Download Monitoring soil erosion in South Africa at a regional scale

Monitoring soil erosion in South Africa at a regional scale
ARC-ISCW report no GW/A/2011/23
Project GW 59/004 Task 50
FEBRUARY 2011
REPORT FOR THE
COUNCIL OF GEOSCIENCE
GEOHAZARDS PROJECT
BY
Le Roux, J.J.
AGRICULTURAL RESEARCH COUNCIL
INSTITUTE FOR SOIL, CLIMATE AND WATER
PRIVATE BAG X79
PRETORIA
0001
Tel: 012 310-2684
Fax: 012 323-1157
E-mail: [email protected]
Executive summary
Soil erosion is a major soil degradation problem, confronting land and water resource
management throughout South Africa. Soil erosion involves the loss of fertile topsoil and
reduction of soil productivity, as well as serious off-site impacts related to increased
mobilization of sediment and delivery to rivers.
Before prevention of soil erosion or
remediation can be undertaken, however, the spatial extent of the problem should be
established. The Council of Geoscience proposed the development of a decision support or
risk-management system to assess the impacts of different geohazards including soil
erosion (in association with the Agricultural Research Council – Institute for Soil, Climate
and Water). Remote sensing techniques are very promising for assessing and monitoring
soil erosion, especially with the development in sensor technology and the availability of
space-borne data with improved spectral, spatial and temporal resolution.
After a brief description of soil erosion impacts and cost implications, this report reviews
mechanisms leading to sheet-rill and gully development including rainfall erosivity,
topographical variables, parent material-soil associations and land use-cover interactions.
The second objective of this report is a brief description of the methodologies for regional
water erosion assessment using satellite remote sensing addressing mainly erosion
detection of eroded areas and features; followed by the assessment of off-site impacts and
erosion controlling factors, and finally data integration for erosion modelling. Third, the
report describes two most recent spatial inventories namely a gully location map of South
Africa created by visual interpretation and vectorization from SPOT 5 satellite imagery, and
a water erosion prediction map of South Africa created by simplification of the Universal Soil
Loss Equation (emphasizing sheet-rill erosion). Results from the latter mentioned spatial
inventories confirm that erosion is a major soil degradation problem in South Africa. In
terms of gully erosion, the Northern Cape (160 885 ha) and Eastern Cape (151 759 ha)
provinces are the most severely affected, followed by KwaZulu-Natal (87 522 ha), Free
State (64 674 ha), Limpopo (58 669 ha), Western Cape (25 403 ha), Mpumalanga (17 420
ha), North West (10 782 ha) and Gauteng (110 ha) provinces. In terms of sheet-rill erosion,
surface areas classified as having a moderate to high erosion risk (where the average
annual soil loss rate exceeds 12 t/ha.yr), the Eastern Cape (6 188 581 km2) is the most
severely affected province, followed by the Free State (5 503 875 km2), Northern Cape (5
407 138 km2), Limpopo (4 422 688 km2), Mpumalanga (2 578 363 km2), KwaZulu-Natal (2
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138 038 km2), Western Cape (2 065 319 km2), North West (1 485 263 km2), and Gauteng
(775 688 km2) provinces.
Finally, the report emphasizes the importance of spatially modelling areas that are currently
erosion free, but under threat and in need of area-specific management e.g. protection of
the current vegetation cover. The assessment of areas that are susceptible to erosion is
important in policy terms because it indicates those areas which are inherently susceptible
to erosion (potential risk), but which are presently protected by vegetation. Due to limited
financial resources it will not be feasible, for example, to rehabilitate large gullies with large
and expensive structures at a catchment scale. In addition to rehabilitating existing gullies,
currently vegetated gully-free areas susceptible to gully development should be identified
and protected/sustainably managed. A previous catchment scale study is described for this
purpose.
After quantifying the influence of factors in gully development (topographical
variables, parent material-soils interactions and remotely sensed cover management), the
identification of vegetated gully-free areas susceptible to gully development can be achieved
by means of overlay analysis.
Such a remote sensing/modelling approach is relatively
simple, realistic and practical, and it can be applied or expanded to other areas of South
Africa at a regional scale, thereby providing a tool to help with the implementation of a
decision support or risk-management system for soil conservation and sustainable
management (of problem soils as a geohazard).
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Table of Contents
Acknowledgements .............................................................................................................. 5
1. Introduction: Soil erosion impacts and cost implications ................................................... 6
2. Mechanisms leading to sheet-rill and gully development .................................................. 7
2.1 Factors influencing sheet and rill erosion ................................................................ 7
2.2 Factors influencing gully erosion............................................................................10
3. Review of current assessment/monitoring techniques .....................................................12
3.1 Eroded areas .........................................................................................................12
3.2 Erosion features ....................................................................................................14
3.3 Assessment of off-site impacts ..............................................................................15
3.4 Erosion controlling factors......................................................................................15
3.5 Data integration for erosion modelling ...................................................................18
4. Review of latest spatial inventories/datasets on sheet-rill and gully erosion at a national
scale ...................................................................................................................................18
4.1 Gully erosion mapping ...........................................................................................18
4.2 Water erosion modelling (emphasizing sheet-rill erosion) ......................................19
5. The extent of sheet-rill and gully erosion in South Africa .................................................20
5.1 Extent of gully erosion ...........................................................................................20
5.2 Extent of sheet-rill erosion .....................................................................................22
6. Assessment of susceptible areas ....................................................................................24
6.1 Assessment of areas susceptible to gully erosion by means of GIS and remote
sensing: Case study in the Eastern Cape ....................................................................24
7. Conclusion and recommendations ..................................................................................27
8. References......................................................................................................................29
List of Figures
Figure 1: Surface area (ha) of provinces affected by gully erosion ......................................20
Figure 2: Gully location map of South Africa........................................................................21
Figure 3: Surface area (km2) of provinces where the mean soil loss exceeds 12 t/ha.yr
(moderate to high)...............................................................................................................22
Figure 4: Water erosion prediction map of South Africa (emphasizing sheet-rill erosion).....23
Figure 5: Gully locations map of the catchment in the Eastern Cape Province ....................25
Figure 6: Areas susceptible to gully erosion in the catchment, Eastern Cape Province .......27
List of Tables
Table 1:
Satellites and sensor acronyms and names mentioned in text………………12
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Acknowledgements
This report necessitated close cooperation between the Agricultural Research Council Institute for Soil, Climate and Water (ARC-ISCW) and the Council of Geoscience (CG). More
specifically, the author would like to thank (in alphabetical order) Mr. S. Foya, Mr. J. Hugo,
Mr. D. Sebake and colleagues at the CG for initiating the project, as well as for funding this
report. At the ARC-ISCW, thanks to Dr. H.J. Smith for managerial support. Editing of the
final report by Dr. T.P. Fyfield is very much appreciated.
