Monitoring the seasonal and interannual variation of the carbon sequestration in a temperate deciduous forest with MODIS time series data

2013 ◽  
Vol 306 ◽  
pp. 150-160 ◽  
Author(s):  
Xuguang Tang ◽  
Zongming Wang ◽  
Jing Xie ◽  
Dianwei Liu ◽  
Ankur R. Desai ◽  
...  
Keyword(s):  
Time Series ◽  
Carbon Sequestration ◽  
Interannual Variation ◽  
Time Series Data ◽  
Deciduous Forest ◽  
Series Data ◽  
Temperate Deciduous Forest ◽  
Temperate Deciduous

scholarly journals Vegetation Carbon Sequestration Mapping in Herbaceous Wetlands by Using a MODIS EVI Time-Series Data Set: A Case in Poyang Lake Wetland, China

2020 ◽  
Vol 12 (18) ◽  
pp. 3000
Author(s):  
Xue Dai ◽  
Guishan Yang ◽  
Desheng Liu ◽  
Rongrong Wan
Keyword(s):  
Time Series ◽  
Carbon Sequestration ◽  
Vegetation Index ◽  
Time Series Data ◽  
Poyang Lake ◽  
Carbon Stocks ◽  
Series Data ◽  
Biomass Carbon ◽  
Vegetation Carbon ◽  
Poyang Lake Wetland

The carbon sequestration capacity of wetland vegetation determines carbon stocks and changes in wetlands. However, modeling vegetation carbon sequestration of herbaceous wetlands is still problematic due to complex hydroecological processes and rapidly changing biomass carbon stocks. Theoretically, a vegetation index (VI) time series can retrieve the dynamic of biomass carbon stocks and could be used to calculate the cumulative composite of biomass carbon stocks during a given interval, i.e., vegetation carbon sequestration. Hence, we explored the potential for mapping vegetation carbon sequestration in herbaceous wetlands in this study by using a combination of remotely sensed VI time series and field observation data. This method was exemplarily applied for Poyang Lake wetland in 2016 by using a 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) time series. Results show that the vegetation carbon sequestration in this area was in the range of 193–1221 g C m−2 year−1 with a mean of 401 g C m−2 year−1 and a standard deviation of 172 g C m−2 year−1 in 2016. The approach has wider spatial applicability in wetlands than the currently used global map of vegetation production (MOD17A3) because our carbon estimation in areas depicted by ‘no data’ in the MOD17A3 product is considerable, which accounts for 91.2–91.5% of the total vegetation carbon sequestration of the wetland. Thus, we determined that VI time series data shows great potential for estimating vegetation carbon sequestration in herbaceous wetlands, especially with the continuously improving quality and frequency of satellite VI images.

scholarly journals Growing season moisture drives interannual variation in woody productivity of a temperate deciduous forest

2019 ◽  
Vol 223 (3) ◽  
pp. 1204-1216 ◽  
Author(s):  
Ryan Helcoski ◽  
Alan J. Tepley ◽  
Neil Pederson ◽  
Jennifer C. McGarvey ◽  
Victoria Meakem ◽  
...  
Keyword(s):  
Interannual Variation ◽  
Deciduous Forest ◽  
Growing Season ◽  
Temperate Deciduous Forest ◽  
Temperate Deciduous

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.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data
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