Estimating long-term spatio-temporal variations of urban vegetation using Landsat time series: a case study of Kyiv city, Ukraine

2021 ◽  
Author(s):  
D. Svidzinska
Keyword(s):  
Time Series ◽  
Temporal Variations ◽  
Urban Vegetation ◽  
Spatio Temporal

Seabird bycatch vulnerability in pelagic longline fisheries based on modelling of a long-term dataset

2021 ◽  
pp. 1-16
Author(s):  
CAN ZHOU ◽  
NIGEL BROTHERS
Keyword(s):  
Capture Event ◽  
Retrieval Rate ◽  
Competitive Environments ◽  
Global Concern ◽  
Competitive Species ◽  
Spatio Temporal ◽  
Longline Fisheries ◽  
Pelagic Longline

Summary The incidental mortality of seabirds in fisheries remains a serious global concern. Obtaining unbiased and accurate estimates of bycatch rates is a priority for seabird bycatch mitigation and demographic research. For measuring the capture risk of seabird interactions in fisheries, the rate of carcass retrieval from hauled gear is commonly used. However, reliability can be limited by a lack of direct capture observations and the substantial pre-haul bycatch losses known to occur, meaning incidence of seabird bycatch is underestimated. To solve this problem, a new measure (bycatch vulnerability) that links an observed interaction directly to the underlying capture event is proposed to represent the capture risk of fishery interactions by seabirds. The new measure is not affected by subsequent bycatch loss. To illustrate how to estimate and analyse bycatch vulnerability, a case study based on a long-term dataset of seabird interactions and capture confirmation is provided. Bayesian modelling and hypothesis testing were conducted to identify important bycatch risk factors. Competition was found to play a central role in determining seabird bycatch vulnerability. More competitive environments were riskier for seabirds, and larger and thus more competitive species were more at risk than smaller sized and less competitive species. Species foraging behaviour also played a role. On the other hand, no additional effect of physical oceanic condition and spatio-temporal factors on bycatch vulnerability was detected. Bycatch vulnerability is recommended as a replacement for the commonly used bycatch rate or carcass retrieval rate to measure the capture risk of an interaction. Combined with a normalized contact rate, bycatch vulnerability offers an unbiased estimate of seabird bycatch rate in pelagic longline fisheries.

scholarly journals CONSTRUCTION AND ANALYSIS OF LONG-TERM SURFACE TEMPERATURE DATASET IN FUJIAN PROVINCE

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1223-1227 ◽  
Author(s):  
W. E. Li ◽  
X. Q. Wang ◽  
H. Su
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface ◽  
Rapid Development ◽  
Temporal Distribution ◽  
Heat Fluxes ◽  
Urban Region ◽  
Fujian Province ◽  
Spatio Temporal

Land surface temperature (LST) is a key parameter of land surface physical processes on global and regional scales, linking the heat fluxes and interactions between the ground and atmosphere. Based on MODIS 8-day LST products (MOD11A2) from the split-window algorithms, we constructed and obtained the monthly and annual LST dataset of Fujian Province from 2000 to 2015. Then, we analyzed the monthly and yearly time series LST data and further investigated the LST distribution and its evolution features. The average LST of Fujian Province reached the highest in July, while the lowest in January. The monthly and annual LST time series present a significantly periodic features (annual and interannual) from 2000 to 2015. The spatial distribution showed that the LST in North and West was lower than South and East in Fujian Province. With the rapid development and urbanization of the coastal area in Fujian Province, the LST in coastal urban region was significantly higher than that in mountainous rural region. The LST distributions might affected by the climate, topography and land cover types. The spatio-temporal distribution characteristics of LST could provide good references for the agricultural layout and environment monitoring in Fujian Province.

Time Series Forecasting in Retail Sales Using LSTM and Prophet

2021 ◽  
pp. 241-262
Author(s):  
Clony Junior ◽  
Pedro Gusmão ◽  
José Moreira ◽  
Ana Maria M. Tome
Keyword(s):  
Time Series ◽  
Data Science ◽  
Short Term Memory ◽  
Special Kind ◽  
Time Series Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Use Of Time

Data science highlights fields of study and research such as time series, which, although widely explored in the past, gain new perspectives in the context of this discipline. This chapter presents two approaches to time series forecasting, long short-term memory (LSTM), a special kind of recurrent neural network (RNN), and Prophet, an open-source library developed by Facebook for time series forecasting. With a focus on developing forecasting processes by data mining or machine learning experts, LSTM uses gating mechanisms to deal with long-term dependencies, reducing the short-term memory effect inherent to the traditional RNN. On the other hand, Prophet encapsulates statistical and computational complexity to allow broad use of time series forecasting, prioritizing the expert's business knowledge through exploration and experimentation. Both approaches were applied to a retail time series. This case study comprises daily and half-hourly forecasts, and the performance of both methods was measured using the standard metrics.

Long-term dynamics of natural populations of Schistosoma mansoni among Rattus rattus in patchy environment

Parasitology ◽  
1992 ◽  
Vol 104 (2) ◽  
pp. 291-298 ◽  
Author(s):  
A. Théron ◽  
J. P. Pointier ◽  
S. Morand ◽  
D. Imbert-Establet ◽  
G. Borel
Keyword(s):  
Schistosoma Mansoni ◽  
Rattus Rattus ◽  
Natural Populations ◽  
Ecological Factors ◽  
Temporal Variations ◽  
Distribution Analysis ◽  
Patchy Environment ◽  
Human Parasite ◽  
Spatio Temporal

SUMMARYDynamics of natural populations of Schistosoma mansoni were studied during 8 consecutive years among Rattus rattus populations from 8 transmission sites of the marshy forest focus of Guadeloupe (French West Indies). The schistosome population is over-dispersed (k = 0·119) within the murine hosts and ecological factors linked to the patchy environment may be responsible for such aggregated distribution. Analysis of the spatio-temporal variations in prevalences, intensities and abundances showed limited variations of the infection during the 8 years at the level of the whole parasite population but great spatial heterogeneity at the level of local schistosome populations. Inter-populational genetic variability linked to the degree of adaptation of this human parasite to the murine host may explain differences in transmission dynamics between the local populations of S. mansoni.

scholarly journals Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

2020 ◽  
Vol 12 (3) ◽  
pp. 576 ◽  
Author(s):  
Zhonghua He ◽  
Liping Lei ◽  
Yuhui Zhang ◽  
Mengya Sheng ◽  
Changjiang Wu ◽  
...  
Keyword(s):  
Temporal Variations ◽  
Atmospheric Co2 ◽  
Total Carbon ◽  
Kriging Method ◽  
Good Reliability ◽  
Potential Applications ◽  
Satellite Sensors ◽  
Spatio Temporal ◽  
Atmospheric Co2 Concentrations

Column-averaged dry air mole fraction of atmospheric CO2 (XCO2), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO2 concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO2 from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO2. The approach integrated XCO2 from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO2 (GM-XCO2) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO2 precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R2 of 0.97 from cross-validation. GM-XCO2 showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO2 or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO2 product may be also used in different carbon cycle research applications with different precision requirements.

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