scholarly journals Phenological Characterization of Desert Sky Island Vegetation Communities with Remotely Sensed and Climate Time Series Data

2010 ◽  
Vol 2 (2) ◽  
pp. 388-415 ◽  
Author(s):  
Willem J.D. Van Leeuwen ◽  
Jennifer E. Davison ◽  
Grant M. Casady ◽  
Stuart E. Marsh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Remotely Sensed ◽  
Series Data ◽  
Vegetation Communities ◽  
Climate Time Series ◽  
Sky Island

A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data

2021 ◽  
Vol 24 ◽  
pp. 100618
Author(s):  
Philipe Riskalla Leal ◽  
Ricardo José de Paula Souza e Guimarães ◽  
Fábio Dall Cortivo ◽  
Rayana Santos Araújo Palharini ◽  
Milton Kampel
Keyword(s):  
Time Series ◽  
Extreme Events ◽  
Time Series Data ◽  
Remotely Sensed ◽  
Series Data ◽  
Precipitation Time Series ◽  
New Approach

Characterization of Time Series Data

2017 ◽  
pp. 211-230
Author(s):  
W. Kulp Christopher ◽  
J. Niskala Brandon
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data

scholarly journals Missing data imputation of high‐resolution temporal climate time series data

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
E Afrifa‐Yamoah ◽  
U. A. Mueller ◽  
S. M. Taylor ◽  
A. J. Fisher
Keyword(s):  
Time Series ◽  
Missing Data ◽  
High Resolution ◽  
Time Series Data ◽  
Series Data ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Climate Time Series

scholarly journals Deep learning classification of temporal data in ecology

2020 ◽  
Author(s):  
César Capinha ◽  
Ana Ceia-Hasse ◽  
Andrew M. Kramer ◽  
Christiaan Meijer
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Meteorological Data ◽  
Species Distribution Modelling ◽  
Series Data ◽  
Temporal Data ◽  
Climate Time Series ◽  
Biological Entities

AbstractTemporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach transforms the temporal data into static predictors of the classes. However, recent deep learning techniques can perform the classification using raw time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We present a general approach for time series classification that considers multiple deep learning algorithms and illustrate it with three case studies: i) insect species identification from wingbeat spectrograms; ii) species distribution modelling from climate time series and iii) the classification of phenological phases from continuous meteorological data. The deep learning approach delivered ecologically sensible and accurate classifications, proving its potential for wide applicability across subfields of ecology. We recommend deep learning as an alternative to techniques requiring the transformation of time series data.

scholarly journals CHARACTERIZATION OF LAND COVER SEASONALITY IN SENTINEL-1 TIME SERIES DATA

2020 ◽  
Vol V-3-2020 ◽  
pp. 97-104
Author(s):  
C. Dubois ◽  
M. M. Mueller ◽  
C. Pathe ◽  
T. Jagdhuber ◽  
F. Cremer ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Coniferous Forest ◽  
Series Data ◽  
Urban Land Cover ◽  
Multi Temporal ◽  
Forested Areas

Abstract. In this study, we analyze Sentinel-1 time series data to characterize the observed seasonality of different land cover classes in eastern Thuringia, Germany and to identify multi-temporal metrics for their classification. We assess the influence of different polarizations and different pass directions on the multi-temporal backscatter profile. The novelty of this approach is the determination of phenological parameters, based on a tool that has been originally developed for optical imagery. Furthermore, several additional multitemporal metrics are determined for the different classes, in order to investigate their separability for potential multi-temporal classification schemes. The results of the study show a seasonality for vegetation classes, which differs depending on the considered class: whereas pastures and broad-leaved forests show a decrease of the backscatter in VH polarization during summer, an increase of the backscatter in VH polarization is observed for coniferous forest. The observed seasonality is discussed together with meteorological information (precipitation and air temperature). Furthermore, a dependence of the backscatter of the pass direction (ascending/descending) is observed particularly for the urban land cover classes. Multi-temporal metrics indicate a good separability of principal land cover classes such as urban, agricultural and forested areas, but further investigation and use of seasonal parameters is needed for a distinct separation of specific forest sub-classes such as coniferous and deciduous.

scholarly journals On the influence of spatial sampling on climate networks

2014 ◽  
Vol 21 (3) ◽  
pp. 651-657 ◽  
Author(s):  
N. Molkenthin ◽  
K. Rehfeld ◽  
V. Stolbova ◽  
L. Tupikina ◽  
J. Kurths
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Voronoi Cell ◽  
Spatial Sampling ◽  
Series Data ◽  
Cell Size Distribution ◽  
Reconstructed Network ◽  
Climate Time Series ◽  
The Impact ◽  
Climate Networks

Abstract. Climate networks are constructed from climate time series data using correlation measures. It is widely accepted that the geographical proximity, as well as other geographical features such as ocean and atmospheric currents, have a large impact on the observable time-series similarity. Therefore it is to be expected that the spatial sampling will influence the reconstructed network. Here we investigate this by comparing analytical flow networks, networks generated with the START model and networks from temperature data from the Asian monsoon domain. We evaluate them on a regular grid, a grid with added random jittering and two variations of clustered sampling. We find that the impact of the spatial sampling on most network measures only distorts the plots if the node distribution is significantly inhomogeneous. As a simple diagnostic measure for the detection of inhomogeneous sampling we suggest the Voronoi cell size distribution.

Analyzing trends in Land Surface Temperature using remotely sensed time series data and the BFAST method

2020 ◽  
Author(s):  
Alexandra Gemitzi ◽  
George Falalakis
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Missing Values ◽  
Structural Changes ◽  
Time Series Data ◽  
Remotely Sensed ◽  
Series Data ◽  
Global Changes

<p>The present work deals with the time series analysis of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST). While many works have been published concerning the trends of nighttime and daytime LST at the regional or local scale, little attention has been paid to structural changes observed within the LST time series in various sub-periods. This could be of much interest not only for climate studies but also for unveiling the possible relation between natural disasters such as wildfires and global changes. In this work we tested the hypothesis of a constant trend in LST time series from 2000 to 2019 and highlighted the existence of periods with changing trends. The methodology was applied in an area of approximately 17.000 km<sup>2</sup> located in NE Greece and South Bulgaria. The nighttime and daytime LST time series data were initially subjected to a gap filling algorithm to account for missing values and were then aggregated at the catchment level. Furthermore, LST time series were analyzed using the Breaks For Additive Season and Trend (BFAST) method. Results indicated that an abrupt change in both nighttime and daytime LST trends was observed in all examined time series, indicating a transition from a decreasing LST regime from 2002 to 2006 to an abrupt increasing thereafter until today. An initial comparison with the existing inventory of wildfires in the area for the last 20 years indicated an increase of wildfire events which coincides with the LST breakpoint, indicating thus possible connections between rising LST and wildfire events.</p>

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