Remotely sensed soil data analysis using artificial neural networks. A case study of El-Fayoum depression, Egypt

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F14%3A00078217" target="_blank" >RIV/00216224:14310/14:00078217 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Remotely sensed soil data analysis using artificial neural networks. A case study of El-Fayoum depression, Egypt

Popis výsledku v původním jazyce

Earth observation and monitoring of soil quality, long term changes of soil characteristics and deterioration processes such as degradation or desertification are among the most important objectives of remote sensing. The georeferenciation of such information contribute to the development and progress of Digital Earth project in the framework of information globalization process. Earth observation and soil quality monitoring via remote sensing are mostly based on the use of satellite spectral data. Advanced techniques are available to predict the soil or land use/cover categories from satellite imagery data. Artificial Neural Networks (ANNs) are among the most widely used tools for modeling and prediction purposes in various field of science. The assessment of satellite images quality and suitability for analysing the soil conditions (e.g., soil classification, land use/cover estimation, etc.) is fundamental.

Název v anglickém jazyce

Remotely sensed soil data analysis using artificial neural networks. A case study of El-Fayoum depression, Egypt

Popis výsledku anglicky

Earth observation and monitoring of soil quality, long term changes of soil characteristics and deterioration processes such as degradation or desertification are among the most important objectives of remote sensing. The georeferenciation of such information contribute to the development and progress of Digital Earth project in the framework of information globalization process. Earth observation and soil quality monitoring via remote sensing are mostly based on the use of satellite spectral data. Advanced techniques are available to predict the soil or land use/cover categories from satellite imagery data. Artificial Neural Networks (ANNs) are among the most widely used tools for modeling and prediction purposes in various field of science. The assessment of satellite images quality and suitability for analysing the soil conditions (e.g., soil classification, land use/cover estimation, etc.) is fundamental.

Druh

O - Ostatní výsledky

CEP obor

CB - Analytická chemie, separace

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů