Comparison of SRTM-V4.1 and ASTER-V2.1 for Accurate ... · The obtained results indicate that the SRTM DEM performs better than ASTER in terms of micro-topography, hydrologic-network

Journal of Earth Science and Engineering 8 (2018) 8-30 doi: 10.17265/2159-581X/2018.01.002

Comparison of SRTM-V4.1 and ASTER-V2.1 for Accurate

Topographic Attributes and Hydrologic Indices

Extraction in Flooded Areas

A. Bannari, G. Kadhem, A. El-Battay and N. Hameid

Department of Geoinformatics, College of Graduate Studies, Arabian Gulf University, Manama, P.O. Box 26671, Kingdom of Bahrain

Abstract: This research compares the potential of SRTM-V4.1 and ASTER-V2.1 with 30-m pixel size to derive topographic attributes (elevation, slopes, aspects, and flow accumulation) and hydrologic indices such as STI (sediment transport index), CTI (compound topographic index) and SPI (stream power index) to detect areas associated with flash floods caused by rainfall storms and sediment accumulation. The study area is Guelmim city in Morocco, which has been flooded several times over the past 50 years, and which was declared a “disaster area” in December 2014 after violent rainfall storms killed 46 people and caused significant damage to the infrastructure. The obtained results indicate that the SRTM DEM performs better than ASTER in terms of micro-topography, hydrologic-network and structural information characterization. In addition, with reference to a topographic contours map (1:50000), the derived global height surfaces accuracies are ±3.15 m and ±9.17 m for SRTM and ASTER, respectively. These accuracies are significantly influenced by topography; errors are larger (SRTM = 11.34 m, ASTER = 19.20 m) for high altitude terrain with strong slopes, while they are smaller (SRTM = 1.92 m, ASTER = 3.76 m) in the low to medium-relief areas with indulgent slopes. Moreover,

all the considered hydrological indices are significantly characterized with SRTM compared to ASTER. They demonstrated that the

rainfall and the topographic morphology are the major contributing factors in flash flooding and catastrophic inundation in this area. The runoff waterpower delivers vulnerable topsoil and contributes strongly to the erosion and transport of soil material and sediment to the plain areas through waterpower and gravity. Likewise, the role of the lithology associated with the terrain morphology is decisive in the erosion risk and land degradation in this region. Key words: DEM, SRTM-V4.1, ASTER-V2.1, flood-storm, runoff, topographic attributes, hydrologic indices, sediment transport, soil erosion, GIS.

1. Introduction

Topography represented by a DEM (digital

elevation model) can be used in several applications

such as erosion and landslides [1], disaster and

environmental process management, hydrological

attribute modeling and extraction [2, 3], satellite

image ortho-rectification [4], volcanic 3D modeling

[5], sea level rise and flooding risk assessment [6] and

coastal zone modelling. Based on the requested

accuracy and/or the nature of the project, which is

often determined by economic factors (investment vs.

Corresponding author: Abderrazak Bannari, professor,

research fields: remote sensing, GIS, topography, modelling, environment.

accuracy), as well the conditions of surveying the

environment (i.e. terrain accessibility, topography and

geometry, vegetation cover, etc.) DEM can be created

by several methods. These include surveying

engineering, stereo-photogrammetry, altimetric GPS

in situ measurements, lidar altimetry, radar

interferometry (InSAR), topographic map contours,

and stereoscopic pairs of optical satellite imageries [7].

However, although several DEMs such as SRTM

(Shuttle Radar Topography Mission) and ASTER

(Advanced Spaceborne Thermal Emission and

Reflection Radiometer) GDEM (Global Digital

Elevation Model) are freely available today on the

web, choosing the appropriate data for a specific

project remains a difficult decision [8].

D DAVID PUBLISHING

Comparison of SRTM-V4.1 and ASTER-V2.1 for Accurate Topographic Attributes and Hydrologic Indices Extraction in Flooded Areas

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Furthermore, DEM has a dominant hold on

hydrology and influences the spatial distribution of

flow direction and accumulation. It is essential to

model flood impact on land erosion and degradation,

sediment transport and accumulation because water

tends to flow and accumulate in response to gradients

in gravitational potential energy [9]. It has a vital role

in the spatial variation of hydrological conditions such

as soil formation and moisture, groundwater flow and

slope stability. Topographic attributes have, therefore,

been used to describe spatial soil elevation, slope,

aspect and delineation of stream networks [10].

Additionally, in the literature, scientists have proposed

many hydrologic semi-empirical models (or indices) to

map soil erosion, soil sediment transport and

accumulation, and soil moisture conditions based on

continuous field models [11], in which the various

properties are mapped considering certain numbers of

pixels with continuous coverage across the landscape.

This approach has been greatly supported by the

development of computer science, GIS technologies,

satellite remote sensing and image processing methods

[9, 10, 12].

In the literature, there are many relationships

available to estimate sediment transport and

accumulation using DEM. According to Kothyari et al.

