Designing erosion management plans in Lebanon using remote sensing, GIS and decision-tree modeling

Landscape and Urban Planning 88 (2008) 54–63

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Designing erosion management plans in Lebanon using remote sensing, GIS and decision-tree modeling Rania Bou Kheir a,∗ , Chadi Abdallah a , Micael Runnstrom b , Ulrik Martensson b a b

National Council for Scientific Research/Remote Sensing Center, P.O. Box 11-8281, Beirut, Lebanon GIS Centre, Department of Physical Geography and Ecosystems Analysis, Lund University, Sweden

a r t i c l e

i n f o

Article history: Received 30 April 2008 Received in revised form 23 July 2008 Accepted 17 August 2008 Available online 8 October 2008 Keywords: Landscape units Erosion maps Decision-trees GIS Structural image classifications

a b s t r a c t Soil erosion by water represents a serious threat to the natural and human environment in Mediterranean countries, including Lebanon, which represents a good case study. This research deals with how to use Geographic Information Systems (GIS), remote sensing, and, more specifically, structural classification techniques and decision-tree modeling to map erosion risks and design priority management planning over a representative region of Lebanon. The structural classification organization and analysis of spatial structures (OASIS) of Landsat TM satellite imagery (30 m) was used to define landscapes that prevail in this area and their boundaries, depending on their spectral appearance. The landscape map produced was overlaid sequentially with thematic erosion factorial maps (i.e., slope gradient, drainage density, rainfall quantity, vegetal cover, soil infiltration, soil erodibility, rock infiltration and rock movement). The overlay was visual and conditional using three visual interpretation rules (dominance, unimodality and scarcity conservation), and landscape properties were produced. Rills and gullies were measured in the field, and a decision-tree regression model was developed on the landscapes to statistically explain gully occurrence. This model explained 88% of the variability in field gully measurements. The erosion risk map produced corresponds well to field observations (accuracy of 82%). The landscapes were prioritized according to anti-erosive remedial measures: preventive (Pre), protective (Pro), and restorative (Res). This approach seems useful in Lebanon, but can also serve in other countries with similar geo-environmental conditions or those lacking detailed geospatial data. © 2008 Elsevier B.V. All rights reserved.

1. Introduction Soil erosion by water is a major degradation problem in semihumid to semi-arid Mediterranean landscapes. Its negative impacts are tremendous, including reduction of soil productivity, silting of dams, pollution of water courses, deficits in water availability, serious damages to properties by soil-laden runoff increasing indemnities demands, and desertification of natural environments. Terrains particularly susceptible to such phenomena generally have a combination of unconsolidated rock type, vulnerable soils, steep slopes, and rapid land use changes. The latter are mostly expressed, in the second half of the 20th century, as harmful human activities comprising chaotic excavation for quarrying, disorganized urban encroachment, deforestation (fire, wood cutting and overgrazing), and intensification of agricultural exploitation at the expense of forests and well-managed arable lands.

∗ Corresponding author. Tel.: +961 4 409 845/6; fax: +961 4 409 847. E-mail address: [email protected] (R.B. Kheir). 0169-2046/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.landurbplan.2008.08.003

Recent studies addressing land use change detection in Lebanon indicate that the number of quarries in mountainous areas increased by 8% between 1987 and 1997. Forestry service reports (LMOA, 1998) indicate that a high number of forest fire events take place each year (e.g., 1413 forest fires in 1997) with resultant large burn areas (e.g., 3000 ha during 1998). Agricultural lands declined significantly over the period from 1963 to 1987, with variable proportions (Ba’albaki and Mahfouz, 1985; Masri et al., 2002). Olives decreased 30%, compared to 72% and 82% for fruit trees and vineyards, respectively. This means a considerable increase in bare lands, which now occupy almost 30% of Lebanon (Akrimi, 2000). Despite this severe situation, appropriate management plans are still lacking. Water erosion risk maps do not exist, and strategies or policies have not been designed in Lebanon to address this. Moreover, a lack of awareness of the community clearly reflects a misunderstanding of the risks, and contractors are reluctant to implement geotechnical site analyses that may raise costs. In addition, engineering firms and the legislation lack obligatory preventive measures. Therefore, regional planning of soil protection measures is needed. Erosion assessment using current empirical or deterministic Mediterranean regional soil erosion models (e.g. Coordination of

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Fig. 1. Map showing geomorphic units of the study area.

