Assessment of dryland condition using spatial

International Journal of Remote .Sensing Vol. 26, No. 18, 20 September 2005, 4045 4065

Assessment of dryland condition using spatial anomalies of vegetation index values M. M. BOER* and J. P U I G D E F A B R E G A S Estacion Experimental de Zonas Aridas, Consejo Superior de Investigaciones Cientifieas, General Segura 1, E-Q4001 Almeria, Spain {Received 13 May 2003: in final form 21 October 2004) A landscape's long-term capacity to retain, utilize and recycle local resources is an objective basis for assessing its ecological functionality or condition. In subtropical and tropical drylands, where plant growth is moisture-iimited for mueh of the time, land condition is retlected in the local water balance. The ratio of long-term actual evapotranspiration and precipitation {EJP) is proposed as an objective indicator of dryland condition, A spatial modelling framework is developed for the quantification of E.JP over large areas using remotely sensed vegetation density patterns. Model parameters arc defined by two particular situations: (i) non-vegetated sites, where EJP depends on the long-term runofi' coefficient of bare soil surfaees {RCbJ, and (ii) non-degraded sites with a vegetation density close to the potential value for which EjP= 1.0. Specht's evaporative coenicient is used as an independent variable for the prediL'tion of the potential vegetation density, whereas RChs is estimated with the curve number method. The performanee of the method was evaluated in a 900 km" area in south-east Spain, where predicted land condition was found to be in good agreement with qualitative field observations on the nature and intensity of land degradation proecsses,

1.

Introduction

Global assessments (Batjes 1996, Bridges and Oldeman 1999) show that land degradation (Barrow 1991) is widespread in the arid and semiarid zones of the world. Spatially distributed information on land condition at regional scales, which is crucial for sustainable land managetnent. is lacking in many of those areas. Due to the mere size of the management units and/or poor accessibility of the land, remote sensing frequently is the only practical means of data collection for regional assessments of land condition. Land condition refers to the ecological functionality or integrity of the land and cannot be directly measured on the ground or on remotely sensed imagery. Assessment methods must make use of indicators that, when measured, quantify the ecosystem's capacity to perform basic ecological functions (Mouat ct al. 1992). Primary production is one of those key functions. In dryland environments, primary production is moisture-limited during mueh of the time {Noy-Meir 1973, Eischer and Turner 1978). Nutrients, especially nitrogen, may limit primary production *Corresponding author. Current address: School of Plant Biology, The University of Western Australia. 35 Stirling Highway, Crawley 6009 WA, Australia. Email: mboer@ plants,uwa.edu,au International Journal of Remote Sensing ISSN 0143-1161 priiit/tSSN 1366-5901' online < 2005 Taylor & Francis liltp://www. tandr.cti.uk/journals DOI; 10.1080/01431160512331338014

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M. M. Boer and J. Puigdefdbregas

