MD Petra G A Van Boeckel
email: p.g.a.vanboeckel@umcutrecht.nl

Alette Sijbring

Frank P Vleggaar

Peter D Siersema

Systematic review: Temporary stent placement for benign ruptures or anastomotic leaks with special emphasis on stent type

Keywords: Oesophagus < Organ-based, Devices < Topics, Endoscopy < Topics, Outcomes research <

APT-1037-2010: Stent placement for benign ruptures or anastomotic leaks: a pooled analysis with special emphasis on stent type" .

INTRODUCTION

Esophageal ruptures and leaks are rare, but may occur spontaneously, as in Boerhaave syndrome, or as a complication after an endoscopic or surgical procedure [START_REF] Siersema | Treatment of esophageal perforations and anastomotic leaks: the endoscopist is stepping into the arena[END_REF]. Despite improvement in diagnostic procedures and (surgical) interventions, ruptures and leaks in the esophagus are still potentially life threatening injuries associated with a high morbidity and mortality if treated surgically [START_REF] Adamek | Management of esophageal perforations after therapeutic upper gastrointestinal endoscopy[END_REF][START_REF] Attar | Esophageal perforation: a therapeutic challenge[END_REF][START_REF] Fernandez | Treatment of endoscopic esophageal perforation[END_REF][START_REF] Jougon | Mortality from iatrogenic esophageal perforations is high: experience of 54 treated cases][END_REF][START_REF] Okten | Management of esophageal perforation[END_REF][START_REF] Port | Thoracic esophageal perforations: a decade of experience[END_REF][START_REF] Wesdorp | Treatment of instrumental oesophageal perforation[END_REF].

Surgical treatment has long been the 'gold standard' for these emergencies (1;5;9-12).

Over the past few years, new minimally-invasive endoscopic treatment options have emerged, i.e., closure with clips or sutures, sealing with biologic glue and sealing with stent placement [START_REF] Amrani | From iatrogenic digestive perforation to complete anastomotic disunion: endoscopic stenting as a new concept of "stent-guided regeneration and re-epithelialization[END_REF].

Endoscopic stent placement is an effective treatment for malignant dysphagia in a palliative setting [START_REF] Homs | Single-dose brachytherapy versus metal stent placement for the palliation of dysphagia from oesophageal cancer: multicentre randomised trial[END_REF]. Recently, a good outcome with low morbidity and mortality has been reported for treating benign esophageal ruptures and leaks with temporary placement of a fully (FSEMS) or partially (PSEMS) covered self-expanding metal stent or a self-expanding plastic stent (SEPS) (15)[START_REF] Hunerbein | Treatment of thoracic anastomotic leaks after esophagectomy with self-expanding plastic stents[END_REF][START_REF] Kauer | Stent implantation as a treatment option in patients with thoracic anastomotic leaks after esophagectomy[END_REF][START_REF] Langer | Management of postoperative esophageal leaks with the Polyflex self-expanding covered plastic stent[END_REF][START_REF] Fischer | Nonoperative treatment of 15 benign esophageal perforations with self-expandable covered metal stents[END_REF][START_REF] Freeman | Esophageal stent placement for the treatment of iatrogenic intrathoracic esophageal perforation[END_REF]. Stents were found to be able to effectively seal esophageal leaks or ruptures and allow healing of the esophageal wall, particularly when concurrent adequate drainage of fluid collections in the mediastinum or pleural cavity is performed. The main drawbacks of stent placement are stent migration and tissue in-or overgrowth, which both necessitate a repeat intervention. Reactive nonmalignant tissue in-or overgrowth is mainly causing a problem when stents are inserted for a longer period and has been reported to occur more commonly with PSEMS than with FSEMS or SEPS [START_REF] Conigliaro | Polyflex stents for malignant oesophageal and oesophagogastric stricture: a prospective, multicentric study[END_REF][START_REF] Eloubeidi | Novel removable internally fully covered self-expanding metal esophageal stent: feasibility, technique of removal, and tissue response in humans[END_REF][START_REF] Verschuur | New design esophageal stents for the palliation of dysphagia from esophageal or gastric cardia cancer: a randomized trial[END_REF].

Experience with temporary stent placement for benign esophageal ruptures or anastomotic leaks is until now only limited with most studies reporting small case series. In addition, studies comparing SEPS, FSEMS and PSEMS for the treatment of benign esophageal ruptures and leaks are not available. We therefore performed a systematic review of the currently available literature to assess clinical effectiveness and safety of treating benign esophageal ruptures and anastomotic leaks with temporary placement of a stent with special emphasis on different stent designs.

Methods

Literature search

A literature search was performed in MEDLINE and EMBASE to identify all related studies on stent placement for benign esophageal ruptures or leaks. Each search was performed for studies in the English language until June 30, 2010, using the key words esophagus, esophageal, rupture, leakage, leak, perforation, tear, Boerhaave syndrome and stent. Boolean operators (NOT, AND, OR) were used, when appropriate, to widen or narrow the search.

Then, a scan of the reference lists of each article was undertaken to identify other relevant articles that were missed in the search. Studies that met the following inclusion criteria were selected for our pooled analysis: (a) patients with a benign esophageal rupture or leak, (b) endoscopic stent placement, and (c) results on a specific stent design (SEPS, FSEMS and PSEMS). Studies that were not in the English language, letters, editorials, reviews, animal studies, case reports with fewer than four patients, and studies in patients with a malignant indication for stent placement were excluded. Patients with incomplete follow up after stent placement and patients dying from an esophageal malignancy within 1 year of follow-up were excluded from our analysis.

Date extraction

Data on year of publication, country of origin, stent design, total number of patients included, number of patients with complete follow up, gender, age, etiology and size of rupture or leak, additional drainage of fluid collections in the mediastinum or pleural cavity, time between success, total time of stent placement, procedure-related and stent related complications, reinterventions and mortality were extracted. All abstracts and titles of studies were screened and data extraction from the selected studies were independently performed by two investigators (PVB and AS) and differences in opinion were resolved by consensus opinion.

Definitions

We used the following definitions:

-Time of stent placement: time between stent placement and removal -Successful sealing: sealing a perforation without complications -Clinical success: healed perforation at the end of follow up without fatal complications -Technical success: technically successful stent placement and removal -Complication: adverse event due to the stent placement procedure (procedure-related complication except pneumonia, for example perforation and hemorrhage) or type of stent used (stent related complication, for example stent migration, tissue in-or overgrowth, etc.).

-Reintervention: endoscopic or surgical procedure needed to resolve complications or because of failure of stent placement

Statistical analysis

After data extraction data were pooled according to stent design. Data comparison between the stent designs was performed by using the Chi-squared test. A p-value <0.05 was considered statistically significant. Statistical analyses were conducted using SPSS version 15 (SPSS Inc, Chicago, Ill. USA).

RESULTS

F

Search results

We detected 392 articles in MEDLINE and 601 articles in EMBASE. Of these, 25 articles met our inclusion criteria for the pooled analysis (Figure 1). A total of 13 studies reported results on SEPS placement(15;16;18;24-32) , 5 on FSEMS placement (13;33-36) and 7 on PSEMS placement for the treatment of benign oesophageal ruptures and leaks (19;37-42).

Descriptive analysis

All studies were published between 2001 and 2009. Sixteen studies were performed in Europe, seven in the US, one in Australia and one in China (Table 1). Twenty-five studies evaluated 267 patients with completed follow up, of whom 159 were treated with SEPS, 34 with FSEMS and 74 with PSEMS. Most of the stents used in the studies were 18-mm diameter stents. Overall, 71% of the patients were male and the mean age across studies was 60 (range 17-91) years.

The etiology of the ruptures was an anastomotic leak in 137 (51%) patients, iatrogenic (post endoscopic) in 66 (25%) patients, Boerhaave's syndrome in 46 (17%) patients, a (benign) fistula in 9 (4%) patients and other causes in the remaining 8 (3%) patients. In the PSEMS group, 4 (14%) perforations occurred during dilation, with 2 (7%) of these performed for achalasia. Two (8%) other perforations occurred during rigid esophagoscopy, 2 (2%) during ERCP and 18 (69%) following endoscopic procedures of unknown origin. In the FSEMS group, 1 (20%) perforation occurred after dilation for achalasia, 1 (20%) post EMR and 3 (60%) following endoscopic procedures of unknown origin. In the SEPS group, 7 (22%) perforations occurred during dilation, 3 (10%) during EUS, 3 (10%) during stent removal, 2 (7%) as a consequence of taking biopsies, 1 (3%) post EMR and 17 (48%) following endoscopic procedures of unknown origin. In all but five studies (18;27;32;37;40), detailed data on size of rupture or leak was missing. In these five studies, rupture or leak size varied between 10 and 30 mm and/or consisted of 20-100% of the circumference. The overall mean time between occurrence of rupture or leak and stent placement was 10 (range, 50 min -72 days) days.

Concurrent drainage of fluid accumulations outside the esophageal lumen was performed in 59% of patients (55% of patients with SEPS, 60% of patients with FSEMS and 61% of patients with PSEMS) (Table 3).