[13], the best-suited one for integration with GIS is the

relationship proposed by Moore and Wilson [14] based

on unit-stream power theory and called the STI

(sediment transport index). Another semi-empirical

equation within the runoff model is the CTI (compound

topographic index), originally developed by Beven and

Kirkby [15]. Moreover, the SPI (stream power index),

which is a semi-empirical model, was also developed

by Moore et al. [10] and used to describe the ability to

transfer sediment in channel streams, to estimate the

sediment rate in basin hydrology, and to assess the

flood risks. Nevertheless, Vaze et al. [16] demonstrated

that the source and spatial resolution size of the used

DEMs have a serious impact on the hydrologic indices

or models calculated from the DEMs. Because several

factors affect the DEM accuracy, such as data

collection methods, algorithms used to generate the

DEM, as well topographic complexity of the landscape

[17]. During this decade, many scientists have explored

the opportunity of space-borne DEMs in several

applications, different geographic locations and

different environments. However, mountainous

topography such as the Moroccan Atlas has significant

DEM errors contributed by terrain complexity,

especially for hydrologic applications regarding

flooded areas. Hence, this research compares the

potential of SRTM-V4.1 and ASTER-V2.1 with 30-m

pixel size in flooded areas to derive topographic

attributes (elevation, slopes, aspects, and flow

accumulation) and hydrologic indices (STI, SPI and

CTI) to detect areas associated with flash floods,

erosion caused by rainfall storm, and sediment

transport and accumulation. The SRTM-V4.1 DEM

was acquired with an active remote sensing system

(InSAR) and processed using interferometric method.

Whereas, the ASTER-V2.1 DEM was recorded by one

optical remote sensing sensor and processed using

stereo correlation photogrammetric procedures, to

achieve our objectives, the DEMs data were

downloaded from USGS data explorer gate [18], and

were preprocessed and processed using ArcGIS [19].

2. Material and Methods

2.1 Study Site

Guelmim is a city in the south of Morocco (28º59′02′′

N, 10º03′37′′ W) located at the foot of the western

Anti-Atlas Mountains with peaks rising to over 2,100

m and it follows the course of underground shallow

aquifers and dry rivers (Fig. 1). Settled agriculture

developed around underground water sources and dry

riverbeds that flood during the rainy season. It is

characterized by a semi-arid and arid subtropical

climate. Temperature range varies from 12 °C in

January to 49 °C in July. Annual rainfall averages

between 70 and 120 millimeters/year [20]. The Assaka

River drains a large watershed on the southwest border


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Fig. 1 Map of Morocco geographic location (left), and Landsat-8 image of the study site, Guelmim city and regions (right).

of the Anti-Atlas and its Saharan edges in the Guelmim

area [21]. Its watershed covers approximately 7,000

km2 and develops on the southern slopes of the

Anti-Atlas [22]. In the lower part of its course, their

vervalley shows the stepping and the nesting of several

alluvial terraces intersecting the Appalachian relief of

the Anti-Atlas Mountains and depressions, which offer

a landscape of hills and small mountains depending on

the resistance of rocks to erosion. The Assaka River,

the confluence of three wadis, Seyyad, Noun and Oum

Al-Achar, crosses the last folded chains of the

Anti-Atlas before flowing into the Atlantic Ocean. In

this part of its course, it has aggraded a large system of

alluvial terraces.

The geological formations that feed alluvium are

granite, schist, quartzite, sandstone, limestone,

dolomite, marl, conglomerate, andesite and rhyolite

(Fig. 2). They correspond to the top of the Precambrian

(Ifni boutonniere) and lower Paleozoic rock, which

forms part of the Anti-Atlas sedimentary cover [22].

From a geological point of view [23], the study region

constitutes a complex synclinal, framed and

surrounded in the N, W and S by three Precambrian

anticlinal inlets (boutonnieres): Kerdous-Tazeroualt,

Ifni and Guir. The two mean structural unities in the

region are the carbonate plateaus and the folded Bani

hills. The most important Infra-Cambrian and

Cambrian carbonate plateaus are located north,

consisting of a continuous area bordering from West to

East the Ifni-Inlet, Akhsass plateau and the southern

flank of the Kerdous inlet. The second one, located

south, is formed by the external part of Jabal

Guir-Taissa. These plateaus are surrounded by schist

and sandstone formations of the Georgian age. At their

foot begin large and elongated plains, named “feijas”

and consisting of Acadian schist covered by

Quaternary deposits (mainly lacustrine carbonate and

silts). At the centre of the Guelmim basin lies Jabal

Tayert, this is formed by green Upper Acadian schist

which is covered at the top by hard sandstone and

quartzite bars. The Bani Jabal is a folded structure

consisting of several aligned and NE-SW oriented

synclinals alternating with narrow anticlinals formed

by Acadian or Ordovician sandstones and quartzites

[23].

In December 2014, violent storms caused flooding

and impressive river floods in Guelmim city and

regions, and in a large part of southern Morocco.

According to SIGMA [24], this natural catastrophe

caused the death of more than 46 persons and

Spain

Spain

Guelmim


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Fig. 2 Lithologic map of the study site.

significant damage to the infrastructure: villages were

inundated, causing thousands of houses to collapse;

there was destruction of many oasis and agricultural

fields, power and telephone networks were damaged,

and several bridges and roads were destroyed (Fig. 3).

Total losses were estimated at approximately US$ 0.6

billion [24, 25]. Consequently, the region of Guelmim

was declared a “disaster area” by the Moroccan

government. This was not the first time that Guelmim

city and its regions were devastated; these regions have

been flooded several times over the past 50 years: 1968,

1985, 1989, 2002, 2010 and 2014 [25]. All these

hydrological and geo-morphological characteristics

and flooding repetitiveness have motivated our choice

to select this area as a study site for this research.

