A Band Grouping Based Approach for Phenotype-Class Mapping of Tree Genotypes Using Spectro-Temporal Information in Hyperspectral Time-Series UAV Data

2021 ◽  
Author(s):  
Aravind Harikumar ◽  
Siyu Wang ◽  
Ingo Ensminger
Keyword(s):  
Time Series ◽  
Temporal Information ◽  
Phenotype Class

scholarly journals Systematic Water Fraction Estimation for a Global and Daily Surface Water Time-Series

2021 ◽  
Vol 13 (14) ◽  
pp. 2675
Author(s):  
Stefan Mayr ◽  
Igor Klein ◽  
Martin Rutzinger ◽  
Claudia Kuenzer
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Spatial Resolution ◽  
Pure Water ◽  
Feature Space ◽  
Observation Time ◽  
Temporal Information ◽  
Landsat 8 ◽  
Water Fraction ◽  
Classification Probability

Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product’s performance regarding mixed water/non-water pixels by an average of 11.6% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series.

scholarly journals J48SS: A Novel Decision Tree Approach for the Handling of Sequential and Time Series Data

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 21 ◽  
Author(s):  
Andrea Brunello ◽  
Enrico Marzano ◽  
Angelo Montanari ◽  
Guido Sciavicco
Keyword(s):  
Time Series ◽  
Decision Tree ◽  
Time Series Data ◽  
Temporal Information ◽  
Frequent Pattern ◽  
Series Data ◽  
The Novel ◽  
C4.5 Decision Tree ◽  
Business Setting ◽  
Interpretable Models

Temporal information plays a very important role in many analysis tasks, and can be encoded in at least two different ways. It can be modeled by discrete sequences of events as, for example, in the business intelligence domain, with the aim of tracking the evolution of customer behaviors over time. Alternatively, it can be represented by time series, as in the stock market to characterize price histories. In some analysis tasks, temporal information is complemented by other kinds of data, which may be represented by static attributes, e.g., categorical or numerical ones. This paper presents J48SS, a novel decision tree inducer capable of natively mixing static (i.e., numerical and categorical), sequential, and time series data for classification purposes. The novel algorithm is based on the popular C4.5 decision tree learner, and it relies on the concepts of frequent pattern extraction and time series shapelet generation. The algorithm is evaluated on a text classification task in a real business setting, as well as on a selection of public UCR time series datasets. Results show that it is capable of providing competitive classification performances, while generating highly interpretable models and effectively reducing the data preparation effort.

scholarly journals Future streamflow regime changes in the United States: assessment using functional classification

2020 ◽  
Vol 24 (8) ◽  
pp. 3951-3966 ◽  
Author(s):  
Manuela I. Brunner ◽  
Lieke A. Melsen ◽  
Andrew J. Newman ◽  
Andrew W. Wood ◽  
Martyn P. Clark
Keyword(s):  
Climate Change ◽  
United States ◽  
Time Series ◽  
Functional Data ◽  
Rocky Mountains ◽  
Classification Scheme ◽  
Temporal Information ◽  
Ungauged Basins ◽  
Regime Changes ◽  
Streamflow Regime

Abstract. Streamflow regimes are changing and expected to further change under the influence of climate change, with potential impacts on flow variability and the seasonality of extremes. However, not all types of regimes are going to change in the same way. Climate change impact assessments can therefore benefit from identifying classes of catchments with similar streamflow regimes. Traditional catchment classification approaches have focused on specific meteorological and/or streamflow indices, usually neglecting the temporal information stored in the data. The aim of this study is 2-fold: (1) develop a catchment classification scheme that enables incorporation of such temporal information and (2) use the scheme to evaluate changes in future flow regimes. We use the developed classification scheme, which relies on a functional data representation, to cluster a large set of catchments in the conterminous United States (CONUS) according to their mean annual hydrographs. We identify five regime classes that summarize the behavior of catchments in the CONUS: (1) intermittent regime, (2) weak winter regime, (3) strong winter regime, (4) New Year's regime, and (5) melt regime. Our results show that these spatially contiguous classes are not only similar in terms of their regimes, but also their flood and drought behavior as well as their physiographical and meteorological characteristics. We therefore deem the functional regime classes valuable for a number of applications going beyond change assessments, including model validation studies or predictions of streamflow characteristics in ungauged basins. To assess future regime changes, we use simulated discharge time series obtained from the Variable Infiltration Capacity hydrologic model driven with meteorological time series generated by five general circulation models. A comparison of the future regime classes derived from these simulations with current classes shows that robust regime changes are expected only for currently melt-influenced regions in the Rocky Mountains. These changes in mountainous, upstream regions may require adaption of water management strategies to ensure sufficient water supply in dependent downstream regions. Highlights. Functional data clustering enables formation of clusters of catchments with similar hydrological regimes and a similar drought and flood behavior. We identify five streamflow regime clusters: (1) intermittent regime, (2) weak winter regime, (3) strong winter regime, (4) New Year's regime, and (5) melt regime. Future regime changes are most pronounced for currently melt-dominated regimes in the Rocky Mountains. Functional regime clusters have widespread utility for predictions in ungauged basins and hydroclimate analyses.

scholarly journals PhenoRice: A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series

