar images[END_REF] was then applied using a window of 3 × 3 pixels to reduce speckle noise. The image was then geocoded using the Shuttle Radar Topography Mission (SRTM) data to correct the topographic deformations. The images were also geometrically corrected to the Lambert-RGF93/IGN-69 system using 55 ground control points selected from orthophotoplan images with a 0.5 m resolution. The geometric correction accuracy is less than one pixel (i.e., 1 m). The backscattering coefficients σ° HH and σ° VV were then calculated in decibels (dB):

(3)

Polarimetric Parameters

A 2 × 2 covariance (C 2 ) matrix was firstly extracted from the scattering matrix images (Figure 4) using PolSARpro v4.0 software (Polarimetric SAR Data Processing and Educational Toolbox) [START_REF] Pottier | Recent Advances in the Development of the Open Source Toolbox for Polarimetric and Interferometric Polarimetric SAR Data Processing: The PolSARpro v4.1.5 Software[END_REF]. A Lee refined filter [START_REF] Lee | Speckle analysis and smoothing of synthetic aperture radar images[END_REF] was applied using a window of 3 × 3 pixels to reduce speckle noise. The geocoding process was in this case directly applied on the elements of the 2 × 2 C 2 matrix which are independent of the polarimetric absolute phase [START_REF] Pottier | Polarimetric Radar Imaging: From Basics to Applications[END_REF]. The Shannon entropy (SE), which corresponds to the sum of two contributions related to the intensity and the degree of polarization [START_REF] Pottier | Polarimetric Radar Imaging: From Basics to Applications[END_REF], was then calculated from the 2 × 2 covariance matrix C 2 . SE measures the disorder encountered in polarimetric SAR images. Each pixel of the TerraSAR-X images is defined as a complex 2D target vector k that follows a 2D circular Gaussian process with a zero mean and a covariance C 2 matrix [START_REF] Pottier | Polarimetric Radar Imaging: From Basics to Applications[END_REF]: [START_REF] Burel | Effect of landscape structure and dynamics on species diversity in hedgerow networks[END_REF] where T* stands for transpose-conjugate.

The intensity (I C ) and the degree of polarization (P C ) can be defined from the averaged covariance matrix C 2 using the following expressions: [START_REF] Brandle | Windbreaks in North American Agricultural Systems[END_REF] where T r (.) and |.| stand respectively for the trace and determinant of the matrix.

The Shannon Entropy SE is defined, for a general density function, by: (

where stands for complex 2D integration. In the case of a circular Gaussian process, the SE can be decomposed as the sum of two terms: the intensity contribution (SE I ) that depends on the total backscattered power and the polarimetric contribution (SE P ) that depends on the Barakat degree of polarization P C [START_REF] Ré Fré Gier | Shannon entropy of partially polarized and partially coherent light with Gaussian fluctuations[END_REF]. In other words, the SE measures the randomness of scattering of a pixel which can be due to the variation of backscattering power or the variation of the backscattering polarization.

(

) (8) 7
In parallel a 2 × 2 coherency (T 2 ) matrix was extracted from the scattering matrix S using the Pauli spin elements [START_REF] Pottier | Polarimetric Radar Imaging: From Basics to Applications[END_REF]:

The first element of the diagonal T 11 and the second one T 22 were used to study the single bounce and double bounce, respectively.

Image Processing

Extraction of Hedgerows

Figure 5 shows the general classification scheme applied to extract hedgerows. An object-oriented approach was used to classify the Shannon entropy and the single bounce (HH + VV) layers using eCognition Developer V 8 software [START_REF] Definiens | Ecognition Professional: User Guide 4[END_REF].

