16 research outputs found
Impacts of streamflow alteration on benthic macroinvertebrates by mini‐hydro diversion in Sri Lanka
No full textOur study focused on quantifying the alterations of streamflow at a weir site due to the construction of a mini-hydropower plant in the Gurugoda Oya (Sri Lanka), and evaluating the spatial responses of benthic macroinvertebrates to altered flow regime. The HEC-HMS 3.5 model was applied to the Gurugoda Oya sub-catchment to generate streamflows for the time period 1991-2013. Pre-weir flows were compared to post-weir flows with 32 Indicators of Hydrologic Alteration using the range of variability approach (RVA). Concurrently, six study sites were established upstream and downstream of the weir, and benthic macroinvertebrates were sampled monthly from May to November 2013 (during the wet season). The key water physico-chemical parameters were also determined. RVA analysis showed that environmental flow was not maintained below the weir. The mean rate of non-attainment was similar to 45% suggesting a moderate level of hydrologic alteration. Benthic macroinvertebrate communities significantly differed between the study sites located above and below the weir, with a richness reduction due to water diversion. The spatial distribution of zoobenthic fauna was governed by water depth, dissolved oxygen content and volume flow rate. Our work provides first evidence on the effects of small hydropower on river ecosystem in a largely understudied region. Studies like this are important to setting-up adequate e-flows
A Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET-GEE)
No full textA Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET)
This is a Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET) developed by the Surface Dynamics and Modeling Lab at the University of Alabama that calculates flood depth using a flood extent layer and a digital elevation model. This research is made possible by the CyberSeed Program at the University of Alabama. Project name: WaterServ: A Cyberinfrastructure for Analysis, Visualization and Sharing of Hydrological Data.
Please see the associated publications:
1. Peter, B.G., Cohen, S., Lucey, R., Munasinghe, D., Raney, A. and Brakenridge, G.R., 2020. Google Earth Engine Implementation of the Floodwater Depth Estimation Tool (FwDET-GEE) for rapid and large scale flood analysis. IEEE Geoscience and Remote Sensing Letters, 19, pp.1-5.
https://ieeexplore.ieee.org/abstract/document/9242297
2. Cohen, S., Peter, B.G., Haag, A., Munasinghe, D., Moragoda, N., Narayanan, A. and May, S., 2022. Sensitivity of remote sensing floodwater depth calculation to boundary filtering and digital elevation model selections. Remote Sensing, 14(21), p.5313.
https://www.mdpi.com/2072-4292/14/21/5313>https://www.mdpi.com/2072-4292/14/21/5313
GitHub Repository (ArcMap and QGIS implementations): https://github.com/csdms-contrib/fwdet
3. Cohen, S., A. Raney, D. Munasinghe, J.D. Loftis J, A. Molthan, J. Bell, L. Rogers, J. Galantowicz, G.R. Brakenridge7, A.J. Kettner, Y. Huang, Y. Tsang, (2019). The Floodwater Depth Estimation Tool (FwDET v2.0) for Improved Remote Sensing Analysis of Coastal Flooding. Natural Hazards and Earth System Sciences, 19, 2053–2065. https://doi.org/10.5194/nhess-19-2053-2019
4. Cohen, S., G. R. Brakenridge, A. Kettner, B. Bates, J. Nelson, R. McDonald, Y. Huang, D. Munasinghe, and J. Zhang (2018), Estimating Floodwater Depths from Flood Inundation Maps and Topography, Journal of the American Water Resources Association, 54 (4), 847–858. https://doi.org/10.1111/1752-1688.12609
Sample products and data availability:
https://sdml.ua.edu/models/fwdet/
https://sdml.ua.edu/michigan-flood-may-2020/
https://cartoscience.users.earthengine.app/view/fwdet-gee-mi
https://alabama.app.box.com/s/31p8pdh6ngwqnbcgzlhyk2gkbsd2elq0
GEE implementation output: fwdet_gee_brazos.tif
ArcMap implementation output (see Cohen et al. 2019): fwdet_v2_brazos.tif
iRIC validation layer (see Nelson et al. 2010): iric_brazos_hydraulic_model_validation.tif
Brazos River inundation polygon access in GEE: var brazos = ee.FeatureCollection('users/cartoscience/FwDET-GEE-Public/Brazos_River_Inundation_2016')
Nelson, J.M., Shimizu, Y., Takebayashi, H. and McDonald, R.R., 2010. The international river interface cooperative: public domain software for river modeling. In 2nd Joint Federal Interagency Conference, Las Vegas, June (Vol. 27).
