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Landsat-based approaches for mapping of land degradation prevalence
and soil functional properties in Ethiopia
Tor-G. Vågen a,, Leigh A. Winowiecki b, Assefa Abegaz c, Kiros M.
Hadgu d
a World Agroforestry Centre (ICRAF), P.O. Box 30677, 00100 GPO,
Nairobi, Kenya b International Center for Tropical Agriculture
(CIAT), P.O. Box 823-00621, Nairobi, Kenya c Department of
Geography and Environmental Studies, Addis Ababa University, P.O.
Box 1176, Addis Ababa, Ethiopia d World Agroforestry Centre
(ICRAF), P.O.Box 2658, Addis Ababa, Ethiopia
a b s t r a c ta r t i c l e i n f o
Article history: Received 14 November 2012 Received in revised form
7 March 2013 Accepted 7 March 2013 Available online xxxx
Keywords: Soil organic carbon pH Soil erosion Land degradation
Agriculture Mapping Landsat Ethiopia
Agriculture is the basis of the Ethiopian economy, accounting for
the majority of its employment and export earnings. Land
degradation is, however, widespread and improved targeting of land
management interven- tions is needed, taking into account the
variability of soil properties that affect agricultural
productivity and land degradation risk across landscapes. In the
current study we demonstrate the utility of Landsat ETM + imagery
for landscape-level assessments of land degradation risk and soil
condition through a com- bination of systematic field
methodologies, infrared (IR) spectroscopy and ensemble modeling
techniques. The approaches presented allow for the development of
maps at spatial scales that are appropriate for mak- ing spatially
explicit management recommendations. Field data and soil samples
collected from 38 sites, each 100 km2, were used to develop
predictive models that were applied as part of a case study to an
independent dataset from four sites in Ethiopia. The predictions
based on Landsat reflectance were robust, with R-squared values of
0.86 for pH and 0.79 for soil organic carbon (SOC), and were used
to create predicted surfaces (maps) for these soil properties.
Further, models were developed for the mapping of the occurrence of
soil erosion and root depth restrictions within 50 cm of the soil
surface (RDR50), with an accuracy of about 80% for both variables.
The maps generated from these models were used to assess the
spatial distribution of soil pH and SOC, which are important
indicators of soil condition, and land degradation risk factors in
order to target relevant management options.
© 2013 Elsevier Inc. All rights reserved.
1. Introduction
Since agriculture remains the basis of the Ethiopian economy
up-to-date information on soil functional properties is needed for
un- derstanding soil constraints in order to improve and maintain
land productivity. In its country strategy paper (2011–2015) for
Ethiopia, the African Development Bank (ADB) reports that the
agricultural sector accounts for 42% of GDP, 80% of employment and
85% of its ex- port earnings, with a mainly smallholder dominated
structure. In order to achieve the formulated goals for
agricultural development of Ethiopia, establishment of sustainable
agricultural strategies is a pre-requisite, for which development
of appropriate soil resource in- formation and management systems
is one of the tools.
Despite their economic importance, agricultural and rangeland sys-
tems in Ethiopia are subject to continuous and widespread
disequilibri- um dynamics (Coppock, 1993) through severe land
degradation due to its topography, population growth and adverse
agricultural and range- land land-use practices over centuries
(Berry, 2003; Bishaw, 2001; Desta & Coppock, 2004; Hurni, 1988,
1993; Taddese, 2001). Ethiopia's
geological history and diverse parent materials, coupled with its
com- plex topography, are reflected in its diversity of soil types.
However, na- tional soil mapswere developed between 30 and 40 years
ago at a scale of 1:2 M (FAO, 1998a) and are hence both outdated
and have a coarse spatial resolution. Traditional soil maps,
generally based on classifica- tion of soils into taxonomic units,
are potentially useful for understand- ing the occurrence of soil
types across landscapes and their properties may determine the
constraint envelopes for dynamic soil properties (Norfleet et al.,
2003). However, such maps are static (use-invariant) and do not
capture soil degradation prevalence or risk, nor do they re- flect
the dynamic nature of soil functional properties. There is
therefore a need for soil information that reflects use-dependent
or dynamic soil properties at landscape scale and land use/cover
characteristics that are sensitive tomanagement. This information
can be used to better inform land management decision-making, as
well as for modeling of soil loss, hydrology or crop suitability.
Given the increasing pressure on the soil resource base, it has
become critical to develop maps of such dynamic soil properties at
scales appropriate for targeted soil management, also in
smallholder systems.
While many studies have assessed land degradation, and specifi-
cally soil erosion, in Ethiopia (Daba, 2003; Descheemaeker et al.,
2006; Grepperud, 1996; Hurni, 1993; Nyssen et al., 2008;
Sonneveld
Remote Sensing of Environment 134 (2013) 266–275
Corresponding author. Tel.: +254 724332508; fax: +254 20 7224001.
E-mail address: t.vagen@cgiar.org (T.-G. Vågen).
0034-4257/$ – see front matter © 2013 Elsevier Inc. All rights
reserved. http://dx.doi.org/10.1016/j.rse.2013.03.006
Contents lists available at SciVerse ScienceDirect
Remote Sensing of Environment
j ourna l homepage: www.e lsev ie r .com/ locate / rse
Author's personal copy
& Keyzer, 2003) spatially explicit approaches at
fine-resolution scales that incorporate diverse climatic
conditions, dynamic soil properties and topography are still needed
to better assess land health and target soil conservation
activities. Many of the assessments of soil erosion in Ethiopia
apply the Universal Soil Loss Equation (USLE) (Wischmeier &
Smith, 1962) or some form of revision of the USLE, such as the
MUSLE (Smith et al., 1984) or the RUSLE (Renard & Ferreira,
1993). While the RUSLE incorporates mean annual rainfall, soil
data, land cover, slope length and steepness, as well as land man-
agement information to estimate soil loss, we present an
alternative method that uses systematically collected field data to
train models for the prediction of soil erosion prevalence at 30 m
resolution, using Landsat reflectance data as predictors.
