Temporal and spatial variability of chlorophyll a concentration in Lake Taihu using MODIS time-series data

Hydrobiologia ◽  
2010 ◽  
Vol 661 (1) ◽  
pp. 235-250 ◽  
Author(s):  
Yuchao Zhang ◽  
Shan Lin ◽  
Xin Qian ◽  
Qin’geng Wang ◽  
Yu Qian ◽  
...  
Keyword(s):  
Time Series ◽  
Spatial Variability ◽  
Chlorophyll A ◽  
Time Series Data ◽  
Lake Taihu ◽  
Series Data ◽  
Chlorophyll A Concentration ◽  
Temporal And Spatial Variability ◽  
Temporal And Spatial

scholarly journals Fishing Ground Mapping Based on Chlorophyll-A Distribution Using Aqua Modis Satellite Imagery in The Fisheries Management Area (FMA) 712

2021 ◽  
Vol 324 ◽  
pp. 01007
Author(s):  
Yulius Yulius ◽  
Vaninda Aidina ◽  
Muhammad Ramdhan ◽  
August Daulat
Keyword(s):  
Chlorophyll A ◽  
Fisheries Management ◽  
Satellite Imagery ◽  
Time Series Data ◽  
Monsoon Season ◽  
Fishing Ground ◽  
Series Data ◽  
Chlorophyll A Concentration ◽  
Management Area ◽  
Java Sea

The fishing ground is identical and closely related to chlorophyll-a abundance in the waters, associated with its fisheries potential. This research has been conducted to determine the concentration and distribution of chlorophyll-a in the seas, especially in the Fisheries Management Area (FMA) 712 encompassing the Java Sea. The analysis used monthly time-series data collected from the satellite imagery over the last 11 years, taken through the ERDDAP website. The mean concentration of chlorophyll-a in the study area was 1.13 mg/l in June 2013. Based on 11 years of data processing, it can be concluded that the highest concentration of chlorophyll-a occurred in the west monsoon season from November to April, where increased rainfall caused the nutrient wash from terrestrial to the open waters. The lowest chlorophyll-a concentration happened in the east monsoon season from April to November, where the dry season arrived. The existence of pelagic fishes in FMA 712, such as Sardinella Lemurs and Sardinella Fimbriata, in waters highly influenced by the chlorophyll-a concentration, while Thunnus Sp. and Exocoetidae Sp. not affected significantly to the existence of chlorophyll-a concentration and its distribution.

Multi-dimensional Prediction Model for Cell Traffic in City Scale

2020 ◽  
pp. 2150010
Author(s):  
Hong Wang ◽  
Liqun Wang ◽  
Shufang Zhao ◽  
Xiuming Yue
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Base Stations ◽  
Series Data ◽  
Traffic Prediction ◽  
Percentage Error ◽  
Spatial Dimensions ◽  
Temporal And Spatial

Traffic prediction is a classical time series prediction which has been investigated in different domains, but most existing models are proposed based on limited time or spatial scale. Mobile cellular network traffic prediction is of paramount importance for quality-of-service (QoS) and power management of the cellular base stations, especially in the 5G era. Through the statistical analysis of the real historical traffic data obtained in a city scale spanning across multiple months, this paper makes an in-depth study of the temporal characteristics and behavior rules of the model data traffic. Considering that the time series data show different changing rules under the different time dimensions, spatial dimensions and independent dimensions, a multi-dimensional recurrent neural network (MDRNN) prediction model is established to predict the future cell traffic volume over various temporal and spatial dimensions. The data of this paper are trained and tested over real data of a city, and the granularity of the proposed prediction model can be drilled down to the cell level. Compared with the traditional trend fitting method, the proposed model achieves mean absolute percentage error (MAPE) reduction of 6.56%, and provides guidance for energy efficiency optimization and power consumption reduction of base stations in various temporal and spatial dimensions.

scholarly journals POTENSIAL FISHING GROUND MAPPING OF SKIPJACK AND FRIGATE TUNA IN NORTH NANGGROE ACEH DARUSSALAM WATERS