during periods of rapid growth under prolonged conditiotis of high soil moisture content (Crawford and Gosz 1982), but such conditions are generally infrequent under (semi)arid climates. It therefore makes sense to express dryland condition in terms of the ecosystem's capacity to retain and utilize local water resources {Ludwig et al. 1997). This concept of ecological functionality, which is extensively used in Australian drylands (Tongway 1994), has so far found little application in remote sensing-based assessments. Ludwig ct al. (2002) used videography images of high spatial resolution (i.e. 0.2-1.0 m) to map the fine-scale organization of vegetated patches and clearings, and to calculate so-called ieakiness indices' that assess the site's capacity to retain water- or windborne resources (e.g. run-on, sediment, nutrients, seeds). Their approach requires imagery of a spatial resolution that is compatible with prevailing patch sizes and resource redistribution distances (Aguiar and Sala 1999). In many dryland environments, vegetation patches are too small and water redistribution distances are too short (e.g. Noy-Meir 1981, Bergkamp 1996. Puigdefabregas et al. 1999) to meet this requirement with common satellite imagery having spatial resolutions of several decimetres to kilometres. The general moisture limitation of plant growth in arid and semiarid environments allows the water resources retention capacity of the landscape also to be quantified indirectly by measuring (changes in) vegetation density in relation to precipitation (e.g. Pickup and Chewings 1994, Bastin ct al. 1996, Prince ct al. 1998). By focusing on vegetation density, as the result of resource redistribution, rather than on spatial vegetation patterns as an indicator for the landscape's resistance to water or windborne flows across the landscape, the requirements for the spatial resolution of the imagery can be relaxed, opening up possibilities for assessing the resource retention capacity from Landsat Thematic Mapper (TM) data and other common satellite imagery. A requisite for such an approach to work is the capacity to separate the effects of short-term variability in (climatic) driving forces and spatial heterogeneity in terrain, soil, and other land attributes from long-term changes that affect essential landscape functions (Ludwig and Tongway 1992, Mouat et al. 1992). One way of minimizing confusion about the cause of an observed response at a particular site is by comparing the actually observed vegetation response with the potential response corresponding to an ecologically defined and spatially distributed reference condition (Stoms and Flargrove 2000, Boer and Puigdefabregas 2003). In this paper we present a framework for the quantification of the water resources retention capacity from remotely sensed vegetation indices, and for its use as an indicator of dryland condition. Our approach only considers the canopy density or cover fraction of the vegetation and ignores changes in the composition that are associated with some forms of land degradation (e.g. shrub encroachment in rangelands. Archer et al. 1988). In the first part of the paper we discuss the rationale for our approach and outline the cartographic modelling procedures to run the method on widely available environmental data. In the second part of the paper we report on a regional case study in which the method was used to map dryland degradation status in a 900 km" area in south-east Spain. 2. 2.1

Approach Rationale

In moisture-limited environments the capacity to retain and utilize local water resources is the key property of landscape systems that determines its primary

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production potential and other aspects of ecological integrity (e.g. biodiversity, Ludwig et al. 1999). Under the assumption that local precipitation (P) is the principal water input to a site, the partitioning of/* into evapotranspiration (£.,) and drainage (/)) components can be considered as a basic expression of the water resources retention capacity, and hence as an objective indicator of dryland condition. The fraction EJP represents the water resources that are potentially available for primary production, whereas the water that drains from the site through percolation beyond the root zone or lateral (sub)surface flow, DIP. is essentially lost for local plant production. The partitioning of P into £,, and D depends on the spatial and temporal scale considered. D can be a substantial fraction of P at the plot scale (e.g. 1-lOOm"). will normally be a much smaller fraction at the hillslope scale (e.g. lOOm^ lOha), and is often a negligible fraction of P at the basin scale (e.g. >IOha) (Bergkamp 1996, Puigdelabrcgas et al. 1999). A similar decrease of DIP can be observed with decreasing temporal resolution. Our assessment method focuses on the hillslope and landscape scales, in agreement with the context of the conventional landscape analysis and ground-based observations in which the resource retention concepts were developed (Ludwig et al. 1997), and aims at quantifying EJP over long timescales (i.e. years decades) to ensure the detection of changes in land condition rather than short-term variation. At such long timescales the water balance of a site can be simplified to; P-E,^D

=O

(1)

as changes in the soil water store are normally negligible. Methods for the spatial interpolation of precipitation records arc relatively well established (e.g. Hutchinson 1995, Thornton el al. 1997), but current methods for the spatially distributed estimation of long-term E^ and D still have many limitations. Remotely sensed surface infrared temperatures have been used, together with local micrometeorological measurements, to estimate instantaneous £;, rates over large areas. including dryland environments (e.g. Kustas et al. 1994). The high temporal resolution of the E-j estimates and the requirement to collect micro-meteorological measurements simultaneously with the acquisition of the image data make this approach unpractical for the estimation of long-term £.,. Distributed estimates of long-term drainage are even more difficult to obtain. In areas of impermeable bedrock, stream discharge or hillslope runoff records may provide accurate estimates of D but such measurements would normally not be an option for dryland condition assessments due to the high expenses involved and the erratic occurrence of runoff events. As an alternative approach for the assessment of the ratio EJP we propose to use spatial anomalies in spectral vegetation index values. 2.2 Assessment framework 2.2.1 Vegetation density-evapotranspiration. Actual evapotranspiration (E.J from a vegetated surface has three main components: transpiration (Ei) and evaporation of intercepted rainfall (£,) from the plant canopy, and evaporation from the soil surface (E^). For given climate conditions. long-term E, and E\ increase with the density of the vegetation canopy {i.e. leaf area index, LAI), whereas E^ decreases (e.g. Saugier and Katerji 1991, Daamen ct al. 1995, Domingo et al. 1998). When expressed as fractions ot" the local precipitation, P. these evaporative fluxes approximately vary with LAI as illustrated in figure 1.

