The original article has been corrected.

High spatial resolution images as well as image processing and object detection algorithms are recent technologies that aid the study of biodiversity and commercial plantations of forest species. This paper seeks to contribute knowledge regarding the use of these technologies by studying randomly dispersed native palm tree. Here, we analyze the automatic detection of large circular crown (LCC) palm tree using a high spatial resolution panchromatic GeoEye image (0.50 m) taken on the area of a community of small agricultural farms in the Brazilian Amazon. We also propose auxiliary methods to estimate the density of the LCC palm tree Attalea speciosa (babassu) based on the detection results. We used the “Compt-palm” algorithm based on the detection of palm tree shadows in open areas via mathematical morphology techniques and the spatial information was validated using field methods (i.e. structural census and georeferencing). The algorithm recognized individuals in life stages 5 and 6, and the extraction percentage, branching factor and quality percentage factors were used to evaluate its performance. A principal components analysis showed that the structure of the studied species differs from other species. Approximately 96% of the babassu individuals in stage 6 were detected. These individuals had significantly smaller stipes than the undetected ones. In turn, 60% of the stage 5 babassu individuals were detected, showing significantly a different total height and a different number of leaves from the undetected ones. Our calculations regarding resource availability indicate that 6870 ha contained 25,015 adult babassu palm tree, with an annual potential productivity of 27.4 t of almond oil. The detection of LCC palm tree and the implementation of auxiliary field methods to estimate babassu density is an important first step to monitor this industry resource that is extremely important to the Brazilian economy and thousands of families over a large scale.

Marine resources are under increasing pressure from a wide variety of threats such as overfishing, offshore energy development, and climate change. As marine ecosystems degrade, so do the well-being and livelihoods of humans that depend directly on the ecosystem goods and services they provide. Marine protected areas have been proposed to protect biodiversity, restore damaged ecosystems, sustain fisheries, and rebuild overexploited stocks. The effectiveness of marine protected areas depends in part on their effectiveness as connected networks, linked over large areas by ecological processes such as larval dispersal. Here, we applied a biophysical model driven by ocean currents derived from satellite altimetry to evaluate connectivity between Western Indian Ocean reefs. We applied graph-theoretic analysis, including clustering and a betweenness centrality metric. Our results show high interconnectivity within several regions (Mozambique Channel, Mascarene archipelago) and lower connectivity across the WIO region. We compared the results with the current MPA network, and proposed sites/reefs that should be considered priority sites for MPA implementation: Pebane, Cosmoledo, Majunga, Masoarivo, Platte Island, Farquhar, Agalega and Geyser bank. Our results are timely, considering the oil and gas exploration that is ongoing in the region. We discuss implications for transboundary marine policies and regional cooperation in the Western Indian Ocean, and advocate the creation of a regional-scale organization to structure interactions among the different actors.

Estimating sugarcane biomass is difficult to achieve when working with highly variable spatial distributions of growing conditions, like on Reunion Island. We used a dataset of in-farm fields with contrasted climatic conditions and farming practices to compare three methods of yield estimation based on remote sensing: (1) an empirical relationship method with a growing season-integrated Normalized Difference Vegetation Index NDVI, (2) the Kumar-Monteith efficiency model, and (3) a forced-coupling method with a sugarcane crop model (MOSICAS) and satellite-derived fraction of absorbed photosynthetically active radiation. These models were compared with the crop model alone and discussed to provide recommendations for a satellite-based system for the estimation of yield at the field scale. Results showed that the linear empirical model produced the best results (RMSE = 10.4 t∙ha−1). Because this method is also the simplest to set up and requires less input data, it appears that it is the most suitable for performing operational estimations and forecasts of sugarcane yield at the field scale. The main limitation is the acquisition of a minimum of five satellite images. The upcoming open-access Sentinel-2 Earth observation system should overcome this limitation because it will provide 10-m resolution satellite images with a 5-day frequency.

The objective of this study was to assess the efficiency of the assimilation of the fraction of intercepted photosynthetically active radiation (fIPAR) data derived from Satellite Pour l'Observation de la Terre SPOT images into the MOSICAS sugarcane crop growth model for estimating the yield at field scale on Reunion Island. Over 3 years, time series of SPOT satellite imagery were used to estimate the daily evolution of NDVI for 60 plots located on two climatically contrasted farms. Ground measurements of the fIPAR were performed on 5 reference fields and used to calibrate a relationship with the corresponding NDVI values. Forced and not forced simulations were run and compared with respect to their ability to predict the final observed yield. Forcing MOSICAS with fIPAR values derived from SPOT images improved the accuracy of the model for the yield estimation (RMSE = 12.2 against 14.8 t ha(-1)) closer to the 1:1 line. However, underestimations of the yield by the forced model suggest that some of the model parameters were not optimal. The maximal radiation use efficiency parameter (RUEm) was optimised for each field, and an analysis of variance showed the significant effect of the ratoon number of the field, of its cultivar and of the farm where it is planted. Accordingly, the RUEm was recalibrated for each cultivar for the number of ratoons and farms. New RUEm values ranged from 3.09 to 3.77 gMJ(-1), and new computations were run using the optimised values of RUEm The results indicate that recalibrating the maximal radiation use efficiency according to the number of ratoons improved the yield estimation accuracy by as much as 10.5 t ha-1 RMSE. This study highlights the potential of time series of satellite images to enhance the estimation of the yield by a forced ecophysiological model and to obtain better knowledge about the ecophysiological processes that are involved in crop dynamics with the recalibration method. (C) 2014 Elsevier B.V. All rights reserved.