The mean time between rupture or leak and stent placement was shortest for PSEMS placement (4 days), followed by SEPS placement (13 days) and longest for FSEMS placement (16 days). Total time of stent placement was longest for SEPS (8 weeks, range 3-19) weeks), followed by PSEMS and FSEMS placement (both 6 weeks, range 3-17 and 5-9 weeks, respectively). Results of the individual studies are summarized in Tables 2a-2c.

Outcome and survival

The overall technical success rate of stent placement was 99% and was not different between stent types (99% for SEPS, 100% for FSEMS and 98% for PSEMS, p=0.65) (Table 3).

Removal of SEPS and FSEMS was mostly uncomplicated (99% and 100%, respectively), however removal of PSEMS was reported as complicated in 8% (92% uncomplicated)(see below).

Clinical success was achieved in 85% of all patients and was not significantly different between SEPS (84%) FSEMS (85 %) and PSEMS (86%) (p=0.97) (Table 3). Mortality was 13% in all patients and was lowest with SEPS (11%) followed by FSEMS and PSEMS (both 18%). In most patients the cause of death was a septic complication due to infected fluid or an abscess outside the esophageal lining.

Complications and reinterventions

Eight (3%) patients had a procedure related complication (bleeding (n=6) or perforation due to stent misplacement (n=2)), whereas in 65 (34%) patients a stent related complication was seen (stent migration (n=56), tissue-in or overgrowth (n=9)). Stent migration occurred more often with SEPS (n=47 (31%)) and FSEMS (n=7 (26%)) than with PSEMS (n=2 (12%)) (p= <0.001), while there was no significant difference in tissue in-or overgrowth between PSEMS (n=2 (12%)) than with FSEMS (n=2(7%)) and SEPS (n=5(3%)) (p=0.68).

An endoscopic reintervention was performed in 53 (25%) patients; however more with SEPS or FSEMS (both 26%, n= 41 and 9, respectively) than with PSEMS (13%, n=3)( p= <0.001). A surgical intervention for incomplete sealing, a procedure related or stent related complication was performed in 13% of patients and was not different between stent designs.

DISCUSSION

To our knowledge, this is the first pooled data analysis reporting the results of different stent designs in patients with a benign esophageal rupture or leak with results of 267 treated patients. There was a lack of randomized controlled trials. It is important to note that these results are only based on case series in which mainly small patient numbers were included.

Clinical success of stent placement, i.e., healing of the perforation or leak, was achieved in 85% of reported patients with no differences between PSEMS, FSEMS and SEPS. The mean time of stent placement that was needed for healing was 7 weeks and was not different between different stent types (range, 6-8 weeks). Animal studies have suggested that 4 weeks should be sufficient for tissue healing. Based on the results of this review; however, it seems advisable to remove esophageal stents after a period of approximately 7 weeks.

An absolute prerequisite for healing is adequate drainage of fluid or abscess cavities that are in continuity with the perforation or leak (43). The time between the occurrence of an (3;10;12;19). A longer delay between a rupture or leak and treatment is associated with a worsening of the prognosis due to septic complications from an infected fluid accumulation in the mediastinum or pleural cavity. Tilanus et al. found that extension of an esophageal rupture into the pleural cavity was an independent risk factor for mortality (44). Most patients included in this review also died from septic complications due to an abscess cavity outside the esophageal lining. Treatment, i.e., sealing the rupture or leak with concurrent drainage of fluid collections of abscess cavities in the pleural cavity, mediastinum and/or even peritoneal cavity should therefore be performed as early as possible.

Drainage can be performed by endoscopic, radiologic or surgical means [START_REF] Schubert | Endoscopic treatment of thoracic esophageal anastomotic leaks by using silicone-covered, selfexpanding polyester stents[END_REF].

The mean time between esophageal perforation or leak and stent placement was 11 days with significant differences between different stent types (FSEMS 16 days, SEPS 13 days, and PSEMS 4 days). Remarkably, this did not result in differences in clinical success and mortality rates in favor of any stent type. This is probably due to the fact that the time between perforation or leak and stent placement differed to a large extent between the included studies, varying between 50 minutes and 50 days! Stent migration necessitating a reintervention occurred in 25% of patients and was most commonly seen with fully covered stents, both SEPS (26%) and FSEMS (26%), compared to PSEMS (13%) (p=<0.001). This is explained by the known reduced anchoring capacity of FSEMS and SEPS compared to PSEMS resulting in an increased migration rate of the former stent type (23;45). Furthermore, as the far majority of these patients have no obstructive lesion keeping the stent in place, the relatively high rate of stent migration with fully covered stent designs is not unexpected.

In contrast, tissue in-and/or overgrowth was higher with PSEMS (12%) compared to SEPS (3%) and FSEMS (7%) (p=0.68), although this result was not significant. The cover of FSEMS en SEPS that is applied along its whole length prevents tissue from growing into the stent meshes. It has been shown that this benign tissue reaction particularly occurs at the uncovered part of FSEMS and is caused by a local fibrotic reaction and/or the proliferation of granulation tissue. This hyperplastic tissue reaction can be clinically manifest as early as 2 weeks after stent placement but also at a later stage (46). Moreover, tissue in-and/or overgrowth may complicate removal of PSEMS in patients, resulting in a second esophageal perforation (47). A technique to remove embedded PSEMS is to place a fully covered stent of the same diameter inside the FSEMS. This so-called stent-in-stent method causes necrosis of the hyperplastic tissue in-and/or overgrowth. In our experience, both these stents can be removed uneventfully after a period of 7-14 days (48).

Although all three stent designs were found to be effective in sealing benign esophageal ruptures or leaks, they all have their pros and cons. The main limitations of the presently used stent types are migration and hyperplastic tissue in-and/or overgrowth..These stent types are as yet not available in a covered version. Another option could be the use of biodegradable formulations to cover ruptures or anastomotic leaks. This material has been shown to stimulate connective tissue and vascular ingrowth and displays only minor hyperplastic tissue formation (49). The technique to applicate this material into an esophageal leak or rupture needs however further development.

The mortality rate associated with stent placement for this indication (13%) may well compare favorably to surgical management (12%-50%) [START_REF] Fischer | Nonoperative treatment of 15 benign esophageal perforations with self-expandable covered metal stents[END_REF]. There is currently no guideline which type of esophageal rupture or leak should be treated with stent placement or primary surgery. Stent placement has been proposed for ruptures or leaks less than 70% of the circumference, with surgery being reserved for larger ruptures or leaks [START_REF] Schubert | Endoscopic treatment of thoracic esophageal anastomotic leaks by using silicone-covered, selfexpanding polyester stents[END_REF]. However, Doniec et al. reported a patient with a complete dehiscence that was treated with stent placement, resulting in complete closure without a complicated course [START_REF] Doniec | Therapy of anastomotic leaks by means of covered self-expanding metallic stents after esophagogastrectomy[END_REF]. The only true evidence will 11 come from a randomized trial comparing these two treatment modalities in a well defined population. Nevertheless, the limited number of patients for such a trial and the promising results of stent placement as is summarized in this review make it difficult if not impossible to perform such a trial. This review has several limitations which should be taken into account before concluding that a particular stent type is favorable in patients with a benign esophageal rupture or leak.

First, no randomized trials have been conducted. It is important to note that these results are only based on case series in which mainly small patient numbers were included. This is probably due to the limited number of patients in each center. Moreover, both FSEMS and PSEMS are not approved by the Food and Drug Administration (FDA) for temporary use in benign indications.

Second, a variety of treatment protocols have been employed in the included studies. In some patients, stent removal or exchange was performed at shorter intervals than in others and concurrent treatment, such as drainage of fluid collections or abscesses, was also not standard treatment in all studies. Consequently this could have affected clinical success rate, but also complication and mortality rates. Furthermore, it was not possible to analyse outcome on stent placement based on aetiology of the underlying disorder due to a lack of data in the source papers.

Finally, selection bias cannot be excluded in this patient group, since it has still not been elucidated which patients could benefit from stenting and which patients from primary surgery.

In conclusion, this review demonstrates that covered stents placed for a period of 6-8 weeks are effective and safe for benign esophageal ruptures or anastomostic leaks to heal. A prerequisite for successful stent placement is adequate drainage of fluid collections in the mediastinum or pleural cavity. As efficacy between PSEMS, FSEMS and SEPS was not found to be significantly different, stent choice should depend on expected risks of stent migration and/or tissue in-or overgrowth with a particular stent type. We are increasingly using fully covered stents, particularly FSEMS, for this indication as these are flexible enough and when used in large diameters (23 mm body diameter) show acceptable migration rates.

Further randomized trials are however needed to compare different stent types on the one hand and the ideal stent design that comes out of these trials with surgical treatment on the other hand; however, due to the limited number of patients this is unlikely to occur. -

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F o r P e e r R e v i e w

F o r P e e r R e v i e w

RESULTS

Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.

Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.

Table 2

Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.

  and stent placement, technical success of stent placement and removal, clinical

  leak and the actual treatment, either surgical or endoscopic, is one of the most critical prognostic factors

11 FUNDINGFunding 27

 1127 Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review.