We use the following definition of land health: “The capacity of
land to sustain delivery of essential ecosystem services” (Vågen et
al., 2012), and propose an approach that uses dynamic soil
properties, instead of soil classes, and indicators of soil
degradation to model and map soil and land health. For example,
mapping the distribution of soil erosion prevalence across
landscapes is important to better target land management practices
that reduce degradation risk or rehabili- tate already degraded
areas, and can be coupled with additional infor- mation about
landscape attributes and socioeconomic factors that may contribute
to increasing degradation prevalence (Daba, 2003; Dragan et al.,
2003; Piot et al., 1995). Sensitivity to soil erosion, or soil
erosion risk, is a commonly used indicator in agricultural man-
agement. Similarly, root depth restrictions may strongly inhibit
plant growth, particularly in cropping systems, and also influence
the hydrological characteristics of the soil, including
infiltration and water holding capacities. Land management
practices that expose soil, deform soil via traffic and/or
cultivation or grazing increase soil erodibility, while in grazing
areas, over-stocking may lead to soil compaction, reducing
infiltration capacity and increasing runoff due to collapse of
macro-pores.
Studies of soil and ecosystem health often rely heavily on chemical
analysis of soil samples (Foley et al., 1998). Soil analysis using
tradi- tional wet chemistry is generally costly, and since studies
are often unable to afford all the required analyses, the result is
less than ideal sampling schemes and poor levels of replication.
Soil infrared spectroscopy can reduce these constraints by
providing quick, non-destructive and quantitative analyses of an
enormous range of organic and other constituents of soil material,
including for the dis- crimination between complex mixtures, and is
now an established technology (Jong, 1994; McCarty et al., 2002;
Osborne, 1986). This technology has been used for the
characterization of soils for more than two decades (Ben-Dor &
Banin, 1995; Chang et al., 2001), and has been shown to have many
applications for approaches to the de- velopment of soil condition
indices as well (Vågen et al., 2006). In the last decade in
particular, there has been a strong increase in the use of IR
spectroscopy for soil and plant characterization, and the method is
now in routine use (Odlare et al., 2005; Shepherd & Walsh,
2002; Terhoeven-Urselmans et al., 2010; Zornoza et al.,
2006).
An important component for rigorously assessing land health is a
systematic sampling design that includes sufficient sampling
densities and replication across landscapes at different spatial
scales. The current studywas undertaken to explore the development
of predictionmodels for moderate to high resolution (≤30 m)mapping
of soil condition, fo- cusing on soil organic carbon (SOC) and soil
pH. Both of these are con- sidered important indicators of soil
condition (Baldock et al., 2009). In addition, the prediction and
mapping of land degradation risk factors such as erosion prevalence
and root depth restrictions were explored. Field measurements from
38 sites, which were surveyed using system- atic field sampling
procedures referred to as the Land Degradation Sur- veillance
Framework (LDSF) (Vågen et al., 2012) as part of several projects,
were used to develop models for prediction of soil condition and
land health indicators. Earlier studies suggest that models based
on satellite imagery have significant potential in predicting
soil
condition and land degradation processes (Hill & Schütt, 2000;
Monastersky, 1989; Pinet et al., 2006; Vågen et al., 2006). Also,
several studies have applied Landsat data for predicting soil
taxonomic units and drainage classes (Alaily & Pohlmann, 1995;
Kilic, 2009; Su et al., 1989), while others have applied image
reflectance data in the predic- tion of soil salinity
(Melendez-Pastor et al., 2012; Panah & Pouyafar, 2005;
Shrestha, 2006). In the current study, a combination of ap-
proaches using remote sensing and soil IR spectroscopy was applied
to a case study of four study sites in Ethiopia with the aim to
explore methods for development of cost-effective mapping
techniques that can be applied across larger areas (i.e.
landscapes). We propose to build on these studies and develop an
assessment of land degradation and soil health for diverse
environments in Ethiopia at scales appropri- ate for
management.
2. Study sites
Four sites were used in this case study from Ethiopia, each
randomly locatedwithin four different LandsatWRS2 path/row tiles,
representing three different Köppen–Geiger climate zones. The
climate zones includ- edwere tropicalwet savanna (Dambidolo), humid
subtropical highland climate (Kutaber) and warm semi-arid savanna
(Mega andMerar) (cli- mate classification after Kottek et al.,
2006) (Fig. 1). Existing data on dominant soil types indicate that
Mega is dominated by Cambisols and Leptosols; Merar by Cambisols,
Leptosols and Vertisols; Kutaber by Leptosols and Dambidolo by
Phaeozems (FAO, 1998b).
A cluster sampling design (Thompson, 1991)was used by first divid-
ing each site into 16 tiles (2.5 × 2.5 km in size), then generating
one random centroid location per tile, and finally generating 15
random sampling plots, each 1000 m2, within a 564 m radius of each
cluster centroid. Five of these plots were used as alternate plots
and hence 10 were characterized and sampled in each cluster. Thus,
the data for each site consisted of 160 stratified-random sampling
plots with an area of 1000 m2 each. Within each individual plot,
four sub-plots were established, each with an area of 100 m2, one
in the center and three on a radial arm with 120° angles between
them. This form of stratified cluster sampling allows the
assessment of variability of soil properties as different spatial
scales (in our case: subplot, plot, cluster, site, be- tween sites)
by applyingmodels incorporating random effects that rep- resent
different groups, including spatial nested scales (Pfeffermann
& Nathan, 1981; Raudenbush & Bryk, 2002). Assessing and
understanding spatial variability is essential for developing
accurate predictive models and understanding landscape
dynamics.
3. Materials and methods
The LDSF approach to mapping of soil properties and land degrada-
tion prevalence makes use of (i) systematic and unbiased field
surveys to collect soil information and other ecological
parameters, (ii) laboratory analyses using wet chemistry and soil
IR spectroscopy, and (iii) remote sensing information. The aim of
this study was to explore the potential of applying analytical
frameworks that utilize this information as well as statistical
approaches to develop predictive models and create maps of various
indicators of land health that may be used to developmanage- ment
recommendations.
