A Dupuy

S Jouneau

C Droitcourt

Dr Henri Adamski
email: henri.adamski@chu-rennes.fr

A Polat

E Polard

M Kerjouan

B Arnouat

M Le Garrec

E Oger

Pirfenidone photosensitization in patients with idiopathic pulmonary fibrosis: a case series Running head: Pirfenidone photosensitization

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Accepted Article

The oral antifibrotic agent, pirfenidone (PFD), 5-methyl-l-phenyl-[1H]-pyridine, is used to treat idiopathic pulmonary fibrosis (IPF), a chronic and fatal lung disease. In trials, PFD reduces disease progression and decreases mortality. The most common side events of PFD are skin manifestations (25%), described as a photosensitivity or rash, but they are not well characterised [START_REF] Lancaster | Safety of pirfenidone in patients with idiopathic pulmonary fibrosis: integrated analysis of cumulative data from 5 clinical trials[END_REF] . The objective of the present real-life study was to address the question of skin manifestations in patients treated with PFD for IPF.

We performed a single-centre cross-sectional study of 54 patients treated with PFD for IPF (85% men, median age 74 years, median exposure time 11.9 months), in the Department of Pulmonology (Competence Centre for Rare Lung Diseases), at Rennes University Hospital (CHU), France, between April 2014 and January 2017. The study was approved by the CHU Ethics Committee and all patients signed informed consent in accordance with the principles of the Helsinki Declaration.

Of the 54 patients treated with PFD, 13 (22.2%) experienced skin manifestations. All were declared to the Rennes Pharmacovigilance. This database showed that 12 patients had photosensitivity and one urticaria. Eight patients, none of whom had a history of photosensitive diseases, were assessed by a dermatologist (Table 1). The mean duration between starting PFD and a skin manifestation was 5.5 months. They developed burning erythema followed by hyperpigmentation which was sharply limited to sun-exposed areas (bald head, face, neck, upper chest and/or dorsa of forearms and hands), where sunscreen has not been applied one day after UV exposure. These findings were consistent with a moderate phototoxic reaction. Skin biopsies performed in cases 1, 3 and 6, showed epidermal spongiosis with a lichenoid reaction and moderate dermal perivascular lymphocytic infiltration. Apoptotic keratinocytes were observed in case 1. All patients were successfully treated with topical corticosteroid within 8 days. Three patients discontinued PFD due to gastrointestinal disorders and fatigue. No patient relapsed. Other long-term medication was continued.

Photobiological explorations were realized on the back of patients with an ultraviolet (UV)A lamp (Waldmann ® 182, Reischtett, France) and a solar simulator (Dermolum UM-UW Müller Elektronik ® , Moosinning, Germany) emitting polychromatic spectrum (95%UVA/5%UVB). Polychromatic minimal erythema dose (MED) was evaluated 24 hours after exposure for 5 patients tested in normal values. UVA MED was normal (> 20J/cm²) at baseline in all of the 3 cases evaluated. After skin reaction, the reactivity threshold was lower in UVA: an erythema appeared for 20J/cm² 24 hours after exposure in 6 of 6 patients tested. We examined 5 patients using PFD photopatches (contents of Esbriet® 267mg capsule, 30% petrolatum). The irradiated site of 4 patients was positive one and two days after UVA-irradiation (7J/cm²) with 3 having strong crescendo eczematous reaction. The nonirradiated patch showed no reaction.

Accepted Article

Porphyrins in the blood and urine were assayed at PFD introduction and during the skin manifestations in 3 patients: all were normal. The niacin values of 2 of the 3 patients tested were initially low and were not significantly altered after photosensitivity.

To our knowledge, this study represents the largest documented series of PFD photosensitivity because such sporadic case reports have been only published. [START_REF] Papakonstantinou | Pirfenidone-induced severe phototoxic reaction in a patient with idiopathic lung fibrosis[END_REF][START_REF] Tsuruta | Pirfenidone-induced photoleukomelanoderma in a patient with idiopathic pulmonary fibrosis[END_REF][START_REF] Reinholz | Photoallergic contact dermatitis due to treatment of pulmonary fibrosis with pirfenidone[END_REF][START_REF] Caruana | Cutaneous reactions to pirfenidone: a new kid on the block[END_REF][START_REF] Park | Pirfenidoneinduced photo-allergic reaction in a patient with idiopathic pulmonary fibrosis[END_REF] One fifth of our patients were photosensitive, consistent with data from PFD safety analysis 1 . Our patients seen by a dermatologist had clinical features of phototoxicity. All were treated with maximum dose of PFD. Photobiochemical studies demonstrated the phototoxicity of PFD [START_REF] Seto | Photosafety assessments on pirfenidone: photochemical, photobiological, and pharmacokinetic characterization[END_REF] , confirmed by clinical reported cases. [START_REF] Papakonstantinou | Pirfenidone-induced severe phototoxic reaction in a patient with idiopathic lung fibrosis[END_REF][START_REF] Tsuruta | Pirfenidone-induced photoleukomelanoderma in a patient with idiopathic pulmonary fibrosis[END_REF] Our results do not indicate that phototoxicity is linked to the metabolism of porphyrins or niacin. In patients with low niacin serum concentration, we did not assess their diets and found no drug-induced niacin deficiency.

Furthermore, three cases of PFD photoallergic reaction were recently published. [START_REF] Reinholz | Photoallergic contact dermatitis due to treatment of pulmonary fibrosis with pirfenidone[END_REF][START_REF] Park | Pirfenidoneinduced photo-allergic reaction in a patient with idiopathic pulmonary fibrosis[END_REF] Photoallergic dermatitis is characterised by eczematous eruption starting in light-exposed areas and later spreading to covered sites. This clinical presentation was not found in our patients, but histology (lichenoid pattern) and photopatch testing (crescendo eczematous reaction) were in accordance with photoallergic features. Therefore, we believe the mechanism underlying the PFD photosensitivity involves a combination of photoallergic and phototoxic effects.

Our photobiological explorations showed that UVA irradiation influenced PFD photosensitivity, as in most drug-induced photosensitization. [START_REF] Khandpur | Drug-induced photosensitivity: new insights into pathomechanisms and clinical variation through basic and applied science[END_REF] Very few cases with PFD phototesting have been reported. [START_REF] Tsuruta | Pirfenidone-induced photoleukomelanoderma in a patient with idiopathic pulmonary fibrosis[END_REF][START_REF] Park | Pirfenidoneinduced photo-allergic reaction in a patient with idiopathic pulmonary fibrosis[END_REF] In one case, UVA and UVB MEDs were decreased. [START_REF] Tsuruta | Pirfenidone-induced photoleukomelanoderma in a patient with idiopathic pulmonary fibrosis[END_REF] Lastly, only one patient had UVA PFD patch and was positive [START_REF] Park | Pirfenidoneinduced photo-allergic reaction in a patient with idiopathic pulmonary fibrosis[END_REF] , as in most of our cases tested.

The great photosensitivity of PFD requires optimal management including photoprotection and a close collaboration between dermatologists, pulmonologists and general practitioners.

ccepted Article

Table 1 .

 1 Clinical, biological and photobiological characteristics of skin manifestations for patients with idiopathic pulmonary fibrosis treated with pirfenidone and seen by a dermatologist. : years. M: male. F: female. Grade II: according to the Common Terminology Criteria Adverse Event (CTCAE). PFD: pirfenidone. MED: minimal erythema dose. ND: not done. NI: non-irradiated PFD patch test. -: negative reaction. + or ++ : positive reaction according International Contact Dermatitis Research Group (ICDRG) system. * More than six months ** In our laboratory, the normal values for UVA MED were 21-80 J/cm2. MED was considered to be pathological after positive reaction in response to 20J/cm², 24 hours after UVA exposure.

	Case	Age	Sex	Phototype	Long-term therapy*	Type of skin side	Period of use	PFD dose on	Treatment	Polychromatic	UVA phototest	UVA	PFD	Niacin dosage	Porphyrins
		(y)		(Fitzpatrick		event and	PFD before	onset of		MED (Normal >	before PFD	phototest	patch	before/during	dosage in
				's		dermatological	skin event, in	photosensitivity		1 J/cm²) before/	starting (20	during skin	test	PFD treatment	blood and
				classificatio		examination	months	(mg/day)		during PFD	J/cm²)**	reaction (20		(Normal > 38	urine
				n)						treatment		J/cm²)**		µmol/L)	before/during
															PFD
															treatment
	1	74	M	III	Lansoprazole, irbesartan,	Phototoxicity	4	2403	Topical	ND	ND	ND	ND	ND	ND
					rosuvastatin	(Grade II) on head			corticosteroids,
						and dorsa of hands			photoprotective
									measures,
									PFD continued
	2	68	M	III	Lansoprazole, prednisone,	Phototoxicity	10	2403	Photoprotective	Normal: 1.5/1.25	-	Erythema	NI : -	40/52	Normal/
					simvastatin, valsartan,	(Grade II) on dorsa of			measures,				UVA: +		Normal
					budesonide/formoterol	hands			PFD continued
	3	77	F	II	Amlodipine, salbutamol,	Phototoxicity	3	2403	High potent topical	ND	-	Erythema	ND	22/31	Normal/
					indacaterol/glycopyrronium	(Grade II) on			corticosteroids,						Normal
					, alendronic acid,	forehead and dorsa of			photoprotective
					metoclopramide	hands			measures.
									Decrease PFD dose
									(1602mg) due to
									digestive disorders
	4	68	M	II	Lercanidipine, olmesartan,	Phototoxicity	11	2403	Photoprotective	ND/Normal: 1.25	ND	Erythema	NI :-	ND/35	ND/Normal
					fenofibrate, lansoprazole	(Grade II) on head			measures				UVA :-
						and neck			PFD continued
	5	75	M	II	Ramipril/hydrochlorothiazi	Phototoxicity	5	2403	Topical	ND/Normal: 1.75	-	Erythema	NI: -	22/51	Normal/
					de, acetylsalicylate,	(Grade II) on head,			corticosteroids,PFD				UVA: ++		Normal
					bosiprolol, lansoprazole,	neck, scalp, ears, and			discontinued
					rosuvastatin, amlodipine	dorsum of hands
	6	79	M	II	Acetylsalicylate,	Phototoxicity	2	2403	Topical	ND/Normal: 1.75	ND	Erythema	NI : -	ND / 35	ND/ Normal
					hydrocortisone,	(Grade II) on head,			corticosteroids,				UVA : ++
					levothyroxine, testostérone,	neck and dorsa of			PFD discontinued
					bisoprolol, atorvastatin,	hands
					ramipril, lansoprazole
	7	68	F	II	Pantoprazole, rosuvastatin,	Phototoxicity	7	2403	Topical	ND/Normal: 1.75	ND	Erythema	NI : -	ND / 42	ND/Normal
					paroxetine, domperidone,	(Grade II) on neck,			corticosteroids,				UVA : ++
					lebrikizumab	upper chest, dorsa of			photoprotective
						forearms and hands			measures,
									PFD continued
	8	66	M	II	None	Phototoxicity	2	2403	Topical	ND	ND	ND	ND	ND	ND
						(Grade II) on			corticosteroids,
						head and dorsa of			PFD discontinued
						hands

y

Acknowledgments

The English text was edited by Dr Owen Parkes Table Table 1. Clinical, biological, and photobiological characteristics of skin manifestations for patients with idiopathic pulmonary fibrosis treated with pirfenidone and seen by a dermatologist.

