A “simple” geomatics-based approach for assessing water erosion hazard at montane areas

1 23

Arabian Journal of Geosciences ISSN 1866-7511Volume 7Number 1 Arab J Geosci (2014) 7:1-12DOI 10.1007/s12517-012-0782-4

A “simple” geomatics-based approach forassessing water erosion hazard at montaneareas

Abdelkrim Arar & Haroun Chenchouni

1 23

Your article is protected by copyright and

all rights are held exclusively by Saudi

Society for Geosciences. This e-offprint is

for personal use only and shall not be self-

archived in electronic repositories. If you wish

to self-archive your article, please use the

accepted manuscript version for posting on

your own website. You may further deposit

the accepted manuscript version in any

repository, provided it is only made publicly

available 12 months after official publication

or later and provided acknowledgement is

given to the original source of publication

and a link is inserted to the published article

on Springer's website. The link must be

accompanied by the following text: "The final

publication is available at link.springer.com”.

ORIGINAL PAPER

A “simple” geomatics-based approach for assessing watererosion hazard at montane areas

Abdelkrim Arar & Haroun Chenchouni

Received: 25 December 2011 /Accepted: 21 November 2012 /Published online: 18 December 2012# Saudi Society for Geosciences 2012

Abstract Soil erosion by water is recognised as a world-wide land degradation issue, particularly in arid and semi-arid regions. The aim of this study is to apply the powerfulcapabilities of advanced remote sensing and geographicinformation system techniques to identify the areas at riskto water erosion. This study presents the assessment ofwater erosion in mountainous areas (eastern Aures,Algeria) based on three main factors: the friability of thebedrock, degree of slope and density of vegetation cover.Alsat1 image was used to produce land use and vegetation(NDVI) maps. Digital elevation model was used in con-structing the slope gradient map. The erosion risk map wasobtained by the combination of indices assigned to themat-ic layers following multicriteria decision rules. Water ero-sion was generally not concerning in the eastern Aures(slight risk 0 33 %, moderate risk 0 44 % of the area).This simple–qualitative approach gave good results forassessing soil erosion equally to quantitative methods since89.55 % of field verifications were accurate. The non-alarming state and the low rate of severe and extremelysevere risk to erosion are due to (1) the low steep slopes,(2) the good quality of vegetation (forests with thick un-dergrowth), and which are occurring on (3) resistant mate-rials of the substratum, and (4) the low human pressure.Results of this study, which may be conducted with rea-sonable costs and accuracy over large areas, are of signif-icant help in prioritising areas in decision making andsustainable planning.

Keywords Algeria . GIS . Remote sensing . Soil runoff .

Water erosion . Land degradation . Mountain landscape

Introduction

Soil erosion by water is the common and the most importantland degradation problem worldwide, after climate change(Eswaran et al. 2001; Lal 2001; Terranova et al. 2009),which continues to take significant proportions especiallyon slopes because of the nature of torrential rains, highvulnerability of land (soft rock, fragile soils, stiff slopesand vegetation often degraded), overgrazing and negativeimpact of human activities: deforestation, fires, improperconduct of agricultural activities, chaotic urbanisation, quar-rying, etc. (FAO 1989; Bou Kheir et al. 2008; Nekhay et al.2009). Even if some authors question its impact on globalfood security (Crosson 1997), soil erosion creates strongenvironmental impacts and high economic costs by its effecton agricultural production, infrastructure and water quality(Pimentel et al. 1995; Lal 1998). Furthermore, erosionresults in emission of soil organic carbon to the atmospherein the form of CO2 and CH4, causing impact on globalwarming (Lal 2004). Global warming in turn is expectedto increase erosion rates (Nearing et al. 2004).

Taking into account the soil erosion within environmentalpolicy and agricultural development, it represents a keypriority because it is a character of irreversibility. The ero-sion causes damage to farmlands but also has implicationsbeyond the soil itself since it degrades water quality, dis-regarding sediment transport that must then be managed(Pimentel et al. 1995; Zhang et al. 2010).

