scholarly journals Polarimetric SAR Time-Series for Identification of Winter Land Use

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5574
Author(s):  
Julien Denize ◽  
Laurence Hubert-Moy ◽  
Eric Pottier
Keyword(s):  
Land Use ◽  
Time Series ◽  
Time Series Data ◽  
High Spatial Resolution ◽  
Synthetic Aperture ◽  
Series Data ◽  
Band Frequency ◽  
The Past ◽  
Land Use Types ◽  
L Band

In the past decade, high spatial resolution Synthetic Aperture Radar (SAR) sensors have provided information that contributed significantly to cropland monitoring. However, the specific configurations of SAR sensors (e.g., band frequency, polarization mode) used to identify land-use types remains underexplored. This study investigates the contribution of C/L-Band frequency, dual/quad polarization and the density of image time-series to winter land-use identification in an agricultural area of approximately 130 km² located in northwestern France. First, SAR parameters were derived from RADARSAT-2, Sentinel-1 and Advanced Land Observing Satellite 2 (ALOS-2) time-series, and one quad-pol and six dual-pol datasets with different spatial resolutions and densities were calculated. Then, land use was classified using the Random Forest algorithm with each of these seven SAR datasets to determine the most suitable SAR configuration for identifying winter land-use. Results highlighted that (i) the C-Band (F1-score 0.70) outperformed the L-Band (F1-score 0.57), (ii) quad polarization (F1-score 0.69) outperformed dual polarization (F1-score 0.59) and (iii) a dense Sentinel-1 time-series (F1-score 0.70) outperformed RADARSAT-2 and ALOS-2 time-series (F1-score 0.69 and 0.29, respectively). In addition, Shannon Entropy and SPAN were the SAR parameters most important for discriminating winter land-use. Thus, the results of this study emphasize the interest of using Sentinel-1 time-series data for identifying winter land-use.

scholarly journals Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

2020 ◽  
Vol 12 (17) ◽  
pp. 2735 ◽  
Author(s):  
Carlos M. Souza ◽  
Julia Z. Shimbo ◽  
Marcos R. Rosa ◽  
Leandro L. Parente ◽  
Ane A. Alencar ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Google Earth ◽  
Series Data ◽  
Data Series ◽  
Forest Conversion ◽  
Pixel Resolution ◽  
Historical Land Use

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

scholarly journals Reconsidering the importance of the past in predator–prey models: both numerical and functional responses depend on delayed prey densities

2013 ◽  
Vol 280 (1768) ◽  
pp. 20131389 ◽  
Author(s):  
Jiqiu Li ◽  
Andy Fenton ◽  
Lee Kettley ◽  
Phillip Roberts ◽  
David J. S. Montagnes
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Prey Abundance ◽  
Series Data ◽  
Functional Responses ◽  
New Approach ◽  
Predator Prey ◽  
The Past ◽  
Predator Prey Models ◽  
Underlying Mechanisms

We propose that delayed predator–prey models may provide superficially acceptable predictions for spurious reasons. Through experimentation and modelling, we offer a new approach: using a model experimental predator–prey system (the ciliates Didinium and Paramecium ), we determine the influence of past-prey abundance at a fixed delay (approx. one generation) on both functional and numerical responses (i.e. the influence of present : past-prey abundance on ingestion and growth, respectively). We reveal a nonlinear influence of past-prey abundance on both responses, with the two responding differently. Including these responses in a model indicated that delay in the numerical response drives population oscillations, supporting the accepted (but untested) notion that reproduction, not feeding, is highly dependent on the past. We next indicate how delays impact short- and long-term population dynamics. Critically, we show that although superficially the standard (parsimonious) approach to modelling can reasonably fit independently obtained time-series data, it does so by relying on biologically unrealistic parameter values. By contrast, including our fully parametrized delayed density dependence provides a better fit, offering insights into underlying mechanisms. We therefore present a new approach to explore time-series data and a revised framework for further theoretical studies.

scholarly journals Time-Series Causality with Missing Data

2021 ◽  
Vol 6 (1) ◽  
pp. 1-4
Author(s):  
Bo Yuan Chang ◽  
Mohamed A. Naiel ◽  
Steven Wardell ◽  
Stan Kleinikkink ◽  
John S. Zelek
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Missing Values ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Gaussian Process Regression ◽  
Series Data ◽  
Causal Relationships ◽  
Sampled Data ◽  
The Past

Over the past years, researchers have proposed various methods to discover causal relationships among time-series data as well as algorithms to fill in missing entries in time-series data. Little to no work has been done in combining the two strategies for the purpose of learning causal relationships using unevenly sampled multivariate time-series data. In this paper, we examine how the causal parameters learnt from unevenly sampled data (with missing entries) deviates from the parameters learnt using the evenly sampled data (without missing entries). However, to obtain the causal relationship from a given time-series requires evenly sampled data, which suggests filling the missing data values before obtaining the causal parameters. Therefore, the proposed method is based on applying a Gaussian Process Regression (GPR) model for missing data recovery, followed by several pairwise Granger causality equations in Vector Autoregssive form to fit the recovered data and obtain the causal parameters. Experimental results show that the causal parameters generated by using GPR data filling offers much lower RMSE than the dummy model (fill with last seen entry) under all missing values percentage, suggesting that GPR data filling can better preserve the causal relationships when compared with dummy data filling, thus should be considered when dealing with unevenly sampled time-series causality learning.

scholarly journals Estimating time-varying selection coefficients from time series data of allele frequencies

2020 ◽  
Author(s):  
Iain Mathieson
Keyword(s):  
Time Series ◽  
East Asia ◽  
Ancient Dna ◽  
Experimental Evolution ◽  
Time Series Data ◽  
Natural Populations ◽  
Allele Frequencies ◽  
Series Data ◽  
Alcohol Metabolism ◽  
The Past

