scholarly journals Distribution of Chlorophyll in Coastal Borneo Island Using Modis Terra Satellite Data

2021 ◽  
Vol 934 (1) ◽  
pp. 012011
Author(s):  
N F Yunita ◽  
M Usman ◽  
D Merdekawati
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Chlorophyll Concentration ◽  
Fishing Ground ◽  
Series Data ◽  
Distribution Area ◽  
Data Series ◽  
Aquatic Area ◽  
Marine Aquaculture ◽  
Chlorophyll Distribution

Abstract Clorophyll is the colour pigment most common found in phytoplankton. Its concentration is one of the indicator of the high of productivity of aquatic area, especially in coastal area. Information of chlorophyll concentration and distribution is very important to determine the suitable location of marine aquaculture and prediction of fishing ground. The aims of this research were to: 1) find out and analyze the concentration of chlorophyll and its distribution in Borneo Island Indonesia and 2) the pattern of chlorophyll distribution for each provinces using modis terra data for five years (from January 2016 to December 2020) in monthly and annually data series. In addition, it used Seadas 7.5.3 for data visualization. The result of this research showed that the chlorophyll concentration ranged 0,045 – 20 mg/m3 and clorophyll distribution affected by the location that seen in all variation data series. In annually time series data, the highest value of concentration shown by west borneo province and central borneo province with the distribution area were larger as well. The distribution of chlorophyll in monthly data showed almost same with annually data time series. The difference was just in large area distribution. The pattern of chlorophyll distribution also showed that in the west Kalimantan and central Kalimantan area had the highest values.

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 An ECM Approach for Long Run Relationship Between Real Exchange Rate and Output Growth: Evidence from Bangladesh

2015 ◽  
Vol 63 (2) ◽  
pp. 105-110 ◽  
Author(s):  
Khnd Md Mostafa Kamal
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Real Exchange Rate ◽  
Time Series Data ◽  
Output Growth ◽  
Series Data ◽  
Data Series ◽  
Currency Exchange ◽  
Long Run ◽  
Short Run

Currency exchange rate is an important aspect in modern economy which indicates the strength of domestic currency with respect to international currency. This study uses 42 years’ (1972 to 2013) time series data for Bangladesh in order to empirically determine whether the real exchange rate has significant impact on output growth for Bangladesh by using error correction model (ECM).The time series econometrics properties of the data series have been thoroughly investigated to apply ECM approach. The empirical evidence suggests mixed results; in the short run low exchange rate has positive significant effect while in the long run output growth is positively affected high exchange rate pass through.Dhaka Univ. J. Sci. 63(2):105-110, 2015 (July)

scholarly journals Time Series Outlier Detection Based on Sliding Window Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Yufeng Yu ◽  
Yuelong Zhu ◽  
Shijin Li ◽  
Dingsheng Wan
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Time Series Data ◽  
Series Data ◽  
Forecasting Model ◽  
Data Series ◽  
Hydrologic Time Series ◽  
Prediction Confidence ◽  
Evaluation Of Data ◽  
Operation And Management

In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI), which can be calculated by the predicted value and confidence coefficient. The use ofPCIas threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

scholarly journals Detection and mapping of burnt areas from time series of MODIS-derived NDVI data in a Mediterranean region

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Miguel García ◽  
José Alloza ◽  
Ángeles Mayor ◽  
Susana Bautista ◽  
Francisco Rodríguez
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Mediterranean Region ◽  
Time Series Data ◽  
Series Data ◽  
Data Series ◽  
Regional Government ◽  
Fire Event ◽  
Fire Scar ◽  
Burnt Areas

AbstractModerate resolution remote sensing data, as provided by MODIS, can be used to detect and map active or past wildfires from daily records of suitable combinations of reflectance bands. The objective of the present work was to develop and test simple algorithms and variations for automatic or semiautomatic detection of burnt areas from time series data of MODIS biweekly vegetation indices for a Mediterranean region. MODIS-derived NDVI 250m time series data for the Valencia region, East Spain, were subjected to a two-step process for the detection of candidate burnt areas, and the results compared with available fire event records from the Valencia Regional Government. For each pixel and date in the data series, a model was fitted to both the previous and posterior time series data. Combining drops between two consecutive points and 1-year average drops, we used discrepancies or jumps between the pre and post models to identify seed pixels, and then delimitated fire scars for each potential wildfire using an extension algorithm from the seed pixels. The resulting maps of the detected burnt areas showed a very good agreement with the perimeters registered in the database of fire records used as reference. Overall accuracies and indices of agreement were very high, and omission and commission errors were similar or lower than in previous studies that used automatic or semiautomatic fire scar detection based on remote sensing. This supports the effectiveness of the method for detecting and mapping burnt areas in the Mediterranean region.

scholarly journals Modelling aboveground net primary production (ANPP) of an Atlantic mountain grassland based on time series approach

