RiMARS: An automated river morphodynamics analysis method based on remote sensing multispectral datasets

Shahrood, Abolfazl Jalali; Menberu, Meseret Walle; Darabi, Hamid; Rahmati, Omid; Rossi, Pekka M.; Kløve, Bjørn; Haghighi, Ali Torabi

doi:10.1016/j.scitotenv.2020.137336

Cited by 18 publications

(7 citation statements)

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“…The correction method can be called geometric correction. The pixel coordinates are changed and the pixel gray value is collected for correction preparation (Shahrood et al, 2020). This article chooses the polynomial correction method, and the specific expression method is as follows:…”

Section: Uav Multispectral Remote Sensing Image and Its Preprocessingmentioning

confidence: 99%

Dynamic monitoring of environmental quality in tourist attractions based on UAV multispectral remote sensing

Liu¹,

Chen²,

Cheng³

2022

IJETM

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In order to overcome the problems of low recognition rate, high spatial accuracy error and low monitoring accuracy existing in traditional methods, this paper proposes a dynamic monitoring study of environmental quality in tourist scenic spots based on UAV multi-spectral remote sensing. In this method, DJI Matrice600Pro UAV equipped with multispectral camera is used to collect remote sensing images and preprocess them to obtain hyperspectral remote sensing images. The classification regression tree method is used to extract the environmental remote sensing information of scenic spots, and the camera calibration objective function is constructed according to the world coordinates corresponding to the feature points to dynamically monitor the environmental quality. The experimental results show that the recognition rate of remote sensing image is close to 100%, the spatial accuracy error is less than 4%, the system error data value is 0.15, and the monitoring accuracy is as high as 95%.

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Section: Uav Multispectral Remote Sensing Image and Its Preprocessingmentioning

confidence: 99%

Dynamic monitoring of environmental quality in tourist attractions based on UAV multispectral remote sensing

Liu¹,

Chen²,

Cheng³

2022

IJETM

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“…They are particularly useful for assessing recent river morphological changes (Langat et al 2019). Different methodologies have been developed that allow analysing, inter alia, changes in river morphology (Shahrood et al 2020), river morphological status (Bechter et al 2018), physical habitat and river health (Zhao et al 2019), and even river gauging (Hou et al 2020) based on remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

Landscape Reading for Alpine Rivers: A Case Study from the river Biya

Schmalfuß

Hauer

Yanygina

et al. 2023

GES

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Anthropogenic stressors have altered the hydromorphological characteristics of rivers worldwide. Environmental guiding principles are essential for planning sustainable river restoration measures. The alpine river Biya, located in the Russian Altai mountains, originates from Lake Teletskoye and joins the Katun near Biysk, forming the Ob. The Biya represents a hydromorphological reference system in anthropogenically ‘least-disturbed’ condition. The presented study aimed to assess the river’s undisturbed morphology in relationship with the geological history of three different river stretches based on an adapted landscape reading approach using remote sensing information (ASTER GDEM v3). The established widths of the active channel, active floodplain and morphological floodplain as well as the longitudinal section were used to explain the differences between upper, middle, and lower Biya. The results confirm differences in the geological origins between the upper Biya, which has previously been described as the least developed and narrowest, and the other two stretches based on the analyses of morphological parameters. Morphological floodplain width could best explain the differences between upper (0-86 km), middle (86-196 km), and lower Biya (196-301 km). The study further showed a clear relationship between the variations in river patterns and adjacent topographic structures (valley confinements, tributary interactions), highlighting that any assessment of river morphology must consider the wider surroundings of a river stretch. The presented morphological observations and analyses of the Biya show that easily obtainable parameters can detect differences in the morphological history of river stretches within the same catchment, supporting process understanding.

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“…Different channel planforms are thought to reflect differences in river behaviour, and planform assessment remains central to all modern river channel classification schemes (Carling et al, 2014). However, quantitative assessment of river planviews is considered a challenging task in river channel analyses, inclusive of channel evolution, migration and bank erosion (Miller, 1988;Osman and Thorne, 1988;Richardson, 2002;Smith and Pain, 2009;Kleinhans et al, 2013;Grabowski et al, 2014;Yousefi et al, 2016;Li et al, 2017;Shahrood et al, 2020).…”

Section: Introductionmentioning

confidence: 99%