†ESPACE Unit, Institut de Recherche pour le Développement, Montpellier, France ‡Current affiliation: European Commission, Joint Research Center, Institute for the Protection and Security of the Citizen, Ispra, Italy §Laboratoire d’Informatique Gaspard-Monge, Université Paris-Est, Equipe A3SI, ESIEE Paris, France ¶IUP Génie Physiologique et Informatique, Faculté des Sciences Fondamentales et Appliquées, Université de Poitiers, Poitiers, France

The pelagic larval period is probably the least understood life stage of reef fish, yet the processes of larval dispersal and settlement exert a strong influence on the persistence of reef fish populations. A thorough understanding of these processes is essential to determining whether distant populations are connected and how to adapt management plans to patterns in connectivity. Managers may erroneously assume that local populations are isolated when they are actually replenished by distant reefs beyond their jurisdiction. Researchers increasingly rely on numerical hydrodynamic models that simulate the spatio-temporal dispersal of larvae by ocean currents to elucidate these connections and guide marine spatial planners, yet relatively little work has been done to validate these models with empirical data. In this study, we tested a dispersal simulation model against in situ observations of young post-larval fish to investigate a whether larvae settling at La Reunion (in the western Indian Ocean) might have originated at Mauritius, 200 km distant. First, we collected post larval specimens of honeycomb grouper (Epinephelus merra) shortly after an episodic mass settlement that occurred in 2002 at La Reunion. Using sclerochronology, we established the age of the fish from their otoliths. Finally, we simulated dispersal of larvae from La Reunion, Mauritius, and other reefs in the region by ocean currents using a 20 Eulerian advection-diffusion model driven by current velocities derived from satellite remote sensing. The simulation suggested that larvae spawned at la Reunion were carried away from the island while larvae spawned at Mauritius were carried to La Reunion. The otolith-derived ages of the fish were compatible with this hypothesis, when we accounted for the time required for larvae to drift from Mauritius to La Reunion. The combined results suggest a dispersal connection from Mauritius to la Reunion. To best maintain populations of adult reef fish at La Reunion, managers should protect stocks spawning at Mauritius. Although more study is needed to characterize patterns of regional connectivity and account for seasonal and inter-annual variations in these patterns, the example presented here demonstrates the possibility of distant connections in the western Indian Ocean. We urge managers in the region to look beyond their own jurisdictions, view their jurisdictions as part of a connected network, and undertake a collaborative approach to protecting the network as a whole. (C) 2013 Elsevier Ltd. All rights reserved.

Coupling remotely sensed data with crop model is known to improve the estimation of crop variables by the model. The recalibration coupling approach tends to reduce the differences between observation and simulation by optimizing the value of one of the model's parameter. In this study, we used this approach with a sugarcane model and Crop Water Stress Index calculated using remotely sensed thermal infrared data in order to optimize the value of the root depth parameter thanks to measured and simulated AET/MET ratio. The effect of the root depth recalibration has also been assessed on the yield estimation, which showed good trends with a significant enhancement of the estimated yield.

Afin de gerer au mieux les periodes de recolte de la canne a sucre, une prevision suffisamment fine de son rendement est des plus utiles. Il a ete montre a plusieurs reprises que le couplage d'un modele de simulation de croissance d'une culture vegetale avec des donnees obtenues par teledetection ameliore significativement la pertinence du rendement issu du modele (Delecolle et al., 1992 ; Dorigo et al., 2007). Nous presentons ici plusieurs methodes de couplage (forcage, assimilation et calibration) ainsi que les applications potentielles du modele de simulation de croissance de la canne a sucre MOSICAS, concu pour simuler la croissance de parcelles experimentales de canne a sucre. L'experimentation a ete menee sur cinq parcelles agricoles situees dans le Sud de l'ile de La Reunion sur une periode de neuf mois (octobre 2011 a juin 2012). Les resultats montrent une amelioration du rendement simule par le modele apres couplage avec l'indice d'efficience d'interception estime a partir des donnees de teledetection. (Resume d'auteur)