3.1. Field data collection
Field survey data collection in Ethiopia was conducted between
February and June 2011. The field team was led by the staff from
Mekelle and Addis Ababa Universities, with assistance from the
Inter- national Center for Tropical Agriculture (CIAT) drivers, all
of whom were trained and supervised by CIAT and the World
Agroforestry Centre (ICRAF) scientists. Local extension agents and
farmers from each Woreda were also involved and assisted in the
field surveys. The surveys included soil sampling, vegetation
measurements, root
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depth restriction and soil erosion prevalence scores, which were
conducted at plot and subplot levels. Soil samples were collected
at two vertical depths (0–20 and 20–50 cm) at each subplot (n = 4)
using a soil auger. Subplot soil samples were subsequently combined
to form one composite sample for 0–20 cm depth and one composite
sample for 20–50 cm depth per plot, making a total of 320 soil sam-
ples per site. Prevalence and typology of soil erosion (sheet, rill
and gully) were recorded at each subplot, while root depth
restrictions were recorded by measuring the depth (in cm) from the
soil surface to the auger depth in cases where the auger could not
penetrate the soil. In the current study we assessed the presence
of root depth re- strictions in the upper 50 cm of the soil profile
(RDR50) within each plot, by scoring these occurrences from 0
(none) to 4 (in cases where all subplots had restrictions).
Vegetation was also scored at the subplot level, rating woody and
herbaceous cover as: absent; b4; 4–15; 15–40; 40–65 and >65%.
Tree and shrub counts were conducted for density estimates with
shrubs classified as woody plants between 1.5 and 3 m in height,
while woody vegetation taller than 3 mwas classified as trees. In
addition, ecosystem impact factors such as agriculture, grazing,
fire and tree cutting were scored for each plot, based on methods
adapted from Moat and Smith (2007).
Field data from 38 sites, surveyed in the period 2009 to 2012 using
the LDSFmethodology (Vågen et al., 2010; Vågen et al., 2012),were
col- lected as part of several projects, but using the same
consistentmethod- ology. The countries surveyed were Ethiopia (4
sites), Kenya (3 sites),
Tanzania (7 sites), Malawi (2 sites), Mozambique (6 sites),
Democratic Republic of Congo (2 sites), Ghana (2 sites), Burkina
Faso (2 sites), South Africa (1 site), Nigeria (3 sites), Zambia (4
sites) and Zimbabwe (2 sites). These data were used to train and
validate predictive models for SOC, pH, soil erosion prevalence and
occurrence of RDR50 based on a total of 3378 topsoil samples for
SOC and pH and observations from 6237 plots for soil erosion and
RDR50. All field data were collected in near real time using an
electronic data entry systemdeveloped by the first author, building
on the CyberTracker platform (Liebenberg, 2003) for handheld PDAs,
with duplicate entries on paper forms as backup.
3.2. Soil laboratory analysis
All soil samples were air-dried, crushed using a wooden rolling pin
and sieved using a two millimeter mesh certified sieve. Samples
scanned on the HTS-XT Fourier transform mid-infrared (FT-MIR)
spectrometer (Bruker Optics) were further ground to 0.05 μm before
analysis. All soil samples were characterized using MIR
spectroscopy following procedures outlined in Terhoeven-Urselmans
et al. (2010) at ICRAF in Nairobi, Kenya. Soil pH, base cations and
other soil fertility characteristics were analyzed on 10% of the
samples using a 1:1 H2O solution at the Crop Nutrition Laboratory
in Nairobi, Kenya. On the same 10% of the samples, soil texture was
analyzed using laser dif- fraction and soil organic carbon (SOC)
was analyzed using flash
Fig. 1.Map of Ethiopia showing annual average precipitation (MAP)
and the locations of the sites included in this study. The map was
derived with data fromWorldClim version 1.4 (Hijmans et al.,
2005).
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266–275
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dynamic combustion using a Thermo Scientific CN analyzer at ICRAF,
after acidifying the samples with HCl at 80 °C.
3.3. Remote sensing data analysis
In the current study, we used Landsat ETM + imagery from May
(Merar) and November (Kutaber, Dambidolo and Mega) 2009 to map land
degradation risk factors and soil functional properties at
landscape scale. Prior to model development, satellite digital num-
bers (DNs) were converted to ground reflectance through radiomet-
ric calibrations and corrections of the imagery, including for
sensor gains and offsets, solar irradiance and solar zenith angles.
A set of semi-automated scripts developed by Gumbricht (2011) were
used to generate an archive of standardized surface reflectance
(SRF) im- ages following methods developed and adapted from Chavez
(1996).
3.4. Model development
3.4.1. Soil IR spectral data pre-processing and calibration A soil
MIR spectral library consisting of 3378 samples was used,
which included 764 reference soil samples that had analytical data
on SOC and pH using the soil analytical methods described above.
The cal- ibration models (see Terhoeven-Urselmans et al., 2010)
developed based on the training dataset were applied to the full
library (N = 3,378) and then related to satellite image reflectance
(Section 3.4.2). Prior to the development of calibration models,
MIR spectra were z-score normalized and linear feature selection
(decorrelation) was ap- plied for data reduction using a linear
correlation filter (Yu & Liu, 2003) to retain only the
information-rich parts of the spectra.
3.4.2. Statistical procedures and generation of maps A range of
statistical analytical methods and machine learning
techniques are available for multivariate calibration, multilevel
modeling of ecological variables, and clustering and classification
of soil IR spectral libraries and remote sensing imagery. In recent
years, ensemble methods have been introduced to increase prediction
accuracy in cases where base learners are weak, such as in
classifica- tion trees. In these approaches, variable selection
bias can be elimi- nated through the use of split selection
criteria (Strobl et al., 1993). In the current study random forest
(RF) models (Breiman, 2001; Svetnik et al., 2003) and gradient
boosting techniques (Friedman, 1999) were applied for
classification of land degradation risk factors and for prediction
of soil pH and SOC, using Landsat ETM + image
reflectance bands as predictors. These model classes have become
in- creasingly important for ecological applications (Cutler et
al., 2007; Prasad et al., 2006) and have been applied for mapping
of soil carbon based on terrain attributes (Grimm et al., 2008),
although their appli- cation is relatively rare in soil science. In
a random forest model, a dif- ferent bootstrap sample from the
original data is used during the construction of each tree,
generating a test set classification for each case in about
one-third of the trees. At the end of the run, take j to be the
class that got most of the votes every time case n was out-of-bag.
The proportion of times that j is not equal to the true class of n
averaged over all cases is the out-of-bag error estimate. This has
been proven to be unbiased in many tests. We assessed var- iable
importance for each predictor by calculating the total decrease in
node impurities from splitting on the variable, averaged over all
trees (Genuer et al., 2010).
Independent training and test data were generated by randomly
selecting 64% of the original data for model training and using the
remaining independent random subset of 36% for validation. We also
assessed the influence of the predictors included in the models.
