Copyright 2020 - copyright UMR ESPACE-DEV - 2017

Nos dernières publications - Août 2020

icone documentation publicationBegue A., Leroux L., Soumare M., Faure Jean-François, Diouf A. A., Augusseau X., Toure L., Tonneau J. P. (2020). Remote sensing products and services in support of agricultural public policies in Africa : overview and challenges. Frontiers in Sustainable Food Systems, 4.

http://dx.doi.org/10.3389/fsufs.2020.00058

WOS:000556663900001

In the last decade, governments, international institutions, donors, the private sector, etc. have shown a renewed interest in agricultural issues in Sub-Saharan Africa (SSA). This interest came with a strong need for information in countries where the lack of reliable and timely basic information can be a problem. Thanks to its capacity to observe the Earth at local, regional, and global scales and from various vantage points, satellite remote sensing is a powerful tool to streamline the monitoring and improvement of the existing systems, and thus to support decision-making. However, the path from satellite images to public policy decisions is not straightforward, and today, only few operational information services are available in SSA countries (e.g., early warning systems for food security and desert locust plagues prevention, rangeland production forecasting). This paper aims to analyze the gap between the technical aspects of the remote sensing sciences and the pragmatic need for relevant data to address agricultural policies in Africa and produce operational recommendations. To achieve this goal, the authors (1) determine the information, and in particular the geoinformation, needed to develop, implement and evaluate agricultural public policies (2) illustrate the role of remote sensing as a public policy tool in SSA through an overview of the current off-the-shelf products and services derived from Earth Observation systems, and (3) propose an analysis of the existing gap between the remote sensing research community and the policy makers. Based on this review, the authors conclude that to benefit from this technological advancement and bridge the gap between technical analysts and policy makers, some key points are fundamental: capacity building, political will and institutional commitment, public-private partnership, and proofs of concept.

Affiliation IRD : UMR 228 (ESPACE-DEV)

Copublication Sud avec : Mali ; Sénégal

Lien Horizon FDI : http://www.documentation.ird.fr/hor/fdi:010079465

Descr. géo. Horizon : AFRIQUE SUBSAHARIENNE

 

Crochelet Estelle, Barrier Nicolas, Andrello M., Marsac Francis, Spadone A., Lett Christophe. (2020). Connectivity between seamounts and coastal ecosystems in the Southwestern Indian Ocean. Deep-Sea Research Part II : Topical Studies in Oceanography, 176.

http://dx.doi.org/10.1016/j.dsr2.2020.104774

WOS:000556810400003

Understanding larval connectivity patterns is critical for marine spatial planning, particularly for designing marine protected areas and managing fisheries. Patterns of larval dispersal and connectivity can be inferred from numerical transport models at large spatial and temporal scales. We assess model-based connectivity patterns between seamounts of the Southwestern Indian Ocean (SWIO) and the coastal ecosystems of Mauritius, La Reunion, Madagascar, Mozambique and South Africa, with emphasis on three shallow seamounts (La Perouse [LP], MAD-Ridge [MR] and Walters Shoal [WS]). Using drifter trajectory and a Lagrangian model of ichthyo-plankton dispersal, we show that larvae can undertake very long dispersion, with larval distances increasing with pelagic larval duration (PLD). There are three groups of greater connectivity: the region between the eastern coast of Madagascar, Mauritius and La Reunion islands; the seamounts of the South West Indian Ridge; and the pair formed by WS and a nearby un-named seamount. Connectivity between these three groups is evident only for the longest PLD examined (360 d). Connectivity from seamounts to coastal ecosystems is weak, with a maximum of 2% of larvae originating from seamounts reaching coastal ecosystems. Local retention at the three focal seamounts (LP, MR and WS) peaks at about 11% for the shortest PLD considered (15 d) at the most retentive seamount (WS) and decreases sharply with increasing PLD. Information on PLD and age of larvae collected at MR and LP are used to assess their putative origin. These larvae are likely self-recruits but it is also plausible that they immigrate from nearby coastal sites, i.e. the southern coast of Madagascar for MR and the islands of La Reunion and Mauritius for LP.

