scholarly journals Satellite-based forest monitoring: spatial and temporal forecast of growing index and short-wave infrared band

2016 ◽  
Vol 11 (1) ◽  
Author(s):  
Caroline Bayr ◽  
Heinz Gallaun ◽  
Ulrike Kleb ◽  
Birgit Kornberger ◽  
Martin Steinegger ◽  
...  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Time Series Data ◽  
Short Wave ◽  
Forest Monitoring ◽  
Random Regression ◽  
Series Data ◽  
Infrared Band

For detecting anomalies or interventions in the field of forest monitoring we propose an approach based on the spatial and temporal forecast of satellite time series data. For each pixel of the satellite image three different types of forecasts are provided, namely spatial, temporal and combined spatio-temporal forecast. Spatial forecast means that a clustering algorithm is used to group the time series data based on the features normalised difference vegetation index (NDVI) and the short-wave infrared band (SWIR). For estimation of the typical temporal trajectory of the NDVI and SWIR during the vegetation period of each spatial cluster, we apply several methods of functional data analysis including functional principal component analysis, and a novel form of random regression forests with online learning (streaming) capability. The temporal forecast is carried out by means of functional time series analysis and an autoregressive integrated moving average model. The combination of the temporal forecasts, which is based on the past of the considered pixel, and spatial forecasts, which is based on highly correlated pixels within one cluster and their past, is performed by functional data analysis, and a variant of random regression forests adapted to online learning capabilities. For evaluation of the methods, the approaches are applied to a study area in Germany for monitoring forest damages caused by wind-storm, and to a study area in Spain for monitoring forest fires.

scholarly journals Functional Data Analysis of Weather Variables Linked to Fusarium Head Blight Epidemics in the United States

2019 ◽  
Vol 109 (1) ◽  
pp. 96-110 ◽  
Author(s):  
D. A. Shah ◽  
E. D. De Wolf ◽  
P. A. Paul ◽  
L. V. Madden
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Time Series Data ◽  
The United States ◽  
Series Data ◽  
Head Blight ◽  
Weather Variables ◽  
The Mean

In past efforts, input weather variables for Fusarium head blight (FHB) prediction models in the United States were identified after following some version of the window-pane algorithm, which discretizes a continuous weather time series into fixed-length windows before searching for summary variables associated with FHB risk. Functional data analysis, on the other hand, reconstructs the assumed continuous process (represented by a series of recorded weather data) by using smoothing functions, and is an alternative way of working with time series data with respect to FHB risk. Our objective was to functionally model weather-based time series data linked to 865 observations of FHB (covering 16 states and 31 years in total), classified as epidemics (FHB disease index ≥ 10%) and nonepidemics (FHB disease index < 10%). Altogether, 94 different time series variables were modeled by penalized cubic B-splines for the smoothing function, from 120 days pre-anthesis to 20 days post-anthesis. Functional mean curves, standard deviations, and first derivatives were plotted for FHB epidemics relative to nonepidemics. Function-on-scalar regressions assessed the temporal trends of the magnitude and significance of the mean difference between functionally represented weather time series associated with FHB epidemics and nonepidemics. The mean functional weather-variable curve for epidemics started to deviate, in general, from that for nonepidemics as early as 40 days pre-anthesis for several weather variables. The greatest deviations were often near anthesis, the period of maximum susceptibility of wheat to FHB-causing fungi. The most consistent separations between the mean functional curves were seen with the daily averages of moisture-related variables (such as average relative humidity) and with variables summarizing the daily variation in temperature (as opposed to the daily mean). Functional data analysis was useful for extending our knowledge of relationships between weather variables and FHB epidemics.

scholarly journals Weather Patterns Associated with DON Levels in Norwegian Spring Oat Grain: A Functional Data Approach

Plants ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 73
Author(s):  
Anne-Grete Roer Hjelkrem ◽  
Heidi Udnes Aamot ◽  
Morten Lillemo ◽  
Espen Sannes Sørensen ◽  
Guro Brodal ◽  
...  
Keyword(s):  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Time Series Data ◽  
Weather Conditions ◽  
Weather Data ◽  
Series Data ◽  
Growth Stages ◽  
Head Blight ◽  
Weather Patterns

Fusarium graminearum is regarded as the main deoxynivalenol (DON) producer in Norwegian oats, and high levels of DON are occasionally recorded in oat grains. Weather conditions in the period around flowering are reported to have a high impact on the development of Fusarium head blight (FHB) and DON in cereal grains. Thus, it would be advantageous if the risk of DON contamination of oat grains could be predicted based on weather data. We conducted a functional data analysis of weather-based time series data linked to DON content in order to identify weather patterns associated with increased DON levels. Since flowering date was not recorded in our dataset, a mathematical model was developed to predict phenological growth stages in Norwegian spring oats. Through functional data analysis, weather patterns associated with DON content in the harvested grain were revealed mainly from about three weeks pre-flowering onwards. Oat fields with elevated DON levels generally had warmer weather around sowing, and lower temperatures and higher relative humidity or rain prior to flowering onwards, compared to fields with low DON levels. Our results are in line with results from similar studies presented for FHB epidemics in wheat. Functional data analysis was found to be a useful tool to reveal weather patterns of importance for DON development in oats.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

scholarly journals Topological Data Analysis Approaches to Uncovering the Timing of Ring Structure Onset in Filamentous Networks

