Remote sensing is the science and art of obtaining information about an object through the analysis of data acquired by a device that is not in contact with the object (Lillesand and Keifer, 1994). Remotely sensed data can be of many forms, including variations in force distribution, acoustic wave distribution or electromagnetic energy distributions and can be obtained from a variety of platforms, including satellite, airplanes, remotely pilot vehicles, handheld radiometers or even bucket trucks. They may be gathered by different devices, including sensors, film camera, digital cameras, video recorders. Our eyes acquire data on variations in electromagnetic radiations. Instruments capable of measuring electromagnetic radiation are called sensors. Sensors can be differentiated in two main groups.

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Introduction

Satellite remote sensing technology and the science associated with the evaluation of the resulting data are constantly evolving. To meet the growing needs related tothis industry, a team of personnel that understands the fundamental science as well asthe scientific applications related to remote sensing is essential. Therefore, the futureworkforce that will excel in this field requires individuals who not only have a strongacademic background, but have practical hands-on experience with remotely senseddata, and have developed knowledge of its real-world applications. NASA’s DEVELOPProgram has played an integral role in fulfilling this need.

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A B S T R A C T

A satellite retrieval of surface solar irradiance based on METEOSAT SEVIRI-derived cloud properties is presented and validated for the Netherlands with one year of pyranometer measurements from 35 stations. The approach requires two independent steps: 1. Cloud properties are determined from narrow-band satellite radiances. 2. These cloud properties are used together with data onwater vapor column and surface albedo to calculate the atmospheric flux transmittance. The retrieved irradiance is biased low by about 3–4 W/m2 throughout the year, corresponding to an underestimate in atmospheric flux transmittance of about 0.015 in summer and 0.04 in winter. From a least-squares linear regression, residual standard deviations of 56 W/m2 (0.072, 17.0%), 11 W/m2 (0.052, 10.8%), and 4 W/m2 (0.021, 4.2%) are found for hourly, daily and monthly mean irradiance (transmittance, relative error), respectively. These findings indicate that the accuracy of the retrieval is comparable to first-class pyranometers in the summer half year (5% of daily-mean values), but significantly lower in winter. Two aspects requiring further investigation have been identified: 1. For thin clouds, the atmospheric flux transmittance is strongly underestimated. 2. The retrieval accuracy is reduced for snow-covered surfaces.

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Abstract
We have developed novel integrated remote sensing and hydrodynamic simulation methods for cost effective design, evaluation, inspection, maintenance, and repair of wastewater disposal infrastructure. Wastewater diffusers are needed to meet minimum dilution requirements within a regulatory mixing zone, a limited region around the discharge structure where the initial dilution occurs. We have created new methods for the CORMIX software system to provide comprehensive analysis of point source mixing zones. The CorHyd internal diffuser hydraulics simulation tool is introduced for design and analysis of multiport discharges. We have developed new methods to assess diffuser infrastructure physical condition using various remote sensing technologies. Our patent-pending aerial remote sensing platform monitors mixing zone water quality and provides assessment of outfall physical condition through diffuser performance monitoring. Our approach integrates simulation and sensor networks to provide advanced information technology on wastewater disposal infrastructure to designers, consultants, regulators, facility managers, and maintenance crews.

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Remote sensing is the science of obtaining and interpreting information from a distance, using sensors that are not in physical contact with the object being observed. Though you may not realize it, you are familiar with many examples. Biological evolution has exploited many natural phenomena and forms of energy to enable animals (including people) to sense their environment. Your eyes detect electromagnetic energy in the form of visible light. Your ears detect acoustic (sound) energy, while your nose contains sensitive chemical receptors that respond to minute amounts of airborne chemicals given off by the materials in our surroundings. Some research suggests that migrating birds can sense variations in Earth’s magnetic field, which helps explain their remarkable navigational ability.

