FRENCH RESEARCH ON MANGROVES. DIRECTORY OF RESEARCHERS, ENGINEERS, POST-DOCTORAL AND DOCTORAL STUDENTS: DIRECTORY OF RESEARCHERS, ENGINEERS, POST-DOCTORAL AND DOCTORAL STUDENTS

2015 is declared as the Mangrove Year by the CNRS-INEE (www.cnrs.fr) and the IRD (www.ird.fr), two leading French research institutes on natural ecosystems. As a contribution to this initiative, we prepared the directory of persons conducting scientific works on mangroves from French research laboratories, national or local organizations and private agencies. We collated the countries of study and the fields of expertise developed by each person. PhD and post-doctoral students are also included in this database since we consider them as the people of the future. It is our strong belief that sharing the basic data of these specialists constitutes pivotal information for strengthening national and international scientific works dedicated to knowledge acquisition on mangroves ecosystems and coasts. It is the only way we have to claim the necessity of mangrove preservation worldwide. So, we hope that this directory could be linked to others built in other mangrove countries

Mud bank colonization by opportunistic mangroves: A case study from French Guiana using lidar data

Mud bank colonization by mangroves on the Amazon-influenced coast of French Guiana was studied using light detection and ranging (lidar) data which provide unique information on canopy geometry an sub-canopy topography. The role of topography was assessed through analysis of vegetation characteristics derived from these data. Measurements and analyses of mangrove expansion rates over space and time led to the identification of two distinct colonization processes. The first involves regular step-by-step mangrove expansion to the northwest of the experimental site. The second is qualified as ‘opportunistic’ since it involves a clear relationship between specific ecological characteristics of pioneer Avicennia and mud cracks affecting the mud bank surface and for which probabilities of occurrence were computed from terrain elevations. It is argued from an original analysis of the latter relationship that mud cracks cannot be solely viewed as water stress features that reflect desiccation potentially harmful to plant growth. Indeed, our results tend to demonstrate that they significantly enhance the propensity for mangroves to anchor and take root, thus leading to the colonization of tens of hectares in a few days. The limits and potential of lidar data are discussed with reference to the study of muddy coasts. Finally, the findings of the study are reconsidered within the context of a better understanding of both topography and vegetation characteristics on mangrove-fringed muddy coasts

Biomass prediction in tropical forests : the canopy grain approach

18 pagesThe challenging task of biomass prediction in dense and heterogeneous tropical forest requires a multi-parameter and multi-scale characterization of forest canopies. Completely different forest structures may indeed present similar above ground biomass (AGB) values. This is probably one of the reasons explaining why tropical AGB still resists accurate mapping through remote sensing techniques. There is a clear need to combine optical and radar remote sensing to benefit from their complementary responses to forest characteristics. Radar and Lidar signals are rightly considered to provide adequate measurements of forest structure because of their capability of penetrating and interacting with all the vegetation strata. However, signal saturation at the lowest radar frequencies is observed at the midlevel of biomass range in tropical forests (Mougin et al. 1999; Imhoff, 1995). Polarimetric Interferometric (PolInsar) data could improve the inversion algorithm by injecting forest interferometric height into the inversion of P-band HV polarization signal. Within this framework, the TROPISAR mission, supported by the Centre National d'Etudes Spatiales (CNES) for the preparation of the European Space Agency (ESA) BIOMASS program is illustrative of both the importance of interdisciplinary research associating forest ecologists and physicists and the importance of combined measurements of forest properties. Lidar data is a useful technique to characterize the vertical profile of the vegetation cover (e.g. Zhao et al. 2009) which in combination with radar (Englhart et al. 2011) or optical (e.g. Baccini et al. 2008; Asner et al. 2011) and field plot data may allow vegetation carbon stocks to be mapped over large areas of tropical forest at different resolution scales ranging from 1 hectare to 1 km². However, small-footprint Lidar data are not yet accessible over sufficient extents and with sufficient revisiting time because its operational use for tropical studies remains expensive. At the opposite, very-high (VHR) resolution imagery, i.e. approximately 1-m resolution, provided by recent satellite like Geoeye, Ikonos, Orbview or Quickbird as well as the forthcoming Pleiades becomes widely available at affordable costs, or even for free in certain regions of the world through Google Earth®. Compared to coarser resolution imagery with pixel size greater than 4 meters, VHR imagery greatly improves thematic information on forest canopies. Indeed, the contrast between sunlit and shadowed trees crowns as visible on such images (Fig. 1) is potentially informative on the structure of the forest canopy while new promising methods now exist for analyzing these fine scale satellite observations (e.g. Bruniquel-Pinel & Gastellu-Etchegorry, 1998; Malhi & Roman-Cuesta, 2008; Rich et al. 2010). Besides, we believe that there is also a great potential in similarly using historical series of digitized aerial photographs that proved to be useful in the past for mapping large extents of unexplored forest (Le Touzey, 1968; Richards, 1996) for quantifying AGB changes through time. This book chapter presents the advancement of a research program undertaken by our team for estimating high biomass mangrove and terra firme forests of Amazonia using canopy grain from VHR images (Couteron et al. 2005; Proisy et al. 2007; Barbier et al., 2010; 2011). We present in a first section, the canopy grain notion and the fundamentals of the Fourier-based Textural Ordination (FOTO) method we developed. We then introduce a dual experimental-theoretical approach implemented to understand how canopy structure modifies the reflectance signal and produces a given texture. We discuss, for example, the influence of varying sun-view acquisition conditions on canopy grain characteristics. A second section assesses the potential and limits of the canopy grain approach to predict forest stand structure and more specifically above ground biomass. Perspectives for a better understanding of canopy grain-AGB relationships conclude this work

