Force distribution in a scalar model for non-cohesive granular material

We study a scalar lattice model for inter-grain forces in static, non-cohesive, granular materials, obtaining two primary results. (i) The applied stress as a function of overall strain shows a power law dependence with a nontrivial exponent, which moreover varies with system geometry. (ii) Probability distributions for forces on individual grains appear Gaussian at all stages of compression, showing no evidence of exponential tails. With regard to both results, we identify correlations responsible for deviations from previously suggested theories.Comment: 16 pages, 9 figures, Submitted to PR

Deep learning for agricultural land use classification from Sentinel-2

[ES] En el campo de la teledetección se ha producido recientemente un incremento del uso de técnicas de aprendizaje profundo (deep learning). Estos algoritmos se utilizan con éxito principalmente en la estimación de parámetros y en la clasificación de imágenes. Sin embargo, se han realizado pocos esfuerzos encaminados a su comprensión, lo que lleva a ejecutarlos como si fueran “cajas negras”. Este trabajo pretende evaluar el rendimiento y acercarnos al entendimiento de un algoritmo de aprendizaje profundo, basado en una red recurrente bidireccional de memoria corta a largo plazo (2-BiLSTM), a través de un ejemplo de clasificación de usos de suelo agrícola de la Comunidad Valenciana dentro del marco de trabajo de la política agraria común (PAC) a partir de series temporales de imágenes Sentinel-2. En concreto, se ha comparado con otros algoritmos como los árboles de decisión (DT), los k-vecinos más cercanos (k-NN), redes neuronales (NN), máquinas de soporte vectorial (SVM) y bosques aleatorios (RF) para evaluar su precisión. Se comprueba que su precisión (98,6% de acierto global) es superior a la del resto en todos los casos. Por otra parte, se ha indagado cómo actúa el clasificador en función del tiempo y de los predictores utilizados. Este análisis pone de manifiesto que, sobre el área de estudio, la información espectral y espacial derivada de las bandas del rojo e infrarrojo cercano, y las imágenes correspondientes a las fechas del período de verano, son la fuente de información más relevante utilizada por la red en la clasificación. Estos resultados abren la puerta a nuevos estudios en el ámbito de la explicabilidad de los resultados proporcionados por los algoritmos de aprendizaje profundo en aplicaciones de teledetección.[EN] The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.Este trabajo ha sido subvencionado gracias al Convenio 2019 y 2020 de colaboración entre la Generalitat Valenciana, a través de la Conselleria d’Agricultura, Medi Ambient, Canvi Climàtic i Desenvolupament Rural, y la Universitat de València – Estudi General.Campos-Taberner, M.; García-Haro, F.; Martínez, B.; Gilabert, M. (2020). Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2. Revista de Teledetección. 0(56):35-48. https://doi.org/10.4995/raet.2020.13337OJS3548056Baraldi, A., Parmiggiani, F. 1995. An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 293-304. https://doi.org/10.1109/36.377929Bengio, Y., Simard, P., Frasconi, P. 1994. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157-166. https://doi.org/10.1109/72.279181Breiman, L., Friedman, J., Olshen, R.A., Stone, C.J. 1984. Classification and regression trees. Taylor & Francis: London, UK.Breiman, L. 2001. Random forests. Machine learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324Campos-Taberner, M., Romero-Soriano, A., Gatta, C., Camps-Valls, G., Lagrange, A., Le Saux, B., Beaupère, A., Boulch, A., Chan-Hon-Tong, A., Herbin, S., Randrianarivo, H., Ferecatu, M., Shimoni, M., Moser, G., Tuia, D. 2016. Processing of extremely high-resolution Lidar and RGB data: outcome of the 2015 IEEE GRSS data fusion contest-part a: 2-D contest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(12), 5547-5559. https://doi.org/10.1109/JSTARS.2016.2569162Campos-Taberner, M., García-Haro, F.J., Martínez, B., Sánchez-Ruíz, S., Gilabert, M.A. 2019a. A Copernicus Sentinel-1 and Sentinel-2 Classification Framework for the 2020+ European Common Agricultural Policy: A Case Study in València (Spain). Agronomy, 9(9), 556. https://doi.org/10.3390/agronomy9090556Campos-Taberner, M., García-Haro, F.J., Martínez, B., Sánchez-Ruiz, S., Gilabert, M.A. 2019b. Evaluación del potencial de Sentinel-2 para actualizar el SIGPAC de la Comunitat Valenciana. En: XVIII Congreso de la Asociación Española de Teledetección. Valladolid, España, 24-27, septiembre. pp 11-14.Camps-Valls, G., Tuia, D., Bruzzone, L., Benediktsson, J.A. 2013. Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE Signal Processing Magazine, 31(1), 45-54. https://doi.org/10.1109/MSP.2013.2279179Chuvieco, E. 2008. Teledetección Ambiental. La observación de la Tierra desde el espacio. Madrid: Ariel.Cover, T., Hart, P. 1967. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. https://doi.org/10.1109/TIT.1967.1053964Estrada, J., Sánchez, H., Hernanz, L., Checa, M.J., Roman, D. 2017. Enabling the Use of Sentinel-2 and LiDAR Data for Common Agriculture Policy Funds Assignment. ISPRS International Journal of Geo-Information, 6(8), 255. https://doi.org/10.3390/ijgi6080255Friedl, M.A., McIver, D.K., Hodges, J.C., Zhang, X.Y., Muchoney, D., Strahler, A.H., Baccini, A. 2002. Global land cover mapping from MODIS: algorithms and early results. Remote sensing of Environment, 83(1-2), 287-302. https://doi.org/10.1016/S0034-4257(02)00078-0Graves, A., Mohamed, A.R., Hinton, G. 2013. Speech recognition with deep recurrent neural networks. En 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645-6649. https://doi.org/10.1109/ICASSP.2013.6638947Gilabert, M.A., González-Piqueras, J., García-Haro, J., 1997. Acerca de los índices de vegetación. Revista de teledetección, 8, 1-10. Disponible en http://www.aet.org.es/?q=revista8-4González-Guerrero, O., y Pons, X., 2020. The 2017 Land Use/Land Cover Map of Catalonia based on Sentinel-2 images and auxiliary data. Revista de Teledetección, 55, 81-92. https://doi.org/10.4995/raet.2020.13112Gregrio, A., Jansen, J. 2000. Land cover classification system (LCCS); Classification concepts and user manual for software version 2. Roma: FAO.Griffiths, P., Nendel, C., Hostert, P. 2019. Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping. Remote Sensing of Environment, 220, 135-151. https://doi.org/10.1016/j.rse.2018.10.031Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.Z. 2019. XAI-Explainable artificial intelligence. Science Robotics, 4(37). https://doi.org/10.1126/scirobotics.aay7120Haralick, R.M., Shanmugam, K., Dinstein, I.H. 1973. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, 3(6), 610-621. https://doi.org/10.1109/TSMC.1973.4309314Haykin, S. 1994. Neural networks: a comprehensive foundation. River: Prentice Hall.Immitzer, M., Vuolo, F., Atzberger, C. 2016. First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sensing, 8(3), 166. https://doi.org/10.3390/rs8030166Hochreiter, S., Schmidhuber, J. 1997. Long short-term memory. Neural computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A. 2017. Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778-782. https://doi.org/10.1109/LGRS.2017.2681128Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., Johnson, B.A. 2019. Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166-177. https://doi.org/10.1016/j.isprsjprs.2019.04.015Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P. 2016. Convolutional neural networks for large-scale remotesensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(2), 645-657. https://doi.org/10.1109/TGRS.2016.2612821Mikolov, T., Kombrink, S., Burget, L., Černocký, J., Khudanpur, S. 2011. Extensions of recurrent neural network language model. En 2011 IEEE International Conference on acoustics, speech and signal processing (ICASSP). Praga, República Checa, 22-27 Mayo. pp. 5528-5531. https://doi.org/10.1109/ICASSP.2011.5947611Montavon, G., Samek, W., Müller, K.R. 2018. Methods for interpreting and understanding deep neural networks. Digital Signal Processing, 73, 1-15. https://doi.org/10.1016/j.dsp.2017.10.011Mou, L., Ghamisi, P., Zhu, X.X. 2017. Deep recurrent neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(7), 3639-3655. https://doi.org/10.1109/TGRS.2016.2636241Mountrakis, G., Im, J., Ogole, C. 