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1. Introduction: Soil erosion impacts and cost implications
Soil erosion is a major problem confronting land and water resources throughout South
Africa (SA). Previous research indicates that more than 70% of South Africa (SA) is affected
by varying intensities of soil erosion (Garland et al., 2000; Le Roux et al., 2008a). Although
soil erosion is a natural process it is often accelerated by human activities, for example by
the clearing of vegetation or overgrazing (Snyman, 1999).
Erosion is a process of
detachment and transportation of soil materials by wind or water (Morgan, 1995). Although
25% of SA is highly susceptible to wind erosion (Hoffman and Todd, 2000), water is the
dominant agent causing erosion in SA and therefore the focus of this report. Water erosion
occurs mostly through rain-splash, in unconcentrated flow as sheet erosion, as well as in
concentrated flow as rill and/or gully erosion. Sheet and rill erosion are usually not as
conspicuous as gully erosion; however, several authors agree that it is the dominant cause
for loss of fertile topsoil throughout SA.
Soil erosion not only involves the loss of fertile topsoil and reduction of soil productivity, but
is also coupled with serious off-site impacts related to increased mobilization of sediment
and delivery to rivers.
Flügel et al. (2003) states that eroded soil material leads to
sedimentation/siltation of reservoirs, as well as an increase in pollution due to suspended
sediment concentrations in streams which affects water use and ecosystem health. For
example, due to siltation, the storage capacity of the Welbedacht Dam near Dewetsdorp in
the Free State reduced rapidly from the original 115 million m3 to approximately 16 million
m3 within twenty years since completion in 1973 (DWA, 2011). With a catchment area of 15
245 km2, its purpose was to supply water to the city of Bloemfontein via the 115 km long
Caledon-Bloemfontein pipeline. Due to the high sediment concentration in the water, the
transfer from Welbedacht Dam has to be purified at the purification plant downstream.
According to the latest State of Environment Report of SA (Hoffman and Ashwell, 2001;
cited in Gibson et al., 2006), the off-site purification of silted dam water caused by soil
erosion costs an estimated R2 billion per annum. It is therefore imperative to prevent these
negative impacts and to remediate affected areas.
However, before prevention of soil
erosion or remediation can be undertaken, the spatial extent of the problem should be
established and continually monitored by means of remote sensing. A brief overview of the
mechanisms leading to sheet-rill and gully development follows.
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2. Mechanisms leading to sheet-rill and gully development
Soil erosion processes are complex and influenced by several soil erosion factors that
respond differently, even in the same area. In addition, different processes and interactions
are likely to emerge as dominant when crossing scale boundaries (Kirkby et al., 1996;
Wilson and Gallant, 2000). Nevertheless, soil erosion by water mainly depends on the
combined and interactive effects of five erosion factors, namely rainfall erosivity, soil
erodibility, slope steepness and slope length, crop management, and support practice.
These factors are also known as the Universal Soil Loss Equation (USLE) factors developed
by Wischmeier and Smith (1978), emphasizing the sheet and rill aspects of the erosion
cycle. A brief overview of these factors is discussed below, followed by a description of
factors which specifically promote gullying.
2.1 Factors influencing sheet and rill erosion
Foremost, sheet erosion involves the detachment and transport of soil particles by raindrop
impact (rainsplash erosion) and transport by shallow overland flow (Lal and Elliot, 1994),
whereas rill erosion is a process in which numerous small channels of several centimetres
up to about 30 cm are formed (Bergsma et al., 1996).
Causal actors are discussed
individually, despite the interdependency that exists between them.
Climate erosivity
Erosivity is the ability of rainfall and runoff to cause soil detachment and transport. The
ability of rain to cause detachment and transport is partly the result of raindrop impact, and
partly due to the runoff that rainfall generates (Lal and Elliot, 1994). As a result, rainfall
erosivity is based on the kinetic energy of the rain, which is a function of the rainfall intensity
and duration, and the mass, diameter and velocity of the raindrops (Morgan, 1995). Rainfall
erosivity indices, such as the widely used EI30 index, were developed to correlate rainfall
with potential soil loss (Wischmeier and Smith, 1978). EI30 is a product of the total kinetic
energy (E) of the storm multiplied by its maximum 30-minute intensity (I30).
I30 is
calculated as twice the greatest amount of rain falling in any 30 consecutive minutes. Some
areas in SA are occasionally characterized by high intensity rainstorms. For instance, the
erosivity values for the northern and eastern parts of SA are four to eight times higher than
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the southern and western parts of the country (Smithen and Schulze, 1982). Short-lived
intense storms, where the infiltration capacity of the soil is exceeded, are usually
responsible for the bulk of seasonal soil loss (e.g. Rydgren, 1996).
Soil erodibility
Soil erodibility (K) accounts for the resistance of soil to the impacting forces of rainfall and
runoff. Therefore, soil erodibility is a measure of the susceptibility of a given soil to particle
detachment and transport (Wischmeier and Smith, 1978).
The physical, as well as
chemical, soil properties and their interactions that affect K-values are many and varied.
