Measuring river planform changes from remotely-sensed data: A Monte-Carlo approach to assess the impact of spatially-variable geometric error.

2020 ◽  
Author(s):  
Timothée Jautzy ◽  
Pierre-Alexis Herrault ◽  
Valentin Chardon ◽  
Laurent Schmitt ◽  
Gilles Rixhon
Keyword(s):  
Monte Carlo ◽  
Confidence Level ◽  
Anthropogenic Activities ◽  
Independent Set ◽  
Remotely Sensed ◽  
Geometric Error ◽  
Remotely Sensed Data ◽  
River Planform ◽  
Active Channels ◽  
The Impact

<p>A majority of European rivers have been extensively affected by diverse anthropogenic activities, including e.g. channelization, regulation and sediment mining. Against this background, the planimetric analysis based on remotely-sensed data is frequently used to evaluate historical planform changes, eventually leading to quantification of migration rates. However, geometric spatially-variable (SV) error inherently associated with these data can result in poor or even misleading interpretation of measured changes, especially on mid-sized rivers. We therefore address the following issue: What is the impact of spatially-variable error on the quantification of surfacic river planform changes?</p><p>Our test river corresponds to a 20 m wide meandering sub-tributary of the Upper Rhine, the Lower Bruche. Within four, geomorphologically-diverse sub-reaches, the active channel is digitised using diachronic orthophotos (1950; 1964) and the SV-error affecting the data is interpolated with an Inverse Distance Weighting technique based on an independent set of ground control points. As a second step, the main novelty of our approach consists in running Monte-Carlo (MC) simulations to randomly translate active channels according to the interpolated SV-error. This eventually allows to display the relative margin of error (RME) associated with measured eroded and/or deposited surfaces for each sub-reach through MC simulations, illustrating the confidence level in the respective measurements of our river planform changes.</p><p>Our results suggest that (i) SV-error strongly affects the significance of measured changes and (ii) the confidence level might be dependent not only on magnitude of changes but also on their shapes. Taking SV-error into account is strongly recommended, regardless of the remotely-sensed data used. This is particularly true for mid-sized rivers and/or low amplitude river planform changes, especially in the aim of their sustainable management and/or restoration. Finally, our methodology is transferrable to different fluvial styles.</p>

scholarly journals Measuring river planform changes from remotely sensed data – a Monte Carlo approach to assessing the impact of spatially variable error

2020 ◽  
Vol 8 (2) ◽  
pp. 471-484
Author(s):  
Timothée Jautzy ◽  
Pierre-Alexis Herrault ◽  
Valentin Chardon ◽  
Laurent Schmitt ◽  
Gilles Rixhon
Keyword(s):  
Monte Carlo ◽  
Remotely Sensed ◽  
Lateral Migration ◽  
River Channels ◽  
Remotely Sensed Data ◽  
Channel Changes ◽  
Aerial Photos ◽  
River Planform ◽  
Active Channels ◽  
The Impact

Abstract. Remotely sensed data from fluvial systems are extensively used to document historical planform changes. However, geometric and delineation errors inherently associated with these data can result in poor or even misleading interpretation of measured changes, especially rates of channel lateral migration. It is thus imperative to take into account a spatially variable (SV) error affecting the remotely sensed data. In the wake of recent key studies using this SV error as a level of detection, we introduce a new framework to evaluate the significance of measured channel migration. Going beyond linear metrics (i.e. migration vectors between diachronic river centrelines), we assess significance through a channel polygon method yielding a surficial metric (i.e. quantification of eroded, deposited, or eroded-then-deposited surfaces). Our study area is a mid-sized active wandering river: the lower Bruche, a ∼20 m wide tributary of the Rhine in eastern France. Within our four test sub-reaches, the active channel is digitised using diachronic orthophotos (1950 and 1964), and the SV error affecting the data is interpolated with an inverse-distance weighting (IDW) technique. The novelty of our approach arises from then running Monte Carlo (MC) simulations to randomly translate active channels and propagate geometric and delineation errors according to the SV error. This eventually leads to the computation of percentage of uncertainties associated with each of the measured planform changes, which allows us to evaluate the significance of the planform changes. In the lower Bruche, the uncertainty associated with the documented changes ranges from 15.8 % to 52.9 %. Our results show that (i) orthophotos are affected by a significant SV error; (ii) the latter strongly affects the uncertainty of measured changes; and (iii) the significance of changes is dependent on both the magnitude and the shape of the surficial changes. Taking the SV error into account is strongly recommended even in orthorectified aerial photos, especially in the case of mid-sized rivers (<30 m width) and/or low-amplitude river planform changes (<1 m2m-1yr-1). In addition to allowing detection of low-magnitude planform changes, our approach is also transferable as we use well-established tools (IDW and MC): this opens new perspectives in the fluvial context (e.g. multi-thread river channels) for robustly assessing surficial channel changes.