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247- 259. https://doi.org/10.1016/j.isprsjprs.2010.11.001Ndikumana, E., Ho Tong Minh, D., Baghdadi, N., Courault, D., Hossard, L. 2018. Deep recurrent neural network for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sensing, 10(8), 1217. https://doi.org/10.3390/rs10081217Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N. 2019. Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204. https://doi.org/10.1038/s41586-019-0912-1Ruiz, L.A., Almonacid-Caballer, J., Crespo-Peremarch, P., Recio, J.A., Pardo-Pascual, J.E., SánchezGarcía, E. 2020. Automated classification of crop types and condition in a mediterranean area using a fine-tuned convolutional neural network. En The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. Niza, Francia, 31 Agosto - 2 Septiembre (en línea). pp. 1061-1068. https://doi.org/10.5194/isprs-archivesXLIII-B3-2020-1061-2020Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., Muller, K.R. (Eds.). 2020. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Cham: Springer Nature. https://doi.org/10.1007/978-3-030-28954-6Schmedtmann, J., Campagnolo, M.L. 2015. Reliable crop identification with satellite imagery in the context of common agriculture policy subsidy control. Remote Sensing, 7(7), 9325-9346. https://doi.org/10.3390/rs70709325Schuster, M., Paliwal, K.K. 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673-2681. https://doi.org/10.1109/78.650093Sitokonstantinou, V., Papoutsis, I., Kontoes, C., Lafarga Arnal, A., Armesto Andrés, A.P., Garraza Zurbano, J.A. 2018. Scalable parcel-based crop identification scheme using sentinel-2 data time-series for the monitoring of the common agricultural policy. Remote Sensing, 10(6), 911. https://doi.org/10.3390/rs10060911Story, M., Congalton, R.G. 1986. Accuracy assessment: a user's perspective. Photogrammetric Engineering and Remote Sensing, 52(3), 397-399.Tapsall, B., Milenov, P., Tasdemir, K. 2010. Analysis of RapidEye imagery for annual landcover mapping as an aid to European Union (EU) common agricultural polic. En ISPRS Technical Commission VII Symposium - 100 Years ISPRS. Viena, Austria, 5-7 Julio. pp. 568-573.Van Den Oord, A., Kalchbrenner, N., Kavukcuoglu, K. 2016. Pixel recurrent neural networks. En Proceedings of the 33rd International Conference on International Conference on Machine Learning. New York, EEUU., 20-22 Junio. pp. 1747-1756.Vuolo, F., Neuwirth, M., Immitzer, M., Atzberger, C., Ng, W.T. 2018. How much does multi-temporal Sentinel-2 data improve crop type classification? International Journal of Applied Earth Observation and Geoinformation, 72, 122-130. https://doi.org/10.1016/j.jag.2018.06.007Wardlow, B.D., Egbert, S.L., Kastens, J.H. 2007. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sensing of Environment, 108(3), 290-310. https://doi.org/10.1016/j.rse.2006.11.021Watson, R.T., Noble, I.R., Bolin, B., Ravindranath, N.H., Verardo, D.J., Dokken, D.J. 2000. Land use, land-use change and forestry: a special report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.Zhan, X., Defries, R., Townshend, J.R.G., Dimiceli, C., Hansen, M., Huang, C., Sohlberg, R. 2000. The 250 m global land cover change product from the Moderate Resolution Imaging Spectroradiometer of NASA's Earth Observing System. International Journal of Remote Sensing, 21(6-7), 1433-1460. https://doi.org/10.1080/014311600210254Zhang, L., Zhang, L., Du, B. 2016. Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4(2), 22-40. https://doi.org/10.1109/MGRS.2016.2540798Zhong, L., Hu, L., Zhou, H. 2019. Deep learning based multi-temporal crop classification. Remote sensing of environment, 221, 430-443. https://doi.org/10.1016/j.rse.2018.11.032Zhu, Z., Woodcock, C.E. 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152-171. https://doi.org/10.1016/j.rse.2014.01.011Zhu, X.X., Tuia, D., Mou, L., Xia, G.S., Zhang, L., Xu, F., Fraundorfer, F. 2017. Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), 8-36. https://doi.org/10.1109/MGRS.2017.276230

