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Dray S.,University of Lyon | Dray S.,University Claude Bernard Lyon 1 | Pelissier R.,Montpellier University | Pelissier R.,Institute Francais Of Pondichery | And 18 more authors.
Ecological Monographs | Year: 2012

Species spatial distributions are the result of population demography, behavioral traits, and species interactions in spatially heterogeneous environmental conditions. Hence the composition of species assemblages is an integrative response variable, and its variability can be explained by the complex interplay among several structuring factors. The thorough analysis of spatial variation in species assemblages may help infer processes shaping ecological communities. We suggest that ecological studies would benefit from the combined use of the classical statistical models of community composition data, such as constrained or unconstrained multivariate analyses of site-by-species abundance tables, with rapidly emerging and diversifying methods of spatial pattern analysis. Doing so allows one to deal with spatially explicit ecological models of beta diversity in a biogeographic context through the multiscale analysis of spatial patterns in original species data tables, including spatial characterization of fitted or residual variation from environmental models. We summarize here the recent progress for specifying spatial features through spatial weighting matrices and spatial eigenfunctions in order to define spatially constrained or scale-explicit multivariate analyses. Through a worked example on tropical tree communities, we also show the potential of the overall approach to identify significant residual spatial patterns that could arise from the omission of important unmeasured explanatory variables or processes. © 2012 by the Ecological Society of America.


Ploton P.,Institute Francais Of Pondichery | Ploton P.,University of Yaounde I | Pelissier R.,Institute Francais Of Pondichery | Pelissier R.,IRD Montpellier | And 5 more authors.
Ecological Applications | Year: 2012

Reducing Emissions from Deforestation and Forest Degradation (REDD) in efforts to combat climate change requires participating countries to periodically assess their forest resources on a national scale. Such a process is particularly challenging in the tropics because of technical difficulties related to large aboveground forest biomass stocks, restricted availability of affordable, appropriate remote-sensing images, and a lack of accurate forest inventory data. In this paper, we apply the Fourier-based FOTO method of canopy texture analysis to Google Earth's very-high-resolution images of the wet evergreen forests in the Western Ghats of India in order to (1) assess the predictive power of the method on aboveground biomass of tropical forests, (2) test the merits of free Google Earth images relative to their native commercial IKONOS counterparts and (3) highlight further research needs for affordable, accurate regional aboveground biomass estimations. We used the FOTO method to ordinate Fourier spectra of 1436 square canopy images (125 × 125 m) with respect to a canopy grain texture gradient (i.e., a combination of size distribution and spatial pattern of tree crowns), benchmarked against virtual canopy scenes simulated from a set of known forest structure parameters and a 3-D light interception model. We then used 15 1-ha ground plots to demonstrate that both texture gradients provided by Google Earth and IKONOS images strongly correlated with field-observed stand structure parameters such as the density of large trees, total basal area, and aboveground biomass estimated from a regional allometric model. Our results highlight the great potential of the FOTO method applied to Google Earth data for biomass retrieval because the texture-biomass relationship is only subject to 15% relative error, on average, and does not show obvious saturation trends at large biomass values. We also provide the first reliable map of tropical forest aboveground biomass predicted from free Google Earth images. © 2012 by the Ecological Society of America.


Vega C.,Institute National Of Linformation Geographique Et Forestiere | Vega C.,Institute Francais Of Pondichery | Vepakomma U.,Pulp and Paper Research Institute of Canada | Morel J.,Institute Francais Of Pondichery | And 8 more authors.
Remote Sensing | Year: 2015

Light Detection and Ranging (Lidar) is a state of the art technology to assess forest aboveground biomass (AGB). To date, methods developed to relate Lidar metrics with forest parameters were built upon the vertical component of the data. In multi-layered tropical forests, signal penetration might be restricted, limiting the efficiency of these methods. A potential way for improving AGB models in such forests would be to combine traditional approaches by descriptors of the horizontal canopy structure. We assessed the capability and complementarity of three recently proposed methods for assessing AGB at the plot level using point distributional approach (DM), canopy volume profile approach (CVP), 2D canopy grain approach (FOTO), and further evaluated the potential of a topographical complexity index (TCI) to explain part of the variability of AGB with slope. This research has been conducted in a mountainous wet evergreen tropical forest of Western Ghats in India. AGB biomass models were developed using a best subset regression approach, and model performance was assessed through cross-validation. Results demonstrated that the variability in AGB could be efficiently captured when variables describing both the vertical (DM or CVP) and horizontal (FOTO) structure were combined. Integrating FOTO metrics with those of either DM or CVP decreased the root mean squared error of the models by 4.42% and 6.01%, respectively. These results are of high interest for AGB mapping in the tropics and could significantly contribute to the REDD+ program. Model quality could be further enhanced by improving the robustness of field-based biomass models and influence of topography on area-based Lidar descriptors of the forest structure. © 2015 by the authors; licensee MDPI, Basel, Switzerland.


