TISSBERT: una referencia para la validación y la comparación de métodos para la reconstrucción de series temporales de NDVI

[EN] This paper introduces the Time Series Simulation for Benchmarking of Reconstruction Techniques (TISSBERT) dataset, intended to provide a benchmark for the validation and comparison of time series reconstruction methods. Such methods are routinely used to estimate vegetation characteristics from optical remotely sensed data, where the presence of clouds decreases the usefulness of the data. As for their validation, these methods have been compared with previously published ones, although with different approaches, which sometimes lead to contradictory results. We designed the TISSBERT dataset to be generic so that it could simulate realistic reference and cloud-contaminated time series at global scale. To that end, we estimated both cloud-free and cloud-contaminated Normalized Difference Vegetation Index (NDVI) statistics for randomly selected control points and each day of the year from the Long Term Data Record Version 4 (LTDR-V4) dataset by assuming different statistical distributions. The best approach was then applied to the whole dataset, and validity of the results were estimated through the Kolmogorov-Smirnov statistic. The dataset elaboration is described thoroughly along with how to use it. The advantages and drawbacks of this dataset are then discussed, which emphasize the realistic simulation of the cloud-contaminated and reference time series. This dataset can be obtained from the authors upon demand. It will be used in a next paper to compare widely used NDVI time series reconstruction methods.[ES] En este trabajo se presenta la base de datos titulada Time Series Simulation for Benchmarking of Reconstruction Techniques (TISSBERT) con el propósito de ofrecer una herramienta para la validación y la comparación de métodos para la reconstrucción de series temporales. Tales métodos se usan de manera rutinaria para la estimación de características de la vegetación a partir de datos obtenidos por teledetección óptica, donde la presencia de nubes disminuye su utilidad. En cuanto a su validación, estos métodos se han comparado con otros publicados anteriormente, aunque desde perspectivas diferentes, lo cual conduce a resultados contradictorios. La base de datos TISSBERT se ha diseñado como una herramienta genérica para una simulación realista a escala global de series temporales de referencia o contaminadas por nubes. Para ello, se estimaron estadísticas de Normalized Difference Vegetation Index (NDVI) con y sin contaminación de nubes para unos píxeles de control seleccionados de manera aleatoria, y para cada día del año, usando la base de datos Long Term Data Record Version 4 (LTDR-V4), y probando con varias distribuciones estadísticas. La mejor metodología se aplicó al conjunto de la base de datos, y la validez de los resultados se comprobó con la prueba de Kolmogorov-Smirnov. La elaboración de la base de datos se describe detalladamente así como la manera de usarla. Finalmente, se analizan las ventajas y los inconvenientes de la base de datos TISSBERT, los cuales enfatizan la simulación realista de series temporales de referencia y con contaminación nubosa. Esta base de datos se puede obtener gratuitamente de los autores, y se usará en un futuro para comparar métodos usuales de reconstrucción de series temporales de NDVI.This work was supported by the Spanish Ministerio de Economía y Competitividad (CEOS-SPAIN2, project ESP2014-52955-R and SIM, project PCIN-2015-232). The authors also thank NASA for the free access to the LTDRV4 data.Julien, Y.; Sobrino, JA. (2018). TISSBERT: A benchmark for the validation and comparison of NDVI time series reconstruction methods. Revista de Teledetección. (51):19-31. https://doi.org/10.4995/raet.2018.9749SWORD193151Beck, P., Atzberger, C., Hogda, K.A., Johansen, B. Skidmore A. 2006. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sensing of Environment, 100, 321-334. https://doi.org/ 10.1016/j.rse.2005.10.021Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B., Eklundh, L. 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment, 91, 332-334. https://doi.org/ 10.1016/j.rse.2004.03.014Cho, AR., Suh, M.S. 2013. Detection of contaminated pixels based on the short-term continuity of NDVI and correction using spatio-temporal continuity. Asia-Pacific Journal of Atmospheric Sciences, 49(4), 511-525. https://doi.org/10.1007/s13143-013- 0045-7Geng, L., Ma, M., Wang, X., Yu, W., Jia, S. and Wang, H. 2014. Comparison of eight techniques for reconstructing multi-satellite sensor time-series NDVI data sets in the Heihe river basin, China. Remote Sensing, 2014, 6, 2024-2049Hird, J.N., McDermid, G.J. 2009. Noise reduction of NDVI time series: An empirical comparison of selected techniques. Remote Sensing of Environment, 113, 248-258. https://doi.org/10.3390/rs6032024Holben, B.N. 1986. Characteristics of maximum-value composite image from temporal AVHRR data. International Journal of Remote Sensing, 7, 1417- 1434. https://doi.org/10.1080/01431168608948945Jönsson, P., Eklundh, L. 2004. TIMESAT - A program for analyzing time-series of satellite sensor data. Computers and Geoscience, 30, 833-845. https://doi.org/10.1016/j.cageo.2004.05.006Julien, Y., Sobrino, J.A. 2009. Global land surface phenology trends from GIMMS database. International Journal of Remote Sensing, 30(13), 3495-3513. https://doi.org/ 10.1080/01431160802562255Julien, Y., Sobrino, J.A. 2010. Comparison of cloudreconstruction methods for time series of composite NDVI data. Remote Sensing of Environment, 114, 618-625. https://doi.org/10.1016/j.rse.2009.11.001Julien, Y., Sobrino, J.A. 2012. Correcting Long Term Data Record V3 estimated LST from orbital drift effects. Remote Sensing of Environment, 123, 207- 219. https://doi.org/10.1016/j.rse.2012.03.016Julien, Y., Sobrino, J.A., Verhoef, W. 2006. Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999. Remote Sensing of Environment, 103, 43-55. https://doi.org/10.1016/j.rse.2006.03.011Ke, L., Ding, X., Song, C. 2013. Reconstruction of time series MODIS LST in central Qinghai-Tibet plateau using geostatistical approach. IEEE Geoscience and Remote Sensing Letters, 10(6), 1602-1606. https://doi.org/10.1109/LGRS.2013.2263553Lin, C.H., Lai, K.H., Chen, Z.B., Chen, J.Y. 2014. Patch-based information reconstruction of cloud-contaminated multitemporal images. IEEE Transactions on Geoscience and Remote Sensing, 52(1), 163-174. https://doi.org/10.1109/ TGRS.2012.2237408Ma, M., Veroustraete, F. 2006. Reconstructing pathfinder AVHRR land NDVI timeseries data for the Northwest of China. Advances in Space Research, 37, 835-840. https://doi.org/10.1016/j.asr.2005.08.037Michishita, R., Jin, Z., Chen, J., Xu, B. 2014. Empirical comparison of noise reduction techniques for NDVI time-series based on a new measure. ISPRS Journal of Photogrammetry and Remote Sensing, 91, 17-28. https://doi.org/10.1016/j.isprsjprs.2014.01.003Moreno, A., García-Haro, F.J., Martínez, B., Gilabert, M.A. 2014. Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter. Remote Sensing, 6, 8238-8260. https://doi.org/10.3390/rs6098238Munyati, C., Mboweni, G. 2012. Variation in NDVI values with change in spatial resolution for semi-arid savanna vegetation: a case study in northwestern South Africa. International Journal of Remote Sensing, 34(7), 2253-2267. https://doi.org/10.1080/01431161.2012.743692Pedelty, J., Devadiga, S., Masuoka, E., Brown, M., Pinzon, J., Tucker, C., et al. 2007. Generating a long-term land data record from the AVHRR and MODIS instruments. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2007, 1021-1025, https://doi.org/10.1109/IGARSS.2007.4422974Poggio, L., Gimona, A., Brown, I. 2012. Spatiotemporal MODIS EVI gap filling under cloud cover: an example in Scotland. ISPRS Journal of Photogrammetry and Remote Sensing, 72, 56-72. https://doi.org/10.1016/j.isprsjprs.2012.06.003Roerink, G.J., Menenti, M., Verhoef, W. 2000. Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9), 1911-1917. https://doi.org/10.1080/014311600209814Rouse, J.W., Haas, R.H., Scheel, J.A., Deering, D.W. 1974. Monitoring Vegetation Systems in the Great Plains with ERTS. 3rd Earth Resource Technology Satellite (ERTS) Symposium Proceedings, Vol. 1, 48-62.Sobrino, J.A. Julien, Y. 2011. Global trends in NDVI derived parameters obtained from GIMMS data. International Journal of Remote Sensing, 32(15), 4267-4279. https://doi.org/10.1080/01431161.2010 .486414Sobrino, J.A., Julien, Y. 2016. Exploring the validity of the Long Term Data Record V4 database for land surface monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 99, 1-8, https://doi.org/10.1109/ JSTARS.2016.2567642Swinnen, E., Veroustraete, F. 2008. Extending the SPOT-VEGETATION time series (1998-2006) back in time with NOAA-AVHRR data (1985- 1998) for Southern Africa. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 558-572. https://doi.org/10.1109/TGRS.2007.909948Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127-150. https://doi.org/10.1016/0034-4257(79)90013-0Tucker, C.J., Pinzon, J.E., Brown, M.E., Slayback, D.A. Pak, E.W., Mahoney, R., Vermote, E.F., El Saleous, N. 2005. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing, 26(20), 4485-4498. https://doi.org/10.1080/01431160500168686van Dijk, A., Callis, S., Sakamoto, C. and Decker, W. 1987. Smoothing vegetation index profiles: An alternative method for reducing radiometric disturbance in NOAA/AVHRR data. Photogrammetric Engineering and Remote Sensing, 53, 1059-1067.Viovy, N., Arino, O., Velward, A. 1992. The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series International Journal of Remote Sensing, 13, 1585-1590. https://doi.org/10.1080/01431169208904212Weiss, D.J., Atkinson, P.M., Bhatt, S., Mappin, B., Hay, S.I., Gething, P.W. 2014. An effective approach for gap-filling continental scale remotely sensed time-series. ISPRS Journal of Photogrammetry and Remote Sensing, 98, 106-118. https://doi.org/10.1016/j.isprsjprs.2014.10.001White, M.A., De Beurs, K.M., Didan, K., Inouye, D. W., Richardson, A.D., et al. 2009. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006. Global Change Biology, 15, 2335-2359. https://doi.org/10.1111/j.1365-2486.2009.01910.xXiao, Z., Liang, S., Wang, T., Liu, Q. 2015. Reconstruction of satellite-retrieved land-surface reflectance based on temporally-continuous vegetation indices. Remote Sensing, 7, 9844-9864. https://doi.org/10.3390/rs70809844Xu, L., Li, B., Yuan, Y., Gao, X., Zhang, T. 2015. A temporal-spatial iteration method to reconstruct NDVI time series datasets. Remote Sensing, 7, 8906- 8924. https://doi.org/10.3390/rs70708906Yang, G., Shen, H., Zhang, L., He, Z. and Li, X. 2015. A moving weighted harmonic analysis method for reconstructing high-quality SPOT VEGETATION NDVI time-series data. IEEE Transactions on Geoscience and Remote Sensing, 53(11), 6008- 6021. https://doi.org/10.1109/TGRS.2015.2431315Zhou, J., Jia, L. and Menenti, M. 2015. Reconstruction of global MODIS NDVI time series: Performance of Harmonic ANalysis of Time Series (HANTS). Remote Sensing of Environment, 163, 217-228. https://doi.org/10.1016/j.rse.2015.03.01

