Improved Understanding of Suspended Sediment Transport Process Using Multi-Temporal Landsat Data: A Case Study From the Old Woman Creek Estuary (Ohio)

Yeo, In‐Young; Lang, Megan W.; Eric, Vermote

doi:10.1109/jstars.2013.2265191

Cited by 11 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to mapping open waters, mapping wetlands and associated inundated areas is more challenging due to the complex physical environmental characteristics of wetlands. In wetlands, the viewing of standing water is limited by canopy cover, and observed spectral responses are strongly mixed with vegetation and soils [25,26,28,32,33]. Therefore, multiple spectral indices and bands, which are sensitive to vegetation, bare soils, and water, have been used in some of the previous studies to map wetland inundation by defining thresholds with decision trees (e.g., [13,33,34]).…”

Section: Introductionmentioning

confidence: 99%

A Random Forest-Based Multi-Index Classification (RaFMIC) Approach to Mapping Three-Decadal Inundation Dynamics in Dryland Wetlands Using Google Earth Engine

2023

Self Cite

View full text Add to dashboard Cite

Australian inland riparian wetlands located east of the Great Dividing Range exhibit unique, hydroecological characteristics. These flood-dependent aquatic systems located in water-limited regions are declining rapidly due to the competitive demand for water for human activities, as well as climate change and variability. However, there exist very few reliable data to characterize inundation change conditions and quantify the impacts of the loss and deterioration of wetlands. A long-term time record of wetland inundation maps can provide a crucial baseline to monitor, assess, and assist the management and conservation of wetland ecosystems. This study presents a random forest-based multi-index classification algorithm (RaFMIC) on the Google Earth Engine (GEE) platform to efficiently construct a temporally dense, three-decadal time record of inundation maps of the southeast Australian riparian inland wetlands. The method was tested over the Macquarie Marshes located in the semiarid region of NSW, Australia. The results showed a good accuracy when compared against high-spatial resolution imagery. The total inundated area was consistent with precipitation and streamflow patterns, and the temporal dynamics of vegetation showed good agreement with the inundation maps. The inundation time record was analysed to generate inundation probability maps, which were in a good agreement with frequently flooded areas simulated by a hydrodynamic model and the distribution of flood-dependent vegetation species. The long-term, time-dense inundation maps derived from the RaFMIC method can provide key information to assess the condition and health of wetland ecosystems and have the potential to improve wetland inventory with spatially explicit water regime information. RaFMIC can be adapted over other dryland wetlands, as an effective semiautomated method of mapping long-term inundation dynamics.

show abstract

Section: Introductionmentioning

confidence: 99%

A Random Forest-Based Multi-Index Classification (RaFMIC) Approach to Mapping Three-Decadal Inundation Dynamics in Dryland Wetlands Using Google Earth Engine

2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…A Geographical Information System (GIS) can be used to model bathymetry and the spatial distribution of sediments (Evans et al, 2002). Several attempts have been made to calculate the quantity of sediment using remote sensing technology (Yeo et al, 2014;Narasayya et al, 2013;Sri Sumantyo et al, 2012).Using the Remote Sensing techniques, it has become very efficient and convenient to quantify the sedimentation in a reservoir and to assess its distribution and deposition pattern (Narasayya et al, 2013). Remote sensing technology, offers data acquisition over a long period of time and broad spectral range, can provide synoptic, repetitive and timely information regarding the sedimentation characteristics in a reservoir.…”

Section: Introductionmentioning

confidence: 99%

Sedimentation Study in a Reservoir Using Remote Sensing Technique

Merina¹

2016

Appl Ecol Env Res

View full text Add to dashboard Cite

Abstract.Reservoir sedimentation is a severe problem faced by dams caused due to soil erosion which is almost immeasurable at source and occurs in the catchment areas of the reservoirs. The present study describes the evaluation of sedimentation carried out for Vaigai reservoir situated in Tamil Nadu, India. Vaigai reservoir nourishes the inhabitants through water storage and supply for irrigation and water power. Nowadays, capacity loss occurs in the reservoir due to sedimentation. As it is highly tedious and uneconomical to do hydrographic surveys, the frequentness in finding the sediment yield becomes impossible. But the recent application of remote sensing and GIS technologies in the field of Civil Engineering make it possible. The Satellite Remote Sensing (SRS) method for prediction of reservoir sedimentation uses directly the water-spread area of the reservoir at a particular elevation on the date of pass of the satellite. With known area and the difference in level of water, the capacity and thereby the loss in capacity of the reservoir due to sedimentation can also be estimated. This paper illustrates the prediction of sedimentation at Vaigai reservoir using remote sensing and ArcGIS.

show abstract

Evaluation of Reservoir Sedimentation Using Satellite Data—A Case Study

Reddy

Pramada

2021

Water Resources Management and Reservoir Operation

View full text Add to dashboard Cite

Improved Understanding of Suspended Sediment Transport Process Using Multi-Temporal Landsat Data: A Case Study From the Old Woman Creek Estuary (Ohio)

Cited by 11 publications

References 32 publications

A Random Forest-Based Multi-Index Classification (RaFMIC) Approach to Mapping Three-Decadal Inundation Dynamics in Dryland Wetlands Using Google Earth Engine

A Random Forest-Based Multi-Index Classification (RaFMIC) Approach to Mapping Three-Decadal Inundation Dynamics in Dryland Wetlands Using Google Earth Engine

Sedimentation Study in a Reservoir Using Remote Sensing Technique

Evaluation of Reservoir Sedimentation Using Satellite Data—A Case Study

Contact Info

Product

Resources

About