. Jouneau has acted as Investigator, Consultant or in another capacity from Laboratoire Roche.


Stéphane Mounier
email: stephane.mounier@univtln.fr

Rozane Valente Marins

Drude Luiz

Lacerda

Stéphane Jean

Louis Mounier

Rozane Valente

Marins £ Luiz

Drude Lacerda

Characterization of the Ceara Costal Zone Organic Matter Inputs

Keywords: mangrove, organic matter, fluorescence, metal complexation, urban pressure

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Introduction :

The global dispersion of manmade pollutants in pristine systems and the high levels of metals raises serious questions about their effects on the ecosystems. These contaminants accumulate in organisms and undergo biomagnification in the food web, reaching harmful concentrations, including to human health by the consumption of animals of high trophic level species [START_REF] Doney | The Growing Human Footprint on Coastal and OpenOcean Biogeochemistry[END_REF] [START_REF] Bisi | Trophic relationships and mercury biomagnification in Brazilian tropical coastal food webs[END_REF]. The continent ocean interface encompasses processes in the drainage basins of affluent rivers, oceanic dynamics, both on the continental shelf and in the deep ocean and atmospheric processes. These define the magnitude and variability of flows and material dynamics through the landsea gradient. These processes are affected by anthropogenic actions both locally and globally, and are also influenced by global climate change, which affects both continental and oceanic forcing [START_REF] Jennerjahn | The Tropical Brazilian Continental Margin[END_REF]. It is in the estuary, where all these processes interact, that environmental changes are more significant Estuaries are natural traps that "capture" continental materials transported by rivers. The gentle slope of the coastal strip creates extensive estuarine areas where the residence time of the waters is greater. During transport at the continentocean interface significant biogeochemical transformations of the transported materials occur and the intensity of these transformations is mostly related to the residence time of the water mass in the estuary, where the hydrochemical and hydrological dynamics is more complex. The residence time of the water body in estuaries is a function of the magnitude of the fluvial discharge and the intensity of the oceanic forcing on the continental shelf and on the estuarine region varying according to the interaction between these forces.

In mangroves, the residence time is greater than in estuaries and many pollutants are retained permanently. This accumulation leads to a specific biochemical environment that can induced changes in ecosystem's functioning. The objectives of this work are to investigated at the organic matter level the impact of urbanization and the tidal effect on the organic matter from local mangroves in river/mangrove systems along the coast of Ceará State in Northeasetrn Brazil.

Sites study.

The metropolitan region of Fortaleza is located in the northeastern state of Cearà, Brazil. It is made up of nine cities (Aquiraz, Caucaia, Eusébio, Fortaleza, Guaiúba, Itaitinga, Maracanaú, Maranguape and Pacatuba) and witnessed a great expansion in the recent years [START_REF] Brandão | Mapa Geológico da Região Metropolitana de Fortaleza[END_REF]. This region has several river mouths summing up 1,522 hectares of mangrove [START_REF] Godoy | Alterações nas áreas de mangue em estuários no Estuário do Ceará devido à mudanças nos usos do solo e mudanças climáticas[END_REF]. These areas host several species that sustain biodiversity and also the local economy (SDU, 1994).

The three most important mangrove areas of the metropolitan region are fed by the Cocó, Pacoti and Ceara rivers (figure 1), these areas has remained relatively stable from 2000 to 2004, as well as the total mangrove area in the State, totalling about 14,000 ha. Annual precipitation is 1,000 mm and occurs principally in March and June. In 2004, however, at the east coast of Ceará state, from Fortaleza to the Jaguaribe basin area, rainfall was abnormally high. In January, for example, month rainfall reached 404 mm and 378 mm, respectively, against a historical average of 105 mm and 82 mm, respectively [START_REF] Xavier | Chuvas em janeiro/fevereiro 2004 no Ceará e dificuldades para a previsão durante anos neutros no Pacífico exposição na ACECIAcademia Cearense de Ciências, 01/março[END_REF].The average temperature ranges from 24 to 26 °C with a variability of 6 °C at maximum.

The fourth study area is the Jaguaribe river, the largest basin area to the state and the largest mangrove area in the eastern coast. It is also the major freshwater supply to the metropolitan region through a series of diversion canals. Description of the studied basins.

The Cocó river

This river initiated in the Serra da Aratanha (Pacatuba) and has a length of 50 km with a basin area of 485 km 2 . The river crosses the cities of Aquiraz, Pacatuba and Fortaleza. At the meeting with the Coaçu river, basin area of 375 hectares, most of them covered by mangroves that expands up to 11 km inland [START_REF] Mavignier | Levantamento e quantificação das áreas de manguezais no Estado do Ceará (Brasil) através de sensoriamento remoto[END_REF]SDU, 1994). Human impact of men on the river is important because of garbage, cattle ranching, industrial activity and water use [START_REF] Mavignier | Levantamento e quantificação das áreas de manguezais no Estado do Ceará (Brasil) através de sensoriamento remoto[END_REF], particularly over the last 24 km. Sixty percent of the metropolitan region is covered by the Cocó River basin. Detailed information is available in [START_REF] Beltrão | Abastecimento de Água da Área Metropolitana da Cidade de Fortaleza -CE[END_REF] and for climatology, geology and hydrology in the report of the "Study of the life of the Cocó River" AUMEF / Government of Ceará 1988. Mainly four types of geological formation are found in the basin: sand dunes covered by vegetation or already urbanized, clayey sediments rich in organic clay, sandyclayey districts and in the interior with crystalline formation.

The mangrove vegetation at the estuary is composed by Avecinia germinans (black mangrove), Laguncularia racemosa (white magrove) and Rhizophora mangle (red mangrove). The landmangrove interface harbors many species of grasses and shrubs, coconut, hose, cashew. Mangrove vegetation harbors a great variety of fish, 23 out of 25 are marine. Upstream the river is hypereutrophic, with algae blooms and abundant aquatic macrophytes. In the rainy season, the materials transported are deposited on beaches and are responsible for black spots generally confused with pollution by the population.

The industrial district of Maracanaú is situated 15 km from the centre of Fortaleza. It counts 41 companies (chemical, fabric, metallurgy, food, mineral and nonmetallic exploitation, ...) [START_REF] Mavignier | Levantamento e quantificação das áreas de manguezais no Estado do Ceará (Brasil) através de sensoriamento remoto[END_REF]. The waste water treatment station (WWTS) built since beginning of the area development is already oversized. Since (1992) retention basins have been implanted. The last one of them leaks in the Maranguapinho River. An anaerobic lagoon followed by ripening lagoons (792,000 m 3 ) allows a residence time of 28 days with a flow rate of 840 L.s 1 . But it is not the totality of the urban effluents that are collected.

The contaminant content in the river seems low for fecal coliform, heavy metals and Biological Oxygen Demand (BOD). [START_REF] Lacerda | Estimating the importance of natural and anthropogenic sources on N and P emission to estuaries along the Ceará State Coast NE Brazil[END_REF]SEMACE, 2005).

There is an amplification of the concentrations of copper (Cu) and lead (Pb) in the animals across the food chain [START_REF] Mavignier | Levantamento e quantificação das áreas de manguezais no Estado do Ceará (Brasil) através de sensoriamento remoto[END_REF]). Leaching appears to be the main source of pollutants, however metal concentrations in waters are lower than Brazilian legal standards (CONAMA standards). The leachate composition shows a very high organic matter concentration. (Analyzed from 21/02/90 21/05/91).

The Pacoti River

The Pacoti river has its source in Baturite Mountain chain and has a length of 150 km. It 1,800 km 2 basin includes the contributory rivers Agua Verde, Jua, Guaiuba. The Pacoti river flows through some small towns and possesses many small islands. The mangroves in its estuary cover 158 hectares [START_REF] Mavignier | Levantamento e quantificação das áreas de manguezais no Estado do Ceará (Brasil) através de sensoriamento remoto[END_REF]SDU, 1994). The influence of the sea is measured up to 15 km from the mouth in the dry season (Freire et al., 1991;George Satander Sá Freire et al., 1991). In this river human impact is weak because the activities are dominated by cattle raising and exploitation of the forest. This mangrove is chemically reductive because of the presence of large amount of organic matter (Freire et al., 1991;Freire and Maia, 1991).

The Ceara river

The source of the river Ceara is born in the Maranguape hill. The length is 60 km with a basin of 900 km 2 . The river cross the cities of Maranguape, Caucaia and Fortaleza. The main tributary is the Maranguapinho with a confluence located about 7 km from the mouth. Mangrove land is 675 ha [START_REF] Mavignier | Levantamento e quantificação das áreas de manguezais no Estado do Ceará (Brasil) através de sensoriamento remoto[END_REF] with an expanse up to 14 km inland (SDU, 1994). The industrial district of Maracanaú is situated 15 km from the centre of Fortaleza. It harbours 41 companies (chemical, fabric, metallurgy, food, mineral and nonmetallic exploitation) [START_REF] Mavignier | Levantamento e quantificação das áreas de manguezais no Estado do Ceará (Brasil) através de sensoriamento remoto[END_REF] . The waste water treatment station (WWTS) built since beginning of the area development is already oversized. Since 1992 retention basins have been implanted. The last one of them leaks in the Maranguapinho River. An anaerobic lagoon followed by ripening lagoons (792,000 m 3 ) allows a residence time of 28 days with a flow rate of 840 L.s 1 , but it is not the totality of the urban effluents that are collected.

