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

Výsledek na webu

<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>

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 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.

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 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10406 - Analytical chemistry

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

ISPRS International Journal of Geo-Information

ISSN

2220-9964

e-ISSN

—

Svazek periodika

4

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

20

Strana od-do

677-696

Kód UT WoS článku

000358987600014

EID výsledku v databázi Scopus

2-s2.0-84948949282