Overuse Injuries in Professional Ballet

Ballet is an athletic activity with a marked artistic component, that need a highest technical requirement and repetitive movements. In this way, Overuse injuries, as we have been able to demonstrate in our studies, will be the most frequent injuries in ballet. The technical requierements of ballet will influence both injury specificity for each discipline and for both sexes, usually with higher technical requirements among women and higher athletic requirements among men. The patellofemoral syndrome is the most frequent overuse injuries in ballet, related to decompensating mechanisms to increase a naturally weak in turnout or dehors. This injury and others as the snapping hip, are more common among women, with higher technical requirements than men, and in the more technically demanding disciplines such as classical ballet. Other important injuries in ballet are Achilles tendinopathy, the mechanical low back pain, or the Os trigonum Syndrome. It will be very important to know about, the biomechanic and pathomechanic of the Ballet specific technical gesture, the intrinsecal and environmental risk factors involved in ballet injuries, the injury-based differences among ballet disciplines and among age and professional seniority, as well as the most important preventive measures in ballet

Competitive Segmentation Performance on Near-lossless and Lossy Compressed Remote Sensing Images

Image segmentation lies at the heart of multiple image processing chains, and achieving accurate segmentation is of utmost importance as it impacts later processing. Image segmentation has recently gained interest in the field of remote sensing, mostly due to the widespread availability of remote sensing data. This increased availability poses the problem of transmitting and storing large volumes of data. Compression is a common strategy to alleviate this problem. However, lossy or near-lossless compression prevents a perfect reconstruction of the recovered data. This letter investigates the image segmentation performance in data reconstructed after a near-lossless or a lossy compression. Two image segmentation algorithms and two compression standards are evaluated on data from sev- eral instruments. Experimental results reveal that segmentation performance over previously near-lossless and lossy compressed images is not markedly reduced at low and moderate compression ratios. In some scenarios, accurate segmentation performance can be achieved even for high compression ratios

Methylation-Dependent Gene Silencing Induced by Interleukin 1β via Nitric Oxide Production

Interleukin (IL)-1β is a pleiotropic cytokine implicated in a variety of activities, including damage of insulin-producing cells, brain injury, or neuromodulatory responses. Many of these effects are mediated by nitric oxide (NO) produced by the induction of NO synthase (iNOS) expression. We report here that IL-1β provokes a marked repression of genes, such as fragile X mental retardation 1 (FMR1) and hypoxanthine phosphoribosyltransferase (HPRT), having a CpG island in their promoter region. This effect can be fully prevented by iNOS inhibitors and is dependent on DNA methylation. NO donors also cause FMR1 and HPRT gene silencing. NO-induced methylation of FMR1 CpG island can be reverted by demethylating agents which, in turn, produce the recovery of gene expression. The effects of IL-1β and NO appear to be exerted through activation of DNA methyltransferase (DNA MeTase). Although exposure of the cells to NO does not increase DNA MeTase gene expression, the activity of the enzyme selectively increases when NO is applied directly on a nuclear protein extract. These findings reveal a previously unknown effect of IL-1β and NO on gene expression, and demonstrate a novel pathway for gene silencing based on activation of DNA MeTase by NO and acute modification of CpG island methylation

Land surface temperature representativeness in a heterogeneous area through a distributed energy-water balance model and remote sensing data

Abstract. Land surface temperature is the link between soil-vegetation-atmosphere fluxes and soil water content through the energy water balance. This paper analyses the representativeness of land surface temperature (LST) for a distributed hydrological water balance model (FEST-EWB) using LST from AHS (airborne hyperspectral scanner), with a spatial resolution between 2–4 m, LST from MODIS, with a spatial resolution of 1000 m, and thermal infrared radiometric ground measurements that are compared with the representative equilibrium temperature that closes the energy balance equation in the distributed hydrological model. Diurnal and nocturnal images are analyzed due to the non stable behaviour of the thermodynamic temperature and to the non linear effects induced by spatial heterogeneity. Spatial autocorrelation and scale of fluctuation of land surface temperature from FEST-EWB and AHS are analysed at different aggregation areas to better understand the scale of representativeness of land surface temperature in a hydrological process. The study site is the agricultural area of Barrax (Spain) that is a heterogeneous area with a patchwork of irrigated and non irrigated vegetated fields and bare soil. The used data set was collected during a field campaign from 10 to 15 July 2005 in the framework of the SEN2FLEX project

Cambios en la representación polínica de los ecosistemas fluvio-marinos de transición del entorno de la Ría de Vigo durante los últimos 1500 años

XV lnternational A.P.L.E. Symposium of Palynolog

A Few Descriptive and Optimization Issues on the Material Flow at a Research-Academic Institution: The Role of Simulation

Lately, significant work in the area of Intelligent Manufacturing has become public and mainly applied within the frame of industrial purposes. Special efforts have been made in the implementation of new technologies, management and control systems, among many others which have all evolved the field. Aware of all this and due to the scope of new projects and the need of turning the existing flexible ideas into more autonomous and intelligent ones, i.e.: Intelligent Manufacturing, the present paper emerges with the main aim of contributing to the design and analysis of the material flow in either systems, cells or work stations under this new "intelligent" denomination. For this, besides offering a conceptual basis in some of the key points to be taken into account and some general principles to consider in the design and analysis of the material flow, also some tips on how to define other possible alternative material flow scenarios and a classification of the states a system, cell or workstation are offered as well. All this is done with the intentions of relating it with the use of simulation tools, for which these have been briefly addressed with a special focus on the Witness simulation package. For a better comprehension, the previous elements are supported by a detailed layout, other figures and a few expressions which could help obtaining necessary data. Such data and others will be used in the future, when simulating the scenarios in the search of the best material flow configurations