Information on the Environment [CORINE], 1992; Pouliot et al., 1994; De Jong and Riezebos, 1997; Quinton, 1997; Sobocka and Jambor, 1998; Cerdan et al., 2002; Kirkby et al., 2002; Renschler and Harbor, 2002; Martinez-Casasnovas et al., 2003; Hessel and Jetten, 2007; Wang et al., 2008) is inhibited in Lebanon due to their complexity and their need for a huge amount of input spatial data that are difficult to acquire. In this context, this study focuses on building a statistical decision-tree model to predict the geographic distribution of erosion processes in diverse landscape units (derived from remote sensing data), based on calibrating field estimations of gully volume to erosion-controlling factors, in the frame of Geographic Information Systems (GIS), for a simple, realistic, and practical method. The resulting erosion risk map at a 1:100,000 cartographic scale is used to design priority soil conservation planning, including anti-erosive measures ascribed as preventive, protective and restorative (curative) measures. The applied approach helps decision-making in Lebanon, but also appears useful for many other Mediterranean countries with similar environmental conditions or developing countries worldwide suffering from a lack of detailed input spatial data. 2. Study area The study area was chosen because it represents diversified landscape types in terms of geology, morphology, soil, hydrography, and climate conditions, and is under a number of land uses representative of Mediterranean environments. In addition, its selection depended on its inclusion of local sites that have experienced major soil-erosion problems (Bou Kheir et al., 2001a,b), the absence of quantitative measures of soil loss rates (tons/ha)

and a lack of governmental solutions and resource management plans. The study area covers approximately 6.5% of the total area of Lebanon (676 km2 ), extending 33 km from west to east across the middle part of the country (Fig. 1), and can be divided into two major geomorphic units, Mount Lebanon and the Bekaa. Mount Lebanon, which comprised 76% of the study area, runs parallel to the shoreline, dipping steeply seaward, with an east–west gradient of 7.5–10%. It can be divided into three major parts: the lower slopes (100–500 m altitude), the upper sloping plateaus (500–1500 m altitude), and the elevated crests (>1500 m altitude). The lower slopes, consisting of clastic and oolitic limestone, sandstone, and clayey rocks of the Lower Cretaceous and Upper Jurassic (Dubertret, 1945), are dominated by bare soils and residential/commercial urban areas. The upper sloping plateaus are covered with coniferous (mainly Pinus pinea), oak (mainly Quercus calliprinos), and broadleaf (Quercus infectoria) forests and shrub lands on dolomite, limestone, and dolomitic limestone with patches of basalt, sandstone, and clay materials. The elevated crests are covered by grass and herbaceous vegetation on limestone and marly limestone Cenomanian and dolomitic limestone Jurassic rocks. Mount Lebanon is structurally affected by parallel faults trending SW–NE and separated from the Bekaa by the shed line of the Dead Sea Fault Zone and the NE–SW-trending “Yammounah Fault”. The Bekaa comprises the hills (1000–1500 m altitude; 6% of total area) located between the crests of Mount Lebanon and the Bekaa plain (500–1000 m altitude; 18% of total area). The hills are dominated by marl and marly limestone of the Cenomanian, Senonian, and Tertiary, and are cultivated with deciduous fruit trees. The valley bottom (plain), considered a flat area, is cultivated with field crops on quaternary alluvial rocks.

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Fig. 2. Flow diagram of the modeling approach to elaborate priority management planning against erosion in the study area.

Three perennial coastal rivers (El-Kalb, Beirut, Damour) and one interior river (Litani) traverse the study area, in addition to six intermittent streams. The coastal rivers have a total length less than 50 km and general E–W orientations. A total of 37 different soil units were identified on the available soil map (Gèze, 1956), of which 22 are soil series and 15 are soil associations. These soils are typically Mediterranean in character and just three of them (eutric arenosols, lithic leptosols, luvisols “haplic and leptic”) cover 50% of the study area. The area has a Mediterranean climate characterized by mild wet winters and dry hot summers. Annual precipitation ranges between 500 mm on the lower slopes of Mount Lebanon to more than 1400 mm over the crests, before decreasing again to 600 mm in the Bekaa valley. The precipitation is torrential in nature, and although hourly data are sparse, daily totals ≥100 mm give some indication of the high intensity storms in this region. 3. Modeling approach Our mapping of erosion risks and subsequent design of a priority erosion management plans was realized in several steps (Fig. 2). We used satellite imagery to produce landscape unit maps. These were combined with some thematic erosion-controlling factorial maps to specify landscape properties, with field surveys to allocating gullies. The landscape units refer to the portions of the land surface that contains a set of ground conditions that differ from adjacent units across definable boundaries (Coeterier, 1996; Roth et al., 1996; Grabaum and Meyer, 1998; Landres et al., 1999; Orland et al., 2001; Lewis and Sheppard, 2006). They are considered spatially homo-

