Risk Assessment of Alien Azolla filiculoides Lam in Anzali Lagoon Using Remote Sensing Imagery

Marzvan, Saeideh; Moravej, Kamran; Felegari, Shilan; Sharifi, Alireza; Askari, Mohammad Sadegh

doi:10.1007/s12524-021-01362-1

Cited by 12 publications

(7 citation statements)

References 28 publications

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“…There are most likely two mechanisms at work here, the most efficient of which is light deprivation for immature weed seedlings due to sunlight obstruction (Ouedraogo et al, 2021). The other is a dense, interlocking Azella mat that provides physical resistance to the emergence of weed seedlings (Marzvan et al, 2021). Rice seedlings are not harmed by Azolla's weed suppression function because they are planted above the Azolla mat.…”

Section: Controlling the Spread Of Aquatic Weedsmentioning

confidence: 99%

Water Fern- Azolla: A Review

Chandrababu¹,

Parvathy

2022

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The Azolla-Anabaena symbiosis is distinguished by its high productivity and ability to fix nitrogen at high rates. As a result, numerous studies on this association have been conducted over the last few decades, with insufficient synthesis and coordination. As a result, this paper attempts to review and summarise previous and recent findings on the biology and applications of azolla in the hope of facilitating increased future collaborative research on this green gold mine. Azolla is a plant in the Azolla genus. Azolla derived from water can be used as human food, animal feed, green manure, organic fertiliser and to increase soil fertility, as well as for biological wastewater remediation and salt soil reclamation. Because of its high nutritional quality and protein content, azolla is suitable for human consumption as well as as a feed additive for a variety of animals such as fish, ducks, cattle, poultry and others to reduce feed costs. It is also used in the production of biogas and hydrogen, as well as as astronaut food in space. This review provides an overview of Azolla’s benefits as well as new developments in its various fields of application.

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Section: Controlling the Spread Of Aquatic Weedsmentioning

confidence: 99%

Water Fern- Azolla: A Review

Chandrababu¹,

Parvathy

2022

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“…[1][2][3]. On the other hand, segmentation, defined as the categorization of image pixels into homogeneous regions, whereas each of them corresponds to a particular land-cover type, is an important processing step in many applications and analysis related to RS science such as anomaly, change, object, and target detection, classification, automatic pictorial pattern recognition, understanding plant/land distribution, and target tracking [4][5][6]. Considering the papers in the domain of RS image segmentation analysis (and spatially the panchromatic ones that have the best spatial resolution and are our study subject), there are two types of uncertainties in satellite images that must be modeled and considered.…”

Section: Introductionmentioning

confidence: 99%

“…Another uncertainty, named as inherent, exists in relation to measuring and sensing applications and sciences due to inaccurate measuring and problems in the collecting of data [7,10]. Some additional reasons such as related to sensor, geographical and atmospheric ones increase inherent uncertainty in satellite images case [6,11,12]. There are two methods: parametric and deterministic, to analyze and model the inherent uncertainty in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…There are two methods: parametric and deterministic, to analyze and model the inherent uncertainty in the literature. In the parametric method, inherent uncertainty is modeled by a probability density function [6,13,14]. Difficulties in allocating a suitable distribution (distribution type and parameters determining) for each point in the image and a lack of statistical independence of points from each other to consider an independent probability function for each point are the key problems in the parametric method utilizing RS applications.…”

Section: Introductionmentioning

confidence: 99%

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Ultrafusion: Optimal Fuzzy Fusion in Land-Cover Segmentation Using Multiple Panchromatic Satellite Images

Mahdipour,

Sharifi,

Sookhak

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Handling and quantifying two types of uncertainties, spatial and inherent, in land-cover segmentation using multiple satellite images constitutes a primary concern within the domain of segmenting multiple remote sensing images. Despite the comprehensive examination of the spatial uncertainty, the lack of a mathematical model addressing the inherent uncertainty in satellite images is notable. This paper endeavors to address this gap by considering and focuses on the latter. Leveraging multiple high-resolution panchromatic remote sensing (HR-PRS) images as input data, the study aims to model all procedures in the image formation and preprocessing stages (applied to each input image before segmentation) as the producing factors of inherent uncertainty in the segmentation process. The input images, conceptualized as events in random processes, undergo fusion using the mean estimator, referred to as the proposed "ultrafusion" method in this paper. The study demonstrates that the results of ultra-fusion can be effectively modeled by normal-type fuzzy numbers. The parameters of these fuzzy numbers are estimated using the maximum likelihood method. Subsequently, Fuzzy C-Means (FCM), as the most renowned clustering method, is employed for segmentation. Analytical comparisons reveal the notable performance of the proposed algorithm in terms of accurate segmentation and computational efficiency. Affirming the efficacy of the proposed approach, simulation results validate the advantages by improving the Overall Accuracy, Kappa, and F1-Score indices by about 0.86, 0.52, and 1.03 percent respectively, in compared to the most recent and similar method.

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“…In Anzali Lagoon and its watershed, many attempts have been made to assess the parameters related to the water quality of the lagoon [27,28], or to create chronologies of the sediment accumulated in the lagoon [29,30]. However, there has been no study of the sedimentation rate or an assessment of the effects of the major rivers' discharge and erosion at the seven selected stations in the study area.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Variations of Discharge and Sediment in Rivers Flowing into the Anzali Lagoon

2022

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In the last few years, trend identification has become an important issue in hydrological time-series analyses; it is also a difficult task, due to the variety of models and the impact of climate change on the river flow regime. Due to the vital importance of the Anzali Lagoon to the environment of the region, and the threat to its health caused by the volume or amount of inlet sediments, we decided to study the changes in flow and sediment in the rivers flowing into the Anzali Lagoon. For the present study, the long-term monthly, seasonal, and annual sediment and discharge data of seven stations were obtained during the period 1985–2019. According to the available information, the trend of sediment load variation was investigated at different time scales. In this study, the Mann–Kendall statistical test, the double-mass curve, and performance fitting were used to assess the seasonal and annual trends in sediment and river flow. The results showed that at Aghamahale station, the low relationship between discharge and sediment compared with that at other stations was due to the low slope and constant water of the Behmbar River, which caused the sediments to settle and decreased their carrying rate. Moreover, Nokhaleh station had the largest share of sediment transfer to the lagoon during 2002–2012. Sediment details also show that the highest amount of sediment in all stations occurred in non-crop seasons—i.e., from October to January—and was directly dependent on the amount of rainfall in these areas. The results of the sediment analysis also indicate that the discharge and the subsequent sediment loads from upstream to downstream were high over the summer. Furthermore, the rivers downstream demonstrated springtime peaks in the sediment loads and discharge, probably owing to snow melting.

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Risk Assessment of Alien Azolla filiculoides Lam in Anzali Lagoon Using Remote Sensing Imagery

Cited by 12 publications

References 28 publications

Water Fern- Azolla: A Review

Water Fern- Azolla: A Review

Ultrafusion: Optimal Fuzzy Fusion in Land-Cover Segmentation Using Multiple Panchromatic Satellite Images

Spatio-Temporal Variations of Discharge and Sediment in Rivers Flowing into the Anzali Lagoon

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