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. 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Structure-guided examination of the mechanogating mechanism of PIEZO2

Piezo channels are mechanically activated ion channels that confer mechanosensitivity to a variety of different cell types. Piezos oligomerize as propeller-shaped homotrimers that are thought to locally curve the membrane into spherical domes that project into the cell. While several studies have identified domains and amino acids that control important properties such as ion permeability and selectivity as well as inactivation kinetics and voltage sensitivity, only little is known about intraprotein interactions that govern mechanosensitivity—the most unique feature of PIEZOs. Here we used site-directed mutagenesis and patch-clamp recordings to investigate the mechanogating mechanism of PIEZO2. We demonstrate that charged amino acids at the interface between the beam domain—i.e., a long α-helix that protrudes from the intracellular side of the “propeller” blade toward the inner vestibule of the channel—and the C-terminal domain (CTD) as well as hydrophobic interactions between the highly conserved Y2807 of the CTD and pore-lining helices are required to ensure normal mechanosensitivity of PIEZO2. Moreover, single-channel recordings indicate that a previously unrecognized intrinsically disordered domain located adjacent to the beam acts as a cytosolic plug that limits ion permeation possibly by clogging the inner vestibule of both PIEZO1 and PIEZO2. Thus, we have identified several intraprotein domain interfaces that control the mechanical activation of PIEZO1 and PIEZO2 and which might thus serve as promising targets for drugs that modulate the mechanosensitivity of Piezo channels

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

The molecular mechanism and physiological role of silent nociceptor activation

Silent nociceptors are sensory afferents that are insensitive to noxious mechanical stimuli under normal conditions but become sensitized to such stimuli during inflammation. Using RNA-sequencing and quantitative RT-PCR we demonstrate that inflammation selectively upregulates the expression of the transmembrane protein TMEM100 in silent nociceptors and electrophysiology revealed that over-expression of TMEM100 is required and sufficient to un-silence silent nociceptors. Moreover, we show that mice lacking TMEM100 do not develop secondary hyperalgesia, i.e. pain hypersensitivity that spreads beyond the site of inflammation, in a mouse model of knee joint inflammation and that AAV-mediated overexpression of TMEM100 in articular afferents in the absence of inflammation is sufficient to induce secondary hyperalgesia in remote skin regions without causing knee joint pain. Thus, our work identifies TMEM100 as a key regulator of silent nociceptor un-silencing and reveals a physiological role for this hitherto enigmatic afferent subclass in triggering spatially remote secondary hyperalgesia during inflammation

Differential modulation of voltage-gated sodium channels by nerve growth factor in three major subsets of TrkA-expressing nociceptors

Nerve growth factor is an inflammatory mediator that induces long-lasting hyperalgesia, which can partially be attributed to nerve growth factor-induced sensitization of primary afferent nociceptors. It was shown that nerve growth factor increases the excitability of polymodal C-fibre nociceptors by modulating tetrodotoxin-sensitive and tetrodotoxin-resistant voltage-gated sodium channels, but hitherto only little is known about the effects of nerve growth factor on sodium currents in other nociceptor subtypes that express the nerve growth factor receptor TrkA. We previously characterized two reporter mouse lines that allow the unequivocal identification of two important subclasses of TrkA-expressing nociceptors – i.e. neuropeptide Y receptor type 2 (NPY2R+) Aδ-fibre nociceptors that mediate pinprick pain and nicotinic acetylcholine receptor alpha-3 subunit (CHRNA3+) silent nociceptors, which are the most abundant TrkA+ nociceptors in visceral organs and deep somatic tissues. Here, we utilized these mouse lines to investigate the expression patterns and the possible nerve growth factor-dependent modulation of sodium channels in these neurons using whole-cell patch-clamp recordings and quantitative real-time polymerase chain reaction. We demonstrate that NPY2R+ nociceptors, CHRNA3+ ‘silent’ nociceptors and polymodal C-fibre nociceptors express different combinations of sodium channel α- and β-subunits and accordingly exhibit functionally different sodium currents. Moreover, we demonstrate that nerve growth factor produces robust hyperpolarizing shifts in the half-activation voltage of tetrodotoxin-resistant currents in NPY2R+ nociceptors and polymodal C-fibre nociceptors and also shifts the half-activation of tetrodotoxin-sensitive currents in polymodal C-fibre nociceptors. In silent nociceptors, however, nerve growth factor solely increases the current density of the tetrodotoxin-resistant current but does not alter other sodium channel properties. Considering the different peripheral target tissues and the previously reported roles in different forms of pain of the nociceptor subpopulations that were examined here, our results suggest that nerve growth factor differentially contributes to the development visceral and cutaneous pain hypersensitivity and highlights the importance of developing different therapeutic strategies for different forms of pain