geneous units in terms of both instability factor characteristics and erosion degree (with uniform gully volume). We then explored a GIS decision-tree model on the collected gullies and landscape properties, and used it to map erosion within the landscapes using GIS. Priority management planning scenarios were then proposed for the landscapes, depending on the gully volumes and the specific properties of each landscape. 3.1. Delineation of landscape boundaries from remote sensing data Digital satellite data from the Landsat TM sensor, with a spatial resolution of 30 m acquired in September 2003, was used to exactly delineate landscape boundaries in the study area. The chosen image was free of snow cover and allowed us to interpret diverse types of soils in cultivated lands harvested at that time. Data selection was also influenced by the availability and relatively low cost of Landsat images. Data were registered and ortho-rectified (RMS error of about 0.7 pixels) using ground control points (GCPs) and a digital terrain model DTM (cell size 50 m) generated from topographic map sheets (Direction of geographic affairs [DGA], 1963) at a scale of 1:50,000 with contour intervals of 10 m. A radiometric correction was applied using Erdas Imagine software (version 8.7) on the image to achieve means and variance equalization, i.e., the conformity. A red-green-blue false color composite (FCC) image was constructed from the Landsat bands 4, 3 and 2, and a supervised non-hierarchical structural classification OASIS, Organisation et Analyse de la Structure des Informations Spatialisées (organization

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and analysis of spatial structures), was employed to produce a reliable spatial subdivision of the area (OASIS, 2000; Lillesand and Kiefer, 1994). This method classifies each pixel taking into account the neighbouring ones (Girard and Girard, 2003; Bou Kheir et al., 2004). It is based on fuzzy group theory, which uses spectral and textural characteristics for heterogeneity analysis. It consequently differs from current common textural methods of classification, such as hierarchical ascending classification, mobile centres (or k-means), parallelepiped classification, and maximum likelihood, classifying a given image pixel by pixel (Girard and Girard, 2003; Schowengerdt, 2007). The objective of the OASIS method is to classify all pixels according to neighbourhood parameters. Each class is characterized by a pattern defined by several classes of objects. This classification is based on nuclei defined by the thematic specialist and assumed to characterize the patterns being searched. The result produces zones that are more compact than those obtained by textural methods. Three factors justify our use of the OASIS method. First, we see the presence of heterogeneous pixels (resolution of 900 m2 ), whose spectral behaviours are controlled by diverse terrestrial feature objects inside the same pixel. Thus, by taking into account the neighbourhood concept, several pixels are integrated, and the result becomes statistically robust. Second, entities are characterized by similar (vegetative) features; for example, dense grasslands and herbaceous areas may be included in the same entity. Third, we can compare the map classified using OASIS to any thematic map with larger polygons, but a smaller number of classes. On the FCC image, 15 homogenous reference areas (nuclei or patterns) related to urban, soil and vegetation (either natural or cultivated) occurrence were delineated visually by one or several polygons traced on the computer monitor, covering around 10% of the area. During the OASIS classification, each pixel is assigned to the nucleus located the shortest Euclidian distance away in feature space. A cubic convolution re-sampling using a 7 × 7 window of 49 pixels (210 m on 210 m) was used to generalize and produce compact landscape units.

based on the CORINE land cover methodology (level 4). Three classes of vegetation cover were distinguished on the map: (1) high vegetation cover (>95%), mainly with oak trees with persisting leaves (Q. calliprinos); (2) medium vegetation cover (35–95%), including grasslands, degraded coniferous forests, and plantations of lemons or bananas; and (3) low to no vegetation cover (0–35%), including horticulture/agricultural lands, olive yards, vineyards, deciduous fruit trees, bare rocks, badlands and urban areas. These classes based on the LNCSR-LMOA (2002) at the 172 visited field sites agree completely with field observations. Soil and rock infiltration (permeability) and erodibility/movement were derived by classifying soil units and lithological formations of the soil (Gèze, 1956) and geological (Dubertret, 1945) maps at 1:200,000 and 1:50,000, respectively.