2017 ◽  
Vol 194 ◽  
pp. 347-365 ◽  
Author(s):  
Mirco Boschetti ◽  
Lorenzo Busetto ◽  
Giacinto Manfron ◽  
Alice Laborte ◽  
Sonia Asilo ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Temporal Information ◽  
Automatic Extraction ◽  
Spatio Temporal

scholarly journals EXPLOITATION OF SPECTRAL AND TEMPORAL INFORMATION FOR MAPPING PLANT SPECIES IN A FORMER INDUSTRIAL SITE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 559-566
Author(s):  
R. Gimenez ◽  
G. Lassalle ◽  
R. Hédacq ◽  
A. Elger ◽  
D. Dubucq ◽  
...  
Keyword(s):  
Time Series ◽  
Feature Selection ◽  
Plant Species ◽  
Vegetation Indices ◽  
Principal Component ◽  
Temporal Information ◽  
Support Vector ◽  
Species Discrimination ◽  
High Temporal Resolution ◽  
Periodic Monitoring

Abstract. Characterization and seasonal (periodic) monitoring of plant species distribution in the context of former industrial activity is crucial to assess long-term anthropogenic footprint on vegetated area. Species discrimination has shown promising results using both HyperSpectral (HS) and MultiSpectral (MS) images. Airborne HS instruments enable high spatial and spectral resolution imagery while time series of satellite MS images provide high temporal resolution and phenological information. This paper aims to compare supervised classification results obtained with non-parametric (Random Forest, RF, Support Vector Machine, SVM) and parametric methods (Regularized Logistic Regression, RLR) applied on both kinds of images acquired on an industrial brownfield. The studied site is a complex vegetated environment due to species diversity: 8 dominant species are retained. The performance obtained by preliminary feature selection based on principal component analysis and vegetation indices, to improve separability of spectral or temporal information according to species, is analysed. The best performance is obtained by RLR method applied on HS data without feature selection (global accuracy of 93 %). Feature selection is found to be a necessary step to perform classification with time series of MS images. Species that are difficult to distinguish from the HS image, namely Salix and Populus, are well separated using Sentinel-2 images (precision around 70%).

scholarly journals A Stochastic Multivariate Irregularly Sampled Time Series Imputation Method for Electronic Health Records

2021 ◽  
Vol 1 (3) ◽  
pp. 166-181
Author(s):  
Muhammad Adib Uz Zaman ◽  
Dongping Du
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Electronic Health Records ◽  
Missing Values ◽  
Time Series Data ◽  
Temporal Information ◽  
Series Data ◽  
High Dimensional ◽  
Health Records ◽  
Electronic Health

Electronic health records (EHRs) can be very difficult to analyze since they usually contain many missing values. To build an efficient predictive model, a complete dataset is necessary. An EHR usually contains high-dimensional longitudinal time series data. Most commonly used imputation methods do not consider the importance of temporal information embedded in EHR data. Besides, most time-dependent neural networks such as recurrent neural networks (RNNs) inherently consider the time steps to be equal, which in many cases, is not appropriate. This study presents a method using the gated recurrent unit (GRU), neural ordinary differential equations (ODEs), and Bayesian estimation to incorporate the temporal information and impute sporadically observed time series measurements in high-dimensional EHR data.

scholarly journals Future streamflow regime changes in the United States: assessment using functional classification

2020 ◽  
Author(s):  
Manuela I. Brunner ◽  
Lieke A. Melsen ◽  
Andrew J. Newman ◽  
Andrew W. Wood ◽  
Martyn P. Clark
Keyword(s):  
Climate Change ◽  
United States ◽  
Time Series ◽  
General Circulation ◽  
Classification Scheme ◽  
The United States ◽  
Hydrologic Model ◽  
Temporal Information ◽  
Large Set ◽  
Regime Changes

Abstract. Streamflow regimes are changing and expected to further change under the influence of climate change with potential impacts on flow variability and the seasonality of extremes. However, not all types of regimes are going to change in the same way. Climate change impact assessments can therefore benefit from identifying classes of catchments with similar streamflow regimes. Traditional catchment classification approaches have focused on specific meteorological and/or streamflow indices usually neglecting the temporal information stored in the data. The aim of this study is two-fold: (1) develop a catchment classification scheme that allows for the incorporation of such temporal information and (2) use the scheme to evaluate changes in future flow regimes. We use the developed classification scheme, which relies on a functional data representation, to cluster a large set of catchments in the conterminous United States (CONUS) according to their mean annual hydrographs. We identify five regime classes that summarize the behavior of catchments in the CONUS: (1) Intermittent regime, (2) weak winter regime, (3) strong winter regime, (4) New Year's regime, and (5) melt regime. Our results show that these spatially contiguous classes are not only similar in terms of their regimes, but also their flood and drought behavior, as well as their physiographical and meteorological characteristics. We therefore deem the functional regime classes valuable for a number of applications going beyond change assessments including model validation studies or the prediction of streamflow characteristics in ungauged basins. To assess future regime changes, we use simulated discharge time series obtained from the Variable Infiltration Capacity hydrologic model driven with meteorological time series generated by five general circulation models. A comparison of the future regime classes derived from these simulations with current classes shows that robust regime changes are expected only for currently melt-influenced regions in the Rocky Mountains. These changes in mountainous, upstream regions may require the adaptation of water management strategies to ensure sufficient water supply in dependent downstream regions.

Determination of temporal information granules to improve forecasting in fuzzy time series

2014 ◽  
Vol 41 (6) ◽  
pp. 3134-3142 ◽  
Author(s):  
Lizhu Wang ◽  
Xiaodong Liu ◽  
Witold Pedrycz ◽  
Yongyun Shao
Keyword(s):  
Time Series ◽  
Temporal Information ◽  
Fuzzy Time Series ◽  
Information Granules
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