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Using remote sensing approach and surface landscape conditions for optimization of watershed management in Mediterranean regions
Physics and Chemistry of the Earth 36 (2011) 213–220
Contents lists available at ScienceDirect
Physics and Chemistry of the Earth journal homepage: www.elsevier.com/locate/pce
Using remote sensing approach and surface landscape conditions for optimization of watershed management in Mediterranean regions Zeyad Makhamreh ⇑ Department of Geography, University of Jordan, 11942 Amman, Jordan
a r t i c l e
i n f o
Article history: Received 7 March 2010 Received in revised form 29 July 2010 Accepted 22 August 2010 Available online 17 September 2010 Keywords: Landsat Watershed management Organic carbon Soil colour Jordan
a b s t r a c t Identification of potential sites for water harvesting is a prerequisite for improving watershed management in the semi-arid and dry Mediterranean regions. The main objective of this work is to identify and optimize the potential water-harvesting sites in Jordan based on the characterization of surface landscape conditions using DEM and remote sensing techniques. In this study, the vegetation abundance and distribution was derived using spectral mixture analysis. In order to characterize the surface landscape conditions, a mathematical model has been established between soil colour and soil surface properties to derive the spatial distribution of soil organic and inorganic content using Landsat images. Based on these results the current distribution of the hydrological pattern has been derived, and hence the spatial pattern of run-on and run-off areas have been determined. The run-on areas have been determined with high details utilizing organic carbon distribution, while the run-off areas have been determined based on spatial distribution of inorganic carbon. The high concentration of organic carbon indicate presence of excessive water moisture content and drainage network and hence the potential for water-harvesting sites is high. This approach takes into consideration both the physical and actual landscape conditions in allocation of suitable sites for water harvesting. Thus, integration of landscape information and DEM are useful for efficient management of watersheds and identification of potential water-harvesting sites on a sub-catchment basis. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Water harvesting and watershed management is an essential step for improving water availability in the semi-arid and dry Mediterranean regions. Subsequently, identification of potential sites for water harvesting is a prerequsite in such regions. Water scarce countries are subject to various hydrological constraints particularly within rural communities that are reliant on rainfed agriculture (Kahinda et al., 2005). Currently, the issue of water scarcity and resources management in Jordan is becoming an increasing concern on a national level. Jordan is classified among the top ten poorest countries worldwide in water resource availability. In addition, the agricultural water sector accounts for over 65% of the total consumption of water in the country. However, with rapidly growing demands of water for food production, alternative techniques for capturing and improving new water resources are highly required, and consequently, the ability to successfully manage the resulting rain water is extremely important (Sekar and Randhir, 2007).
⇑ Tel.: +962 796766162, office +962 6 5355000x24981; fax: +962 6 5300232. E-mail address: [email protected] 1474-7065/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.pce.2010.08.004
Water resource development programs are applied generally on watershed basis and thus, prioritization is essential for proper planning and management of natural resources for sustainable development (Srinivasa et al., 2008). Catchments and sub-catchments are the fundamental units for the management of land and water resources, and have been identified as the primary planning units for administrative purposes for the conservation of these precious resources (Pandey et al., 2006). Delineation and prioritization of watersheds within a large drainage basin is important for proper planning and management of natural resources for sustainable crop production. Arid and semi-arid areas of Jordan are characterized by low, highly variable and erratic distribution of rainfall. In addition, the climatic and soil surface properties in such areas prevails surface run-off. Due to these circumstances, run-off harvesting is becoming particularly significant because run-off water can be captured and efficiently utilized to maintain agricultural production in a sustainable manner (Ziadat et al., 2006). Approaches related to watershed management have experienced a vast development during the past decade, yet there is no universal methodology for achieving effective analysis. Many analyses approaches are limited by the information available for modelling parameters of concern (Ramakrishnan et al., 2008). However,
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the new developments in remote sensing and geographic information system technologies have evolved as a powerful tool in analysis and management of watershed and landscape conditions (Kumar et al., 2008). The concept of watershed management recognizes the interrelationships between land use, soil and water resources and the linkage between uplands and downstream areas. As the watershed management depend on land use, soil types, geology and drainage flows, this information and its dynamics can be derived from the satellite remote sensing data more precisely due to its synoptic coverage and high spatial and temporal resolutions (Schiettecattea et al., 2005; Winnaar et al., 2007). Remote sensing techniques provide standardized methods for quantitative analysis of landscape conditions at different scales, and thus can be used in order to characterize and optimize the selection of water-harvesting sites and to improve watershed management (Gosain and Sandhya, 2004). The integration of remote sensing data and GIS for hydrological characterization and modelling is particularly important for improving the spatial and temporal resolution of satellite images (Gangodagamage and Clarke, 2001; Mbilinyi et al., 2007), which allow for better understanding and representation of the hydrological processes in the landscape (Korkalainen et al., 2007). Surface landscape conditions are important for the shaping of the hydrological water network on the catchment basis (Kumar et al., 2008). Recent developments in remote sensing analysis techniques have proven to be a valuable contribution to providing information about landscape features (Hill and Schütt, 2000). Derived indicators of landscape conditions using remote sensing data, is of vital importance for deriving indicators about the hydrological network of the land surface (Srinivasa et al., 2008). Therefore, optimization of watershed management (e.g. selection of water-harvesting sites) can be improved by integration of surface actual landscape conditions and other physical factors. The potential of run-off water harvesting in Jordan has not yet been fully explored and therefore remote sensing techniques could be used to explore the spatial and temporal water drainage networks. Therefore, the objective of this paper is to integrate remote sensing and DEM information in order to improve the identification and selection of potential water-harvesting sites for watershed management, while taking into consideration the current landscape conditions.