Affiliation IRD : UMR 228 (ESPACE-DEV) ; (sans mention d'UMR) ; UMR 248 (MARBEC)

Lien Horizon FDI : http://www.documentation.ird.fr/hor/fdi:010079444

Descr. géo. Horizon : OCEAN INDIEN SUD OUEST ; MADAGASCAR ; REUNION ; MAURICE ; MOZAMBIQUE ; AFRIQUE DU SUD ; WALTERS BANC ; LA PEROUSE MONT

 

Dezan C., Zermani S., Hireche C. (2020). Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles. Algorithms, 13 (7).

http://dx.doi.org/10.3390/a13070155

WOS:000556509500001

Bayesian Networks (BN) are probabilistic models that are commonly used for the diagnosis in numerous domains (medicine, finance, transport, robotics, horizontal ellipsis ). In the case of autonomous vehicles, they can contribute to elaborate intelligent monitors that can take the environmental context into account. We show in this paper some main abilities of BN that can help in the elaboration of fault detection isolation and recovery (FDIR) modules. One of the main difficulty with the BN model is generally to elaborate these ones according to the case of study. Then, we propose some automatic generation techniques from failure mode and effects analysis (FMEA)-like tables using the pattern design approach. Once defined, these modules have to operate online for autonomous vehicles. In a second part, we propose a design methodology to embed the real-time and non-intrusive implementations of the BN modules using FPGA-SoC support. We show that the FPGA implementation can offer an interesting speed-up with very limited energy cost. Lastly, we show how these BN modules can be incorporated into the decision-making model for the mission planning of unmanned aerial vehicles (UAVs). We illustrate the integration by means of two models: the Decision Network model that is a straightforward extension of the BN model, and the BFM model that is an extension of the Markov Decision Process (MDP) decision-making model incorporating a BN. We illustrate the different proposals with realistic examples and show that the hybrid implementation on FPGA-SoC can offer some benefits.

Affiliation IRD : UMR 228 (ESPACE-DEV)

Ihantamalala F. A., Herbreteau Vincent, Revillion C., Randriamihaja M., Commins Jérémy, Andreambeloson T., Rafenoarimalala F. H., Randrianambinina A., Cordier L. F., Bonds M. H., Garchitorena Andres. (2020). Improving geographical accessibility modeling for operational use by local health actors. International Journal of Health Geographics, 19 (1).

http://dx.doi.org/10.1186/s12942-020-00220-6

WOS:000550065600003

Background Geographical accessibility to health facilities remains one of the main barriers to access care in rural areas of the developing world. Although methods and tools exist to model geographic accessibility, the lack of basic geographic information prevents their widespread use at the local level for targeted program implementation. The aim of this study was to develop very precise, context-specific estimates of geographic accessibility to care in a rural district of Madagascar to help with the design and implementation of interventions that improve access for remote populations. Methods We used a participatory approach to map all the paths, residential areas, buildings and rice fields on OpenStreetMap (OSM). We estimated shortest routes from every household in the District to the nearest primary health care center (PHC) and community health site (CHS) with the Open Source Routing Machine (OSMR) tool. Then, we used remote sensing methods to obtain a high resolution land cover map, a digital elevation model and rainfall data to model travel speed. Travel speed models were calibrated with field data obtained by GPS tracking in a sample of 168 walking routes. Model results were used to predict travel time to seek care at PHCs and CHSs for all the shortest routes estimated earlier. Finally, we integrated geographical accessibility results into an e-health platform developed with R Shiny. Results We mapped over 100,000 buildings, 23,000 km of footpaths, and 4925 residential areas throughout Ifanadiana district; these data are freely available on OSM. We found that over three quarters of the population lived more than one hour away from a PHC, and 10-15% lived more than 1 h away from a CHS. Moreover, we identified areas in the North and East of the district where the nearest PHC was further than 5 h away, and vulnerable populations across the district with poor geographical access (> 1 h) to both PHCs and CHSs. Conclusion Our study demonstrates how to improve geographical accessibility modeling so that results can be context-specific and operationally actionable by local health actors. The importance of such approaches is paramount for achieving universal health coverage (UHC) in rural areas throughout the world.

Affiliation IRD : UMR 228 (ESPACE-DEV) ; UMR 224 (MIVEGEC)

Copublication Sud avec : Cambodge ; Madagascar

Lien Horizon FDI : http://www.documentation.ird.fr/hor/fdi:010079378

Descr. géo. Horizon : MADAGASCAR ; IFANADIANA

 

Li Z. C., Gurgel H., Dessay Nadine, Hu L. J., Xu L., Gong P. (2020). Semi-supervised text classification framework : an overview of dengue landscape factors and satellite earth observation. International Journal of Environmental Research and Public Health, 17 (12).