2.2 ASTER-V2.1 GDEM Data

The ASTER GDEM is a joint product developed and

made available to the public by the Ministry of

Economy, Trade, and Industry (METI) of Japan and the

United States National Aeronautics and Space

Administration (NASA). It is generated from data

collected from the optical instrument ASTER onboard

the TERRA spacecraft [26]. This instrument was built

in December 1999 with an along-track stereoscopic

capability using its nadir-viewing and backward-viewing

telescopes to acquire stereo image data with a

base-to-height ratio of 0.6 [27]. Since 2001, these

stereo pairs have been used to produce single-scene

(60 × 60 km2) DEM based on a stereo-correlation

matching technique using a WGS84 geodetic reference.

The ASTER system imaged the Earth’s landmass

between 84º N and 84º S latitudes offering greater

coverage over the SRTM Mission. In 2011, NASA and

Japanese partners [29] made the validation and the

accuracies assessment of ASTER-V2.1 GDEM


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Fig. 3 Power of water threatens citizens’ lives, causes destruction of several roads and bridges’ infrastructure and a street and a classroom are filled with mud after the water retreated (Guelmim city region in December 2014: Photos from the web [28]).

products (version-2.1) jointly. The results of this study

showed that the absolute geometrical rectification

accuracies, expressed as a linear error at the 95%

confidence level, are ±8.68 and ±17.01 meters for

planimetry and altimetry, respectively [29]. Overall,

the ability to extract elevations from ASTER

stereo-pairs using stereo-correlation techniques meets

expectations. Studies were conducted by a large group

of international investigators, working under the joint

leadership of U.S. and Japan ASTER Project

participants, to validate the estimated accuracy of the

new ASTER-V2.1 Global DEM product and to identify

and describe artifacts and anomalies found in this

product [30].

2.3 SRTM-V4.1 DEM Data

The SRTM is an international project managed by

the JPL (Jet Propulsion Laboratory) and sponsored by

NASA, the NGIA (National Geospatial-Intelligence

Agency) of the US Department of Defense, the German

Aerospace Center (DLR) and the ISA (Italian Space

Agency). It collected the most complete

high-resolution digital topographic database over 80%

of the Earth’s land surface from 60º N to 56º S during a

11-day mission, which was flown aboard the space

shuttle Endeavour from February 11-22, 2000 [31].

The used radar systems are the SIR-C (Space-borne

Imaging Radar C-band) (5.6 cm) developed by NASA

and the X-SAR (X-Band Synthetic Aperture Radar, 3.1


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cm) developed by DLR with ISA participation [32].

They were flown for tests on two Endeavour missions

in April and October 1994, then modified for the

SRTM mission to collect single-pass interferometry

(InSAR) data using two signals at the same time from

two different radar antennas. The first one was located

on board the space shuttle and used as a transmitter and

receiver, and the second receiver antenna was at the

end of a 60-meter (baseline) mast that extended from

the payload bay [33]. Obviously, the differences

between the two signals allowed the calculation of

surface elevation using stereo-photogrammetry

methods [34]. The fundamental objectives of the

SRTM Mission are to provide important information

for NASA’s Earth Sciences Enterprise, which is

dedicated to understanding the total Earth system and

the effects of human activity on the global environment

[35, 36].

Since 2000, the SRTM data have been provided in

30-m pixel size only within USA territory, while for

the rest of the world the data were available for public

use at 90-m pixel size. On September 23, 2014, the US

government announced that the highest resolution

elevation data generated from NASA’s SRTM in 2000

would be released globally over the next year with the

full resolution of the original measurements, 30-m

pixel size. Data for most of Africa and its surrounding

areas were released in September 2014. Then, in

November 2014, the data were released for South and

North America, most of Europe, and islands in the

eastern Pacific Ocean. The most recent release, in

January 2015, includes most of continental Asia, the

East Indies, Australia, New Zealand, and islands of the

western Pacific [36]. The data are projected in a

geographic coordinates system using a WGS-84

geodetic reference and EGM-96 (Earth Gravitational

Model 1996) vertical datum. According to USGU [36],

at 90% confidence, the absolute vertical height

accuracy is equal or less than ±16 m, there is a relative

vertical height accuracy of less than ±10 m, there is a

circular absolute planimetric error of less than ±20 m,

and a circular relative planimetric error of less than ±15

m [38, 39].

2.4 DEMs Height Accuracies Assessment

For the two considered DEMs height accuracies

validation and assessment purposes, topographic

contours map at 1:50000 was scanned, geo-referenced

and overlaid on both DEM layers in ArcGIS [19]. This

map was established by accurate photogrammetric

stereo-preparation and restitution procedure exploiting

optico-mechanic stereo-plotter by Moroccan geodetic

services. The spot heights and other control points

were selected from contour lines (20-m intervals)

considering various landforms. According to the USGS

recommendation [40], the DEM error estimation is

usually made with a minimum of 28 GCPs (ground

control points). However, Li [41] reported that many

GCPs are needed to achieve a consistency closer to

what is accepted in most statistical tests. Indeed, the

number of validation GCPs is an important factor in

consistency because it conditions the range of

stochastic variations on the RMSE and standard

deviation values [41]. To guarantee the error estimation

stability in this research, 100 GCPs have been

considered assuring a confidence level of 95%. These

GCPs, randomly distributed over the study area, were

selected and used for accuracies statistical analysis.