2. Theoretical background Spectral reflectance of soils is determined by their physical and chemical compositions. Characterization of landscape conditions can be achieved by using soil surface properties such as carbonate and organic matter (Baumgardner et al., 1985). Prediction of the spatial distribution of soil surface properties is a prerequisite for landscape characterizations; this can be obtained by modelling soil organic and inorganic carbon especially for soils developed on Mediterranean conditions (Makhamreh, 2006). Different approaches have been used for the soil modelling purposes such as using selected wavelength, mathematical functions, total spectral reflection or derived parameters such as soil colour. Soil colours are primarily related to their content from organic matter, carbonate and iron oxides (Baumgardner et al., 1985). It is a covariant property, providing information as to the characteristics of soils that facilitate a distinction between soil types and an indirect estimate of other soil properties. Soil colour value has been identified in many studies affecting the amount of energy reflected from soil surfaces using Landsat sensor (Escadafal et al., 1988). Colour science is based on the observations that all colours may be reproduced by adding the three primaries red, green, and blue.
Considering that a spectral reflectance curve is the sum of elementary monochromatic reflections, the colour coordinate of any nontransparent objects can then be easily calculated. The most popular quantitative approaches for colour measurement is the International Commission on Illumination (CIE, 1931) colour system. Many studies have been established quantitative relationships between soil colour parameters and chemical soil properties. For calculation of the CIE colour parameters, the primary valence X, Y, Z can be determined from the spectral functions (x; y; z) values according to Eq. (1) (Escadafal et al., 1988).
X¼
770 nm X
qðkÞ SðkÞ xðkÞ
380 nm
Y¼
770 nm X
qðkÞ SðkÞ yðkÞ
380 nm
Z¼
770 nm X
qðkÞ SðkÞ zðkÞ
ð1Þ
380 nm
where SðkÞ is the spectral irradiance of the light (xÞðk;yðkÞ; zðkÞ). The relationship between the CIE trichromatic coordinate X, Y, Z and the R, G, B primaries colour coefficients are defined by the following equations (Escadafal et al., 1988).
X ¼ 2:7659R þ 1:7519G þ 1:1302B
ð2Þ
Y ¼ R þ 4:5909G þ 0:06012B
ð3Þ
Z ¼ 0:0565G þ 5:59440B
ð4Þ
These components were chosen so that Y corresponds to the degree of brightness according to its definition; consequently X and Z have no physical reality. The relation of the spectral curve to the total spectral value gives the trichromatic coordinates x, y, z to be calculated as follows
x¼
X XþY þZ
y¼
Y XþY þZ
z¼
Z XþY þZ
ð5Þ
where
xþyþz¼1
ð6Þ
The normal colour value Y is proportional to light intensity of colour values and therefore the normal spectral function represent Y as a measurement tool for the colour brightness. Accordingly, the colour can be defined taken into consideration the Eq. (5), colour brightness Y and normal colour value x and y. 3. Methodology The main objective of this paper is to identify the potential water-harvesting sites in the study area, based on the surface landscape conditions by the modelling of soil surface properties using remote sensing techniques and field analysis. For modelling purposes, quantitative analysis of Landsat images require geometric and radiometric correction. Therefore, the main image preparation steps included image pre-processing and the advance analytical techniques for deriving of soil and vegetation thematic indicators. Image pre-processing includes the geometric and radiometric processing steps, while the advanced digital image analysis involves deriving of vegetation and soil surface indicators. Deriving indicators for the evaluation of the vegetation conditions includes analyzing the land use types and vegetation density, On the other hand, deriving indicators for assessment of soil conditions are based on modelling of soil organic and inorganic carbon using CIE colour system as shown in Fig. 1.
Z. Makhamreh / Physics and Chemistry of the Earth 36 (2011) 213–220
3.1. Study area The study area occupies the eastern part of the Mediterranean region in northern Jordan, as shown in Fig. 2. It is located between Latitudes 32°150 and 32°300 north and Longitudes 35°450 and 36°150 East and covers an area of about 1000 km2. The prevailing climate is of arid Mediterranean type and characterized by dry hot summers and mild wet winters. Most of the precipitation occurs during the winter months November–April. Annual and seasonal rainfall variability is very high both in space and time. This diversity ranges from semi-arid conditions where average rainfall amounts are about 150 mm, to semi-humid conditions where the average rainfall is about 650 mm. Monthly variations in temperature ranges from 5.2 °C in the coldest month to 22.0 °C in the warmest month. While the mean daily minimum temperature drops to 2.5 °C for the month of January, the mean daily maximum temperature reaches 28 °C in the month of August. Land cover varies between natural, semi-natural and cultivated areas. The dominant oak forest species is Quercus coccifera, covering the mountains area, while the dominant shrubs are Artemesia herba-alba covering mainly the transitional rainfall zone. Cultivated lands are characterized by rainfed agriculture with two main subdivisions which are fruit tree and field crops. The soil types are developed on quaternary alluvial and colluvial materials derived from limestone. The major soil types found in the region are Inceptisols, Entisols, Aridisols and inclusions of Mollisols and Vertisols. For the research purpose, it is not feasible to take the whole area at once in considering the watershed management work. Rather it is recommended to divide it into several smaller units, as catchments or sub-catchments, by considering its drainage system. Therefore, a pilot study area representing a
Image pre-processing (LANDSAT – TM)
DEM Analysis
Geometric correction Sub-catchments & Water drainage
Atmospheric correction
