Éditeur(s) : HAL CCSD Public Library of Science
Résumé : We examined tree-soil habitat associations in lowland forest communities at Paracou, French Guiana. We analyzed a large dataset assembling six permanent plots totaling 37.5 ha, in which extensive LIDAR-derived topographical data and soil chemical and physical data have been integrated with precise botanical determinations. Map of relative elevation from the nearest stream summarized both soil fertility and hydromorphic characteristics, with seasonally inundated bottomlands having higher soil phosphate content and base saturation, and plateaus having higher soil carbon, nitrogen and aluminum contents. We employed a statistical test of correlations between tree species density and environmental maps, by generating Monte Carlo simulations of random raster images that preserve autocorrelation of the original maps. Nearly three fourths of the 94 taxa with at least one stem per ha showed a significant correlation between tree density and relative elevation, revealing contrasted species-habitat associations in term of abundance, with seasonally inundated bottomlands (24.5% of species) and well-drained plateaus (48.9% of species). We also observed species preferences for environments with or without steep slopes (13.8% and 10.6%, respectively). We observed that closely-related species were frequently associated with different soil habitats in this region (70% of the 14 genera with congeneric species that have a significant association test) suggesting species-habitat associations have arisen multiple times in this tree community. We also tested if species with similar habitat preferences shared functional strategies. We found that seasonally inundated forest specialists tended to have smaller stature (maximum diameter) than species found on plateaus. Our results underline the importance of tree-soil habitat associations in structuring diverse communities at fine spatial scales and suggest that additional studies are needed to disentangle community assembly mechanisms related to dispersal limitation, biotic interactions and environmental filtering from species-habitat associations. Moreover, they provide a framework to generalize across tropical forest sites.
ISSN: 1932-6203

hal-01260289
https://hal.archives-ouvertes.fr/hal-01260289
PRODINRA : 342930
DOI : 10.1371/journal.pone.0141488

Éditeur(s) : HAL CCSD MDPI
Résumé : International audience
LiDAR (Light Detection And Ranging) 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 a coarser acquisition density associated with a global cover. It is therefore valuable to analyze the integration relevance of canopy heights estimated from LiDAR sensors with ancillary data such as geological, meteorological, and phenological variables in order to propose a forest canopy height map with good precision and high spatial resolution.In this study, canopy heights extracted from both airborne and spaceborne LiDAR, were first extrapolated from available environmental data (e.g. geology, slope, vegetation indices, etc.). The estimated canopy height maps using random forest (RF) regression from the airborne or GLAS calibration datasets showed similar precisions (RMSE better than 6.5 m). In order to improve the precision of the canopy height estimates regression-kriging (kriging of random forest regression residuals) was used. Results indicated an improvement in the RMSE (decrease from 6.5 to 4.2 m) for the regression-kriging maps from the GLAS dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on the precision of canopy height estimates, six subsets were derived from the initial airborne LiDAR dataset with flight line spacing of 5, 10, 20, 30, 40 and 50 km (corresponding to 0.29, 0.11, 0.08, 0.05, 0.04, and 0.03 points/km² respectively). Results indicated that using the regression-kriging approach achieved a precision of 1.8 m on the canopy height map with flight line spacing of 5 km and achieved an average RMSE of 4.8m for the configuration for the 50 km flight line spacing.
ISSN: 2072-4292

hal-01361343
https://hal.archives-ouvertes.fr/hal-01361343
https://hal.archives-ouvertes.fr/hal-01361343/document
https://hal.archives-ouvertes.fr/hal-01361343/file/mt2016-pub00048036.pdf
DOI : 10.3390/rs8030240

Éditeur(s) : HAL CCSD Wiley
Résumé : International audience
The influence of conservation policies on environmental governance by indigenous peoples is not well understood. Our goal is to understand the role of conservation policies in the on-going social, spatial and temporal reorganisation of common natural resources management by the Wayãpi and Teko in French Guiana. Using socio-economic and land use variables, we explain their territorial reorganisation. We show how the delimitation of protected areas encourages identity claims and resources re-appropriation, which play a determining role in territorial reorganisation.Finally, we focus on the emergence of a new form of environmental governance based on “multi-sited” land use system.
ISSN: 0261-3050

Droits : info:eu-repo/semantics/OpenAccess
hal-01135244
https://hal.archives-ouvertes.fr/hal-01135244
https://hal.archives-ouvertes.fr/hal-01135244/document
https://hal.archives-ouvertes.fr/hal-01135244/file/Tritsch_al_Toward_revival_indigenous_mobility_Bull.Lat.Am.Research_2015.pdf
DOI : 10.1111/blar.12204