Since RMSE is closely associated with DEM data

generation techniques and accounts for both random

and systematic errors introduced during the data

generation process, it is widely used as an overall

indicator for vertical accuracy assessment of DEMs

[42]. According to the American Society for

Photogrammetry and Remote Sensing [43], the height

precision of each DEM should be expressed by the

RMSEDEM-j (root mean square error) given by the

following relationship:

RMSEDEM-j = ∑ HR HDEM

(4)

where HRef is the reference topographic map GCPs

elevations, HDEM-j is the elevation data from the two


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considered space-borne sources (SRTM and ASTER),

and “n” corresponds to the total number of GCP

elevations used for validation.

2.5 Hydrologic Indices

STI is a non-linear function of specific discharge and

slope and it is derived by considering the transport

capacity limiting sediment flux and catchment

evolution erosion theories [14]. This index is a

fundamental factor in the USLE (Universal Soil Loss

Equation) [43] and its modified and revised forms to

incorporate the influence of topography on soil loss

[45]. It calculates a spatially distributed sediment

transport capacity and may be better suited to

landscape assessment of erosion than the original

empirical equation because it explicitly accounts for

flow convergence and divergence [14, 46]. The CTI

has been developed by Beven and Kirkby [15], well

tested under different DEM resolutions [47], and

different flow routing methods [48]. It was used

extensively to quantify the topography effects on

hydrological processes. It indicates quantitatively the

balance between water accumulation and drainage

conditions at the local scale. In addition, it describes

the tendency for a site to be saturated at the surface

given its contributing area and local slope

characteristics. Likewise, the SPI is a semi-empirical

model, which was developed by Moore et al. [10] to

describe the ability to transfer sediment in channel

streams, to estimate the sediment rate in basin

hydrology, and to assess the flood risks. These indices

are considered in this research, and their equations are

as follows [10, 14, 15]:

STI = [ (m + 1) . (As/ 22.13)m . (sinβ/0.0896)n ]

(1)

CTI = [ ln((As + 0.001) / (() + 0.001) ] (2)

SPI = [ ln ((As + 0.001) . (() + 0.001) ] (3)

where, “As” is the flow accumulation area, “β” is the

slope, and “ln” is the Napierian logarithm. As proposed

by Moore and Wilson [14], m = 0.4 and n = 1.3. Slopes,

aspects, flow direction and accumulations, and these

three indices presented above were implemented and

derived using ArcGIS. “As” determines how much

water is accumulating from upstream areas and

therefore identifies areas that contribute to overland

flow. Theoretically, the recommended value of “As” is

the DEM pixel size [49] which is 30-m in the case of

this study. However, according to morphologic and

topographic characteristics of the study site, and after

several tests using hydrological module in ArcGIS, a

value of 200-mattributed for “As” has provided optimal

results.

As we discussed above (Sections 2.2 and 2.3), the

acquisition modes of the both considered DEMs are

completely different. The SRTM-V4.1 DEM was

acquired with an active remote sensing system (InSAR)

and processed using interferometric method. Whereas,

the ASTER-V2.1 DEM was recorded by one optical

remote sensing sensor and processed using

stereo-correlation photogrammetric procedures,

obviously, these two different methods can have a

significant impact on topographic attributes retrieval

(elevation, slope, aspect, and flow accumulation) and,

consequently, on hydrological indices (STI, SPI, and

CTI) derivation, hence the need of this study.

3. Results Analysis and Discussion

3.1 Topographic Attributes

3.1.1 Elevation

The quality assessment of the used DEMs and the

produced thematic maps is critical for information

extraction and analysis; it is based often on statistical

methods. In contrast, visual methods are generally

neglected despite their potential for derived product

quality assessment. Certainly, the complementarily

between visual and statistical methods would result in a

more efficient improvement of the derived product

quality. Before processing and analysis, the space-borne

DEMs were re-projected into UTM and sink removal

using ArcGIS. Fig. 4 illustrates SRTM and ASTER

DEMs (30-m pixel size) with a digitized and overlaid

hydrological network. For the purposes analysis,


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Fig. 4 SRTM-V4.1 and ASTER-V2.1DEMs with 30-m pixel size and the hydrologic network overlaid on the study site (left), and zooming on hilly areas (right).

number 2 identifies the main Assaka River and the

three confluence wadis, “Oum Al-Achar, Seyyad and

Noun” are numbered as 1, 3 and 4, respectively.

The visual analysis of used DEMs indicates that the

SRTM performs better than ASTER in terms of

micro-topography, hydrologic-network and structural

information characterization and enhancement

capability (Fig. 4). As illustrated by zooming on hilly

areas, the ASTER DEM shows less detail than the

SRTM, especially regarding the micro-topography

regime, which includes height variations and

undulations of lengths comparable to the radar

wavelength (Fig. 4). In fact, radar wavelength ranges

furnish good signal returns from the Earth’s surface.

Moreover, the almost total absence of vegetation cover

in the study area helps the radar system to characterize

the surface topography since its signal adheres very

well to the micro-topography and determines the

intensity and type of the backscattered signal [50].

Indeed, radar is sensitive to surface roughness since

shorter radar wavelengths (X-band) are most sensitive

to micro-topography, while long wavelengths (C-band)

are sensitive to macro-topography. Obviously, these

characteristics advantage SRTM compared to ASTER.

The SRTM DEM values vary between -34 and 2,142

meters describing correctly the topographic zones even

of those with an altitude below zero, while ASTER

values vary from 0 (zero) to 2,106 meters (Fig. 4).