15From:

  Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(6): e1000097. doi:10.1371/journal.pmed1000097

Table 1 : Baseline characteristics of studies on stent placement for benign esophageal leaks and ruptures

 1 Freeman RK, Ascioti AJ, Wozniak TC. Postoperative esophageal leak management with the Polyflex esophageal stent. J Thorac Cardiovasc Surg 2007 Feb;133(2):333-8. Hirdes MMC, Vleggaar FP, van der Linde K, Willems M, Totte E, Siersema PD. Esophageal perforation due to removal of partially covered self-expanding metal stents placed for benign perforation or leak. Endoscopy. In press 2010.

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Table 2a : Outcome of studies on SEPS placement for benign esophageal leaks and ruptures Etiology Technical success Iatrogenic Complications Reintervention

 2a

Table 2c : Outcome of studies on PSEMS placement for benign esophageal leaks and ruptures Etiology Iatrogenic Technical success Complications Reintervention

 2c

Table 3 : Pooled analysis of outcome of studies reporting on SEPS, FSEMS and PSEMS placement for benign esophageal leaks and ruptures

 3

	Etiology	Technical success	Complications	Reintervention
	Iatrogenic

Flowchart of search history on stents for benign esophageal leaks and ruptures

  Risk of bias across studies15 Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies). Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.

	Section/topic	# Checklist item	601 EMBASE studies	Reported on page #
				11
	Additional analyses	16
	392 studies excluded based on abstracts	582 studies excluded based on abstracts
	Reasons for exclusion:	Reasons for exclusion:
	-87 non-English language	-111 non-English language
	-23 reviews		-32 reviews
	-16 letters		-35 letters
	-35 editorials		-47 editorials
	-73 with inclusion of malignant cases	-143 with inclusion of malignant cases
	-59 single case reports	-113 single case reports
	-76 other studies		-101 other studies

Table 1

 1 Risk of bias within studies19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12).

Table 2

 2 Synthesis of results21 Present results of each meta-analysis done, including confidence intervals and measures of consistency.-Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see Item 15).

-Additional analysis

23 Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see

Item 16]

).

Table 3 DISCUSSION

 3 Summary of evidence24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias).11 Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and implications for future research.

	8

*Computation limited to studies in which this information was provided -Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale.

F o r P e e r R e v i e w

4

Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.

4

Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated.

Figure 1

Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).

4

Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.

4-5

Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.

Risk of bias in individual studies

12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.

5

Summary measures 13 State the principal summary measures (e.g., risk ratio, difference in means).

5

Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I

2

) for each meta-analysis.

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Page 1 of 2


Cédric Véga
email: cedric.vega@teledetection.fr

Sylvie Durrieu
email: sylvie.durieu@teledetection.fr

Multi-level filtering segmentation to measure individual tree parameters based on Lidar data: application to a mountainous forest with heterogeneous stands

Keywords: lidar, airborne laser scanning; mountainous forests, tree extraction, canopy surface model

This paper presents a method for individual tree crown extraction and characterisation from a Canopy Surface Model (CSM). The method is based on a conventional algorithm used for localising LM on a smoothed version of the CSM and subsequently for modelling the tree crowns around each maximum at the plot level. The novelty of the approach lies in the introduction of controls on both the degree of CSM filtering and the shape of elliptic crowns, in addition to a multi-filtering level crown fusion approach to balance omission and commission errors. The algorithm derives the total tree height and the mean crown diameter from the elliptic tree crowns generated. The method was tested and validated on a mountainous forested area mainly covered by mature and even-aged black pine (Pinus nigra ssp. nigra [Arn.]) stands. Mean stem detection per plot, using this method, was 73.97 %.

Algorithm performance was affected slightly by both stand density and heterogeneity (i.e. tree diameter classes' distribution). The total tree height and the mean crown diameter were estimated with root mean squared error values of 1.83 m and 1.48 m respectively. Tree heights were slightly underestimated in flat areas and overestimated on slopes. The average crown diameter was underestimated by 17.46 % on average.

Introduction

Acquiring detailed information on tree spatial distribution and tree crown characteristics is a crucial step when attempting to improve our understanding of the structure and function of forest ecosystems and to ensure their sustainable development. Traditional field-based inventories require time-consuming and labour-intensive work that can only be applicable to small areas (Avery and Burkhart, 2001). With the development of very high resolution remote sensing, single tree crowns can be identified, thus enabling an automatic extraction of some tree characteristics at operational scales [START_REF] Leckie | Automated tree recognition in old growth conifer stands with high resolution digital imagery[END_REF].

Scanning laser altimetry or lidar (light detection and ranging) generates detailed and accurate information about forest 3D structure [START_REF] Means | Predicting forest stand characteristics with airborne scanning Lidar[END_REF][START_REF] Popescu | Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass[END_REF], and is used at an operational level in some coniferous forests [START_REF] Naesset | Laser scanning of forest resources: the nordic experience[END_REF]. Lidar systems combine a micropulse laser unit, a Global Positioning Systems (GPS) and Inertial Measurement Units (IMU) to produce high precision measurements of the Earth surface structure [START_REF] Baltsavias | Airborne laser scanning: basis relations and formulas ISPRS[END_REF]. Due to the ability of the signal to partially penetrate canopies, lidar measurements, acquired as 3D point clouds or waveforms, can be used to produce detailed information on both ground topography and vegetation layer (Reutebush et al., 2003). In the last decades high density lidar data -at least 5 points by squared meter [START_REF] Naesset | Effects of different flying altitudes on biophysical stands properties estimated from canopy height and density measured with a small-footprint airborne scanning laser[END_REF]) -were successfully used to retrieve forest parameters at the tree level including tree location, tree height, crown dimensions, tree biomass and even the tree species [START_REF] Brandtberg | Classifying individual tree species under leaf-off and leaf-on conditions using airborne lidar[END_REF][START_REF] Popescu | Estimating biomass of individual pine trees using airborne lidar[END_REF][START_REF] Rowell | Using laser altimetrybased segmentation to refine automated tree identification in managed forests of the black hills, South Dakota[END_REF][START_REF] Solberg | Single Tree Segmentation Using Airborne Laser Scanner Data in a Structurally Heterogeneous Spruce Forest[END_REF]. Such high densities are

Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.com DOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002 required to describe the crown area in adequate detail (Hyyppä et al., 2001) and to increase the rate of tree apices effectively sampled [START_REF] Leckie | Stand delineation and composition estimation using semi-automated individual tree crown analysis[END_REF].

Methods for deriving individual tree parameters from lidar data mainly rely on the processing of a canopy surface model (CSM) describing the outer canopy layer [START_REF] Bongers | Methods to assess tropical rain forest canopy structure: an overview[END_REF]. They were commonly based on techniques applied for the processing of optical imagery [START_REF] Leckie | Stand delineation and composition estimation using semi-automated individual tree crown analysis[END_REF][START_REF] Falkowski | The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data[END_REF]. Widely used approaches are based on geometrical properties (i.e. height, slope, orientation) of CSMs and mainly consist in the identification of local maxima (LM) followed by the construction of crown segments (Morsdorff et al., 2004;[START_REF] Persson | Detecting and measuring individual trees using an airborne laser scanner[END_REF][START_REF] Popescu | Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size[END_REF].

The accuracy of automated tree detection relies heavily on image spatial resolution (see [START_REF] Pouliot | Approaches for optimal automated individual tree crown detection in regenerating coniferous forests[END_REF] for review). Several methods were introduced to optimize LM identification i.e. to minimize either the level of commission (i.e. false tree detection) or omission errors. These include conventional image filtering [START_REF] Persson | Detecting and measuring individual trees using an airborne laser scanner[END_REF], heightbased variable window size filtering [START_REF] Popescu | Fusion of small-footprint lidar and multispectral data to estimate plot-level volume and biomass in deciduous and pine forests in Virginia[END_REF] or template matching [START_REF] Brandtberg | Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America[END_REF]. Approaches used to extract segments from the set of LM include; watershed analysis (Kwak et al., 2007;[START_REF] Mei | Tree-crown delineation from digital elevation models and high resolution imagery[END_REF], region growing algorithms (Hyyppä et al., 2001;[START_REF] Persson | Detecting and measuring individual trees using an airborne laser scanner[END_REF], fitting functions [START_REF] Popescu | Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size[END_REF] or template matching [START_REF] Pollock | The automatic recognition of individual trees in aerial images of forest based on a synthetic tree crown model[END_REF]. As methods for deriving crown characteristics are mostly based on CSM geometry, the resulting crown shapes are directly impacted by the CSM optimization strategy [START_REF] Rowell | Using laser altimetrybased segmentation to refine automated tree identification in managed forests of the black hills, South Dakota[END_REF][START_REF] Solberg | Single Tree Segmentation Using Airborne Laser Scanner Data in a Structurally Heterogeneous Spruce Forest[END_REF].

Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.com DOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002 On the whole, both LM detection and crown modelling approaches reveal high sensitivity to initial parameters [START_REF] Pouliot | Approaches for optimal automated individual tree crown detection in regenerating coniferous forests[END_REF] and optimisations are generally performed on a field knowledge basis, which hinders direct extendibility to different forest ecosystems [START_REF] Popescu | Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size[END_REF]Koch et al., 2006). While methods have been introduced to evaluate the reliability of either the LM or the crown segments [START_REF] Brandtberg | Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America[END_REF][START_REF] Rowell | Using laser altimetrybased segmentation to refine automated tree identification in managed forests of the black hills, South Dakota[END_REF][START_REF] Solberg | Single Tree Segmentation Using Airborne Laser Scanner Data in a Structurally Heterogeneous Spruce Forest[END_REF] more generic and flexible approaches need to be developed to satisfactorily manage various forest types and structures. This paper presents a multi-level filtering approach to extract and characterise individual tree crown parameters from a lidar CSM. The segmentation is based on the extraction of LM and on the modelling of tree crown using ellipsis defined using the local geometrical properties of the CSM surrounding each LM. The method integrates algorithms to control both the degree of CSM filtering and tree crown shape, and to merge tree crown segments derived at various scales. In order to evaluate the extendibility potential of the method the study was conducted within a complex environment, a mountainous conifer forests characterized by steep slopes and diverse stand structures and densities.

Material

Study area

The study site (Fig. 1) is a 108 ha area that was reforested at the end of the [Insert Fig. 1 about here]

Field data

Field inventory data were collected during December 2007 (14 plots) and November 2008

(13 plots). The plots (N=27) were established to represent the whole range of stand conditions and were inventoried according to the French National Forest Inventory protocol (NFI, http://www.ifn.fr). Each plot comprised 3 concentric rings of 6, 9 and 15 m radius from the plot centre, in which trees to be inventoried were selected based on their diameter at breast height (dbh). Within the 6 m radius plot, all the trees having a dbh greater than 7.48 m were considered. The minimum dbh values were set to 22.44 cm and 34.7 cm respectively for the second (6-9 m) and the third (9-15m) rings. For each tree considered, the dbh, the total timber height, and the tree state (dead or alive) were recorded. The protocol was slightly modified for 2 plots characterized by gentle slopes. All the trees having a dbh greater than 7.48 m were inventoried and additional measurements were collected for the crown diameter The structural diversity of the plot was estimated using the Gini diversity index (Lexerød andEid, 2006, Ozdemir et al., 2008). The Gini coefficient (GC) was calculated as follows:

GC = ¦ n j 1 (2j-n-1)ba j / ¦ n j 1 ba j (n-1) (1)
where j is the rank of a tree according to size in ascending order, n is the total number of trees and ba j the basal area for tree j. GC values are inside the [0-1] interval. A value of 0 indicates that all trees in a given area have exactly the same diameter. On the other hand, a value of 1 characterises forest areas in which each tree has a unique dbh value.

The exact plot centre positions were measured using differential GPS (DGPS) or a Leica total station (Leica, Switzerland) whenever the local slope conditions permitted this. Individual tree positions were derived from distance and angle measurements taken from the plot centre.

Distance measurements were made at the tree base using a Vertex III clinometer (Hagloff, Sueden) with slope compensation. Azimuths and tree heights were measured using a Suunto compass (Suunto, Finland). Measurements of crown diameters and trunk circumference at breast height were performed using tapes. The dbh were derived from trunk circumferences.

Due to low DGPS accuracy (around 2 m) within the area, the plot centre positions were visually corrected in the laboratory by visually matching tree location with photo-interpreted tree crowns on the lidar data. Two plots were discarded because of insufficient positional accuracy and the impossibility to obtain a satisfactory matching within the close neighbourhood.

[Insert Table 1 about here]

In addition, a topographic survey was conducted in May 2009 to validate the quality of the lidar ground elevation model. Four forested plots located in areas with different topographical conditions were surveyed, comprising a total of 891 ground control points.

Additional 502 points were collected in open areas leading to a total number N GCP of 1393.

Measurements were made using a Leica total station.

Lidar data acquisition and processing

The lidar data were acquired by Sintegra (Grenoble, France) in April 2007 using a RIEGL LMS-Q560 instrument [START_REF] Wagner | Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner[END_REF]. This small footprint fullwaveform airborne laser scanner operates at a pulse frequency of 111 kHz. The sampling was performed at about 600 m above ground level leading to a mean footprint diameter of 0.25 m at the ground level.

A 30 % overlap was set between adjacent flight lines to avoid slivers (Latipov, 2002). Point density obtained was close to 5.5 pts / m 2 .

Waveform processing was performed using RiAnalyze © solftware from Riegl (Riegl, Austria) calculation of the gravity centre of the echo pulse with the accuracy of a multiple Gaussian pulse fitting method [START_REF] Riegl | Rianalyze 560 for full waveform analysis[END_REF]. During this processing step a set of 3D points are derived from the waveforms, describing the position of targets having interacted with the laser signal [START_REF] Chauve | Processing full-waveform lidar data in an alpine coniferous forest: assessing terrain and tree height quality[END_REF].

The resulting point cloud was classified as vegetation or ground elements to create a DTM and a CSM representing the shape of the canopy surface. To match the average point spacing, the DTM and the CSM were computed at 1 m and 0.5 m resolutions respectively.

The points corresponding to the ground level were extracted by the data provider from the last returns using a TIN-iterative algorithm [START_REF] Axelsson | DEM generation from laser scanner data using adaptative tin models[END_REF] applied with Terrascan software (Terrasolid, http://www.terrasolid.fi/en/products/terrascan). The resulting Triangulated Irregular Network was then converted into a 1 m cell grid.

The "non-ground" returns were interpolated into a Digital Surface Model (DSM) using a two-step method. First, the point cloud was converted into a regular grid with a 0.5 m resolution using neighbouring statistics. Each DSM cell was assigned the maximum height value of the points within it. The Inverse Distance Weighting (IDW) method was used to compute empty cell values by interpolating the point values selected at the previous stage. A CSM at a 0.5 m spatial resolution was finally calculated by subtracting the DTM from the DSM.

Methods

Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.com DOI: 10.1016/j.jag.2011.04.002

General principle of the method

The method we propose to extract individual tree crowns is based on conventional algorithms The novelty of the approach lies in the introduction of i) a hole filling algorithm to locally improve the CSM quality, ii) a threshold-based method to control the degree of CSM filtering, ii) a control of the shape of elliptic crowns and iii) a multi-level filtering crown fusion to balance omission and commission errors (Fig. 2).

[Inset Fig. 2 about here]

Hole-filling algorithm

Lidar CSMs are commonly beset by pit problems due to both lidar sampling design and postprocessing of the raw data including point classification and interpolation (Ben-Aries et al., 2009). We developed a straightforward and automatic algorithm to improve both CSM filtering quality and segmentation accuracy (either with or without CSM filtering). We opted for this solution instead of the semi-automated method proposed by Ben-Aries et al. (2009) as it would have been more complex to parameterize.

The algorithm first calculates if a given pixel value is lower than a given threshold (fixed to 0.5 m here) compared to its 8-connected pixels. If the condition is fulfilled, the pixel tested value is replaced by the mean value of its 8 neighbours. Otherwise, the algorithm tests if the condition could by fulfilled using 4-connected pixels (vertical cross, oblique cross). This meant that holes of more than 1 pixel were filled. Again, if a neighbouring configuration matched the height condition, the tested pixel value is replaced by the corresponding mean value of those 4 connected pixels. The algorithm iterates until no pixel value is modified.

This hole-filled CSM (CSM HF ) was used as a starting point for the segmentation process (Fig. 2).

Segmentation method

The algorithm was coded using Python 2.4 (http://www.python.org/) with Numpy 1.2.1 (http://www.scipy.org/) and the ArcGIS 9.2 geoprocessing tools (www.esri.com/).

2.3.1.

Filtering of the CSM HF

The CSM HF was smoothed using a Gaussian low-pass 3 × 3 filter (Hyyppä et al., 2001) and 1/16 for the diagonal ones. The number of Gaussian runs was optimized for every plot according to a user defined threshold (see section 3.5.1).

Locating local maxima

LMs were created by extracting the centre coordinates of pixels having a higher height value than their 8 connected neighbours. Only the LMs above 3 m in height were considered.

Extracting elliptic tree crowns

Tree crowns around each LM were modelled as ellipsis. The algorithm was based on the measurement of the crown size in the 8 cardinal directions. The measurements were made downwards from the LM, starting from each of the 8 pixels connected to the LM. For any given direction, a pixel was considered as part of the crown if, and only if, its height value and those of two of its neighbours were lower than the last connected pixel fulfilling that condition. The position of the two "constraining" neighbours was set depending on the tested direction as illustrated in Fig. 3. An additional constraint was set to fix the minimum height of the crown outer limit. A 2 m threshold was used for trees exceeding 10 m, and a threshold of 0.5 m otherwise. Such very similar thresholds were set to accurately model tree crowns in open areas and to improve calculation efficiency. The number of pixels composing a crown radius was then translated into distance based on pixel size.

The condition for including a point within a crown radius was relaxed when no Gaussian filtering was used (i.e. iteration 1) to balance the effect of noise within the CSM HF . To include a point within the crown radius measurement, only one out of the two constraining pixels must fulfil the height condition ("or" criteria instead of "and").