Google Earth Engine Code
/* ----------------------------------------------------------------------------------------------------------------------
# FwDET-GEE calculates floodwater depth from a floodwater extent layer and a DEM
Authors: Brad G. Peter, Sagy Cohen, Ronan Lucey, Dinuke Munasinghe, Austin Raney
Emails: [email protected], [email protected], [email protected], [email protected], [email protected]
Organizations: BP, SC, DM, AR - University of Alabama; RL - University of Alabama in Huntsville
Last Modified: 10/08/2020
To cite this code use:
Peter, Brad; Cohen, Sagy; Lucey, Ronan; Munasinghe, Dinuke; Raney, Austin, 2020, "A Google Earth Engine implementation
of the Floodwater Depth Estimation Tool (FwDET-GEE)", https://doi.org/10.7910/DVN/JQ4BCN, Harvard Dataverse, V2
-------------------------------------------------------------------------------------------------------------------------
This is a Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDETv2.0) [1] developed by the
Surface Dynamics and Modeling Lab at the University of Alabama that calculates flood depth using a flood extent layer
and a digital elevation model. This research is made possible by the CyberSeed Program at the University of Alabama.
Project name: WaterServ: A Cyberinfrastructure for Analysis, Visualization and Sharing of Hydrological Data.
GitHub Repository (ArcMap and QGIS implementations): https://github.com/csdms-contrib/fwdet
-------------------------------------------------------------------------------------------------------------------------
How to run this code with your flood extent GEE asset:
User of this script will need to update path to flood extent (line 32 or 33) and select from the processing options.
Available DEM options (1) are USGS/NED (U.S.) and USGS/SRTMGL1_003 (global). Other options include (2) running the elevation
outlier filtering algorithm, (3) adding water body data to the inundation extent, (4) add a water body data layer uploaded by
the user rather than using the JRC global surface water data, (5) masking out regular water body data, (6) masking out 0 m
depths, (7) choosing whether or not to export, (8) exporting additional data layers, and (9) setting an export file name.
The simpleVis option (10) bypasses the time consuming processes and is meant for visualization only; set this option to false
to complete the entire process and enable exporting.
-------------------------------------------------------------------------------------------------------------------------
••••••••••••••••••••••••••••••••••••••••••• USER OPTIONS ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••
Load flood extent layer | Flood extent layer must be uploaded to GEE first as an asset. If the flood extent is a
shapefile, upload as a FeatureCollection; otherwise, if the flood extent layer is a raster, upload it as an image.