However, K depends primarily on the structural stability of the soil and on its ability to absorb
rainfall (i.e. its infiltration capacity). These properties, in turn, vary according to a number of
attributes, including soil texture, organic matter content, carbon content, salinity and pH.
Several studies demonstrate that dispersibility is a fundamental soil property to be
considered in erodibility analysis (e.g. Rienks et al., 2000; Valentin et al., 2005). Dispersion
processes supply fine particles to overland flow for transportation, as well as shift dispersed
particles into pores, decreasing infiltration and accelerating runoff. Several authors state the
importance of parent material in terms of soil erodibility (e.g. Dardis et al., 1988; Laker,
2004; Le Roux and Sumner, 2011). For example, soils from the Tarkastad and Molteno
Formations in northern parts of the Eastern Cape Province are associated with duplex soils
(Land Type Survey Staff, 1972-2008) that are highly erodible with widespread gully erosion
evident (Le Roux et al., 2010). Furthermore, in the Eastern Cape a distinction can be made
between erodible soils developed on the silts and mudstones of the Beaufort Series which
give rise to fine textured dispersive soils and the dolerites which give rise to well structured
clay soils with a lower erodibility (Garland et al., 2000).
Likewise, the quartzitic Table
Mountain sandstones weather more slowly, producing coarse textured soils with very little
silt and clay, thus accounting for the clear waters and widespread distribution of sand bed
rivers in the lowlands of the Western Cape. Botha et al. (1994) cite the importance of the
Quaternary Masotchini Formation, a thick layer of transported colluvial sediments as host of
most of KwaZulu-Natal’s gully systems, whereas in situ soils are far less erodible. Soil
depth is also important since deep soils typically have a higher water-holding capacity, and
thus are able to absorb larger rainfall amounts before overland flow is generated (Van Zyl,
2004). Soil erodibility also depends on topographic position and slope steepness.
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Slope gradient and length
The effects of topography on erosion are partitioned in the effects of slope steepness and
slope length.
Ground slope is important when considering the overall transport of soil
particles (Wang et al., 2000).
As the slope steepens, the proportion of downslope
movement increases as a result of respective increases in velocity and volume of surface
runoff. Gentle slopes usually erode less, because there is more surface ponding and slower
overland flow which protect the surface against the impact of rain. Runoff and erosion also
tend to increase with increasing slope length. Slope length, together with the slope form,
determines the severity of erosion through the type of erosion that will dominate, and the
probability for deposition (Liu et al., 2000). Soil creep upslope (due to rainsplash erosion)
gives way first to interrill erosion (due to broad overland flow) and then to rill erosion further
downslope (due to concentrated flow). Therefore, the presence or absence of rill erosion
and the density of rills are strongly influenced by slope length (Loch, 1996). According to
Van Zyl (2004), the effect of topography also often operates at an essentially local level,
erosion being initiated at specific locations on the slope, or in association with minor
topographic variations. Renard et al. (1994) state that surface runoff usually concentrates in
less than 400 feet (91 m), which is a practical slope-length limit in many situations. Long
steep slopes, a common feature in the KwaZulu-Natal Drakensberg, render the land
extremely susceptible to erosion once the vegetation cover is degraded (Schulze, 1979).
Areas of pronounced relief yield most suspended sediment, including large tracts of the
Drakensberg, the former Transkei and Waterberg Plateau (Rooseboom et al., 1992).
Vegetation cover and land management
Of all hazard factors the cover management code and land use is the most important soil
erosion factor which can rapidly change as a result of human activities (Wischmeier and
Smith, 1978). Not only does it represent conditions that can be managed to reduce erosion,
but it also represents the actual soil erosion risk, which relates to the current risk of erosion
under present vegetation and land use conditions. The cover management factor is mainly
a function of the canopy cover and residual effect (McPhee and Smithen, 1984). However,
the canopy is not always protective against erosion. The effect of the canopy cover on soil
loss depends not only on its density but also on its height. If the canopy height is too high
(>3 m), the fall velocity of the drip is higher than unintercepted rain. According to McPhee
and Smithen (1984), surface cover including mulch and gravel is more effective than
equivalent percentages of canopy cover. Surface cover, such as the rocks and residue
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composition, is considered as one of the most sensitive factors controlling erosion (Renard
et al., 1994; Evans, 2000). Various studies across SA report that good vegetation cover
protects the soil, even on very steep slopes and under highly erosive rainfall (e.g. Smith et
al., 2000; Le Roux et al., 2008a). Boardman et al. (2003) postulate that overgrazing in the
first half of the 20th century in the Karoo (Sneeuberg) was the driving factor for gully
development.
2.2 Factors influencing gully erosion
Gully erosion is a process where surface (or subsurface) water concentrates in narrow flow
paths and removes the soil resulting in incised channels that are too large to be destroyed
by normal tillage operations (Kirkby and Bracken, 2009). Once initiated, individual gullies
can expand into a network of active gullies that contribute significantly to soil loss in a
catchment (e.g. Martinez-Casasnovas et al., 2003).
Several factors contribute to gully
development including topographical variables, parent material-soil associations and land
use-cover interactions (Valentin et al., 2005).
Rainfall
The influence of rainfall erosivity on gullying is not widely researched. Although gullies can
be long-term features of the landscape, most research agrees that high magnitude events
are required for the initiation of gullies (e.g. Rydgren, 1996; Flügel et al., 2003).
Lithological and pedological factors
Gully erosion largely depends on soil and lithological characteristics. At the regional scale,
several authors note that the inherent erodibility of the parent material is the overriding
erosion risk factor (e.g. Watson and Ramokgopa, 1997; Laker, 2004; Le Roux and Sumner,
2011). Several gullies in KwaZulu-Natal developed on transported colluvial and alluvial
sediments (Botha et al., 1994).