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Aerial and Spaceborne Ten-Year Trends
USGS 2008-2018 Africa Remote Sensing Study

Africa is a continent as large as China, India, Argentina, the United States of America and Western Europe combined. With a population of over 922 million (as of 2005 and projected by the United Nations to reach one billion by 2010) Africa has four main regional languages (i.e. English, French, Portuguese and Arabic) and almost 200 local languages. The three largest countries in Africa in terms of population are Nigeria (133 million as of 2006), Egypt (79 million) and Ethiopia (77 million). Africa faces some of the greatest geospatial data needs and issues found anywhere in the world, considering the high levels of poverty, deforestation and desertification, coupled with the political and societal unrest in individual countries. The USGS 2008 Africa Remote Sensing Study received an excellent response rate of 377 respondents from over 30 countries; as compared to a 2005 GIS study in Africa that focused on 600 qualified GIS professionals and resulted in 62 responses from 18 countries. Much of that low response was due to lack of telecommunication infrastructure. The USGS 2008 survey is an excellent sign that wireless technology is making some advances and allowing greater access to the internet and to geospatial data, although infrastructure is still an issue in Africa.

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Abstract: The semi-arid region of the Aurès presents an undeniable diversity of flora and fauna, however weather conditions and actual adverse anthropogenic, caused degradation of the physical environment, which have the form of a regression in the natural forest cover. The objective of this study is to determine the contribution of satellite images in detecting changes in land use and monitoring of the degradation processes in the southern part of the Aurès region. As far as that goes, we used images: Landsat ETM + for 2001 and TM 1987.These last cover the southern region of the Aurès which presents a landscape exposed to the phenomena of degradation including forest Beni-Mloul, Dj.Mezbel and Dj khaddou Ahmar, characterized by endemic species. The adopted step is to treat multi-dates satellite imagery by the method of supervised classification of Maximum likelihood to see global changes of land use that have occurred in this area.

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ABSTRACT

The possibility of using high resolution satellite data on a GIS platform for fast and low-cost map upgrading and digital cartographic database-building over remote areas in Africa is demonstrated. Using just one LANDSAT TM composite image, in combination with DEM (digital elevation model) and the author’s GPS data, a series of base and derived maps (hydrography, territorial boundaries, villages location, road network, relief) was obtained in a digitized, highly accurate, updated and upgradeable GIS-friendly format for the remote Kayamba Chiefdom in Congo. From these digital layer maps, a set of quantitative cartographic metrics (coordinates, distance, length, area, slope, aspect, etc.) was reliably deduced. All these fi ndings provide a solid base for further quantitative eco-climatic analysis or land/resources management and planning for the Chiefdom. Ground truth data are determinant in defi ning and/or identifying the surface features embedded in the satellite imagery. I recommend this analysis approach for those remote areas in developing countries where a full-scale ground survey is too costly or technologically too demanding.

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INTRODUCTION
The success of tourism in any country depends on the ability of that country to sufficiently develop, manage and market the tourism facilities and activities in that country (Briassoulis and Straaten, 1999). In the United Arab Emirates (UAE), tourism is one of the industries with the strongest effect on the economy because it helps in developing other sectors. According to statistics supplied by Department of Economic Development in the Emirate of Ras al Khaimah (RAK) (2007), tourism in the Northern part of the UAE contributes about two percent to the Gross Domestic Product (GDP) (Department of Economic Development, 2007). The tourism sector is estimated to have generated 19 billion US dollars in 2007 for the UAE (Alittihad, 2008). The UAE Government in her quest to diversify her mono-cultural economy, which is heavily dependent on petroleum export, has decided to take some measures to promote travel and tourism (Ministry of Economy, 2007). The aims of these measures to make the UAE the ultimate tourism destination in the Middle East and to make tourism one of the greatest foreign exchange earners in an oil dependent economy.

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Remote sensing is the science and art of obtaining information about an object through the analysis of data
acquired by a device that is not in contact with the object (Lillesand and Keifer, 1994). Remotely sensed data can be of many forms, including variations in force distribution, acoustic wave distribution or electromagnetic energy distributions and can be obtained from a variety of platforms, including satellite, airplanes, remotely pilot vehicles, handheld radiometers or even bucket trucks. They may be gathered by different devices, including sensors, film camera, digital cameras, video recorders. Our eyes acquire data on variations in electromagnetic radiations. Instruments capable of measuring electromagnetic radiation are called sensors. Sensors can be differentiated in two main groups: Passive sensors: without their own source of radiation. They are sensitive only to radiation from a natural origin. Active sensors: which have a built in source of radiation. Examples are Radar (Radio detection and ranging) and Lidar (Light detection and ranging) systems.

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• THE REMOTE SENSING TUTORIAL (0)
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