The Jaguaribe river

The basin of the Jaguaribe River is 75,669 km 2 for a length of 610 km. The river is much greater than the Pacoti, Ceara and Cocó rivers and does not allow to compare with them. The population density was less than 30 hab.km 2 with a total population of 2,064,535 (data of 1996) [START_REF] Gatto | Diagnóstico Ambiantal da Bacia do Rio Jaguaribe Diretrizes Gerais para a Ordenação Territorial[END_REF]. The main uses of the space is traditional agriculture together with livestock. Sixty percent of the basin has a semiarid climate with locally with spots under moist, subhumid and arid climate. Approximately 50% of the basin is in the state of Ceara, the other part is shared with the states of Piauí, Pernambuco, Paraíba and Rio Grande do Norte. The dominant vegetation is the Caatinga (Savanna). The most abundant species are Anadenathera colubrina, Tabebuia chrysotricha, Auxemma oncocalyx, Astronium urundeuva, Caesalpinia pyramidalis, Aspidosperma pyrifolium, Bumelia sartorum, Erythrina velutina Croton spp., Mimosa tenuiflora, Combretum leprosum, and cacti Pilosocereus piauhiensis, Cereus jamacaru. Most rains (80%) occur between January and May (600 to 1,200 mm) and varies in temperature between 24 °C and 27 °C.

In the lower basin inshore fishing, shrimp farming, lobster and crab are the major extractive activities. The Jaguaribe River is a wellregulated river that has many dams (Table 2). The control is done by 17 rain stations, 4 on the main course and 13 on the tributaries. During the low waters the flow of the Jaguaribe is controlled by the large Castanhão dam and is about 20 m 3 .s 1 downstream. As a result of extensive damming of the river, semiarid climate and mesotides make that the Jaguaribe estuary is frequently an importer of seawater and marine materials [START_REF] Lacerda | Pluriannual Watershed Discharges of Hg into a Tropical Semi Arid Estuary of the Jaguaribe River, NE Brazil[END_REF]. All the studied rivers show a varied state of human impact, particularly from Figure 1. The Jaguaribe river Basin compared to the metropolitan basin in Ceará State, north eastern Brazil. The Jaguaribe River Basin receives untreated effluents from urban, agriculture, aquaculture and, to a certain degree, industries. The general environmental characterized situation of the four rivers, relative to pathogenic microorganisms; toxicology and sediment quality and contamination by nutrients and metals [START_REF] Nilin | Sediment quality assessment in a tropical estuary: The case of Cear? River, Northeastern Brazil[END_REF]. A summary of anthropogenic emissions of nutrients and selected trace metals is presented in table 2. The waters of the Jaguaribe River are of the bicarbonatemixed type (Instituto Brasileiro Geografia e Estatistica, IBGE, 1996, http://www.ibge.gov.br/home/estatistica/populacao/contagem/). The Banabuiú river presents mixed waters with chloride and sodium. Heighty percent (80%) of the basin drains a crystalline rock geology with weak hydraulic potentiality. The groundwater is chloromixed to chlorosodium.

Particularity of mangroves.

Mangroves are particularly sensitive ecologically [START_REF] Blasco | Mangroves as indicator of coastal change[END_REF] and chemically [START_REF] Otero | Spatial and seasonal variation in heavy metals in interstitial water of salt marsh soils[END_REF] sensitive ecosystems. Most of the sediments carried by rivers reach the coastal zone with reduced speed, tides also contribute to choke the rivers, whereas mangrove trees trap the sediments as well as most pollutants associated with them, either metallic or organic [START_REF] Da Silva | Fluorescence quenching of anthropogenic fulvic acids by Cu(II), Fe(III) and UO2/2+[END_REF]. Mangrove vegetation is one of the most important driver of these process, also because of the large production of organic matter [START_REF] Alberts | Elemental composition, stable carbon isotope ratios and spectrophotometric properties of humic substances occurring in a salt marsh estuary[END_REF][START_REF] Lacerda | Biogeochemistry of mangrove soil organic matter: a comparison between Rhizophora and Avicennia soils in Southeastern Brazil[END_REF]. This sequestration is permanent due to the low remobilization of pollutants, as far as the natural mangrove biogeochemical conditions are preserved [START_REF] Machado | Trace metal retention in mangrove ecosystems in Guanabara Bay, SE Brazil[END_REF] This permanent sequestration has been reported even in highly polluted urban areas [START_REF] Burke | Release of Metals by the Leaves of the Salt Marsh Grasses Spartina alterniflora and Phragmites australis[END_REF]. However, changes in biogeochemical conditions of the sediments may result in remobilization of pollutants [START_REF] Marins | Effect of Root Metabolism on the PostDepositional Mobilization of Mercury in Salt Marsh Soils[END_REF]. The organic carbon concentration in mangroves ranges from 318 μM to 2,043 μM, depending on season and location and has no visible transition between the terrestrial and the marine endmembre when observing the natural organic matter by fluorescence, for example [START_REF] Jaffé | Source characterization of dissolved organic matter in a subtropical mangrovedominated estuary by fluorescence analysis[END_REF]. The modification of the uses in the basin can modify the flows of pollutants that enter the mangroves. Dam construction modifies physical chemical condition that alter river fluxes, producing an erosion of the coast and a migration or destruction of the mangroves [START_REF] Lacerda | Pluriannual Watershed Discharges of Hg into a Tropical Semi Arid Estuary of the Jaguaribe River, NE Brazil[END_REF][START_REF] Lacerda | River damming and changes in mangrove distribution[END_REF]. For example the transformation of forest into agriculture causes the mercury trapped in the soils to be released [START_REF] Lacerda | The effects of land use change on mercury distribution in soils of Alta Floresta, Southern Amazon[END_REF]. Also the dam construction modifies physicalchemical condition that alter the flow and speed producing an erosion of the coast and a migration or a destruction of the mangroves. As mangroves only exert themselves in specific conditions, they are influenced by all modification of local and global environment conditions. A subtle change in the conditions lead to an important response of the mangrove ecosystem that can die, move, transform [START_REF] Blasco | Mangroves as indicator of coastal change[END_REF].

It is therefore important to have geochemical indicators of the natural state. As organic material is an important mode of transmission of trace metals [START_REF] Lu | Interaction between Hg(II) and natural dissolved organic matter: a fluorescence spectroscopy based study[END_REF][START_REF] Mounier | Copper and mercury complexing capacity of organic matter from a mangrove mud flat environment, Sepetiba Bay, Brazil[END_REF] and how little data exists on the fluorescence spectroscopy as a monitoring tool for mangroves [START_REF] Alberts | Elemental composition, stable carbon isotope ratios and spectrophotometric properties of humic substances occurring in a salt marsh estuary[END_REF], this work proposed to use fluorescence spectroscopy on the Fortaleze city rivers to monitor the quality and quantity of the organic matter as an indicator of the impact of the soil use onto the mangroves geochemistry. Particular attention will be paid to the Pacoti, Cocó and Ceara rivers because their basins are of similar size but drastically different in soil uses.

Strategy and comparison of the Cocó and Pacoti rivers

Cocó and Pacoti rivers were sampled around the region of Fortaleze. Each river is divided into three zones: one near the source called "river zone", one inside the mangrove zone called "mangrove zone" and one at the mouth of the river to have the marine part called "marine zone".

Every month between May 2004 and September 2004 a sample of each zone was made. Filtration was done in the field with a 13 mm diameter burnt glass filter using a syringe and a HDPE filter holder. Six vacuum tubes, two by zone, were filled with the sample, two of them stabilized with 50 μl of 1 M NaN 3 for carbon, fluorescence quenching and anion concentration, and two treated with hydrochloric acid for cation analysis. The tubes were kept in the cold and dark until measurement. The filters were dried and used to measure the particulate organic carbon (POC) with a SSM SHIMADZU TOC instrument.

The physicochemical parameters were measured at the same time using a on field multiparameter instrument for pH, dissolved oxygen, conductivity. The probes were calibrated before each measurement. The results are presented in table 5.

Analyzes

Measurements of the major elements, cations and anions, were done on a Dionex DX 120 ion chromatography with a ISOCRAT AS9HC column and CS12A ISOCRAT column. The eluents are respectively Na 2 CO 3 9Mm, 950mg.L 1 and methane sulfonic acid (M.S.A) 20mM, 1.3 mL.L 1 . The flow rate was set at 1 mL.min 1 and the injection volume set at 100 μL.

Measurement of total organic carbon was done with a SHIMADZU TOC5000. The standard used was a solution of potassium phthalate. Each sample was acidified (100 μL of 5N HCl in the 5 mL sample) and bubbled with nitrogen gas before measurement for eliminating the carbonates.

Fluorescence was measured with an HITACHI4500. Excitation Emission Matrix of Fluorescence (EEM) were made in a quartz cell at natural pH with the following parameters: 250450 nm for excitation, 300600 for emission, a scan speed of 30,000 nm.min 1 and excitation and emission slit fixed at 5 nm.

To study the properties of complexation the fluorescence quenching was used. By placing the sample in a glass vessel, the pH was fixed to 5 with a buffer solution of Na 2 PO 4 •H 2 O, and addition of copper Cu(NO 3 ) 2 with 0.1, 1 and 10 ppm solution till a stabilization of the fluorescence signal extinction. The fluorescence measurement was done with an excitation spectrum from 200 to 400 nm and an emission wavelength fixed at 420 nm, all other parameters were identical to the EEM measurement. The results presented in Figure 3 show that for the Cocó river the concentration of dissolved carbon is always higher in the mangrove area. For Pacoti, on the contrary, there is a systematic increase from the river zone to the marine zone. The salinity suddenly increases at the marine zone in the Cocó river and in the mangrove zone in the Pacoti rivers, except for the month 3 (June 2004) where a low salinity was observed probably due to a low tide conjugated to a high discharge. The pH always increases from the Cocó river zone (~ 7) to the marine zone (~ 8), while it change more stochastically in the Pacoti river. Concerning the particulate organic carbon (POC), there is no trend along the period and among the zones. The average POC concentration is about 0.2 mg of C L 1 (± 0.1) except for the Cocó river at the river zone where a greater variability is observed leading to an average value of 0.5 ± 0.6 mg of C L 1 .

Results

Dissolved and Particulate organic carbon

1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 4 5
With these results it is already possible to say that these two ecosystem do not have the same dynamic. Apparently it seems to have a carbon production in the mangroves of the Cocó River not present in the Pacoti river. The measurement of the fluorescence intensity at peak A (λ ex = 250 / λ em = 450) and peak C (λ ex = 350 / λ em = 450) shows that for the Cocó River intensity is higher for the mangrove area (figure 4, solid lines), as for its organic carbon concentration. In the Pacoti river the only fields that behave in the same way are field 3 and 5 where salinity and pH behave differently. Observing the ratio of the two intensity allows one to see the quality of the organic material [START_REF] Mounier | Fluorescence 3D de la matiere organique dissoute du Fleuve Amazone[END_REF]. When r a,c decreases, maturity increases as intensity from the type C increases more than the type A (figure 4). This ratio does not depend on the carbon concentration [START_REF] Patelsorrentino | Excitationemission fluorescence matrix to study pH influence on organic matter fluorescence in the Amazon basin rivers[END_REF]. All the values are similar and around value of 1, except for the Cocó river in field 4 where the ratio is about 1.5. In the Cocó river there is a decrease from the river zone until the marine zone. This could be due to a maturation of the organic material during the transport. In contrast in the Pacoti river, the variation is not recorded with maximum in mangrove area except in field 2. For the Pacoti river, the maximum often appears in the marine zone, indicating a recent organic material (fields 1, 3, 4, 5 nad 6).