3.2. Construction of thematic erosion-controlling factorial maps

3.4. Field sampling of erosion data

Several factors were chosen as relevant in describing landscape erosion units (Poesen and Hooke, 1997; Morgan, 2005), and thematic digital maps were produced using satellite imagery, ancillary data, and digital terrain models (DTMs). These factors can be divided into two types: (i) basic factors regrouping slope gradient, drainage density and annual precipitation, and (ii) derived factors comprising vegetal cover, rock infiltration (permeability) and movement (e.g., landslide, earth creep, drift), and soil infiltration and erodibility. Slope gradient (angle) was derived from the constructed DTM. Considering the histogram of equalization between distribution of slope gradient and the corresponding number of pixels, the slope gradient was divided into three classes: (1) gentle (<10◦ ), (2) medium (10–18◦ ), and (3) steep (>18◦ ). Drainage density was extracted from a 1:50,000 drainage map (Bou Kheir et al., 2006), and was categorized in three classes: low (<0.19 km/km2 ), medium (0.19–2.83 km/km2 ) and high (2.83–5.84 km/km2 ). The existing digital rainfall map at a 1:200,000 scale (Plassard, 1971), ranging from 500 to more than 1400 mm/year, was reclassified into three classes with an interval of 300 mm. Rainfall intensity data, although highly reliable in inducing erosion, was not taken into account as it was unavailable. The vegetation cover density map was estimated from a recent land cover/use map at a 1:20,000 scale (LNCSR-LMOA, 2002). The map was prepared by visual interpretation of high-resolution Indian satellite images IRS (6 m) acquired in October 1998 and

A field survey was performed involving detailed measurements of rills and gullies. In all, 172 sites were chosen by a stratified random sampling method to cover all landscape units different by one of the following variables: slope gradient, drainage density, rainfall quantity, vegetation cover, soil infiltration and erodibility, and rock infiltration (permeability) and movement. The geographic locations of all sampling sites were determined using a Global Positioning System (GPS) with a precision of about 10 m. The tributaries of each linear system, once found, were counted, and the dimensions measured. Readings of depth and width were taken in 10% intervals of the total curved length of linear channels. The obtained volumes in m3 were converted to tons/ha considering the mean bulk density of Mediterranean soils as 1.5 tons/m3 (Hillel, 2003).

3.3. GIS conditional characterization of landscapes The landscape map derived from the OASIS classification was overlaid sequentially with every thematic vector erosion factorial map. The applied overlay was visual, conditional, and not systematic in order to reduce accuracy errors due to shifting in overlapping factorial map layers being handled at different scales, dissimilar levels of treatment, and various acquisition dates, and so as to easily differentiate diverse landscape elements. To acquire uniform information on erosion factors for each landscape unit, three conditional rules were applied: (1) dominance (a given landscape polygon is characterized by the thematic erosion factorial unit that is dominant in the area); (2) unimodality (if, in a large landscape polygon, a bimodal population exists for an erosion factorial theme, the polygon was divided into two new polygons); and (3) scarcity conservation (if, in a large polygon, there is a theme occupying a small area that does not exist elsewhere, it was saved as a new polygon). Thus, a resulting landscape map with 900 polygons was used as a basis for collecting erosion signs (rills and gullies) in the field and applying the decision-tree model for erosion mapping.

3.5. Decision-tree erosion modeling An ASCII point data file was constructed for the field sample locations. This file contains several columns: geographic site coordinates (x, y), the gully size in tons/ha (target variable), and various landscape properties (predictor erosion factorial values), both ordinal (slope gradient and rainfall quantity) and categorical with classes (drainage density, vegetation cover, rock infiltration and movement, and soil infiltration and erodibility). A decision-tree model was explored using this file since it offers several advantages over other data mining techniques (McKenzie and Ryan, 1999;

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Table 1 Inventory of possible preventive (Pre), protective (Pro) and restorative (Res) measures to reduce soil erosion by water in the study area Landscape properties

Gully erosion volume (tons/ha)