A raster layer may be required if the flood extent is a highly complex geometry -------------------------------------- */
var flood = ee.FeatureCollection('users/username/folder/flood_extent') // comment out this line if using an Image
// var flood = ee.Image('users/username/folder/flood_extent') // comment out this line if using a FeatureCollection
var waterExtent = ee.FeatureCollection('users/username/folder/water_extent') // *OPTIONAL* comment out this line if using an Image
// var waterExtent = ee.Image('users/username/folder/water_extent') // *OPTIONAL* comment out this line if using a FeatureCollection
// Processing options - refer to the directions above
/*1*/ var demSource = 'USGS/NED' // 'USGS/NED' or 'USGS/SRTMGL1_003'
/*2*/ var outlierTest = 'TRUE' // 'TRUE' (default) or 'FALSE'
/*3*/ var addWater = 'TRUE' // 'TRUE' (default) or 'FALSE'
/*4*/ var userWater = 'FALSE' // 'TRUE' or 'FALSE' (default)
/*5*/ var maskWater = 'FALSE' // 'TRUE' or 'FALSE' (default)
/*6*/ var maskZero = 'FALSE' // 'TRUE' or 'FALSE' (default)
/*7*/ var exportLayer = 'TRUE' // 'TRUE' (default) or 'FALSE'
/*8*/ var exportAll = 'FALSE' // 'TRUE' or 'FALSE' (default)
/*9*/ var outputName = 'FwDET_GEE' // text string for naming export file
/*10*/ var simpleVis = 'FALSE' // 'TRUE' or 'FALSE' (default)
// ••••••••••••••••••••••••••••••••• NO USER INPUT BEYOND THIS POINT ••••••••••••••••••••••••••••••••••••••••••••••••••••
// Create buffer around flood area to use for clipping other layers
var area = flood.geometry().bounds().buffer(1000).bounds()
// Load DEM and grab projection info
var dem = ee.Image(demSource).select('elevation').clip(area) // [2,3]
var projection = dem.projection()
var resolution = projection.nominalScale().getInfo()
// Load global surface water layer
var jrc = ee.Image('JRC/GSW1_1/GlobalSurfaceWater').select('occurrence').clip(area) // [4]
var water_image = jrc
// User uploaded flood extent layer
// Identify if a raster or vector layer is being used and proceed with appropriate process
if ( flood.name() == 'FeatureCollection' ) {
var addProperty = function(feature) {
return feature.set('val',0);
};
var flood_image = flood.map(addProperty).reduceToImage(['val'],ee.Reducer.first())
.rename('flood')
} else {
var flood_image = flood.multiply(0)
}
// Optional user uploaded water extent layer
if ( userWater == 'TRUE' ) {
// Identify if a raster or vector layer is being used and proceed with appropriate process
if ( waterExtent.name() == 'FeatureCollection' ) {
var addProperty = function(feature) {
return feature.set('val',0);
};
var water_image = waterExtent.map(addProperty).reduceToImage(['val'],ee.Reducer.first())
.rename('flood')
} else {
var water_image = waterExtent.multiply(0)
}
}
// Add water bodies to flood extent if 'TRUE' is selected
if ( addWater == 'TRUE' ) {
var w = water_image.reproject(projection)
var waterFill = flood_image.mask().where(w.gt(0),1)
flood_image = waterFill.updateMask(waterFill.eq(1)).multiply(0)
}
// Change processing options if 'TRUE' is selected
if ( simpleVis == 'FALSE' ) {
flood_image = flood_image.reproject(projection)
} else {
outlierTest = 'FALSE'
exportLayer = 'FALSE'
}
// Run the outlier filtering process if 'TRUE' is selected
if ( outlierTest == 'TRUE' ) {
// Outlier detection and filling on complete DEM using the modified z-score and a median filter [5]
var kernel = ee.Kernel.fixed(3,3,[[1,1,1],[1,1,1],[1,1,1]])
var kernel_weighted = ee.Kernel.fixed(3,3,[[1,1,1],[1,0,1],[1,1,1]])
var median = dem.focal_median({kernel:kernel}).reproject(projection)
var median_weighted = dem.focal_median({kernel:kernel_weighted}).reproject(projection)
var diff = dem.subtract(median)
var mzscore = diff.multiply(0.6745).divide(diff.abs().focal_median({kernel:kernel}).reproject(projection))
var fillDEM = dem.where(mzscore.gt(3.5),median_weighted)
// Outlier detection and filling on the flood extent border pixels
var expand = flood_image.focal_max({kernel: ee.Kernel.square({