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sub-catchment was selected, wherein micro-watershed prioritization has been carried out using cutting across criteria such as hydro-geological, land use and landscape characteristics. 3.2. Digital elevation model The most important part for an accurate delineation of any watershed is the accuracy of the digital elevation model (DEM). The DEM data was derived from topographic maps of 1:50,000 scale by digitizing the contour lines at 20 m interval with additional 10 m contour lines in the mountain regions of more than 1000 m elevation. Then the contour lines were transferred into digital elevation model format. The accuracy and level of the produced DEM details of the study area is sufficient for watershed and hydrological studies in such areas and it is also comparable for the Landsat TM resolution. The DEM raster layer has been used as a primary imput for the hydrological modelling using GIS analysis tools. 3.3. Satellite image preparation The satellite images used for this study consists of two Landsat TM acquired in June and October 2006, which are comparable to spring and summer seasons. Selection of image combinations was based on the landscape characteristics basically vegetation growth pattern and soil types and provide optimal condition for surface soil modelling. Image pre-processing includes the geometric and radiometric processing steps. The geometric correction was performed using ground control points to establish the transformation between the image and map coordinates. The control points were taken from topographic maps and field work using GPS. The radiometric value assigned to each pixel was calculated using the cubic convolution method. The accuracy of geometric correction was ranged within a half pixel. The atmospheric correction was based on the modelling procedure which incorporates the topographic correction module into a radiative transfer code to correct the atmospheric and terrain-induced illumination effect. The hybrid classification approach which combines ISODATA and Maximum Likehood classification algorithms were applied on the images. The level of classification accuracy was improved by employing multi-temporal images and reached 84.2%. Spectral mixture analysis was used in this study in order to derive the vegetation density and distribution. The endmembers were derived using the pixel purity index method and the derived endmembers are representing the annual vegetation, perennial vegetation soil and rock. 3.4. Soil analysis
Advance image processing
Soil modeling
Inorganic carbon
Organic carbon
Vegetation analysis
Vegetation density
Land use types
Interpretation of Landscape Thematic Indices Fig. 1. Flow diagram shows the image processing procedure of Landsat images and deriving thematic parameters for landscape surface properties.
Soil samples from surface layer (0–1) cm were collected from the studied area. The sampling sites were carefully selected to represent all varieties of parent materials, soil types and of land use pattern. In total, 67 samples were selected along the study area, each sample was split into two sub-samples. One was used for spectral measurements, while the other was analyzed to study the soil chemical properties. The soil samples were air-dried and sieved using 2 mm sieves, and homogenized to a standard grain size. Spectral reflectance of soil samples were measured in the laboratory under sieved and homogenized conditions with an ASD Field Spec II spectrometer in 10 nm intervals between 0.35 and 2.5 lm using a reflectance standard of known reflectivity. The illumination source was positioned at 30° zenith angle and a 1000 W quartz-halogen lamp was putted at a distance of approximately 30 cm. The samples were laid out on a black coated board to minimize the external reflectance or backscattering. Soil chemical analysis including the organic and inorganic carbon content was analyzed by using an infrared cell in a high-frequency induction
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oven (LECO). The analysis involved two phases, one between 200 °C and 550 °C for organic carbon and the other between 550 °C and 1050 °C for inorganic carbon. The CO2 flow is continuously detected by an infrared cell while the temperature is increased at a rate of 200 °C min1. 3.5. Modelling of soil properties
tory. The relationship between CIE colour and inorganic carbon concentration was investigated by using the regression analysis. The best result for linear regression between colour coordinate and inorganic content was achieved using the variable Y and y as given in Eq. (7) with a cross validation values of a r 2cv = 0.79 and RMSEcv = 0.934.
C:inorg ¼ 0:657 Y 0:412 y
The soil colours have been used to model the soil chemical properties in order to facilitate the integration of this model on the satellite images. Quantitative model had been developed between the CIE colour and soil organic and inorganic carbon content. The CIE. normal colour values were calculated from reflectance measurements of soil samples performed in the labora-
ð7Þ
On the other hand, estimation of the organic carbon contents shows a clear relationship between the organic carbon content and the CIE colour variable Y. The best result for linear regression between colour component and organic content was achieved using the variable Y as given in Eq. (8) with a cross validation values of a r2cv = 0.906 and RMSEcv = 0.283.
Fig. 2. Location map of the study area in the northern part of Jordan.
Z. Makhamreh / Physics and Chemistry of the Earth 36 (2011) 213–220
C:org ¼ 0:961 Y
ð8Þ
The spectral measurement data was used to derive the empirical soil reflection curve and incidence of absorption channels. The reflection value for the Landsat channels was calculated from the continuous reflection measurements. Accordingly, the CIE colour functions x, y and Y have been calculated using Eqs. (1)–(5). The next step was transferring the models into the satellite images which involve applying the derived models on the colour parameters, and then prediction the spatial distribution of organic and inorganic carbon concentration using Landsat images. The model has been applied on a summer acquisition image time where the vegetation density reaching minimum values in such regions, and is applied on soil surface having less than 25% vegetation density.
4. Results and discussion The study emphasize on identification and prioritization of subwatersheds for their development and management of the water harvesting on a sustainable basis. The advantage of the applied approach in this study is that it takes into consideration both the physical and actual landscape conditions. It considers the current landscape properties and the effect of man made changes on the surface drainage network and direction of water flow.