Since soil erosion is a complex physical and biologicalprocess, there is an increased need in using potentialapproaches that evaluate the risk including all kind of var-iables and parameters (Nekhay et al. 2009). Risk assessmentof soil erosion at watershed scale requires mapping and

A. ArarDepartment of Natural and Life Sciences, Faculty of Sciences,University of El Hadj Lakhdar, Batna 05000, Algeria

H. Chenchouni (*)Department of Natural and Life Sciences, Faculty of ExactSciences and Natural and Life Sciences, University of Tebessa,Tebessa 12002, Algeriae-mail: [email protected]

Arab J Geosci (2014) 7:1–12DOI 10.1007/s12517-012-0782-4

Author's personal copy

analysis of many factors involved in the erosive processincluding mainly climate (precipitation), topographic factor(slope), geological factor (lithology) and land use includingvegetation cover factor (Wischmeier and Smith 1978;Poesen et al. 1996; Vrieling 2006).

For managing effectively soil loss problem within a sus-tainability context, policymakers need reliable spatial infor-mation that could be offered by relevant and appropriate tools(Ait Fora 1995; Pimentel et al. 1995). Geographical informa-tion system (GIS) allows cross maps with different themes,merging several variables to obtain a final and interactivedatabase of soil erosion factors. GIS includes structured andcoded data of the main factors involved in the erosive processand allows the application of mathematical equations on thenumerical values of these factors (Rahman et al. 2009).Furthermore, remote sensing appears as the data source ofland cover. It is essential within erosion risk analysis since itconstitutes a component that provides data to the geographicaldatabase (Bonn 1998; Vrieling 2006).

Among major empirical models, the soil loss equation(USLE) (Wischmeier and Smith 1978) was the first andmost important; it was based on experimental data. A revi-sion of the USLE model, called RUSLE (Renard et al.1997), has been applied to erosion over extended areasand in different contexts, including forests, rangeland andanthropogenically disturbed areas (Terranova et al. 2009).

The historical, natural and socio-economic conditions ofAlgeria have contributed to the recent rapid degradation ofenvironments, reducing thus vegetation cover and leading tothe development of various forms of erosion. The latter onesresult in stripping of the humiferous horizon and gullyformation evolving in ravines on watersheds, gullies gener-alised, landslides and rambling within valleys of wadis(Arabi and Roose 1989). In addition, soil erosion has longbeen a major concern in several regions in Algeria, wherethis phenomenon is accentuated by socio-economic activi-ties, particularly in arid and semi-arid regions (FAO 1989).Moreover, mountainous North African lands located insemi-arid areas are more susceptible to erosion given thefragility of ecosystems on one hand, and the presence of allthe factors that promote and speed the development of thisphenomenon on the other (Abaoui et al. 2005; Bachaoui etal. 2007; Berkane and Yahiaou 2007; Bou Kheir et al. 2008;Nekhay et al. 2009).

While no study has estimated soil loss at national scale inAlgeria, the few existing regional and/or punctual researcheshave highlighted an alarming situation regarding the evolutionof erosion (Prinz et al. 1994; Ghenim et al. 2007; Megnounif etal. 2007). For example, the estimated soil loss in Aures’ streamsystems was about 63 to 73 m3/ha (Berkane and Yahiaou2007). Additionally, several documented studies used GISand remote sensing (RS) to estimate the risk of soil erosionthen analyse characteristics of its category distributions.

However, overall, observational data are still lacking and moststudies have targeted a specific watershed because of spatialand temporal limitations of data collection and analysis.Furthermore, time-series analyses of soil loss and future fore-casting have never been considered.

In this context, this study aims to assess and map thegeographic distribution of erosion grade in different land-scape units of a montane area in the Aures region (EastAlgeria), based on geomatic data in the frame of a GIS. Asimple–qualitative method, based on sums computation ofindices attributed to three erosion parameters (friability oflithological substratum, slope gradient and vegetation cov-er), was used to map erosion risk in order to propose soilconservation planning, including anti-erosive measures. Theapplied approach gives first data on soil erosion fordecision-making in East Algeria. While this approach maybe conducted with reasonable costs and accuracy over largeareas, hence, it could be applied in other developingcountries with similar environmental conditions, specifical-ly when detailed inputs of geomatic data are deficient.

Materials and methods

Study area description

The Aures region (~10,000 km2) is a mountain range ofnorth-eastern Algeria, belonging to the eastern edge of theSaharan Atlas Mountains and separating the high plains ofConstantine from the Sahara (Benabderrahmane andChenchouni 2010; Arar and Chenchouni 2012). The Auresor ‘Mons Aurasius’ are located on the territory of thewilayas ‘provinces’ of Batna, Biskra and Khenchela, andcontained 42 municipalities.