The TropiSAR campaign has been conducted in August 2009 in French Guiana with the ONERA airborne radar system SETHI. The main objective of this campaign was to collect data to support the Phase A of the 7th Earth Explorer candidate mission, BIOMASS. Several specific questions needed to be addressed to consolidate the mission concept following the Phase 0 studies, and the data collection strategy was constructed accordingly. More specifically, a tropical forest data set was required in order to provide test data for the evaluation of the foreseen inversion algorithms and data products. The paper provides a description of the resulting data set which is now available through the European Space Agency website under the airborne campaign link. First results from the TropiSAR database analysis are presented with two in-depth analyses about both the temporal radiometric variation and temporal coherence at P-band. The temporal variations of the backscatter values are less than 0.5 dB throughout the campaign, and the coherence values are observed to stay high even after 22 days. These results are essential for the BIOMASS mission. The observed temporal stability of the backscatter is a good indicator of the expected robustness of the biomass estimation in tropical forests, from cross-polarized backscatter values as regarding environmental changes such as soil moisture. The high temporal coherence observed after a 22-day period is a prerequisite for SAR Polarimetric Interferometry and Tomographic applications in a single satellite configuration. The conclusion then summarizes the paper and identifies the next steps in the analysis.

Coupling remote sensing data with crop model has been shown to improve accuracy of the model yield estimation. MOSICAS model simulates sugarcane yield in controlled conditions plot, based on different variables, including the interception efficiency index (i). In this paper, we assessed the use of remote sensing data to sugarcane growth modeling by 1) comparing the sugarcane yield simulated with and without satellite data integration in the model, and 2) comparing two approaches of satellite data forcing. The forcing variable is the interception efficiency index (Εi). The yield simulations are evaluated on a data set of cane biomass measured on four on-farm fields, over three years, in Reunion Island. Satellite data are derived from a SPOT 10 m resolution time series acquired during the same period. Three types of simulations have been made: a raw simulation (where the only input data are daily precipitations, daily temperatures and daily global radiations), a partial forcing coupling method (where MOSICAS computed values of Εi have been replaced by NDVI computed Εi for each available satellite image), and complete forcing method (where all MOSICAS simulated Εi have been replaced by NDVI computed Εi). Results showed significant improvements of the yield's estimation with complete forcing approach (with an estimation of the yield 8.3 % superior to the observed yield), but minimal differences between the yields computed with raw simulations and those computed with partial forcing approach (with a mean overestimation of respectively 34.7 and 35.4 %). Several enhancements can be made, especially by optimizing MOSICAS parameters, or by using other remote sensing index, like NDWI.

Ship detection from remote sensing imagery is a crucial application for maritime security, which includes among others traffic surveillance, protection against illegal fisheries, oil discharge control and sea pollution monitoring. In the framework of a European integrated project Global Monitoring for Environment and Security (GMES) Security/Land and Sea Integrated Monitoring for European Security (LIMES), we developed an operational ship detection algorithm using high spatial resolution optical imagery to complement existing regulations, in particular the fishing control system. The automatic detection model is based on statistical methods, mathematical morphology and other signal-processing techniques such as the wavelet analysis and Radon transform. This article presents current progress made on the detection model and describes the prototype designed to classify small targets. The prototype was tested on panchromatic Satellite Pour l'Observation de la Terre (SPOT) 5 imagery taking into account the environmental and fishing context in French Guiana. In terms of automatic detection of small ship targets, the proposed algorithm performs well. Its advantages are manifold: it is simple and robust, but most of all, it is efficient and fast, which is a crucial point in performance evaluation of advanced ship detection strategies.

In New Caledonia (21°S, 165°E), shade-grown coffee plantations were abandoned for economic reasons in the middle of the 20th century. Coffee species (Coffea arabica, C. canephora and C. liberica) were introduced from Africa in the late 19th century, they survived in the wild and spontaneously cross-hybridized. Coffee species were originally planted in native forest in association with leguminous trees (mostly introduced species) to improve their growth. Thus the canopy cover over rustic shade coffee plantations is heterogeneous with a majority of large crowns, attributed to leguminous trees. The aim of this study was to identify suitable areas for coffee inter-specific hybridization in New Caledonia using field based environmental parameters and remotely sensed predictors. Due to the complex structure of tropical vegetation, remote sensing imagery needs to be spatially accurate and to have the appropriate bands for monitoring vegetation cover. Quickbird panchromatic (black and white) imagery at 0.6 to 0.7m spatial resolutions and multispectral imagery at 2.4m spatial resolution were pansharpened and used for this study. The two most suitable remotely sensed indicators, canopy heterogeneity and tree crown size, were acquired by the sequential use of tree crown detection (neural network), image processing (such as textural analysis) and classification. All models were supervised and trained on learning data determined by human expertise. The final model has two remotely sensed indicators and three physical parameters based on the Digital Elevation Model: elevation, slope and water flow accumulation. Using these five predictive variables as inputs, two modelling methods, a decision tree and a neural network, were implemented. The decision tree, which showed 96.9% accuracy on the test set, revealed the involvement of ecological parameters in the hybridization of Coffea species. We showed that hybrid zones could be characterized by combinations of modalities, underlining the complexity of the environment concerned. For instance, forest heterogeneity and large crown size, steep slopes (>53.5%) and elevation between 194 and 429masl, are favourable factors for Coffea inter-specific hybridization. The application of the neural network on the whole area gave a predictive map that distinguished the most suitable areas by means of a nonlinear continuous indicator. The map provides a confidence level for each area. The most favourable areas were geographically localized, providing a clue for the detection and conservation of favourable areas for Coffea species neo-diversity.