Remedial anti-erosive measures Type

Activity

Unstable bare lands (moving soil or rock)

1–5 tons/ha 5–10 tons/ha >10 tons/ha

Pre 1 Pro 1 Res 1 Res 2

Forestation Contour plowing and strip planting Implementation of suitable land use according to soil capability Stopping excavation activities (chaotic quarrying)

Agricultural lands (high drainage density-Pre 2; low infiltration capacity-Pre 3; steep areas-Pro 2)

1–5 tons/ha

Pre 2

Building of water diverging canals and water harvesting

5–10 tons/ha >10 tons/ha

Pre 3 Pro 2 Res 3

Well structured mixed plantation Constructing or maintaining terraces Agri-environmental land protection (e.g., rational use of irrigation, integrated use of chemicals)

1–5 tons/ha 5–10 tons/ha >10 tons/ha

Pre 4 Pro 3 Res 4

Preventing unbalanced change in land use (e.g., wood cutting, forest fire) Optimization of forest maintenance and exploitation Planning access road network for forest fire fighting

Forested areas

Vayssieres et al., 2000; Danielson et al., 2007; McDonald and Urban, 2004; Kim, 2008; Jenhani et al., 2008; Sieling, 2008). Decision-trees are practical, informative, and easy to build and interpret, and can automatically handle interactions between continuous (ordinal, interval) and categorical (nominal) multiple variables using modern computing technologies. They have been extensively exploited for vegetation mapping (Franklin, 1998; Kandrika, 2008), ecological modeling (Michaelsen et al., 1994), soil mapping (Henderson et al., 2005; Bou Kheir et al., 2008), and in remote sensing studies (e.g., land use classification based on threshold values of various band data) (Huang and Jensen, 1997; Friedl et al., 1999). In this study, we tested the use of decision-trees in predictive volumetric estimation of gully erosion. The decision-trees were built over several steps: (1) find the best possible split by examining each predictor erosion-controlling factor, (2) create two child nodes, (3) determine which child node each field sampling point falls into, and (4) continue the process until the criterion of minimum node size is fulfilled. The number of splits to be evaluated is 2(k−1) − 1, where k is the number of categorical classes of predictor variables (Breiman, 2001). For example, if we consider the vegetation cover factor with three classes, three splits must be tried. With an increase in the number of classes, there is exponential growth of splits and computation time. The algorithm used to evaluate the quality of the constructed tree is the Gini splitting method, considered the default method (Breiman, 2001). Each split is chosen to maximize the heterogeneity (node impurity) of the classes of a target variable in child nodes. The Gini method is considered to be slightly better than the entropy tree fitting algorithm (Loh and Shih, 1997; Breiman, 2001). The Gini coefficient is used to measure the degree of inequality of a variable in terms of frequency distribution. It ranges between 0 (perfect equality) and 1 (perfect inequality). The Gini mean difference (GMD) is defined as the mean of the difference between each observation and every other observation (Breiman, 2001): 1  {|Xj − Xk |} N2 N

GMD =

N

j=1 k=1

where X is cumulative percentage (or fraction) of their respective values (j and k), and N is the number of elements (observations). A minimum node size of ten was applied in this study, since a simpler tree is easier to understand and faster to use, and more importantly, smaller trees provide greater predictive accuracy for unseen data. This number has also been used in other studies (Zhang and Singer, 1999; Piramuthu, 2008; Twala et al., 2008). The