The first step aimed to segment and extract objects with high SE values in order to extract the hedgerows using the multi-threshold segmentation technique implemented in eCognition Developer V 8 software [START_REF] Definiens | Ecognition Professional: User Guide 4[END_REF]. This algorithm segments the image into objects based on a threshold value that splits the image object domain and classifies the resulting image objects based on a defined pixel value threshold. This threshold can be user-defined, dividing the selected set of pixels into two subsets so that heterogeneity is increased to a maximum. In general, radar backscattering is lower for an open water body than for other surfaces because the single bounce scattering mechanism that is due to specular reflection from the water surface is dominant. Moreover, grassland present lower backscattering coefficients in spring period than other crops [START_REF] Bargiel | Land Use Classification with High-resolution Satellite Radar for Estimating the Impacts of Land-Use Change on the Quality of Ecosystem Services[END_REF][START_REF] Schuster | Towards detecting swath events in Terrasar-x time series to establish natura 2000 grassland habitat swath management as monitoring parameter[END_REF]. A threshold value was applied to the SE image to eliminate open water and grassland [START_REF] Marechal | One year wetland survey investigations from quad-pol RADARSAT-2 time-series SAR images[END_REF]. Indeed, open water and grassland present a number of backscattering mechanisms lower than that associated with hedgerows or plowed bare soil.

The second step of the classification process aimed to eliminate plowed bare soil. The single bounce image was used for this purpose because this scattering mechanism is higher for this surface condition than for hedgerows for which volume and double bounce mechanisms are dominant. The accuracy of the classification was defined using a Kappa index which expresses the proportional reduction in error generated by a classification process compared with the error of a completely random classification [START_REF] Congalton | A review of assessing the accuracy of classifications of remotely sensed data[END_REF]. This index is commonly used for the assessment of prediction errors in conservation presence/absence models [START_REF] Fielding | A review of methods for the assessment of prediction errors in conservation presence/absence models[END_REF]. Seventy-nine GPS points were taken in hedgerows, hedgerow gaps and grassland agricultural plots on the study site to evaluate the efficiency of radar SAR images to detect linear hedgerow fragmentation.

Assessment of Hedgerow Structure

First, the centre of each hemispherical photograph (assessed using a DGPS) was identified on the radar image. Circular buffers with 4 m radii were then formed around each centre point of field sample plots to collect SAR features. The size of the buffer was chosen in such a way to correspond to the maximum hedgerow width encountered on the study site. Buffers were then intersected with hedgerows to eliminate the values of the radar image from adjacent plots. Then, the mean values of the polarimetric parameters and of the backscattering coefficients were extracted for each buffer area.

Finally, linear regressions were performed between the SAR parameter mean values and the heterogeneity structure index measured from the field hemispherical photographs. Model fit was assessed using the coefficient of determination (R-squared) and p-values. For the best model, model robustness was assessed by the calculation of confidence intervals for the R-squared, slope, Root Mean Square Error (RMSE), average absolute error and average mean error using the bootstrap sampling technique (100,000 runs) [START_REF] Hastier | The Elements of Statistical Learning-Data Mining, Inference, and Prediction[END_REF]. Bootstrapping provides a realistic estimate of the predictive performance of a model [START_REF] Potts | Comparing species abundance models[END_REF]. In this method, the coefficients and statistics of a high number of samples taken from the available data set are calculated. First a sample with random points (picked with replacement, i.e., the same number can be picked more than one time) is selected. The "bootstrap sample" has the same size as the data set (i.e., 55). Linear regression is performed using the bootstrap sample, and the coefficients and statistics are stored. In our case the process was repeated 100,000 times. Thus, using the stored values it is possible to calculate RMSE, average absolute error and average mean error to evaluate if the model could be used to predict or assess hedgerow canopy structure. All statistics were performed with R statistical software, version 2.7.0 (www.r-project.org).

Results

Extraction of Hedgerows

The accuracy of the hedgerow network classification is very high, with an overall agreement of 96% and a Kappa Index of 0.92 (Table 1). This means that the classification process avoided at least 92% of the errors that were generated by a completely random classification. The analysis of the map that shows the classification of the validation points highlights that only 3 out of 79 points were misclassified (Figure 6). Underestimation error (1 point) was due to the fact that very narrow hedges (<1 m) cannot be extracted because of the spatial resolution of the images, while overestimation errors (2 points) can be explained the layover artefacts in radar imaging mainly for hedgerows which were perpendicular to the radar azimuth.