Laker (2004) indicates that in South Africa various
mudstones are susceptible to gully erosion mainly due to highly erodible duplex soils
derived therefrom. The most prominent feature of duplex soils is a permeable horizon
overlying an impermeable one. As a result, water infiltrates and saturates the top layer
above the impermeable one where it moves along as subsurface flow causing tunnel
erosion (Beckedahl, 1998). In addition, several studies agree that soils prone to tunnel
erosion are usually dispersive and easily lose aggregation as a result of high sodium
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absorption (e.g. Rienks et al., 2000; Valentin et al., 2005). The tunnel network is exposed
as gullies where their roofs collapsed.
Topographical factors
In contradiction to interrill and rill erosion, gully erosion cannot be correlated positively with
slope steepness. Several authors agree that gullies in SA are mainly located on gentle
slopes with gradients less than 10˚ (Flügel et al., 2003; Kakembo et al., 2009). Tamene et
al. (2006) found in Ethiopia that gully erosion is less severe on steep slopes, probably due to
steep areas being less accessible and less exposed to human and livestock disturbances.
Another possible reason is provided by Poesen et al. (2003), explaining that the so-called
critical drainage area needed for gully initiation decreases as slope steepens. Likewise,
Kakembo et al. (2009) observed that gullying in several catchments of the Eastern Cape
Province predominantly occurs on gentle slopes where the critical drainage area or upslope
contributing area is large. Areas with large contributing areas have high flow accumulation
(number of upslope cells that flow into each cell) used to identify drainage areas and flow
paths vulnerable to gully erosion (Desmet et al., 1999).
In addition, areas with large
contributing areas are associated with zones of saturation with high surface soil water along
drainage paths where slope is low. These saturated areas favour gully formation since the
surface soils lose their strength as they become wet. Several studies in South Africa state
that gully development is specially favoured in certain terrain units, namely footslopes and
valley floors (e.g. Descroix et al., 2008; Kakembo et al., 2009). Gully development is
favoured in footslopes and valley floors since they represent areas where overland flow is
concentrated into preferred pathways of flow (Beckedahl and Dardis, 1988), especially
concave hollows adjacent to drainage lines where soils are deep, as opposed to upland
convex hillslope sections (Kakembo et al., 2009). Although soil depth is not a topographical
factor per se, it is highly correlated with terrain units usually increasing downslope or
towards the lower hillslope elements (Land Type Survey Staff, 1972–2008). Moreover, gully
development also depends on the availability of deep soils (e.g. Descroix et al., 2008;
Kakembo et al., 2009). Therefore, relatively large fractions of deep soils are affected by
gully erosion, especially where footslopes and valleys are subject to overgrazing.
Land use and vegetation cover
Several studies identify the reduction in vegetation cover as the main driving factor of gully
erosion (e.g. Tamene et al., 2006; Descroix et al., 2008).
As indicated by examples
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worldwide (e.g. Boardman and Foster, 2008; Gutiérrez et al., 2009), gully erosion is often
triggered and/or accelerated by inappropriate land use. Le Roux et al. (2010) observed that
a relatively large portion of cultivated and grassland areas are affected by gully erosion due
to livestock disturbance, including overgrazing and trampling along cattle tracks. Several
studies mention the importance of paths and tracks in creating gullies (e.g. Garland et al.,
2000; Boardman et al., 2003, Hochschild et al., 2003). Clearly, a major problem with gully
erosion is the temporal and spatial scale of reporting and the spatial extent to which the
phenomenon occurs.
3. Review of current assessment/monitoring techniques
The intention of this report is to review methodologies for regional water erosion assessment
using satellite remote sensing. The current remote sensing review adopts a similar structure
provided by Vrieling (2006), addressing mainly erosion detection of eroded areas and
features, followed by the assessment of off-site impacts and erosion controlling factors, and
finally data integration for erosion modelling. See Table 1 for satellite and sensor acronyms
and names mentioned in text.
Table 1:
Satellites and sensor acronyms and names mentioned in text
Acronym
Name
ASTER
IRS - LISS
Landsat MSS
Landsat TM
JERS-1
SAR
SPOT HRV
Advanced Spaceborne Thermal Emission and Reflection Radiometer
Indian Remote Sensing Satellites - Linear Imaging and Self-Scanning Sensor
Landsat Multispectral Scanner
Landsat Thematic Mapper
Japanese Earth Resources Satellite
Synthetic Aperture Radar
(Syste`me Pour l’Observation de la Terre High Resolution Visible)
3.1 Eroded areas
Until recently, satellite data have been limited to detecting large eroded areas, instead of
detecting individual erosion features due to the heterogeneity of the object itself as well as
the environment (King et al., 2005; Vrieling, 2006). For example, until recently only large
areas suffering extensive gully erosion have been mapped with visual interpretation
techniques on optical image composites of different sensors (e.g. Dwivedi et al., 1997a).
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Some studies separated erosion classes based on a combination of data such as vegetation
cover derived from visual interpretation (e.g. Dwivedi and Ramana, 2003) or vegetation and
topographic characteristics (e.g. Yuliang and Yun, 2002).
An alternative for visual
interpretation is direct correlation between erosion and spectral reflectance values,
sometimes permitting the detection of erosion and its intensity (Vrieling, 2006).
For
example, Price (1993) found good correlation between reflectance values of single Landsat
TM bands, especially band 4 (near infrared; NIR), and erosion rates for pinyon-juniper
woodlands.
Pickup and Nelson (1984) successfully distinguished eroding, stable, and
depositional areas for arid rangelands in Australia using the data space defined by the 4/6
and 5/6 band ratios of Landsat MSS imagery (corresponding to green/NIR and red/NIR
respectively).
In addition to abovementioned interpretation techniques is the automatic extraction of
eroded areas from imagery (Vrieling, 2006).
For example, Servenay and Prat (2003)
applied an unsupervised classification algorithm to multispectral SPOT HRV data to
distinguish four stages of erosion. Bocco and Valenzuela (1988) separated several erosion
classes, whereas Floras and Sgouras (1999) determined one erosion class, using the
maximum likelihood classifier after principal component analysis of Landsat TM imagery.