[Insert Fig. 3 about here]

As the accuracy of crown radius measurement may vary depending on the local structure of the CSM, the 8 measured radii of a crown were compared to identify suspicious measurements and to retain the most reliable ones for the elliptic crown shape computation.

First, the crown diameters were computed for the four directions (N-S; E-W; NE-SW; NW-SE) by adding the corresponding crown radii. Then, the radii corresponding to orthogonal diameters having the smallest distance difference were selected as master radii of the crown ellipse. Finally, the four remaining radii values were individually compared to their two encircling master ones. If the tested radius was within the interval of the mean of the two encircling master radii ± twice their standard deviation (SD), then it was integrated within the ellipse definition. Otherwise, the radius was removed. At the end of the process, 4 to 8 radii remained and were used to define the ellipse approximating the crown.

Such elliptic representations of tree crown shapes were obtained by 1) converting the radii into points representing the outer limit of the crown using distance and angle measurement from the LM coordinates and 2) applying a standard deviational ellipse algorithm to the resulting point cloud provided within the ArcGis spatial Analyst toolbox (http://webhelp.esri.com/). that only the crowns having at least a 5 m 2 area were considered. Such a threshold was set according to field data and visual analysis of the CSM.

Cleaning of local maxima and corresponding crowns

Estimating tree parameters

The algorithm recorded the crown area as well as the height of the tree. The latter is defined as the maximum height of the original CSM within the crown.

Optimisation of the segmentation

Controlling the degree of CSM filtering

This step relies on the hypothesis that there is a specific CSM HF filtering degree at which the balance between omission and commission errors would be optimized. Such an optimal filtering degree is expected to vary according to the both the plot structure and the canopy texture. In the present study, these later parameters were controlled introducing to empirically defined parameters hereafter refereed to as "structural parameter" and "segmentation parameter".

The "structural parameter" was used to assess the plot structure. It lies on the hypothesis that the relation between the number of extracted LM and the filtering intensity is closely related to the plot structure. In the present study, the "structural parameter" was defined as the rate of LM suppression between the number of extracted LM using 0 and 1 iteration of the Gaussian filter. As illustrated in Fig. 4, such a parameter was found to be a good estimator of the plot density of homogeneous stands (plot having at least 75% of stems in a same dbh class). Threshold values of 80 and 70 were used to classify plots within three structural classes according to the point clusters (Fig. 4).

The "segmentation parameter" was introduced to measure the proportion of noise within a set of identified LM and was calculated after each segmentation iteration as the ratio between the number of LM remaining after the cleaning step (step 5 of the segmentation process) and the number of LM before the cleaning step. Such a parameter lies on the hypothesis that above a particular threshold value, the selected set of LM offers a good approximation of the spatial structure of dominant trees within a given area. For example, a segmentation parameter threshold set to a value of 0.9 implied that 90 % of the extracted LM had to be converted into tree apices to reach the optimal number of Gaussian run. The threshold values were estimated for each structural class based on a trial-error experiment using 20 randomly selected plots. In the present study, values of 0.98, 0.90 and Note that the minimum number of Gaussian runs was set at 1, and the maximum to 6 according to [START_REF] Solberg | Single Tree Segmentation Using Airborne Laser Scanner Data in a Structurally Heterogeneous Spruce Forest[END_REF] and to experimental results.

[Insert Table 2 about here]

Multi-level filtering crown fusion

The multi-level filtering crown fusion approach was introduced to improve segmentation results, considering that the ideal filtering level of the CSM would vary depending on the crown size distribution within a given area. Filtering the CSM HF with an increasing number of Gaussian runs tends to progressively improve segmentation results on dominant tree crowns to the detriment of the smaller ones. Such an effect may dominate within heterogeneous stands leading to underestimation of plot density.

To balance this effect, some of the crowns extracted using lower Gaussian filtering levels and stored within temporary data bases, were reintroduced, in descending filtering order, to make up the final set of tree crowns. Only crowns, whose corresponding LM were not inside the existing set of crowns, were added.

Statistical analysis

The modelled tree crowns were matched with their corresponding ground measured trees according to tree location and height. If more than one field-measured tree position was found in a modelled crown, the tree having the highest height was considered.

Due to the presence of a significant amount of leaning trees on the field, a certain proportion of modelled crowns could not be easily paired with any field-measured tree. In such cases, the modelled crowns were associated with their closest field-measured tree provided that the latter was not already associated to a crown. Otherwise the modelled crown was deemed a false detection.

Statistical analysis involved estimating the proportion of omission and commission errors at

the plot level. Because of the specificities of the NFI plots, the analysis was made for each tree size category (small, medium and large diameters). For the matched trees, the accuracy of CSM derived tree heights and, when field measurements were available, of crown diameters, were assessed by calculating the mean difference and the root mean squared error (RMSE) of the difference between field and lidar derived measurements. Further investigations were carried out to account for both plot structure and terrain slope characteristics.

Results and Discussion

Author [START_REF] Sithole | Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds[END_REF]). These errors were also caused by issues linked to algorithm parameter optimisation over large and topographically complex areas (Kobler et al., 2007). Underestimation of terrain elevation over a slope may be explained by the propensity of TIN-iterative methods to be influenced by low points. A detailed discussion on filtering algorithms limitations and possible ways of improving them is available in [START_REF] Sithole | Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds[END_REF].

Fig. 5 illustrated the results of the hole-filing algorithm. The resulting CSM HF was only qualitatively evaluated through visual comparison with the original CSM. As expected, pits localized within tree crowns were mostly removed while gaps between trees were well preserved. Such characteristics are required prior to CSM filtering and for extracting trees crowns [START_REF] Rowell | Using laser altimetrybased segmentation to refine automated tree identification in managed forests of the black hills, South Dakota[END_REF].

Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4) [Insert Fig. 5 about here]

Tree detection

The segmentation results are illustrated Fig. 6 for three plots having different densities and slopes. The optimal number of Gaussian runs was found to range from 1 (i.e. the minimal authorized value) to 4 with an average value of 1.8 (± 0.96 SD) (Table 3). Half of the plots (52 %) were segmented using a unique Gaussian filter, 20 % needed two filtering iterations and another 20% 3 iterations. The remaining plots were segmented using 4

Gaussian filters. From all the identified tree stems by plots, 90.3 % of the crowns were obtained from the CSM smoothed with the optimized number of Gaussian filter runs. The multi-level filtering crown fusion approach to crown detection generated about 10 % of overall results, thus, emphasizing how important it is to consider multiple filtering levels when refining tree crown detection as underlined by [START_REF] Rowell | Using laser altimetrybased segmentation to refine automated tree identification in managed forests of the black hills, South Dakota[END_REF]. According to the plot the contribution of crowns derived from lower filtering level varied from 0 to 50% of the total number of extracted crowns.

[Insert Table 3 about here]

[Insert Fig. 6 about here]

Overall, the performance of the algorithm correlated with plot density, the latter parameter explaining 64 % of result variance (Fig. 7). Up to 800 stem/ha, segmentation accuracy was superior to 90 % for 9 plots over 11 and was obtained with significant filtering efforts (i.e. 2 to 4 Gaussian runs) (Fig. 7) which meant that the density and crown dimensions were correctly modelled (Fig. 6A). With increasing plot densities, the number of Gaussian filters tended to decrease to efficiently match plot structure. Similarly, [START_REF] Rowell | Using laser altimetrybased segmentation to refine automated tree identification in managed forests of the black hills, South Dakota[END_REF] reported both an overestimation of stem count without filtering in low density and thinned plots, and an underestimation of stem count with filtering in dense ones.

The general decrease in segmentation accuracy observed with increasing plot density was expected because high plot densities often lead to higher crown closure, the appearance of merged crowns that are difficult to isolate, and an increasing proportion of overtopped trees (Koch et al. 2006[START_REF] Rowell | Using laser altimetrybased segmentation to refine automated tree identification in managed forests of the black hills, South Dakota[END_REF][START_REF] Solberg | Single Tree Segmentation Using Airborne Laser Scanner Data in a Structurally Heterogeneous Spruce Forest[END_REF]. However, by considering the performance of the algorithm by diameter classes, the rates of correctly identified tree crowns were respectively 98.18 % (±6,03 % SD), 92.63% (±11.61 % SD) and 45.66 % (±34,41% SD) for large, medium and small diameter classes (Table 3). The high percentage of trees identified for both large and medium diameter classes underline the utility of the method for management purposes. Indeed forest managers are particularly interested in the estimation of dominant and codominant tree crowns (Koch et al., 2006). Finally, while most of the non detection of tree crowns was due to overtopping, a proportion of error may be attributed to the 5 squared meter threshold used to define an ellipse as a tree crown. However this threshold level was retained, as experimental results gave an increased number of commission errors when using lower values.