Effect of large and small herbivores on seed and seedling survival of Beilschmiedia miersii in central Chile

SUMMARY In the Mediterranean region of Chile, populations of the threatened tree Beilschmiedia miersii have been strongly affected by anthropic disturbances, causing a critical state of conservation. Herbivory has been proposed as the main factor that currently limits the regeneration of this species. We studied the effect of large vs. small herbivores on seed and seedling survival of B. miersii under two contrasting habitat conditions (forest and shrubland), using plots with fenced enclosures which differentially excluded mammalian herbivores according to body size. Results show that herbivory had a significant negative effect on B. miersii. Both large and small herbivores had a significant negative effect on seeds and seedlings in the shrub habitat. In the forest habitat small herbivores had a significant negative effect only on seeds. Our results suggest that different herbivores can have varying effects on seed and seedling survival, but these effects can vary spatially, probably due to different herbivore assemblage of each habitat. Results suggest that restoration plans for B. miersii need to be adjusted according to local conditions. Key words: Beilschmiedia miersii, herbivory, seed predation, seedling survival, restoration. RESUMEN En la zona mediterránea de Chile, las poblaciones de la especie amenazada Beilschmiedia miersii han sido afectadas fuertemente por actividades antrópicas, provocando que actualmente se encuentre en un estado crítico de conservación. La herbivoría ha sido propuesta como el principal factor que limita actualmente la regeneración de esta especie. El efecto de herbívoros grandes versus pequeños, la etapa del ciclo de vida más afectada (semillas vs. plántulas) y si la sobrevivencia depende de las condiciones de hábitat permanece menos conocida. En este estudio evaluamos el efecto de diferentes tipos de herbívoros en la sobrevivencia de semillas y plántulas de B. miersii usando parcelas con cierres perimetrales que excluyeron a los herbívoros mamíferos de acuerdo a su tamaño. También evaluamos la variabilidad espacial del efecto de los herbívoros comparando entre habitas de matorral y bosque. Los resultados muestran que la herbivoría tuvo un efecto significativamente negativo sobre B. miersii. Ambos herbívoros, grandes y pequeños, tuvieron un efecto significativamente negativo sobre semillas y plántulas en el hábitat de matorral, donde la sobrevivencia de plántulas fue de 2,5% para exclusiones parciales y cero para parcelas sin exclusión. La sobrevivencia de semillas fue nula en las exclusiones parciales y en parcelas sin exclusión. En el hábitat de bosque los herbívoros pequeños tuvieron un efecto negativo solo sobre semillas. La sobrevivencia de semillas fue nula en parcelas parcialmente excluidas y parcelas sin exclusión. Nuestros resultados indican que diferentes tipos de herbívoros pueden tener efectos variados sobre semillas y plántulas, pero estos resultados pueden variar espacialmente, debido probablemente a los diferentes ensamblajes de herbívoros en cada hábitat. Los resultados sugieren que los planes de restauración de B. miersii deben ser ajustados de acuerdo al hábitat y al tipo de herbívoro. Palabras clave: Beilschmiedia miersii, herbivoría, depredación de semillas, sobrevivencia de plántulas, restauración

Haemosporidian parasites of Antelopes and other vertebrates from Gabon, Central Africa

Re-examination, using molecular tools, of the diversity of haemosporidian parasites (among which the agents of human malaria are the best known) has generally led to rearrangements of traditional classifications. In this study, we explored the diversity of haemosporidian parasites infecting vertebrate species (particularly mammals, birds and reptiles) living in the forests of Gabon (Central Africa), by analyzing a collection of 492 bushmeat samples. We found that samples from five mammalian species (four duiker and one pangolin species), one bird and one turtle species were infected by haemosporidian parasites. In duikers (from which most of the infected specimens were obtained), we demonstrated the existence of at least two distinct parasite lineages related to Polychromophilus species (i. e., bat haemosporidian parasites) and to sauropsid Plasmodium (from birds and lizards). Molecular screening of sylvatic mosquitoes captured during a longitudinal survey revealed the presence of these haemosporidian parasite lineages also in several Anopheles species, suggesting a potential role in their transmission. Our results show that, differently from what was previously thought, several independent clades of haemosporidian parasites (family Plasmodiidae) infect mammals and are transmitted by anopheline mosquitoes