radius: projection.nominalScale(),
units: 'meters'
})}).reproject(projection)
var demMask = fillDEM.updateMask(flood_image.mask().eq(0))
var boundary = demMask.add(expand)
var medianBoundary = boundary.focal_median({kernel:kernel}).reproject(projection)
var medianWeightedBoundary = boundary.focal_median({kernel:kernel_weighted}).reproject(projection)
var diffBoundary = boundary.subtract(medianBoundary)
var mzscoreBoundary = diffBoundary.multiply(0.6745).divide(diffBoundary.abs().focal_median({kernel:kernel}).reproject(projection))
var fill = fillDEM.where(mzscoreBoundary.gt(3.5),medianWeightedBoundary)
} else {
var fill = dem
}
// cumulativeCost floodwater surface elevation model (adaptation of the cost allocation method from FwDETv2.0)
var mod = fill.updateMask(flood_image.mask().eq(0))
var source = mod.mask()
var val = 10000
var push = 5000
var cost0 = ee.Image(val).where(source,0).cumulativeCost(source,push)
var cost1 = ee.Image(val).where(source,1).cumulativeCost(source,push)
var cost2 = mod.unmask(val).cumulativeCost(source,push)
var costFill = cost2.subtract(cost0).divide(cost1.subtract(cost0))
var costSurface = mod.unmask(0).add(costFill)
// Interpolation method courtesy of Matt Hancher (Earth Engine Co-Founder) posted to the GEE developer forums
// https://groups.google.com/forum/#!forum/google-earth-engine-developers
// Kernel size for low-pass filter
var boxcar = ee.Kernel.square({
radius: 3, units: 'pixels', normalize: true
});
// Floodwater depth calculation and smoothing using a low-pass filter
var costDepth = costSurface.subtract(fill)
.rename('FwDET_GEE')
.convolve(boxcar)
.reproject(projection)
.updateMask(flood_image.eq(0))
var costDepthFilter = costDepth.where(costDepth.lt(0),0)
// Mask out regular water bodies if 'TRUE' is selected
if ( maskWater === 'TRUE' ) {
var w = jrc.reproject(projection)
costDepthFilter = costDepthFilter.updateMask(w.mask().eq(0))
}
// Mask out zero values if 'TRUE' is selected
if ( maskZero === 'TRUE' ) {
costDepthFilter = costDepthFilter.updateMask(costDepthFilter.neq(0))
}
// Add flood depth to console
Map.clear()
Map.setOptions('HYBRID')
Map.centerObject(flood)
// Add message to user
var message = ui.Panel({
layout: ui.Panel.Layout.flow('vertical'),
style: {position: 'bottom-left', border: '1px solid gray', padding: '2px'}
})
message.widgets().set(0,ui.Label('Check tasks tab for export'));
Map.add(message);
// Change options for visualization sample
if ( simpleVis == 'TRUE' ) {
var histOptions = {
title: 'Depth (m)',
fontSize: 11,
legend: {position: 'none'},
series: {0: {color: '7100AA'}}
};
var histogram = ui.Chart.image.histogram({
image: costDepthFilter.updateMask(costDepthFilter.neq(0)),
region: area,
scale: resolution*10
}).setOptions(histOptions)
Map.addLayer(costDepthFilter,{},'FwDET GEE',true)
message.widgets().set(0,ui.Label("set simpleVis to 'FALSE' to export layers"))
message.widgets().set(1,histogram)
} else {
Map.addLayer(flood_image,{},'flood extent',true)
Map.addLayer(costDepthFilter,{},'FwDET GEE',false)
}
// Export function
var exportFunc = function(i,n) {
return Export.image.toDrive({
image: i,
description: outputName+n,
fileNamePrefix: outputName+n,
maxPixels: 1e13,
scale: resolution,
region: area
})
}
// Export output if 'TRUE' is selected
if ( exportLayer === 'TRUE' ) {
exportFunc(costDepthFilter, '_FwDET')
// Export all layers if 'TRUE' is selected
if ( exportAll == 'TRUE' ) {
exportFunc(costSurface, '_costSurface')
exportFunc(dem, '_dem')
exportFunc(fill, '_demFill')
}
}
/* ----------------------------------------------------------------------------------------------------------------------
// Citations
// [1] Cohen, S., A. Raney, D. Munasinghe, J.D. Loftis J, A. Molthan, J. Bell, L. Rogers, J. Galantowicz,
// G.R. Brakenridge7, A.J. Kettner, Y. Huang, Y. Tsang, (2019). The Floodwater Depth Estimation Tool (FwDET v2.0)
// for Improved Remote Sensing Analysis of Coastal Flooding. Natural Hazards and Earth System Sciences, 19, 2053–2065.