217
The various themes have been integrated in order to optimize the water harvesting and management. The main derived themes are soil organic and inorganic carbon, vegetation density, land cover types and digital elevation model, while the secondary layers used in the analysis are drainage network, rainfall map and soil types. The digital elevation model of the study area was used in determining the drainage pattern, direction and the sub-catchments boundary, and in formation of the slope characteristics as given in Fig. 3. The drainage pattern of any terrain reflects the characteristics of the surface as well as sub-surface information. The density of drainage networks indicates the closeness of spacing of channels in the mountains parts, while in the plains it shows low density. Thus drainage density characterizes the run-off in the area and determines the quantum of rainwater that could have infiltrated into sub-surface and vice versa. Slope is one of the major controlling factors in the development and formation of different landforms and controlling the surface and sub-surface water movements. Higher slope area facilitates high run-off allowing less residence time for rainwater, whereas in the gentle slope area the surface run-off is slow, allowing more time for rainwater to percolate and hence comparatively more infiltration. Slope analysis show two distinctive physiographic provinces in the study area. The mountainous regions in the western part, its relief characterized by dissected and exhibited steep
Fig. 3. The digital elevation model of the study area derived from a topographic maps at 20 m contour line intervals.
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Z. Makhamreh / Physics and Chemistry of the Earth 36 (2011) 213–220
slopes, with slopes reaching up to 60%, but mostly within 5–25%. The central part of the study area is characterized by undulating plains with slopes ranging from 1% to 8%. The south and southwest part comprises rolling hills with slopes exceeding 8% and can reach as high as 40%. The eastern plain has generally undulating relief with slope less than 15%, but predominantly less than 5%. Concentration of organic and inorganic carbon is highly related to the climatic conditions, vegetation abundance and also reflects the surface landscape conditions. Investigating the spatial distribution of the soil’s organic and inorganic carbon gives actual representation of the soil, hydrological and vegetation conditions in the study area as shown in Figs. 4 and 5 respectively. High concentrations of organic carbon result from high moisture content and vegetation abundance which contribute to the accumulation of organic matter on the soil surface, while low concentrations are related to the presence of dry conditions, low level of moisture content, and low vegetation density. The predicted values of the organic carbon represent the spatial pattern of the landscape, in which the drainage network shows very obvious boundaries by having higher organic matter content than the surrounding areas. The concentration in the mountains and high elevation areas reached up to 4%, while it reaches up to 0.2% in the eastern plain. The central parts characterized by a low to medium organic matter content ranges between 0.5% and 2%, with a high le-
vel of variability. These differences are explained on basis of the spatial characteristics of the landscape properties, which are in accordance with the topographic characteristics of the DEM result as shown in Fig. 3. On the other hand, concentration of soil inorganic carbon is related to the rainfall amounts and type of parent material. The spatial distribution of inorganic carbon pattern represents the surface characteristics of the landscape, in which the concentration in the western part reaches about 0.5%; it increases in the middle parts between 4% and 6%, while it reaches more than 9% for the built up and construction objects. At the mountain summits and high elevation areas there is a relatively high concentration of inorganic carbon and low concentration of organic carbon due to the presence of rocks, stones and shallow soils on the surface. In addition, low moisture availability accounts for the accumulation of carbonate on the surface and hence gives high concentrations of inorganic carbon. However, concentration of the inorganic carbon in the drainage network reach around 3%, this value corresponds to the content of well developed soil in the study area. Low concentration of inorganic carbon and high concentration of organic carbon in the water drainage network returned to the water flow paths, and water run-off areas. Investigation in the spatial distributions of both indicators indicate that the concentration of organic carbon and inorganic
Fig. 4. Spatial distribution of organic carbon content in the study area. The upper right part of the image (blue colour) is not covered with the Landsat scene. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Z. Makhamreh / Physics and Chemistry of the Earth 36 (2011) 213–220
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Fig. 5. Spatial distribution of inorganic carbon content in the study area. The upper right part of the image (blue colour) is not covered with the Landsat scene. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
carbon are conjoined with each other. These results are considered as evidence that the estimation of organic and inorganic carbon concentrations as well as the performance of both model represent the actual landscape conditions. It is noticeable that spatial distributions of both indicators explain the landscape characteristics and the potential of using these results in delineating the water harvesting catchments and sub-catchments in the study area. These indicators have been integrated in order to improve the allocation and selection of water-harvesting sites based on derived landscape properties and drainage network. Run-on/run-off surface properties have been spatially determined by investigating the distribution of surface properties of organic and inorganic carbon so that the run-off areas have been spatially determined with high detailed using inorganic carbon. On the other hand, run-on areas have been determined by using spatial distribution of organic carbon of the surface. This approach can be applied on different mediterranean and dry land regions and for countries having landscape conditions that are similar to countries such as Syria and Lebanon. Remote sensing derived landscape conditions, DEM and GIS hydrological tool analysis have been integrated in order to optimize the selection of the water-harvesting sites within the different catchments. Integration of soil surface indicators and DEM are very useful in determination and selection of on-farm water
harvesting, allocating dams for sub-catchments, and improve the watershed management practices. 5. Conclusion This paper presents an integrated approach for developing water harvesting and management of sub-watersheds in Mediterranean basins using field analysis and Landsat images. Derived indicators of landscape conditions from remote sensing data, combined with DEM and GIS hydrological analysis tools have been used to optimize delineation and selection of the water-harvesting sites. The run-off areas have been spatially determined with high details using inorganic carbon, while run-on areas have been determined by using spatial distribution of organic carbon of the surface. Integration of soil surface indicators and DEM are very useful to determine and to select on-farm water harvesting, allocating local dams for sub-catchments, and to improve the watershed management practices. The advantage of this approach is that it takes into consideration both the physical and current landscape conditions. It considers the landscape surface properties and the effect of built up areas and human construction objects on the surface drainage network and direction of water flow. While the traditional watershed management approaches consider the physical landscape characteristics and do not consider the actual landscape conditions.