2021 ◽  
Vol 83 (3) ◽  
Author(s):  
Maria-Veronica Ciocanel ◽  
Riley Juenemann ◽  
Adriana T. Dawes ◽  
Scott A. McKinley
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Actin Filaments ◽  
Time Series Data ◽  
Polymer Networks ◽  
Topological Data Analysis ◽  
Series Data ◽  
Topological Features ◽  
Topological Data

AbstractIn developmental biology as well as in other biological systems, emerging structure and organization can be captured using time-series data of protein locations. In analyzing this time-dependent data, it is a common challenge not only to determine whether topological features emerge, but also to identify the timing of their formation. For instance, in most cells, actin filaments interact with myosin motor proteins and organize into polymer networks and higher-order structures. Ring channels are examples of such structures that maintain constant diameters over time and play key roles in processes such as cell division, development, and wound healing. Given the limitations in studying interactions of actin with myosin in vivo, we generate time-series data of protein polymer interactions in cells using complex agent-based models. Since the data has a filamentous structure, we propose sampling along the actin filaments and analyzing the topological structure of the resulting point cloud at each time. Building on existing tools from persistent homology, we develop a topological data analysis (TDA) method that assesses effective ring generation in this dynamic data. This method connects topological features through time in a path that corresponds to emergence of organization in the data. In this work, we also propose methods for assessing whether the topological features of interest are significant and thus whether they contribute to the formation of an emerging hole (ring channel) in the simulated protein interactions. In particular, we use the MEDYAN simulation platform to show that this technique can distinguish between the actin cytoskeleton organization resulting from distinct motor protein binding parameters.

scholarly journals Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

2020 ◽  
Vol 12 (11) ◽  
pp. 1876 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Hideki Saito
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Series Data ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

scholarly journals Using and acquiring time-series data with the EMSO ERIC DataLab

2020 ◽  
Author(s):  
Raul Bardaji ◽  
Jaume Piera ◽  
Juanjo Dañobeitia ◽  
Ivan Rodero
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Web Application ◽  
Time Series Data ◽  
Interdisciplinary Teams ◽  
Series Data ◽  
Published Data ◽  
Oceanographic Data ◽  
Engineering Best Practices ◽  
Parameters Of Interest

&lt;p&gt;In marine sciences, the way in which many research groups work is changing as scientists use published data to complement their field campaign data online, thanks to the large increase in the number of open access observations. Many institutions are making great efforts to provide the data following FAIR principles (findability, accessibility, interoperability, and reusability) and are bringing together interdisciplinary teams of data scientists and data engineers.&lt;/p&gt;&lt;p&gt;There are different platforms for downloading marine and oceanographic data and many libraries to analyze data. However, the reality is that scientists continue to have difficulty finding the data they need. On many occasions, data platforms provide information about the metadata, but they do not show any underlying graph of the data that can be downloaded. Sometimes, scientists cannot download only the data parameters of interest and have to download huge amounts of data with other not useful parameters for their studies. On other occasions, the platform allows to download the data parameters of interest but offers the time-series data as many files, and it is the scientist who has to join the pieces of data into a single dataset to be analyzed correctly. EMSO ERIC is developing a data service that helps reduce the burden of scientists to search and acquire data as much as possible.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;We present the EMSO ERIC DataLab web application, which provides users with capabilities to preview harmonized data from the EMSO ERIC observatories, perform some basic data analyses, create or modify datasets, and download them. Use case scenarios of the DataLab include the creation of a NetCDF file with time-series information across EMSO ERIC observatories.&lt;/p&gt;&lt;p&gt;The DataLab has been developed using engineering best practices and trend technologies for big data management, including specialized Python libraries for web environments and oceanographic data analysis, such as Plotly, Dash, Flask, and the Module for Ocean Observatory Data Analysis (MOODA).&lt;/p&gt;

Examining the Long Run Effects of Export, Import and FDI Inflows on the FDI Outflows from India: A Causality Analysis

2009 ◽  
Vol 10 (1) ◽  
pp. 65-88
Author(s):  
Nandita Dasgupta
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Driving Force ◽  
Time Series Data ◽  
Series Data ◽  
Causality Analysis ◽  
Fdi Inflows ◽  
Long Run ◽  
Time Series Data Analysis ◽  
Trade And Investment

The objective of this paper is to examine the effects of international trade and investment related macro economic variables, namely, exports, imports and FDI inflows on the outflows of FDI from India over 1970 through 2005. Using time series data analysis, the empirical part of the paper finds unidirectional Granger Causality from export and import to FDI outflows but no such causality exists from FDI inflows to the corresponding outflows from India. Results confirm the assumption that lagged imports and exports are a driving force of ing front.

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