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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
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& 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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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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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.
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