5. CONCLUSION
This work was a first attempt at describing and simu- lating MAR spatial variations within a **tree** **canopy**. Despite the relatively correct MAR prediction, the model of Sinoquet and Bonhomme (1992) was unable to simu- late data close to the measured values, especially in the far red. This is probably due to: i) the small amount of locally measured spectra within the **canopy** which could have been used for the comparison; ii) the simplistic treatment of scattering in the model; iii) the high sensi- tivity of input parameters such as the diffuse to incident radiation ratio; iv) the severity of the test, since the model was used to simulate spectral and local fluxes in either shaded or sunlit zones. From our knowledge no model (either based on the turbid medium analogy or ray-tracing techniques in virtual plants) has been tested against filed data at such a small scale. This however suggests that the assessment of MAR distribution in canopies would need more accurate calculation of radia- tion scattering, a precise model of the spectral of the diffuse proportion of diffuse, and probably a fine description of **canopy** structure. Such assumptions will be tested by comparing the model of Sinoquet and Bonhomme (1992) with a 3D model where scattering is better considered (e.g. [7]), and with models based on ray-tracing in virtual plants. In particular, it could be necessary to include leaf and soil bidirectional optical properties instead of assuming them as lambertian diffusers.

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Forest temperatures are often estimated from open ﬁeld weather stations, sometimes located far from study sites, even though **tree** canopies modulate their own microclimate, and sys- tematic differences between temperatures measured in forests and at open ﬁeld weather stations have been identiﬁed ( Kollas et al., 2014 ). As Körner (2016) emphasised, plants experience temper- atures that are rarely reﬂected by average data and “to advance vegetation ecologists need to collect bioclimatic data, rather than rely on weather station data”. Most ecological or physiological stud- ies have used climate data at a resolution of several kilometres or more, whereas organisms experience microclimate at a ﬁner scale, from millimetres to metres ( Suggitt et al., 2011 ). While the general buffering effect of the **tree** **canopy** is known, i.e. a decrease in max- imum temperatures and an increase in minimum temperatures, further investigation is needed into daily variations in temperature, and their relations to solar radiation below the **canopy**.

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in these two areas, and also explain my design and implementation of computer vi- sion and deep learning solutions to quantify urban canopy cover and to measure st[r]

5 HORTSYS - Fonctionnement agroécologique et performances des systèmes de cultures
horticoles Abstract :
**Research focus**: Like many other tropical trees, mango **tree** is characterized by strong phenological asynchronisms between and within trees, entailing patchiness. Patchiness is characterized by clumps of either vegetative or reproductive growth units (GUs) within the **canopy**: while some parts of the **tree** **canopy** develop vegetative GUs, others may remain in rest or produce inflorescences at the same time. These asynchronisms concern more or less large branching systems. The objective here is to define statistical methodology to identify and characterize patchiness patterns.

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Keywords: growth asynchronism; growth unit; Mangifera indica; patchiness; spatio-temporal data analysis. INTRODUCTION
As other tropical trees, mango **tree** is characterised by strong phenological asynchronisms between and within trees entailing patchiness (Chacko, 1986). Patchiness is characterized by clumps of either vegetative or reproductive development within the **canopy**: while some portions of the **tree** **canopy** develop vegetative GUs (i.e. portions of leafy axes developed during an uninterrupted period of growth), other portions may remain in rest or produce inflorescences at the same time. These asynchronisms often correspond to more or less large branching systems, e.g. scaffolds (Ramírez and Davenport, 2010). They entail various agronomical problems, such as the repeated use of pesticides to protect recurrent susceptible phenological stages from pests or a too extended period of fruit maturity, which may lead to difficulties to organize fruit harvesting.

