Remotely Sensed Soil Data Analysis Using Artificial Neural
Networks: A Case Study of El-Fayoum Depression, Egypt

J 2015

Remotely Sensed Soil Data Analysis Using Artificial Neural Networks: A Case Study of El-Fayoum Depression, Egypt

AMATO, Filippo; Josef HAVEL; Abd-Alla GAD a Ahmed Mohamed EL-ZEINY

Základní údaje

Originální název

Remotely Sensed Soil Data Analysis Using Artificial Neural Networks: A Case Study of El-Fayoum Depression, Egypt

Autoři

AMATO, Filippo; Josef HAVEL; Abd-Alla GAD a Ahmed Mohamed EL-ZEINY

Vydání

ISPRS International Journal of Geo-Information, Basel, MDPI AG, Basel, Switzerland, 2015, 2220-9964

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10406 Analytical chemistry

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 0.651

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/15:00114224

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

remote sensing; soil classification; desertification; land use/cover; soil taxonomy; eigenvalues analysis; principal components analysis; artificial neural networks

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 14. 5. 2021 11:04, Mgr. Michal Petr

Anotace

V originále

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.

Návaznosti

ED2.1.00/03.0086, projekt VaV

Název: Regionální VaV centrum pro nízkonákladové plazmové a nanotechnologické povrchové úpravy

GA13-05082S, projekt VaV

Název: Analýza a aplikace plazmatických procesů pro přípravu tenkých vrstev amorfních chalkogenidů

Investor: Grantová agentura ČR, Analýza a aplikace plazmatických procesů pro přípravu tenkých vrstev amorfních chalkogenidů

GA202/07/1669, projekt VaV

Název: Depozice termomechanicky stabilních nanostrukturovaných diamantu-podobných tenkých vrstev ve dvojfrekvenčních kapacitních výbojích

Investor: Grantová agentura ČR, Depozice termomechanicky stabilních nanostrukturovaných diamantu-podobných tenkých vrstev ve dvojfrekvenčních kapacitních výbojích

295031, interní kód MU

Název: SUDSOE—CHARACTERIZATION AND SUSTAINABLE USE OF EGYPTIAN DEGRADED SOILS (Akronym: SUDSOE)

Investor: Evropská unie, SUDSOE—CHARACTERIZATION AND SUSTAINABLE USE OF EGYPTIAN DEGRADED SOILS, Kapacity

Citovat

AMATO, Filippo; Josef HAVEL; Abd-Alla GAD a Ahmed Mohamed EL-ZEINY. Remotely Sensed Soil Data Analysis Using Artificial Neural Networks: A Case Study of El-Fayoum Depression, Egypt. ISPRS International Journal of Geo-Information. Basel: MDPI AG, Basel, Switzerland, 2015, roč. 4, č. 2, s. 677-696. ISSN 2220-9964. Dostupné z: https://doi.org/10.3390/ijgi4020677.

@article{1669581,
   author = {Amato, Filippo and Havel, Josef and Gad, AbdandAlla and ElandZeiny, Ahmed Mohamed},
   article_location = {Basel},
   article_number = {2},
   doi = {https://doi.org/10.3390/ijgi4020677},
   keywords = {remote sensing; soil classification; desertification; land use/cover; soil taxonomy; eigenvalues analysis; principal components analysis; artificial neural networks},
   language = {eng},
   issn = {2220-9964},
   journal = {ISPRS International Journal of Geo-Information},
   title = {Remotely Sensed Soil Data Analysis Using Artificial Neural Networks: A Case Study of El-Fayoum Depression, Egypt},
   url = {https://doi.org/10.3390/ijgi4020677},
   volume = {4},
   year = {2015}
}

TY  - JOUR
ID  - 1669581
AU  - Amato, Filippo - Havel, Josef - Gad, Abd-Alla - El-Zeiny, Ahmed Mohamed
PY  - 2015
TI  - Remotely Sensed Soil Data Analysis Using Artificial Neural Networks: A Case Study of El-Fayoum Depression, Egypt
JF  - ISPRS International Journal of Geo-Information
VL  - 4
IS  - 2
SP  - 677-696
EP  - 677-696
PB  - MDPI AG, Basel, Switzerland
SN  - 22209964
KW  - remote sensing
KW  - soil classification
KW  - desertification
KW  - land use/cover
KW  - soil taxonomy
KW  - eigenvalues analysis
KW  - principal components analysis
KW  - artificial neural networks
UR  - https://doi.org/10.3390/ijgi4020677
L2  - https://doi.org/10.3390/ijgi4020677
N2  - 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.
ER  -

AMATO, Filippo; Josef HAVEL; Abd-Alla GAD a Ahmed Mohamed EL-ZEINY. Remotely Sensed Soil Data Analysis Using Artificial Neural Networks: A Case Study of El-Fayoum Depression, Egypt. \textit{ISPRS International Journal of Geo-Information}. Basel: MDPI AG, Basel, Switzerland, 2015, roč.~4, č.~2, s.~677-696. ISSN~2220-9964. Dostupné z: https://doi.org/10.3390/ijgi4020677.

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