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (>150 Mg/ha, and >300 Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variations in AGB by combining optical and environmental data at regional and global scales. Nevertheless, these maps cannot represent local variations in AGB in tropical forests. In this paper, we hypothesize that the problem of misrepresenting local variations in AGB and AGB estimation with good precision occurs because of both methodological limits (signal saturation or dilution bias) and a lack of adequate calibration data in this range of AGB values. We test this hypothesis by developing a calibrated regression model to predict variations in high AGB values (mean >300 Mg/ha) in French Guiana by a methodological approach for spatial extrapolation with data from the optical geoscience laser altimeter system (GLAS), forest inventories, radar, optics, and environmental variables for spatial inter-and extrapolation. Given their higher point count, GLAS data allow a wider coverage of AGB values. We find that the metrics from GLAS footprints are correlated with field AGB estimations (R 2 =0.54, RMSE=48.3 Mg/ha) with no bias for high values. First, predictive models, including remote-sensing, environmental variables and spatial correlation functions, allow us to obtain "wall-to-wall" AGB maps over French Guiana with an RMSE for the in situ AGB estimates of ~51 Mg/ha and R²=0.48 at a 1-km grid size. We conclude that a calibrated regression model based on GLAS with dependent environmental data can produce good AGB predictions even for high AGB values if the calibration data fit the AGB range. We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics. Thus, we advocate including a greater variety of data, even if less precise and shifted, to better represent high AGB values in global models and to improve the fitting of these models for high values.
ISSN: 0303-2434

Droits : http://creativecommons.org/licenses/by/
hal-01381116
https://hal.archives-ouvertes.fr/hal-01381116
https://hal.archives-ouvertes.fr/hal-01381116/document
https://hal.archives-ouvertes.fr/hal-01381116/file/Fayad2016a.pdf
DOI : 10.1016/j.jag.2016.07.015

Éditeur(s) : HAL CCSD Montpellier : CIRAD
Résumé : Les aménagements futurs nécessaires au développement rapide de la Guyane vont entraîner la conversion de terres forestières, participant ainsi au changement global. Les décideurs guyanais devront conjuguer ces aménagements avec la préservation des services écosystémiques forestiers. Le projet GuyaSim avait comme objectif l’approfondissement des connaissances sur ces services (stock de carbone, biodiversité et qualité du sol)et le transfert d’un logiciel aux décideurs pour faciliter l’intégration de ces connaissances dans les politiques d’aménagement du territoire. L’article présente les caractéristiques et les fonctionnalités de ce logiciel GuyaSim. Il s’agit d’un logiciel libre de type Sig, destiné a priori aux services d’aménagement des collectivités et du domaine forestier de Guyane. Le logiciel offre deux grands types de fonctionnalité : la mise à disposition d’informationset l’aide à l’aménagement. Les informations mises à disposition sont les scénarios de développement socio-économique, les scénarios climatiques et les valeurs des services écosystémiques. L’aide à l’aménagement consiste en des outils de construction des scénarios d’aménagement du territoire (changement d’usage des terres) et d’aménagement forestier (exploitation forestière), fournissant des informations sur l’impact environnemental. Les fonctionnalités du logiciel sont limitées par les connaissances sur les écosystèmes guyanais. Les avancées des projets de recherche en cours permettront de mettre à jour le logiciel à moyen terme
ISSN: 0006-579X

hal-01535354
https://hal.archives-ouvertes.fr/hal-01535354
PRODINRA : 351684

Éditeur(s) : HAL CCSD Elsevier Masson
Résumé : 14 pages
International audience
Central Africa hosts the world's second largest tropical forest after the Amazonian basin. However, as compared to its Amazonian counterpart, the Central Africa forests receive much less rain (∼1500 mm/year in mean spread over two rainy seasons in March-May and September-November, and two dry seasons). They also experience a slower deforestation rate, so that the main threat for the next decades might come from climate variations. Nonetheless, their response to the annual cycle of solar radiation and rainfall/clouds is still poorly known. Analysing high resolution remote sensing data of Enhanced Vegetation Index, rainfall, cloudiness, and solar radiation for a target region located between 0 and 5°N and 12–19°E, we explore the climatic drivers of the forests greenness mean annual cycle. Three main points emerge; first, the diurnal cycle is a key-scale for understanding the mean annual cycles of rainfall and incoming solar radiation at surface, then how climate shapes the greenness mean annual evolution; second, neither the two dry seasons nor the two rainy seasons resemble each other in terms of cloud cover, solar radiation and rainfall, and their links with greenness levels; third, whereas the first rainy season (March-May) appears optimal for greenness especially because of favorable light conditions, water availability is the main controlling factor during the main dry season and at the start of the first vegetative season (February). Regarding the little dry season (mid-June-mid-August) and the second rainy season (September-October), light availability might be the main limiting factor. These findings pave the way for further studies of the climate interannual variability and change impacts on the Central Africa forests, taking into account time-scale interactions.
ISSN: 0168-1923

hal-01309565
https://hal.archives-ouvertes.fr/hal-01309565
DOI : 10.1016/j.agrformet.2016.04.005