scholarly journals Predicting distribution of malaria vector larval habitats in Ethiopia by integrating distributed hydrologic modeling with remotely sensed data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ai-Ling Jiang ◽  
Ming-Chieh Lee ◽  
Guofa Zhou ◽  
Daibin Zhong ◽  
Dawit Hawaria ◽  
...  
Keyword(s):  
Malaria Vector ◽  
Malaria Control ◽  
Remotely Sensed ◽  
Larval Habitat ◽  
Validation Dataset ◽  
Significant Shift ◽  
Remotely Sensed Data ◽  
Larval Habitats ◽  
Common Land Model ◽  
The Impact

AbstractLarval source management has gained renewed interest as a malaria control strategy in Africa but the widespread and transient nature of larval breeding sites poses a challenge to its implementation. To address this problem, we propose combining an integrated high resolution (50 m) distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats. The simulation was performed for the year of 2018 using ParFlow-Common Land Model (CLM) in a sugarcane plantation in the Oromia region, Ethiopia to examine the effects of rainfall and irrigation. The model was calibrated using field observations of larval habitats to successfully predict ponding at all surveyed locations from the validation dataset. Results show that without irrigation, at least half of the area inside the farms had a 40% probability of potential larval habitat occurrence. With irrigation, the probability increased to 56%. Irrigation dampened the seasonality of the potential larval habitats such that the peak larval habitat occurrence window during the rainy season was extended into the dry season. Furthermore, the stability of the habitats was prolonged, with a significant shift from semi-permanent to permanent habitats. Our study provides a hydrological perspective on the impact of environmental modification on malaria vector ecology, which can potentially inform malaria control strategies through better water management.

Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data

2007 ◽  
Vol 109 (3) ◽  
pp. 314-327 ◽  
Author(s):  
Izaya Numata ◽  
Dar A. Roberts ◽  
Oliver A. Chadwick ◽  
Josh Schimel ◽  
Fernando R. Sampaio ◽  
...  
Keyword(s):  
Grazing Intensity ◽  
Remotely Sensed ◽  
Biophysical Properties ◽  
Remotely Sensed Data ◽  
The Impact

scholarly journals Exceptional retreat of Novaya Zemlya's marine-terminating outlet glaciers between 2000 and 2013

2017 ◽  
Author(s):  
J. Rachel Carr ◽  
Heather Bell ◽  
Rebecca Killick ◽  
Tom Holt
Keyword(s):  
Sea Ice ◽  
Sea Level Rise ◽  
Sea Level ◽  
Remotely Sensed ◽  
Glacier Retreat ◽  
Remotely Sensed Data ◽  
Novaya Zemlya ◽  
The Past ◽  
Air Temperatures ◽  
The Impact

Abstract. Novaya Zemlya (NVZ) has experienced rapid ice loss and accelerated marine-terminating glacier retreat during the past two decades. However, it is unknown whether this retreat is exceptional longer-term and/or whether it has persisted since 2010. Investigating this is vital, as dynamic thinning may contribute substantially to ice loss from NVZ, but is not currently included in sea level rise predictions. Here, we use remotely sensed data to assess controls on NVZ glacier retreat between the 1973/6 and 2015. Glaciers that terminate into lakes or the ocean receded 3.5 times faster than those that terminate on land. Between 2000 and 2013, retreat rates were significantly higher on marine-terminating outlet glaciers than during the previous 27 years, and we observe widespread slow-down in retreat, and even advance, between 2013 and 2015. There were some common patterns in the timing of glacier retreat, but the magnitude varied between individual glaciers. Rapid retreat between 2000–2013 corresponds to a period of significantly warmer air temperatures and reduced sea ice concentrations, and to changes in the NAO and AMO. We need to assess the impact of this accelerated retreat on dynamic ice losses from NVZ, to accurately quantify its future sea level rise contribution.

scholarly journals Remote Sensing of Ecosystem Structure: Fusing Passive and Active Remotely Sensed Data to Characterize a Deltaic Wetland Landscape

2020 ◽  
Vol 12 (22) ◽  
pp. 3819 ◽  
Author(s):  
Daniel Peters ◽  
K. Olaf Niemann ◽  
Robert Skelly
Keyword(s):  
Remotely Sensed ◽  
Ecosystem Structure ◽  
Remotely Sensed Data ◽  
Horizontal Structure ◽  
Multiple Sensors ◽  
Data Mining Approach ◽  
Input Variables ◽  
Future Work ◽  
The Impact ◽  
Northern Alberta

A project was constructed to integrate remotely sensed data from multiple sensors and platforms to characterize range of ecosystem characteristics in the Peace–Athabasca Delta in Northern Alberta, Canada. The objective of this project was to provide a framework for the processing of multisensor data to extract ecosystem information describing complex deltaic wetland environments. The data used in this study was based on a passive satellite-based earth observation multispectral sensor (Sentinel-2) and airborne discrete light detection and ranging (LiDAR). The data processing strategy adopted here allowed us to employ a data mining approach to grouping of the input variables into ecologically meaningful clusters. Using this approach, we described not only the reflective characteristics of the cover, but also ascribe vertical and horizontal structure, thereby differentiating spectrally similar, but ecologically distinct, ground features. This methodology provides a framework for assessing the impact of ecosystems on radiance, as measured by Earth observing systems, where it forms the basis for sampling and analysis. This final point will be the focus of future work.

Sign in / Sign up

Export Citation Format

Share Document