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References Baumgardner, M.F., Silva, L.F., Biehl, L.L., Stoner, E.R., 1985. Reflectance properties of soils. Advances in Agronomy 38, 1–44. CIE (International Commision on Illumination), 1931. In: Proc. 8th Session. Cambridge. Escadafal, R., Girard, M.C., Courault, D., 1988. Modelling the relationship between Munsell soil color and soil spectral properties. International Agrophysics 4, 249–261. Gangodagamage, C., Clarke, A.C., 2001. Hydrological modeling using remote sensing and GIS. In: Twenty second Asian Conference on Remote Sensing, Singapore, 5– 9 November, 2001. Gosain, K., Sandhya, R., 2004. GIS based technologies for watershed management. Current Science 87 (7), 948–953. Hill, J., Schütt, B., 2000. Mapping complex patterns of erosion and stability in dry Mediterranean ecosystems. Remote Sensing of Environment 74, 557–569. Kahinda, M.J., Boroto, R.J., Taigbenu, A.E., 2005. Developing an integrated water resources management and rainwater harvesting system in South Africa. In: 12th SANCIAHS Symposium, Midrand, 5–7 September. Korkalainen, H.J., Lauren, A.M., Kokkonen, T.S., 2007. A GIS-based analysis of catchment properties within a drumlin field. Borel Environment Research 12, 489–500. Kumar, M.G., Agarwal, A.K., Rameshwar, B., 2008. Delineation of potential sites for water harvesting structures using remote sensing and GIS. Journal of the Indian Society of Remote Sensing 36, 323–334. Makhamreh, Z., 2006. Evaluation of Soil Quality and Development Stage Using Spectral Reflectance of Soils: a Case Study in Eastern Mediterranean Region. Scientific Workshop of AK BoGeo and Desert Net: Soil and Desertification. Hamburg, Germany.
Mbilinyi, B.P., Tumbo, S.D., Mahoo, H.F.m., Mkiramwinyi, F.O., 2007. GIS based decision support system for identifying potential sites for rainwater harvesting. Physics and Chemistry of the Earth 32, 1074–1081. Pandey, V.K., Panda, S.N., Raghuwanshi, N.S., Sudhakar, S., 2006. Delineation and parametrization of Banikdih watershed using remote sensing and Avawat model. Journal of Indian Society of Remote Sensing 34, 144–152. Ramakrishnan, D., Durga Rao, K.H.V., Tiwari, K.C., 2008. Delineation of potential sites for water harvesting structures through remote sensing and GIS techniques: a case study of Kali watershed, Gujarat, India. Geocarto International 23 (2), 95–108. Schiettecattea, W., Ouessarb, M., Gabrielsa, D., Tanghea, S., Heirmana, S., Abdellib, F., 2005. Impact of water harvesting techniques on soil and water conservation: a case study on a micro catchments in southeastern Tunisia. Journal of Arid Environments 61, 297–313. Sekar, I., Randhir, T.O., 2007. Spatial assessment of conjunctive water harvesting potential in watershed systems. Journal of Hydrology 334, 39–52. Srinivasa, S., Govindaiah, S., Honne, H.G., 2008. Prioritization of sub-watersheds for sustainable development and management of natural resources: an integrated approach using remote sensing, GIS and socio-economic data. Current Science 95 (3), 10. Winnaar, G., Jewitt, W., Horan, M., 2007. A GIS based approach for identifying potential runoff harvesting sites in the Thukela River basin, South Africa. Physics and Chemistry of the Earth 32, 1058–1067. Ziadat, F.M., Mazahreh, S.S., Oweis, T.Y., Bruggeman, A., 2006. A GIS based approach for assessing water harvesting suitability in a Badia benchmark watershed in Jordan. In: Fourteenth International Soil Conservation Organisation Conference: Water Management and Soil Conservation in Semi-Arid Environments, Marrakech, Morocco, 14–19 May.
Contents lists available at ScienceDirect
Physics and Chemistry of the Earth journal homepage: www.elsevier.com/locate/pce
Using remote sensing approach and surface landscape conditions for optimization of watershed management in Mediterranean regions Zeyad Makhamreh ⇑ Department of Geography, University of Jordan, 11942 Amman, Jordan
a r t i c l e
i n f o
Article history: Received 7 March 2010 Received in revised form 29 July 2010 Accepted 22 August 2010 Available online 17 September 2010 Keywords: Landsat Watershed management Organic carbon Soil colour Jordan
a b s t r a c t Identification of potential sites for water harvesting is a prerequisite for improving watershed management in the semi-arid and dry Mediterranean regions. The main objective of this work is to identify and optimize the potential water-harvesting sites in Jordan based on the characterization of surface landscape conditions using DEM and remote sensing techniques. In this study, the vegetation abundance and distribution was derived using spectral mixture analysis. In order to characterize the surface landscape conditions, a mathematical model has been established between soil colour and soil surface properties to derive the spatial distribution of soil organic and inorganic content using Landsat images. Based on these results the current distribution of the hydrological pattern has been derived, and hence the spatial pattern of run-on and run-off areas have been determined. The run-on areas have been determined with high details utilizing organic carbon distribution, while the run-off areas have been determined based on spatial distribution of inorganic carbon. The high concentration of organic carbon indicate presence of excessive water moisture content and drainage network and hence the potential for water-harvesting sites is high. This approach takes into consideration both the physical and actual landscape conditions in allocation of suitable sites for water harvesting. Thus, integration of landscape information and DEM are useful for efficient management of watersheds and identification of potential water-harvesting sites on a sub-catchment basis. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Water harvesting and watershed management is an essential step for improving water availability in the semi-arid and dry Mediterranean regions. Subsequently, identification of potential sites for water harvesting is a prerequsite in such regions. Water scarce countries are subject to various hydrological constraints particularly within rural communities that are reliant on rainfed agriculture (Kahinda et al., 2005). Currently, the issue of water scarcity and resources management in Jordan is becoming an increasing concern on a national level. Jordan is classified among the top ten poorest countries worldwide in water resource availability. In addition, the agricultural water sector accounts for over 65% of the total consumption of water in the country. However, with rapidly growing demands of water for food production, alternative techniques for capturing and improving new water resources are highly required, and consequently, the ability to successfully manage the resulting rain water is extremely important (Sekar and Randhir, 2007).