Table 1. Confusion matrix between the hedgerow classification derived from the

TerraSAR-X image (lines) and the validation set (columns).

Hedgerows Structure Assessment

When analyzing the relationship between the polarimetric parameters and the backscattering coefficients extracted from the SAR image and the heterogeneity structure index extracted from the field measurements, it can be seen that ( 1) three of these regressions are significant (p-value < 0.05) and two of them present a good goodness-of-fit (R-squared = 0.4 for the double bounce parameter and R-squared = 0.85 for the Shannon entropy index); and (2) the Shannon entropy index is the most correlated variable with field data (Table 2 andFigure 7). The analysis of the Shannon entropy index associated with the hedgerows extracted from TerraSAR-X shows a quite large range of values (from -6 to +5), and a wide spatial variability which highlights the heterogeneity of the hedgerow network structure (Figure 8).

The evaluation statistics derived from the bootstrap analysis (Figure 9) indicates a good agreement between HS index and Shannon entropy estimated through the subsampling with R-squared values ranging between 0.82 and 0.90. The interval of RMSE and average mean error is low (0.10) and the average mean error is equal to 1.4. We can also notice that the slope is significantly different from zero (p < 0.05) for the 100,000 bootstrap samples with confident intervals comprise between 10.06 and 12.38.

Discussion

This study is the first to describe the inner structure of woody linear features such as hedgerows using SAR images. This study provides a methodology to detect hedgerow networks using polarimetric parameters. Moreover, the results show that one polarimetric parameter, i.e., the Shannon entropy, is highly sensitive to the canopy structure.

The extraction of hedgerow networks was achieved with an object-oriented method using two polarimetric parameters: the single bounce parameter and the Shannon entropy index derived from one TerraSAR-X image. As proved in this study, SAR imagery can be used to identify hedgerows in agricultural landscapes, the accuracy of hedgerow classification being over 90% (KIA = 0.92). These results are comparable to those from similar studies that aimed to develop methods to automatically extract linear landscape features such as hedgerows from VHRS optical data using object-oriented [START_REF] Vannier | Wooded Hedgerows Characterization in Rural Landscape Using Very High Spatial Resolution Satellite Images[END_REF][START_REF] Deng | Recognition of shelterbelt continuity using remote sensing and waveform recognition[END_REF][START_REF] Bargiel | Capabilities of high resolution satellite radar for the detection of semi-natural habitat structures and grasslands in agricultural landscapes[END_REF]; hybrid pixel-based and object-oriented [START_REF] Sheeren | Discriminating small wooded elements in rural landscape from aerial photography: A hybrid pixel/object-based analysis approach[END_REF], or waveform recognition-based [START_REF] Deng | Recognition of shelterbelt continuity using remote sensing and waveform recognition[END_REF] classification algorithms. [START_REF] Bargiel | Capabilities of high resolution satellite radar for the detection of semi-natural habitat structures and grasslands in agricultural landscapes[END_REF] [START_REF] Bargiel | Capabilities of high resolution satellite radar for the detection of semi-natural habitat structures and grasslands in agricultural landscapes[END_REF] detected semi-natural habitats and hedges composed of trees and shrubs with a global accuracy of 70.4% using textural indexes calculated from backscattering coefficients derived from a TerraSAR-X time series. [START_REF] Bargiel | Capabilities of high resolution satellite radar for the detection of semi-natural habitat structures and grasslands in agricultural landscapes[END_REF] [START_REF] Bargiel | Capabilities of high resolution satellite radar for the detection of semi-natural habitat structures and grasslands in agricultural landscapes[END_REF] and the present study provide good results for the detection of hedgerows. Because of the different approaches adopted, the results cannot be compared directly. Indeed the presented study use only one TerraSAR-X image and Bargiel et al. [START_REF] Bargiel | Capabilities of high resolution satellite radar for the detection of semi-natural habitat structures and grasslands in agricultural landscapes[END_REF] used a supervised classification of multi-temporal TerraSAR-X data. The results of this study shows that polarimetric parameters derived from TerraSAR-X imagery allow good identification of hedgerow networks. Hedgerow orientation relative to radar azimuth does not affect the ability of SAR sensors to detect hedgerows. Indeed, hedgerow discontinuity (Figure 6) is mainly due to the presence of gaps in hedgerows (as shown on the picture in Figure 1). These gaps are due to the presence of pastured grasslands on either side of hedgerows. This point is interesting because compared to optical data mostly acquired in summer during cloudless periods when trees have leaves that hide gaps in hedgerows, radar data should have better detect hedgerow discontinuities which is a reliable information to study species distribution patterns [START_REF] Burel | Structural dynamic of a hedgerow network landscape in Brittany France[END_REF].