2009 ◽  
Vol 1 (1) ◽  
Author(s):  
Muklis Muklis ◽  
Jonson Lumban Gaol ◽  
Domu Simbolon
Keyword(s):  
Chlorophyll A ◽  
Positive Relationship ◽  
Sea Surface ◽  
Fishing Ground ◽  
Chlorophyll A Concentration ◽  
Temporal And Spatial Variability ◽  
Processing Software ◽  
Image Processing Software ◽  
Temporal And Spatial ◽  
Banda Aceh

Potential fishing ground mapping of skipjack and frigate tuna was determined based on sea surface temperature (SST) and chlorophyll-a concentration. The objectives of study were to analyze the temporal and spatial variability of SST and chlorophyll-a and it’s relationship on potential fishing ground. Chlorophyll-a concentration and SST were derived from Moderate Imaging Spectroradiometer (MODIS) sensors using SeaDAS image processing software. Catch data of skipjack and frigate tuna collected from Lampulo fishing base, Banda Aceh from January 2006 until November 2007. Linier regression was used to analysis relationship between catch perunit effort (CPUE) of fish and oceanographic parameters. The analysis of SST and chlorophyll-a concentration showed the temporal and spatial variations in northern part of Aceh seawaters. Both of Chlorophyll-a concentration and SST have positive relationship on CPUE of skipjack and frigate tuna.Keywords: Mapping, potential fishing ground, skipjack and frigate tuna, North Nanggroe Aceh Darussalam Waters, chlorophyll-a, SST.

scholarly journals Hydrography, nutrients and plankton abundance in the hot spot of Abu Qir Bay, Alexandria, Egypt

2001 ◽  
Vol 2 (2) ◽  
pp. 17 ◽  
Author(s):  
N.E. ABDEL-AZIZ ◽  
M.A. FAHMY ◽  
M.M. DORGHAM
Keyword(s):  
Dissolved Oxygen ◽  
Spatial Variability ◽  
Chlorophyll A ◽  
Hot Spot ◽  
Nutrient Concentrations ◽  
Southwestern Part ◽  
Monthly Average ◽  
Temporal And Spatial Variability ◽  
Temporal And Spatial ◽  
Abu Qir Bay

The hydrography, nutrient levels and plankton abundance were investigated monthly for a complete annual cycle in the southwestern part of Abu Qir Bay, the most polluted and biologically productive area on the Egyptian Mediterranean coast. Intense temporal and spatial variability was observed in all measured parameters characteristic of the effects of several effluents discharged into the bay. Based on the present investigation, the southwestern Bay can be divided ecologically and biologically into two parts: one including the near shore strip, which is directly affected by the waste waters, and a second comprising the southwestern part of the coastal strip and the offshore stations, both of which are relatively far away from the land-based effluents.The Bay water was characterized by low transparency (monthly average: 64-280 cm), dissolved oxygen (monthly average 2.0-6.8 mg/l) and surface salinity (monthly average: 24.8-37.9 ppt), the highest limits usually being in the offshore section. Water fertility and plankton production were high in the Bay indicating an occasionally acute degree of eutrophication, particularly nearshore. Great variations occurred in the concentrations of nutrients throughout the year, with monthly averages of 0.8-50.88 mM for ammonia, 0.42-3.28 mM for nitrite, 1.29-17.36 mM for nitrate, 0.32-3.61 mM for reactive phosphate and 1.09-33.34 mM for reactive silicate. Similarly, the abundance of both phytoplankton and zooplankton showed pronounced temporal and spatial variability, whereas the monthly average chlorophyll-a fluctuated between 2.06 and 52.64 mg/l and zooplankton between 31x103 and 248.6x103 ind./m3. However, the absolute values of all parameters indicated remarkably wider ranges of variations.Significant correlation was found between chlorophyll-a and some ecological parameters like temperature, salinity, transparency, dissolved oxygen, nitrite and between zooplankton and temperature, while there was a significant correlation between nutrient concentrations and salinity changes.