Nevertheless, the both DEMs datasets characterized

equally the macro-topography (which is related to large

changes in slopes and aspects of surface facets being

generally related to large parts of the hydrological

network), geological structures, erosion features and

terrain geomorphology.


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To validate the accuracy of the ASTER-V2.1 and the

SRTM-V4.1 DEMs, studies have been conducted by

NASA and international scientific community

considering several types of terrain morphology,

different environments and targets around the world.

According to Rodriguez et al. [51], the expected

absolute accuracy for SRTM-V4.1 over African

countries at a 90% confidence level is ±5.6 m. As well,

at a 95% confidence level, the ASTER-V2.1 accuracy

was estimated at ±17.01 meters [52]. The calculated

global height surface accuracies using Eq. (4) are ±3.15

m and ±9.17 m for SRTM and ASTER, respectively.

We observe also that local open valleys and

depressions surrounded by local hill slopes are

delineated by SRTM with significant precision, but

ASTER has a better ability to extract deeply incised

valleys. Although these accuracies are superior to the

standards discussed above [51, 52], they remain

significantly influenced by topographic variability.

Indeed, errors are relatively larger in high altitude

terrain with strong slopes since foreshortening, layover

and shadow have a strong impact on the elevation

estimation in these regions while errors are smaller in

the low to medium-relief areas with indulgent slopes.

For instance, in low relief areas, the minimum

elevation from the topographic map is 50 m above sea

level, but it is similar (51 m) for both space-borne

DEMs. However, the height elevation from a

topographic map is 1,374 m, while it is 1,396 m and

1,405 m, respectively, for SRTM and ASTER. They

overestimated height by 22 m since radar relief

displacement occurs and the backscattered signals from

slope areas inclined toward radar are compressed and

therefore shadowing. In addition, ASTER

overestimated terrain altitude with a large error of 31 m.

Significant anomalies and artifacts due to sensor

radiometric sensitivity, atmospheric variability, clouds,

stereo-pair image geometry, can cause this error. As

well, the automated algorithm used to generate the final

GDEM based on stereo-correlation procedures.

Moreover, other scientists believe that orbital

parameters of the TERRA-Platform might have an

impact on ASTER DEMs data acquisition [53].

However, these results are globally in agreement with

Suwandana et al’s. [6] results, which show a good

conformity between the topographic map and SRTM,

rather than ASTER, in headwater areas which are

approximately 1,900 m above sea-level. Moreover,

Thomas et al. [54] and Reuter et al. [55] found that

SRTM represents the true terrain in areas without or

with very scattered vegetation cover, which is the case

in this study. Other studies in different geographic

locations around the world showed that, in general,

SRTM has relatively good accuracy compared to

ASTER [6, 42, 54].

3.1.2 Slope

Slope is a primary topographical attribute, which is

derived from the topographical surface. The output

slope map represents the degrees of inclination from

the horizontal. It has a significant influence on the

velocity of surface and subsurface flow, soil water

content, erosion potential, soil formation and several

other earth surface processes [56] and hence an

important parameter in hydrologic and geomorphologic

studies [54]. Fig. 5 illustrated the range of slope

derived from SRTM (0°-45.53°) and from ASTER

(0°-39.64°) with approximately 6° difference, but their

means are much closer to 4.50° for SRTM and 4.80°

for ASTER. The Guelmim region has two main

geomorphologic units, the limestone plateaus of the

Anti-Atlas and the Bani-quartzite ridges as natural

barriers (Fig. 2). It is characterized by broader valleys

and depressions (feijas) surrounded by hills with a

height varying from 153 m to 2,060 m (Fig. 4), and

very strong slopes which converge towards the interior

plain of Guelmim. In the interior and in the middle of

the plain the slope range varies, respectively, from 0.5°

to 4° and from 9.5° to 26° (Fig. 5). Fig. 5 shows how

well radar characterizes slopes of the mountains and

broader valleys, and even the smoothest slope in the

middle of the plain. The topography of this plain is

classified into seven classes, whose altitude ranges


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Fig. 5 Slope maps derived from SRTM and ASTER DEMs.


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vary significantly from 200 to 573.5 m starting from

the northeast to the southwest, with approximately

373.5 m in height difference. This morphology leads to

water retention in the case of rainstorms and,

consequently, contributes to the risk of inundation. The

topography from the NE to SW and from NW to SE

shows a natural barrier, which leads to water retention

in the case of high precipitation intensity (Figs. 4 and 5).

Thus, it is one of the factors supporting the risk of

flash floods. Slopes from NW to SE show an

appropriate morphology for runoff water catchment

with a depth of 250 m between two mountains which

surround the plains in that direction (Jebel Ifni and

Jebel Taissa). As well, from the NE to the SW direction

the terrain morphology shows the area is prone to

inundation. Indeed, the slope orientation and direction

of the Guelmim watershed face the center of the plain.

Also, we observe a hill in the East that forms a natural

barrier with a denivelation of approximately 100 m,

creating a natural basin promoting the accumulation of

water and sediment over 10 km distance. The

topographic variability starting from the foot of

Guelmim city has a strong slope (26°), which ends on a

terrain with concave morphology and a very low slope

(0.5°) over a long distance forming a natural basin,

which facilitates the accumulation of storm floods,

thereby concentrating runoff water, sediment and mud

load.