Evaluation of the LSA-SAF gross primary production product derived from SEVIRI/MSG data (MGPP)

The objective of this study is to describe a completely new 10-day gross primary production (GPP) product (MGPP LSA-411) based on data from the geostationary SEVIRI/MSG satellite within the LSA SAF (Land Surface Analysis SAF) as part of the SAF (Satellite Application Facility) network of EUMETSAT. The methodology relies on the Monteith approach. It considers that GPP is proportional to the absorbed photosynthetically active radiation APAR and the proportionality factor is known as the light use efficiency ε. A parameterization of this factor is proposed as the product of a εmax, corresponding to the canopy functioning under optimal conditions, and a coefficient quantifying the reduction of photosynthesis as a consequence of water stress. A three years data record (2015–2017) was used in an assessment against site-level eddy covariance (EC) tower GPP estimates and against other Earth Observation (EO) based GPP products. The site-level comparison indicated that the MGPP product performed better than the other EO based GPP products with 48% of the observations being below the optimal accuracy (absolute error < 1.0 g m−2 day−1) and 75% of these data being below the user requirement threshold (absolute error < 3.0 g m−2 day−1). The largest discrepancies between the MGPP product and the other GPP products were found for forests whereas small differences were observed for the other land cover types. The integration of this GPP product with the ensemble of LSA-SAF MSG products is conducive to meet user needs for a better understanding of ecosystem processes and for improved understanding of anthropogenic impact on ecosystem services.The objective of this study is to describe a completely new 10-day gross primary production (GPP) product (MGPP LSA-411) based on data from the geostationary SEVIRI/MSG satellite within the LSA SAF (Land Surface Analysis SAF) as part of the SAF (Satellite Application Facility) network of EUMETSAT. The methodology relies on the Monteith approach. It considers that GPP is proportional to the absorbed photosynthetically active radiation APAR and the proportionality factor is known as the light use efficiency epsilon. A parameterization of this factor is proposed as the product of a epsilon(max), corresponding to the canopy functioning under optimal conditions, and a coefficient quantifying the reduction of photosynthesis as a consequence of water stress. A three years data record (2015-2017) was used in an assessment against site-level eddy covariance (EC) tower GPP estimates and against other Earth Observation (EO) based GPP products. The site-level comparison indicated that the MGPP product performed better than the other EO based GPP products with 48% of the observations being below the optimal accuracy (absolute error <1.0 g m(-2) day(-1)) and 75% of these data being below the user requirement threshold (absolute error <3.0 g m(-2) day(-1)). The largest discrepancies between the MGPP product and the other GPP products were found for forests whereas small differences were observed for the other land cover types. The integration of this GPP product with the ensemble of LSA-SAF MSG products is conducive to meet user needs for a better understanding of ecosystem processes and for improved understanding of anthropogenic impact on ecosystem services.Peer reviewe

On-line tools to improve the presentation skills of scientific results

[EN] In experimental sciences and engineering it is essential to communicate and present the results effectively. The authors have participated in several educational innovation projects since 2016, aimed at developing of materials to improve the communication skills of scientific results. An exhaustive and updated compilation of the international rules that constitute the basis for the writted and oral scientific presentations was carried out. The good teaching practices in these fields were also identified. The results of those previous projects have shown the need to incorporate web questionnaires and other interactive content into the educational program. These are adapted to the demands of the students and provide a training feeback. In this contribution, the new materials that are being developed within the innovation project UV-SFPIE_PID19-1096780, funded by the University of Valencia, are presented. They are devoted to facilitate the acquisition of communication skills of scientific results. In particular, these tools combine ICT self-learning environments with traditional classroom teaching (blended learning). The project methodology includes educational data mining aimed at identifying the most effective materials and activities to achieve its objectives. The aim of these mixed learning tools is to facilitate the acquisition by the students of the necessary skills of oral and written communication, improve their presentation skills and, consequently, also their employability as university graduates.This work has been supported by the University of Valencia through project SFPIE_PID19-1096780.Campos-Taberner, M.; Gilabert, M.; Manzanares, J.; Mafé, S.; Cervera, J.; García-Haro, F.; Martínez, B.... (2020). On-line tools to improve the presentation skills of scientific results. IATED. 4907-4910. https://doi.org/10.21125/inted.2020.1342S4907491

