Mapping water salinity using Landsat-8 OLI satellite images (Case study: Karun basin located in Iran)

Ansari, Mohsen; Akhoondzadeh, Mehdi

doi:10.1016/j.asr.2019.12.007

Cited by 28 publications

(19 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study analyzes the changes in precipitation as the most important water inputs into the Lake Urmia watershed and Evapotranspiration (ET) as one of the significant outputs of the Lake Urmia. Lake Urmia watershed is an endorheic watershed, and Lake Urmia is located inside this watershed (Ansari 2020). Therefore, the spatiotemporal analysis of the changes in evapotranspiration and precipitation throughout this watershed would better understand the causes of decreases in the lake's water (Hesami 2016).…”

Section: Spatio-temporal Analysis Of Changes In Evapotranspiration and Precipitationmentioning

confidence: 99%

“…interpreted for salinity identification studies and is the most common type of imagery cited in numerous publications (Mohebzadeh 2019). Researchers have identified how essential thermal bands and infrared spectra help detect and mapping topographies and geological units such as saline lands (Ansari 2020). They have discriminated against the land units based on a single thematic mapper scene that seemed suitable to determine (Fazel 2016).…”

Section: Analyzing Of Changes In Saline Featuresmentioning

confidence: 99%

See 1 more Smart Citation

Assessing climate and human activity effects on lake characteristics using spatio-temporal satellite data and an emotional neural network

et al. 2022

View full text Add to dashboard Cite

Different sensing methods provide valuable information for comprehensive monitoring strategies, which are crucial for the ecological management of lakes and watersheds. Subsequently, the resulting spatio-temporal information can be considered the fundamental knowledge for the water resources management of watersheds.Lake Urmia is deemed one of the most important aquatic habitats in Iran. It has been experiencing significant changes during recent years due to climate change, anthropogenic activities, and a lack of coherent management approaches. Hence, awareness of the hydro-ecological factors during the last few decades is critical for identifying the problems. In this research, the impacts of changes in key parameters such as precipitation, evapotranspiration, water surface temperatures, suspended sediment concentration, saline features, and vegetation are explored using satellite imagery. The primary purpose of this study is to evaluate the Lake Urmia crisis concerning humaninvolved and climate factors such as the agriculture sector and construction of the causeway. In this regard, a limbic based Emotional Artificial Neural Network (EANN) is developed as a non-linear universal mapping and implemented for the first time to demonstrate the interactions between the considered hydro-ecological factors and the sensitivity of the two indicators the lake health. Providing a comprehensive spatio-temporal analysis is another objective of this study in order to detect the onset of deterioration in the parameters. The values of the efficiency criteria were measured to evaluate the sensitivity of the EANN models to the related inputs. The quantitative results confirm that the combination of both climate and anthropogenic factors, including the agricultural sector's overdraft, leads to the most efficient EANN model and, consequently, is considered the leading cause of the crisis.

show abstract

Section: Spatio-temporal Analysis Of Changes In Evapotranspiration and Precipitationmentioning

confidence: 99%

Section: Analyzing Of Changes In Saline Featuresmentioning

confidence: 99%

Assessing climate and human activity effects on lake characteristics using spatio-temporal satellite data and an emotional neural network

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Imagery data acquired from Landsat TM has been effectively interpreted for salinity identification studies and is the most common type of imagery cited in numerous publications (Mohebzadeh 2019). Researchers have identified how essential thermal bands and infrared spectra help detect and mapping topographies and geological units such as saline lands (Ansari 2020). They have discriminated against the land units based on a single thematic mapper scene that seemed suitable to determine (Fazel 2016).…”

Section: Analyzing Of Changes In Saline Featuresmentioning

confidence: 99%

Assessing climate and human activity effects on lake characteristics using spatio-temporal satellite data and an emotional neural network

Mojtahedi

Dadashzadeh

Azizkhani

et al. 2021

Preprint

View full text Add to dashboard Cite

Different sensing methods provide valuable information for comprehensive monitoring strategies, which are crucial for the ecological management of lakes and watersheds. Subsequently, the resulting spatio-temporal information can be considered the fundamental knowledge for the water resources management of watersheds. Lake Urmia is deemed one of the most important aquatic habitats in Iran. It has been experiencing significant changes during recent years due to climate change, anthropogenic activities, and a lack of coherent management approaches. Hence, awareness of the hydro-ecological factors during the last few decades is critical for identifying the problems. In this research, the impacts of changes in key parameters such as precipitation, evapotranspiration, water surface temperatures, suspended sediment concentration, saline features, and vegetation are explored using satellite imagery. The primary purpose of this study is to evaluate the Lake Urmia crisis concerning human-involved and climate factors such as the agriculture sector and construction of the causeway. In this regard, a limbic based Emotional Artificial Neural Network (EANN) is developed as a non-linear universal mapping and implemented for the first time to demonstrate the interactions between the considered hydro-ecological factors and the sensitivity of the two indicators the lake health. Providing a comprehensive spatio-temporal analysis is another objective of this study in order to detect the onset of deterioration in the parameters. The values of the efficiency criteria were measured to evaluate the sensitivity of the EANN models to the related inputs. The quantitative results confirm that the combination of both climate and anthropogenic factors, including the agricultural sector's overdraft, leads to the most efficient EANN model and, consequently, is considered the leading cause of the crisis.

show abstract

“…The ML and deep learning algorithm can develop a model that simulates the relation between output and different variables as inputs (Aghdami-Nia et al, 2022;Rostami et al, 2022b). Nevertheless, ML algorithms have parameters that should be optimized with some methods (Ansari and Akhoondzadeh, 2020;Ranjbar et al, 2021). There are methods for optimizing ML parameters, such as genetic algorithms, some empirical formulas (Arabi et al, 2022), and grid search.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Machine Learning Classifiers for Crop Type Mapping Using Vegetation Indices

Asgari

Hasanlou²

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Timely and accurate mapping of crops is crucial for agriculture management, policy-making, and food security. Due to the differences in the product calendars of various crops, it is possible to classify them by investigating the remote sensing Vegetation Indices (VIs) during crop growth season. This study developed a VI-based mapping approach to specifying crop types based on phenological and spectral metrics derived from the sentinel-2 images. We used six spectral VIs (ARVI, CVI, EVI, LAI, GLI, and NDVI) in three supervised machine learning methods, including Random Forest (RF), GBoost (GB), and K-Nearest Neighborhood (KNN) for crop mapping. Field data consisting of wheat, barley, canola, vegetables, and a bare land class, were collected as the testing and training data set. The classification results were evaluated through test samples showing high overall accuracy (OA) and satisfactory class accuracies for the most dominant crop types across different fields despite the variability of planting and harvesting dates. Among the VIs utilized to crop mapping, the Atmospherically Resistant Vegetation Index (ARVI) in all three classification methods achieved better results. The overall accuracy of RF, GB, and KNN models with the ARVI index was 95%, 88%, and 90%, respectively.

show abstract

Mapping water salinity using Landsat-8 OLI satellite images (Case study: Karun basin located in Iran)

Cited by 28 publications

References 41 publications

Assessing climate and human activity effects on lake characteristics using spatio-temporal satellite data and an emotional neural network

Assessing climate and human activity effects on lake characteristics using spatio-temporal satellite data and an emotional neural network

Assessing climate and human activity effects on lake characteristics using spatio-temporal satellite data and an emotional neural network

A Comparative Study of Machine Learning Classifiers for Crop Type Mapping Using Vegetation Indices

Contact Info

Product

Resources

About