Fluorescence spectroscopy

The 2D fluorescence

3D Fluorescence

3D fluorescence is a map of all the photons emitted by the organic matter that fluoresce (Figures 5). This measurement gives a quantification of the fluorescent organic material as well as qualitative information using the excitation and emission wavelength position. Generally the most popular peaks are the peak A (λ ex = 250 / λ em = 450) and the peak C (λ ex = 350 / λ em = 450). We can observe that the fluorescence is more intense in the Cocó River. 14 The peak T (tyrosynlike) corresponding to the signal of the proteins is not observed. The waters are mainly composed of humic type (peak C) as presented in figures 6 and 7. From the 3D fluorescence of all the excitation emission matrices of fluorescence presented above, the CP/PARAFAC separation algorithm was used. Two components were extracted (CORCONDIA 96%) and are represented by the two components in figures 8. The first component shows a maximum at 300 nm of excitation and 400 nm of emission. Its shape is circular, and related to the fluorescence domain. It represents the humiclike type components linked to fulvic acids. The component C2 exhibit two excitation maxima one secondary at 280 nm and one principal at 350 nm. The emission wavelength is around 480 nm. This later component represents the humic like substances coming from terrestrial origin. No more than two components where found meaning that possible fluorescence biological activity was not statistically extracted.

1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 4 5 6 0,4 0,6 0,8 1 1,2 1,4 1,6 ra,c-Coco ra,c-Pacoti
Their evolution along the rivers and during the time are shown in figure 9. The Cocó river presents an higher C1 contribution in the mangrove zone. Each sampling has the variation of the C1 contribution in mangrove zone higher than the C1 in river and marine zone (figure 9). Particularly during the campaign 4, 5 an d 6 when the difference is bigger. The C2 component on the contrary does not vary. The urban impacted mangrove zone produce more fulvic components. This is confirmed by the r a,c ratio that is systematically higher zone the mangrove zone except for the campaign 1 and 2 (figure 9, grey line).

The Pacoti river shows a different trend from the Cocó river. Indeed, the fulvic contribution decreases regularly from river zone to the marine zone. Moreover the r a,c is also decreasing meaning that the organic matter become more and more terrestrial like during the transport to the mangrove zone. In field 1 to 3, the fulvic contribution was higher than the terrestrial one (C2), but in fields 4, 5 and 6 it is the contrary for mangrove and marine zones. Hence by fluorescence measurement, it is observable that the organic matter produced by the mangrove differs a lot between urban impacted and non impacted mangroves, except on month 3 where the fluorescence organic matter is similar to the Cocó river pattern. More fulvic like components are produced in the Cocó river, maybe as a defence mechanism.

Major elements:

The cations

The concentrations of the cations (figure 10) present high values for the Na in the marine zone, as expected due to stronger sea influence. Indeed it is connected with the proportion of sea water in the system. In the other zones the dominant cation is Na + but also K + and Mg 2+ . It only has anomaly during field campaign 4, where K + and NH 4 + present a higher concentration than Ca 2+ .

The patterns for the Pacoti river are different but have the same relation as the same influence related to the sea, with an increase of the Mg 2+ and Na + concentration. One can see that the Pacoti river is more invaded by the sea intrusion in the mangrove zone (figure 11) than the Cocó river.

The anions

The anion concentrations in the Cocó river are shown in (figure 12 and13). The increase of concentrations results from the influence of seawater. Except for nitrate and sulphate that disappear when salinity increases. 20 The Pacoti river again does not presents the same variation as the Cocó river (Figure 14). From these analyses it is difficult to interpret the results. But one can say that during the study, nitrate concentration is high at the river zone.

Fluorescence quenching

Complexing capacity is the ability of the natural organic matter (NOM) to complex the metals forming a metalNOM complex. For a sample, the site concentration of complexation is expressed in μmol / L of metal, and the complexion capacity is the value in μmol of site / mg of carbon (μmol of Cu 2+ / mg of C).

Using the [START_REF] Ryan | Fluorescence quenching titration for determination of complexing capacities and stability constants of fulvic acid[END_REF] model it is possible to calculate the total site concentration L T and the equilibrium complexation constant K L , which represent the strengh of the complexe bound [START_REF] Ryan | Fluorescence quenching titration for determination of complexing capacities and stability constants of fulvic acid[END_REF].

The obtained results are represented in figure 15 and 16. The Cocó river shows a greater capacity for the river zone and the marine zone. Systematically the complexation capacity is minor for the mangrove zone. This can be explained by a specific production of carbon with weak complexation properties. In the mangrove zone metals are less complexed than in the Cocó river mouth. At the contrary in the Pacoti river the complexation capacity always increases from the river zone until the marine zone. In Pacoti organic matter site density increases continuously and metals have more and more probability to be complexed. There is no production of specific organic material in the mangrove zone of the Pacoti river. Based on the results of quenching of fluorescence it appears that the dynamic of the two rivers is not similar in term of NOM production. The Cocó river, more affected by anthropogenic activities, produces non complexant organic matter in the mangrove zone, while in the Pacoti basin the NOM complexant capacity increases. The results do not show trend on complexant capacity during the 6 months sampling period in both rivers.

Sampling during a tide

The the waters of the Jaguaribe river were sampled along time during a tide period. These 15 samples were sampled in front of the Ilha Grande island (Figure 17, Table 6).

River

Results of the spacial distribution study

Dissolved organic carbon.

It can be observed that the concentration of organic carbon in the Jaguaribe river basin increases in April and September. Figure 18 shows the concentrations observed from the site 1 (Marine) to Itaiçaba (River) throughout the continentsea gradient. There is a decrease in carbon concentration showing dilutive effect from land to sea both in April and September. In April the carbon concentration is very low, particularly downstream the dam of Itaiçaba and in Aracati (downstream the bridge) while the values upstream the bridge are very high. If the value after the dam can be a false response, we can consider a large dilution after the Aracati point (after the bridge). The value of carbon concentration is higher in September than in April. The dilution effect is confirmed by the linear correlation between DOC and Cl concentration (r 2 =0,89) (Figure 18). April the fluorescence decreases drastically. From site 3 to 9 the fluorescence reaches the values of 500 u.a (arbitrary unit) and becomes quasi constant. In contrast, in September fluorescence values are weak (less than 400 u.a., figure 19) and one can observe from point 3 an increase in the fluorescence of peak A which represents the young organic matter. This is confirmed by graph (Figure 19) where the ratio increases from point 3. One can also observe that the r a,c do not increase in April. This in agreement with a production of young organic matter at the end of the river path at the end of the basin when the influence of the sea is great and in the mangrove zone. As the concentration of organic carbon increases in September we can speak of a production of young organic matter in September, the a young dissolved organic carbon input. The r a,c increases only at three points in the basin near the sea, that is to say that the carbon inputs are different up and down. The production of young organic material only takes place in the cruise of September in the basin of the Jaguaribe.

Results of the temporal study

Organic carbon, pH and salinity

The salinity varies from 8 to 20 with a sinuous curve. The minimum salinity happens at 11:35 p.m. and the maximums happen by 6:00 p.m. and 5:00 p.m. The pH value shows maximum during growth and salinity decline. The total organic carbon shows anomaly during the same moments (figure 20).

Fluorescence spectroscopy

The fluorescence of peaks A and C in the Jaguaribe River shows a maximum during low tidewhen the influence of the sea minor the salinity is lowest. The fluorescence increases to 500 versus 300 when the salinity is to the maximum. From the graph in figure 23 we can see that the ratio is quasi constant throughout the tidal period. This means that there is no variation in the quality of the organic material, either in low or high tide. The variation of concentration comes solely from the dilution of river waters by sea waters and does not change the carbon quality. At least we can note that this variation does not match the variations of dissolved organic carbon. One can consider that during the tide the quality of carbon is constant during the event, even if its quantity change. The 3D fluorescence of Jaguaribe samples during the tide shows that the quality of the fluorescent organic matter is constant . There is a peak excited at 380 nm with an emission around 430 nm (figure 24). Only the intensity seems change during the sampled tidal cycle. These data were treated by CP/PARAFAC to extracted subtle variations that occurs during the event.

Variation of the major elements

Space strategy

Changes in cation and anion concentrations in the Jaguaribe River Basin in April and September 2004 are represented respectively respectively in figures 25 and 27. We observed a higher mean concentration for all elements again in September. Particularly Na + with highest concentrations in point 6 (Aracati downstream the bridge). It also has an inland sea influence in September with a strong concentration of K + , Na + and Ca 2+ up to point 6 in September versus point 3 in April. Except for the anomaly of K + in point 4 in April (Raimundinho), the waters are therefore sodic. Representing the K + , Mg 2+ and the Ca 2+ concentration versus the Na + one (figure 26), it is observed that except for the river before the dam, sample site 9, the cations concentration are highly correlated to the Na + , which represent the sea influence. The point 6 differs from its K + and Ca 2+ concentration that confirms it out of sea influence behaviour. The anions concentrations are also controlled by the sea influence for Cl and (HCO 3 ,CO 3 2 ) except for site 2 in September (figure 27). For NO 3 and SO 4 2 anions, it is clear that the concentrations fall down after the sea influence, and after the point 5 in April and 7 in September. In the Jaguaribe, the carbonate are highly stable and represent with the Cl the major anions.

Time strategy

The temporal sampling in April is presented in figure 28 for the cations and in figure 29 for the anions. The graph represents the percentage variation of the initial concentration to accentuate the relationship with the salinity. The salinity starts at 18.2 with a maximum of 20.4 to 08:30. The minimum of salinity occurs in the afternoon around 3:30 p.m. and has a salinity of 4. The results in concentrations are summarized in Table 8. Clearly the concentrations in cations and anions are normally associated with salinity. The most connected elements is Ca 2+ (figure 28). For the other elements concentration, it decreases until the minimum salinity is reached. These results do not show an influence of the activities above the sampling point. Except the first point in the early morning that could by an outlier, there is a perfect correlation between the Na + concentration and the Ca 2+ , Mg 2+ concentration. Considering that Na + is principally coming from sea, this means that during the Jaguaribe tide event, there is only mixing of water. The two nitrate concentrations (10:00 11:00) should pointed out that there is a pulse during this period coming from the mangroves but this must be confirmed. The same trend is observed for the anions where correlation of Cl and SO 4 2 is about r 2 =0,88 and r 2 =0,85 respectively, while the carbonate is quite constant during the tide as it does not represent a good sea end member. In conclusion, there is no entry of major elements during the tidal episode sampled in this experiment.

