scholarly journals MODIS-derived fire spatial and temporal distribution during haze season in Southeast Asia using empirical orthogonal function

2021 ◽  
Vol 880 (1) ◽  
pp. 012003
Author(s):  
Aulia Nisa’ul Khoir ◽  
Maggie Chel Gee Ooi ◽  
Juneng Liew ◽  
Suradi ◽  
Andang Kurniawan ◽  
...  
Keyword(s):  
Southeast Asia ◽  
Biomass Burning ◽  
Empirical Orthogonal Function ◽  
Seasonal Pattern ◽  
Temporal Distribution ◽  
Principal Component ◽  
Total Variance ◽  
Spatial And Temporal Distribution ◽  
Orthogonal Function ◽  
Monthly Total

Abstract One of the efforts to control the forest and land fire disasters which affect on the biomass burning haze is fire hotspots monitoring. Biomass burning haze in Southeast Asia (SEA) has become a recurring annual issue. This study aims to determine the spatial and temporal distribution of fire hotspots along SEA, so that it can serve as guidance for efforts to control them. The hotspot data used is derived from NASA’s Fire Information for Resource Management System (FIRMS) MODIS sensors which is collected from 2001-2020. Spatial analysis of the re-gridded data shows the highest burning activities over SEA occurred in Feb-Apr, with >2000 fire events in the Indo-China area and >1000 fire events in Sumatra and Borneo. Empirical Orthogonal Function (EOF) was performed on monthly total hotspot data for 228 months for determining dominant patterns spatially and temporally. Based on the EOF analysis results, the three major modes have achieved a total variance of 71 %. The first mode (EOF1) explains 65 % of the total variance. The second (EOF2) and third (EOF3) modes account for 3.60 % and 2.97 % of the total variance respectively. The first and the third principal component identified high loadings over the Indo-China and Sumatra-Borneo regions respectively. Whereas the second principal component separates the fire areas into China and Indo-China region. Inter-annual pattern is dominant in the EOF1, while the inter-seasonal pattern is dominant in EOF2 and EOF3. ENSO, IOD, and MJO are factors that influence the pattern of the determined principal components. The result of this study provides general understanding on how the fire events varied over the past two decades in SEA.

Spatial and Temporal Distribution of Evolutionary Regimes of the Green Corona Brightness During the Cycles 20-21

1994 ◽  
Vol 144 ◽  
pp. 119-121
Author(s):  
O. G. Badalyan ◽  
G. V. Kuklin
Keyword(s):  
Temporal Distribution ◽  
Principal Component ◽  
Spatial And Temporal Distribution ◽  
Time Diagram ◽  
Principal Component Method ◽  
Component Method

AbstractThe distributions of the green corona brightness in the cycles 20-21 are analysed using the principal component method. Four regimes of the evolution of brightness distributions were detected. A “latitude-time” diagram was constructed and compared with the specific points of the cyclic Wolf numbers curve.

scholarly journals The distribution and variability of chlorophyll-a bloom in the southeastern tropical Indian Ocean using Empirical Orthogonal Function analysis

2017 ◽  
Vol 18 (4) ◽  
pp. 1546-1555
Author(s):  
ISKHAQ ISKANDAR ◽  
QURNIA WULAN SARI ◽  
DEDI SETIABUDIDAYA ◽  
INDRA YUSTIAN ◽  
BRUCE MONGER
Keyword(s):  
Time Series ◽  
Indian Ocean ◽  
Chlorophyll A ◽  
Empirical Orthogonal Function ◽  
Function Analysis ◽  
Principal Component ◽  
Tropical Indian Ocean ◽  
Ekman Pumping ◽  
Orthogonal Function ◽  
Southeastern Tropical Indian Ocean

Iskandar I, Sari QW, Setiabudidaya D, Yustian I, Monger B. 2017. The distribution and variability of chlorophyll-a bloom in the southeastern tropical Indian Ocean using Empirical Orthogonal Function analysis. Biodiversitas 18: 1546-1555. The Indian Ocean Dipole (IOD) events cause anomalously strong upwelling along the sourthen coast of Sumatra-Java leading to the bloom of chlorophylla. An empirical orthogonal function (EOF) analysis was applied to the time series of the satellite-observed chlorophyll-a, sea surface temperature (SST) and surface winds. Spatial eigen functions of the first EOF mode revealed the broad areas of coherent temporal variation in chlorophyll-a, SST and Ekman pumping, which was observed in the southeastern tropical Indian Ocean (SETIO) region. The corresponding time series of principal component of the first EOF mode revealed a robust seasonal variation and relativley weak inter-annual variation. The second EOF mode exhibited a distinct inter-annual variation with the high surface chlorophyll-a concentration was observed along the southern coast of Sumatra-Java. This high chlorophyll-a concentration is co-located with the low SST, the positive Ekman pumping, and the positive wind-induced mixing. An EOF analysis applied on the seasonal time series showed interesting patterns. The leading EOF mode during the peak IOD season from September to November (SON) showed the high concentration of chlorophyll-a was restricted to the southern coast of Java and was co-located with low SST region. The corresponding time series of principal component of the leading EOF mode showed a significant correlation with the Dipole Mode Index (DMI), however it had no correlation with the Ekman pumping. It could be concluded that the chlorophyll-a bloom during the peak phase of the IOD event was generated by the alongshore upwelling-favorable winds in the preceding season.

scholarly journals Population biology and diet of the southern kingcroaker Menticirrhus americanus (Linnaeus, 1758) (Perciformes: Sciaenidae) in Caraguatatuba Bay, southeastern Brazil