Visualizing Change: Using Cartographic Animation to Explore Remotely-Sensed Data

2001 ◽  
pp. 30-42 ◽  
Author(s):  
Mark Harrower
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Time Series Data ◽  
Series Data ◽  
Remotely Sensed Data ◽  
Process Of Change ◽  
Detection Techniques ◽  
Space Time Structure ◽  
Current Change ◽  
Temporal And Spatial

This research describes a geovisualization tool that is designed to facilitate exploration of satellite time-series data. Current change-detection techniques are insufficient for the task of representing the complex behaviors and motions of geographic processes because they emphasize the outcomes of change rather than depict the process of change itself. Cartographic animation of satellite data is proposed as a means of visually summarizing the complex behaviors of geographic entities. Animation provides a means for better understanding the complexity of geographic change because it can represent both the state of a geographic system at a given time (i.e. its space-time structure) and the behavior of that system over time (i.e. trends). However, a simple animation of satellite time-series data is often insufficient for this task because it overwhelms the viewer with irrelevant detail or presents data at an inappropriate temporal and spatial resolution. To solve this problem, dynamic temporal and spatial aggregation tools are implemented with the geovisualization system to allow analysts to change the resolution of their data on the fly. These tools provide (1) a means of detecting structures or trends that may be exhibited only at certain scales and (2) a method for smoothing or filtering unwanted noise from the satellite data. This research is grounded in a delineation of the nature of change, and proposes a framework of four kinds of geographic change: location, size/extent, attribute and existence. Each of these kinds of change may be continuous (a process) or discrete (an event).

scholarly journals POTENSIAL FISHING GROUND MAPPING OF SKIPJACK AND FRIGATE TUNA IN NORTH NANGGROE ACEH DARUSSALAM WATERS

2009 ◽  
Vol 1 (1) ◽  
Author(s):  
Muklis Muklis ◽  
Jonson Lumban Gaol ◽  
Domu Simbolon
Keyword(s):  
Chlorophyll A ◽  
Positive Relationship ◽  
Sea Surface ◽  
Fishing Ground ◽  
Chlorophyll A Concentration ◽  
Temporal And Spatial Variability ◽  
Processing Software ◽  
Image Processing Software ◽  
Temporal And Spatial ◽  
Banda Aceh

<p>Potential fishing ground mapping of skipjack and frigate tuna was determined based on sea surface temperature (SST) and chlorophyll-a concentration. The objectives of study were to analyze the temporal and spatial variability of SST and chlorophyll-a and it’s relationship on potential fishing ground. Chlorophyll-a concentration and SST were derived from Moderate Imaging Spectroradiometer (MODIS) sensors using SeaDAS image processing software. Catch data of skipjack and frigate tuna collected from Lampulo fishing base, Banda Aceh from January 2006 until November 2007. Linier regression was used to analysis relationship between catch perunit effort (CPUE) of fish and oceanographic parameters. The analysis of SST and chlorophyll-a concentration showed the temporal and spatial variations in northern part of Aceh seawaters. Both of Chlorophyll-a concentration and SST have positive relationship on CPUE of skipjack and frigate tuna.</p><p>Keywords: Mapping, potential fishing ground, skipjack and frigate tuna, North Nanggroe Aceh Darussalam Waters, chlorophyll-a, SST.</p>

scholarly journals Spatial-time motifs discovery

2020 ◽  
Vol 24 (5) ◽  
pp. 1121-1140
Author(s):  
Heraldo Borges ◽  
Murillo Dutra ◽  
Amin Bazaz ◽  
Rafaelli Coutinho ◽  
Fábio Perosi ◽  
...  
Keyword(s):  
Time Series ◽  
Literature Review ◽  
Motif Discovery ◽  
Time Series Data ◽  
Synthetic Data ◽  
Series Data ◽  
Spatial Constraints ◽  
Traditional Methods ◽  
Temporal And Spatial ◽  
Motif Discovery Algorithm

Discovering motifs in time series data has been widely explored. Various techniques have been developed to tackle this problem. However, when it comes to spatial-time series, a clear gap can be observed according to the literature review. This paper tackles such a gap by presenting an approach to discover and rank motifs in spatial-time series, denominated Combined Series Approach (CSA). CSA is based on partitioning the spatial-time series into blocks. Inside each block, subsequences of spatial-time series are combined in a way that hash-based motif discovery algorithm is applied. Motifs are validated according to both temporal and spatial constraints. Later, motifs are ranked according to their entropy, the number of occurrences, and the proximity of their occurrences. The approach was evaluated using both synthetic and seismic datasets. CSA outperforms traditional methods designed only for time series. CSA was also able to prioritize motifs that were meaningful both in the context of synthetic data and also according to seismic specialists.

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