4048

M. M. Boer and J.

Puigdefahregas

1.5 T

1.0 -

0.0

Figure 1. Long-term variation of evaporative lluxes with leaf area index. LAI. for hypothetical shnibland sites in the semiarid zone of south-east Spain. Evaporative lluxes are expressed as fractions of long-term precipitation, P. The ratio E.JP may exceed a value of 1.0 in areas reeeiving lateral water inputs (i.e. run-on). Canopy interception. Ey. and transpiration, £,, modelled after Neilson (1995) using parameter values for shrubland. Soil evaponition, E^. modelled after Daamen et al. (1995).

Over the relatively large range of LAI values shown in figure 1, there is a diminishing sensitivity of £j. £, and E^ for further increments of LAI as the canopy becomes more continuous and deeper causing the efficiency of the leaves in transpiring, shading or intercepting rainfall or radiation to decrease (Jones 1992). As also shown by Kergoat (1998), the sum of these three evaporative fluxes, E.JP, varies in a more linear way with LAI, due to the combination of opposite trends, and intercepts the >'-axis at a positive value smaller than 1.0 as a result of runoff losses from very sparsely vegetated land. We focus on rain-fed vegetation communities in dryland environments that are characterized by relatively open canopies, with LAI usually not exceeding 1.0m"m~~ {e.g. Myneni et al. 2002), and by relatively small drainage losses (Bergkamp 1996, Puigdefabregas et a!. 1999) that cause E.JP to be close to 1.0. For such conditions we assume a linear function for the long-term variation of EJP with LAI: for

(2)

where LAI is the long-term average leaf area index, £(> the long-term potential evapotranspiration. and a and b are fitted parameters. For land with no vegetation cover, LAI=O and: {3} where RChs is the long-term runoff coefficient of a bare soil surface. Here we implicitly assume that water losses via deep drainage can be neglected, which is reasonable given the low infiltration capacities that characterize bare soil surfaces (e.g. Cerda 1995). To estimate the value of parameter a we focus on a non-degraded state. Non-degraded sites can be defined as those having a maximum water resources retention capacity that allows long-term EJP to approach a value of 1.0, when rain-fed only, and to slightly exceed 1.0 when receiving lateral water inputs

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(Domingo et al. 2001). In non-degraded sites the density of the vegetation is assumed to be at its local long-term potential (LAlpoi) and to have co-evolved with the hydrological properties of the soil to a situation that maximizes long-term £",, and minimizes long-term E^. E^ and D for that particular site. The main feedback mechanisms and process linkages that drive this ecological optimization process (Eagleson 1982) arc shown in figure 2. Our definition of a "non-degraded" state implies that LAIp,,i does not necessarily correspond to a dense vegetation cover or vice versa, and that the density of the vegetation cover can only provide information about the condition of the land when interpreted in the context of local climatic boundary conditions. For non-degraded sites where P is the only water input, we assume EJP^ 1.0 and [. so write for a:

Substituting the expressions for a and b, equation (2) becomes: E,,_

RChs X LAI

.^.