This is a particularly useful aspect of random forest models in
remote sensing of ecological variables to better understand the
relative influ- ence of the predictors. These variable importance
measures have also been shown to be useful for model reduction
(e.g. to build simpler models) (Genuer et al., 2010).
For the modeling of presence/absence of soil erosion and RDR50 we
calculated the sensitivity, specificity, Kappa and the area under
the ROC (receiver operating characteristic) curve (or AUC) for each
model (Fielding & Bell, 1977). Sensitivity measures the
proportion of actual positives that are correctly identified.
Specificity is the in- verse and measures the proportion of
correctly predicted true nega- tives. The Kappa statistic is the
proportion of correctly classified locations after accounting for
the probability of chance agreement.
The spatial distribution of soil pH and SOC concentrations in the
top 20 cm of the soil profile were mapped for the study areas and
are presented as continuous surfaces. The prediction performance of
the RF models for SOC and pH were tested by comparing predicted
versus measured values when applying the training model to the in-
dependently generated test data set. To assess the uncertainty of
maps generated for pH and SOC, we calculated the coefficient of
var- iation (CV) between each regression model (tree) in the
ensemble for each image pixel (Sexton & Laake, 2009). Predicted
soil pH values were also classified into classes: acid (3–4.5);
moderately acid (4.5– 6.5); neutral (6.5–7.5) and alkaline
(7.5–9).
Table 1 Summary of ecological site characteristics for the sites
included in this study.
Selected ecological site characteristics Mega Dambidolo Kutaber
Merar
Cultivation (%) 0 75 54 40 Average tree density (trees ha−1) 30 64
159 4 Average shrub density (shrubs ha−1) 88 48 216 140 Average
slope (°) 1.5 7.4 18 2.2 Average herbaceous cover rating (%) 15–40
15–40 4–15 4–15 Presence of soil conservation measures (%)
0 0 50 0
Table 2 Summary statistics of selected basic topsoil properties for
the 10% of soil samples selected for wet chemistry analyses (n =
64). Values are medians by depth, while the values in the brackets
are lower and upper quartiles, respectively.
Site SOC pH Sand Clay Ca Mg K Na
cmolc kg−1
Dambidolo (n = 32) 36.5 (20.8/51.5) 6.4 (6.0/6.5) 13 (8/21) 57
(45/69) 14.8 (9.9/26.3) 4.7 (3.3/6.8) 0.7 (0.5/1.0) 0.2 (0.2/0.2)
Kutaber (n = 30) 18.7 (16.5/24.1) 6.5 (6.3/6.8) 14 (12/16) 56
(50/61) 30.6 (27.4/36.4) 11.0 (10.2/14.2) 0.5 (0.3/1.2) 0.2
(0.2/0.3) Mega (n = 32) 20.0 (15.5/21.2) 8.2 (8.0/8.2) 10 (8/19) 73
(63/77) 40.9 (27.5/51.5) 10.0 (8.5/11.8) 2.2 (1.2/2.6) 0.2
(0.2/0.5) Merar (n = 32) 25.2 (20.6/29.0) 8.1 (8.1/8.2) 9
(5.5/17.1) 68 (51/76) 79.0 (61.9/97.7) 4.5 (3.6/5.4) 1.1 (0.9/1.4)
0.4 (0.3/0.5)
Table 3 Summary of random forest model classification performance
for erosion and RDR50 prevalence overall, including ranking of the
importance of input Landsat ETM + bands.
AUC Sensitivity Specificity Kappa Predictor rankinga
Erosion 0.81 0.72 0.71 0.42 SLOPE,RFRED,PVI,RFGREEN,
RFMID1,RFNIR,RFMID2
RDR50 0.83 0.75 0.74 0.49 RFBLUE,RFMID1,PVI,RFMID2,
RFGREEN,RFRED,RFNIR
a RF = calibrated ground reflectance, PVI = perpendicular
vegetation index (Rich- ardson & Wiegand, 1977).
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4. Results and discussion
4.1. Ecological site characteristics
Reference soil analysis indicates that Dambidolo has the highest
median SOC and lowest pH of the four sites, while Mega and Merar
have the highest median clay contents and also the highest pH
values (Table 2). Merar has extremely high exchangeable calcium
(Ca), as expected given the presence of Vertisols in this site,
while Kutaber has similar SOC to Mega and similar pH to Dambidolo
(Table 2). Land uses and land cover were highly variable between
the study sites, ranging from intensively cultivated areas to
uncultivated savan- na systems. For example, in Dambidolo about 75%
(7500 ha) of the area is under cultivation, while Mega is a
traditional pastoralist sa- vanna, as summarized in Table 1.
Herbaceous cover ratings were rel- atively low in all sites,
ranging between 4–15% (in Kutaber and
Merar) and 15–40% (in Dambidolo and Mega). Average tree (woody
vegetation taller than 3 m) densities varied from 4 trees ha−1 in
Merar to 159 trees ha−1 in Kutaber, with predominantly Eucalyptus
spp. in the latter site. Shrub densities also showed a high level
of var- iability between sites (Table 1).
4.2. Predicting and mapping soil erosion and root depth
restrictions
Soil erosion prevalence (based on field survey data) was high in
all sites, except Merar, which is mainly on a plateau with low
annual rainfall. The most influential predictors for soil erosion
were slope, red reflectance and the perpendicular vegetation index
(PVI —
Richardson & Wiegand, 1977), while blue and mid-infrared bands
were most influential for predicting RDR50, as shown in Table 3. An
AUC of about 0.8 was achieved for predictions of presence/absence
of both soil erosion and RDR50. An AUC of 1 would indicate a
perfect
Fig. 2. Maps of soil erosion (left column) and RDR50 prevalence
(right column), expressed as probability estimates for each pixel
(from top to bottom: Merar, Kutaber, Dambidolo, Mega). The points
(dots) on the maps are sampling locations, while the coordinates
along the side of the maps are decimal degrees (WGS84).
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classification result, while 0.5 would mean that the model perfor-
mance is no better than random.