Benchmarking the performance of a low-cost Magnetic Resonance Control System at multiple sites in the open MaRCoS community

Purpose: To describe the current properties and capabilities of an open-source hardware and software package that is being developed by many sites internationally with the aim of providing an inexpensive yet flexible platform for low-cost MRI. Methods: This paper describes three different setups from 50 to 360 mT in different settings, all of which used the MaRCoS console for acquiring data, and different types of software interfaces (custom-built GUI or PulSeq overlay) to acquire the data. Results: Images are presented from both phantoms and in vivo from healthy volunteers to demonstrate the image quality that can be obtained from the MaRCoS hardware/software interfaced to different low-field magnets. Conclusions: The results presented here show that a number of different sequences commonly used in the clinic can be programmed into an open-source system relatively quickly and easily, and can produce good quality images even at this early stage of development. Both the hardware and software will continue to develop, and it is an aim of this paper to encourage other groups to join this international consortium.Comment: 9 pages, 10 figures, comments welcom

Recent advances in the interrelationship of vegetation and climate in Spain

Póster elaborado para el WCRP Open Science Conference celebrado en Denver del 24 al 28 de octubre de 201

Capability assessment of the SEVIRI/MSG GPP product for the detection of areas affected by water stress

[ES] Se presenta el nuevo producto de producción primaria bruta (GPP) de EUMETSAT derivado a partir de datos del satélite geoestacionario SEVIRI/MSG (MGPP LSA-411) y se evalúa su potencial para detectar zonas afectadas por estrés hídrico (hot spots). El producto GPP se basa en la aproximación de Monteith, que modela la GPP de la vegetación como el producto de la radiación fotosintéticamente activa (PAR) incidente, la fracción de PAR absorbida (fAPAR) y un factor de eficiencia de uso de la radiación (ε). El potencial del producto MGPP para detectar hot spots se evalúa, utilizando un periodo corto de tres años, a escala local y regional, comparando con datos in situ derivados de medidas en torres eddy covariance (EC) y con datos GPP derivados de satélite (producto de 8 días MOD17A2H.v6 a 500 m y producto de 10 días GDMP a 1 km). Los resultados preliminares sobre el uso del producto MGPP en la evaluación de la respuesta del ecosistema a posibles eventos de déficit de agua ponen de manifiesto que este producto, calculado íntegramente a partir de datos MSG (EUMETSAT), ofrece una alternativa prometedora para detectar y caracterizar zonas afectadas por sequía a través de la incorporación de un coeficiente de estrés hídrico.[EN] This study aims to introduce a completely new and recently launched 10-day GPP product based on data from the geostationary MSG satellite (MGPP LSA-411) and to assess its capability to detect areas affected by water stress (hot spots). The GPP product is based on Monteith’s concept, which models GPP as the product of the incoming photosynthetically active radiation (PAR), the fractional absorption of that flux (fAPAR) and a lightuse efficiency factor (ε). Preliminary results on the use of the MGPP product in the assessment of ecosystem response to rainfall deficit events are presented in this work for a short period of three years. The robustness of this product is evaluated at both site and regional scales across the MSG disk using eddy covariance (EC) GPP measurements and Earth Observing (EO)-based GPP products, respectively. The EO-based products belong to the 8-day MOD17A2H v6 at 500 m and the 10-day GDMP at 1 km. The results reveal the MGPP product, derived entirely from MSG (EUMETSAT) products, as an efficient alternative to detect and characterize areas under water scarcity by means of a coefficient of water stress.Trabajo financiado por los proyectos LSA SAF (EUMETSAT) y ESCENARIOS (CGL2012–35831). Agradecemos a los responsables de las torres EC la cesión de los datos de GPP.Martínez, B.; Sánchez-Ruiz, S.; Campos-Taberner, M.; García-Haro, FJ.; Gilabert, MA. (2020). Potencial del producto SEVIRI/MSG GPP en la detección de zonas afectadas por estrés hídrico. Revista de Teledetección. 