The study area that was considered in this work is locatedin the eastern Aures. It covers 11 municipalities of Khenchelawith an area of 3,000 km2. The target area lies between 34°46′N and 35°34′N latitude and 6°29′E and 7°10′E longitude(Fig. 1). The choice of this specific zoning not only meetswith data availability but also to respond some matters relatedto the recent rapid deterioration of natural resources in partic-ular the forest cover in this region due to desertification(Benabderrahmane and Chenchouni 2010).

This mountainous area is occupied by shrubby forests,where the altitudes range between 500 and 2,326 m.Rainfalls are irregular from one season to another and dur-ing the same season. The mean annual rainfall is 280.5 mm.The average annual temperature is 19.2 °C (3 °C minimumtemperature, maximum temperature 43 °C). The averagewind speed is around 10 km/h. The climate is typicallyMediterranean semi-arid with cool winters marked by arainy and cold winter (corresponding to the wet period—October to May) and dry–warm summers (corresponding to

2 Arab J Geosci (2014) 7:1–12


the dry period—June to September). The vegetation is char-acterised by a flora with Holm oaks (Quercus ilex L.), Atlascedar (Cedrus atlantica M.), Aleppo pine (Pinus halepensisMill.) and Phoenicean juniper (Juniperus phoenicea L.).

The influence of human activities on the territory is uneven.Human pressures vary depending on the density of cities andthe socio-economic activity. In general, they are more pro-nounced near settlements (at plains and low elevations) and

Fig. 1 Geographic location ofthe study area including theAures region (East Algeria)

Arab J Geosci (2014) 7:1–12 3


are attenuated at high altitudes. The study area extends overvaried terrains ranging from very resistant rocks such aslimestone massifs to very friable lands like marl (Arar andChenchouni 2012).

Methodological steps

Because most models deal with many variables displayinggreat spatial and temporal variability (Fig. 2), the use ofgeographical information system (GIS) and remote sensingtechniques makes soil erosion assessment and its spatialdistribution possible, with reasonable costs and accuracyover large scale areas (Wang et al. 2003; Zhang et al. 2010).

Many methods and models were developed to evaluate therate of water erosion and its spatiotemporal variation state.However, the choice of a model depends on the watershedground surface variability and accurate data, in particular rainfall(Bonn 1998; Bou Kheir et al. 2008). Moreover, most of theexciting models require data of a very precise sampling providedfrom punctual measurements at narrow plots and detailed maps(at a scale higher than or equal to 1/50,000) (Laflen et al. 1991).

This approach is based on the joint use of GIS and remotesensing as well as other data in order to model water erosionrisk by evaluating and analysing three environmental fac-tors: topographical (slope), geological (lithology, i.e. type ofsubstratum) and land cover (vegetation cover). These threeparameters were combined according to qualitative decision

rules leading thus to the erosion risk map, which would beuseful for environmental management against this degrada-tion phenomenon. The overall methods used in this studyare summarised as a flowchart (Fig. 3). When handling andprocessing geomatic data, we used two kinds of software:ENVI 4.4 (remote sensing software) for processing satelliteimagery and MapInfo 9.0 (GIS software) for erosion data-base creation and the combination ‘overlay’ of data. Erosionhazard assessment can be defined as the predicted responseof the regional environment to changes in these factors(Renard et al. 1997; Wang et al. 2003).

Data collection

The data required for the development of thematic layers(bedrock friability, degree of slope and density of vegetationcover) were extracted from different cartographic material.We have exploited as a raw material the (1) geological mapof the Aures 1/200,000 (Laffite 1939), (2) topographic mapof the Aures 1/200,000 and (3) Alsat1 satellite image (2003)with a spatial resolution of 30 m (Fig. 3).

The geological data permitted the classification ofmaterialsinto three classes of friability: (1) resistant materials, (2)vulnerable and (3) very vulnerable materials. The high-resolution slope map was extracted by scanning contour linesof the topographic map of Aures region. The calculation of theslope was obtained by passing filters on the digital elevationmodel (DEM). These filters are sliding windows whose centrepasses successively on each grid of the DEM and they com-pute a gradient depending on the value of neighbouring pixels.Slope gradient (in degrees) was assigned with three indices:(1) slopes >5°, (2) slopes varying between 5 and 15°, and (3)slopes of 15–35°.