As is evident from the maps (Fig. 2), soil erosion prevalence was
highest in Dambidolo, followed by Mega, while within-site variation
was highest in Merar. The Merar site was characterized by
distinctly different landforms and soils, with part of the site
located on a plateau of predominantly Vertisols (southern and
eastern section). This part of the site showed low prevalence of
soil erosion. Merar also had the lowest occurrence of soil erosion
on average, but there were high- ly eroded areas in the west and
northwest of the site, on landforms consisting of secondary
limestone (Badel & Mishra, 2007) (Fig. 2). The latter areas
were predominantly used as rangelands. In Kutaber, soil erosion was
widespread, particularly on steep slopes, but in gen- eral not as
severe as for example parts of Dambidolo or Mega. Kutaber has steep
slopes and shallow soils (Table 1) and root depth restric- tions
are prevalent throughout the site. In Mega, root depth restric-
tions occurred mainly on hill slopes and at the base of hills,
while the flatter plains (savannas) did not show much evidence of
restric- tions. However, erosion was relatively prevalent also in
much of the savanna systems, even throughout the flatter parts of
the site (Fig. 2), probably due to overgrazing and exposure of
soils. Soils in Dambidolo are relatively deep and root
depth-restrictions were therefore not very prevalent in this site,
despite the high incidence of soil erosion. The two upper panels in
Fig. 6 show the distributions of erosion and RDR50 prevalence,
respectively, based on the pixel values for the predicted surfaces
in Fig. 2. These distributions clearly show the differences between
sites and the intensity of soil erosion in Dambidolo and Mega is
evident, while the much flatter distribution curve for Merar shows
the low erosion prevalence on the plateau (peak around 30%).
Kutaber has an almost normal distribution curve for soil erosion,
with intensities (prevalence rates) around 60% (Fig. 6). The
distribution curve for root depth restrictions is sim- ilar to that
of erosion in Mega, although much flatter, while Dambidolo has a
distinctively different distribution for root depth re- strictions
than soil erosion (Fig. 6). These findings have implications for
management strategies in the different sites, as discussed
later.
4.3. Predicting and mapping SOC and pH
Comparisons of measured versus predicted pH and SOC using Landsat
reflectance yielded R-squared values of 0.86 and 0.79 for pH and
SOC, respectively, for the independent validation data (Figs. 3 and
4). These results indicate that we can predict SOC and pH well from
Landsat ETM + reflectance libraries. Prediction performance for SOC
was similar to that reported by Vågen et al. (2012) using high
resolution satellite imagery as well as Jarmer et al. (2010), and
also confirms the potential of ensemble approaches for the
prediction of SOC using remote sensing data reported by Spencer et
al. (2006). In Fig. 5 we show the results of fitting the above
model to a set of Landsat ETM + images from 2009, covering the four
sites included in the study. These images are part of a
continent-wide archive of im- agery currently stored at ICRAF for
which these models are being ex- plored for mapping of soil
degradation prevalence and soil functional properties across the
African continent.
In Merar, predicted SOC values were highest in Vertisol areas, and
pH was neutral to alkaline (Fig. 5). The distribution curve for
predict- ed SOC in Merar (lower panels in Fig. 6) shows a peak
around 22 g kg−1, which represents these Vertisol areas, while the
average for this site was around 20 g kg−1 (vertical line). A
smaller peak in the distribution is found at about 5 g kg−1,
representing highly erod- ed shallow soils. These results are
comparable to the results of other studies in this region (e.g.
Badel & Mishra, 2007). Most of the cultivat- ed area in Merar
was within the Vertisol areas of the upland plateau, with wheat and
barley cultivation. The savanna grassland Mega site and the steep
highland site at Kutaber had lower SOC concentrations than Merar on
average, with means of about 17 and 18 g kg−1,
respectively. Mega was the only site with significant occurrences
of alkaline soils according to our analysis, mainly occurring in
depres- sions, most likely due to poor drainage. The areas with
higher pH also had higher SOC in this site, while the relationship
between SOC and pH was the opposite in Dambidolo (Fig. 5). On
average, SOC con- centrations were highest in Dambidolo (Fig. 6),
but with a gradient of decreasing SOC from west to east (Fig. 5).
Dambidolo was also the most humid site and had the lowest predicted
pH (Fig. 6), but with high potential SOC storage both due to the
humid climate and to deep soils (i.e. little occurrence of RDR50).
A large proportion of the Dambidolo site was converted to
cultivation (from forest and grass- lands) during the last 7 years,
which may be part of the explanation for the relatively high SOC
contents observed in this site. Predicted SOC concentrations may be
classified into ranges for identifying prior- ity areas for
recommendation of appropriate management interven- tions. However,
thresholds for SOC are widely debated and we therefore do not show
results of these explorations here.
Fig. 3. Predicted versus measured SOC concentrations (g kg−1) for
the independent validation dataset with Landsat reflectance data
for the sites included in this study (n = 473).
Fig. 4. Predicted versus measured pH for the independent validation
dataset with Landsat reflectance data for the sites included in the
study (n = 473).
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4.4. Implications for land management
While acknowledging that sustainable land management is highly
complex in that it has dimensions beyond the ecological variables
presented in this paper, including social, economical and political
as- pects (Bouma, 2002), we give some examples of how assessments
and mapping of soil condition and land degradation risk can be
useful for targeting land management interventions. Through the
development of maps at moderate to high resolution, land
degradation hotspots and areas with soil constraints such as low
SOC, alkaline or acid soils can be identified at scales relevant to
individual farmers and Woreda extension agents (Figs. 2 and 5).
Using such maps, spatially explicit management recommendations can
be made to target for example soil fertility management or soil and
water conservation measures.
In Dambidolo our analysis shows higher prevalence of soil erosion
in the southeastern and eastern parts of the site, as well as 35%
lower
SOC concentrations in eroded versus non-eroded plots. These
findings not only highlight the need for management options that
reduce soil erosion but also manage soil carbon. These particular
areas are still predominantly grasslands and interventions need to
be appropriate for optimizing stocking densities to reduce
overgrazing and compac- tion. They are also likely to be vulnerable
to severe land degradation if converted to agriculture.
Approximately 75% of Dambidolo was culti- vated at the time of the
field survey (Table 1), with conversion to ag- riculture having
taken place largely since 2004, at least in part as a result of
resettlement schemes that began in the 1990s (Woube, 1995). Also,
soil and water conservation measures were largely ab- sent in this
site. Soil conservation measures and possibly conservation
agricultural strategies that incorporate crop residues and increase
or- ganic matter status and stabilize soil carbon could be
beneficial inter- ventions in cultivated areas of Dambidolo. In
addition, 53% of the sampled plots were impacted strongly by fire,
which is a commonly
Fig. 5.Maps of predicted topsoil (0–20 cm) pH (left column) and SOC
(right column) for each study site (from top to bottom: Merar,
Kutaber, Dambidolo, Mega). The points (dots) on the maps are
sampling locations, while the coordinates along the side of the
maps are decimal degrees (WGS84).