We present data collected as part of ValHyBio- VALidation HYperspectral of a BIOgeochemical model in the South Western Tropical Lagoon of New Caledonia, a PNTS-sponsored program dedicated to chlorophyll satellite imaging and validation as affected by bathymetry. The specific goals of ValHyBio are to: - examine time-dependent oceanic reflectance in relation to dynamic surface processes, - construct field/satellite reflectance-based chlorophyll models, - investigate the feasibility of inverting the model to yield surface chlorophyll and turbidity, - validate the biogeochemical model with field/satellite observations. In situ bio-optical parameters include absorption coefficients by CDOM and particles, Secchi disk depth, backscattering coefficient, pigment concentration, suspended matter concentration, and K_dPAR. They are measured every month at 5 stations, of contrasted bathymetry and bottom reflectance, as well as at a reference station situated 4 miles offshore, and on a station over coral reefs. Remote sensing reflectance is calculated from the absorption and backscattering coefficients and compared with satellite data. SeaWIFS and MODIS AQUA match-ups collected over the period 1997-2010 (ValHySat-VALidation HYperspectral SATellite database) are used. Satellite retrievals are examined as a function of bathymetry. The feasibility of a longterm monitoring program of optical water retrieval with satellite remote sensing technique is examined in the frame of the GOPS (South Pacific Integrated Observatory).

The TropiSAR campaign has been conducted in August 2009 in French Guiana with the ONERA airborne system SETHI. The main objective of this campaign was to collect data to support the Phase A of the Earth Explorer candidate mission, Biomass. Several specific questions need to be addressed to answer the recommendations of the ESAC group and the data collection strategy has been constructed accordingly. The first part of the paper lists these specific questions. We then describe the selected test sites, followed by a summary on the radar instrument and the radar configuration (geometry and waveform). The data acquisition plan is provided and the temporal behaviour of the P-Band data is explored.

La problématique du développement durable et la part sans cesse grandissante des TIC dans la société sont deux caractéristiques marquantes de ce début de siècle. Deux volets, complémentaires entre eux, méritent d’être analysés : que peut-on faire, pour minimiser l’empreinte écologique des TIC ? Comment les TIC peuvent-elles diminuer les atteintes à l’environnement des activités dans l’industrie, les services et la vie quotidienne ?

The new SPOT/Envisat direct receiving station (DRS) operating in Cayenne in the framework of the SEAS-Guyane project was used to produce a global cloudless SPOT mosaic over French Guiana for the year 2006. This mosaic was used to perform a land-use, land-use change and forestry (LULUCF) inventory in the framework of the Kyoto Protocol. Nearly 17 000 sample points were laid down on the SPOT mosaic with a stratified sampling design. The land use at each sample point was determined by visual interpretation of the corresponding SPOT image in 2006 and the Landsat Thematic Mapper (TM) image from the Global Land Cover Facility (GLCF) in 1990. Statistics for the period 1990–2006 were computed and integrated in the first voluntary Kyoto inventory.

The advent of a new generation of synthetic aperture radar (SAR) satellites, such as Advanced SAR/Environmental Satellite (C-band), Phased Array Type L-band Synthetic Aperture Radar/Advanced Land Observing Satellite (L-band), and TerraSAR-X (X-band), offers advanced potentials for the detection of urban tissue. In this letter, we analyze and compare the performance of multiple types of SAR images in terms of band frequency, polarization, incidence angle, and spatial resolution for the purpose of operational urban areas delineation. As a reference for comparison, we use a proven method for extracting textural features based on a Gaussian Markov Random Field (GMRF) model. The results of urban areas delineation are quantitatively analyzed allowing performing intrasensor and intersensors comparisons. Sensitivity of the GMRF model with respect to texture window size and to spatial resolutions of SAR images is also investigated. Intrasensor comparison shows that polarization and incidence angle play a significant role in the potential of the GMRF model for the extraction of urban areas from SAR images. Intersensors comparison evidences the better performances of X-band images, acquired at 1-m spatial resolution, when resampled to resolutions of 5 and 10 m.