Tree crown detection in high resolution optical and LiDAR images of tropical forest

International audienceTropical forests are complex ecosystems where the potential of remote sensing has not yet been fully realized. The increasing availability of satellite metric imagery along with canopy altimetry from airborne LiDAR open new prospects to detect individual trees. For this objective, we optimized, calibrated and applied a model based on marked point processes to detect trees in high biomass mangroves of French Guiana by considering a set of 1m pixel images including 1) panchromatic images from the IKONOS sensor 2) LiDAR-derived canopy 2D altimetry and 3) reflectance panchromatic images simulated by the DART-model. The relevance of detection is then discussed considering: (i) the agreement in space of detected crown centers locations with known true locations for the DART images and also the detection agreement for each pair of IKONOS and LiDAR images, and (ii) the comparison between the frequency distributions of the diameters of the detected crowns and of the tree trunks measured in the field. Both distributions are expected to be related due to the allometry relationships between trunk and crown

Mapping local density of young Eucalyptus plantations by individual tree detection in high spatial resolution satellite images

International audienceLocal tree density may vary in young Eucalyptus plantations under the effects of environmental conditions or inadequate management, and these variations need to be mapped over large areas as they have a significant impact on the final biomass harvested. High spatial resolution optical satellite images have the potential to provide crucial information on tree density at an affordable cost for forest management. Here, we test the capacity of this promising technique to map the local density of young and small Eucalyptus trees in a large plantation in Brazil. We use three Worldview panchromatic images acquired at a 50 cm resolution on different dates corresponding to trees aged 6, 9 and 13 months and define an overall accuracy index to evaluate the quality of the detection results. The best agreement between the local densities obtained by visual detection and by marked point process modeling was found at 9 months, with only small omission and commission errors and a stable 4% underestimation of the number of trees across the density gradient. We validated the capability of the MPP approach to detect trees aged 9 months by making a comparison with local densities recorded on 112 plots of ~590 m² and ranging between 1360 and 1700 trees per hectare. We obtained a good correlation (r²=0.88) with a root mean square error of 31 trees/ha. We generalized detection by computing a consistent map over the whole plantation. Our results showed that local tree density was not uniformly distributed even in a well-controlled intensively managed Eucalyptus plantation and therefore needed to be monitored and mapped. Use of the marked point process approach is then discussed with respect to stand characteristics (canopy closure), acquisition dates and recommendations for algorithm parameterization

Extended biomass allometric equations for large mangrove trees from terrestrial LiDAR data

International audienceAccurately determining biomass of large trees is crucial for reliable biomass analyses in most tropical forests, but most allometric models calibration are deficient in large trees data. This issue is a major concern for high-biomass mangrove forests, especially when their role in the ecosystem carbon storage is considered. As an alternative to the fastidious cutting and weighing measurement approach, we explored a non-destructive terrestrial laser scanning approach to estimate the aboveground biomass of large mangroves (diameters reaching up to 125 cm). Because of buttresses in large trees, we propose a pixel-based analysis of the composite 2D flattened images, obtained from the successive thin segments of stem point-cloud data to estimate wood volume. Branches were considered as successive best-fitted primitive of conical frustums. The product of wood volume and height-decreasing wood density yielded biomass estimates. This approach was tested on 36 A. germinans trees in French Guiana, considering available biomass models from the same region as references. Our biomass estimates reached ca. 90% accuracy and a correlation of 0.99 with reference biomass values. Based on the results, new tree biomass model, which had R² of 0.99 and RSE of 87.6 kg of dry matter. This terrestrial LiDAR-based approach allows the estimates of large tree biomass to be tractable, and opens new opportunities to improve biomass estimates of tall mangroves. The method could also be tested and applied to other tree species

Very high resolution satellite images for parameterization of tree-scale forest process-based model

International audienceVery high spatial resolution (VHSR) satellite images provide interesting information for parameterizing tree-scale forest process-based models, and in particular their light absorption submodels, which is at the basis of photosynthesis calculation. Such tree-scale models require a large amount of field measurements to describe the forest ecosystems, i.e. all tree positions, their sizes and shapes, their leaf areas, etc. These data are generally measured directly in the field, which can be tedious for large areas like a forest stand. In this study, we explore the possibility to parameterize such tree-scale models directly or indirectly from panchromatic and multispectral very high resolution images

Tree crown detection in high resolution optical images during the early growth stages of eucalyptus plantations in Brazil

International audienceIndividual tree detection methods are more and more present, and improve, in forestry and silviculture domains with the increasing availability of satellite metric imagery. Automatic detection on these very high spatial resolution images aims to determine the tree positions and crown sizes. In this paper, we used a mathematical model based on marked point processes, which showed advantages w.r.t. several individual tree detection algorithms for plantations, to analyze the eucalyptus plantations in Brazil, with 2 optical images acquired by the WorldView-2 satellite. A tentative detection simultaneously with 2 images of different dates (multi-date) was tested for the first time, which estimates individual tree crown variation during these dates. The relevance of detection was discussed considering the detection performance in tree localizations and crown sizes. Then, tree crown growth was deduced from detection results and compared with the expected dynamics of corresponding populations