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used by farmers in the Gambela region to stimulate regrowth both in
grasslands and cropping systems (Woube, 1998).
Vertisol areas in Merar generally have higher SOC concentrations
and pH than the rest of the site, as expected given the inherent
proper- ties of this soil type, and as reflected also in the maps
in Fig. 5. These areas have low soil erosion prevalence, but our
analysis indicates some presence of root depth restrictions (soil
physical constraints). Amelio- rating soil physical degradation can
be difficult if caused by long-term plowing, which is likely to be
the case in the wheat-producing area of Merar. In contrast to
Dambidolo, Merar is in a low rainfall region and fire was not a
prominent landmanagement practice according to the in- formation
collected at the time of the survey of this site.
Other studies from areas near Kutaber, show that this region has
been under intensive farming for decades (Tekle & Hedlund,
2000), if not centuries. The major soil constraints found in this
site were physical due to steep slopes and shallow soils, and
overall the site has moderately high soil erosion prevalence and
high prevalence of root depth restric- tions (Fig. 2). Soil and
water conservation structures in Kutaber need to be optimized to
reduce erosion and increase soil depth as depth restrictions are
likely to be amajor constraint to agricultural production with
continued soil erosion. Also, management options that
increase
vegetation cover need to be considered both as part of soil
conservation measures and to improve organic matter status.
Mega has significant areas with high erosion and the results show
that root depth restrictions are extensive. This region is
predominantly pastoral, with 100% of the plots surveyed under
grazing. Hence, the management of livestock in this area should
focus on reducing over- grazing and compaction. The results of the
current study show that while tree and shrub densities are
important ecological site descriptors, sites such as Mega do not
have lower erosion prevalence in areas with higher tree densities,
on average. When herbaceous cover was taken into consideration, the
results suggested a reduction in erosion preva- lence of between 25
and 30% for areas with 40% herbaceous cover or more. Hence,
proposed rehabilitation strategies in grasslands may focus on
restoration of herbaceous cover, including perennial grasses, and
the implementation of vegetative and/or structural soil conserva-
tion measures, particularly on steep slopes.
5. Conclusions
There is significant potential in the approaches applied in this
study, and particularly in the combination of systematic field
surveys
Fig. 6. Summary of map predictions (all pixels within each site)
for land degradation risk factors and soil functional properties.
The density plots show distributions of predicted values, with
means superimposed as vertical lines. The mosaic plot for pH shows
predicted values categorized into classes based on acidity levels
(orange = moderately acid, green = neutral and blue = alkaline) by
site. (For interpretation of the references to color in this figure
legend, the reader is referred to the web version of this
article.)
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266–275
Author's personal copy
with remote sensing data. Since the methods used were applied con-
sistently across several projects, we were able to develop models
based on a wide range of soil and environmental conditions and
apply these for site-specific prediction of land degradation risk
factors and indices of soil functional properties to four sites in
Ethiopia. The results indicate that the ensemble modeling
approaches used are able to explain most of the variability in SOC
and pH as well as the oc- currence of soil erosion and root depth
restrictions, when applied to an independent validation dataset.
Further, the models developed can be applied for mapping of these
indicators of soil and land health to generate spatially explicit
recommendations for improved land management in these landscapes.
The results of the analysis presented in this paper shows that soil
erosion is highly prevalent in both agricultural and pastoral land
use systems, but with high levels of spatial variability. This
variability calls for modeling and mapping approaches that are
spatially resolved enough for manage- ment interventions to be
targeted appropriately.
Acknowledgments
This research was conducted with support from the Bill and Melinda
Gates Foundation (BMGF), Wajibu MS (Kenya sites) and Ghent
University (DRC sites). We would like to thank the field team,
which sampled the Ethiopia sites: Biadglign DemissieMulawu (Mekelle
University), Tesema Bekele Silewondim (Addis Ababa University),
Venance Kengwa (CIAT), Chris Ekise (CIAT) and Zelalem Hadush
(Mekelle University). We would also like to thank the individual
Woredas and farmers for allowing us access to con- duct the field
surveys. We would like to thank the following authorities: In
Dambidolo: the agriculture office of Oromiya Region, the
agriculture and administrative offices of Kelem Wollega Zone, and
the agriculture and administrative offices of Hawa Gelan District.
In Kutaber: the zonal agricultural offices in Hayke and Dese. In
Merar: Tulli-Guled district of Somali Regional state and Chinaksen
district of Eastern Harerge Zone of Oromia Regional State and
Jijiga University. In Mega: the Yabello zonal administrative and
agriculture office.
References
Alaily, F., & Pohlmann, J. (1995). Soil mapping of Bir-Tarfawi
region (SW-Egypt) based on digital classification of
Landsat-MSS-data. In H. P. Blume, & S. M. Berkowicz (Eds.),
Arid ecosystems. Catena; advances in geoecology, 28. (pp.
89–107).
Badel, M., & Mishra, B. (2007). Characterization of some soils
of Jijiga plain, Ethiopia. Journal of Food, Agriculture and
Environment, 5, 416–424.
Baldock, J., Grundy, M., Griffin, E., Webb, M., Wong, M., &
Broos, K. (2009). Building a foundation for soil condition
assessment. CSIRO land and water science report.
Ben-Dor, E., & Banin, A. (1995). Near infrared analysis (NIRA)
as a rapid method to simultaneously evaluate several soil
properties. Soil Science Society of America Journal, 59,
364–372.
Berry, L. (2003). Land degradation in Ethiopia: Its extent and
impact. Washington D.C: The World Bank.
Bishaw, B. (2001). Deforestation and land degradation in the
Ethiopian highlands: A strategy for physical recovery. Northeast
African Studies, 8, 7–25.
Bouma, J. (2002). Land quality indicators of sustainable land
management across scales. Agriculture, Ecosystems and Environment,
88, 129–136.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
Chang, C., Laird, D. A., Mausbach, M. J., & Hurburgh, C. R.