Under the assumption that multi-temporal observations of a spectral vegetation index can be used as an indicator for LAI (e.g. Sellers et al. 1994), and that the bare soil runoff coefficient and potential vegetation density can be estimated, equation (5) provides a simple framework for predicting EJP over large areas. 2.2.2 DeHning bare soil conditions. As mentioned, RCbs depends on the temporal and spatial scale considered, as well as on local terrain factors (e.g. bedrock type, slope form and length) and rainfall characteristics (e.g. storm frequency, intensity and duration). RCbs values in the range of 0.05-0.40 have been observed for small, nearly bare, catchments in the Tabernas badlands of south-east Spain (Canton et al. 2001). Collection of field observations of RCbs will normally not be an option in a regional assessment study, nor is there an existing method to extract information on RC},^ from remotely sensed imagery. To minimize effects of spatial variation of RCbs on the land condition assessment our approach consisted of three steps: (i) application of a single regional estimate for RC^s while computing EJP, followed by (ii) a stratification of the study area for Hthology and terrain types, and (iii) a standardization of EJP per Hthology and terrain type to reduce the impact of differences between the regional and local value of RCb, on the interpretation of EJ P in terms of land condition. A regional figure for RCbs t^im be obtained by running the curve number method (Soil Conservation Service 1986) for a bare soil surface on a series of daily rainfall data (e.g. Wilcox et al. 1990, Van Wesemael et al. 1998). 2.2.3 Prediction of potential vegetation density. In heterogeneous dryland environments spatial variation in climate driving forces, parent material and soil type may be substantial, causing the potential vegetation density (LAIp^i) and thereby parameter a (equation (4)) to vary as well. The process linkages that determine the potential vegetation density (figure 2) are relatively well understood and have been successfully modelled over large areas (e.g. Neilson 1995, Kergoat 1998). but application of a physically-based model for the prediction of LAlp^i would normally not be feasible in a land evaluation project. A more practical approach is to use a

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M. M. Boer and J. Puigdefabregas

Piant transpiration

(6) LAi

Soil energy budget

Soil evaporation

Piant transpiration

Soil water

(c) LAI

SOM, roots, etc.

Soii hydrol. props.

Piant transpiration

Soil water

Figure 2 Schematic representation of the main process linkages and feedback mechanisms involved in the optimization between vegetation density and site water balance, {a) Constraints on canopy growth through increasing water use and interception losses, {h) Short-term effects of canopy growth on the soil energy budget, soil evaporation losses and plant-available soil moisture, (r) Long-term effects of canopy growth on soil hydrological properties and plantavailable soil moisture. SOM, soil organic matter.

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simple model that accounts for the key processes and can be calibrated with satellite sensor data (e.g. Pickup 1995). We use a regression model for this purpose with Specht's (1972) evaporative coefficient as the independent variable representing long-term topoclimatic moisture conditions. Specht (1972), working in Australian drylands, observed that the monthly ratio of £y and Eu in full-grown communities of evergreen plant species varied as a linear function with soil water availability. W: ^-kxW

(6)

EQ

The slope ofthe function, k, corresponded to the £;, rate that allowed the vegetation to thoroughly use the local water resources without ever completely depleting the soil moisture store. The evaporative coefficient can also be calculated in an iterative manner from mean monthly precipitation, /*,. and potential evapotranspiration rates, Et),, by running a simple water balance model with increasing values for k until monthly actual evapotranspiration rates, E.^,, are maximized and yet the soil water store, S,, remains positive for all months t ofthe year (Specht 1972): E,,=kxW,xEo.,

(7)

where: W, = P, + S>-i S,^W,-E,,

(8) (9)

In ecophysiological terms, k can be interpreted as an indicator of the maximum long-term canopy conductance, g^, that allows an evergreen vegetation community to fully exploit local water resources and at the same time sustain positive transpiration rates throughout the year (Boer 1999). For a given vegetation type and climate the long-term value of ^^. is mainly a function ofthe surface area of green leaves per unit ground area (Jones 1992). Therefore, k can be expected to be an accurate predictor of LAlp^,, in dryland environments (Specht and Specht 1989). Boer and Puigdefabregas (2003) demonstrated the suitability of A: as a predictor of potential vegetation density in large areas of complex terrain. Using multi-tcmporul TM observations of the normalized difference vegetation index (NDVI) as an indicator for LAI, they obtained a simple function for the prediction of potential vegetation density (i.e. NDVIp^,,) by fitting curves through the upper and lower boundaries of the /r-NDVI data envelope. Further details on this procedure are given in the next section. 3. Case study As part of the MFDALUS project (Mediterranean Desertification and Land Use, see Brandt et al. 2002) we evaluated the practicality and performance of the proposed land condition assessment method in a 30x30km^ area of the Rio Guadalentin basin in south-east Spain (figure 3). 3.1 Research area In terms of environmental conditions, land use (change) and land degradation processes, the Rio Guadalentin basin (3300 km") is representative for large areas in