Fig. 2 The network structure of the soil erosion hazard assessmentissue (redrawn according to Nekhay et al. 2009)

Fig. 3 Flowchart of general method followed for the soil erosion riskassessment

4 Arab J Geosci (2014) 7:1–12


Supervised classification of Alsat1 satellite image, taken in2003, gave the map of land use. The satellite image had thefollowing characteristics: (1) spatial resolution equal to 30 m,(2) large spectral coverage up to the infrared, (3) large sizecovering the study area and (4) availability of several onlineacquisition dates. The maximum likelihood classifier was usedafter principal component analysis of Alsat1 imagery to sepa-rate phenomena with same radiometric particularities in a givencolour composite. It directly provides a graphics output map-ping in conventional colours per pixel established based onground-truth data (Arar and Chenchouni 2012). When carryingout a supervised classification of satellite images, field verifi-cation is required for database validation in accordance with thenature and the realities on the ground (Vrieling 2006; Rahmanet al. 2009). Supervised classification also requires the identi-fication of stations, randomly selected, while having priorinformation about the land use practices. We used the GPS asa tool to locate exactly sampled stations (latitude, longitude,altitude, field profile and route) where 67 GPS surveys werecarried out for accuracy of ground truth (Benabderrahmane andChenchouni 2010). The verification stations were selectedfollowing these criteria: (1) accessibility in terms of roads ortracks, (2) sufficient number to ensure representativeness andcoverage of the study area, (3) covering all types of climatesand/or vegetation landscapes, and (4) covering confusion orunclear areas (Benabderrahmane and Chenchouni 2010).These surveyed points were also used to assess erosion riskaccuracy (see “Accuracy assessment” below).

The vegetation cover (VC) is one of the most importantfactors for scoring the vegetation coverage on the land surfaceand is an important factor in assessing risk of soil erosion(Wang et al. 2002, 2003; Vrieling 2006; BouKheir et al. 2008;Nekhay et al. 2009). VC was estimated according to land usemap achieved after a supervised classification using maxi-mum likelihood, then compared and verified with the normal-ised difference vegetation index (NDVI), which is derivedfrom the near infrared band and red band of the remote sensingdata (for more method details, see Zhang et al. 2010). Fourclasses of VC were distinguished: (1) dense vegetation, (2)sparse vegetation, (3) farmland and (4) bare land.

The potential erosion was obtained by combining twofactors (slope gradient and land use, Fig. 3) according toqualitative decision rules (Table 1). Soil erosion risk wasdivided into four classes based on qualitative multicriteriadecision rules (Table 2), which combined friability factorand potential erosion index.

Accuracy assessment

An accuracy assessment was performed on erosion assessmentresults in the study area. About 67 samples were carried out(details on sampled plots in Benabderrahmane and Chenchouni2010). The accuracy of the erosion risk assessment results was

evaluated by determining whether the erosion risk is correctlyassessed at each sample point. The accuracy is defined by a rate(%) by dividing the number of samples that are correctly assessedby the total number of samples.

Results

Mapping input geomatic data involved in erosion-riskassessment

Friability factor

The classification of substratum friability along the mainlithologic units of the study area showed a predominance ofa substratum with vulnerable materials (marl, sandstone,marly limestone, dolomite saccharoidal) with 71.8 % of thetotal area (Table 3). This class of materials was distributedover the entire study area. Very vulnerable materials (22.1 %)(represented by spreading of scree and alluvium, clays, siltsand eluvium) and resistant materials (6.1 %) represented bylimestone were mainly located in peripheral areas (Fig. 4).

DEM and slope gradient map

Slope gradient was granted from the DEM (see “Materials andmethods”). The landscape of the study area appears as a

Table 1 Index cross-table of potential erosion index (I: slight, II:moderate, III: severe, IV: extremely severe)

Slope gradient index

1 2 3

Vegetation cover index 1 I II III

2 I II III

3 II III III

4 II IV IV

Table 2 Qualitative multicriteria decision rules for identifying erosionrisk

Material friability index

1 2 3

Potential erosion index I ST ST MT

II ST ST SR

III MT MT ES

IV SR ES ES

ST slight, MT moderate, SR severe, ES extremely severe

Arab J Geosci (2014) 7:1–12 5


mosaic of terrain with different degrees of slope which doesnot exceed 35° (Fig. 5). Three classes of slope gradient wereretained based on their frequency of occurrence and theirspatial distribution. Although the study area has a mountain-ous affinity, the low slope class (0–5°) represented 58.5 % ofthe total area. It is followed by the class indexed as ‘moderate’with 39.5 % (Table 3).