AbstractTime series data of allele frequencies are a powerful resource for detecting and classifying natural and artificial selection. Ancient DNA now allows us to observe these trajectories in natural populations of long-lived species such as humans. Here, we develop a hidden Markov model to infer selection coefficients that vary over time. We show through simulations that our approach can accurately estimate both selection coefficients and the timing of changes in selection. Finally, we analyze some of the strongest signals of selection in the human genome using ancient DNA. We show that the European lactase persistence mutation was selected over the past 5,000 years with a selection coefficient of 2-2.5% in Britain, Central Europe and Iberia, but not Italy. In northern East Asia, selection at the ADH1B locus associated with alcohol metabolism intensified around 4,000 years ago, approximately coinciding with the introduction of rice-based agriculture. Finally, a derived allele at the FADS locus was selected in parallel in both Europe and East Asia, as previously hypothesized. Our approach is broadly applicable to both natural and experimental evolution data and shows how time series data can be used to resolve fine-scale details of selection.

scholarly journals Comparative analysis of hydrologic signatures in two agricultural watersheds in east-central Illinois: legacies of the past to inform the future

2013 ◽  
Vol 17 (11) ◽  
pp. 4607-4623 ◽  
Author(s):  
M. A. Yaeger ◽  
M. Sivapalan ◽  
G. F. McIsaac ◽  
X. Cai
Keyword(s):  
Land Use ◽  
Comparative Analysis ◽  
Time Series Data ◽  
Past History ◽  
Series Data ◽  
Agricultural System ◽  
Current Response ◽  
Geologic History ◽  
The Past ◽  
Central Illinois

Abstract. Historically, the central Midwestern US has undergone drastic anthropogenic land use change, having been transformed, in part through government policy, from a natural grassland system to an artificially drained agricultural system devoted to row cropping corn and soybeans. Current federal policies are again influencing land use in this region with increased corn acreage and new biomass crops proposed as part of an energy initiative emphasizing biofuels. To better address these present and future challenges it is helpful to understand whether and how the legacies of past changes have shaped the current response of the system. To this end, a comparative analysis of the hydrologic signatures in both spatial and time series data from two central Illinois watersheds was undertaken. The past history of these catchments is reflected in their current hydrologic responses, which are highly heterogeneous due to differences in geologic history, artificial drainage patterns, and reservoir operation, and manifest temporally, from annual to daily timescales, and spatially, both within and between the watersheds. These differences are also apparent from analysis of the summer low flows, where the more tile-drained watershed shows greater variability overall than does the more naturally drained one. In addition, precipitation in this region is also spatially heterogeneous even at small scales, and this, interacting with and filtering through the historical modifications to the system, increases the complexity of the problem of predicting the catchment response to future changes.

scholarly journals Relative Contribution of Greenhouse Gases and Ozone-Depleting Substances to Temperature Trends in the Stratosphere: A Chemistry–Climate Model Study

2010 ◽  
Vol 23 (1) ◽  
pp. 28-42 ◽  
Author(s):  
Richard S. Stolarski ◽  
Anne R. Douglass ◽  
Paul A. Newman ◽  
Steven Pawson ◽  
Mark R. Schoeberl
Keyword(s):  
Time Series ◽  
Greenhouse Gases ◽  
Climate Model ◽  
Time Series Data ◽  
Temperature Time Series ◽  
Series Data ◽  
Future Evolution ◽  
The Past ◽  
Ozone Depleting Substances ◽  
Temperature Time

Abstract The temperature of the stratosphere has decreased over the past several decades. Two causes contribute to that decrease: well-mixed greenhouse gases (GHGs) and ozone-depleting substances (ODSs). This paper addresses the attribution of temperature decreases to these two causes and the implications of that attribution for the future evolution of stratospheric temperature. Time series analysis is applied to simulations of the Goddard Earth Observing System Chemistry–Climate Model (GEOS CCM) to separate the contributions of GHGs from those of ODSs based on their different time-dependent signatures. The analysis indicates that about 60%–70% of the temperature decrease of the past two decades in the upper stratosphere near 1 hPa and in the lower midlatitude stratosphere near 50 hPa resulted from changes attributable to ODSs, primarily through their impact on ozone. As ozone recovers over the next several decades, the temperature should continue to decrease in the middle and upper stratosphere because of GHG increases. The time series of observed temperature in the upper stratosphere is approaching the length needed to separate the effects of ozone-depleting substances from those of greenhouse gases using temperature time series data.

Land use/land cover (LULC) classification with MODIS time series data and validation in the Amur River Basin

2011 ◽  
Vol 32 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Kaishan Song ◽  
Zongmin Wang ◽  
Qingfeng Liu ◽  
Dianwei Liu ◽  
V. V. Ermoshin ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
River Basin ◽  
Time Series Data ◽  
Series Data ◽  
Amur River Basin ◽  
Land Use Land Cover ◽  
Amur River ◽  
The Amur River

scholarly journals Likelihood Inference for Generalized Integer Autoregressive Time Series Models

Econometrics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 43 ◽  
Author(s):  
Harry Joe
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Probability Generating Function ◽  
Likelihood Estimation ◽  
Model Fit ◽  
Likelihood Inference ◽  
Series Data ◽  
Time Series Models ◽  
Autoregressive Time Series ◽  
The Past

For modeling count time series data, one class of models is generalized integer autoregressive of order p based on thinning operators. It is shown how numerical maximum likelihood estimation is possible by inverting the probability generating function of the conditional distribution of an observation given the past p observations. Two data examples are included and show that thinning operators based on compounding can substantially improve the model fit compared with the commonly used binomial thinning operator.

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