scholarly journals Observation of Oceanic Eddy in the Northeastern Arabian Sea Using Multisensor Remote Sensing Data

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
R. K. Sarangi
Keyword(s):  
Remote Sensing ◽  
Arabian Sea ◽  
Chlorophyll Concentration ◽  
Climatic Conditions ◽  
Late Winter ◽  
Surrounding Water ◽  
Noaa Avhrr ◽  
Oceanic Eddy ◽  
Remote Sensing Satellite ◽  
Cold Core

An oceanic eddy of size about 150 kilometer diameter observed in the northeastern Arabian Sea using remote sensing satellite sensors; IRS-P4 OCM, NOAA-AVHRR and NASA Quickscat Scatterometer data. The eddy was detected in the 2nd week of February in Indian Remote Sensing satellite (IRS-P4) Ocean Color Monitor (OCM) sensor retrieved chlorophyll image on 10th February 2002, between latitude 16°90′–18°50′N and longitude 66°05′–67°60′E. The chlorophyll concentration was higher in the central part of eddy (~1.5 mg/m3) than the peripheral water (~0.8 mg/m3). The eddy lasted till 10th March 2002. NOAA-AVHRR sea surface temperature (SST) images generated during 15th February-15th March 2002. The SST in the eddy’s center (~23°C) was lesser than the surrounding water (~24.5°C). The eddy was of cold core type with the warmer water in periphery. Quickscat Scatterometer retrieved wind speed was 8–10 m/sec. The eddy movement observed southeast to southwest direction and might helped in churning. The eddy seemed evident due to convective processes in water column. The processes like detrainment and entrainment play role in bringing up the cooler water and the bottom nutrient to surface and hence the algal blooming. This type of cold core/anti-cyclonic eddy is likely to occur during late winter/spring as a result of the prevailing climatic conditions.

scholarly journals Methodology for prediction of corn yield using remote sensing satellite data in Central Mexico

2012 ◽  
pp. 61 ◽  
Author(s):  
Jesús Soria Ruiz ◽  
Yolanda Fernández Ordóñez ◽  
Rebeca Granados Ramírez
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Central Mexico ◽  
Corn Yield ◽  
Noaa Avhrr ◽  
Multi Temporal ◽  
Remote Sensing Satellite

El principal reto de la agricultura en cualquier país, es garantizar la producción de alimentos para su población. La heterogeneidad de condiciones de cultivo del maíz en muchos países, especialmente en México, hace más difícil hacer predicciones de sitios de ocurrencia y de rendimiento antes de la cosecha. Las predicciones anticipadas a la cosecha son necesarias al gobierno para estimar la cantidad de grano que requiere importar con el fin de afrontar los déficit de producción. Por ello, en este artículo se desarrolla una metodología para la estimación anticipada a la cosecha del rendimiento de maíz, bajo las condiciones de sistemas de producción intensivos en el Centro de México. El método se basa en el análisis multi-temporal de los índices de vegetación normalizados (NDVI) de imágenes de satélite NOAA-AVHRR, grados-calor (DD) y los índices de área foliar (LAI) para predecir ocurrencia en maíz y su rendimiento. En este artículo se discuten los resultados de la aplicación de una metodología generada para identificar áreas de siembra de maíz y predecir su rendimiento en el Centro de México.

Landscape Change in the Steppe of Algeria South-West Using Remote Sensing

2018 ◽  
Vol 18 (1) ◽  
pp. 41-52
Author(s):  
Tayeb Sitayeb ◽  
Ishak Belabbes
Keyword(s):  
Remote Sensing ◽  
Social Systems ◽  
Bare Soil ◽  
Climatic Conditions ◽  
Landscape Dynamics ◽  
Landsat 8 ◽  
Natural Ecosystems ◽  
Stipa Tenacissima ◽  
Steppe Species ◽  
Sparse Vegetation

Abstract Landscape dynamics is the result of interactions between social systems and the environment, these systems evolving significantly over time. climatic conditions and biophysical phenomena are the main factors of landscape dynamics. Also, currently man is responsible for most changes affecting natural ecosystems. The objective of this work is to study the dynamics of a typical landscape of western Algeria in time and space, and to map the distribution of vegetation groups constitute the vegetation cover of this ecosystem. as well as using a method of monitoring the state of a fragile ecosystem by remote sensing to understand the processes of changes in this area. The steppe constitutes a large arid area, with little relief, covered with low and sparse vegetation. it lies between the annual isohyets of 100 to 400 mm, subjected to a very old human exploitation with an activity of extensive breeding of sheep, goats, and camels. Landsat satellite data were used to mapping vegetation groups in the Mecheria Steppe at a scale of 1: 300,000. Then, a comparison was made between the two maps obtained by a classification of Landsat-8 sensor Operational Land Imager (OLI) acquired on March 18, 2014, and Landsat-5 sensor Thematic Mapper (TM) acquired on April 25, 1987. The results obtained show the main changes affecting the natural distribution of steppe species, a strong change in land occupied by the Stipa tenacissima steppe with 65% of change, this steppe is replaced by Thymelaea microphylla, Salsola vermiculata, lygeum spartum and Peganum harmala steppe. an absence from the steppe Artemisia herba-alba that has also been replaced by the same previous steppes species. The groups with Quercus ilex and Juniperus phoenicea are characterized by a strong regression that was lost 60% of its global surface and transformed by steppe to stipa tenacissima and bare soil.

Remote sensing of phytoplankton fluorescence in northern Arabian Sea

2018 ◽  
Vol 51 (2) ◽  
Author(s):  
Imran Ahmed Khan ◽  
Lubna Ghazal ◽  
Mudassar Hassan Arsalan ◽  
Muhammad Faheem Siddiqui
Keyword(s):  
Remote Sensing ◽  
Arabian Sea ◽  
Northern Arabian Sea

scholarly journals Modeling and Simulation of Mission Planning Problem for Remote Sensing Satellite Imaging

2018 ◽  
Vol 179 ◽  
pp. 03024 ◽  
Author(s):  
Yao Pan ◽  
Zhong Ming Chi ◽  
Qi Long Rao ◽  
Kai Peng Sun ◽  
Yi Nan Liu
Keyword(s):  
Remote Sensing ◽  
Time Window ◽  
Constraint Satisfaction Problem ◽  
Optimal Path ◽  
Time Constraint ◽  
Mission Planning ◽  
Planning Problem ◽  
Satellite Imaging ◽  
Problem Model ◽  
Remote Sensing Satellite

Mission planning problem for remote sensing satellite imaging is studied. Firstly, the time constraint satisfaction problem model is presented after analyzing the characteristic of time constraint. Then, An optimal path searching algorithm based on the discrete time window is proposed according to the non-uniqueness for satellite to mission in the visible time window. Simulation results verify the efficiency of the model and algorithm.

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