(2001). Near-Infrared Reflec-
tance Spectroscopy—Principal components regression analyses of soil
properties. Soil Science Society of America Journal, 65,
480–490.
Chavez, P. S. (1996). Image-based atmospheric corrections —
Revisited and improved. Photogrammetric Engineering and Remote
Sensing, 62, 1025–1036.
Coppock, D. L. (1993). Vegetation and pastoral dynamics in the
southern Ethiopian rangelands. Implications for theory and
management. In R. Behnke, I. Scoones, & C. Kerven (Eds.), Range
ecology at disequilibrium: New models of natural variability and
pastoral adaptation in African savannas. Proceedings of a meeting
held 19–21 November, 1990. (pp. 42–61). Woburn, United Kingdom: The
Commonwealth Sec- retariat and the Overseas Development
Institute.
Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K.
T., Gibson, J., et al. (2007). Random forests for classification in
ecology. Ecology, 88, 2783–2792.
Daba, S. (2003). An investigation of the physical and socioeconomic
determinants of soil erosion in the Hararghe Highlands, Eastern
Ethiopia. Land Degradation and De- velopment, 81, 69–81.
Descheemaeker, K., Nyssen, J., Poesen, J., Raes, D., Haile, M.,
Muys, B., et al. (2006). Run- off on slopes with restoring
vegetation: A case study from the Tigray highlands, Ethiopia.
Journal of Hydrology, 331, 219–241.
Desta, S., & Coppock, D. L. (2004). Pastoralism under pressure:
Tracking system change in Southern Ethiopia. Human Ecology, 32,
465–486.
Dragan, M., Feoli, E., Fernetti, M., & Zerihun, W. (2003).
Application of a spatial decision support system (SDSS) to reduce
soil erosion in northern Ethiopia. Environmental Modelling &
Software, 18, 861–868.
FAO (1998a). Digital soil map of the world and derived soil
properties (CD-ROM). Land and water digital media series no
1.
FAO (1998b). The soil and terrain database for northeastern Africa
(CD-ROM). Land and water digital media series no 2.
Fielding, A. H., & Bell, J. F. (1977). A review of methods for
the assessment of prediction errors in conservation
presence/absencemodels. Environmental Conservation, 24,
38–49.
Foley, W. J., McIlwee, A., Lawler, I., Aragones, L., Woolnough, A.
P., & Berding, N. (1998). Ecological applications of near
infrared reflectance spectroscopy — a tool for rapid,
cost-effective prediction of the composition of plant and animal
tissues and aspects of animal performance. Oecologia, 116,
293–305.
Friedman, J. H. (1999). Stochastic gradient boosting. Computational
Statistics & Data Analysis, 38, 367–378.
Genuer, R., Poggi, J., & Tuleau-malot, C. (2010). Variable
selection using random forests. Pattern Recognition Letters, 31,
2225–2236.
Grepperud, S. (1996). Population pressure and land degradation: The
case of Ethiopia. Journal of Environmental Economics and
Management, 30, 18–33.
Grimm, R., Behrens, T., Märker, M., & Elsenbeer, H. (2008).
Soil organic carbon concen- trations and stocks on Barro Colorado
Island — Digital soil mapping using Random Forests analysis.
Geoderma, 146, 102–113.
Gumbricht, T. (2011). Automated multispectral satellite reflectance
calibration using SML. Hijmans, R., Cameron, S., Parra, J., Jones,
P., & Jarvis, A. (2005). WorldClim, version 1.3.
edu/worldclim/worldclim.htm Hill, J., & Schütt, B. (2000).
Mapping complex patterns of erosion and stability in dry
Mediterranean ecosystems. Remote Sensing of Environment, 74,
557–569. Hurni, H. (1988). Degradation and conservation of the
resources in the Ethiopian high-
lands. Mountain Research and Development, 8, 123–130. Hurni, H.
(1993). Land degradation, famine, and land resource scenarios in
Ethiopia. In D.
Pimentel (Ed.), World soil erosion and conservation (pp. 27–61). :
CAB International. Jarmer, T., Hill, J., Lavee, H., & Sarah, P.
(2010). Mapping topsoil organic carbon in
non-agricultural semi-arid and arid ecosystems of Israel.
Photogrammetric Engi- neering and Remote Sensing, 75, 85–94.
Jong, S. de (1994). Applications of reflective remote sensing for
land degradation studies in a Mediterranean environment. Amsterdam
and Utrech: Koninklijk Nederlands Aardrijkskundig
Genootschap.
Kilic, S. (2009). Mapping soil drainage classes of Amik Plain using
Landsat images. African Journal of Agricultural Research, 4,
847–851.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F.
(2006). World map of the Köppen– Geiger climate classification
updated. Meteorologische Zeitschrift, 15, 259–263.
Liebenberg, L. (2003). A new environmental monitoring methodology.
wwwcybertrackercozaMethodologyhtml
McCarty, G. W., Reeves, J. B., III, Reeves, V. B., Follett, R. F.,
& Kinble, J. M. (2002). Mid-infrared and near-infrared diffuse
reflectance spectroscopy for soil carbon measurement. Soil Science
Society of America Journal, 66, 640–646.
Melendez-Pastor, I., Hernández, E. I., Navarro-Pedreño, J., &
Gómez, I. (2012). Mapping soil salinization of agricultural coastal
areas in Southeast Spain. In B. Escalante (Ed.), Remote sensing
applications (pp. 117–140). : InTech.
Moat, J., & Smith, P. P. (2007). CEPF Madagascar vegetation
mapping [WWW document].
http://www.kew.org/science-research-data/directory/projects/CEPFMadaVegMapping.
htm
Monastersky, R. (1989). Spotting erosion from space. Science News,
136, 61. Norfleet, M. L., Ditzler, C. A., Puckett, W. E., Grossman,
R. B., & Shaw, J. N. (2003). Soil
quality and its relationship to pedology. Soil Science, 168,
149–155. Nyssen, J., Poesen, J., Moeyersons, J., Haile, M., &
Deckers, J. (2008). Dynamics of soil
erosion rates and controlling factors in the Northern Ethiopian
Highlands —
towards a sediment budget. Earth, 711, 695–711. Odlare, M.,
Svensson, K., & Pell, M. (2005). Near infrared reflectance
spectroscopy for
assessment of spatial soil variation in an agricultural field.
Geoderma, 126, 193–202.