Despite generally satisfactory performance, the algorithm produced inconsistent results for some plots characterized by variable densities and structural characteristics as reflected by the GC values ploted as labels on Fig. 7. These results concern poorly textured canopies (i.e. canopy with fused or/and relatively flat crowns). Within such plots, the structural parameter failed to optimize the number of filtering iterations, leading to over filtering of the CSM HF and under-segmentation of the tree crowns that could not be counterbalanced by et al., 2006;Kwak et al., 2007). In the present study, such poor results can be attributed to a failure to optimize the filtering process and might be mostly explained by the underlying hypothesis of the "segmentation parameter" regarding the modelling of dominant tree crowns. This weakness of the method could be over passed by introducing approaches using fully textural indices to control the degree of filtering of the CSM HF , e.g. indices derived either from wavelets transforms (Falkowski et al., 2006) or textural ordination based on Fourier spectral decomposition [START_REF] Couteron | Textural ordination based on Fourier spectral decomposition: a method to analyze and compare landscape patterns[END_REF], as both methods can be used to approximate crown size distribution. In the same way, texturebased segmentation approaches such as the one proposed by [START_REF] Lucieer | Texture-based landform segmentation of LiDAR imagery[END_REF] may also improve the robustness of the method by both minimizing effects of plot local structure on the optimization process and extending the method from the plot to the stand level.

[Insert Table 4 about here]

[Insert Fig. 7 about here]

Height and crown characteristics

Tree heights were evaluated for 245 stems (Table 5). Lidar tree heights were on average 0.84 m (± 1.63 m SD) above the field measurements. Minimum and maximum absolute

Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.com DOI: 10.1016/j.jag.2011.04.002 differences were 0 m and 8.26 m respectively. The RMSE was 1.83 m. Overall the linear relationship accounted for 94 % of the total variance (Fig. 8).

Average difference was found to increase slightly with diameter, but converse results were found for standard deviation (Table 5). Nevertheless, height errors were closely correlated with slope.

A 0.1 m (± 0.65 m SD) tree height underestimation was found for terrain slopes lower than 25 %. The RMSE was 0.65 m. Such an underestimation was reported in most previous studies [START_REF] Persson | Detecting and measuring individual trees using an airborne laser scanner[END_REF]Naesset and Økland, 2002) and was explained both by the discrete nature of the lidar sampling -increased density resulted in a higher proportion of sampled tree apices (Naesset and Økland, 2002) -and a partial penetration of the lidar signal within the canopy before a return could be detected by the lidar receiver [START_REF] Gaveau | Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data[END_REF]. m and 2.5 m respectively. Similar results were reported in other studies related to mountainous environments (Hollaus et al., 2006;[START_REF] Takahashi | Estimating individual tree heights of sugi (Cryptomeria japonica D. Don) plantations in mountainous areas using smallfootprint airborne lidar[END_REF]. [START_REF] Heurich | Laser scanning for identification of forest structures in the Bavarian Forest National Park[END_REF] suggested that lidar-derived tree height measurements must be corrected above 20° terrain slopes (e.g. 36.4 %). Such overestimations may be explained by several factors. One source of tree height overestimation may be attributed to DTM errors. As reported in section 4.1, DTM error differences around 0.7 m were found between roads and steep wooded areas.

DTM underestimation contributed significantly to tree height measurement errors for steep terrain. Another source of tree height overestimation may be explained by a difference between field and lidar tree heights. In the field, tree height was defined as the vertical distance from the tree apex to the upslope root crown (Fig. 9). The lidar-derived tree heights were simply calculated as the maximum value of the CSM within the crown area. Such calculation differences may lead to tree height overestimations for slopes as indicated by [START_REF] Takahashi | Estimating individual tree heights of sugi (Cryptomeria japonica D. Don) plantations in mountainous areas using smallfootprint airborne lidar[END_REF]. A 1 m horizontal difference between the tree apex and the tree base within a 100 % terrain slope produces a 1 m error in height measurement. [START_REF] Heurich | Laser scanning for identification of forest structures in the Bavarian Forest National Park[END_REF] also pointed out that increased leaning trees and/or steeper terrain slopes generated increased systematic tree height errors.

[Insert Table 5 about here]

[Insert Fig. 8 about here] Quality of crown diameter estimations was evaluated for 53 trees (Table 6). The plots were located in gentle terrain slopes having average values of 6 % (plot 1, Fig. 6A) and 13 % (plot 2, Fig. 6B). Plot densities were respectively 313 and 746 stem/ha for plot 1 (N Crown = 13) and 2 (N Crown = 40).

Plot 1 was segmented using a CSM HF obtained after 3 Gaussian runs against a unique one for plot 2. The error measurement increased slightly with density. While plot 1 showed a 0.79 m (± 1.14 m SD, 12.34%) mean underestimation, it reached 1 m (± 1.21 m SD, 19.11%) for plot 2. A asymmetry was also found when considering measurement direction (Table 6). The underestimation may be explained by both the field measurement strategy and the plot structure. In mountainous areas tree crowns are often dissymmetric and characterized by long branches exceeding the crown that may lead to an overestimation of the crown diameter.

With increasing densities, the spatial arrangement of trees can generate a high level of merged crowns [START_REF] Maier | Characterising mountain forest structure using landscape metrics on LiDAR-based canopy surface models[END_REF]. Such characteristics could not be efficiently modelled using CSM geometrical properties. The reported varying error levels as a function of measurement direction highlight the impact of plot structure on crown measurement accuracy. The more compact a set of crown is, the greater the individual crown underestimation. Possible ways of improvements may include the use of more sophisticated filtering methods as suggested by Koch et al. (2006).

[Insert Table 6 about here]

[Insert Fig. 9 about here]

Conclusion

This research presents a method for extracting tree crown parameters from a lidar CSM acquired over mountainous areas. The method was based on conventional algorithms used to detect tree apices and model crown shape. The novelty of the approach lies in 1) the introduction of a hole-filling algorithm to improve the initial CSM quality, 2) the optimization of the CSM filtering via the introduction of 2 parameters to quantitatively evaluate the plot spatial structure, 3) the introduction of a control method to optimize the shape of the identified tree crowns and 4) the application of a multi-level filtering crown fusion approach to improve the performance of the algorithm. The method performed quite well for most plots but failed to optimize filtering for some plots due to the complex structure or spatial organisation of the latter. This was partly due to the fact that the optimization procedure was based on the presupposed behaviour of the identified dominant tree number when Gaussian iteration number increases. To account for this problem future developments will focus on textural indices to improve both the description of the forest structure at the stand level and the robustness of the filtering process. Taking into account the variability of the forest structure within the study site, we are confident that the method can be used in various forests conditions. However in very dense even aged young stands the canopy height patterns will probably not allow a correct crown segmentation, even with very high lidar point density, since intra-crown height variations can be for some species similar to inter-crown ones. Furthermore, this research confirms some inconsistencies in height and crown width measurement regarding terrain slope and vegetation density. This issue would be addressed in future work dedicated to producing robust models of forest parameters and to improving management strategies within mountainous environments.

Figure captions list

Fig. 1: 3D view of the study site.

Fig. 2: Workflow of the proposed segmentation method (i and j are iteration variables used to control Gaussian smoothing and crown fusion respectively).

  used to locate LM on a smoothed version of the CSM and to subsequently model the tree crowns around each maximum at the plot level (Fig.2, Segmentation box). The segmentation included the following 5 steps: 1) Hole-filling 2) Gaussian filtering of the CSM HF , 3) Extraction of LM, 4) Modelling of elliptic crown shapes, 5) Cleaning of LM and corresponding crowns. The latter step involved filtering out LM and corresponding crowns to keep a single LM per crown (priority given to the LM associated with the largest crown) (see section 3.3.4).

  Author-produced version of the article published in International Journal of Applied EarthObservation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.com DOI: 10.1016/j.jag.2011.04.002Depending on the spatial arrangement ofLMs the elliptic crowns associated with each LM may partially overlap. The algorithm authorized crown overlapping under the condition that overlapping areas did not included any LM (i.e. a LM could only be inside a unique crown). The later rule lies on the hypothesis that a LM falling within the crown of a neighbouring LM may be a false apex detection. Each LM located within at least 2 crowns were tested and false apex detection were set for LM whose corresponding crown had a lower area than the other considered crowns. Therefore, the cleaning step consisted in removing such identified LM and their corresponding crown. Note

  Tree heights were overestimated for the 3 other terrain slope classes. Average values were respectively 0.18 m (± 0.65 m), 0.83 m (± 1.31 m SD) and 1.58 m (± 0.65 m SD) for [25 -50 %[, [50 -75 %[, and [75 -∞] terrain slope classes. Corresponding RMSE were 0.97 m, 1.54

Fig. 3 :

 3 Fig. 3: Position of the constraining pixels according to the measurement direction.

Fig. 4 :

 4 Fig. 4: Variation of the structural parameter according to field measured plot density (N=12).

Fig. 5 :Fig. 6 :

 56 Fig. 5: Subset (66 m x 56 m) of the CSM before (left) and after (right) hole-filling. Numbered squares show examples of correction using 8-connected pixels (1), and 4 connected pixels (2: oblique cross, 3-4: vertical cross).

Fig. 7 :

 7 Fig. 7: Performance of the crown detection according to plot density. Symbols represented the optimized number of Gaussian runs (N = 25). Color scale indicated the dominant dbh class (white: small; grey: medium; black: large) and labels the GC index.

