RivaMap: An automated river analysis and mapping engine

Isikdogan, Furkan; Bovik, Alan C.; Passalacqua, Paola

doi:10.1016/j.rse.2017.03.044

Cited by 112 publications

(104 citation statements)

References 25 publications

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“…With the assistance of a genetic algorithm, they established a characteristic scaling law called a river's at‐many‐station hydraulic geometry, which has been explained in Gleason and Wang () in details. At‐many‐station hydraulic geometry relationships enable river discharge to be estimated solely from river width, which can be automatically derived and easily monitored from satellite images (Isikdogan et al, ; Pavelsky & Smith, ). Gleason et al () followed the work of Gleason and Smith () with an updated methodology and a thorough sensitivity analysis of 34 rivers worldwide and found continued satisfactory performance for most river morphologies.…”

Section: Progresses and Challengesmentioning

confidence: 99%

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Huang

Chen

Zhang

et al. 2018

Reviews of Geophysics

515

267

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Observation of surface water is a functional requirement for studying ecological and hydrological processes. Recent advances in satellite-based optical remote sensors have promoted the field of sensing surface water to a new era. This paper reviews the current status of detecting, extracting, and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field, including spatio-temporal scale issues, integration with in situ hydrological data and elevation data, obscuration caused by clouds and vegetation, and the growing need to map surface water at a global scale. Historically, sensors have exhibited a contradiction in resolutions. Techniques including pixel unmixing and reconstruction, and spatio-temporal fusion have been developed to alleviate this contradiction. Spatio-temporal dynamics of surface water have been modeled by combining remote sensing data with in situ river flow. Recent studies have also demonstrated that the river discharge can be estimated using only optical remote sensing imagery, providing valuable information for hydrological studies in ungauged areas. Another historical issue for optical sensors has been obscuration by clouds and vegetation. An effective approach of reducing this limitation is to combine with synthetic aperture radar data. Digital elevation model data have also been employed to eliminate cloud/terrain shadows. The development of big data and cloud computation techniques makes the increasing demand of monitoring global water dynamics at high resolutions easier to achieve. An integrated use of multisource data is the future direction for improved global and regional water monitoring.Plain Language Summary Observing surface water is essential for ecological and hydrological studies. This paper reviews the current status of detecting, extracting, and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field. For example, it was found that pixel unmixing and reconstruction, and spatio-temporal fusion are two common and low-cost approaches to enhance surface water monitoring. Remote sensing data have been integrated with in situ river flow to model spatio-temporal dynamics of surface water. Recent studies have also proved that the river discharge can be estimated using only optical remote sensing imagery. This will be a breakthrough for hydrological studies in ungauged areas. Optical sensors are also easily obscured by clouds and vegetation. This limitation can be reduced by integrating optical data with synthetic aperture radar data and digital elevation model data. There is increasing demand of monitoring global water dynamics at high resolutions. It is now easy to achieve with the development of big data and cloud computation techniques. Enhanced global or regional water monitoring in the future requires integrated use of multiple sources of remote sensing data.

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Section: Progresses and Challengesmentioning

confidence: 99%

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Huang

Chen

Zhang

et al. 2018

Reviews of Geophysics

515

267

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“…Recent developments in image processing technology and increasing provision of freely-available remotely sensed data products could facilitate the semi-or complete automation of the geomorphological classification approach proposed in this GOOGLE EARTH/DISCRIMINATING RIVER TYPES paper. Isikdogan et al, 2017;Monegaglia et al, 2018), and object-based image analysis techniques have great potential for identifying spatially-distinctive features (e.g. It has enabled analyses with a spatial and temporal richness that would previously have been considered unachievable, including tracking surface water dynamics (Pekel et al, 2016), quantifying the status and distribution of sensitive habitats (Giri et al, 2010) and monitoring urban development (Patel et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…Wheaton et al, 2015), image processing workflows capable of extracting channel networks and their geometric properties have been developed (e.g. Isikdogan et al, 2017;Monegaglia et al, 2018), and object-based image analysis techniques have great potential for identifying spatially-distinctive features (e.g. Demarchi et al, 2016;Demarchi et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Google Earth as a data source for investigating river forms and processes: Discriminating river types using form‐based process indicators

