We propose a new model-free segmentation method, JULES, which combines recent statistical multiresolution techniques with local deconvolution for idealization of ion channel recordings. The multiresolution criterion takes into account scales down to the sampling rate enabling the detection of flickering events, i.e., events on small temporal scales, even below the filter frequency. For such small scales the deconvolution step allows for a precise determination of dwell times and, in particular, of amplitude levels, a task which is not possible with common thresholding methods. This is confirmed theoretically and in a comprehensive simulation study. In addition, JULES can be applied as a preprocessing method for a refined hidden Markov analysis. Our new methodolodgy allows us to show that gramicidin A flickering events have the same amplitude as the slow gating events. JULES is available as an R function jules in the package clampSeg.

Most of the vegetation growth in Mexico shows clear responses to the availability and abundance of surface water which can almost exclusively be linked to rainfall. This study employs four years of time series with daily precipitation and modeled surface reflectance at a spatial resolution of 0.05° which were analyzed for lag-dependent linear serial correlation. Positive lags of, on average, 1 to 1.5 months between precipitation and NDVI as a surrogate for vegetation growth as well as statistically significant correlation coefficients (p<;0.05) were found for vegetated surfaces. Vegetation types which are physiologically more dependent on precipitation for seasonal vegetation growth show a shorter response time and stronger relationship to precipitation than evergreen forests and shrublands. The relationship with respect to precipitation variations among several years remains inconclusive.

Earth observation (EO) data play a crucial role in monitoring ecosystems and environmental processes. Time series of satellite data are essential for long-term studies in this context. Working with large volumes of satellite data, however, can still be a challenge, as the computational environment with respect to storage, processing and data handling can be demanding, which sometimes can be perceived as a barrier when using EO data for scientific purposes. In particular, open-source developments which comprise all components of EO data handling and analysis are still scarce. To overcome this difficulty, we present Tools for Analyzing Time Series of Satellite Imagery (TATSSI), an open-source platform written in Python that provides routines for downloading, generating, gap-filling, smoothing, analyzing and exporting EO time series. Since TATSSI integrates quality assessment and quality control flags when generating time series, data quality analysis is the backbone of any analysis made with the platform. We discuss TATSSI’s 3-layered architecture (data handling, engine and three application programming interfaces (API)); by allowing three APIs (a native graphical user interface, some Jupyter Notebooks and the Python command line) this development is exceptionally user-friendly. Furthermore, to demonstrate the application potential of TATSSI, we evaluated MODIS time series data for three case studies (irrigation area changes, evaluation of moisture dynamics in a wetland ecosystem and vegetation monitoring in a burned area) in different geographical regions of Mexico. Our analyses were based on methods such as the spatio-temporal distribution of maxima over time, statistical trend analysis and change-point decomposition, all of which were implemented in TATSSI. Our results are consistent with other scientific studies and results in these areas and with related in-situ data.

La península de Yucatán (LPY) alberga 32% de las selvas tropicales de México. Por ello esta zona presenta una alta densidad de nubes a lo largo del año, característica que resulta un reto particular para cualquier estudio de monitoreo de la vegetación a mediano y largo plazo basado en series de tiempo de imágenes satelitales. En este artículo presentamos los resultados de un análisis de clasificación de tendencias de una serie de tiempo (11 imágenes Landsat-7 ETM+ y 150 Landsat 8 OLI) del Índice de Vegetación de Diferencia Normalizada (NDVI, por sus siglas en inglés) por tipo de suelo y vegetación en la región oriental de Escárcega, Campeche, en LPY de 2014 a 2020. Aplicamos el algoritmo bfast01 para clasificar pixeles por tendencias lineales, globales (una línea con pendiente positiva o negativa a lo largo del periodo de estudio) o locales (dos segmentos de línea con pendiente positiva o negativa en cada segmento). A partir del análisis se deduce que la mayor parte de la región estudiada tiene NDVI con tendencias lineales globales (pardeamiento: 47%; enverdecimiento: 15.39%) y en menor grado tendencias lineales locales (pardeamiento demorado: 20.66%; pardeamiento a enverdecimiento: 6.04%; enverdecimiento demorado: 5.26%; enverdecimiento a pardeamiento: 3.88%). Consideramos que el enverdecimiento generalizado (el agregado de las clases enverdecimiento, enverdecimiento demorado y pardeamiento a enverdecimiento) y el pardeamiento generalizado (el agregado de las clases pardeamiento, pardeamiento demorado y enverdecimiento a pardeamiento) pueden interpretarse como dinámicas con indicios significativos de recuperación y degradación del NDVI, respectivamente. Dichas dinámicas fueron identificadas principalmente en la selva mediana subperennifolia (enverdecimiento generalizado: 10.26%; pardeamiento generalizado: 25.43%), selva baja espinosa subperennifolia (7.66 y 21.76) y en la vegetación secundaria arbórea de selva mediana (3.26 y 10.93). En 2017 y 2018 se identificaron las áreas más grandes con alguna clase de tendencia local lineal.