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Since the late 90 ′s, airborne LiDAR point clouds (or Airborne Laser Scanning, ALS) have become the state-of-art remote sensing technique to characterize the 3D structure of forest ( Michez et al., 2016 ). ALS forest characterization approaches have been in the focus of research for two decades and are now an important component of operational large-scale FI ( Næsset, 2014 ). As ALS surveys remain expensive, there is a need for alternative technology like Digital Aerial Photogrammetry (DAP). Aerial photography is the traditional source of information for forest characterization which has been completed by satellite imagery since the 80′s and by 3D point clouds since the late 90′s. The devel- opment of DAP renewed the interest for the use of aerial imagery in forest monitoring which has tended to fade in the late 1990s with the advent of ALS. In the context of forest characterization, when a quality Digital Terrain Model (DTM) is available, DAP can produce photo- grammetric **Canopy** Height Model (pCHM) which describes the **tree** **canopy** height. Leberl et al. (2010) identiﬁed 4 main innovations which eased the implementation of DAP: cost-free increase of overlap between digitally sensed images, an improved radiometry, the development of multi-view matching algorithm and the ability to run the process on Graphics Processing Unit (GPU). These innovations have made DAP work ﬂows very practical and automated, potentially reaching sub-pixel 3D total accuracy. DAP is a cost-eﬀective alternative to ALS, reducing the cost of the survey to one half to one third ( Leberl et al., 2010 ; White et al., 2013 ) while it presents similarities with ALS in terms of data structure (i.e. point clouds). Nevertheless, the most important diﬀer- ence from ALS is that DAP is limited to characterizing the outer **canopy** envelope while ALS provides precious information about the sub ca- nopy layers. DAP can be processed from aerial photos routinely ac- quired for general base maps updates as highlighted by Ginzler and Hobi (2015) . As such systematic surveys (ALS and aerial photos) are more and more carried out in many European and North-American countries, DAP could be used to produce 3D data on a national scale at little or no additional cost.

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Sub-**tree** Pair Selection for Reconfiguration of a Light-**tree** Pair
Amanvon Ferdinand Atta, Bernard Cousin, Joel Christian Adepo, Souleymane Oumtanaga
Abstract
Reconfiguration of unicast or multicast connections in an optical network is a critical task. Indeed, if it is not carried out correctly, it can lead to optical flow (also called flow) interruptions that can cause damage to the network operator. It is therefore common to perform the reconfigu- ration in several steps. In this study, we focused on multicast connection reconfiguration because multicast connection become more attractive and efficient technique to transmit flow of multicast applications. A Multicast connection in an optical network can be represented by a point-to- multipoint all-optical path called light-**tree**. In short, we explain how to select a pair of sub-trees (current sub-**tree**, new sub-**tree**) to be reconfigured at a given step of the reconfiguration process. Keywords— Light-**tree**; Sub-**tree** Pair Selection; Flow Migration; Routing Reconfiguration; Optical Network

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Trees were grafted onto M9 rootstock and planted in March 2007 at INRA - Montpellier experimental field (south-east France, 43°36’N 3°58’W). Forty trees per treatment were planted at 5x2 m apart, in a complete randomized design. One main shoot per **tree** was left to develop after grafting. Two pruning treatments were applied in August 2007, i.e. during the first year of growth: heading cuts of the trunk (HCT; which reduce the length of the trunk by ca. 50% of their internodes at the time of pruning) and thinning cuts of the laterals (TCL; which completely remove all current lateral branches; terminology according to Barritt, 1992) (Fig.1). Control trees (C) were left without any pruning.

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Nature Forest Environment, Rue du Moulin, 7c, 6929 Porcheresse, Belgium
11 Natural Sciences Teaching and Research Unit, Nangui Abrogoua University, 01 BP 4403, Abidjan, Ivory Coast
ABSTRACT
Size at reproduction is a key aspect of species life history that is relatively understudied for long-lived tropical trees. Here, we quantiﬁed reproductive diameter for 31 major timber species across 11 sites in Cameroon, Congo, and Central African Republic. Speciﬁcally, we examined whether (1) between-species variability is correlated with other species traits; (2) reproductive diameter varies within-species among sites; (3) reproductive status varies with crown exposure; and (4) the minimum cutting diameter limits (MCDL) imposed by national forest regulations enable seed trees to persist after logging operations. Consistent with studies conducted elsewhere in the trop- ics, we found great variability in diameter at reproduction among species, which correlated with adult stature (maximum diameter and height). For some species, reproductive diameter thresholds substantially varied between sites, and crown exposure had a signiﬁcant pos- itive effect on reproductive status. Most MCDLs were found to be suitable, with trees having a high probability of being seed trees at MCDL. Our ﬁndings have implications for the sustainable management of production forests, and they highlight questionable MCDLs for some species and between-site variation in reproductive diameter. The study also highlights the need for long-term phenological monitoring of **tree** species spanning a large range of ecological strategies to address both theoretical (species life history, allocation trade- offs) and practical questions (MCDL).