Henshaw

Sekarsari

Zolezzi

et al. 2019

Earth Surf Processes Landf

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Google Earth provides potential for exploiting an enormous reservoir of freely‐available remotely sensed data to support river science and management. In this paper, we consider how the platform can support investigation of river physical forms and processes by developing an empirically‐based reach‐scale classification of semi‐natural European single thread to transitional rivers. Using strict reach and image selection criteria, we identified 194 reaches of 68 rivers for analysis. Measurements of channel dimensions and counts of in‐channel and floodplain features, standardised for reach length and channel width where necessary, were used to derive a series of geomorphologically‐relevant process indicators. A suite of multivariate analyses were then applied to this data set, resulting in the discrimination of five river types: laterally stable, laterally active sinuous‐meandering; transitional (near‐braided); bedrock; and cascade/step dominated. The results of the classification were tested by examining the characteristics and distribution of the river classes in relation to known independent controls of river form including reach‐scale energy and valley confinement conditions. Our results show that if methods of data extraction are carefully developed, physically meaningful river reach discrimination can be achieved using Google Earth. Although there are limits to the types of information that can be extracted such that field investigations cannot always be avoided, there is enormous potential to mine Google Earth across different space and time scales, supporting the assembly of large, reliable data sets relevant to river forms and processes in a very cost‐effective way. © 2019 John Wiley & Sons, Ltd.

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“…Because of the difficulty in extracting delta networks and the manual labor involved, studies up to date have analyzed metrics only in small systems or have focused on bulk metrics that do not capture how the characteristics of 5 the delta may vary spatially and temporally. We expect that further development in automatic approaches for delta network extraction (Isikdogan et al, 2017b(Isikdogan et al, , 2018 and for the analysis of network change over time (Jarriel et al, 2019) will enable similar analyses at the global scale and over time.…”

Section: Metrics Importance and Applicability Of The Approach To Othementioning

confidence: 99%

Dominant process zones in a mixed fluvial-tidal delta are morphologically distinct

Perignon¹,

Adams²,

Overeem³

et al. 2020

Preprint

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Abstract. The morphology of deltas is determined by the spatial extent and variability of the geomorphic processes that shape them. While in some cases resilient, deltas are increasingly threatened by natural and anthropogenic forces, such as sea level rise and land use change, which can drastically alter the rates and patterns of sediment transport. Quantifying process patterns can improve our predictive understanding of how different zones within delta systems will respond to future change. Available remotely sensed imagery can help but appropriate tools are needed for pattern extraction and analysis. We present a method for extracting information about the nature and spatial extent of active geomorphic processes across deltas from ten parameters quantifying the geometry of each of 1239 islands and the channels around them using machine learning. The method consists of a two-step unsupervised machine learning algorithm that clusters islands into spatially continuous zones based on the ten morphological metrics extracted from remotely sensed imagery. By applying this method to the Ganges–Brahmaputra–Meghna Delta, we find that the system can be divided into six major zones. Classification results show that active fluvial island construction and bar migration processes are limited to relatively narrow zones along the main Ganges River and Brahmaputra and Meghna corridors, whereas zones in the mature upper delta plain, with smaller fluvial distributary channels stand out as their own morphometric class. The classification also shows good correspondence with known gradients in the influence of tidal energy with distinct classes for islands in the backwater zone and in the purely tidally-controlled region of the delta. Islands at the delta front, under the mixed influence of tides, fluvial-estuarine construction, and local wave reworking have their own characteristic shape and channel configuration. The method does not distinguish between islands with embankments (polders) and natural islands in the nearby mangrove forest (Sundarbans), suggesting that human modifications have not yet altered the gross geometry of the islands beyond their previous natural morphology. These results demonstrate that machine learning and remotely sensed imagery are useful tools for identifying the spatial patterns of geomorphic processes across delta systems.

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RivaMap: An automated river analysis and mapping engine

Cited by 112 publications

References 25 publications

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Google Earth as a data source for investigating river forms and processes: Discriminating river types using form‐based process indicators

Dominant process zones in a mixed fluvial-tidal delta are morphologically distinct

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