Fig. 8 :

 8 Fig. 8: Field versus Lidar measured tree height (m) (N = 245) according to slope classes.

Fig. 9 :

 9 Fig. 9: Difference between field-derived and lidar-derived tree height on steep slope.

  Figure caption list

  Author-produced version of the article published in International Journal of Applied EarthObservation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.com DOI: 10.1016/j.jag.2011.04.002 Provence) and is part of an Observatory for Research on the Environment (ORE) dedicated to the monitoring of erosion and hydrological processes in mountainous areas. The site is mainly covered by mature and even-aged black pine (Pinus nigra ssp. nigra [Arn.]) stands growing at elevations ranging between 802 m and 1 263 m. The mean slope is about 53 % but can locally reach up to 100 %. The whole forest is currently subject to intensive management (selective cutting) to renew the forest and to contribute to the regeneration of native species. These activities have a direct impact on the forest mosaic characterized by varying stand structures.

19 th century. It is located in the southern French Alps, to the north of Digne-les-Bains (Alpes de Haute

Such limited crown measurements were established considering (1) the time available for field measurements, (2) the difficulty to have reliable field crown measurements on slope terrains and (3) the possibility to have a visual control of crown delineation quality using high resolution images complementary to field measurements.

  Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.com DOI: 10.1016/j.jag.2011.04.002 (measurements taken in North-South and Est-West directions).

  based on a Gaussian pulse estimation technique. The method combines the rapid

	Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656.
	The original publication is available at http://www.elsevier.com
	DOI: 10.1016/j.jag.2011.04.002

  Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.comDOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002

  Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.comDOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002

	. The
	filter weights were 1/4 for the central pixel, 1/8 for the 4 nearest pixels (Y and Y direction)

  Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.comDOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002

  Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.comDOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002

  Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.com DOI: 10.1016/j.jag.2011.04.002 0.80 were used respectively for high, medium and low values of the structural parameter, as illustrated on Table2.

  Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.comDOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002

  -produced version of the article published in International Journal of Applied EarthObservation and Geoinformation, 2011, 13(4), 646-656.

	The original publication is available at http://www.elsevier.com
	DOI: 10.1016/j.jag.2011.04.002
	3.1.	DTM and Hole-filled CSM quality
	DTM quality was assessed using the 1393 ground control points. Over open areas (mainly
	roads), the average difference between field and lidar (i.e. field minus lidar) data was -0.05
	m (± 0.36 m SD). The minimum and maximum error values were -0.57 and 4.77 m
	respectively. Over forested areas (N =891), the average difference was 0.24 m (± 0.81 m
	SD), minimum and maximum values were -2.88 and 4.22 m respectively. The plot-level
	DTM error increased as a function of slope from 0.02 m (± 0.3 m SD) to 0.65 m (±0.86 m

SD) for plot-averaged mean slopes of 59.42 % and 86.03 %. Such increased slope related errors were explained by the difficulties involved in filtering algorithms to work within rough and forested terrain due to the underlying assumption regarding the bare Earth structure in a local neighbourhood

  , 646-656. The original publication is available at http://www.elsevier.comDOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002

  Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.comDOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002

  Author-produced version of the article published in International Journal of Applied EarthObservation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.com DOI: 10.1016/j.jag.2011.04.002 multi-level filtering crown fusion (see for example Fig. 6 D). Similar underestimations due to low height variance and poor CSM texture were found for different forests structures and types (Koch

  Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.comDOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002

  Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.comDOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002

	Overall, lidar-derived values underestimated field measurements by 0.95 m (± 1.14 m
	SD) representing 17.45 % of the field measured values. Minimum and maximum values were
	-2.08 m and 6.02 m respectively. The 1.48 m RMSE was of the same range as mentioned in
	Popescu et al. (2003), which reported a 1.36 m RMSE for dominant trees of various pine
	trees species using a similar method. The Pearson correlation between field and lidar derived
	values was r = 0.63.

  Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.comDOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002

Table 1

 1 Descriptive statistics of the mean plot values field inventory data (N=25). Note that statistics were computed according to the IFN protocol.

		Density	Dbh	Height	GC
		(stem/ha)	(cm)	(m)
	Minimum	78.59	12.34	5.86	0.03
	Mean	960	23,62	14.54	0.28
	Median	874.36	24.03	16.35	0.27
	Maximum	2234.45	37.48	23	0.58
	Standard Deviation	632.55	6.93	5.11	0.12

Table Author -

 Author produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.com DOI: 10.1016/j.jag.2011.04.002

	Table 2
	Threshold parameter values used to constrain the segmentation
	Structural parameter	> 80	<=80 and =>70	< 70
	Segmentation parameter	0.98	0.85	0.8

Table 2

 2 Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.comDOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002

Table 3

 3 Statistics of the segmentation parameters for the 25 plots

		Nb of Gaussian		Crowns from	Crowns from lower
			Nb LM/Crown
		filter runs		optimized filtering	filtering levels
	Min 1	1	50	0
	Mean 1.8	1.12	90.28	9.72
	Max 4	1.37	100	50
	SD	0.96	0.12	11.12	11.12

Table 3

 3 Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.comDOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002

Table 4

 4 This table presents results of the segmentation performance for the 25 field plots. Note that commission errors were evaluated within a 6 m radius from the plot centre.

	Diameter class		All	Large	Medium	Small
	Nb of stems		391	32	156	203
	Detection (%)	Min	30.76	80	14	0
		Mean	73.97	98.18	92.63	45.66
		Max	100	100	100	100
		SD	23.66	6.03	11.61	34.41
	Omission (%)	Min	0	0	0	0
		Mean	21.02	1.81	6.54	54.33
		Max	69.23	20	36.36	100
		SD	23.66	6.03	11.61	34.41
	Commission (%) (6 m	Min	0	-	-	-
	radius)
		Mean	5.91	-	-	-
		Max	25	-	-	-
		SD	8.41	-	-	-

Table 4

 4 Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.comDOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002

Table 5

 5 Differences between lidar and field measurements (ΔH = field -lidar) for the 245 identified trees (N Stem ). Statistics are reported for all trees, dbh classes and terrain slope classes.

			All		Large		Medium		Small
	dbh classes (cm)
					[ 34.7 -α [	[ 22.44 -34.7 [	[ 7.48 -22.44 [
		ΔH	|ΔH|	ΔH	|ΔH|	ΔH	|ΔH|	ΔH	|ΔH|
	N Stem	245	245	29	29	130	130	86	86
	Mean (m)	-0.84	1.23	-1.30	1.37	-0.83	1.19	-0.70	1.22
	Min (m)	-8.26	0	-4.57	0.07	-6.48	0	-8.26	0.02
	max (m)	3.16	8.26	0.85	4.57	2.24	6.48	3.16	8.26
	SD (m)	1.63	1.36	1.24	1.15	1.51	1.24	1.89	1.59
	RMSE (m) 1.83	-	1.78		1.72		2.00
	slope classes (%)		<=25	>25 & <=50	>50 & <=75		>75
		ΔH	|ΔH|	ΔH	|ΔH|	ΔH	|ΔH|	ΔH	|ΔH|
	N	40	40	54	54	51	51	100	100
	Mean (m)	0.10	0.38	-0.18	0.72	-0.83	1.13	-1.58	1.88
	Min(m)	-0.78	0.03	-3.8	0.02	-3.7	0.02	-8.26	0
	max(m)	3.16	3.16	2.04	3.8	1.15	3.7	2.72	8.26
	SD (m)	0.65	0.53	0.96	0.66	1.31	1.06	1.95	1.67
	RMSE (m) 0.65		0.97		1.54		2.50

Table 5

 5 Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.comDOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002

Table 6

 6 Evaluation of the lidar derived crown diameters (N Crown = 53): difference between ground truth and lidar-derived values. NS and EW represent the North-South and the East-West directions respectively. The Mean Diameter is the mean of the two later values.

		All directions		North-South		Est-West
		All	Plot 1	Plot 2	All	Plot 1	Plot 2	All	Plot 1	Plot 2
	Min	-2.08	-0.68	-2.08	-1.36	-0.68	-1.36	-2.08	-0.48	-2.08
	Mean (m)	0.95	0.79	1.00	1.19	0.62	1.37	0.72	0.96	0.63
	Mean (%)	17.46	12.34	19.11	21.90	11.53	25.26	13.02	13.15	12.97
	Max	6.02	2.84	6.02	6.02	2.48	6.02	2.84	2.84	2.66
	SD	1.14	0.88	1.21	1.18	0.77	1.23	1.07	0.98	1.09
	RMSE	1.48	1.17	1.57	1.66	0.97	1.83	1.27	1.34	1.25

Table 6

 6 Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.comDOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002

Acknowledgements

Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.com DOI: 10.1016/j.jag.2011.04.002

The authors would like to thank Laurent Albrech and Tristan Allouis for their valuable help in field data collection and post-processing. This work is part of the ExFOLIO project and was realized thanks to the financial support of the CNES (Centre National d'Etudes Spatiales). The authors would also like to deeply thank the GIS Draix for providing the fullwaveform lidar data and for helping in ground truth surveys. They are grateful to INSU for its support to GIS Draix through the ORE program.