2019 ◽  
Vol 45 (2) ◽  
pp. 551
Author(s):  
A. Salaberria ◽  
G. García-Baquero ◽  
I. Odriozola ◽  
A. Aldezabal
Keyword(s):  
Time Series ◽  
Primary Production ◽  
Time Series Data ◽  
Net Primary Production ◽  
Series Data ◽  
Data Series ◽  
Treatment Level ◽  
Aboveground Net Primary Production ◽  
Smoothing Methods ◽  
Mountain Grassland

Because primary productivity is related both with the energy that sustains food webs and with species diversity, it is usually considered a key ecosystem property and a reliable indicator of available forage. In this work the aboveground net primary production (ANPP) of an Atlantic mountain grassland system was modelled in order to attempt producing short-term forecasts. Since grazing influences productivity, two treatment levels (grazing and exclusion) were experimentally applied in each of three field sites. Monthly ANPP data were then collected over three consecutive vegetative periods (2006-2008), thereby obtaining six time series (one per plot). Since no significant differences among sites (within treatments) were found, these six series were later reduced through averaging to only two series (one per treatment level). Two kinds of statistical models were then used to attempt monthly ANPP forecasting: exponential smoothing methods and ARIMA models. Both methodologies turned out to produce inadequate forecasts due to the presence of marked local features (innovative outliers) in our relatively short time-series data. Nonetheless, useful information for a more innovative shepherding management was revealed (e.g. the presence of within-year variation in ANPP, and differences between the grazing and exclusion treatments). Longer data series, which would require a more demanding effort in sampling investment, are likely necessary in order to obtain adequate forecasts using these time series methodologies.

scholarly journals Detection of Changes in Hydrological Time Series During Recent Decades

2020 ◽  
Vol 28 (2) ◽  
pp. 56-62
Author(s):  
Mária Ďurigová ◽  
Kamila Hlavčová ◽  
Jana Poórová
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Time Series Data ◽  
Kendall Test ◽  
Change Points ◽  
Series Data ◽  
Data Series ◽  
Time Data ◽  
Air Temperatures ◽  
Pettitt’S Test

AbstractAn analysis of a hydrological time-series data offers the possibility of detecting changes that have arisen due to climate change or change in land use. This paper deals with the detection of changes in the hydrological time data series. The trend analysis was applied at 58 stage-discharge gauging stations that are located throughout Slovakia, with the measurement period from 1962 to 2017. The Mann-Kendall test show a declining trends in the summer and a few rising trends in the winter in discharges. In the town of Banská Bystrica at a station on the Hron River, decades of discharges, air temperatures, and precipitation totals were analyzed. The five decades from the 1960s to the 2000s were used. The hydrological time data series were also analyzed by the Pettitt’s test, which is used to detect change points. The decadal analysis at the Banská Bystrica station shows an increase in the air temperature but insignificant changes in discharges and precipitation. Pettitt’s test identified many change points in the 1990s in the air temperature.

scholarly journals Mixing ARMA Models with EGARCH Models and Using it in Modeling and Analyzing the Time Series of Temperature

2021 ◽  
pp. 2307-2326
Author(s):  
Abduljabbar Ali Mudhir
Keyword(s):  
Time Series ◽  
Mixed Model ◽  
Time Series Data ◽  
Series Data ◽  
Data Series ◽  
Arma Models ◽  
Prediction Ability ◽  
Proposed Model ◽  
Egarch Model ◽  
Unconditional Variance

In this article our goal is mixing ARMA models with EGARCH models and composing a mixed model ARMA(R,M)-EGARCH(Q,P) with two steps, the first step includes modeling the data series by using EGARCH model alone interspersed with steps of detecting the heteroscedasticity effect and estimating  the model's parameters and check the adequacy of the model. Also we are predicting the conditional variance and verifying it's convergence to the unconditional variance value. The second step includes mixing ARMA with EGARCH and using the mixed (composite) model in modeling time series data and predict future values then asses the prediction ability of the proposed model by using prediction error criterions.

A Solution for Absent Spatial Data: The Common Correlated Effects Estimator

2020 ◽  
pp. 016001762095913
Author(s):  
Michael Beenstock ◽  
Daniel Felsenstein
Keyword(s):  
Time Series ◽  
Spatial Data ◽  
Time Series Data ◽  
Housing Stock ◽  
National Level ◽  
Series Data ◽  
Data Series ◽  
Regional Data ◽  
Common Correlated Effects ◽  
The Common

Informed regional policy needs good regional data. As regional data series for key economic variables are generally absent whereas national-level time series data for the same variables are ubiquitous, we suggest an approach that leverages this advantage. We hypothesize the existence of a pervasive “common factor” represented by the national time series that affects regions differentially. We provide an empirical illustration in which national FDI is used in place of panel data for FDI, which are absent. The proposed methodology is tested empirically with respect to the determinants of regional demand for housing. We use a quasi-experimental approach to compare the results of a “common correlated effects” (CCE) estimator with a benchmark case when absent regional data are omitted. Using three common factors relating to national population, income and housing stock, we find mixed support for the common correlated effects hypothesis. We conclude by discussing how our experimental design may serve as a methodological prototype for further tests of CCE as a solution to the absent spatial data problem.

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