⇑ Tel.: +962 796766162, office +962 6 5355000x24981; fax: +962 6 5300232. E-mail address: [email protected] 1474-7065/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.pce.2010.08.004
Water resource development programs are applied generally on watershed basis and thus, prioritization is essential for proper planning and management of natural resources for sustainable development (Srinivasa et al., 2008). Catchments and sub-catchments are the fundamental units for the management of land and water resources, and have been identified as the primary planning units for administrative purposes for the conservation of these precious resources (Pandey et al., 2006). Delineation and prioritization of watersheds within a large drainage basin is important for proper planning and management of natural resources for sustainable crop production. Arid and semi-arid areas of Jordan are characterized by low, highly variable and erratic distribution of rainfall. In addition, the climatic and soil surface properties in such areas prevails surface run-off. Due to these circumstances, run-off harvesting is becoming particularly significant because run-off water can be captured and efficiently utilized to maintain agricultural production in a sustainable manner (Ziadat et al., 2006). Approaches related to watershed management have experienced a vast development during the past decade, yet there is no universal methodology for achieving effective analysis. Many analyses approaches are limited by the information available for modelling parameters of concern (Ramakrishnan et al., 2008). However,
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the new developments in remote sensing and geographic information system technologies have evolved as a powerful tool in analysis and management of watershed and landscape conditions (Kumar et al., 2008). The concept of watershed management recognizes the interrelationships between land use, soil and water resources and the linkage between uplands and downstream areas. As the watershed management depend on land use, soil types, geology and drainage flows, this information and its dynamics can be derived from the satellite remote sensing data more precisely due to its synoptic coverage and high spatial and temporal resolutions (Schiettecattea et al., 2005; Winnaar et al., 2007). Remote sensing techniques provide standardized methods for quantitative analysis of landscape conditions at different scales, and thus can be used in order to characterize and optimize the selection of water-harvesting sites and to improve watershed management (Gosain and Sandhya, 2004). The integration of remote sensing data and GIS for hydrological characterization and modelling is particularly important for improving the spatial and temporal resolution of satellite images (Gangodagamage and Clarke, 2001; Mbilinyi et al., 2007), which allow for better understanding and representation of the hydrological processes in the landscape (Korkalainen et al., 2007). Surface landscape conditions are important for the shaping of the hydrological water network on the catchment basis (Kumar et al., 2008). Recent developments in remote sensing analysis techniques have proven to be a valuable contribution to providing information about landscape features (Hill and Schütt, 2000). Derived indicators of landscape conditions using remote sensing data, is of vital importance for deriving indicators about the hydrological network of the land surface (Srinivasa et al., 2008). Therefore, optimization of watershed management (e.g. selection of water-harvesting sites) can be improved by integration of surface actual landscape conditions and other physical factors. The potential of run-off water harvesting in Jordan has not yet been fully explored and therefore remote sensing techniques could be used to explore the spatial and temporal water drainage networks. Therefore, the objective of this paper is to integrate remote sensing and DEM information in order to improve the identification and selection of potential water-harvesting sites for watershed management, while taking into consideration the current landscape conditions.
2. Theoretical background Spectral reflectance of soils is determined by their physical and chemical compositions. Characterization of landscape conditions can be achieved by using soil surface properties such as carbonate and organic matter (Baumgardner et al., 1985). Prediction of the spatial distribution of soil surface properties is a prerequisite for landscape characterizations; this can be obtained by modelling soil organic and inorganic carbon especially for soils developed on Mediterranean conditions (Makhamreh, 2006). Different approaches have been used for the soil modelling purposes such as using selected wavelength, mathematical functions, total spectral reflection or derived parameters such as soil colour. Soil colours are primarily related to their content from organic matter, carbonate and iron oxides (Baumgardner et al., 1985). It is a covariant property, providing information as to the characteristics of soils that facilitate a distinction between soil types and an indirect estimate of other soil properties. Soil colour value has been identified in many studies affecting the amount of energy reflected from soil surfaces using Landsat sensor (Escadafal et al., 1988). Colour science is based on the observations that all colours may be reproduced by adding the three primaries red, green, and blue.
Considering that a spectral reflectance curve is the sum of elementary monochromatic reflections, the colour coordinate of any nontransparent objects can then be easily calculated. The most popular quantitative approaches for colour measurement is the International Commission on Illumination (CIE, 1931) colour system. Many studies have been established quantitative relationships between soil colour parameters and chemical soil properties. For calculation of the CIE colour parameters, the primary valence X, Y, Z can be determined from the spectral functions (x; y; z) values according to Eq. (1) (Escadafal et al., 1988).
