Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data

Polar Record ◽  
1995 ◽  
Vol 31 (177) ◽  
pp. 179-190 ◽  
Author(s):  
Carl J. Markon ◽  
Michael D. Fleming ◽  
Emily F. Binnian
Keyword(s):  
Time Series ◽  
Photosynthetic Activity ◽  
Time Series Data ◽  
Landscape Patterns ◽  
Series Data ◽  
Vegetation Phenology ◽  
Regional Patterns ◽  
Landscape Processes ◽  
Composite Maps ◽  
Landscape Scales

AbstractAdvanced Very High Resolution Radiometer (AVHRR) satellite data were acquired and composited into twice-a-month periods from 1 May 1991 to 15 October 1991 in order to map vegetation characteristics of the Alaskan landscape. Unique spatial and temporal qualities of the AVHRR data provide information that leads to a better understanding of regional biophysical characteristics of vegetation communities and patterns. These data provided synoptic views of the landscape and depicted phenological diversity, temporal vegetation phenology (green-up, peak of green, and senescence), photosynthetic activity, and regional landscape patterns. Products generated from the data included a phenological class map, phenological composite maps (onset, peak, and duration), and photosynthetic activity maps (mean and maximum greenness). The time-series data provide opportunities to study phenological processes at small landscape scales over time periods of weeks, months, and years. Regional patterns identified on some of the maps are unique to specific areas; others correspond to biophysical or ecoregional boundaries. The data provide new insights to landscape processes, ecology, and landscape physiognomy that allow scientists to look at landscapes in ways that were previously difficult to achieve.

Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology

2012 ◽  
Vol 123 ◽  
pp. 400-417 ◽  
Author(s):  
Peter M. Atkinson ◽  
C. Jeganathan ◽  
Jadu Dash ◽  
Clement Atzberger
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Vegetation Phenology ◽  
Satellite Sensor

scholarly journals Discovering Spatial-temporal Patterns of COVID-19 Pandemic in South Korea: A Data-driven Approach using Dynamic Mode Decomposition

2021 ◽  
Author(s):  
Sungchan Kim ◽  
Minseok Kim ◽  
Sunmi Lee ◽  
Young Ju Lee
Keyword(s):  
Time Series ◽  
South Korea ◽  
Time Series Data ◽  
Temporal Patterns ◽  
Dynamic Mode Decomposition ◽  
Series Data ◽  
Dynamic Mode ◽  
Regional Patterns ◽  
Effective Prevention ◽  
Mode Decomposition

Abstract A novel severe acute respiratory syndrome coronavirus 2 emerged in December 2019, and it took only a few months for WHO to declare COVID-19 as a pandemic in March 2020. It is very challenging to discover complex spatial-temporal transmission mechanisms. However, it is crucial to capture essential features of regional-temporal patterns of COVID-19 to implement prompt and effective prevention or mitigation interventions. In this work, we develop a novel framework of compatible window-wise dynamic mode decomposition (CwDMD) for nonlinear infectious disease dynamics. The compatible window is a selected representative subdomain of time series data, in which compatibility between spatial and temporal resolutions is established so that DMD can provide meaningful data analysis. A total of four compatible windows have been selected from COVID-19 time-series data from January 20, 2020, to May 10, 2021, in South Korea. The spatiotemporal patterns of these four windows are then analyzed. Several hot and cold spots were identified, their spatial-temporal relationships, and some hidden regional patterns were discovered. Our analysis reveals that the first wave was contained in Daegu and Gyeongbuk area but it spread rapidly to the whole of South Korea after the second wave. Later on, the spatial distribution is seen to become more homogeneous after the third wave. Our analysis also identifies that some patterns are not related to regional relevance. These findings have then been analyzed and associated with the inter-regional and local characteristics of South Korea. Thus, the present study is expected to provide public health officials helpful insights for future regional-temporal specific mitigation plans.

scholarly journals Spatio-temporal monitoring of vegetation phenology in the dry sub-humid region of Nigeria using time series of AVHRR NDVI and TAMSAT datasets

2018 ◽  
Vol 10 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Babatunde Adeniyi Osunmadewa ◽  
Worku Zewdie Gebrehiwot ◽  
Elmar Csaplovics ◽  
Olabinjo Clement Adeofun
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Anthropogenic Activities ◽  
Phase 1 ◽  
Climatic Variability ◽  
Annual Rainfall ◽  
Series Data ◽  
Vegetation Phenology ◽  
The Impact

Abstract Time series data are of great importance for monitoring vegetation phenology in the dry sub-humid regions where change in land cover has influence on biomass productivity. However few studies have inquired into examining the impact of rainfall and land cover change on vegetation phenology. This study explores Seasonal Trend Analysis (STA) approach in order to investigate overall greenness, peak of annual greenness and timing of annual greenness in the seasonal NDVI cycle. Phenological pattern for the start of season (SOS) and end of season (EOS) was also examined across different land cover types in four selected locations. A significant increase in overall greenness (amplitude 0) and a significant decrease in other greenness trend maps (amplitude 1 and phase 1) was observed over the study period. Moreover significant positive trends in overall annual rainfall (amplitude 0) was found which follows similar pattern with vegetation trend. Variation in the timing of peak of greenness (phase 1) was seen in the four selected locations, this indicate a change in phenological trend. Additionally, strong relationship was revealed by the result of the pixel-wise regression between NDVI and rainfall. Change in vegetation phenology in the study area is attributed to climatic variability than anthropogenic activities.

scholarly journals Discovering spatiotemporal patterns of COVID-19 pandemic in South Korea

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sungchan Kim ◽  
Minseok Kim ◽  
Sunmi Lee ◽  
Young Ju Lee
Keyword(s):  
Time Series ◽  
South Korea ◽  
Time Series Data ◽  
Spatiotemporal Patterns ◽  
Dynamic Mode Decomposition ◽  
Series Data ◽  
Dynamic Mode ◽  
Regional Patterns ◽  
Effective Prevention ◽  
Mode Decomposition

AbstractA novel severe acute respiratory syndrome coronavirus 2 emerged in December 2019, and it took only a few months for WHO to declare COVID-19 as a pandemic in March 2020. It is very challenging to discover complex spatial–temporal transmission mechanisms. However, it is crucial to capture essential features of regional-temporal patterns of COVID-19 to implement prompt and effective prevention or mitigation interventions. In this work, we develop a novel framework of compatible window-wise dynamic mode decomposition (CwDMD) for nonlinear infectious disease dynamics. The compatible window is a selected representative subdomain of time series data, in which compatibility between spatial and temporal resolutions is established so that DMD can provide meaningful data analysis. A total of four compatible windows have been selected from COVID-19 time-series data from January 20, 2020, to May 10, 2021, in South Korea. The spatiotemporal patterns of these four windows are then analyzed. Several hot and cold spots were identified, their spatial–temporal relationships, and some hidden regional patterns were discovered. Our analysis reveals that the first wave was contained in the Daegu and Gyeongbuk areas, but it spread rapidly to the whole of South Korea after the second wave. Later on, the spatial distribution is seen to become more homogeneous after the third wave. Our analysis also identifies that some patterns are not related to regional relevance. These findings have then been analyzed and associated with the inter-regional and local characteristics of South Korea. Thus, the present study is expected to provide public health officials helpful insights for future regional-temporal specific mitigation plans.

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

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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