Hamrouni A.,Institute Francais Of Pondichery | Vega C.,IGN | Renaud J.-P.,ONF | Durrleu S.,IRSTEA | Bouvier M.,IRSTEA
Revue Francaise de Photogrammetrie et de Teledetection | Year: 2015

We developed an-object based framework to assess individual tree volume from airborne LiDAR data in a pine-dominated forest. Individual tree crowns were extracted using a point-based segmentation algorithm and total tree volume was estimated using height and either tree or crown bounding volume information using nonlinear models. Tree-level models provided root mean squared errors (RMSE) around 30%. Scaling volume at the plot level allows to reduce RMSE by a factor 2, i.e. around 15%. This scale change may benefits from error compensation associated to segmentation involving false tree detections or tree omissions leading to crown fusions. Along with height, crown volume was found to be a good predictor of tree volume, but suffers from computational issues that may further induce variability in the models. Future work should integrate an analysis of tree neighborhood in order to improve tree- models by the use of indices reflecting competition and growth conditions.


Vega C.,Institute Francais Of Pondichery | Vega C.,Institute National Of Linformation Geographique Et Forestiere | Hamrouni A.,Institute Francais Of Pondichery | El Mokhtari A.,Institute Francais Of Pondichery | And 5 more authors.
International Journal of Applied Earth Observation and Geoinformation | Year: 2014

This paper introduces PTrees, a multi-scale dynamic point cloud segmentation dedicated to forest tree extraction from lidar point clouds. The method process the point data using the raw elevation values (Z) and compute height (H = Z - ground elevation) during post-processing using an innovative procedure allowing to preserve the geometry of crown points. Multiple segmentations are done at different scales. Segmentation criteria are then applied to dynamically select the best set of apices from the tree segment sextracted at the various scales. The selected set of apices is then used to generate a final segmentation. PTrees has been tested in 3 different forest types, allowing to detect 82% of the trees with under 10% of false detection rate. Future development will integrate crown profile estimation during the segmentation process in order to both maximize the detection of suppressed trees and minimize false detections. © 2014 Elsevier B.V.


Vega C.,Institute Francais Of Pondichery | Vega C.,IRSTEA | Durrieu S.,IRSTEA | Morel J.,Institute Francais Of Pondichery | Allouis T.,IRSTEA
Computers and Geosciences | Year: 2012

This paper introduces a sequential iterative dual-filter method for filtering Lidar point clouds acquired over rough and forested terrain and computing a digital terrain model (DTM). The method belongs to the family of virtual deforestation algorithms that iteratively detect and filter objects above-the ground surface. The method uses both points and raster models to do so. The algorithm performance was first tested over a complex badlands environment and compared to a reference model obtained using a traditional TIN-Iterative approach. It was further tested on a benchmark site of the ISPRS (site 5) representing mainly forests and slopes. Over badlands, the resulting DTM elevation RMSE was 0.14. m over flat areas, and increased to 0.28. m under forested and rough terrain. The later value was 12.5% lower than the one obtained with a TIN-Iterative approach. Over the ISPRS site, the TIN-Iterative model provided better results for 3 out of the 4 sample sites. But the proposed algorithm, still worked fairly well provided a total classification error of 5.52%, and is well ranked compared with other algorithms. While the TIN-iterative approach might work better with low density, the proposed one is a good alternative to process high density point cloud and compute DTMs suitable for modeling either hydrodynamic or morphological processes under forest cover at a local scale. © 2012 Elsevier Ltd.