maximum tree elevation or size was not specified in this study. A too-large tree needs to be pruned back to its optimal size (i.e., backward pruning) on the basis of a V-fold cross-validation (Berk, 2003), with several assumptions such as random-row-holdback validation, fixed number of terminal nodes, and smooth minimum spikes. The V-fold cross-validation does not require a separate, independent dataset, which reduces the data used to build the tree. It partitions the dataset to build a reference unpruned tree into a number of groups (i.e., folds). A 10 V-fold value was adopted in this study, since a larger value increases computation time and may not result in a more optimal tree. After 10 test trees were built, their classification error rate (cross-validation cost) was averaged as a function of tree size. The tree size that produces the minimum cross-validation cost (error) corresponds to the optimal tree. 3.6. Constructing erosion risk and priority management plan maps Using the results of the pruned decision-tree model, we converted the landscape unit map into a predictive map of erosion risk in a GIS environment. The obtained gully volumes shown in the terminal tree nodes were grouped into three erosion level classes with an equal range: 1–5 tons/ha (low erosion risk), 5–10 tons/ha (medium erosion risk), and more than 10 tons/ha (high erosion risk), in addition to urban and stable areas (without linear channels). Where different end results (erosion classes) characterize field sites within a given landscape unit, new sub-polygons were delineated. When results were similar, unit polygons were joined. The map was validated based on field surveys. An independent dataset was chosen randomly for all landscape units, consisting of 20% (34 sites) of the total number of field sites, and an error matrix was established. The differentiation of stable landscapes with no evidence of any active linear erosion processes (rills and gullies) from unstable ones (low, medium and high) is useful for regional priority erosion management planning. The former landscapes are due to the predominant stabilizing effect of one or several erosion-controlling factors, generating a state of morpho-dynamic equilibrium. Thus, the obtained erosion risk map showing different unstable and stable areas was reclassified in terms of land use planning, specifying for each landscape the particular remedial anti-erosive measures that must be applied on a preventive, protective or restorative basis (Table 1). These remedial measures were designed according to the gully volume in tons/ha and the causative erosion-controlling factors (e.g., topography, vegetation cover, soil erodibility) characterizing each landscape unit.

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Fig. 3. Landscape unit map of the study area derived from structural classification of Landsat TM satellite imagery and improved using GIS conditional analysis by thematic erosion factorial data.

Fig. 4. Pruned regression tree for predicting gully erosion based on landscape properties. Note: SG = slope gradient, drain = drainage density, rain = annual rainfall, vegcov = vegetation cover, infsoil = soil infiltration, erosoil = soil erodibility, infrock = rock infiltration, movrock = rock movement. A value of 1 for each erosion factor refers to low erosion, while a value of 3 indicates high erosion.

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Table 2 Error matrix for the modelled erosion map of landscapes and field observation on rills and gullies

Pu: user’s precision; Pr: producer’s precision; Ee: excess errors or commission; Ed: deficit errors or omission; Oa: overall accuracy.

4. Results and discussion 4.1. Model performance evaluation The exploratory regression tree model with 56 terminal nodes correctly classified 95% of the variance, but V-fold cross-validation indicated that this model would correctly classify 88% of an independent dataset, with 37 total nodes (shown as rectangular boxes) and 17 terminal (or leaf) nodes without child nodes (Fig. 3). The number of observations (N) per terminal node ranged from 1 to 7, and the node did not split if N is less than 10 observations. A gully volume in tons/ha in a given terminal node is the estimated mean value of the rows falling in this node. The relative importance of the predictor erosion-controlling factorial data in building the tree is represented as follows:

• slope gradient as the initial split-100% with a higher proportion of sample points, • vegetation density-82%, • soil infiltration-78%, • soil movement-75%, • drainage density-72%, • rock movement-69%, • rock infiltration-65%, and • rainfall quantity-45%. 4.2. Accuracy of the erosion risk map The results of the pruned decision-tree model were applied on the landscape unit map, and an erosion risk map at a 1:100,000 cartographic scale was produced (Fig. 4). This map indicates that 34% of

Fig. 5. Erosion risk map of the study area.

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Fig. 6. Priority management plan for the study area.

the area is affected by high erosion volume (>20 tons/ha), whereas medium volume covers 25% of the region, indicating a widespread possibility of terrain degradation by erosion if insufficient management is applied. The medium erosion class has the largest number of polygons (30%), reflecting a wide distribution over the studied area. The confusion matrix between the measured erosion classes and the modelled ones indicates a good overall accuracy of 82% (Table 2). This accuracy is different from the explained variance of the pruned decision-tree model (88%), since it is dedicated to validating all adopted approaches combining the integration of landscape unit map and quantification of linear channels volumes and decisiontree modeling. In contrast, the explained variance reflects only the rills and gullies chosen for model training. The user’s accuracy ranges from 73% to 100% and the producer’s between 75% and 89%. Modeling overestimates low erosion volumes; this can be considered as a positive point for management planning considerations because the possibility of overlooking actual risks decreases.