X¼
770 nm X
qðkÞ SðkÞ xðkÞ
380 nm
Y¼
770 nm X
qðkÞ SðkÞ yðkÞ
380 nm
Z¼
770 nm X
qðkÞ SðkÞ zðkÞ
ð1Þ
380 nm
where SðkÞ is the spectral irradiance of the light (xÞðk;yðkÞ; zðkÞ). The relationship between the CIE trichromatic coordinate X, Y, Z and the R, G, B primaries colour coefficients are defined by the following equations (Escadafal et al., 1988).
X ¼ 2:7659R þ 1:7519G þ 1:1302B
ð2Þ
Y ¼ R þ 4:5909G þ 0:06012B
ð3Þ
Z ¼ 0:0565G þ 5:59440B
ð4Þ
These components were chosen so that Y corresponds to the degree of brightness according to its definition; consequently X and Z have no physical reality. The relation of the spectral curve to the total spectral value gives the trichromatic coordinates x, y, z to be calculated as follows
x¼
X XþY þZ
y¼
Y XþY þZ
z¼
Z XþY þZ
ð5Þ
where
xþyþz¼1
ð6Þ
The normal colour value Y is proportional to light intensity of colour values and therefore the normal spectral function represent Y as a measurement tool for the colour brightness. Accordingly, the colour can be defined taken into consideration the Eq. (5), colour brightness Y and normal colour value x and y. 3. Methodology The main objective of this paper is to identify the potential water-harvesting sites in the study area, based on the surface landscape conditions by the modelling of soil surface properties using remote sensing techniques and field analysis. For modelling purposes, quantitative analysis of Landsat images require geometric and radiometric correction. Therefore, the main image preparation steps included image pre-processing and the advance analytical techniques for deriving of soil and vegetation thematic indicators. Image pre-processing includes the geometric and radiometric processing steps, while the advanced digital image analysis involves deriving of vegetation and soil surface indicators. Deriving indicators for the evaluation of the vegetation conditions includes analyzing the land use types and vegetation density, On the other hand, deriving indicators for assessment of soil conditions are based on modelling of soil organic and inorganic carbon using CIE colour system as shown in Fig. 1.
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3.1. Study area The study area occupies the eastern part of the Mediterranean region in northern Jordan, as shown in Fig. 2. It is located between Latitudes 32°150 and 32°300 north and Longitudes 35°450 and 36°150 East and covers an area of about 1000 km2. The prevailing climate is of arid Mediterranean type and characterized by dry hot summers and mild wet winters. Most of the precipitation occurs during the winter months November–April. Annual and seasonal rainfall variability is very high both in space and time. This diversity ranges from semi-arid conditions where average rainfall amounts are about 150 mm, to semi-humid conditions where the average rainfall is about 650 mm. Monthly variations in temperature ranges from 5.2 °C in the coldest month to 22.0 °C in the warmest month. While the mean daily minimum temperature drops to 2.5 °C for the month of January, the mean daily maximum temperature reaches 28 °C in the month of August. Land cover varies between natural, semi-natural and cultivated areas. The dominant oak forest species is Quercus coccifera, covering the mountains area, while the dominant shrubs are Artemesia herba-alba covering mainly the transitional rainfall zone. Cultivated lands are characterized by rainfed agriculture with two main subdivisions which are fruit tree and field crops. The soil types are developed on quaternary alluvial and colluvial materials derived from limestone. The major soil types found in the region are Inceptisols, Entisols, Aridisols and inclusions of Mollisols and Vertisols. For the research purpose, it is not feasible to take the whole area at once in considering the watershed management work. Rather it is recommended to divide it into several smaller units, as catchments or sub-catchments, by considering its drainage system. Therefore, a pilot study area representing a
Image pre-processing (LANDSAT – TM)
DEM Analysis
Geometric correction Sub-catchments & Water drainage
Atmospheric correction
215
sub-catchment was selected, wherein micro-watershed prioritization has been carried out using cutting across criteria such as hydro-geological, land use and landscape characteristics. 3.2. Digital elevation model The most important part for an accurate delineation of any watershed is the accuracy of the digital elevation model (DEM). The DEM data was derived from topographic maps of 1:50,000 scale by digitizing the contour lines at 20 m interval with additional 10 m contour lines in the mountain regions of more than 1000 m elevation. Then the contour lines were transferred into digital elevation model format. The accuracy and level of the produced DEM details of the study area is sufficient for watershed and hydrological studies in such areas and it is also comparable for the Landsat TM resolution. The DEM raster layer has been used as a primary imput for the hydrological modelling using GIS analysis tools. 3.3. Satellite image preparation The satellite images used for this study consists of two Landsat TM acquired in June and October 2006, which are comparable to spring and summer seasons. Selection of image combinations was based on the landscape characteristics basically vegetation growth pattern and soil types and provide optimal condition for surface soil modelling. Image pre-processing includes the geometric and radiometric processing steps. The geometric correction was performed using ground control points to establish the transformation between the image and map coordinates. The control points were taken from topographic maps and field work using GPS. The radiometric value assigned to each pixel was calculated using the cubic convolution method. The accuracy of geometric correction was ranged within a half pixel. The atmospheric correction was based on the modelling procedure which incorporates the topographic correction module into a radiative transfer code to correct the atmospheric and terrain-induced illumination effect. The hybrid classification approach which combines ISODATA and Maximum Likehood classification algorithms were applied on the images. The level of classification accuracy was improved by employing multi-temporal images and reached 84.2%. Spectral mixture analysis was used in this study in order to derive the vegetation density and distribution. The endmembers were derived using the pixel purity index method and the derived endmembers are representing the annual vegetation, perennial vegetation soil and rock. 3.4. Soil analysis
Advance image processing
Soil modeling
Inorganic carbon
Organic carbon
Vegetation analysis
Vegetation density
Land use types
Interpretation of Landscape Thematic Indices Fig. 1. Flow diagram shows the image processing procedure of Landsat images and deriving thematic parameters for landscape surface properties.