Land use factor

Supervised classification using maximum likelihood,performed on the satellite image, yielded the map ofland use, which provides back mapping for identifyingvarious components of landscapes of the study area.Eight types of land use were considered in this study

Table 3 Area, percentage anddescription of each classexplaining the three factors (fri-ability index, slope gradient andvegetation cover) used to assesserosion risk

Factor classes Index Description Superficies

(km2) (%)

Friability

Limestone 1 Resistant materials 184.53 6.1

Marls, sandstone, marly limestones,saccharoidal dolomite

2 Vulnerable materials 2,153.00 71.8

Spreading of scree and alluvium,clays, silts and eluvium

3 Very vulnerable materials 662.47 22.1

Slope gradient (°)

>5 1 Slight 1,756.62 58.5

5–15 2 Moderate 1,184.64 39.5

15–35 3 High 58.74 2.0

Vegetation cover

Dense vegetation 1 Slight vulnerability 872.72 29.1

Sparse vegetation 2 Moderate vulnerability 1,213.53 40.5

Farmland 3 High vulnerability 286.58 9.5

Bare land 4 Very high vulnerability 627.17 20.9

Fig. 4 Map of substratumfriability of the study area.Bedrock material were classedinto three classes of friability,where resistant and moderatelyresistant materials were mergedinto one class

6 Arab J Geosci (2014) 7:1–12


Fig. 5 Slope gradient mapdivided into seven classes withlimits of 4, 8, 12, 16, 20 and 24degrees (°)

Fig. 6 Land-use map of thestudy area showing eight type-classes defined after carryingout a supervised classificationwith maximum likelihood onAlsat1 (2003) satellite image

Arab J Geosci (2014) 7:1–12 7


(Fig. 6). Land use types were grouped into four classesto define vegetation cover categories while referring toNDVI (Fig. 7): (1) dense vegetation (represented byforests and shrubs), (2) sparse vegetation (representedby pastures and steppes), (3) cultivated land and (4)vacant land (including sandy soils). Each class wasassigned by an index from 1 to 4, respectively, knowingthat ‘1’ represents the class that is less vulnerable and‘4’ represents the most vulnerable class. The majority ofthe vegetation in study area was not vulnerable toerosion since main vegetation consists of dense andsparse vegetation with a cover of 29.1 % and 40.5 %,respectively. Bare grounds (20.9 %) were considered asthe most vulnerable landscapes (Table 3).

Map of potential erosion

The map of potential erosion (Fig. 8) was obtained bycombining data of slope gradient and vegetation cover.This index reflects the aptitude of land to be erodedfollowing the previous two factors. Lands of the studyarea have largely moderate (51.4 %) or slight (37.7 %)potential to erosion. Areas with severe and extremely

severe potential erosion were poorly represented(10.9 %) (Table 4).

Soil erosion risk map

The erosion risk of the study area (Fig. 9) was computedaccording to two maps of factors: potential erosion andsubstratum friability. Erosion risk was divided into fourclasses, where areas of moderate and slight risk were repre-sented by 44.1 % and 32.9 %, respectively (Table 4). The 67erosion risk samples covered several different land-usetypes. By comparing the field data with the assessed erosionrisk, we calculated the overall producer accuracy, which was89.55 %. The accuracy varied among land-use types: 97 %for forested land and 81 % for farmland.

Discussion

This study has demonstrated that it is an effective approachfor assessing soil erosion risk. Simple–qualitative assess-ment method can give an equally good result as of quanti-tative methods for prioritising areas for conservation and

Fig. 7 Normalised differencevegetation index (NDVI) mapof the study area

8 Arab J Geosci (2014) 7:1–12


remediation decisions (Zhang et al. 2010; Arar andChenchouni 2012). With this approach, we find that watererosion is generally low (77 %) in the eastern Aures. This isbecause of the large and homogeneous distribution of low-middle slopes on the entire area (~98 %), and the goodquality of vegetation (forests with thick undergrowth) thathas a cover often greater than 60 % and occupying almosthalf of the study area.

Although erosion remains moderate (44 %), it is originallygenerated from degradation of natural resources that are diffi-cultly renewable (Lal 1998; Eswaran et al. 2001). Moreover,this degradation starts from forest clearing, irregular rainfall(often in the form of showers or heavy rains), increase induration and/or intensity of drought that leads to vegetationcover degradation and increased socio-economic pressures, in

addition to the harsh conditions occurring in an already fragileenvironment (FAO 1989; Arar and Chenchouni 2012).

Erosion occurs in several forms in the Aures regionwhere the most visible form is gully erosion especially inthe south of study area where the rarity of vegetation and theharsh climate play a negative role on the erosive process(Nearing et al. 2004).

Among the factors that control, significantly, water ero-sion is the friability of lithological substratum which seemsmore contributing in this phenomenon (Bachaoui et al.2007; Bou Kheir et al. 2008). The DEM is a digital repre-sentation of ground in terms of altitude. It allows the deter-mination of topographic attributes (elevation, orientation,slope, surface curvature) that influence the various quanti-ties involved directly in the erosion process (Wang et al.2003; Rahman et al. 2009) especially in mountainousregions (Abaoui et al. 2005; Bachaoui et al. 2007). In fact,both qualitative and quantitative studies cannot be achievedwithout the vital support of this tool.

The interpretation of water erosion risk classes must takeinto account their different components.

– The class of slight risk of erosion (32.9 % of the totalarea) is occupied by forest vegetation almost not sub-jected to human pressure which explains the stability ofthe ground. This area of the watershed represents also

Fig. 8 Map of potential erosionof the study area

Table 4 Superficies of different types of potential erosion index anderosion risk classes

Classes description Potential erosion Erosion risk

(km2) (%) (km2) (%)

Slight (ST) 1,130.93 37.7 988.01 32.9

Moderate (MT) 1,541.55 51.4 1,321.64 44.1

Severe (SR) 143.59 4.8 403.77 13.5

Extremely severe (ES) 183.93 6.1 286.58 9.5

Arab J Geosci (2014) 7:1–12 9


resistant and moderately resistant lithological composi-tions dominated by massive limestone, marl and sand-stone. In addition, this category consists particularly oflow and middle slopes from 0° to 15°. However, despitethe steep slopes (15° to 35°) taking place in the west ofthe study area, the erosion risk was low; this could beexplained by the role of the dense forest with the under-growth (Arar and Chenchouni 2012), which offers a goodresistance against soil runoff on the one hand and protectsthe surface against the kinetic force of rain droplets on theother. Moreover, these woodlands have higher soil pres-ervation value and legislative limitations.

– Moderate erosion risk class is distributed over the entirestudy area. It is found specifically in mountain areas,plains and open/moderately dense forests (bush andmountain pastures). It covers areas lithologically moreor less resistant which are generally formed by marl,sandstone and limestone–marl. This degree of risk resultsmainly of the quality of vegetation that is herbaceous andsteppe type in addition to seasonal crops. However, theseasonality of the vegetation cover provides temporaryprotection against the impact of rain on the ground sur-face. Erosion may have a significant effect on agriculturalproduction in these regions (Crosson 1997; Lal 1998).

– Areas with severe erosion risk have a poor vegetationquality, mainly dominated by sparse, very thin, tempo-ral vegetation and highly subjected to grazing problems.Lithologically, terrain with highly vulnerable materialslike alluvial, sandy soils or sandstone and marl, quitecommon in the Aures (Berkane and Yahiaou 2007),promotes erosion. Indeed, the presence of friable soilmaterials with the absence of protective vegetation layeron the ground surface allows the transport of soil par-ticles by runoff even on non-steep slopes (FAO 1989;Abaoui et al. 2005; Bachaoui et al. 2007).

– Extremely severe risk of erosion occurs in the lands ofsteep slopes (>25°), which accelerate runoff and trans-port of solid particles (Rahman et al. 2009; Zhang et al.2010; Park et al. 2011). This category also coincideswith the presence of a large urban fabric (KhenchelaYabous, El Oueldja and Tamza) established on highlyfriable materials. This gives a clear insight on the im-pact of human activities, in particular seasonal and/orperennial cultivation of bare lands that characterisessemiarid and pre-Saharan pastures.