// https://doi.org/10.5194/nhess-19-2053-2019
// [2] Gesch, D., Oimoen, M., Greenlee, S., Nelson, C., Steuck, M. and Tyler, D., 2002. The national elevation dataset.
// Photogrammetric engineering and remote sensing, 68(1), pp.5-32.
// [3] Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E.,
// Roth, L. and Seal, D., 2007. The shuttle radar topography mission. Reviews of geophysics, 45(2).
// [4] Pekel, J.F., Cottam, A., Gorelick, N. and Belward, A.S., 2016. High-resolution mapping of global surface
// water and its long-term changes. Nature, 540(7633), pp.418-422.
// [5] Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.
---------------------------------------------------------------------------------------------------------------------- */
</pre
Global River Delta Morphology Response to Fluvial Sediment Change and Anthropogenic Stress
No full textElectronic Thesis or DissertationRiver deltas, home to almost half a billion people around the world, are important coastal depositional systems. Valuable natural resources, fertile grounds, and convenient locations for trade have proven deltaic land to become hot spots for urbanization, industrialization, and food production over the last few decades. In this Dissertation the following research questions are investigated: (1) what remote sensing-based algorithms are most efficient in river delta shoreline detection? (2) what changes do we observe of shorelines of individual deltas historically? (3) how is human modification on river delta plains contributing to delta plain erosion? (4) are changes in fluvial sediment flux to the delta are directly linked to decadal changes in delta morphology? A novel multifaceted research approach is used that combines (1) remote sensing analysis of past delta morphology changes, (2) numerical modeling of fluvial sediment fluxes, and (3) GIS/Statistical analysis of shoreline migration rates to answer the intricacies of the aforesaid spatio-temporal questions. This study (a) provides recommendations on different shoreline extraction techniques and make the transfer of knowledge to lesser studied deltaic systems done informatively, (b) provides quantitative understandings of historical shoreline change rates of deltas, (c) quantitative understandings of delta plain erosion from humans having modified delta plains from their pristine conditions, and (d) how shoreline mobility is informed based on riverine fluvial sediment, overall, at a global scale. The outcomes of this study yield several novel insights and scientific advancements of delta morphology changes of the last four decades, and not only transforms our analytical capabilities for studying human influences on river deltas, globally, but also provide a predictive platform that could assist decision makers to make better informed decisions for long-term sustainability of deltas
Riparian vegetation response to streamflow alteration due to dam construction in a range of rivers across the United States
No full textElectronic Thesis or DissertationHydrologic variability plays a major role in structuring the riparian vegetation within river ecosystems. This study evaluates the spatial and temporal response of riparian vegetation to altered flow regimes below 16 river dams across the contiguous United States using a combination of a holistic Environmental Flow Assessment approach and satellite remote sensing. River flows were characterized using thirty-three (33) different Indicators of Hydrologic Alteration (IHA) using the Range of Variability Approach (RVA). The alterations of riverflows were determined for post-dam scenarios comparing between the pre-dam and post-dam IHAs. Of the 16 locations assessed, 2 showed low levels, 11 moderate and 3 high levels of alteration. Change detection of riparian vegetation revealed an increase at majority of the sites (10 of the 16) immediately after the construction of the dam. Also, in a majority of the locations a decrease (10 of the 16) in vegetation was observed at the 1 year post-dam completion mark. Analyses show that vegetation change effects due to flow regime alterations below smaller dams occurred at shorter time spans (1-year post-completion) than larger dams (5-year post completion). It is inferred that categorizing dams based on capacity was successful in understanding effects on the vegetation extents better. In addition to the in-stream flow paradigm, regional climate and geomorphology are also identified as driving factors of riparian vegetation regulation. The need for a multi-factor model that drives annual changes in riparian zones is recognized to make better-informed decisions on sustainable dam operations
A Review of Satellite Remote Sensing Techniques of River Delta Morphology Change
No full textRiver deltas are important coastal depositional systems that are home to almost half a billion people worldwide. Understanding morphology changes in deltas is important in identifying vulnerabilities to natural disasters and improving sustainable planning and management. Satellite remote sensing has shown to be a useful technology for analyzing these morphology changes owing largely to its capability to provide spatially continues observations. In this paper, we critically review the literature about satellite remote sensing techniques that were used to study delta morphology changes.