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M. M. Boer and J. Puigdejdhregas

583000

593000

Figure 3. Location and relief of the test area in the Rio Guadalentin basin in south-east Spain. Source: Boer (1999). Spain and other semiarid zones of the Mediterranean Region (e.g. Poesen and Hooke 1997. Conacher and Sala 1998). It is characterized by mountainous headwaters {ca 1500 2000m a.s.l.). and 'meseta'-Wkt plains in the upper sections, undulating landscape with long pediments and (incised) river terraces in the middle section and a wide and open, rift-type, valley in the lower section. Underlying bedrock types include (marly) limestones, dolomites, quartzites. gneisses, shales, greywackes, marls and phyllites. Footslopes and pediments often have a welldeveloped caliche layer. Soils tend to be thin, and stony, to have little horizon development, and to vary in a rather consistent pattem with topographic position (Boer ct al. 1996). The climate varies from semiarid to sub-humid Mediterranean, with altitude and exposition to rain-bearing easterly winds as the major source of spatial variation. Mean annual precipitation ranges from less than 300 mm in the lower valley to more than 500 mm in the highest mountain ranges. Rainfall tends to be most abundant in autumn and spring and least in summer but the main proportion of the annual total may fall any time of the year. Mean annual temperatures vary from 12'C in the mountains to I8'C in the lower areas. As a result, potential evapotranspiration rates may vary from less thati 500 to more than 1500mmyear"' (Satichez 1993, Sanchez and Noguera 1995). Droughts, centred on the summer season, commonly last for more than 4-5 months. The potential vegetation type for most of the area would be a typical Mediterranean-type shrubland {'nmtorral'). Remtiants of this vegetation still exist in the study area (Ruiz

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de la Torre 1990). Current land use consists of irrigated agriculture on terraces along the main stream courses, dryland farming (e.g. cereals, olives, almonds) on the more level terrain, and extensive grazing by sheep and goats, combined with forestry and nature conservation, in most of the uplands. Our assessment focuses on the nonagricultural land. 3.2

Materials and methods

As is commonly the case in dryland regions, spatially continuous environmental data are scarce in the study area. Our database (for details see Boer 1999) consisted of: • a digital elevation model (DEM) at 30 m spatial resolution; • monthly rainfall and temperature records for the period I960 1995 from ca 200 stations located within a radius of 100km from the study area: • a 2-year record (i.e, 1992-1993) of monthly temperature and pan 'A' evaporation measurements from 32 agro-meteorological stations from Murcia Province, partly including the Rio Guadalentin basin (Sanchez 1993. Sanchez and Noguera 1995); and • a set of six Landsat TM images for the period September 1993 to August 1994. The DEM was constructed from digital contours at 20 m interval using the ANUDEM interpolation algorithm with drainage enforcement (Hutchinson 1989) (figure 3). A set of terrain attributes (i.e, slope angle, aspect, specific catchment area, wetness index, plan curvature, profile curvature: e,g. Moore et al. 1991) was calculated from the DEM using PCRaster software (Karssenberg 1996), Spatially continuous estimates of mean monthly precipitation were obtained by a combination of multiple regression, with elevation, latitude and longitude as independent variables, and kriging of regression residuals (rigure4((/)). The Hargreaves and Samani (1982) formula was used for the estimation of mean monthly £,0 values after calibration with the pan evaporation measurements. The DEM and calculated mean lapse rates were used to spatially distribute the input parameters of the Hargreaves and Samani formula (i.e. extraterrestrial radiation, minimum, maximum and mean monthly temperatures) (figure4(/?)), Specht's evaporative coefficient (figure4(()) was calculated tVom the maps of mean monthly P and E,) using equations (7) (9). The Landsat TM image dates {29 September 1993, 3 December 1993, 29 March 1994. 27 April 1994, 28 May 1994 and 31 July 1994) were selected to cover key stages of tbe annual growth cycle. Image correction and processing consisted of: (i) georegistration using I : 50000 topographic maps, (ii) conversion of digital numbers to radiance units using standard values for minimum and maximum brightness (Markham and Barker 1986). and (iii) a simple correction for atmospheric effects using the darkest-pixel subtraction method (e.g. Campbell 1996). The mean value of the normalized difference vegetation index (NDVI). calculated from the six TM images, was used as an indicator of average vegetation density (figure 6((/)). A regression model for the prediction of the potential mean NDVI was obtained from the observed mean NDVI and k values at 15 000 randomly selected locations. The sample locations were drawn from a population of grid cells with a drainage area of one grid cell, identified through analysis of local drain directions on the 30 m DEM (Boer and Puigdefabregas 2003), We assumed that: (i) these grid cells do not receive additional water inputs by run-on so that at long timescales E^^P, and (ii) land degradation processes have, by definition, a negative effect on the retention and