Usually, the higher resolution SPOT data performs better in classifying eroded areas,
whereas the larger number of spectral bands of Landsat TM resulted in a better
classification of land cover and land use (Dwivedi et al., 1997b). Metternicht and Zinck
(1998) achieved highest classification accuracy using a combination of images (Landsat TM
and JERS-1; SAR data).
Change detection methods can also supply direct information on erosion occurrence
(Vrieling, 2006). A great variety of methods for change detection from satellite imagery
exists (Coppin et al., 2004). These include albedo and spectral image differencing, spectral
change vector analysis, repeat-pass SAR interferometry, digital elevation model (DEM)
extraction and slight deformation measurements (Rosen et al., 2000; Dhakal et al., 2002).
In this context, Vrieling (2006) states that derived interferometric coherence imagery has
most potential for erosion detection, even for the assessment of erosion volumes (Smith et
al., 2000). However, Vrieling (2006) stressed the need to integrate coherence imagery with
additional spatial data, like optical imagery, due to multiple causes of temporal decorrelation.
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Furthermore, spatial resolutions such as offered by Landsat do not allow gully growth
analysis with sequential imagery (Bocco and Valenzuela, 1993).
3.2 Erosion features
Until recently, it was difficult to detect erosion features with satellite data due to coarse scale
resolution (Hochschild et al., 2003). Most remote sensing techniques have been based on
the use of airphoto interpretation and photogrammetry to map erosion features such as
gullies (Martinez-Casasnovas, 2003). With the development in sensor technology, spaceborne data with improved spectral, spatial and temporal resolution is now available. New
high-resolution satellite imagery such as SPOT 5, IKONOS and Quickbird are very
promising for measurement of erosion features, e.g. individual gullies. Le Roux and Sumner
(2011) based gully erosion mapping in a large catchment in the Eastern Cape Province on
analysis of SPOT 5 imagery from various acquisition dates in 2008. A gully erosion map
was created for the catchment by means of manual vectorization at a scale of 1:10 000.
SPOT 5 satellite imagery was utilized because the panchromatic sharpened images at 2.5
m resolution provides high resolution air photo-like quality for gully mapping (Taruvinga,
2008) and was acquired from government agencies for the whole country. In order to speed
up the processing of data and to exclude subjectivity of manual interpretation, the study first
considered different techniques of classification, as well as object-based modelling (i.e.
eCognition software) (e.g. Lück, 2002). However, latter mentioned techniques could not
express individual gullies with acquired accuracy due to their spectral complexity, especially
over such a large area. The spectral reflectance between gullies varies significantly and
depends on vegetation cover inside gullies, as well as several soil properties such as the
soil organic matter and soil moisture contents.
Other regional studies that utilized
classification techniques in semi-arid regions of SA confirm this trend, i.e. could not rapidly,
nor accurately, define individual gullies from bare soil (e.g. Le Roux et al., 2008b).
Taruvinga (2008) demonstrated that classification techniques (i.e. support vector machine
applied on SPOT 5 imagery) mainly identifies large (>3.5 ha) prominent gullies (continuous),
omitting small intermittent gullies (discontinuous).
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3.3 Assessment of off-site impacts
Most studies that have applied satellite imagery to assess the off-site impacts of erosion
focus on the sedimentation of reservoirs and lakes (Vrieling, 2006).
For example,
sedimentation volumes (water-spread areas at varying depths) have been estimated for
Indian reservoirs using simple classification algorithms on multi-temporal IRS LISS-2 and
LISS-3 imagery (Jain et al., 2002). Suspended sediment concentration can be assessed
using relationships between in situ determined suspended sediment concentration and
spectral reflectance (spectral bands between 500 and 800 nm within VNIR range) (e.g.
Nellis et al., 1998). However, these relationships are site-specific due to the influence of
different sediments, chlorophyll and algae on water reflection (Liu et al., 2003).
3.4 Erosion controlling factors
Most remote sensing studies of soil erosion concentrated on the assessment of erosion
controlling factors, especially soil erodibility, vegetation attributes and to a lesser extent
conservation practices and topography (King et al., 2005; Vrieling, 2006). These inputs are
usually integrated for erosion modelling.
Soil erodibility
Different soils can be classified and mapped with satellite remote sensing on the basis of
factors such as soil properties, climate, vegetation, topography and lithology (McBratney et
al., 2003; Vrieling, 2006).
Soil classification, especially visual interpretation of optical
satellite imagery, has been used to assess spatial differences in soil erodibility (e.g. Reusing
et al., 2000). For example, Wang et al. (2003) assigned soil erodibility values determined in
the field to a soil classification.
Erodibility values were then extrapolated to the whole
sampling region using geostatistical methods, while Landsat TM band 7 was used to
reproduce the spatial variability of the erodibility.
Singh et al. (2004) found significant
relationships between soil colours defined by the Munsell system and optical satellite
imagery. Dwivedi (2001) points out that the soil’s spectral reflectance is influenced by the
topsoil characteristics such as soil texture, moisture content, iron oxides and minerals. This,
however, can be a limitation for determining one particular property, but useful to classify the
surface state such as surface crusting and the uncovering of subsoil (Mathieu et al., 1997;
Escel et al., 2004). An increase in radiation relates to a decreasing surface state, whereas a
decreasing albedo indicates improved conditions (Ritchie, 2000). A difficulty in measuring
15
topsoil reflectance with satellite data is the influence of vegetation cover, which greatly limits
satellite-based soil studies for temperate and humid areas, unless agricultural practices
leave the soil bare periodically (Vrieling, 2006). A commonly used technique to separate the
soil from the vegetation signal in (semi-)arid environments is linear spectral unmixing (Smith
et al., 1990).
So called pure components or end-members are those elements that
represent the spectral variability of the landscape, e.g. soil and rock outcrops (De Jong et
al., 1999). Spectral unmixing requires reference spectra (or end-members) that represent
spectrally pure elements. Therefore, this approach is difficult to apply at a regional scale
due to spectral variability of different soils and, especially, erosion features.
