Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Fereydoon SARMADIAN"'
Publikováno v:
EQA, Vol 64, Pp 48-67 (2024)
The study assesses the ecological capacity and land use planning in Alborz Province, Iran for different purposes namely irrigated agriculture, dryland farming, orchards and forestry, rangelands, residential and industrial areas, as well as conservati
Externí odkaz:
https://doaj.org/article/4a6414d773824044a4368e7c0c402fd9
Publikováno v:
Eurasian Journal of Soil Science, Vol 8, Iss 4, Pp 340-350 (2019)
Soil moisture is an influential parameter in land surface hydrology and precise soil moisture data that can help researcher to realize the climate changes and land-atmosphere interactions. A initial struggle for the utilize of soil moisture data from
Externí odkaz:
https://doaj.org/article/a8912464fd2449c38aa0837b9a152291
Autor:
Ruhollah Taghizadeh-Mehrjardi, Karsten Schmidt, Alireza Amirian-Chakan, Tobias Rentschler, Mojtaba Zeraatpisheh, Fereydoon Sarmadian, Roozbeh Valavi, Naser Davatgar, Thorsten Behrens, Thomas Scholten
Publikováno v:
Remote Sensing, Vol 12, Iss 7, p 1095 (2020)
Understanding the spatial distribution of soil organic carbon (SOC) content over different climatic regions will enhance our knowledge of carbon gains and losses due to climatic change. However, little is known about the SOC content in the contrastin
Externí odkaz:
https://doaj.org/article/85115f9d0d1e46b2afcc312307bd6cec
Publikováno v:
Egyptian Journal of Remote Sensing and Space Sciences, Vol 18, Iss 2, Pp 127-135 (2015)
Artificial neural network (ANN) model was developed and tested for estimating soil phosphorus (P) in Kouhin watershed area (1000 ha), Qazvin province, Iran using terrain analysis. Based on the soil distribution correlation, vegetation growth pattern
Externí odkaz:
https://doaj.org/article/5b47dcc013474011a9043550bd054da6
Autor:
Abbas TAATI, Fereydoon SARMADIAN, Amin MOUSAVI, Chamran Taghati Hossien POUR, Amir Hossein Esmaile SHAHIR
Publikováno v:
Walailak Journal of Science and Technology, Vol 12, Iss 8, Pp 681-687 (2015)
Nowadays, remote sensing images have been identified and exploited as the latest information to study land cover and land uses. These digital images are of significant importance, since they can present timely information, and capable of providing la
Externí odkaz:
https://doaj.org/article/26b2ef72df6f4fa4ba5a9791b5c5b31a
Publikováno v:
Eurasian Journal of Soil Science, Vol 3, Iss 1, Pp 1-6 (2014)
The two common methods used to develop PTFs are multiple-linear regression method and Artificial Neural Network. One of the advantages of neural networks compared to traditional regression PTFs is that they do not require a priori regression model, w
Externí odkaz:
https://doaj.org/article/c3ea49ee44934441ba2d539c6a196ccf
Publikováno v:
Acta Agriculturae Slovenica, Vol 107, Iss 1 (2016)
In this study appraisal of four different agricultural land evaluation methods including the so-called Storie method, square root method, maximum limitation method and fuzzy sets method, was done. The study was performed in Bastam region, located in
Externí odkaz:
https://doaj.org/article/d917076f088f48079ab032eca2f92b1c
Autor:
Abbas TAATI, Fereydoon SARMADIAN, Amin MOUSAVI, Chamran Taghati Hossien POUR, Amir Hossein Esmaile SHAHIR
Publikováno v:
Walailak Journal of Science and Technology, Vol 12, Iss 8 (2014)
Nowadays, remote sensing images have been identified and exploited as the latest information to study land cover and land uses. These digital images are of significant importance, since they can present timely information, and capable of providing la
Externí odkaz:
https://doaj.org/article/0ae4bb832a3c46ef8a2eec71cb358014
Autor:
Amin Sharififar, Fereydoon Sarmadian
Publikováno v:
European Journal of Soil Science. 74
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::29d8605b123b25370fe6006049148fb2
https://doi.org/10.1111/ejss.13368
https://doi.org/10.1111/ejss.13368
This research aims to develop a novel deep learning-based model for predicting soil properties based on visible and near-infrared (VIS-NIR) spectroscopy data. Soil samples were collected from the European topsoil dataset provided by the LUCAS project
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d8cd5410bbeda523c0f83cdc52fddff9
https://doi.org/10.21203/rs.3.rs-2715755/v1
https://doi.org/10.21203/rs.3.rs-2715755/v1