Conclusion

This work allowed to make a first characterization of the natural organic matter in two zones of mangrove, one urban (Cocó river) and another less affected by urban activities (Pacoti river). A characterization of the Jaguaribe river basin was also made temporally and spatially. The results showed that comparing the mangroves in the Fortaleza metropolitan area, in general, the two mangrove zones are very similar from the point of view of the larger elements and the 2D or 3D fluorescence. But they showed slight differences in the quality of the organic matter in relation to the metals complexing and the intensity of fluorescence. The Cocó river basin seems to have seems to have a production of specific organic matter. The properties of complexation then can appear weak for the river Cocó with a higher production of young (fulvic like) less complexant organic matter. Also the ability to fluoresce is greater for the organic matter of this zone. These differences appear throughout the sampling period showing that it is not in relation to time but to human activities 35

Geochemistry of the Jaguaribe River

For the lower part of the Jaguaribe river measurements of dissolved organic carbon and major elements were made. A comparison of the results showed a strong seasonal variability, with concentrations generally higher in September (dry season) than in April (rainy season). There is a steady decrease in concentrations from more inland stations to seainfluenced ones. No external or unusual inputs were detected. The influence of the sea is visible up to point 5 (Aracati) in September but only to point 3 (Ilha Grande) in April. During the tide there is only variation of the elements with the salinity. The fluorescence failed to detect any specific processes during the tidal cycle.

General conclusion

During this study many measures were performed. It has been shown a strong influence of the urbanization on the geochemistry of the organic material produced in the mangrove zones. This modification may play an important role in the transport of pollutants such as metals.

In the future it would be necessary to do a more complete study on the flows coming in and out of the mangrove zone. Also a study on metals at the same time could be important to really understand what happens in that zone and the consequences on coastal geochemistry.

Tableau 1 :

 1 Figure 1: Location of the studied river basins in Ceara state. The Ceara river (up right), the Cocó river (middle up right), Pacoti river (middle down right) and Jaguaribe river (down right) in the hydrological network of the state of Ceará. This map was produced by the Labomar (UFC, Ceara) and Google Earth pictures.

Figure 2 :

 2 Figure 2: Results of dissolved organic carbon concentration in ppm (black square), salinity (blue triangle) and pH (grey losange) for the Cocó and Pacoti rivers. Points 1, 2, 3 representing the river, mangrove and marine zones respectively.

Figure 3 :

 3 Figure 3: Fluorescence of Ic and Ia (dark and grey solid, respectively) of the Cocó river and fluorescence Ic and Ia of the Pacoti river samples (dark and grey dashed, respectively)

  13

Figure 4 :

 4 Figure 4: Ratio for the fluorescence peaks of the Cocó and Pacoti rivers

Figure 5 :

 5 Figure 5: Excitation emission fluorescence matrices example of the Cocó water river (left) and the Pacoti river (right)).

Figure 6 :

 6 Figure 6: Excitation Emission Matrices of the Cocó river false colour representation. Column 1,2 and 3 represent the river, mangrove and marine zones and line represent the line correspond to field campaign 1,2,3,4,5 and 6.

Figure 7 :

 7 Figure 7: Excitation Emission Matrices of the Pacot river false colour representation. Column 1,2 and 3 represent the river, mangrove and marine zones and line represent the line correspond to field campaign 1,2,3,4,5 and 6.

Figure 8 :

 8 Figure 8: PARAFAC component extracted from the Cocó and Pacoti rivers excitation emission matrices of fluorescence.

Figure 9 :

 9 Figure 9: Evolution of the contributio of the CP/PARAFAC components along the rivers. The component C1 and C2 are represented by the blue and red solid line respectively. The ratio of their contribution C1/C2 is represented by the grey dashed line.

Figure 11 :

 11 Figure 11: Variations of cation concentrations in the Pacoti River.

Figure 10 :Figura 12 :

 1012 Figure 10: Variation of cation concentration in the Cocó River

Figura 14 :

 14 Figura 14: Variation of anion concentrations in the Pacoti river

Figura 15 :

 15 Figura 15: Distribution of complexation capacity for copper in the Pacoti river and Cocó river along the river. 1 -River; 2 -mangrove; 3 -marine zones.

Figure 16 :

 16 Figure 16: Distribution of complexation constant for copper in the Pacoti river and Cocó river along the river. 1 -River; 2 -mangrove; 3 -marine zones.

Figure 18 :

 18 Figure 18: Variation of the dissolved organic carbon during the two sampling campaigs and the relation to salinity.

Figure 19 :

 19 Figure 19: Fluorescence of peaks A (rose) and C (dark blue) (Coble 1996) in the Jaguaribe river basin, in April (left) and September (right)

Figure 21 :

 21 Figure 21: Variation of carbon, pH and salinity during a tidal episode.

Figure 22 :

 22 Figure 22: Fluorescence of peaks A and C for the Jaguaribe River in April during the temporal sampling.

Figure 23 :

 23 Figure 23: Values the ratio Ia / Ic for the Jaguaribe river in April during the temporal sampling.

Figure 24 :

 24 Figure 24: Excitation Emission Matrices of fluorescence of the Jaguaribe representing fluorescent organic matter during tide event (in false colour).

Figure 25 :

 25 Figure 25: Variation of cations in the Jaguaribe river basin in April (left) and in September (right).

Figure 26 :

 26 Figure 26: Sodium to cations concentration relation for the Jagaribe river along downtream.

Figure 27 :

 27 Figure 27: Variation of the anions in the Jaguaribe basin in April (left) and in September (right).

Figure 28 :

 28 Figure 28: Variation of the cations in the Jaguaribe basin during the temporal sampling.

Figure 29 :

 29 Figure 29: Variation of the anions in the Jaguaribe basin during the temporal sampling.

  Table1summarizes the geographical information for the four river basins studied.

	River	Extention (km)	Basin area (km 2 )	Mangrove area (km 2 )
	Cocó	50	485	4.52
	Ceará	60	900	7.26
	Pacoti	150	1,800	3.40
	Jaguaribe	610	75,669	7.29

  The general parameters observed in the Cocó River water are summarized in the table 1.

	Fluxes	Cu	Zn	Pb	Cd	Hg	P	N
	Ceará	7.09 102.2 7.37 0.77 0.072 393 815
	Cocó	35.1 84.0 103.0 4.80 0.464 738 2,588
	Pacoti	4.04 19.4 2.91 0.31 0.026 697 1,097
	Jaguaribe 9.2 4.79 0.45 0.15 0.011 330 798

Table 2 :

 2 Annual anthropogenic emissions (ton.yr1) of N, P, Cu, Zn, Pb, Cd and Hg to the lower basins of the four estudied rivers

Dam localisation River Capacity 1000 m 3 Alt. Made in ...

			Alto Jaguaribe
	Caiçaras	Tauá	Riacho Carrapateira	1,072	6
	Favelas	Tauá	Riacho Favela	30,100	18
	Forquilha I	Tauá	Riacho Carrapateira	3,000	12.6
	Orós	Orós	Jaguaribe	2,100,000	54
	P. da Pedra	C. sales	Conceição	52,000	31.4
	V. do Boi	Tauá	Riacho Carrapateira	51,900	17.4
			Médio Jaguaribe
	Ema	Iracema	Iracema	10,395	18.5
	Feiticeiro	Jaguaribe	Feiticeiro	24,105	20.5
	N. Floresta	Jaguaribe	M. Lopes	7,619	15.6
	P. do Barro	M. Nova	Livramento	54,704	23
	R. Sangue	Solonópole	R. Sangue	68,194	24
	Velame	Jaguaribara	R. Velame	2,566	8.5
	Castanhão	Jaguaribara	R. Jaguaribe	6,700,000	10
			Baixo Jaguaribe
	L. Pombas	Aracati	L. Pombas	2,231	4.6
	S.A. Russas	Russas	Palhano	24,000	14
			Bacia do rios Salgado
	Gomes	Mauriti	R. Gomes	2,394	29
	L. Campos	Icó	R.S. João	66,382	19
	Quixibinha	Mauriti	R. Bois	32,150	35
	R. Carneiros	Caririaçu	R. Carneiros	37,181	35,3
	Umari	Crato	Carás	28,788	28,7
			Bacia do rio Banabuiú
	Banabuiú	Banabuiú	Banabuiú	1,700,000	57,7
	Cedro	Quixadá	Sitiá	125,684	20
	M.J. Cândido B. Viagem	R. Fernandes	3,863	19.5
	Patu	S. Pompeu	Patu	71,829	44.4
	P. Brancas	Quixadá	Sitiá	434,049	33.6
	Quixeramobim Quixeramobim Quixeramobim	54,000	13.8

Table 3 :

 3 Dams on the Jaguaribe River Basin (ANEL)

Table 4 :

 4 Values of the physicochemical parameters of river water samples in the river, mangrove and marine zones of the Pacoti and Cocó rivers, Ceará State, north eastern Brazil.