Spectral
unmixing allows erosion assessment if the regional soil types are known, as well as their
respective climax and degradation forms in combination with their spectral characteristics
(Haboudane et al., 2002). Finally, SAR systems can assess soil properties including surface
roughness, texture and to a lesser extent soil moisture. For more detail about SAR systems
readers should refer to Walker et al. (2004).
Topography
Nearly all regional erosion models require DEM input for the assessment of slope
characteristics. DEMs can be obtained from contour lines, stereo aerial photography, and
from satellite data, such as stereo optical imagery provided by SPOT and ASTER (Toutin
and Cheng, 2003) or SAR imagery (Toutin and Gray, 2000).
Vegetation attributes
Until now, most remote sensing studies deal with the measurement of inputs for erosion
models, especially for vegetation cover.
The reason is remote sensing data have the
advantage to account for seasonal vegetation dynamics. Land use classification is often
used to map vegetation types to assign USLE C-values reported in literature (Vrieling,
2006). Land use classification has been performed with either optical satellite systems
through visual interpretation of image composites (e.g. Khan et al., 2001), or automated
classification approaches. Classification accuracy can be further improved by direct linear
regression between image bands or ratios and C-values determined in the field (Wang et al.,
2002).
A specific class of spectral band ratios is known as vegetation indices. Vegetation indices
often exploit the fact that green vegetation has high reflectance in the NIR and low
16
reflectance in the red part of the spectrum (Vrieling, 2006). The Normalized Difference
Vegetation Index (NDVI) is the most common index which has been used directly as an
indication of the protective cover of vegetation (Thiam, 2003) or was related to vegetation
cover with regression analysis (Symeonakis and Drake, 2004). The NDVI is sometimes
inaccurate due to the effect of soil reflectance and the sensitivity to the vitality of the
vegetation. To account for soil reflectance, several soil adjusted vegetation indices (e.g.
Transformed Soil Adjusted Vegetation Index; TSAVI) have been developed. Although soil
adjusted indices perform better for the assessment of low vegetation covers than NDVI
(Flügel et al., 2003; Hochschild et al., 2003), they have difficulty in accounting for spatially
variable soil types.
One of the most important vegetation vitality effects occur during
vegetation senescence when vegetation indices usually decrease even when the cover
remains the same. Since senescent vegetation offers the same protection to the soil as
vigorous vegetation, it is important to also detect dry vegetation or crop residues (French et
al., 2000).
Conservation practices
Few studies assessed conservation practices with satellite remote sensing, mainly due to
the coarse resolution. Tillage practice is the most frequently detected conservation practice
by satellite remote sensing because it affects the surface roughness and amount of crop
residues (Vrieling, 2006).
For tillage detection, remote sensing techniques include
classification and visual interpretation (DeGloria et al., 1986), and logistic regression
techniques (Van Deventer et al., 1997).
Although the authors mentioned above used
different band ratios, both included TM band 5 (shortwave infrared; SWIR), which has been
related to crop residue.
Image timing is very important since tillage operations are
performed during a specific time of the year. Cloud cover sometimes restricts acquisitions of
optical imagery at the time of tillage, especially in temperate regions (Vrieling, 2006).
Therefore, SAR data has been used to assess tillage because the radar return is dependent
on surface roughness. McNairn et al. (1998) stress that multi-temporal SAR imagery is
required for effective class separation. Moran et al. (2002) stress that integration of SAR
and optical imagery provides more information on tillage than separate analysis of both data
sources.
17
3.5 Data integration for erosion modelling
From the above discussion it is apparent that remote sensing data assist erosion mapping
through direct erosion detection or through the use of erosion controlling factors. Erosion
models integrate erosion controlling factors, whereas satellite imagery has the potential to
provide regional spatial data for several input parameters of erosion models. Until recently,
however, most published studies merely use optical satellite data to assess the vegetation
component (Vrieling, 2006). The only model which was developed with the intention to be
used with satellite data is the Soil Erosion Model for Mediterranean regions, SEMMED (De
Jong et al., 1999). It is based on the Morgan et al. (1984) model, but modifications were
made to enable the input from satellite imagery to assess the rainfall interception of
vegetation at different moments.
King et al. (2005) state that the nature of models is
changing, with a strong development of physical models which explicitly incorporate spatial
generalization.
4. Review of latest spatial inventories/datasets on sheet-rill
and gully erosion at a national scale
Due to the increasing threat to land and water resources of SA by soil erosion, several
spatial inventories have been compiled. Two most recent studies are listed below.
4.1 Gully erosion mapping
The most recent spatial inventory includes a gully erosion map for SA created by visual
interpretation and vectorization from SPOT 5 satellite imagery (Le Roux et al., 2010;
Mararakanye and Le Roux, 2011).
SPOT 5 satellite imagery was utilized because the
panchromatic sharpened images at 2.5 m resolution provide high resolution air photo-like
quality for gully mapping (Taruvinga, 2008) and were acquired from government agencies
for the whole of South Africa. As a result, the study successfully mapped gully erosion
features and established the spatial extent of the problem at a national level (displayed as
gully erosion maps in a GIS; see results in Section 5.1). The study resolved to map gully
erosion for the whole country by means of manual vectorization at a scale of 1:10 000.
Although the technique is time-consuming, automated mapping techniques could not
express individual gullies with the required accuracy due to their spectral complexity over
18
such a large area. The proportion of the provinces and each of their local municipalities that
is affected by gully erosion was determined by means of zonal functions in the Spatial
Analyst extension of ArcGIS 9.3.
The next recommended step is to utilize the gully maps to estimate the degree of erosion in
conjunction with collateral classification techniques and the quantification of the influence of
factors in gully development. This can mainly be achieved by quantifying the influence of
different gully contributing factors by correlating existing gully maps with other available
spatial datasets in a GIS (e.g. Geology, Land Types, DEMs, Land Cover etc.). Quantifying
the influence of factors in gully development will facilitate the selection of suitable
alternatives for preventing or reducing soil loss, especially at municipal and provincial levels.