Regarding the spatial behavior of radar signal in hedgerow structure assessment, the results of this study show that only the Shannon entropy index presents a very good correlation with the heterogeneity structure index that reflects the canopy fragmentation. Regarding the σ° HH and σ° VV parameters, i.e., the single and double bounces, the correlations are low (respectively NS and 0.4 for the double bounce parameter). This result is in accordance with radar theory because even if the double bounce mechanism is higher than the single bounce in vegetation cover [START_REF] Pottier | Polarimetric Radar Imaging: From Basics to Applications[END_REF], it is not the dominant backscattering mechanism. Indeed, volume scattering (HV polarization) is generally more sensitive to the tree structure [START_REF] Pottier | Polarimetric Radar Imaging: From Basics to Applications[END_REF]. This was not studied here because the TerraSAR-X image we used presented only HH and VV polarizations. In spotlight mode, only HH-VV dual pol TerraSAR-X data are available with a very high spatial resolution (1 m to 2 m). HV images in dual pol mode are only delivered in stripmap mode [START_REF] Roth | TerraSAR-X: A New Perspective for Applications Requiring High Resolution Spaceborne SAR Data[END_REF] with a lower spatial resolution which is not compatible with the extraction of hedgerow networks.

This approach was applied on a TerraSAR-X image acquired at the beginning of the spring period. At this period, the maize agricultural plots were at a plowed bare soil stage on the study site. The rule set developed to automatically extract hedgerows was adapted to this particular period but would need to be adapted for a SAR image acquired during another period for instance in summer. Indeed, the surface statuses vary across the seasons with vegetation growth, for example the maize crop is at sowing stage in spring and harvest stage in late summer. In this particular case, the double bounce image could be used to identify crops especially at the beginning of their growth, because the double bounce mechanisms increase due to the interactions between the radar pulse, the vegetation stems and the underlying surface [START_REF] Baghdadi | Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island[END_REF]. Regarding the hedgerow structure characterization, it is important to note that the TerraSAR-X image used was acquired during a leaf-off period. This study could not have been conducted at another time period, because the X-band is not able to penetrate a dense crown cover. We can also notice that the orientation of hedgerows relative to the radar azimuth is an important feature for the canopy fragmentation interpretation. Indeed, layover has to be considered when hedgerows are perpendicular to radar azimuth, since they increase the number of mixed pixels within hedgerows.

By revealing the capability of TerraSAR-X images for the classification of hedgerows and the detection of their inner canopy structure, the present study supplies interesting information about the possibilities for the determination of ecological metrics based on remote sensing data. In further work, it would be interesting to calculate landscape metrics such as for instance the landscape grain [START_REF] Vannier | Multiscale ecological assessment of remote sensing images[END_REF] or to quantify the linear hedgerow fragmentation. These metrics should be used to study the spatial distribution of species and could help to understand how some species use hedgerows for their dispersion and how far species immigrate into them. Indeed the identification and characterization of hedgerows are often required in landscape ecology studies to evaluate the response of some species to landscape structure, composition and configurations [START_REF] Ouin | The species-area relationship in the hoverfly (Diptera, Syrphidae) communities of forest fragments in southern France[END_REF][START_REF] Balent | Modelling bird communities/landscape patterns relationships in a rural area of South-Western France Landsc[END_REF]. Different studies [START_REF] Hill | Bandas de lineales vegetación de selvas tropicales como potenciales corredores dispersores para insectos de la selva tropical[END_REF][START_REF] Tischendorf | A simulation experiment on the potential of hedgerows as movement corridors for forest carabids[END_REF] have also considered the potential of hedgerows as ecological corridors for the movement of plants and animals in shady (forest like) conditions, thus, with a dense canopy structure. Therefore, determining the linear fragmentation of the hedgerow network is essential for hedgerow management for biodiversity [START_REF] Baudry | Institut National de la Recherche Agronomique[END_REF]. The windbreak function is also highly dependent on the continuity of hedgerow networks and hedgerow canopy cover.