Furthermore, the frequent natural or man-caused fires,occurring mainly in summer, affect negatively soil and

Fig. 9 Map of erosion risk ofthe Eastern Aures (Algeria)

10 Arab J Geosci (2014) 7:1–12


water resources. Fire, soil erosion and drought are the mostcommon threats to forest stands that accelerate desertifica-tion process in north Algeria (Benabderrahmane andChenchouni 2010). To control soil erosion, conservationmeasures need to be implemented at the field, hillslope orwatershed scale (Vrieling 2006; Zhang et al. 2010). Giventhe poor edaphic and climatic conditions of the region,which make vegetation recover and regeneration ‘ifdestroyed’ very difficult, areas located nearby and/or sur-rounding large settlements should be strictly protected fromhuman activities, which are evidently a key cause of vege-tation degradation and soil erosion acceleration.

Conclusion and perspectives

A qualitative method of soil erosion assessment may beused to prioritise conservation areas without the need formore complex quantitative methods. Integration of recentenvironmental changes in erosion risk while developingdecision rules can reveal future priority areas and mayprovide useful information for decision making. The pro-posed method is a significant contribution to research soilerosion; moreover, it may facilitate conservation planning.

GIS and remote sensing have proven fundamental toassess and map the surface states vis-à-vis water erosion.They offer the ability to identify, locate, analyse and classifysets of information related to land use and land cover toallow ecosystem monitoring with the aim to address prob-lems that could lead to their disruption.

The map of water-erosion risk is a relevant tool thathighlights surfaces classified according to the level of thisphenomenon, especially areas with ‘severe’ and ‘extremelysevere’ risk, which facilitate remediation decisions andplanning-conservation actions.

Although the method adopted in this study was based ona qualitative approach, its relevance has been justified inseveral studies in Mediterranean regions. However, it isdesigned to evolve through further experiments that willallow better characterisation of diagnostic criteria. In addi-tion, to improve results of this study, we make the followingresearch prospects:

– Similar studies would be done with rain simulator tomodel results closer to reality,

– Consider gully erosion and integrate it into a globalmodel of soil loss,

– Make measurements at the plot scale and investigate thephenomenon in its natural context at the level of land-scape and watershed to better identify the nature of riskslinked to runoff and recommend a suitable solution tothe problem.

References

Abaoui J, El Ghmari A, El Harti A, Bachaoui EM, Bannari A, ElBouadili A (2005) Cartographie de l’érosion hydrique en zonemontagneuse: Cas du bassin versant des Ait Bou Goumez, HautAtlas, Maroc. Estudios Geológicos 61:33–39

Ait Fora A (1995) Modélisation spatiale de l’érosion hydrique dans unbassin versant du Rif marocain: Validation de l’approchegéomatique par la sédimentologie, les traceurs radioactifs et lasusceptibilité magnétique des sédiments. PhD thesis, Univ.Sherbrooke, Canada

Arabi M, Roose E (1989) Influence de quatre systèmes de productionen région méditerranéenne de moyenne montagne algérienne.ORSTOM Montpellier, Bull Réseau Erosion 9:39–51

Arar A, Chenchouni H (2012) How could geomatics promote ourknowledge for environmental management in Eastern Algeria? JEnviron Sci Technol 5:291–305

Bachaoui B, Bachaoui EM, El Harti A, Bannari A, El Ghmari A (2007)Cartographie des zones à risque d’érosion hydrique: exemple duhaut Atlas marocain. Teledetection 7:393–404

Benabderrahmane MC, Chenchouni H (2010) Assessing environmen-tal sensitivity areas to desertification in Eastern Algeria usingMediterranean desertification and land use “MEDALUS” model.Int J Sustain Water Environ Syst 1:5–10

Berkane A, Yahiaou A (2007) L’érosion dans les Aurès. Sécheresse18:213–216

Bonn F (1998) La spatialisation des modèles d’érosion des sols à l’aidede la télédétection et des SIG: possibilités, erreurs et limites.Sécheresse 9:185–192

Bou Kheir R, Abdallah C, Khawlie M (2008) Assessing soil erosion inMediterranean karst landscapes of Lebanon using remote sensingand GIS. Eng Geol 99:239–254

Crosson P (1997) Will erosion threaten agricultural productivity?Environment 39:4–31

Eswaran H, Lal R, Reich PF (2001) Land degradation: an overview. In:Bridges EM, Hannam ID, Oldeman LR, Penning de Vries FWT,Scherr SJ, Sombatpanit S (eds) Response to land degradation.Science, Enfield, pp 20–35