It is worth mentioning here that sheet and rill erosion features could not be interpreted from
SPOT 5 imagery with the required accuracy. The reason is that it is not possible to discern
where the boundaries of such eroded areas exactly are. Sheet and rill eroded areas are
usually ambiguous, especially as observed from SPOT 5 imagery (changing gradually from
eroded to bare to vegetated areas). It is postulated that modelling approaches are required
in order to express/map areas subjected to sheet and rill erosion at a regional scale.
4.2 Water erosion modelling (emphasizing sheet-rill erosion)
Prior to above-mentioned study, a simplification of the Universal Soil Loss Equation (USLE)
was applied to the whole country (Le Roux et al., 2008a). The model primarily estimates
rainfall erosion and combines sufficient simplicity for application on a national scale with
incorporation of the main factors causing soil erosion; emphasizing the sheet and rill aspects
of the erosion cycle.
Indicators of erosion susceptibility of the physical environment,
including climate erosivity, soil erodibility and topography were improved by feeding current
available data into advanced algorithms.
Actual soil-erosion risk, which relates to the
current risk of erosion under contemporary vegetation and land use conditions, was
accounted for by regression equations between vegetation cover and MODIS-derived
spectral index. Subsequently, a significant update was provided on previous assessments
of erosion by inclusion of improved or new national datasets which were not available until
recently.
19
5. The extent of sheet-rill and gully erosion in South Africa
Results from the studies mentioned above confirm that erosion is a major soil degradation
problem in SA, confronting both land and water resource management throughout the
country. The results support the general perception that most highly degraded areas have
combinations of sheet, rill and gully erosion.
5.1 Extent of gully erosion
The results of Le Roux et al. (2010) and Mararakanye and Le Roux (2011) show that all
provinces are affected by gully erosion (see Figures 1 and 2). The Northern Cape (160 885
ha) and Eastern Cape (151 759 ha) are the most severely affected, followed by KwaZuluNatal (87 522 ha), Free State (64 674 ha), Limpopo (58 669 ha), Western Cape (25 403 ha),
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
G
au
te
ng
No
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Area affected by gully erosion (ha)
Mpumalanga (17 420 ha), North West (10 782 ha) and Gauteng (110 ha).
Figure 1: Surface area (ha) of provinces affected by gully erosion
20
Figure 2: Gully location map of South Africa
21
5.2 Extent of sheet-rill erosion
The USLE-based results (emphasizing sheet-rill erosion) of Le Roux et al. (2008a) illustrate
that areas with high erosion risk occur mostly in the eastern parts of SA (see Figures 3 and
4). Areas are classified as having a moderate to high erosion risk when the average annual
soil loss rate exceeds 12 t/ha.yr (Bergsma et al., 1996). In this context, the Eastern Cape (6
188 581 km2) is the most severely affected province, followed by the Free State (5 503 875
km2), Northern Cape (5 407 138 km2), Limpopo (4 422 688 km2), Mpumalanga (2 578 363
km2), Kwazulu Natal (2 138 038 km2), Western Cape (2 065 319 km2), North West (1 485
263 km2), and Gauteng (775 688 km2) provinces.
In quantitative terms, the average
predicted soil loss rate for SA is 12.6 t/ha.yr. It should be stressed that these results give a
broad overview of the general pattern of the relative differences, rather than providing
accurate absolute erosion rates. It is also noteworthy that differences between sediment
yield and soil loss can be very high (Garland et al., 2000). Research findings of Scott and
Schulze (1991) suggest that soil loss within a catchment can be up to five times greater than
sediment yield due to the reduction of the total eroded volume by deposition within the
catchment.
Consequently, a soil-erosion figure of 12.6 t/ha.yr could correspond with a
6,000,000
5,000,000
4,000,000
3,000,000
2,000,000
1,000,000
au
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at
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0
G
Area where mean soil loss>12 t/ha.yr (sq km)
sediment yield of 2.5 t/ha.yr.
Figure 3: Surface area (km2) of provinces where the mean soil loss exceeds 12 t/ha.yr (moderate to high)
22
Figure 4: Water erosion prediction map of South Africa (emphasizing sheet-rill erosion)
23
Compared to Australia, the average predicted soil loss rate for SA is three times as much as
that estimated (4.1 t/ha.yr) by Lu et al. (2003). SA has a higher soil loss rate than Australia
presumably due to extensive cultivation and overgrazing. A total of 62% of the country is
currently under commercial and subsistence farming (National Land Cover, 2000). The
areas predicted to be greatly affected by soil loss when compared to the National Land
Cover appear to be the degraded unimproved grasslands.
Unimproved grasslands are
associated with subsistence agriculture where overgrazing of livestock has been excessive.
This problem is exacerbated by the inherent erodibility of the parent materials in SA that
give rise to erodible soils (Laker, 2004). In terms of management intervention it is especially
important to highlight areas that are intrinsically susceptible to erosion (before being
extrinsically triggered or accelerated by land use and human-induced reduction of the
vegetation cover).
6. Assessment of susceptible areas
The assessment of areas that are susceptible to erosion is important in policy terms
because it indicates those areas which are inherently susceptible to erosion (potential risk),
but which are presently protected by vegetation.
For example, due to limited financial
resources it will not be feasible to rehabilitate large gullies with large and expensive
structures at a catchment scale.
In addition to rehabilitating existing gullies, currently
vegetated gully-free areas susceptible to gully development should be identified and
protected/sustainably managed. Le Roux and Sumner (2011) selected a specific catchment
located in the Eastern Cape Province for this purpose (coded as tertiary catchment 35 by
the Department of Water Affairs).
6.1 Assessment of areas susceptible to gully erosion by means of GIS
and remote sensing: Case study in the Eastern Cape
The catchment lies between 30º 46' 58'' and 31º 28' 55'' south and 27º 55' 56'' and 29º 13'
47'' east in the Eastern Cape Province of South Africa, north of Mthatha. The catchment
was chosen for its high erosion risk on high potential agricultural land (Le Roux et al.,
2008a; b). Soils from the Tarkastad and Molteno Formations in the central part of the
catchment are associated with duplex soils (Land Type Survey Staff, 1972-2008) that are
24
highly erodible with widespread gully erosion evident (Le Roux et al. 2008b). Gully erosion
mapping was based on analysis of SPOT 5 imagery from various acquisition dates in 2008.
