An integrated approach of deep learning convolutional neural network and google earth engine for salt storm monitoring and mapping

Aghazadeh, Firouz; Ghasemi, Mohammad; Garajeh, Mohammad Kazemi; Feizizadeh, Bakhtiar; Karimzadeh, Sadra; Morsali, Reyhaneh

doi:10.1016/j.apr.2023.101689

Cited by 11 publications

(3 citation statements)

References 93 publications

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“…The reason for this is that it is used to extract the features of an image. A back-propagation algorithm optimizes several convolutional kernels in the convolutional layer 64 , 65 . The output of the convolutional layer will be the input for the next layer 66 .…”

Section: Methodsmentioning

confidence: 99%

Monitoring the impacts of crop residue cover on agricultural productivity and soil chemical and physical characteristics

Kazemi Garajeh,

Hassangholizadeh,

Bakhshi Lomer

et al. 2023

Sci Rep

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To the best of our knowledge, the impacts of crop residue cover (CRC) on agricultural productivity and soil fertility have not been studied by previous researchers. In this regard, this study aims to apply an integrated approach of remote sensing and geospatial analysis to detect CRC and monitor the effects of CRC on agricultural productivity, as well as soil chemical and physical characteristics. To achieve this, a series of Landsat images and 275 ground control points (GCPs) collected from the study areas for the years 2013, 2015, and 2021 were used. A convolutional neural network (CNN), a class of artificial neural network has commonly applied to analyze visual imagery, was employed in this study for CRC detection in two classes (Not-CRC and CRC) for the years 2013, 2015, and 2021. To assess the effects of CRC, the Normalized Difference Vegetation Index (NDVI) was applied to Landsat image series for the years 2015 (22 images), 2019 (20 images), and 2022 (23 images). Furthermore, this study evaluates the impacts of CRC on soil fertility based on collected field observation data. The results show a high performance (Accuracy of > 0.95) of the CNN for CRC detection and mapping. The findings also reveal positive effects of CRC on agricultural productivity, indicating an increase in vegetation density by about 0.1909 and 0.1377 for study areas 1 and 2, respectively, from 2015 to 2022. The results also indicate an increase in soil chemical and physical characteristics, including EC, PH, Na, Mg, HCO3, K, silt, sand, and clay from 2015 to 2022, based on physical examination. In general, the findings underscore that the value of an integrated approach of remote sensing and geospatial analysis for detecting CRC and monitoring its impacts on agricultural productivity and soil fertility. This research can offer valuable insight to researchers and decision-makers in the field of soil science, land management and agriculture.

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Section: Methodsmentioning

confidence: 99%

Monitoring the impacts of crop residue cover on agricultural productivity and soil chemical and physical characteristics

Kazemi Garajeh,

Hassangholizadeh,

Bakhshi Lomer

et al. 2023

Sci Rep

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“…The result's classification validation is a critical phase in image analysis to complete a preliminary assessment of the structural model and conceptual framework [59][60][61]. This study utilized ROC curves to assess the accuracy of the flood models.…”

Section: Output Validationmentioning

confidence: 99%

Harnessing the Power of Remote Sensing and Unmanned Aerial Vehicles: A Comparative Analysis for Soil Loss Estimation on the Loess Plateau

Kariminejad,

Kazemi Garajeh,

Hosseinalizadeh

et al. 2023

Drones

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This study explored the innovative use of multiple remote sensing satellites and unmanned aerial vehicles to calculate soil losses in the Loess Plateau of Iran. This finding emphasized the importance of using advanced technologies to develop accurate and efficient soil erosion assessment techniques. Accordingly, this study developed an approach to compare sinkholes and gully heads in hilly regions on the Loess Plateau of northeast Iran using convolutional neural network (CNN or ConvNet). This method involved coupling data from UAV, Sentinel-2, and SPOT-6 satellite data. The soil erosion computed using UAV data showed AUC values of 0.9247 and 0.9189 for the gully head and the sinkhole, respectively. The use of SPOT-6 data in gully head and sinkhole computations showed AUC values of 0.9105 and 0.9123, respectively. The AUC values were 0.8978 and 0.9001 for the gully head and the sinkhole using Sentinel-2, respectively. Comparison of the results from the calculated UAV, SPOT-6, and Sentinel-2 data showed that the UAV had the highest accuracy for calculating sinkhole and gully head soil features, although Sentinel-2 and SPOT-6 showed good results. Overall, the combination of multiple remote sensing satellites and UAVs offers improved accuracy, timeliness, cost effectiveness, accessibility, and long-term monitoring capabilities, making it a powerful approach for calculating soil loss in the Loess Plateau of Iran.

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“…This powerful platform has found effective applications across various Earth science disciplines [25]. It has been utilized in deforestation analysis [26,27], land use mapping [28,29], monitoring the impacts of climate change [30], and air pollution monitoring [31,32]. One of the key features of the GEE is its ability to perform automated parallel processing, making use of Google's fast computing platform.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Spatio-Temporal Distribution of Soil Salinity Using Google Earth Engine for Detecting the Saline Areas Susceptible to Salt Storm Occurrence

Kazemi Garajeh

2024

Pollutants

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Recent droughts worldwide have significantly affected ecosystems in various regions. Among these affected areas, the Lake Urmia Basin (LUB) has experienced substantial effects from both drought and human activity in recent years. Lake Urmia, known as one of the hypersaline lakes globally, has been particularly influenced by these activities. The extraction of water since 1995 has resulted in an increase in the extent of salty land, leading to the frequent occurrence of salt storms. To address this issue, the current study utilized various machine learning algorithms within the Google Earth Engine (GEE) platform to map the probability of saline storm occurrences. Landsat time-series images spanning from 2000 to 2022 were employed. Soil salinity indices, Ground Points (GPs), and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products were utilized to prepare the training data, which served as input for constructing and running the models. The results demonstrated that the Support Vector Machine (SVM) performed effectively in identifying the probability of saline storm occurrence areas, achieving high R2 values of 91.12%, 90.45%, 91.78%, and 91.65% for the years 2000, 2010, 2015, and 2022, respectively. Additionally, the findings reveal an increase in areas exhibiting a very high probability of saline storm occurrences from 2000 to 2022. In summary, the results of this study indicate that the frequency of salt storms is expected to rise in the near future, owing to the increasing levels of soil salinity resources within the Lake Urmia Basin.

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An integrated approach of deep learning convolutional neural network and google earth engine for salt storm monitoring and mapping

Cited by 11 publications

References 93 publications

Monitoring the impacts of crop residue cover on agricultural productivity and soil chemical and physical characteristics

Monitoring the impacts of crop residue cover on agricultural productivity and soil chemical and physical characteristics

Harnessing the Power of Remote Sensing and Unmanned Aerial Vehicles: A Comparative Analysis for Soil Loss Estimation on the Loess Plateau

Monitoring the Spatio-Temporal Distribution of Soil Salinity Using Google Earth Engine for Detecting the Saline Areas Susceptible to Salt Storm Occurrence

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