3.1.3 Aspect

Aspect is an anisotropic topographic attribute, i.e.,

depends on a specific geographical direction, such as to

the Sun’s azimuth [57]. In addition, it has a significant

influence on vegetation cover distribution, biodiversity,

and agricultural productivity because solar radiation

received at a location on the terrain depends on the

aspect and shadows cast by the terrain [54]. Moreover,

aspect characterizes the topographic curve changes

(concave and convex), which control the flow direction

and accumulation. The derived aspect maps (Fig. 6)

indicate the direction of slope gradients and the aspect

categories represent the number of degrees of East

increasing in a counter-clockwise direction, and an

aspect value of -1 is generally assigned for flat areas.

Although aspect is not a direct erosion pertinent

parameter, it plays a very significant role in delineating

the flow-lines and subsequently the flow accumulation

in sub-catchment areas [1]. The discrepancies and

similarities between the aspect values derived from

SRTM and ASTER can be determined using rose

diagrams. The latter are a circular histogram plots

which displays geographic directional aspect classes (0°

to 360°) and their frequencies which are a function of

the length of the radius of the rose. It is commonly used

in sedimentary and structural geology, topography,

erosion and hydrology for directional features

characteristics interpretation. Fig. 6 illustrates the

output aspect maps and rose diagrams. Visual

interpretation of this figure showed no significant

difference between the derived aspect maps from the

two space-borne DEMs. However, the SRTM rose

diagram shows that aspects appear more pronounced in

all directions by 11% compared with ASTER. This

improvement is automatically consistent with the

derived slope values from SRTM. The dominant

highest aspect percentages are located toward the

Atlantic-Ocean in the N-W direction (270º to 360º),

while the lowest percentages are in the N-E direction

(0º to 90º).

3.1.4 Flow Accumulation

The Guelmim watershed covers a total area of

approximately 7,000 km2, forming a network of wadis

(rivers), along which there are several spreading

floodwater areas (Fig. 4). The hydrographic network is

made up of three sub-watersheds of the following main

wadis: wadi OumAl-Achar, wadi Noun and wadi

Seyyad. Wadi OumAl-Achar (numbered 1 in Fig. 4),

with a watershed of 1,170 km2, crosses a wide plain of

7 km and is located between the Tayert hill and Ifni

boutonnière. It drains the southern slopes of the

Akhsass region, and its main tributaries are located in

the plain. Wadi Seyyad (numbered 3 in Fig. 4) originates

at 1,200 m on the southern slopes of the Anti-Atlas


19

Fig. 6 Aspect maps and rose diagrams derived from SRTM and ASTER DEMs.

Mountains. It flows in an E-W direction, and mainly

receives numerous tributaries of its right bank; its

watershed is about 2,860 km2. Finally, wadi Noun

(numbered 4 in Fig. 4) drains the southern area, where

the bit is marked with riverbeds which promote natural

flooding. With a length of 143 km, its watershed

comprises about 2,240 km2. All three wadis lie on

schistous impermeable large valleys or feijas, covered

by low permeable Quaternary carbonates and

fluvio-lacustrine silts. The confluence of the three

wadis, downstream from Guelmim city, forms Wadi

Assaka (numbered 1 in Fig. 4), which begins in the

Akhsass massif and reaches a 1,150 m altitude (Fig. 4).

It goes through the corridor between the Jabal Adrar

and then along Guelmim west, eventually discharging

into the Atlantic Ocean after crossing narrow gorges.

This hydrographic system is often inactive especially

during the summer, when the flow is very low, but it

becomes active during the winter period (December to

March). This configuration is the cause of many

talwegs and wadis draining the area. All the runoffs

are thus directed automatically to the city of Guelmim,

which is subject to a hydrological regime unregulated

surface. Fig. 7 illustrates the flow accumulation

network derived from the both space-borne DEMs.

Because ASTER does not characterize properly the

micro-topography, it describes the flow accumulation

network with difficulty in a discontinuous way.

Secondary and even predominant streams are broken

and not well connected; also, watersheds are delineated

with difficulty. These limitations are inherent to

ASTER DEM accuracy, which probably limits its

hydrological applications. These remarks are in

agreement with Hengl et al.’s [58] research conclusions.

Nonetheless, SRTM flow accumulation shows a good

stream and wadi (Oum Al-Achar and Assaka numbered


20

Fig. 7 Flow accumulation maps derived from SRTM and ASTER DEMs.


21

1 and 2 in Fig. 4) continuity related to the steep slopes

and ridges, reflecting the real hydrographic network

with good watershed delineation. The starting stream

points are clearly identified following waterway

directions in low-gradient floodplain and coastal plain

landscapes which obviously promote floods (Fig. 7).

3.2 Hydrologic Indices

3.2.1 Sediment Transport index

Furthermore, the considered hydrologic indices

based on the topographic attributes (slope, aspect, and

flow accumulation) and linked to the drainage system

demonstrate their potential to model flood impact on

land erosion and degradation, and sediment

accumulation. Fig. 8 shows the STI derived maps with

a wide value range, 0 to 324 for SRTM and 0 to 298 for

ASTER. Overall, both maps illustrated a uniform

spatial distribution of sediment transport and

accumulation. However, the derived STI from SRTM

DEM highlights the hydrographic network, especially

wadi Oum-Al-Achar and Wadi Assaka (numbers 1 and

2 in Figs. 8 and 4), and all the secondary streams that

feed them flowing dejection cones from the mountain

to the plain. Furthermore, sediment transport and

accumulation are well illustrated following strong

slopes and channels toward the Atlantic Ocean. Such

improvement is automatically related to SRTM

capability to derive slope, aspect and flow

accumulation correctly. Fig. 8 illustrates the spatial

distribution of the sediment transport capacity and

accumulation. The STI’s highest values (150-324) are

related to the steep slopes and ridges corresponding to

“feija” schist and soft Quaternary deposits, ravines and

water streams. These areas are associated with a

significant degree of sediment transportation and,

consequently, significant soil erosion and degradation.

