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%2F15%3A00114224" target="_blank" >RIV/00216224:14310/15:00114224 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/ijgi4020677" target="_blank" >https://doi.org/10.3390/ijgi4020677</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/ijgi4020677" target="_blank" >10.3390/ijgi4020677</a>

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 contributes to the development and progress of the Digital Earth project in the framework of the 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 fields of science. The assessment of satellite image quality and suitability for analysing the soil conditions (e.g., soil classification, land use/cover estimation, etc.) is fundamental. In this paper, methodology for data screening and subsequent application of ANNs in remote sensing is presented. The first stage is achieved via: (i) elimination of outliers, (ii) data pre-processing and (iii) the determination of the number of distinguishable soil "classes" via Eigenvalues Analysis (EA) and Principal Components Analysis (PCA). The next stage of ANNs use consists of: (i) building the training database, (ii) optimization of ANN architecture and database cleaning, and (iii) training and verification of the network. Application of the proposed methodology is shown.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10406 - Analytical chemistry

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2015

Confidentiality

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

Name of the periodical

ISPRS International Journal of Geo-Information

ISSN

2220-9964

e-ISSN

—

Volume of the periodical

4

Issue of the periodical within the volume

2

Country of publishing house

CH - SWITZERLAND

Number of pages

20

Pages from-to

677-696

UT code for WoS article

000358987600014

EID of the result in the Scopus database

2-s2.0-84948949282