http://dx.doi.org/10.3390/ijerph17124509

WOS:000549562200001

In recent years there has been an increasing use of satellite Earth observation (EO) data in dengue research, in particular the identification of landscape factors affecting dengue transmission. Summarizing landscape factors and satellite EO data sources, and making the information public are helpful for guiding future research and improving health decision-making. In this case, a review of the literature would appear to be an appropriate tool. However, this is not an easy-to-use tool. The review process mainly includes defining the topic, searching, screening at both title/abstract and full-text levels and data extraction that needs consistent knowledge from experts and is time-consuming and labor intensive. In this context, this study integrates the review process, text scoring, active learning (AL) mechanism, and bidirectional long short-term memory (BiLSTM) networks, and proposes a semi-supervised text classification framework that enables the efficient and accurate selection of the relevant articles. Specifically, text scoring and BiLSTM-based active learning were used to replace the title/abstract screening and full-text screening, respectively, which greatly reduces the human workload. In this study, 101 relevant articles were selected from 4 bibliographic databases, and a catalogue of essential dengue landscape factors was identified and divided into four categories: land use (LU), land cover (LC), topography and continuous land surface features. Moreover, various satellite EO sensors and products used for identifying landscape factors were tabulated. Finally, possible future directions of applying satellite EO data in dengue research in terms of landscape patterns, satellite sensors and deep learning were proposed. The proposed semi-supervised text classification framework was successfully applied in research evidence synthesis that could be easily applied to other topics, particularly in an interdisciplinary context.

Affiliation IRD : (sans mention d'UMR) ; UMR 228 (ESPACE-DEV)

Copublication Sud avec : Brésil ; Chine

Lien Horizon FDI : http://www.documentation.ird.fr/hor/fdi:010079377

 

Nuninger L., Verhagen P., Libourel T., Opitz R., Rodier X., Laplaige C., Fruchart C., Leturcq S., Levoguer N. (2020). Linking Theories, Past Practices, and Archaeological Remains of Movement through Ontological Reasoning. Information, 11 (6).

http://dx.doi.org/10.3390/info11060338

WOS:000551236800036

The amount of information available to archaeologists has grown dramatically during the last ten years. The rapid acquisition of observational data and creation of digital data has played a significant role in this "information explosion". In this paper, we propose new methods for knowledge creation in studies of movement, designed for the present data-rich research context. Using three case studies, we analyze how researchers have identified, conceptualized, and linked the material traces describing various movement processes in a given region. Then, we explain how we construct ontologies that enable us to explicitly relate material elements, identified in the observed landscape, to the knowledge or theory that explains their role and relationships within the movement process. Combining formal pathway systems and informal movement systems through these three case studies, we argue that these systems are not hierarchically integrated, but rather intertwined. We introduce a new heuristic tool, the "track graph", to record observed material features in a neutral form which can be employed to reconstruct the trajectories of journeys which follow different movement logics. Finally, we illustrate how the breakdown of implicit conceptual references into explicit, logical chains of reasoning, describing basic entities and their relationships, allows the use of these constituent elements to reconstruct, analyze, and compare movement practices from the bottom up.

Affiliation IRD : UMR 228 (ESPACE-DEV)

 

Satge Frédéric, Hussain Y., Molina-Carpio J., Pillco R., Laugner C., Akhter G., Bonnet Marie-Paule. (2020). Reliability of SM2RAIN precipitation datasets in comparison to gauge observations and hydrological modelling over arid regions. International Journal of Climatology, [Early access].

http://dx.doi.org/10.1002/joc.6704

WOS:000550978700001

Numerous satellite-based precipitation datasets have been successively made available. Their precipitation estimates rely on clouds properties derived from microwave and thermal sensors in a so-named 'top-down' approach. Recently, a 'bottom-up' approach to infer precipitation from soil moisture (SM) estimates has resulted in the release of two new precipitation datasets (P-datasets). One uses satellite-based SM estimates from the European Spatial Agency (ESA) Climate Change Initiative (CCI) (SM2RAIN-CCI) while the other uses satellite-based SM from European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Advanced SCATterometer (ASCAT) (SM2RAIN-ASCAT). This study assesses SM2RAIN-ASCAT and -CCI reliability over two arid regions: Bolivian and Peruvian Altiplano and Pakistan (South Asia) using (a) direct comparisons with rain gauges and (b) testing the sensitivity of streamflow modelling to the P-datasets. Selecting two different regions and different indicators helps to assess whether the P-dataset reliability varies depending on the assessment method and location. For comparison purposes, the most reliable P-datasets from the literature are also considered (IMERG-E v.6, IMERG-L v.6, IMERG-F v.6, CHIRPS v.2 and MSWEP v.2.2). Compared to rain gauge observations and based on the modified Kling-Gupta Efficiency (KGE) values, the SM2RAIN-ASCAT and -CCI are more accurate in the Altiplano than in Pakistan. This difference is explained by a more favourable physical context for satellite-based SM estimates in the Altiplano. Over the Altiplano and despite an overall positive bias, SM2RAIN-ASCAT describes rain gauges temporal dynamics as well as IMERG-F v.6, CHIRPS v.2 and MSWEP v.2.2 and provides streamflow simulations very close to those obtained when using IMERG-F v.6, CHIRPS v.2 and MSWEP v.2.2 as forcing data.

Affiliation IRD : UMR 228 (ESPACE-DEV)

Copublication Sud avec : Bolivie ; Pakistan

Lien Horizon FDI : http://www.documentation.ird.fr/hor/fdi:010079415

 

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