0(55):17-29. https://doi.org/10.4995/raet.2020.13285OJS1729055Anyamba, A., Glennie, E., Small, J. 2018. Teleconnections and Interannual Transitions as Observed in African Vegetation: 2015-2017. Remote Sensing, 10, 1038. https://doi.org/10.3390/rs10071038Bahta, Y.T., Jordaan, A., Muyambo, F. 2016. Communal Farmers' perception of drought in South Africa: policy implication for drought risk reduction. International Journal of Disaster Risk Reduction, 20, 39-50. https://doi.org/10.1016/j.ijdrr.2016.10.007Bartholome, E. Belward, A.S. 2005. GLC2000: a new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 26(9), 1959-1977. https://doi.org/10.1080/01431160412331291297Bojinski, S., Verstraete, M., Peterson, T.C., Richter, C., Simmons, A., Zemp, M. 2014. The concept of essential climate variables in support of climate research, applications, and policy. American Meteorologial Society, 95(9), 1431-1443. https://doi.org/10.1175/BAMS-D-13-00047.1CGLOPS1. 2018. Copernicus Global Land Operations 'Vegetation and Energy' Product User Manual for Dry Matter Productivity (DMP) and Gross Dry Matter Productivity (GDMP). Collection 1 km, version 2- CGLOPS1_PUM_DMP1km-V2, February 2018, 47 pp.Chamaillé-Jammes, S. Fritz, H. 2009. Precipitation-NDVI relationships in eastern and southern African savannas vary along a precipitation gradient. International Journal of Remote Sensing, 30(13), 3409-3422. https://doi.org/10.1080/01431160802562206Flaming, G.M. 2004. Measurement of global precipitation. In: International Geoscience and Remote Sensing Symposium. 9, Anchorage, AK, EUA.Fuster, B., Sánchez-Zapero, J., Camacho, F., García- Haro, F.J., Campos-Taberner, M. 2017. Validation of the Climate Data Record of EUMETSAT LSA SAF SEVIRI/MSG LAI, FAPAR and FVC products. Proceedings of the V RAQRS conference, Torrent, September 2017. pp. 191-196.Garbulsky, M.F., Peñuelas, J., Papale, D., Ardo, J., Goulden, M.L., Kiely, G., et al. 2010. Patterns and controls of the variability of radiation use efficiency and primary productivity across terrestrial ecosystems. Global Ecology and Biogeography, 19, 253-267. https://doi.org/10.1111/j.1466-8238.2009.00504.xGarcía-Haro, F.J., Campos-Taberner, M., Sabater, N., Belda, F., Moreno, A., Gilabert, M.A., Martínez, B., Pérez-Hoyos, A., Meliá, J. 2014. Vulnerabilidad de la vegetación a la sequía en España. Revista de Teledetección, 42, 29-37. https://doi.org/10.4995/raet.2014.2283García-Haro, F.J., Campos-Taberner, M., Muñoz- Mari, J., Laparra, V., Camacho, F., Sánchez- Zapero, J., Camps-Valls, G. 2018. Derivation of global vegetation biophysical parameters from EUMETSAT polar system. ISPRS Journal of Photogrammetry and Remote Sensing, 139, 57-74. https://doi.org/10.1016/j.isprsjprs.2018.03.005García-Haro, F.J., Camacho, F., Martínez, B., Campos- Taberner, M., Fuster, B., Sánchez-Zapero, J., Gilabert, M.A. 2019. Climate Data Records of Vegetation Variables from Geostationary SEVIRI/MSG Data: Products, Algorithms and Applications. Remote Sensing, 11, 2103. https://doi.org/10.3390/rs11182103Gilabert, M.A., Moreno, A., Maselli, F., Martínez, B., Chiesi, M., Sánchez-Ruíz, S., et al. 2015. Daily GPP estimates in Mediterranean ecosystems by combining remote sensing and meteorological data. ISPRS Journal of Photogrammetry and Remote Sensing, 102, 184- 197. https://doi.org/10.1016/j.isprsjprs.2015.01.017Jones, L.A., Kimball, J.S., Reichle, R.H., Madani, N., Glassy, J., Ardizzone, J.V., et al. 2017. The SMAP level 4 carbon product for monitoring ecosystem land-atmosphere CO2 exchange. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6517- 6532. https://doi.org/10.1109/TGRS.2017.2729343Kummerowa, C., Simpson, J., Thielea, O., Barnesa, W., Changa, A.T.C., Stockera, E., 2000. The Status of the Tropical Rainfall Measuring Mission (TRMM) after Two Years in Orbit. Journal of Applied Meteorology, 39, 1965-1982.Liu, Z., Ostrenga, D., Teng, W., Kempler, S. 2012. Tropical Rainfall Measuring Mission (TRMM) Precipitation Data and Services for Research and Applications. Bulletin of the American Meteorological Society, 93, 1317-1325. https://doi.org/10.1175/BAMS-D-11-00152.1Liu, N., Harper, R. J., Dell, B., Liu, S., Yu, Z. 2017. Vegetation dynamics and rainfall sensitivity for different vegetation types of the Australian continent in the dry period 2002-2010. International Journal of Remote Sensing, 17, 2761-2782. https://doi.org/10.1002/eco.1811Malo, A.R Nicholson, S.E. 1990. A study of rainfall and vegetation dynamics in the African Sahel using normalized difference vegetation index. Journal of Arid Environments, 19, 1-24. https://doi.org/10.1016/S0140-1963(18)30825-5Martínez, B., Sánchez-Ruiz, S., Gilabert, M.A., Moreno, A., Taberner, M.C., García-Haro, F.J., et al. 2018. Retrieval of daily gross primary production over Europe and Africa from an ensemble of SEVIRI/MSG products. International Journal of Applied Earth Observation and Geoinformation, 65, 124-136. https://doi.org/10.1016/j.jag.2017.10.011Martínez, B., Gilabert, M.A., Sánchez-Ruiz, S., Taberner, M.C., García-Haro, F.J. 2020. Evaluation of the LSA-SAF gross primary production product derived from SEVIRI/MSG data (MGPP). ISPRS Journal of Photogrammetry and Remote Sensing, 159, 220-236. https://doi.org/10.1016/j.isprsjprs.2019.11.010McKee, T.B., Doesken, N.J., Kleist, K. 1993. The relationship of drought frequency and duration to time scale. In: Proceedings of the Eighth Conference on Applied Climatology, Anaheim, California, 17- 22 January 1993. Boston, American Meteorological Society, 179-184.Monteith, J. L. 1972. Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology, 9, 747-766. https://doi.org/10.2307/2401901Running, S.W., Zhao, M. 2015. Daily GPP and Annual NPP (MOD17A2/A3) Products NASA Earth Observing System MODIS Land Algorithm. User's Guide. Version 3.0 For Collection 6.Sánchez-Ruiz, S., Moreno, A., Piles, M., Maselli, F., Carrara, A., Running, S., Gilabert, M.A. 2017. Quantifying water stress effect on daily light use efficiency in Mediterranean ecosystems using satellite data. International Journal of digital Earth, 10(6), 623-638. https://doi.org/10.1080/17538947.2016.1247301Simpson, J., Adler, R., North, G.A. 1988. Proposed tropical rainfall measuring mission (TRMM) satellite. Bulletin of the American Meteorological Society, 69(3), 278-295. https://doi.org/10.1175/1520- 0477(1988)069%3C0278:APTRMM%3E2.0.CO;2Vicente-Serrano, S.M., Azorín-Molina, C., Peña- Gallardo, M., Tomas-Burguera, M., Domínguez- Castro, F., et al. 2015. A high-resolution spatial assessment of the impacts of drought variability on vegetation activity in Spain from 1981 to 2015. Natural Hazards and Earth System Sciences, 19, 1189- 1213. https://doi.org/10.5194/nhess-19-1189-2019Zhang, Y., Yu, G., Yang, J., Wimberly, M.C., Zhang, X., Tao, J., et al. 2014. Climate driven global changes in carbon use efficiency. Global Ecology and Biogeography, 23, 144-155. https://doi.org/10.1111/geb.12086Zhao, M., Running, S.W., Heinsch, F.A., Nemani, R.R. 2011. MODIS derived terrestrial primary production. Land Remote Sensing and Global Environmental Change. Springer, New York, pp. 635-660. https://doi.org/10.1007/978-1-4419-6749-7_2