Conclusions

This paper explores the use of radar SAR imagery to (i) detect hedgerow networks; and (ii) describe hedgerow canopy heterogeneity using TerraSAR-X imagery. The results show that the hedgerow network and its fragmentation can be identified with a very good accuracy. This study also reveals the importance of the use of one polarimetric parameter, the Shannon entropy, to study the canopy fragmentation, which cannot be achieved with optical images. Therefore, VHSR radar images can both accurately detect the presence of wooded hedgerow networks and characterize their structure. This information can be exploited for landscape ecology studies, for example in calculating landscape metrics in order to analyze the function of hedgerows as dispersal corridors of forest species.

Figure 1 .

 1 Figure 1. Linear fragmentation of a hedgerow network (a) presenting different canopy structures; (b), (c), with (d) or without (e) underlaying and pruned (drawing by Y. Le Flem).

Figure 2 .

 2 Figure 2. Study site location and examples of two hedgerow structures.

Figure 3 .

 3 Figure 3. Workflow of field data processing. Legend: (1) High canopy cover and high fragmentation level; (2) High canopy cover and medium fragmentation level; (3) Medium canopy cover and medium fragmentation level; (4) Low canopy cover and low fragmentation level.

2. 3 . 1 .

 31 Image Pre-Processing 2.3.1.1. Backscattering Coefficients TerraSAR images were firstly radiometrically calibrated according to the following equation [35]: (2) where: Ks is the calibration constant DN is the digital number of each pixel (amplitude of the backscattering signal) NEBN is the Noise Equivalent Beta Naught (sensor noise) is the radar incidence angle This equation transforms the digital number of each pixel into a backscattering coefficient on a linear scale.

Figure 4 .

 4 Figure 4. Workflow of the radar data pre-processing.

Figure 5 .

 5 Figure 5. Workflow of hedgerow extraction using a TerraSAR-X image.

Figure 6 .

 6 Figure 6. Map of the classified validation points.

Figure 7 .

 7 Figure 7. Linear regression between the Shannon entropy index and the heterogeneity structure index.

Figure 8 .

 8 Figure 8. Map of the Shannon entropy index of the hedgerows extracted from the TerraSAR-X image.

Figure 9 .

 9 Figure 9. Evaluation of the model robustness between the HS index and the Shannon entropy derived from the TerraSAR-X image using bootstrap sampling method (100,000 runs). (1) Frequency of slopes; (2) Frequency of R-squared; (3) Frequency of RMSE; and (4) Frequency of Average mean error.

Table 2 .

 2 Linear regression between the polarimetric parameters and the backscattering coefficients extracted from the SAR image and the heterogeneity structure index extracted from ground surveys.

	Radar	R 2	p-value
	σ° HH		NS
	σ° VV		NS
	Single Bounce	0.1	0.001
	Double Bounce	0.4	<0.0001
	Shannon Entropy	0.85	<0.0001

y = 11.027x -2.3241 R 2 = 0.85

© 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

Acknowledgments

This work was supported by the DIVA 3-AGRICONNECT program, the CNES (PhD grant to J. Betbeder) and the DLR by providing TerraSAR-X imagery, and the Zone Atelier Armorique. Air Papillon took the aerial photos.

Author Contributions

All authors contributed extensively to the work presented in this paper. Julie Betbeder and Eric Pottier processed the radar images; Julie Betbeder, Jean Nabucet and Jacques Baudry developed the field methodology and led an interpretation of the results. Laurence Hubert-Moy initiated the project. Julie Betbeder and Laurence Hubert-Moy drafted the manuscript, which was revised by all authors. All authors read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.