We identify and categorize the techniques reported in the literature into 3 major classes: 1) One-step change detection, 2) Two-step change detection, and 3) Ensemble Classifications. In total we offer a review of 18 techniques within these categories. Example studies, the strengths and caveats in relation to the deltaic environment are discussed for each technique. Our synthesis of the literature reveals that sub-pixel-based algorithms perform better than pixel-based ones. Machine learning techniques rank second to sub-pixel techniques although an ensemble of techniques can be used just as effectively to achieve high feature detection accuracies.
We evaluate the 7 most commonly used techniques in literature (Conventional Techniques: (1) Modified Normalized Difference Water Index (MNDWI), 2) Normalized Difference Water Index (NDWI), 3) PCA analysis, 4) Unsupervised Classification, and 5) Supervised Classification)]. Machine Learning techniques: 6) Random Forest Classifier, and 7) Support Vector Machine) on a sample of global deltas, for delta morphological feature extraction performance. Findings show the Unsupervised Classification significantly outperforms the others and is recommended as a first order feature extraction technique in previously unknown, or, data sparse deltaic territories.
We propose four pathways for future advancement in satellite remote sensing of delta morphology: 1) utilizing new high-resolution imagery and development of more efficient data mining techniques, 2) moving toward universal applicability of algorithms and their transferability across satellite platforms, 3) improvement of the availability and use of ancillary data in image processing algorithms, and 4) development of a global-scale repository of deltaic data for the sharing of scientific knowledge across regions and disciplines
Suitability Analysis of Remote Sensing Techniques for Shoreline Extraction of Global River Deltas
No full textHigh frequency flooding, sea level rise and changes to riverine sediment fluxes have threatened the habitable land area of river deltas, where close to half a billion people live, globally. Understanding shoreline positions is important for overall sustainable planning of deltaic communities and delta evolution predictive modeling. However, a gap in literature is recognized where there is a) no understanding of the most effective shoreline extraction method for a delta, and b) comparisons across techniques to infer on the performance metrics of techniques across deltas in different climate regions. This makes it difficult to apply existing knowledge to lesser studied, data sparse deltaic regions worldwide.
In addressing these gaps, we evaluated the performance of 5 different remote sensing techniques against a hand-digitized shoreline vector of 44 river deltas globally, representing the 3 different morphological types of deltas (river-, tide- and wave-dominated), across 4 Köppen Climate Classes using Landsat 8 imagery. We propose a new metric (Robustness: R) to evaluate the performance of a given technique.
The results show that 1) the best performing method for the majority of the deltas (35/44) was Unsupervised Classification, 2) there is no geographical significance in the performance of the tested techniques, and 3) wave dominated deltas showed the highest classification robustness while tide dominated deltas showed the lowest. Recommendations are made for the application of techniques in different types of deltas and unknown deltaic territories worldwide
Gridded Global Revisit Periods of Landsat, Sentinel-1, Sentinel-2 Satellites and their Combination
No full textThis is a global dataset of revisit periods of individual satellites and their combination based on a 0.5-degree resolution grid.
Revisit periods are defined as the time between two consecutive observations of a particular point on the surface, for the satellite missions Landsat, Sentinel-2 and Sentinel-1. The grid was created using ArcMap 10.8.1 and intersections of the grid were used to create points. For each individual point, average revisit times (i.e., to account for irregular revisits, downlink issues) were calculated for each individual satellite and the composite of the three satellites. Averaged revisit times for each of these points were calculated based on the number of image tiles that intersected a particular grid point with more than a 30-minute time difference between each other acquired between 01 Jan 2016 and 31 Dec 2020.
