Multivariate curve resolution of organic pollution patterns in mangrove forest sediment from Qeshm Island and Khamir Port—Persian Gulf, Iran

Dadashi, Mahsa; Ghaffari, Sanaz; Bakhtiari, Alireza Riyahi; Tauler, Romà

doi:10.1007/s11356-017-0450-z

Cited by 20 publications

(3 citation statements)

References 63 publications

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“…Before the multivariate analysis, the data matrix was either autoscaled (for PCA) or undergo min–max normalization (for MCR-ALS). More details concerning the implementation of PCA and MCR could be found in [ 53 ].…”

Section: Methodsmentioning

confidence: 99%

Chemometric Assessment of Bulgarian Wastewater Treatment Plants’ Effluents

2020

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Surface water quality strongly depends on anthropogenic activity. Among the main anthropogenic sources of this activity are the wastewater treatment plant (WWTP) effluents. The discharged loads of nutrients and suspended solids could provoke serious problems for receiving water bodies and significantly alter the surface water quality. This study presents inventory analysis and chemometric assessment of WWTP effluents based on the mandatory monitoring data. The comparison between the Bulgarian WWTPs and previously reported data from other countries reveals that discharged loads from investigated WWTPs are lower. This is particularly valid for total suspended solids (TSS). The low TSS loads are the reason for the deviations of the typical calculated WWTP effluent ratios of Bulgarian WWTPs compared to the WWTPs worldwide. The performed multivariate analysis reveals the hidden factors that determine the content of WWTP effluents. The source apportioning based on multivariate curve resolution analysis provides detailed information for source contribution profiles of the investigated WWTP effluent loads and elucidate the difference between WWTPs included in this study.

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Section: Methodsmentioning

confidence: 99%

Chemometric Assessment of Bulgarian Wastewater Treatment Plants’ Effluents

2020

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“…Unfortunately, tourism and fishing industries affect the mangrove ecosystem adversely. Also, oil leakage is another issue that is affecting this ecosystem due to oil tanker transportation nearby, like the Hormuz strait (Dadashi et al, 2018).…”

Section: Study Areamentioning

confidence: 99%

Identifying Suitable Locations for Mangrove Plantation Using Geospatial Information System and Remote Sensing

Sahraei

Ghorbanian²,

Kanani-Sadat³

et al. 2023

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

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Abstract. Mangroves provide numerous environmental benefits, such as carbon sequestration, water purification, climate change mitigation, and flood and Tsunami impact reduction. Despite these unique advantages, mangroves are threatened by the combined adverse impacts of human activities and climate change. Therefore, it is essential to implement reasonable practices to avoid further degradation of mangroves and provide efficient workflows to increase their extent. Accordingly, better plantation policies are principally required for their conservation and rehabilitation. In this study, we desired to detect suitable locations for mangrove plantation in coastal areas of Hormozgan Province, Iran. We considered a relatively new Multi Criteria Decision Making (MCDM) technique to combine ten criteria derived from remote sensing in a GIS environment. The Best Worst Method (BWM), as an MDCM technique, was implemented to determine the relative importance of each criterion. Afterward, all criteria were aggregated using the Weighted Linear Combination (WLC) method to produce a mangrove plantation suitability map. Statistical measures, including Overall Accuracy (OA = 95%), Kappa Coefficient (KC = 87.9%), and Area Under Curve (AUC = 98.79%), indicated the high applicability of the implemented method for mangrove plantation site allocation. The produced map could give managers a profound insight into finding optimal spots to plant mangroves.

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“…This mangrove species cultivate in sediments with low-oxygen and high-salinity concentrations and are made of light gray bark and thick, glossy, bright green leaves [8,42]. This region has been impacted by anthropogenic practices of local and regional communities including leaf-cutting, fishing, boat journeys for tourism, oil leakage, and petrochemical industries [55][56][57]. Accordingly, developing efficient approaches is vital to obtain reliable information for the sustainable management of such valuable natural resources.…”

Section: Study Areamentioning

confidence: 99%

Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery

Ghorbanian

Ahmadi

Amani³

et al. 2022

Water

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Mangroves, as unique coastal wetlands with numerous benefits, are endangered mainly due to the coupled effects of anthropogenic activities and climate change. Therefore, acquiring reliable and up-to-date information about these ecosystems is vital for their conservation and sustainable blue carbon development. In this regard, the joint use of remote sensing data and machine learning algorithms can assist in producing accurate mangrove ecosystem maps. This study investigated the potential of artificial neural networks (ANNs) with different topologies and specifications for mangrove classification in Iran. To this end, multi-temporal synthetic aperture radar (SAR) and multi-spectral remote sensing data from Sentinel-1 and Sentinel-2 were processed in the Google Earth Engine (GEE) cloud computing platform. Afterward, the ANN topologies and specifications considering the number of layers and neurons, learning algorithm, type of activation function, and learning rate were examined for mangrove ecosystem mapping. The results indicated that an ANN model with four hidden layers, 36 neurons in each layer, adaptive moment estimation (Adam) learning algorithm, rectified linear unit (Relu) activation function, and the learning rate of 0.001 produced the most accurate mangrove ecosystem map (F-score = 0.97). Further analysis revealed that although ANN models were subjected to accuracy decline when a limited number of training samples were used, they still resulted in satisfactory results. Additionally, it was observed that ANN models had a high resistance when training samples included wrong labels, and only the ANN model with the Adam learning algorithm produced an accurate mangrove ecosystem map when no data standardization was performed. Moreover, further investigations showed the higher potential of multi-temporal and multi-source remote sensing data compared to single-source and mono-temporal (e.g., single season) for accurate mangrove ecosystem mapping. Overall, the high potential of the proposed method, along with utilizing open-access satellite images and big-geo data processing platforms (i.e., GEE, Google Colab, and scikit-learn), made the proposed approach efficient and applicable over other study areas for all interested users.

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Multivariate curve resolution of organic pollution patterns in mangrove forest sediment from Qeshm Island and Khamir Port—Persian Gulf, Iran

Cited by 20 publications

References 63 publications

Chemometric Assessment of Bulgarian Wastewater Treatment Plants’ Effluents

Chemometric Assessment of Bulgarian Wastewater Treatment Plants’ Effluents

Identifying Suitable Locations for Mangrove Plantation Using Geospatial Information System and Remote Sensing

Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery

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