Osborne, B. (1986). Near infrared spectroscopy in food analysis.
Encyclopedia of analyt- ical chemistry (pp. 14).
Panah, S. K. A., & Pouyafar, A. M. (2005). Potentials and
constraints of soil salinity studies in two different conditions of
Iran using Landsat TM data. 31st International Symposium on Remote
Sensing of Environment.
Pfeffermann, D., & Nathan, G. (1981). Regression analysis of
data from a cluster sample. Journal of the American Statistical
Association, 76, 681–689.
Pinet, P., Kaufmann, C., & Hill, J. (2006). Imaging
spectroscopy of changing Earth's surface: A major step toward the
quantitative monitoring of land degradation and desertification.
Comptes Rendus Geoscience, 338, 1042–1048.
Piot, Y., Blaikie, P. M., Jackson, C., & Palmer-Jones, R.
(1995). Rethinking research on land degradation in developing
countries. World Bank discussion papers.
Prasad, A. M., Iverson, L. R., & Liaw, A. (2006). Newer
classification and regression tree techniques: Bagging and random
forests for ecological prediction. Ecosystems, 9, 181–199.
Raudenbush, S., & Bryk, A. S. (2002). Hierarchical linear
models: Applications and data anal- ysis methods. Advanced
Quantitative Techniques in the Social Sciences Series 1, XXIV,
485.
Renard, K. G., & Ferreira, V. A. (1993). RUSLE model
description and database sensitiv- ity. Journal of Environmental
Quality, 22, 458–466.
274 T.-G. Vågen et al. / Remote Sensing of Environment 134 (2013)
266–275
Author's personal copy
Richardson, A., & Wiegand, C. (1977). Distinguishing vegetation
from soil background information (by gray mapping of Landsat MSS
data). Photogrammetric Engineering and Remote Sensing, 43,
1541–1552.
Sexton, J., & Laake, P. (2009). Standard errors for bagged and
random forest estimators. Computational Statistics & Data
Analysis, 53, 801–811.
Shepherd, K. D., & Walsh, M. G. (2002). Development of
reflectance libraries for char- acterization of soil properties.
Soil Science Society of America Journal, 66, 988–998.
Shrestha, R. P. (2006). An investigation on the relation between
the remote sensing data and soil salinity. Asian Association on
Remote Sensing 27th Asian Conference on Remote Sensing ACRS (pp.
83–88).
Smith, S. J., Williams, J. R., Menzel, R. G., & Coleman, G. A.
(1984). Prediction of sedi- ment yield from southern plains
grasslands with the modified universal soil loss equation. Journal
of Range Management, 37, 295–297.
Sonneveld, B. G. J. S., & Keyzer, M. A. (2003). Land under
pressure: soil conservation con- cerns and opportunities for
Ethiopia. Land Degradation & Development, 14, 5–23.
Spencer, M. J., Whitfort, T., McCullagh, J., & Bui, E. (2006).
Dynamic ensemble approach for estimating organic carbon using
computational intelligence. ACST06 Proceed- ings of the 2nd IASTED
International Conference on Advances in Computer Science and
Technology (pp. 186–192). : ACTA Press.
Strobl, C., Zeileis, A., Boulesteix, A. L., & Hothorn, T.
(1993). Variable selection bias in classification trees and
ensemble methods. Book of abstracts (pp. 159). : Citeseer.
Su, H., Ransom, M. D., & Kanemasu, E. T. (1989). Detecting soil
information on a native prairie Using Landsat TM and spot satellite
data. Soil Science Society of America Jour- nal, 53,
1479–1483.
Svetnik, V., Liaw, A., Tong, C., Culberson, J. C., Sheridan, R. P.,
& Feuston, B. P. (2003). Random forest: A classification and
regression tool for compound classification and QSAR modeling.
Journal of Chemical Information and Computer Science, 43,
1947–1958.
Taddese, G. (2001). Land degradation: A challenge to Ethiopia.
Environmental Management, 27, 815–824.
Tekle, K., & Hedlund, L. (2000). Land cover changes between
1958 and 1986 in Kalu district, southern Wello, Ethiopia. Mountain
Research and Development, 20, 42–51.
Terhoeven-Urselmans, T., Vagen, T. -G., Spaargaren, O., &
Shepherd, K. D. (2010). Prediction of soil fertility properties
from a globally distributed soil mid-infrared spectral library.
Soil Science Society of America Journal, 74, 1–8.
Thompson, S. K. (1991). Adaptive cluster sampling: Designs with
primary and second- ary units. Biometrics, 47, 1103.
Vågen, T. -G., Davey, F., & Shepherd, K. D. (2012). Land health
surveillance: Mapping soil carbon in Kenyan rangelands. In P. K. R.
Nair, & D. Garrity (Eds.), Agroforestry —
The future of global land use. Springer. Vågen, T. -G., Shepherd,
K. D., & Walsh, M. G. (2006). Sensing landscape level change
in
soil fertility following deforestation and conversion in the
highlands of Madagascar using Vis–NIR spectroscopy. Geoderma, 133,
281–294.
Vågen, T. -G., Shepherd, K. D., Walsh, M. G., Winowiecki, L. A.,
Tamene Desta, L., & Tondoh, J. E. (2010). AfSIS technical
specifications — Soil health surveillance, Africa. Nairobi, Kenya:
CIAT (the AfSIS project).
Wischmeier, W. H., & Smith, D. D. (1962). Soil-loss estimation
as a tool in soil and water management planning. International
Association of Scientific Hydrology Publi- cation, 59,
148–159.
Woube, M. (1995). Ethnobotany and the economic role of selected
plant species in Gambela, Ethiopia. Journal of Ethiopian Studies,
28, 69–86.
Woube, M. (1998). Effect of fire on plant communities and soils in
the humid tropical savannah of Gambela, Ethiopia. Land Degradation
Development, 9, 275–282.
Yu, L., & Liu, H. (2003). Feature selection for
high-dimensional data: A fast correlation- based filter solution.
Twentieth International Conference on Machine Learning (ICML-2003),
8 (Washington, DC).
Zornoza, R., Guerrero, C., Mataix, J., Morales, J., Mayoral, M.,
& Mart, M. (2006). The use of near infrared spectroscopy (NIR)
for soil quality and soil degradation assess- ment. Geophysical
research abstracts (pp. 05033). : European Geoscience Union.