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

Result code in 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>

Result on the web

—

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

O - Miscellaneous

CEP classification

CB - Analytical chemistry, separation

OECD FORD branch

—

Project

—

Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2014

Confidentiality

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