	River Field number	Date	Reference T (°C)	Cond.	pH	Sal.	O2	Zone
						(µS.cm -1 )			(mg.L -1 )
	Cocó	1	07/05/04 Co101	29,5	764	6,14 0,4	6,1	River
	Cocó	1	07/05/04 Co102	28,1	949	6,38 0,5	0,48 Mangrove.
	Cocó	1	07/05/04 Co103	29,1	46600 7,8 30	2,5	Marine
	Pacoti	1	25/05/04	Pa101	28,7	2008 6,38 0,9	3,8	River
	Pacoti	1	25/05/04	Pa102	29,5	54300 8,2 35,4	4,6	Mangrove
	Pacoti	1	25/05/04	Pa103	29,1	54500 8,2 35,1	5,2	Marine
	Cocó	2	09/06/04 Co201	26,4	770	6,54 0,4	6,3	River
	Cocó	2	09/06/04 Co202	26,9	982	6,65 0,5	1,8	Mangrove
	Cocó	2	09/06/04 Co203	27,5	53100 8,12 35	3,2	Marine
	Pacoti	2	09/06/04	Pa201	27	3332 6,75 1,7	4,5	River
	Pacoti	2	09/06/04	Pa202	27,4	56600 8,23 35,7	4,6	Mangrove
	Pacoti	2	09/06/04	Pa203	27,2	56400 8,23 35,7	4,5	Marine
	Cocó	3	30/06/04 Co301	28,3	756	6,45 0,4	9,8	River
	Cocó	3	30/06/04 Co302	27,4	378	7,09 0,2	5,8	Mangrove
	Cocó	3	30/06/04 Co303	28,3	52700 8,1 34,6	4,7	Marine
	Pacoti	3	30/06/04	Pa301	27	916	5,68 0,4	3,83	River
	Pacoti	3	30/06/04	Pa302	28,9	12220 6,25 6,4	3,63 Mangrove.
	Pacoti	3	30/06/04	Pa303	28,3	29770 6,93 17,2 3,95	Marine
	Cocó	4	04/08/04 Co401	25,8	753	7,07 0,3	5	River
	Cocó	4	04/08/04 Co402	25,8	2566 7,67 1,3	3,6	Mangrove
	Cocó	4	04/08/04 Co403	25,8	52100 8,21 32,9	3,3	Marine
	Pacoti	4	04/08/04	Pa401	25,7	3772 8,04 1,9	4,9	River
	Pacoti	4	04/08/04	Pa402	25,5	54000 8,2 34,3	4	Mangrove
	Pacoti	4	04/08/04	Pa403	25,5	54100 8,29 34,4	4,9	Marine
	Cocó	5	27/08/04 Co501	-	878	6,62 0,4	5,49	River
	Cocó	5	27/08/04 Co502	-	839	6,9 0,5	4,98	Mangrove
	Cocó	5	27/08/04 Co503	-	30670 7,01 20,5	5	Marine
	Pacoti	5	27/08/04	Pa501	-	8190 8,33 4,5	6,04	River
	Pacoti	5	27/08/04	Pa502	-	41420	8 26,6 4,52	Mangrove
	Pacoti	5	27/08/04	Pa503	-	49250 7,31 32,3 4,45	Marine
	Cocó	6	17/09/04 Co601	27,6	752	6,99 0,4	4,9	River
	Cocó	6	17/09/04 Co602	26	7310 7,01 4	3,3	Mangrove
	Cocó	6	17/09/04 Co603	27,2	51400 8,09 33,7	3,5	Marine
	Pacoti	6	17/09/04 Co601	27,5	8550 8,12 4,7	4,5	River
	Pacoti	6	17/09/04 Co602	26,9	53500 8,13 35,3	5,2	Mangrove
	Pacoti	6	17/09/04 Co603	26,7	53300 8,18 35,2	5,4	Marine

Table 6 :

 6 Samples from the 20/06/04 field campaign for temporal studies durng a tidal cycle.

		Number	Hour Reference Cu 2+ (µg/L) POC (mg/L) DOC (mg/L) IC (mg/L) T°C Cond. Sal.	pH
	Jaguaribe	1	05h40 TJa01	2,87	1	17,9	37,03	26 30,65 18,2 7,91
	Jaguaribe	2	06h35 TJa02	2,86	1,65	19,27	34,48	27 14,8 8,1 7,88
	Jaguaribe	3	07h30 TJa03	2,84	3,15	19,88	29,11	28 1,19 0,6 8,01
	Jaguaribe	4	08h35 TJa04	-	1,89	4,33	31,11	-35,16 20,4 8,05
	Jaguaribe	5	09h35 TJa05	2,48	2,09	0,27	30,21	-33,39 19,1 8,03
	Jaguaribe	6	10h40 TJa06	2,65	2,59	0,28	-	-29,29 16,7 7,93
	Jaguaribe	7	11h35 TJa07	2,5	2,33	20,6	31,53	-24,33 13,8 7,95
	Jaguaribe	8	12h50 TJa08	2,77	1,75	-	27,63	29	18 10,4 8,02
	Jaguaribe	9	13h35 TJa09	3,18	2,27	20,74	26,08 29,2 15,68 8,4 8,17
	Jaguaribe	10	14h30 TJa10	3,1	2,24	22,68	26,68	-14,29 7,6 8,38
	Jaguaribe	11	15h35 TJa11	3,13	2,61	-	30,31 28,1 7,76	4 8,22
	Jaguaribe	12	16h35 TJa12	3	2,16	0,23	28,9	-	19,7 10,8 8,14
	Jaguaribe	13	17h35 TJa13	7,38	2,02	18,92	26,12	-25,17 14,2 -
	Jaguaribe	14	18h35 TJa14	-	3,28	20,34	25,61	-29,31 16,6 8,09
	Jaguaribe	15	19h25 TJa15	2,34	1,75	21,86	23,77	-30,89 17,8 8,13

Table 7 :

 7 Results on larger elements of temporal sampling

	Mg/L of Cl and HCO3+2*CO3
	mg/L of Na+
	Cl (April)	R²=0,76
	R²=0,51	Cl(Sept.)
	HCO3+2*CO32 (Sept.)	HCO3+2*CO32

Acknowledgment :

This work was supported by the Brasilian Research Center (CNPq, Processo n°3 09342/30034), the Toulon University (CRCT Campaign 2004) and PQ 309718/20163 to RVM. The FUNCAPCNPq PRONEX Project PR2010100052.01.00/15, also supported part of this study.

Sampling strategy on the Jaguaribe River

Spatial sampling

Figure 17: The mangrove zone of the Jaguaribe River (Ilha Grande sampling stations) Two fields campaigns were carried out (Table 5) in April and September 2004 to have variation of physicalchemical parameters of the waters of the lower Jaguaribe basin. The mangrove zone of the Jaguaribe occurs between the point 2 (Barra) and 4 (Raimundinho) (figure 17).


Nathan Crombez

Ralph Seulin

Olivier Morel

David Fofi

Cédric Demonceaux

Multimodal 2D Image to 3D Model Registration via a Mutual Alignment of Sparse and Dense Visual Features

Many fields of application could benefit from an accurate registration of measurements of different modalities over a known 3D model. However, aligning a 2D image to a 3D model is a challenging task and is even more complex when the two have a different modality. Most of the 2D/3D registration methods are based on either geometric or dense visual features. Both have their own advantages and their own drawbacks. We propose, in this paper, to mutually exploit the advantages of one feature type to reduce the drawbacks of the other one. For this, an hybrid registration framework has been designed to mutually align geometrical and dense visual features in order to obtain an accurate final 2D/3D alignment. We evaluate and compare the proposed registration method on real data acquired by a robot equipped with several visual sensors. The results highlights the robustness of the method and its ability to produce wide convergence domain and a high registration accuracy.

I. INTRODUCTION

Nowadays, it becomes relatively simple to create a 3D virtual representation of a real environment. Indeed, visionbased 3D reconstruction methods like SLAM (Simultaneous Localisation and Mapping) [START_REF] Mur-Artal | Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras[END_REF], SfM (Structure from Motion) [START_REF] Wu | Towards linear-time incremental structure from motion[END_REF] or MultiView-Stereo (MVS) [START_REF] Furukawa | Accurate, dense, and robust multiview stereopsis[END_REF] are more and more mature. In parallel, technological advances have enabled the development of tools like terrestrial laser scanners (TLS). These methods and devices can now be considered as outof-the-box solutions to create a 3D model of a real scene.

After an environment has been digitized, it may be interesting and useful to supplement the 3D model with novel measurements that come from different visual sensors. However, the use of a diversity of sensors is a source of issues generally grouped under the term "multimodality". Registering a 3D model over 2D images is already a challenging task and is even more complex when the two have been obtained with different visual sensors. Of course, the types of modality that are used depend on the purpose of the application. For instance, near-infrared (NIR) spectral images are commonly used for precision agriculture applications. In [START_REF] Das | Devices, systems, and methods for automated monitoring enabling precision agriculture[END_REF] authors have designed and developed a multi-spectral 3D imaging device that can be used for creating a 3D point cloud of a field. In addition to geometric and photometric information, each 3D point of the resulting model has also a NDVI (Normalized Difference Vegetation Index) value which is an important indicator of plant vigor. Combining long-wavelength infrared (LWIR) images with a photometric 3D model can reveal information which may not be present neither in the model or in the LWIR images. This facilitates the visual detection, Authors are from the LE2I laboratory (Laboratoire d'Electronique, Informatique et Image), University of Burgundy, 12 Rue de la Fonderie, 71200 Le Creusot, France email : firstname.lastname@u-bourgogne.fr recognition and segmentation of objects like windows on building fac ¸ades [START_REF] Iwaszczuk | Detection of windows in ir building textures using masked correlation[END_REF]. Fusion of thermal radiation and a 3D model is also useful for monitoring energetic performances of buildings [START_REF] Hoegner | Evaluation of methods for coregistration and fusion of rpas-based 3d point clouds and thermal infrared images[END_REF] or to study the thermal properties of materials [START_REF] Bennis | Contours based approach for thermal image and terrestrial point cloud registration[END_REF]. In cultural heritage documentation, registration of visual data acquired from various 2D and 3D sensing modalities is also a crucial point for the visualisation of big multimodal data [START_REF] Kim | Multi-modal visual data registration for web-based visualisation in media production[END_REF] or for photorealistic modeling [START_REF] Pintus | Techniques for Seamless Color Registration and Mapping on Dense 3D Models, ser. Sensing the Past -From artifact to historical site[END_REF]. Finally, registration of 2D image to 3D range scans collected in urban scenarios serves as a core module in many applications [START_REF] Zhao | 2d-image to 3d-range registration in urban environments via scene categorization and combination of similarity measurements[END_REF]. All these works are some examples among many others that show the importance of an accurate multimodal 2D/3D registration.

II. RELATED WORK

The registration of a 3D model over an image can be seen as the alignment of visual correspondences extracted from these two data. These visual correspondences are generally referred as "visual features". As for classical registration (as opposed to the notion of multimodality), the state-of-the-art approaches for multimodal 2D/3D registration may be broadly classified according to the type of used visual features. They can be sparse or dense and both have advantages and drawbacks.

Sparse feature-based registration requires the extraction and matching of corresponding visual features in the real image and in a virtual image rendered within the 3D model. The geometrical features that are the most commonly used are interest points. For instance, [START_REF] Weinmann | Thermal 3d mapping for object detection in dynamic scenes[END_REF] proposed a robust approach for detecting reliable feature correspondences between an image acquired with a range camera and a thermal image by exploiting wavelength independent properties. An EPnP (Efficient Perspective-n-Point) algorithm is used on the resulting set of 2D/3D correspondences to estimate the parameters of the thermal camera and perform the thermal mapping on the 3D data. Other geometrical features like lines have also been used. For instance, contours-based 2D/3D registration method has been used in [START_REF] Russell | Automatic alignment of paintings and photographs depicting a 3d scene[END_REF] to align historical painting over 3D model obtained from current images of a scene. After an initial coarse alignment using a shape descriptor, oriented edge points are extracted and matched from contours that are detected in the historical painting and in 3D model renders. Finally, an ICP-like approach is applied on these matched edges to perform the registration. Dominant lines are often preferred in the case of man-made environments as in [START_REF] Liu | A systematic approach for 2d-image to 3drange registration in urban environments[END_REF]. Geometrical sparse features require an accurate detection in images of different modalities. They also have to be correctly matched together and even tracked for some approaches. Even if features detection, matching and tracking have been deeply studied, they are still hard challenges but are crucial to the success of sparse feature-based 2D/3D registration.

As opposed to sparse features, the second category of 2D/3D registration methods are based on dense features. Dense features concept is based on the global appearance of a scene instead of its geometrical shape. A dense feature uses all image pixels. The most commonly used for multimodal 2D/3D registration is the Mutual Information (MI). This statistical measure of non-linear correlation between two data sources was first introduced for the registration of multimodal medical images. The use of MI has been extended to 2D/3D multimodal registration in [START_REF] Corsini | Imageto-Geometry Registration: a Mutual Information Method exploiting Illumination-related Geometric Properties[END_REF]. Indeed, authors proposed to estimate the camera parameters by maximizing the correlation between a real image and different attributes of illumination of the 3D model (ambient occlusion, specularity, normal field). Very recently, [START_REF] Zhao | 2d-image to 3d-range registration in urban environments via scene categorization and combination of similarity measurements[END_REF] proposed to only use similarity measurements between a chosen set of 2D/3D attribute-pairs that could be dominant in a specific scene. The choice of the attributes-pairs results of a preliminary training phase. Finally, all the selected attributespairs are combined into a reliable similarity measurement: Normalized Mutual Information (NMI). NMI have also been used for autonomous vehicles localization based on a LIDAR map of urban environments [START_REF] Pascoe | FARLAP: Fast Robust Localisation using Appearance Priors[END_REF], [START_REF] Wolcott | Visual localization within lidar maps for automated urban driving[END_REF]. Dense features have the advantages to avoid the detection, matching or tracking stages and offers a very accurate registration [START_REF] Corsini | Imageto-Geometry Registration: a Mutual Information Method exploiting Illumination-related Geometric Properties[END_REF]. However, the convergence domain is very tight, thus to guarantee a correct 2D/3D registration, the real image and the 3D model must be initially coarsely aligned.

These two types of visual features are typically used in two consecutive stages. First, sparse features are used to estimate a first coarse alignment, then a dense feature is used to obtain a fine registration. However, the accuracy of such approaches is highly related to the success of the first phase. An hybrid method has also been studied in [START_REF] Sottile | Mutual correspondences: an hybrid method for image-to-geometry registration[END_REF]. Authors introduced Mutual Correspondences (MC), a semi-automatic 2D/3D registration method based on a minimization function that combines sparse correspondences and MI measure. MC is defined as a simple weighted sum of the two.

In this paper, we propose a robust method that perform accurate multimodal 2D/3D registration that is comparable to an hybrid approach. We did not try to develop a new hybrid similarity measure but we take advantages of both geometrical and dense visual features strengths in an elegant framework. The proposed framework has been designed so that geometrical and dense visual features mutually improve the registration in order to perform a correct and accurate final 2D/3D alignment. Thanks to the use of both feature types, the method has wide convergence domain and a high registration accuracy, regardless of the quality of both the image and the 3D model. Even if the method has been developed in order to perform the alignment of multimodal data, it remains generic and also usable in a classical 2D/3D registration. Moreover, the proposed approach is not limited to a specific pair of geometrical/dense feature types.

This paper is organized as follows. Section III states the problem and describes the several stages of the proposed registration method. Then, experimental results, including qualitative results and quantitative evaluation are presented in Section IV. Finally, conclusions are given in Section V.

III. PROPOSED FRAMEWORK

The registration of a real 2D image over a virtual 3D model can be seen as an estimation of the parameters (intrinsic and extrinsic) of the real camera. This is commonly formalized as an optimization problem. The optimization techniques that are generally used are based on the gradient or the Hessian of the cost function such as Gauss-Newton, Levenberg-Marquardt or Barzilai-Borwein methods. In this work, we propose to use a PSO (Particle Swarm Optimization) approach to perform the registration. PSO solves a problem by having a population (swarm) of candidate solutions (particles) that move around in the search-space. Each particle displacement is influenced by the best particle in its nearest neighborhood and by the best particle in the complete search-space. The particle velocities updated in this way are expected to iteratively move the swarm toward the best solution. PSO is well suited to solve 2D/3D registration problem. First, an analytical derivative of the cost function w.r.t. the camera parameters is not required. Second, having a several virtual cameras appears to be beneficial for both geometrical and dense visual features (Section III-C).

A. Problem formulation

The process inputs are a 3D model noted O (for Object) of an environment and a real image I d that may have a different modality. The 3D model is composed of points P that have 3D coordinates and an intensity value I. The coordinate of a point expressed in the 3D model is noted

o P = [ o X, o Y, o Z, 1] ⊤ . A 3D model is then a list of N points and intensities: O = [ o P 1 , I 1 ], [ o P 2 , I 2 ], ..., [ o P N , I N ].
The pose of a virtual camera C (for Camera) is described by the following homogeneous transformation matrix c M o :

c M o(4×4) = c R o(3×3) c t o(3×1) 0 0 0 1 (1)
where c R o is a rotation matrix and c t o is a translation vector.

In the following, we also expressed a camera pose by a 6element

vector c r o = [ c t X o , c t Y o , c t Z o , c θ X o , c θ Y o , c θ Z o ] where c t o = [ c t X o , c t Y o , c t Z o ] and [ c θ X o , c θ Y o , c θ Z o ] are Euler angles.
The velocity of camera c expressed relatively to the model is

noted c v o = c ṙo .
We express a virtual image rendered by the virtual camera C by:

I = Kpr( c M o O) (2)
where the matrix K contains the intrinsic parameters (focal length in terms of pixels, principal point and distortion parameters) and the operation pr(.) denotes the projection model of the camera (e.g. perspective, fisheye, omnidirectional...). The aim of a 2D/3D registration is to find the optimal virtual camera pose that maximizes the similarity, or minimize the difference, between I c and I d , which can be expressed as: Fig. 1: General overview of the 2D image to 3D model registration method. The stages colored in red represent the core of the approach. For ease of reading, the swarm contains only 4 virtual cameras.

c Mo = arg max c M o [S(f c , f d )] (3)
where f c and f d describes the features extracted from respectively I c and I d and S(.) is a similarity metric (in the case of a maximization).

A general overview of the proposed framework is illustrated in Fig. 1. The method consists in a swarm of N virtual cameras C i for i = [0, ..., N] that move inside the 3D model O trying to reach a desired pose represented by the real image I d . Each virtual camera, in other words particle, is defined by :

C i            I c i : Image c i M o : Pose K c i : Intrinsic Parameters c i v o : Velocity s c i : Similarity Score
The main parts of the proposed registration method are described in the following.

B. Initialization of the swarm

To be able to correctly register the 3D model over the real image, the pose of the real camera has to be inside the searchspace of the swarm. Every camera pose c i M o is initialized from a random position and a random orientation. The position of the N virtual cameras in the 3D model are randomly initialized inside a sphere around a specific position. Orientations are randomly initialized within a cone. The sphere radius and the angle ranges that defined the orientation cone represent the limits of the search-space. The velocity of every virtual camera is zero in the initial state of the method.

If the real camera has been calibrated, its intrinsic parameters K d are already known and all the virtual cameras can be configured with it

K c i = K d ∀ i = [0, ..., N].
Otherwise the intrinsics parameters of the virtual cameras have to be initialized randomly around a reasonably range and will be estimated in addition to the pose. For ease of reading in the following, we consider that the real camera has been calibrated, thus only the pose is optimized.

At this point, we have N virtual cameras intrinsically and extrinsically initialized and N virtual images rendered from every camera pose. The next stages of the method represent the core of the registration.

C. Evolution of the swarm

The core of the registration process consists of two phases: virtual cameras displacement and virtual camera creation. Each phase is based on a different type of visual features.

1) Displacement phase:

The displacement of the N virtual cameras is based on dense visual features. Thanks to the use of every pixel of the images, dense features have the advantage to provide a global minimum clearly defined leading to a very accurate registration. However, it is well known that their cost function are highly non-linear. Having many virtual cameras theoretically reduces the probability of being trapped in a local minimum during the registration process. Considering the different modalities between the 3D model (by extension, the virtual images) and the real image, the state-of-the-art has shown that the Mutual Information (MI) and its derivatives are well suited as similarity metrics [START_REF] Zhao | 2d-image to 3d-range registration in urban environments via scene categorization and combination of similarity measurements[END_REF] [START_REF] Corsini | Imageto-Geometry Registration: a Mutual Information Method exploiting Illumination-related Geometric Properties[END_REF]. In the case of MI as dense metric, equation (3) then becomes:

c Mo = arg max c i M o [MI(I d , I c i )] ∀ i = [1, ..., N] (4)
with

MI(I d , I c i ) = H(I d ) + H(I c i ) -H(I d , I c i ) = s c i ( 5
)
where H(I d ) and H(I c ) are individual entropies and H(I d , I c ) is the joint entropy.

We assume that the nearer a virtual camera is to the real camera, the more its rendered image is similar to the real one. Consequently we consider that the more a virtual camera has a high similarity score the better is its pose in the search-space. At each iteration of the PSO algorithm, the N virtual cameras move in the direction of the camera which has the highest similarity score. Their movements are also influenced by the best particle in their nearest neighborhood. More precisely, velocities are updated at each iteration following:

c i v o (t + 1) =γ c i v o (t) + µ 1 ω 1 ( c g r o (t) -c i r o (t)) + µ 2 ω 2 ( c l r o (t) -c i r o (t)) (6)
where γ is an inertia factor, µ 1 and µ 2 are acceleration constants and ω 1 and ω 2 are random weight that are distributed uniformly in [0, 1]. C g denotes the best camera of the complete swarm and C l denotes the best camera in the local neighborhood of each camera. The configuration of the PSO parameters is discussed in the experiments section (Section IV-B).

The pose of every virtual camera in the 3D model is updated following:

c i M o (t + 1) = c i M o (t) e [ c i v o (t+1)] (7)
where e[.] is an exponential map of special Euclidean group SE(3) used to determine a displacement from a velocity vector. The velocities of the cameras are updated iteratively until a stop criterion is reached (e.g. a maximum number of iterations or a threshold on the spatial distribution of the cameras). At the end of the process, all the cameras are supposed to have iteratively converged to the best solution c Mo in order to solve equation (4). Even if PSO has a high global search capacity, considering the high non-linearity of our cost function (equation ( 3)) we have no guarantee to find the optimal solution. To overcome this weakness, just before the computation of the cameras velocity (Fig. 1), a second phase implementing sparse features, tries to create a new virtual camera to add to the swarm.