SPOT 5 satellite imagery was utilized because the panchromatic sharpened images at 2.5
m resolution provides high resolution air photo-like quality for gully mapping (Taruvinga,
2008) and was acquired from government agencies for the whole of SA (as mentioned
above in Section 4.1). The study resolved to map gully erosion for the whole catchment by
means of manual vectorization at a scale of 1:10 000. Although the technique is timeconsuming, automated mapping techniques could not express individual gullies with the
required accuracy due to their spectral complexity over such a large area. Subsequently, 4
253 gullies were mapped in the catchment, in total affecting an area of approximately 5 273
ha (1.1% of the catchment) (see Figure 5).
Figure 5: Gully locations map of the catchment in the Eastern Cape Province
In order to determine the influence of factors in gully development, the study correlated the
above-mentioned gully map with other spatial datasets in a GIS including DEM-derived
topographical variables (i.e. slope, contributing area, a topographical wetness index, terrain
units), parent material-soils interactions (i.e. geology maps, land types, soil erodibility maps)
25
and remotely sensed cover management (i.e. land use and vegetation cover). Since not all
gully factors can be taken into account at a regional scale, the study considered
incorporation of the most important factors for which regional data already existed, or that
could be readily derived by means of remote sensing techniques or in a GIS for the whole
catchment. Descriptions of the gully contributing factors, methods of derivation and data
sources are detailed in Le Roux and Sumner (2011).
Each gully factor layer was
categorized into 5 expert-based rankings or classes that, according to observations,
uniquely influence gully development. Due to the spatially thematic configuration of the gully
factor layers it was decided to determine the proportion that each of the 5 classes are
affected by gully erosion (by means of zonal functions in the Spatial Analyst extension of
ArcGIS 9.3). The study of Le Roux and Sumner (2011) confirms that factors leading to the
development of gullies in the catchment are consistent with other studies. Factors leading to
the development of gullies are gentle slopes in zones of saturation along drainage paths
with a large contributing area, erodible duplex soils derived from mudstones, and poor
vegetation cover due to overgrazing. When integrated with drainage networks, gullies
expand from valley floors and footslopes onto concave midslopes where the soils are deep.
A combination of overgrazing and susceptible mudstones proves to be key factors that
consistently determine the development of gullies in the catchment.
After quantifying the influence of factors in gully development, the identification of vegetated
gully-free areas susceptible to gully development was achieved by means of overlay
analysis. Figure 6 illustrates areas that are intrinsically susceptible to gully erosion, yet are
vegetated and gully-free (estimated at approximately 7 260 ha).
Appropriate strategies
need to be designed for these susceptible areas in order to protect the current vegetation
cover. This approach proved to be relatively simple, realistic and practical, and it can be
applied or expanded to other areas of SA at a regional scale; thereby providing a tool to help
with the implementation of plans for soil conservation and sustainable management (Kheir
et al., 2007).
26
Figure 6: Areas susceptible to gully erosion in the catchment, Eastern Cape Province
7. Conclusion and recommendations
In this report, a brief description of soil erosion impacts and cost implications is followed by a
review of mechanisms leading to sheet-rill and gully development including rainfall erosivity,
topographical variables, parent material-soil associations and land use-cover interactions.
The ability to map erosion features based on satellite imagery is illustrated similar to Vrieling
(2006), addressing mainly erosion detection of eroded areas and features, followed by the
assessment of off-site impacts and erosion controlling factors, and finally data integration for
erosion modelling. The report further describes two most recent spatial inventories, namely
a gully location map of South Africa created by visual interpretation and vectorization from
SPOT 5 satellite imagery, and a water erosion prediction map of South Africa created by
simplification of the Universal Soil Loss Equation (emphasizing sheet-rill erosion). Results
from the latter mentioned spatial inventories confirm that erosion is a major soil degradation
problem in South Africa.
Finally, the report emphasizes the importance of spatially
27
modelling areas that are currently erosion free, but under threat and in need of area-specific
management, e.g. protection of the current vegetation cover. The assessment of areas that
are susceptible to erosion is important in policy terms because it indicates those areas
which are inherently susceptible to erosion (potential risk), but which are presently protected
by vegetation. The catchment scale study of Le Roux and Sumner (2011) is described for
this purpose. After quantifying the influence of factors in gully development (topographical
variables, parent material-soils interactions and remotely sensed cover management), the
study identified vegetated gully-free areas susceptible to gully development (estimated at
approximately 7 260 ha) by means of overlay analysis.
It is recommended that the above-mentioned remote sensing/modelling approach be applied
or expanded to other areas of South Africa at a regional scale. With a catchment area of 15
245 km2, the Welbedacht Dam catchment (near Dewetsdorp in the Free State) will constitute
a good case study area. Due to siltation, the storage capacity of the Welbedacht Dam near
Dewetsdorp in the Free State reduced rapidly from the original 115 million m3 to
approximately 16 million m3 within twenty years since completion in 1973 (DWA, 2011).
Therefore, the causal factors need to be assessed by means of remote sensing and
modelling techniques similar to the study of Le Roux and Sumner (2011). This will allow
identification of areas within the Welbedacht Dam catchment and other similar areas that
are intrinsically susceptible to erosion (before being extrinsically triggered or accelerated by
land use and human-induced reduction of the vegetation cover). Further refinement will be
possible given additional research, including investigation of the effect of land use history
and vegetation conditions prior to erosion development, using a combination of different
optical and multi-temporal data, assessment of sediment yields and distinction between
active and passive erosion features. These tools will assist the implementation of a decision
support or risk-management system for soil conservation and sustainable management (of
problem soils as a geohazard).
28
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