2012 ◽  
Vol 60 (3) ◽  
pp. 343-352 ◽  
Author(s):  
Alexander Turra ◽  
Flávia Borges Santos ◽  
Eduardo Bessa ◽  
Wellington Silva Fernandez ◽  
Ligia Coletti Bernadochi ◽  
...  
Keyword(s):  
Wave Energy ◽  
Population Biology ◽  
Sandy Beach ◽  
Seasonal Pattern ◽  
Temporal Distribution ◽  
Wave Activity ◽  
Southeastern Brazil ◽  
Spatial And Temporal Distribution ◽  
Varied Diet ◽  
Spatio Temporal

This study evaluated the spatio-temporal distribution, population biology and diet of Menticirrhus americanus in Caraguatatuba Bay. Samples were taken monthly between August 2003 and October 2004, by trawling in two previously selected areas. The northern area is more exposed to wave activity and is influenced by a river, functioning as a small estuary. In contrast, the southern area is relatively sheltered from wave energy and influenced to a lesser degree by smaller rivers. The fishes' length was measured, and the sex and gonadal stage macroscopically identified. The abundance of this species was compared between areas and among months. The diet was identified and quantified. M. americanus occurred in equal proportions in the two study areas, being most abundant in April 2004, followed by December 2003 and January 2004. The population was dominated by small immature individuals. The few individuals in maturation or mature that were captured showed no seasonal pattern of distribution. This species had a varied diet, feeding on worms (nemerteans, sipunculans and echiurans), mollusks (bivalves and cephalopods), polychaetes, crustaceans and fish. The presence of intact nematodes in the intestine suggests that these are parasites. The results demonstrated that M. americanus has a homogeneous spatial and temporal distribution in Caraguatatuba Bay, being uniformly distributed between the south and north areas as well as across the months. This species can be considered a carnivorous predator, showing a preference for consuming benthic sandy-beach species such as glycerids and other polychaetes, crustaceans, and bivalve siphons.

scholarly journals Principal Component Analysis of Geomagnetic Activity: New Information on Solar Wind

2017 ◽  
Vol 13 (S335) ◽  
pp. 197-200
Author(s):  
Kalevi Mursula ◽  
Lauri Holappa
Keyword(s):  
Principal Component Analysis ◽  
Geomagnetic Activity ◽  
Empirical Orthogonal Function ◽  
High Speed ◽  
Principal Component ◽  
Component Analysis ◽  
Latitudinal Distribution ◽  
Orthogonal Function ◽  
Ap Index ◽  
Highly Correlated

AbstractWe use the principal component analysis to study geomagnetic activity at annual resolution using a network of 26 magnetic stations in 1966-2015, and an extended network of 40 stations in 1980-2015. The first principal component describes the long-term evolution of global geomagnetic activity, and has an excellent correlation with indices like the Kp/Ap index. The two networks give identical results for the first principal component. The second principal component is highly correlated with the annual percentage of high-speed streams (HSS). The extended network has a slightly higher sensitivity to HSSs. We verify the non-trivial latitudinal distribution of the second empirical orthogonal function. We find that the amplitude of the 22-year variation of geomagnetic activity has a closely similar latitudinal distribution as the second empirical orthogonal function. This verifies that the 22-year variation of geomagnetic activity is related to HSSs. The most likely cause is the Russell-McPherron mechanism.

scholarly journals ANALISIS EMPIRICAL ORTHOGONAL FUNCTION (EOF) BERBASIS EIGEN VALUE PROBLEM (EVP) PADA DATASET SUHU PERMUKAAN LAUT INDONESIA

2016 ◽  
Vol 15 (1) ◽  
pp. 1
Author(s):  
S. M. ROBIAL ◽  
S. NURDIATI ◽  
A. SOPAHELUWAKAN
Keyword(s):  
Principal Component Analysis ◽  
Relative Error ◽  
Empirical Orthogonal Function ◽  
Principal Component ◽  
Component Analysis ◽  
Data Grid ◽  
Error Norm ◽  
Orthogonal Function ◽  
Eigen Value ◽  
Norm Error

<p>Data global Suhu Permukaan Laut (SPL) hasil observasi dari tahun ke tahun dibatasi penggunaannya untuk menentukan variasi spasial dan temporal. Analisis dilakukan terhadap data SPL di wilayah perairan Indonesia selama 600 bulan. Metode yang digunakan untuk analisis tersebut adalah metode Empirical Orthogonal Function (EOF) berbasis Eigen Value Problem (EVP). Metode ini lebih dikenal sebagai metode Principal Component Analysis (PCA). Metode EOF bertujuan mereduksi data yang berukuran besar menjadi beberapa mode tanpa menghilangkan informasi dari data yang diamati. Analisis dengan metode tersebut menghasilkan empat komponen utama terbesar yang diinisialkan dengan mode EOF1, EOF2, EOF3 dan EOF4. Mode EOF1 menjelaskan 51.4% dari variasi total dan merupakan pola dominan yang mewakili hampir seluruh data. Mode EOF2 menunjukkan 26.7% dari variasi total. Mode EOF3 dan EOF4 masing-masing menjelaskan 11.2% dan 4.9% dari variasi total. Setiap mode EOF mengandung koefisien yang memuat variabel berupa data grid dan vektor eigen. Data grid menggambarkan letak geografis dan vektor eigen menggambarkan dimensi ruang. Efektifitas dari empat mode EOF yang dihasilkan tersebut dipertahankan untuk dapat menghampiri data asli. Hampiran data asli diperoleh dengan menentukan nilai norm error dari hasil reduksi menggunakan teknik error norm matriks. Teknik ini menghasilkan pola hubungan antara tingkat kesalahan relatif (relative error) dan mode EOF. Pola hubungan yang diperoleh memperlihatkan bahwa semakin banyak mode yang diambil, maka kesalahan relatif akan semakin kecil.</p>

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