the low erosion volume terrains (27% of the total area), reflecting the importance of a well-structured plantation, since most soils in the study area show a low infiltration capacity. The other preventive classes (Pre 2, Pre 3, and Pre 4) are also applied, but to a lesser extent: Pre 2-2%, Pre 3-4%, and Pre 4-6%. In areas with medium erosion class (5–10 tons/ha), the first type of protective measures is most important (Pro 1-50%). Pro 2 and Pro 3 have nearly equal areas: class “Pro 2” (29%) is distributed in several morphological units (i.e., the upper sloping plateaus, hills of the Bekaa and Bekaa plain), and class “Pro 3” (31%) often corresponds to terra rosa soils. Restorative measures are concordant with landscapes characterized by dangerous erosion levels (i.e., volume exceeding 20 tons/ha), and their application is optimal for sustainably managing natural resources. Of high erosion areas, 46% require restorative measures of the first type (Cur 1). Cur 2, Cur 3, and Cur 4 can be applied to 54% of these areas (18% each) (Figs. 5 and 6).

4.3. Priority management planning of landscapes units

Structural classification of Landsat TM imagery coupled with a GIS-based conditional analysis of thematic erosion factor maps and decision-tree modeling allowed us to define an erosion risk map for a region in central Lebanon. This map was validated in the field with an accuracy of 82%. Applying diverse management scenarios to these landscapes produced another map that can meet the scientific needs of researchers, governmental authorities and institutions, as well as decision-makers. Selecting priority remedial measures

The erosion volume coupled with erosion-controlling factorial data produces a priority management plan against erosion for each landscape unit in the study area (Fig. 4). In this map, 11 classes were distinguished showing preventive, protective and curative (restorative) measures. These classes are applied in the study area with various proportions. Class “Pre 1” covers the largest part (15%) of

4.4. Advantages and problems of the adopted approach

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can prevent money expenditures without approval/consistency and assure natural resource sustainability by surveying land use changes. The regression model explained 88% of the variance in gully erosion volume for a series of field sites in the studied region. This could be improved by using other erosion predictor variables, such as slope curvature, slope aspect, slope length, and distance to springs. This is an important future research topic, as the importance of such variables in explaining additional variance can be tested. This approach can be extrapolated to other areas in the country if the functional capacities of remote sensing and GIS are used. The map of vegetation cover density is more dependent on time than other erosion factor data, and must be updated as it may change drastically due to human intervention/activities. However, many difficulties have been encountered. The use of one lumped value for the rainfall factor in erosion modeling is an oversimplification of the real situation, in which rainfall shows complex spatial and temporal patterns. Future studies should focus on capturing this variability by incorporating rainfall intensity into erosion methods. In addition, the low resolution DTM (50 m) and coarse thematic maps (e.g., soil map at 1:200,000) reduce the quality of the erosion risk map. These can be improved and enriched by including more detailed data. In addition, using more precise satellite imagery with finer spectral and spatial resolutions, such as pan-sharpened IKONOS (1 m), SPOT 5 (2.5 m) imagery, may allow erosion to be mapped within landscapes at more detailed scales (1:50,000 or larger) to help municipalities and companies plan infrastructure, housing, and industrial projects. 5. Conclusion Engineers, earth scientists, and planners are interested in assessing and mapping erosion within landscapes due to the following considerations: (1) these maps identify and delineate unstable areas, so that environmental regeneration programs can be initiated to adopt suitable mitigation measures; and (2) these maps help planners choose favorable locations for site development schemes, such as building and road construction. For this, a new approach has been proposed that benefits from advances achieved using remote sensing, GIS techniques, and modeling (decision-tree). The structural classification of satellite imagery has proven to be efficient in mapping landscape boundaries in the study area. The GIS-based conditional analysis of these boundaries with erosion factorial maps has permitted us to assess landscape properties specifically related to erosion occurrence. The overall approach can be done quickly and relatively cheaply, and its efficiency should be tested in other countries. Although the chosen scale of the obtained products (erosion risk map and priority management plan-1:100,000) seems to be sufficient for estimating gully volume and considering strategies for land protection, the map can be improved for more localized erosion assessment if more detailed datasets are available (higher resolution DEMs and more detailed GIS maps). Acknowledgments This research is a part of a project (2004–2007) of the Remote Sensing Center, the Lebanese National Council for Scientific Research (LNCSR), in cooperation with the GIS Centre, Department of Physical Geography and Ecosystems Analysis, Lund University (Sweden) and was funded by Swedish International Development Agency (SIDA). We acknowledge all responsibilities of LNCSR, Lund University and SIDA.

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