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II. MODEL OF THE **CANOPY** FLOW A. The mean flow
To obtain the mean flow on top of which small amplitude perturbations are superimposed, the procedure outlined by Ghisalberti and Nepf 15 and recently closely followed by Zampogna et al. 5 is used. For the sake of conciseness, the procedure—which relies on several empirical correlations—is not repeated here, aside from a few brief comments. A mildly inclined water channel is consid- ered, with a **canopy** formed by rigid cylindrical dowels of height h equal to 13.8 cm and diameter d = 0.64 cm. The frontal area of the vegetation per unit volume, i.e., the packing density of the elements, is either a = 0.04 cm −1 or 0.08 cm −1 ; the free surface is positioned at a level H = 46.7 cm from the bottom plate and the flow velocity at the free surface, U 2 , varies from 4.4 to 13.7 cm /s. The

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Naesset E. [2002]. Determination of Mean **Tree** Height of Forest Stands by Digital Photogrammetry. Scandinavian Journal of Forest Research 17 (5), 446–459.
Pierrot-Deseilligny M., Clery I. [2011]. APERO, An Open Source Bundle Adjustment Software for Automatic Calibration and Orientation of Set of Images. IAPRS&SIS 38 (5/W16), 113–124.
V´ ega C., St-Onge B. [2008]. Height growth reconstruction of a boreal forest **canopy** over a period of 58 years using a combination of photogrammetric and lidar models. Remote Sensing of

Université de Toulouse, Institut de Mécanique des Fluides de Toulouse, UMR 5502, Allée du Pr. Camille Soula, F31400 Toulouse, France
( Received 28 July 2016; accepted 22 November 2016; published online 16 December 2016)
Two models of the flow over and through an immersed, vegetated layer are examined to study the onset of instability waves across the layer and to assess the e ffect of mild variations in the mean flow and in the drag force exerted by the **canopy** onto the frequency and growth rate of the monami instability. One of the two models, based on the use of Darcy’s equation, with a tensorial permeability, within the **canopy** is more robust than the other (which uses a scalar drag coe fficient), i.e., it is less sensitive to the inevitable imperfections or approximations in the input data. Published by AIP Publishing. [ http: //dx.doi.org/10.1063/1.4971789 ]

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• **Canopy** aerodynamic distance and correlation coefficients :
– Momentum correlation coefficient (r uw ) is strongly linked to z-d. Characteristic of the roughness sublayer.
– Heat and CO 2 correlation coefficients (r uw , r wc , r wT ) independent of z-d. More homogeneous sources-sinks distribution.