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M. M. Boer and J. Puigdefabregas

UTM Zone 30 -10 km grid

55 cm year^

41S8180

im en

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di EH EH EH EH

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583000

593000 0.071 cm'

4188180- •

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nn CD EH 1=1 0.032 cm-1

4178180-

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593000

Figure 4. Predicted climate attributes for the study area in the Rio Guadalentin basin. {a) Mean annual precipitation, (/>) mean annual potential evapotranspiration, {c) Speeht's evaporative coefficient. Source: Boer (1999).

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utilization of a grid cell's soil moisture resources and therefore on its vegetation density. Hence, the upper limit of the A-NDVl data cloud (figure5(«)) was interpreted as corresponding to the maximum, or potential, vegetation density (i,e. NDVIp,,,) thai can be sustained under local topocHmatic conditions. Similarly, the lower boundary of the data cloud was assumed to represent mean NDVI values corresponding to bare soil surfaees (NDVIhs). Functions for the upper and lower boundaries were obtained by sorting the grid cells on the value of A-, then dividing the sample in 15 sub-samples of 1000 grid cells each, and finally fitting an exponential equation to, respectively, the 95% and 5'V;, valties of the mean NDVI and the middle of the A" interval for each sub-sample (n- 15) (see i'lgurc 5{h)): - 4 , 4 4 x lO''exp(-6,61 - 6 . 4 4 x 10^ exp{-5,56x

x\O-k))

(10)

IO'A-))

(11)

where NDVIp,,, and NDVl^s are. respectively, the 95' value and the 5'V^i value of the I, observed mean NDVI. and k is the evaporative coefficient (cm Equations (10) and (11) were applied to spatially distribute the potential and bare soil values of the mean NDVI over the study area (see figure 6(/))). Equation (5) was adapted to the use of the NDVI as an indicator of LAI and then applied to predict figure6(r)): NDVl-NDVlbs

(12)

~P

where NDVI. NDVIhs and NDVIp^t refer to the mean vegetation index value for Ihe actual situation, bare soil conditions and the potential situation, respectively. The long-term runoff coefficient for bare soil surfaces (RC^s) was estimated at 0.15 by running the Curve Number method (Soil Conservation Service 1986) for 'dirt roads' (i.e. Curve Nurnber=9()) on a 13-year record of daily rainfall observations from the Rambla Honda Field Site in Almeria. south-east Spain.

0,7 X (h)

0.6 0.5 f 0.4 -0.3 -: 0.2 -:

0.1 I 0,0 0.032

0.036

0.040 k

0.044

0.032

0,036

0.040

0.044

k

Figure 5. ((') Scattergram ol" the mean NDVI, calculated tVom six Landsat TM images for the 19^3 1994 hydrological year, iigainst the evaporative coefficient (cm ') for a sample of pixels with drainage area of one grid cell (n= 15 000). (^) The upper ( • , continuous curve) and lower boundary ( . \ dashed curve) of the data envelop, as defined by. respectively, the 95th and 5th percentile values of the mean NDVI of each k class. Upper boundary: R^\ 0.97; /7) Predicted potential mean NDVI. ((•) Predicted EJP. Source; Boer (1999).