The following equation is used to calculate revisit periods:
Average revisit time for a grid point = (Number of days between 01 Jan 2016 and 31 Dec 2020 (1827)) / (Total Number of Images captured)
Only revisits occurring between 82.5 N and 55 S of land grid points are considered; Antarctica is omitted from analysis. For satellite missions that consist of two spacecraft orbiting simultaneously (Sentinel-1 A/B, and Sentinel-2 A/B), images acquired by both satellites were used in average revisit period calculation for a given grid point. Sum totals of image tiles of all three missions are used to calculate composite point-based revisit times.This dataset was produced as part of the manuscript "A multi-sensor approach for increased measurements of floods and their societal impacts from space" which is currently in review
Sensitivity of Remote Sensing Floodwater Depth Calculation to Boundary Filtering and Digital Elevation Model Selections
No full textThe Floodwater Depth Estimation Tool (FwDET) calculates water depth from a remote sensing-based inundation extent layer and a Digital Elevation Model (DEM). FwDET’s low data requirement and high computational efficiency allow rapid and large-scale calculation of floodwater depth. Local biases in FwDET predictions, often manifested as sharp transitions or stripes in the water depth raster, can be attributed to spatial or resolution mismatches between the inundation map and the DEM. To alleviate these artifacts, we are introducing a boundary cell smoothing and slope filtering procedure in version 2.1 of FwDET (FwDET2.1). We present an optimization analysis that quantifies the effect of differing parameterization on the resulting water depth map. We then present an extensive intercomparison analysis in which 16 DEMs are used as input for FwDET Google Earth Engine (FwDET-GEE) implementation. We compare FwDET2.1 to FwDET2.0 using a simulated flood and a large remote sensing derived flood map (Irrawaddy River in Myanmar). The results show that FwDET2.1 results are sensitive to the smoothing and filtering values for medium and coarse resolution DEMs, but much less sensitive when using a finer resolution DEM (e.g., 10 m NED). A combination of ten smoothing iterations and a slope threshold of 0.5% was found to be optimal for most DEMs. The accuracy of FwDET2.1 improved when using finer resolution DEMs except for the MERIT DEM (90 m), which was found to be superior to all the 30 m global DEMs used
Global datasets to evaluate a multi-sensor approach for observation of floods
No full text1. Overview
This repository contains datasets used to evaluate potential improvements to flood detectability afforded by combining data collected by Landsat, Sentinel-2, and Sentinel-1 for the first time globally. The datasets were produced as part of the manuscript "A multi-sensor approach for increased measurements of floods and their societal impacts from space" which is currently in review.
2. Dataset Descriptions
There are two datasets included here.
(a) A global grid of revisit periods of Landsat, Sentinel-1, Sentinel-2 Satellites and their combination [GlobalMedianRevisits.zip]
A global dataset of revisit periods of individual satellites and their combination based on a 0.5-degree resolution grid.
Revisit periods are defined as the time between two consecutive observations of a particular point on the surface, for the satellite missions Landsat, Sentinel-2 and Sentinel-1. The grid was created using ArcMap 10.8.1 and intersections of the grid were used to create points. For each individual point, average revisit times (i.e., to account for irregular revisits, downlink issues) were calculated for each individual satellite and the composite of the three satellites. Averaged revisit times for each of these points were calculated based on the number of image tiles that intersected a particular grid point with more than a 30-minute time difference between each other acquired between 01 Jan 2016 and 31 Dec 2020.
The following equation is used to calculate revisit periods:
Average revisit time for a grid point = (Number of days between 01 Jan 2016 and 31 Dec 2020 (1827)) / (Total Number of Images captured)
Only revisits occurring between 82.5 N and 55 S of land grid points are considered; Antarctica is omitted from analysis. For satellite missions that consist of two spacecraft orbiting simultaneously (Sentinel-1 A/B, and Sentinel-2 A/B), images acquired by both satellites were used in average revisit period calculation for a given grid point. Sum totals of image tiles of all three missions are used to calculate composite point-based revisit times.
(b) Average revisit periods of satellites for flood records in the DFO database [FloodInfo.zip]
Average Revisit Times of Landsat, Sentinel-1, Sentinel-2 and their ensemble are calculated for 5130 flood records in the Dartmouth Flood Observatory's (DFO) flood record database. These were appended to the already existing attributes of the database
