Interdisciplinarité et quiproquos : témoignage à plusieurs voix

Un programme de recherche sur les systèmes de production dans les Andes du Pérou a été l'occasion de prendre conscience de quiproquos qui se manifestent dans les termes et dans la façon de concevoir une recherche. Comme le souligne notre témoignage, ces quiproquos trouvent leur source dans des divisions entre sensibilités individuelles, entre écoles, entre disciplines, entre groupes de disciplines. Pour y mettre fin, quelques précautions doivent être respectées : l'interdisciplinarité ne s'impose pas ; elle se construit et se modifie. (Résumé d'auteur

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

Atmospheric and emissivity corrections for ground-based thermography using 3D radiative transfer modelling

Methods to retrieve urban surface temperature (Ts) from remote sensing observations with sub-building scale resolution are developed using the Discrete Anisotropic Radiative Transfer (DART, Gastellu-Etchegorry, Grau and Lauret, 2012) model. Corrections account for the emission and absorption of radiation by air between the surface and instrument (atmospheric correction), and for the reflected longwave infrared (LWIR) radiation from non-black-body surfaces (“emissivity” correction) within a single modelling framework. The atmospheric correction a) can use horizontally and vertically variable distributions of atmosphere properties at high resolution (< 5 m); b) is applied here with vertically extrapolated weather observations and MODTRAN atmosphere profiles; and c) is a solution to ray tracing and cross section (e.g. absorption) conflicts (e.g. cross section needs the path length but it is typically unavailable during ray tracing). The emissivity correction resolves the reflection of LWIR radiation as a series of scattering events at high spatial (< 1 m) and angular (ΔΩ ≈ 0.02 sr) resolution using a heterogeneous distribution of radiation leaving the urban surfaces. The method is applied to a novel network of seven ground-based cameras measuring LWIR radiation across a dense urban area (extent: 420 m x 420 m) where a detailed 3-dimensional representation of the surface and vegetation geometry is used. Our unique observation set allows the method to be tested over a range of realistic conditions as there are variations in: path lengths, view angles, brightness temperatures, atmospheric conditions and observed surface geometry. For pixels with 250 (± 10) m path length the median (5th and 95th percentile) atmospheric correction magnitude is up to 4.5 (3.1 and 8.1) K at 10:10 on a mainly clear-sky day. The detailed surface geometry resolves camera pixel path lengths accurately, even with complex features such as sloped roofs. The atmospheric correction method evaluation, with simultaneous “near” (~15 m) and “far” (~155 m) observations, has a mean absolute error of 0.39 K. Using broadband approximations, the emissivity correction has clear diurnal variability, particularly when a cool and shaded surface (e.g. north facing) is irradiated by warmer (up to 17.0 K) surfaces (e.g. south facing). Varying the material emissivity with bulk values common for dark building materials (ε = 0.89 → 0.97) alters the corrected roof (south facing) surface temperatures by ~3 (1.5) K, and the corrected cooler north facing surfaces by less than 0.1 K. Corrected observations, assuming a homogeneous radiation distribution from surfaces (analogous to a sky view factor correction), differ from a heterogeneous distribution by up to 0.25 K. Our proposed correction provides more accurate Ts observations with improved uncertainty estimates. Potential applications include ground-truthing airborne or space-borne surface temperatures and evaluation of urban energy balance models

Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes

International audienceSatellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It has been developed since 1992. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification. It is freely distributed for research and teaching activities. This paper presents DART physical bases and its latest functionality for simulating imaging spectroscopy of natural and urban landscapes with atmosphere, including the perspective projection of airborne acquisitions and LIght Detection And Ranging (LIDAR) waveform and photon counting signals

14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

Chemistry and materials science are complex. Recently, there have been great successes in addressing this complexity using data-driven or computational techniques. Yet, the necessity of input structured in very specific forms and the fact that there is an ever-growing number of tools creates usability and accessibility challenges. Coupled with the reality that much data in these disciplines is unstructured, the effectiveness of these tools is limited. Motivated by recent works that indicated that large language models (LLMs) might help address some of these issues, we organized a hackathon event on the applications of LLMs in chemistry, materials science, and beyond. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines

DART radiative transfer modelling for sloping landscapes

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Suivi des mangroves par télédétection optique à très haute résolution spatiale

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