Downscaling rice yield simulation at sub-field scale using remotely sensed LAI data

Crop modeling and remote sensing are key tools to gain deeper understanding on cropping system dynamics and, ultimately, to increase the sustainability of agricultural productions. This study presents a system to estimate rice yields at sub-field scale based on the integration of a biophysical model and remotely sensed products. Leaf area index (LAI) data derived from decametric optical imageries (i.e., Landsat-8, Landsat-7 and Sentinel\u20132A) were assimilated into the WARM rice model via automatic recalibration of crop parameters at a fine spatial resolution (30 m 7 30 m), targeting the lowest error between simulated and remotely sensed LAI. The performance of the system was evaluated by comparing simulated yield using default and recalibrated parameters at sub-field scale with yield maps generated by a GPS-equipped harvester. The training dataset included 40 paddy fields in Northern Italy, which were sampled during three cropping seasons, from 2014 to 2016. The assimilation of remotely sensed LAI into model parameters increased the accuracy of the system: MAE and RRMSE were 0.66 t ha-1 [CI: 0.54 t ha-1\u20130.78 t ha-1] and 13.8% [CI: 11.7%\u201315.7%], respectively, whereas they were 0.82 t ha-1 [CI: 0.68 t ha-1\u20130.96 t ha-1) and 15.7% [CI: 14.1%,\u201317.4%] without assimilation. Moreover, the system allowed to properly reproduce the within-field yield variability, thus laying the basis for possible applications in precision agriculture advisory services

Intercomparison of instruments for measuring leaf area index over rice

Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. LAI estimates can be classified as direct or indirect methods. Direct methods are destructive, time consuming, and difficult to apply over large fields. Indirect methods are non-destructive and cost-effective due to its portability, accuracy and repeatability. In this study, we compare indirect LAI estimates acquired from two classical instruments such as LAI-2000 and digital cameras for hemispherical photography, with LAI estimates acquired with a smart app (PocketLAI) installed on a mobile smartphone. In this work it is shown that LAI estimates obtained with the classical instruments and with a smartphone are well correlated. Consequently, results presented in this work allow considering PocketLAI as a powerful alternative to the classical instruments for LAI monitoring during field campaigns

USH2A is a Meissner's corpuscle protein necessary for normal vibration sensing in mice and humans

Fingertip mechanoreceptors comprise sensory neuron endings together with specialized skin cells that form the end-organ. Exquisitely sensitive, vibration-sensing neurons are associated with Meissner's corpuscles in the skin. In the present study, we found that USH2A, a transmembrane protein with a very large extracellular domain, was found in terminal Schwann cells within Meissner's corpuscles. Pathogenic USH2A mutations cause Usher's syndrome, associated with hearing loss and visual impairment. We show that patients with biallelic pathogenic USH2A mutations also have clear and specific impairments in vibrotactile touch perception, as do mutant mice lacking USH2A. Forepaw rapidly adapting mechanoreceptors innervating Meissner's corpuscles, recorded from Ush2a(-/-) mice, showed large reductions in vibration sensitivity. However, the USH2A protein was not found in sensory neurons. Thus, loss of USH2A in corpuscular end-organs reduced mechanoreceptor sensitivity as well as vibration perception. Thus, a tether-like protein is required to facilitate detection of small-amplitude vibrations essential for the perception of fine-grained tactile surfaces