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To minimize the possibilities for confusion of anomalous EJP values resulting from spatial variation in parent rock type, topographic position, soil type, or otber relatively static land properties, witb spatial variation in land condition we performed the following additional steps to produce the final land condition map (for details see Boer 1999): 1. Analysis of lithological effects on EJP.. using a Kruskal-Wallis median test on sampled values («-1000) from the 10 main lithologieal units in the study area, and subsequent rcclassification of the study area into four significantly different lilhological classes. 2. A multivariate classification in terrain types per lithological class using numerical taxonomy algorithms (Del Barrio et al. 1996) on raster overlays of slope angle, plan curvature, profile curvature, specific catchment area, wetness index and length-slope factor (Moore ct ul. 1991). and subsequent stratification of the study area. By stratifying for lithology and terrain type we aimed at implicitly accounting for the impact of gross variation in soil type on EJP. and thereby for spatial variation in soil properties that are relatively constant in time and form boundary conditions for land degradation processes rather than being indicative for a specific degradation state. A previous analysis of over 600 geo-referenced soil profiles in the Rio Guadalentin basin showed that spatial variation in soil depth and other physical soil attributes (e.g. texture) is often correlated with topographic position (Boer ct al. 1996). 3. A standardization of EjP per lithology terrain class, and composition of the final land condition map in three broad classes (see figure 7(a)): • 'poor condition': local EJP0.7), but still acceptable, classification probabilities are obtained in other upland areas. The lowest classification probabilities (0.4-0.6) are found in the flattest parts of the study area at lower elevations. For the entire study area about half of all the grid cells can be classified with less than 35% uncertainty (see figure 10).

M. M. Boer and J. Puigdefdhrcgas

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Figure 9. Frequeney distributions and experimental variogranis of the mean monthly coefficient of variation of potential evapotranspiration (a, h). precipitation (f, (/). and the evaporative coefficient k ie.f). Souree: Boer (1999).

spatially distributed estimates of Speeht's evaporative coefficient. A, in a large number of rclercnce sites. The practiciility and performance of the method as a dryland assessment tool was tested in a 900 km^ test area in south-east Spain. The evaporative coefilcient was found to explain most of the spatial variation in the maximum and minimum values of tbe mean NDVI observed on TM satellite imagery for the 1993 1994 hydrological year. The ratio of the observed and predicted potential mean NDVI, together with

M. M. Boer and J. Puigdefabregas

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0,8

1.0

Uncertainty Figure 10. Cumulative frequency distribution of the tmccrtainty in the land condition classification. Source: Boer (1999). a.n estimate of RCb,^. was used to produce a map of EJP. Predicted hind condition classes, obtained after stratification and standardization of EJP for lithoiogy and terrain types, showed good agreement with field evidence on land degradation process activity- Confusion of negative anomalies in vegetation density due to natural causes with sites of poor land condition occurred in some areas, such as on scree slopes or large rock outcrops. An analysis of the error sources and range of uncertainty in the predicted land condition classification showed that the evaporative coefficient, k. can be estimated with reasonable accuracy. Uncertainty in the value of A mainly stems from uncertainty in the estimates of mean monthly £,, and is spatially independent. The spatial distribution of A' and the strong non-linearity of the A-NDVI reference function causes the uncertainty of the final land condition assessment to be greatest in the relatively fiat areas at low elevation and to be least in the more humid uplands. Acknowledgements The research for this paper was originally carried out as part of the MEDALUS (Mediterranean Desertification and Land Use) collaborative research project and a post-doctoral fellowship, provided by the Commission of the European Communities (contract number EV5V-CT-94-5242) to M. M. Boer. Further support was received from the LADAMER project (Land Degradation Assessment in Mediterranean Europe). MEDALUS and LADAMER were funded by the European Union, respectively under its Environment Programme (contract EV5V-CT92-0128), and its Global Monitoring for Environment and Security Action Plan (contract EVK2-2002-0599). the support being gratefully acknowledged. We thank colleagues at EEZA in Almcria, Spain, and former colleagues (of M.M.B.) at CSIRO in Alice Springs, Australia, for their helpful comments and suggestions on earlier versions of the manuscript. References AGL iAR, M.R. and SAL.A, O.E., 1999, Palch structure, dynamics and implications for the functioning of arid ecosystems. Trends in Ecology unit Evcliition, 14, pp. 27? 277. ARCHER, S., SCIFRES. C , BASSHAM, C . and MACKiio. R.. 19f^8, Autogenic succession in a

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