Hollaus, M., Wagner, W., Eberhofer, C., Karel, W., 2006. Accuracy of large-scale canopy heights derived from LiDAR data under operational constraints in a complex alpine environment. ISPRS J. Photogramm. Remote Sens. 60, 323-338. Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4), 646-656. The original publication is available at http://www.elsevier.com DOI: 10.1016DOI: 10. /j.jag.2011.04.002 .04.002


Laurent Ranno
email: laurent.ranno@grenoble.cnrs.fr

N T Nam

Unconventional GMR angular dependence using a compensated ferrimagnet

Unconventional GMR angular dependence using a compensated ferrimagnet

Introduction

The giant magnetoresistance (GMR) effect has been the subject of many fundamental and applied studies since its discovery in 1988 [1]. GMR has already had a huge industrial impact in the field of magnetic field sensing especially for data recording devices. More recently, a new sub-field of spin electronics has emerged, current induced magnetic reversal and GHz generation [2]. For such spin torque devices, compensated ferrimagnetic alloys such as GdCo have already been shown to be interesting model systems [9]. Their spin polarisation is large enough to get magnetoresistive effects [3] and they can be tuned by changing the temperature [8]. Their magnetic anisotropy can also be designed to induce out-of-plane or in-plane easy-axis depending on deposition conditions and heat treatments.

We used such a compensated ferrimagnet to get a more detailed understanding of the microscopic contributions to GMR when a ferrimagnetic electrode is used. At the same time, GMR allowed us to get a better understanding of the magnetic configuration of such a ferrimagnetic alloy in high fields close to compensation.

A ferrimagnetic material contains two magnetic sublattices antiferromagnetically coupled. It is difficult to get a direct measurement of the contributions of both sublattices to magnetisation and transport. A physical effect sensitive to only one sublattice has to be used (for example X-ray Circular Magnetic Dichroism XMCD [5], extraordinary Hall effect EHE or magneto-optical effect). However close to compensation, i.e. to the composition x comp or temperature T comp where both ferrimagnetic sublattice magnetisations compensate, a large magnetic field (larger than the spin flop field B sf ) is sufficient to create a non colinear magnetic configuration in the ferrimagnet. The measurement of GMR in this regime gives new insights on the GMR effect and on the ferrimagnetic configuration.

Yang et al. [6] synthesised GdCo/Cu/Fe based GMR trilayers. They observed a GMR ratio which varies in amplitude but more surprisingly in sign with the GdCo layer thickness, keeping its nominal composition constant. They analysed this in terms of interface/volume spin-dependent scatterings.

Bai et al. [7] synthesised TMR junctions based on Gd(FeCo)-Al2O3-FeCo trilayer and also observed a change of the TMR sign at room temperature as a function of the ferrimagnetic layer composition.

In our experience, different GdCo thin layers may have similar nominal compositions but different magnetic composition due to intermixing, preferential resputtering and incorporation of impurities (oxygen). A 1% change in the Gd content changes the compensation temperature by as much as 75 K. We chose not to change the GdCo composition but to vary the temperature across T comp to study the Gd-dominated and Co-dominated magnetisation regimes using the same sample.

Experimental details

Fe 20 Ni 80 (bottom)/Cu/Gd 20 Co 80 (top) multilayers were deposited using the dc magnetron sputtering technique. The Si substrate was kept at room temperature during deposition. Ta buffer and capping layers were added to protect the structure against oxidation. Both Ta layers act as short circuits, which would reduce the amplitude of the spin-dependent transport effects. A magnetic field was applied during deposition to induce an easy axis in the FeNi layer. Data from a Si/Ta(14nm)/FeNi(10nm)/Cu(2nm)/GdCo(15nm)/Ta(8nm) multilayer are presented here.

Magnetic configuration

When the Cu spacing layer is 2 nm-thick, both magnetic electrodes are magnetically decoupled. The Fe 20 Ni 80 electrode has in-plane anisotropy with a 0.5 mT coercivity along its field-induced easy-axis. Its out-of-plane saturation field is 1.1 Tesla, which corresponds to its demagnetising field. The GdCo layer has a spontaneous perpendicular anisotropy at room temperature due to a depositioninduced anisotropy. Polar Kerr microscopy is only sensitive to the out-of-plane component of magnetisation. The capping layer is thin enough to observe the GdCo magnetic domain structure and the image reveals a 5 micron domain structure at 300 K and zero-field in the demagnetised state (Fig. 1).

A Hall effect loop measured at 300 K with the applied field perpendicular to the film (Fig. 2) evidences the spontaneous perpendicular magnetisation of GdCo and the 1.1 Tesla saturation field for FeNi. GdCo extraordinary Hall effect dominates the Hall loop. Ordinary Hall effect dominates the high-field region.

Low field magnetoresistance

When measuring the resistance of the multilayer as a function of a magnetic field applied in-plane, a large change of resistance occurs in a field larger than Figure 2: 300 K Hall effect loop measured with perpendicular applied magnetic field.

the FeNi saturation field (Fig. 3). This magnetoresistance reaches -3% at low temperature. The saturation field reaches a maximum around 210 K and the magnetoresistance changes its sign (Fig. 4). 210 K is also the temperature where the Hall effect loop changes its sign . It corresponds to the compensation temperature of the ferrimagnetic layer [10]. The GdCo saturation field diverges because of the decrease of magnetisation and thus Zeeman energy close to T comp .

At low temperature, an inverse giant magnetoresistance effect is observed as measured by Bellouard et al. using a Gd-rich GdFeCo electrode [3]. In this temperature range the cobalt magnetisation is antiparallel to the GdCo magnetisation. Since we chose a composition which allows for compensation, at high temperature (above T comp ), normal giant magnetoresistance is observed. One may conclude that GdCo has the same polarisation as FeNi (i.e. positive) at high temperature due to either positive spin polarisation from Co or negative spin polarisation from Gd. Below compensation, the spin polarisation reverses because both sublattices reverse compared to the net magnetisation of GdCo [8].

5 High field magnetoresistance Fig. 5 shows the high field magnetoresistance below the compensation temperature for 25 K, 100 K and 150 K. The resistance decreases above a critical field. The critical field decreases when heating up to T comp and increases again above T comp . Below T comp the resistance vs field is not monotonous in the first quadrant above the saturation field of FeNi (1 mT).

Close to the compensation temperature, in a ferrimagnet without anisotropy, the applied magnetic field can break the antiparallel alignment above the spin flop field and B sf = λ|M Co -M Gd |. λ is the molecular field coefficient which couples the Gd and Co sublattices. At the compensation temperature, B sf goes to zero linearly with |T-T com |. In our system, there is some perpendicular anisotropy so B sf 's minimum is of the order of 2 Tesla. This value is less than the experimentally available fields.

Discussion

The GMR effect is related to the spin dependent scattering in a trilayer system. When a current passes in the plane of the multilayer (current-in-plane (cip) geometry), spin dependent scattering takes place at the interfaces and in the bulk of the magnetic electrodes. The angle dependence of the GMR effect, defining the parallel R 0 • and antiparallel R 180 • resistances is then given by a cosine law.

R = R 0 • + R 0 • -R 180 • 2 (cos(M 1 , M 2 ) -1)
The amplitude of the effect is given by the spin polarisations of the electrodes and decays with the thickness of the spacer [4]. Both electrodes do not always have the same sign of spin polarisations and normal GMR (same polarisation signs, low resistance when saturated) and inverse GMR are known to exist. High field negative magnetoresistance can be measured in systems where high magnetic fields reduce the magnetic disorder (colossal magnetoresistance system).

Observing the spin flop field is a more direct proof that the antiparallel alignment is destroyed and that the measured MR is related to the magnetisation reorientation above B sf . The GdCo conduction band is mainly of Co-3d origin [11]. Gd-4f levels are far from it and do not participate in transport. The GdCo conduction band is similar to that of pure cobalt, which explains the dominant role of the cobalt sublattice.

Conclusions

Using a compensated GdCo ferrimagnetic electrode, we have synthesised a GMR trilayer. -3% GMR is measured at low temperature. GMR changes its sign at the compensation temperature. High field measurements revealed an unusual angle dependence of the effect. Below and above the spin flop field, the GMR depends on the angle between cobalt sublattice magnetisation and FeNi not anymore on the angles the magnetisations of both electrodes. These measurements show also that in this GdCo alloy cobalt dominates the GMR mechanism.

Figure 1 :

 1 Figure 1: Zero field polar Kerr image of GdCo domains evidencing the spontaneous perpendicular magnetisation of GdCo at 300 K

Figure 3 :

 3 Figure 3: Magnetoresistance as a function of temperature with applied field in plane

Figure 4 :Figure 6 :

 46 Figure 4: Amplitude of GMR as a function of temperature. The GMR is normalised to the resistance at 0.4 Tesla

Acknowledgments

This work was supported by ACI 'ECRIN' project from CNRS. N.T.N. acknowledges a grant from the Vietnamese government (project 322).