2) Creation phase: having many virtual viewpoints of the scene increases the chance of finding correspondences between images. This is particularly interesting since it is already difficult to accurately detect and match visual feature between real and virtual images and it is even more challenging when these images have different modalities. Very recently, points matching techniques between multimodal pairs of images (visible, thermal, TLS intensity and range images) have been evaluated [START_REF] Gesto-Diaz | Feature matching evaluation for multimodal correspondence[END_REF]. Authors have shown that good results are obtained when point features are detected using MSD (Maximal Self-Dissimilarity) and described with SIFT (Scale-invariant feature transform). Based on this study, our creation phase exploit this combination of detector and descriptor.

The N virtual cameras are reordered in a decreasing order regarding to the visual dense similarity scores computed in the previous phase:

s c i > s c i+1 ∀ i = [1, ..

., N]. Point features are matched between the real image I d and the best virtual image

I c 1 that gives us a list of theoretical 2D/2D correspondences:

d u c 1 ←→ c 1 u (8) d x c 1 ←→ c 1 x (9)
where d u c 1 and c 1 u are expressed in the image space and where d x c 1 and c 1 x are expressed in the normalized metric space following respectively:

d x c 1 = K -1 d d u and c 1 x = K -1 c 1 c 1 u. The 2D points c 1 x are back-projected in 3D using: c 1 X = pr -1 ( c 1 x).
The resulting 3D points are then expressed in the frame of the model O with: o X c 1 = o M c 1 c 1 X. This leads to a set of 2D/3D correspondences:

d x c 1 ←→ o X c 1 ( 10
)
Knowing these theoretical 2D/3D correspondences, the pose computation of the real camera consists in solving the well known PnP problem:

d x c 1 = pr( c Mo o X c 1 ) (11)
Due to the differences in terms of the type, visual aspect, modality and due to the 3D model accuracy, it is challenging to find reliable correspondences between the real and the virtual images. Consequently some matches among the set of correspondences (equation ( 10)) are wrong. For this reason we solved the PnP problem with a RANSAC (Random Sample Consensus) approach. Indeed, RANSAC uses the smallest set of potential correspondences (4 matches in our case) and iteratively tries to expand this set with consistent data. The algorithm provides the camera pose c Mo that solves equation [START_REF] Weinmann | Thermal 3d mapping for object detection in dynamic scenes[END_REF] and also classifies the 2D/3D matches (equation ( 10)) as inliers and outliers.

If the inliers are good enough, the estimated camera parameters c

Mo should be inside the initial search-space (Section III-B). In this case a new virtual camera that is added to the swarm with c

Mo as initial pose and we progress to the next step (Figure 1). If c

Mo is outside the initial search-space, we extract and match point features between the real image and the next best image I c 2 of the swarm. The resulting 2D/3D correspondences are added to the inliers of the previous points matching:

inlers( d x c 1 ), d x c 2 ←→ {inliers( o X c 1 ), o X c 2 } (12)
This new set of correspondences is used as before to solve the PnP problem (equation ( 11)). This procedure is repeated until the estimated c Mo falls within the initial search-space. If c Mo is outside the search-space even using every virtual images, no camera is added during the current creation phase. However, at the next iteration, all the cameras will have moved towards the current best one (highest dense score), consequently the virtual images will be different and the next creation phase has more chance to lead to a better set of correspondences.

Adding a new virtual camera to the swarm is actually always beneficial. If the new camera is actually well estimated, it may be the future global best camera of the swarm. The others will then move in its direction and the dense feature-based PSO should locally improve this solution. If the new camera is estimated inside the search-space but is not close to the desired solution, it will still be a new particle of the swarm that will explore differently the PSO search-space and may be the source of other interesting visual features.

IV. ANALYSIS AND EVALUATION

In a first time, our 2D/3D multimodal acquisition process is detailed. Then, the performances of the proposed approach are qualitatively and quantitatively evaluated.

A. Dataset acquisition

A ground mobile robot (Fig. 2) with several visual sensors has been instrumented to acquire images of different modalities and to simultaneously create a 3D model of the environment. The robotic base is a Summit XL from Robotnik [START_REF]Robotnik automation -mobile robot summit[END_REF] that has been equipped with a Kinect 2.0, a near-infrared camera (AVT Marlin F-131B NIR) and a polarization camera (4D Technology PolarCam). The near-infrared camera with a Fig. 2: Dataset acquisition -Robotnik Summit XL equipped with a Kinect 2.0, a near-infrared camera and a polarization camera 2/3" sensor and a 8mm f1.4 lens has a narrow field of view. The polarization camera with a 1/2" sensor and a 1.4mm lens has super wide angle up to 185 • . The three cameras have been calibrated. Thus, their intrinsic parameters and the relative transformations between each sensor are known.

The complete system architecture including camera interfaces, localization and mapping is ROS (Robot Operating System) [START_REF] Quigley | Ros: an open-source robot operating system[END_REF]. The visual SLAM ORB-SLAM2 [START_REF] Mur-Artal | Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras[END_REF] is applied on the Kinect data to compute the robot trajectory. The keyframes selected by the ORB-SLAM2 algorithm are very well localized thanks to local bundle adjustment, loop closure detection and pose graph optimization. Knowing the pose of the Kinect (and by extension the pose of the two other cameras) at every keyframe, we merge every 3D point clouds acquired at every keyframe in order to build a dense 3D reconstruction with true scale of the environment. Fig. 3 shows a 3D model created following this approach on 470 keyframes. The robot did a loop trajectory of 47.2 meters to create a 3D model that covers an area of about 16 × 24 meters.

In addition to the Kinect data, the near-infrared and the polarization images and their corresponding camera poses are Fig. 3: A 3D model of an indoor building environment created from Kinect data acquired by the Summit XL robot also saved for every keyframes. In summary, the complete dataset contains the 3D model, the 470 images acquired by the three cameras and their poses expressed in the 3D model reference frame. These camera poses serve as ground truth to evaluate the proposed registration method in the next section.

B. Experimental results

For practical reasons, we have evaluated our method on 50 keyframes randomly selected among the 470 of the dataset. To highlight the advantage of using both visual feature types, we compare the estimation of the poses of the near-infrared and the polarization cameras using the proposed method (DENSE+SPARSE), an approach that uses only sparse features (SPARSE) and another one that only use dense features (DENSE).

The SPARSE approach computes the camera pose using point features (MSD+SIFT) matched between the real image and a virtual image rendered from the Kinect pose. The resulting PnP problem is solved following a standard RANSAC scheme. The DENSE approach estimates the real camera pose by minimizing the mutual information between the real image and virtual images using a PSO. In order to test the convergence properties of our method (DENSE+SPARSE) we deliberately initialize the virtual cameras of the swarm in a very large search-space. The performance of PSO depends on the parameters configuration: number of particles, initialization of the swarm, inertia factor, acceleration constants, stop criteria. Many variants of the PSO algorithm have been proposed to initialize and to optimize the evolution of these parameters. This is not the point of this experiment which is a proof of concept, the parameters are thus chosen empirically. For every keyframe, we initialize a swarm of 30 virtual cameras in a 2 meters diameter sphere centered on the current Kinect position. The cameras orientation is initialized in a range of ±20 • around the 3 axes. The number of virtual cameras that can be created (Section III-C2) during the registration is limited to 20. The DENSE approach has been initialized with the same parameters.

Fig. 4 shows a comparison between the ground truth and the trajectories of the near-infrared camera and the polarization camera estimated using the 3 methods. As expected, because estimated with our method, the sparse features only and the dense features only approaches of the very large initial search-space and the very tight convergence domain of the method based only on the MI metric, the poses estimated with the DENSE approach are often very far from the desired solution. On the other hand, because of the difficulties to find reliable sparse correspondences between the real and the virtual images, the poses computed with the SPARSE approach are often very badly estimated or even not estimated at all (Fig. 4b). Despite these problems the proposed framework that combines dense and sparse features takes advantages of both and provides an accurate pose estimation. More precisely, Fig. 5 gives a visual idea about the dissimilarity aspect between the real images and the virtual ones. It also gives a qualitative evaluation of the accuracy of the multimodal 2D/3D registration using the proposed method. Indeed, the two first rows show respectively 4 near-infrared images and the virtual images rendered at the estimated camera poses. Similarly, the two last rows show respectively 4 polarization images and the virtual images rendered at the estimated poses.

It should be kept in mind that the ground truth is directly related to the accuracy of the visual SLAM. The mean errors of estimation have also to be put in perspective with the very high initial search space (±1 meter along the 3 axes and ±20 • around them). One can note that we chose MI as similarity metric for the displacement phase but it is not a limit of the method, and other metric could be used and maybe preferable regarding the modalities. Similarly, other matching approaches for the creation phase, maybe more robust to multimodality and image distortions, could be preferred.

V. CONCLUSION

Usual 2D/3D registration approaches are relied either on sparse or on dense visual features. Sparse features offer a high domain of convergence but the resulting alignment is highly related to the reliability of geometric features that have to be extracted and matched between images. Densebased methods avoid feature detection and matching and provide a very accurate registration but have a very small domain of convergence. Furthermore, the use of a diversity of sensors increases the drawbacks of both types of visual features. In this paper, we have proposed a way to combine sparse and dense features in order to perform automatic and accurate 2D/3D registration. The proposed framework smartly employs both feature types to dramatically increase their strength points. Their combination makes 2D/3D alignment achievable regardless the modalities of both the image and the 3D model. The method has obtained promising results in terms of registration accuracy and robustness even to large initial condition. Of course, the current approach in not free from drawbacks. For further improvements, we plan to study more deeply the influence of the PSO parameters on the quality of the registration. Fig. 5: Near-infrared and polarization images given in inputs of the registration method (first and third rows), virtual images rendered at the virtual camera poses estimated using the proposed registration method (second and fourth rows)

Fig. 4 :

 4 Fig. 4: Comparison of the ground truth and the trajectories of the near-infrared camera (a) and the polarization camera (b) estimated with our method, the sparse features only and the dense features only approaches

  TABLE I summarises the mean estimation errors depending on the registration methods.

		Near-Infrared	Polarization
	SPARSE	[51.11cm, 52.47cm, 52.77cm, 3.25 • , 4.97 • , 3.11 • ]	No results
	DENSE	[62.83cm, 92.12cm, 89.76cm, 6.96 • , 6.64 • , 7.37 • ]	[42.42cm, 70.44cm, 90.05cm, 5.69 • , 7.01 • , 6.30 • ]
	SPARSE DENSE	[6.5cm, 7.3cm, 8.1cm, 0.65 • , 0.72 • , 0.61 • ]	[15.15cm, 20.86cm, 18.07cm, 3.02

• , 5.15 • , 4.04 • ]

TABLE I :

 I Mean pose estimation errors of the two cameras according to the registration method.

ACKNOWLEDGMENT

This work is part from a project entitled VIPeR (Polarimetric Vision Applied to Robotics Navigation) funded by the French National Research Agency ANR-15-CE22-0009-VIPeR.