Osuri A.M.,Tata Institute of Fundamental Research | Osuri A.M.,Nature Conservation Foundation | Ratnam J.,Tata Institute of Fundamental Research | Varma V.,Tata Institute of Fundamental Research | And 14 more authors.
Nature Communications | Year: 2016

Defaunation is causing declines of large-seeded animal-dispersed trees in tropical forests worldwide, but whether and how these declines will affect carbon storage across this biome is unclear. Here we show, using a pan-tropical data set, that simulated declines of large-seeded animal-dispersed trees have contrasting effects on aboveground carbon stocks across Earth's tropical forests. In our simulations, African, American and South Asian forests, which have high proportions of animal-dispersed species, consistently show carbon losses (2-12%), but Southeast Asian and Australian forests, where there are more abiotically dispersed species, show little to no carbon losses or marginal gains (±1%). These patterns result primarily from changes in wood volume, and are underlain by consistent relationships in our empirical data (B2,100 species), wherein, large-seeded animal-dispersed species are larger as adults than small-seeded animal-dispersed species, but are smaller than abiotically dispersed species. Thus, floristic differences and distinct dispersal mode-seed size-adult size combinations can drive contrasting regional responses to defaunation.


Sieler R.,Institute Francais Of Pondichery
Medical anthropology quarterly | Year: 2014

It is often argued that biomedicine alienates patients from doctors, from ailments and from understanding treatment processes, while indigenous and alternative healing systems are portrayed as respectful of patients and their experience. Specifically, South Indian siddha medicine has been seen as diverging from biomedicine in empowering its patients. This approach not only assumes biomedicine to be a homogeneous practice, but also lumps together diverse therapeutic techniques under the labels of "traditional" or "alternative." Analysis of a manual subdiscipline of siddha medicine cautions against such analytic imprecision and active/passive binaries in physician-patient encounters. Practitioners of vital spot medicine claim to "heal the hidden." They rarely communicate diagnostic insights verbally and object to auxiliary devices. However, their physical engagement with patients' ailing bodies highlights the corporeal nature of manual medicine in particular and processual, situational, and reciprocal characteristics of curing in general. © 2014 by the American Anthropological Association.


PubMed | Malaysian Forest Research Institute, MUSE Science Museum of Trento, Tata Institute of Fundamental Research, Smithsonian Tropical Research Institute and 7 more.
Type: | Journal: Nature communications | Year: 2016

Defaunation is causing declines of large-seeded animal-dispersed trees in tropical forests worldwide, but whether and how these declines will affect carbon storage across this biome is unclear. Here we show, using a pan-tropical data set, that simulated declines of large-seeded animal-dispersed trees have contrasting effects on aboveground carbon stocks across Earths tropical forests. In our simulations, African, American and South Asian forests, which have high proportions of animal-dispersed species, consistently show carbon losses (2-12%), but Southeast Asian and Australian forests, where there are more abiotically dispersed species, show little to no carbon losses or marginal gains (1%). These patterns result primarily from changes in wood volume, and are underlain by consistent relationships in our empirical data (2,100 species), wherein, large-seeded animal-dispersed species are larger as adults than small-seeded animal-dispersed species, but are smaller than abiotically dispersed species. Thus, floristic differences and distinct dispersal mode-seed size-adult size combinations can drive contrasting regional responses to defaunation.


PubMed | Institute Francais Of Pondichery
Type: Journal Article | Journal: Ecological applications : a publication of the Ecological Society of America | Year: 2012

Reducing Emissions from Deforestation and Forest Degradation (REDD) in efforts to combat climate change requires participating countries to periodically assess their forest resources on a national scale. Such a process is particularly challenging in the tropics because of technical difficulties related to large aboveground forest biomass stocks, restricted availability of affordable, appropriate remote-sensing images, and a lack of accurate forest inventory data. In this paper, we apply the Fourier-based FOTO method of canopy texture analysis to Google Earths very-high-resolution images of the wet evergreen forests in the Western Ghats of India in order to (1) assess the predictive power of the method on aboveground biomass of tropical forests, (2) test the merits of free Google Earth images relative to their native commercial IKONOS counterparts and (3) highlight further research needs for affordable, accurate regional aboveground biomass estimations. We used the FOTO method to ordinate Fourier spectra of 1436 square canopy images (125 x 125 m) with respect to a canopy grain texture gradient (i.e., a combination of size distribution and spatial pattern of tree crowns), benchmarked against virtual canopy scenes simulated from a set of known forest structure parameters and a 3-D light interception model. We then used 15 1-ha ground plots to demonstrate that both texture gradients provided by Google Earth and IKONOS images strongly correlated with field-observed stand structure parameters such as the density of large trees, total basal area, and aboveground biomass estimated from a regional allometric model. Our results highlight the great potential of the FOTO method applied to Google Earth data for biomass retrieval because the texture-biomass relationship is only subject to 15% relative error, on average, and does not show obvious saturation trends at large biomass values. We also provide the first reliable map of tropical forest aboveground biomass predicted from free Google Earth images.

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