18 research outputs found
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. 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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
Vegetation vulnerability to drought in Spain
Revista oficial de la Asociación Española de Teledetección[EN] Frequency of climatic extremes like long duration droughts has increased in Spain over the last century.The use of remote sensing observations for monitoring and detecting drought is justified on the basis that vegetation vigor is closely related to moisture condition. We derive satellite estimates of bio-physical variables such as fractional vegetation cover (FVC) from MODIS/EOS and SEVIRI/MSG time series. The study evaluates the strength of temporal relationships between precipitation and vegetation condition at time-lag and cumulative rainfall intervals. From this analysis, it was observed that the climatic disturbances affected both the growing season and the total amount of vegetation. However, the impact of climate variability on the vegetation dynamics has shown to be highly dependent on the regional climate, vegetation community and growth stages. In general, they were more significant in arid and semiarid areas, since water availability most strongly limits vegetation growth in these environments.[EN] Los extremos climáticos se han incrementado en España a los largo del último siglo; por ello, su análisis se ha convertido en una línea prioritaria de conocimiento con objeto fundamental de diseñar planes para la gestión y mitigación de sus efectos. Los datos de satélite permiten analizar las variaciones en la actividad de la vegetación a varias escalas temporales y su respuesta a la variabilidad climática. En este trabajo se pone de manifiesto la vulnera-bilidad de la vegetación en España ante condiciones ambientales extremas a través de las correlaciones entre índices meteorológicos de sequía (SPI) y variables biofísicas extraídas de datos MODIS/EOS y SEVIRI/MSG. Las anomalías en la vegetación, como indicadores de las condiciones de humedad de la misma, pueden ayudar a cuantificar y gestionar episodios meteorológicos extremos y hacer un seguimiento de la misma. Las mayores correlaciones se han obtenido en las regiones áridas y semiáridas y durante los meses de máxima actividad de la vegetación, generalmente entre mayo y junio.Este trabajo se enmarca en los proyectos DULCINEA (CGL2005–04202), RESET CLIMATE (CGL2012–35831), LSA SAF
(EUMETSAT) y ERMES (FP7-SPACE-2013, Contract 606983).García-Haro, F.; Campos-Taberner, M.; Sabater, N.; Belda, F.; Moreno, A.; Gilabert, M.; Martínez, B.... (2014). Vulnerabilidad de la vegetación a la sequía en España. Revista de Teledetección. (42):29-38. https://doi.org/10.4995/raet.2014.2283SWORD29384
Changes in health behaviors, mental and physical health among older adults under severe lockdown restrictions during the covid-19 pandemic in spain
We used data from 3041 participants in four cohorts of community-dwelling individuals aged =65 years in Spain collected through a pre-pandemic face-to-face interview and a telephone interview conducted between weeks 7 to 15 after the beginning of the COVID-19 lockdown. On average, the confinement was not associated with a deterioration in lifestyle risk factors (smoking, alcohol intake, diet, or weight), except for a decreased physical activity and increased sedentary time, which reversed with the end of confinement. However, chronic pain worsened, and moderate declines in mental health, that did not seem to reverse after restrictions were lifted, were observed. Males, older adults with greater social isolation or greater feelings of loneliness, those with poorer housing conditions, as well as those with a higher prevalence of chronic morbidities were at increased risk of developing unhealthier lifestyles or mental health declines with confinement. On the other hand, previously having a greater adherence to the Mediterranean diet and doing more physical activity protected older adults from developing unhealthier lifestyles with confinement. If an-other lockdown were imposed during this or future pandemics, public health programs should spe-cially address the needs of older individuals with male sex, greater social isolation, sub-optimal housing conditions, and chronic morbidities because of their greater vulnerability to the enacted movement restrictions. © 2021 by the authors. Licensee MDPI, Basel, Switzerland
CIBERER : Spanish national network for research on rare diseases: A highly productive collaborative initiative
Altres ajuts: Instituto de Salud Carlos III (ISCIII); Ministerio de Ciencia e Innovación.CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research
Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries
Background
Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres.
Methods
This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries.
Results
In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia.
Conclusion
This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries
Algunas consideraciones derivadas de la simulación de reflectividad en escenas con vegetación dispersa
En este trabajo se ha simulado, por medio de un modelo lineal de reflectividad, la influencia de algunos factores sobre la reflectividad de superficies con vegetación dispersa -tales como el tamaño pixel, la variabilidad que presentan las reflectividades de las componentes macroscópicas (suelo, vegetación y sombra) y el error que afecta a la reflectividad de la escena- los cuales son inherentes a su estudio mediante teledetección. Estos factores determinan la respuesta espectral de las superficies naturales y, por tanto, la precisión con que se estiman los parámetros en teledetección. En primer lugar se ha utilizado un modelo de reflectividad geométrico para simular la reflectividad en las bandas TM3 (rojo) y TM4 (infrarrojo cercano) de una escena con vegetación dispersa. Ello ha permitido analizar la distribución de reflectividad en el espacio bidimensional TM3 - TM4 en función de los factores considerados en este estudio. Finalmente, se ha invertido el modelo lineal de reflectividad con el fin de cuantificar el error que producen tanto el error con que se calcula la reflectividad como la propia heterogeneidad espacial de la escena en la estimación de la fracción de vegetación
Acerca de los índices de vegetación
En este trabajo se abordan los índices de vegetación desde un punto de vista docente, al objeto de explicar qué son y cuáles son las mejores que han ido experimentando a lo largo de los últimos años. La interpretación y justificación de los principales índices de vegetación que han aparecido en la bibliografía para normalizar los efectos del suelo, así como el estudio comparado de los mismos, se realiza con ayuda de una serie de medidas radiométricas correspondientes a una experiencia que se realizó en el laboratorio
Sputtering process during pulsed laser deposition of Zn
COLA'03, Crete, Greece, October 5-10, 2003N
Combining hyperspectral UAV and multispectral Formosat-2 imagery for precision agriculture applications
Remote sensing is a key tool for precision agriculture applications as it is capable of capturing spatial and temporal variations in crop status. However, satellites often have an inadequate spatial resolution for precision agriculture applications. High-resolution Unmanned Aerial Vehicles (UAV) imagery can be obtained at flexible dates, but operational costs may limit the collection frequency. The current study utilizes data fusion to create a dataset which benefits from the temporal resolution of Formosat-2 imagery and the spatial resolution of UAV imagery with the purpose of monitoring crop growth in a potato field. The correlation of the Weighted Difference Vegetation Index (WDVI) from fused imagery to measured crop indicators at field level and added value of the enhanced spatial and temporal resolution are discussed. The results of the STARFM method were restrained by the requirement of same-day base imagery. However, the unmixing-based method provided a high correlation to the field data and accurately captured the WDVI temporal variation at field level (r=0.969).</p