This index shows the increased erosivity of channel

flow in the downstream, and sediment deposition in the

plain. The values nearest to zero are associated with

very hard Precambrian magmatic rocks (andesite and

rhyolite), Adoudounian limestone and dolomite,

Acadian sandstone and Ordovician quartzite, which are

located at the top of the mountains (Fig. 2). These areas

are characterized with different rock types and they are

subject to different degrees of erodibility, but in

general it is associated with a low erosion risk.

However, the values ranging from 10 and 30 are

located in the plain progressively following the slope

and the flow accumulation from the NE to the SW. At

the very low slopes (≤ 2º) in the plain, the STI values

(around 5) reflect the slow mobility and, consequently,

the accumulation of the sediment. This index illustrated

well the landscape erosion assessments because it

explicitly demonstrates the sediment flow convergence

and divergence from the top of the mountains to the

areas prone to inundation and sediment accumulation.

3.2.2 Stream Power Index

The SPI describes the ability to transfer sediment in

a channel’s streams and evaluate the flood risks in

basin hydrology (Fig. 9). Similarly to STI, the derived

SPI from SRTM highlight more water streams than

ASTER, particularly the main wadi (Oum-Al-Achar,

Assaka and Seyyad, respectively, numbered 1, 2 and 3

in Figs. 9 and 4). Furthermore, all the streams following

the long steep slopes to the plain are precisely extracted.

Fig. 10 shows a zoom on the red square in Fig. 9; we

see the significant contribution of SRTM to emphasize

the tributaries of wadi Assaka in the north and those of

wadi Oum Al-Arach in the NE. The SPI shows negative

values for areas with topographic potential for sediment

deposition and positive values for potential erosive

zones. Areas with the highest values (approximately -1

to 5.9) represent the streams and the drainage system,

depressions and broader-valleys situated at height

altitudes (500-2,100 m), and related to a strong slope

gradient (≥ 15°). This terrain morphology contributes

significantly to the erosion’s aggressiveness and land

degradation risk process. The SPI values around -8 are

located at the top of the mountain with a significant

slope gradient varying from 9° to 13°. Hard rocks such

as Precambrian quartzite, Adoudounian limestone and

dolomite, Ordovician quartzite and sandstone, and


22

Fig. 8 STI maps derived from SRTM and ASTER DEMs.


23

Fig. 9 SPI maps derived from SRTM and ASTER DEMs.


24

Fig. 10 Zoom on the red box in the Fig. 9 presenting SPI maps derived from SRTM and ASTER DEMs.


25

Fig. 11 CTI maps derived from SRTM and ASTER DEMs.


26

Georgian black limestone (Fig. 2) characterize these

zones. The lowest values (between -13.8 and -4)

represent relatively flat areas with a low slope (≤ 4°)

from the NE to the SW direction, which is the

hydrographic network direction, and morphological

factors influencing the susceptibility to flooding and

sediment deposition and accumulation. We can

observe also that the SPI reflects the tendency of water

accumulation in the landscape and highlights areas

prone to both fast moving and pooling water.

3.2.3 Compound Topographic Index

Fig. 11 illustrates the CTI derived maps from the two

space-borne DEMs. We can observe that the CTI and

the SPI reflect identically the tendency of water

accumulation in the landscape and highlighted areas

prone to both fast moving and pooling water. Likewise

to the considered hydrological indices presented above,

SRTM outperforms ASTER. The watershed areas, the

principal wadi-network (numbered 1, 2, 3 and 4 in Figs.

11 and 4) and their tributaries are clearly identified.

These maps exhibit approximately the same range

values for both DEM sources (-6.00 to 14.90 for SRTM

and -5.94 to 15.56 for ASTER), and the means are

relatively smaller corroborating Thomas et al.’s [54]

finding. Furthermore, we observe that the higher an

area’s values, the greater the potential for that area to

be saturated with water based upon its contributing

neighborhood and local slope characteristic, which is

low. Moreover, high CTI values reveal the potential of

pixels to be wet before other surrounding and

contributing pixels. Therefore, these areas are more

susceptible to flash flooding and inundation as

compared to those with low CTI. Concerning the

intermediate values, they are related to the steep slopes

and ridges, ravines and water streams, which are the

most important contributing factors to erosion and the

land degradation process (talus and alluvial cones,

Quaternary lacustrine limestone, spreading silts and

salt crusts; see Fig. 2). However, the areas with very

low CTI values (around -6) are characterized by high

altitudes and strong slopes highlighting the local

drainage system conditions. Globally, this index shows

clearly how the water flows down the slopes over a

long distance, then the vast morphology of the

watershed and the opposite side slopes support the

flood accumulation, runoff, and sediment load (Fig.

11). Undeniably, the lowest and flattest areas were

affected most by the flash floods because water tends to

flow and accumulate in response to gradients in

gravitational potential energy. The CTI describes the

ability to transfer sediment in a channel’s streams and

evaluate the flood risks in basin hydrology similarly to

the SPI (Fig. 11). This index has been successfully

applied because it parameterizes correctly the water

movement from topographic information [59].