A BSTRACT
Remote sensing has facilitated the techniques used for the mapping, modeling and understanding of forest parameters. Remote sensing applications usually use information from either passive optical systems or active radar sensors. These systems have shown satisfactory results for estimating, for example, aboveground biomass in some biomes. However, they presented significant limitations for ecological applications, as the sensitivity from these sensors has been shown to be limited in forests with medium levels of aboveground biomass. On the other hand, LiDAR remote sensing has been shown to be a good technique for the estimation of forest parameters such as **canopy** heights and above ground biomass. Whilst airborne LiDAR data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne LiDAR data acquired from the Geoscience Laser Altimeter System (GLAS) have low acquisition density with global geographical cover. It is therefore valuable to analyze the integration relevance of **canopy** heights estimated from LiDAR sensors with ancillary data (geological, meteorological, slope, vegetation indices etc.) in order to propose a forest **canopy** height map with good precision and high spatial resolution. In addition, estimating forest **canopy** heights from large-footprint satellite LiDAR waveforms, is challenging given the complex interaction between LiDAR waveforms, terrain, and vegetation, especially in dense tropical and equatorial forests. Therefore, the research carried out in this thesis aimed at: 1) estimate, and validate **canopy** heights using raw data from airborne LiDAR and then evaluate the potential of spaceborne LiDAR GLAS data at estimating forest **canopy** heights. 2) evaluate the fusion potential of LiDAR (using either sapceborne and airborne data) and ancillary data for forest **canopy** height estimation at very large scales. This research work was carried out over the French Guiana.

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Furthermore, an empirically derived power consumption model was found through a series of flight tests in the MIT wind tunnel and was also used to inform the minimum[r]

113 En savoir plus

FIC, Universidad Adolfo Ib´a˜nez, Santiago, Chile 5
WMS, AGH, University of Science and Technology, Krakow, Poland
Abstract. **Tree**-Decompositions are the corner-stone of many dynamic programming algorithms for solving graph problems. Since the complexity of such algorithms generally depends exponentially on the width (size of the bags) of the decomposition, much work has been devoted to compute **tree**-decompositions with small width. However, practical algorithms computing **tree**-decompositions only exist for graphs with treewidth less than 4. In such graphs, the time-complexity of dynamic programming algorithms based on **tree**-decompositions is dominated by the size (number of bags) of the **tree**-decompositions. It is then interesting to minimize the size of the **tree**-decompositions. In this report, we consider the problem of computing a **tree**-decomposition of a graph with width at most k and minimum size. More precisely, we focus on the following problem: given a fixed k ≥ 1, what is the complexity of computing a **tree**-decomposition of width at most k with minimum size in the class of graphs with treewidth at most k? We prove that the problem is NP-complete for any fixed k ≥ 4 and polynomial for k ≤ 2; for k = 3, we show that it is polynomial in the class of trees and 2-connected outerplanar graphs.

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Lightning scar IN33
Fire scars at the lower trunk usually have a triangular shape and are located at the base of the **tree** on the leeward trunk side. Fire scars are associated with charred wood and eventually resin flow on exposed sapwood or bark.

all possible **tree**-decompositions of G. If T is constrained to be a path, (T, X ) is called a path-decomposition of G. The pathwidth of G, denoted by pw(G), is the minimum width over all possible path-decompositions of G.
**Tree**-decompositions are the corner-stone of many dynamic programming algo- rithms for solving graph problems. For example, the famous Courcelle’s Theorem states that any problem expressible in MSOL can be solved in linear-time in the class of bounded treewidth graphs [7]. Another framework based on graph decompositions is the bi-dimensionality theory that allowed the design of sub-exponential-time algo- rithms for many problems in the class of graphs excluding some fixed graph as a minor (e.g., [8]). Given a **tree**-decomposition with width w and size n, the time-complexity of most of such dynamic programming algorithms can often be expressed as O(2 w n)

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Two of the important sources of homogenization arising in present approaches to eq. 1 involve the terms λB (the source-strength term) and f(x) (the dispersal term). There are a number of problems with the source-strength term that we will take up in a separate paper (e.g., the assumption that light receipt is unimportant, and thus, a small **tree** at the edge of a gap will produce fewer seeds than a large sub- **canopy** stem within an intact stand). Here, we merely point out that while basal area may well be our single best predic- tor of seed production for a species (cf. Greene and Johnson 1994); nonetheless, the proportion of variation explained is typically only on the order of 0.5 (e.g., Greene and Johnson 1998; Greene et al. 2002). Our first objective is to show that accounting for this unexplained variation in seed production will indeed increase the expected CV of predicted seed or seedling density in a nontrivial manner and, further, will lead to a higher correlation for observed versus predicted re- cruit density.

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