FAO (1989) Soil and water conservation in semi-arid areas. FAO SoilsBulletin 57. URL: http://www.fao.org/docrep/T0321E/T0321E00.htm

Ghenim A, Terfous A, Seddini A (2007) Étude du transport solide ensuspension dans les régions semi-arides méditerranéennes: cas dubassin-versant de l’oued Sebdou (Nord-Ouest algérien).Sécheresse 18:39–44

Laffite R (1939) Étude géologique de l’Aurès. Doctorate thesis, Univ.Paris, France

Laflen JM, Leonard JL, Foster GR (1991) WEPP: a new generation oferosion predicting technology. J Soil Water Conserv 46:34–38

Lal R (1998) Soil erosion impact on agronomic productivity andenvironment quality. Crit Rev Plant Sci 17:319–464

Lal R (2001) Soil degradation by erosion. Land Degrad Dev 12:519–539Lal R (2004) Soil carbon sequestration impacts on global climate

change and food security. Science 304:1623–1627Megnounif A, Terfous A, Ghenaim A, Poulet J-B (2007) Key process-

es influencing erosion and sediment transport in a semi-aridMediterranean area: the Upper Tafna catchment, Algeria.Hydrolog Sci J 52:1271–1284

Nearing MA, Pruski FF, O’Neal MR (2004) Expected climate changeimpacts on soil erosion rates: a review. J Soil Water Conserv59:43–50

Nekhay O, Arriaza M, Boerboom L (2009) Evaluation of soil erosionrisk using Analytic Network Process and GIS: a case study fromSpanish mountain olive plantations. J Environ Manage 90:3091–3104

Arab J Geosci (2014) 7:1–12 11


http://www.fao.org/docrep/T0321E/T0321E00.htm

Park S, Oh C, Jeon S, Jung H, Choi C (2011) Soil erosion risk inKorean watersheds, assessed using the revised universal soil lossequation. J Hydrology 399:263–273

Pimentel D, Harvey C, Resosudarmo P, Sinclair K, Kurz D, McNair M,Crist S, Shpritz L, Fitton L, Saffouri R, Blair R (1995)Environmental and economic costs of soil erosion and conserva-tion benefits. Science 267:1117–1123

Poesen JW, Boardman J, Wilox B, Valentin C (1996) Water erosionmonitoring and experimentation for global changes studies. J SoilWater Conserv 5:386–390

Prinz D, Gomer D, Belz S (1994) Studies of the causes of soil erosionon marl soils in northern Algeria: the role of traditional soiltillage. Land Degrad Dev 5:271–280

Rahman MR, Shi ZH, Chongfa C (2009) Soil erosion hazard evalua-tion—an integrated use of remote sensing, GIS and statisticalapproaches with biophysical parameters towards managementstrategies. Ecol Model 220:1724–1734

Renard KG, Foster GR, Weesies GA, McCool DK, Yoder DC(1997) Predicting soil erosion by water: a guide to conserva-tion planning with the revised universal soil loss equation

(RUSLE). Agriculture Handbook, No.703, US Department ofAgriculture, Washington DC

Terranova O, Antronico L, Coscarelli R, Iaquinta P (2009) Soil erosionrisk scenarios in the Mediterranean environment using RUSLEand GIS: an application model for Calabria (southern Italy).Geomorphology 112:228–245

Vrieling A (2006) Satellite remote sensing for water erosion assess-ment: a review. Catena 65:2–18

Wang G, Wente S, Gertner GZ, Anderson A (2002) Improvement inmapping vegetation cover factor for the universal soil loss equa-tion by geostatistical methods with Landsat Thematic Mapperimages. Int J Remote Sens 23:3649–3667

Wang G, Gertner G, Fang S, Anderson AB (2003) Mapping multiplevariables for predicting soil loss by geostatistical methods with TMimages and a slope map. Photogramm Eng Rem Sens 69:889–898

Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses: aguide to conservation planning. Agricultural Handbook no 537,U.S. Department of Agriculture, Washington DC

Zhang X, Wu B, Ling F, Zeng Y, Yan N, Yuan C (2010) Identificationof priority areas for controlling soil erosion. Catena 83:76–86

12 Arab J Geosci (2014) 7:1–12


A “simple” geomatics-based approach for assessing water erosion hazard at montane areas

Documents

Transcript of A “simple” geomatics-based approach for assessing water erosion hazard at montane areas