In general, these derived hydrologic indices,

especially from SRTM DEM, demonstrate that rainfall

and topographic attributes are the major contributing

factors to flash flooding and catastrophic inundation.

These conclusions are in agreement with Marchi et

al.’s [60] results, in which they characterized the

extreme flash floods and implications for flood risk

management in Europe. According to these indices

derived maps, after a flood-storm in the Guelmim basin,

the runoff waterpower delivers vulnerable topsoil and

contributes strongly to the erosion and land

degradation process, and then transports soil material

and sediment to the plain through the natural action, i.e.

waterpower and gravity. As illustrated by the photos in

Fig. 3, which were acquired during the same day of the

flood storm, the water color was dark red because of its

turbidity as it was very rich with delivered sediment

and eroded particles. In addition, the classroom of a

primary school and city streets (Fig. 3) were filled with

mud after the water retreated. Certainly, the role of the

lithology associated with the terrain morphology is

decisive in the erosion risk and land degradation in this

region.

4. Conclusions

This research compares the potential of

SRTM-V4.1 and ASTER-V2.1 with 30-m pixel size to


27

derive topographic attributes (slopes, aspects, and flow

accumulation) and hydrologic indices (STI, CTI and

SPI) to detect areas associated with flash floods caused

by rainfall storm and sediment accumulation. The

obtained results indicate that the SRTM DEM performs

better than ASTER in terms of micro-topography,

hydrologic-network and structural information’s

characterization. Indeed, radar wavelength ranges are

sensitive to surface roughness since shorter radar

wavelengths (X-band) are most sensitive to

micro-topography, while long wavelengths (C-band)

are sensitive to macro-topography. They supply good

signal returns (backscatter) from the Earth’s surface

with excellent adhesion to the micro-topography.

Obviously, these characteristics advantage SRTM

compared to ASTER. Moreover, the near total absence

of vegetation cover in the study area helps the SRTM

system to characterize the surface topography better

than ASTER. By reference to the topographic contours

map (1:50000), the derived global height surfaces

accuracies are ±3.15 m and ±9.17 m for SRTM and

ASTER, respectively. For both space-borne systems,

these accuracies are significantly influenced by

topographic variability. Indeed, errors are relatively

larger (SRTM = 11.34 m, ASTER = 19.20 m) in a high

altitude terrain with strong slopes (i.e. foreshortening

and shadow have an impact on the elevation estimation

in these regions), while errors are smaller (SRTM =

1.92 m, ASTER = 3.76 m) in the low to medium-relief

areas with indulgent slopes. In addition, local open

valleys and depressions surrounded by local hill slopes

are delineated by SRTM with significant precision, but

ASTER has a better ability to extract deeply incised

valleys.

Furthermore, all the considered hydrological indices

are significantly characterized with SRTM compared

to ASTER. The SPI reflects the tendency of water

accumulation in the landscape and highlights areas

prone to both fast moving and pooling water. The CTI

describes the ability to transfer sediment in a channel’s

streams and evaluates the flood risks in basin

hydrology similarly to the SPI. This index was

successful because it correctly parameterized the water

movement from DEM. However, the STI illustrated

well the landscape erosion assessments since it

explicitly demonstrates the sediment flow convergence

and divergence from the top of the mountains to the

areas prone to inundation and sediment accumulation.

The synergy of the derived information from these

indices demonstrates that the rainfall and the

topographic morphology are the major contributing

factors to flash flooding and catastrophic inundation in

the study area. The runoff water power delivers

vulnerable topsoil and contributes strongly to the

erosion and transports soil material and sediment to the

plain areas through water power and gravity. Likewise,

the role of the lithology associated with the terrain

morphology is decisive in the erosion risk and land

degradation in this region.

There is therefore an urgency to improve the

management of water regulation structures, since there

is an imperative need to develop a methodology to

maximize the water storage capacity and to reduce the

risks caused by floods in the Guelmim regions.

Currently, active and passive remote sensing

technologies associated with GIS and auxiliary data

have become the fundamental solution for flood

monitoring, understanding, and its impact assessment

to provide a solid basis strategy for regional policies to

address the real causes of problems and risks.

Moreover, digital topography information is

considered crucial for flood hazard mapping, and the

most effective means to estimate flood depth from

hydrologic models and, consequently, to provide ideas

about how flood waters must be channeled, diverted

and drained. Methodologies that are developed around

the world should perhaps be adapted and applied to this

region. For instance, to increase flood inundation

prevention, information technology and the integration

of new approaches have developed a warning system

based on a “Satellite-Ground-Sensor-Web” covering

several regional stations and measuring weather and


28

soil moisture conditions in real time. This system will

challenge decision makers to focus more on long-term

resilience. Without doubt, solutions must be considered

from different perspectives (social, human, financial,

etc.) and at different levels of public policy and

decision-making. Obviously political commitments

must be sincere to apply these flood resilience

measurements and strategies.

Acknowledgements

The authors would like to thank the Arabian Gulf

University for their financial support. We would like to

thank the LP-DAAC NASA-USGS for SRTM and

ASTER DEMs datasets. Our gratitude goes to many

people who have made the used photos available on the

web for consultation and public use. Finally, we

express gratitude to the anonymous reviewers for their

constructive comments.

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Comparison of SRTM-V4.1 and ASTER-V2.1 for Accurate ... · The obtained results indicate that the SRTM DEM performs better than ASTER in terms of micro-topography, hydrologic-network