Soil samples from surface layer (0–1) cm were collected from the studied area. The sampling sites were carefully selected to represent all varieties of parent materials, soil types and of land use pattern. In total, 67 samples were selected along the study area, each sample was split into two sub-samples. One was used for spectral measurements, while the other was analyzed to study the soil chemical properties. The soil samples were air-dried and sieved using 2 mm sieves, and homogenized to a standard grain size. Spectral reflectance of soil samples were measured in the laboratory under sieved and homogenized conditions with an ASD Field Spec II spectrometer in 10 nm intervals between 0.35 and 2.5 lm using a reflectance standard of known reflectivity. The illumination source was positioned at 30° zenith angle and a 1000 W quartz-halogen lamp was putted at a distance of approximately 30 cm. The samples were laid out on a black coated board to minimize the external reflectance or backscattering. Soil chemical analysis including the organic and inorganic carbon content was analyzed by using an infrared cell in a high-frequency induction
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oven (LECO). The analysis involved two phases, one between 200 °C and 550 °C for organic carbon and the other between 550 °C and 1050 °C for inorganic carbon. The CO2 flow is continuously detected by an infrared cell while the temperature is increased at a rate of 200 °C min1. 3.5. Modelling of soil properties
tory. The relationship between CIE colour and inorganic carbon concentration was investigated by using the regression analysis. The best result for linear regression between colour coordinate and inorganic content was achieved using the variable Y and y as given in Eq. (7) with a cross validation values of a r 2cv = 0.79 and RMSEcv = 0.934.
C:inorg ¼ 0:657 Y 0:412 y
The soil colours have been used to model the soil chemical properties in order to facilitate the integration of this model on the satellite images. Quantitative model had been developed between the CIE colour and soil organic and inorganic carbon content. The CIE. normal colour values were calculated from reflectance measurements of soil samples performed in the labora-
ð7Þ
On the other hand, estimation of the organic carbon contents shows a clear relationship between the organic carbon content and the CIE colour variable Y. The best result for linear regression between colour component and organic content was achieved using the variable Y as given in Eq. (8) with a cross validation values of a r2cv = 0.906 and RMSEcv = 0.283.
Fig. 2. Location map of the study area in the northern part of Jordan.
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C:org ¼ 0:961 Y
ð8Þ
The spectral measurement data was used to derive the empirical soil reflection curve and incidence of absorption channels. The reflection value for the Landsat channels was calculated from the continuous reflection measurements. Accordingly, the CIE colour functions x, y and Y have been calculated using Eqs. (1)–(5). The next step was transferring the models into the satellite images which involve applying the derived models on the colour parameters, and then prediction the spatial distribution of organic and inorganic carbon concentration using Landsat images. The model has been applied on a summer acquisition image time where the vegetation density reaching minimum values in such regions, and is applied on soil surface having less than 25% vegetation density.
4. Results and discussion The study emphasize on identification and prioritization of subwatersheds for their development and management of the water harvesting on a sustainable basis. The advantage of the applied approach in this study is that it takes into consideration both the physical and actual landscape conditions. It considers the current landscape properties and the effect of man made changes on the surface drainage network and direction of water flow.
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The various themes have been integrated in order to optimize the water harvesting and management. The main derived themes are soil organic and inorganic carbon, vegetation density, land cover types and digital elevation model, while the secondary layers used in the analysis are drainage network, rainfall map and soil types. The digital elevation model of the study area was used in determining the drainage pattern, direction and the sub-catchments boundary, and in formation of the slope characteristics as given in Fig. 3. The drainage pattern of any terrain reflects the characteristics of the surface as well as sub-surface information. The density of drainage networks indicates the closeness of spacing of channels in the mountains parts, while in the plains it shows low density. Thus drainage density characterizes the run-off in the area and determines the quantum of rainwater that could have infiltrated into sub-surface and vice versa. Slope is one of the major controlling factors in the development and formation of different landforms and controlling the surface and sub-surface water movements. Higher slope area facilitates high run-off allowing less residence time for rainwater, whereas in the gentle slope area the surface run-off is slow, allowing more time for rainwater to percolate and hence comparatively more infiltration. Slope analysis show two distinctive physiographic provinces in the study area. The mountainous regions in the western part, its relief characterized by dissected and exhibited steep
Fig. 3. The digital elevation model of the study area derived from a topographic maps at 20 m contour line intervals.
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slopes, with slopes reaching up to 60%, but mostly within 5–25%. The central part of the study area is characterized by undulating plains with slopes ranging from 1% to 8%. The south and southwest part comprises rolling hills with slopes exceeding 8% and can reach as high as 40%. The eastern plain has generally undulating relief with slope less than 15%, but predominantly less than 5%. Concentration of organic and inorganic carbon is highly related to the climatic conditions, vegetation abundance and also reflects the surface landscape conditions. Investigating the spatial distribution of the soil’s organic and inorganic carbon gives actual representation of the soil, hydrological and vegetation conditions in the study area as shown in Figs. 4 and 5 respectively. High concentrations of organic carbon result from high moisture content and vegetation abundance which contribute to the accumulation of organic matter on the soil surface, while low concentrations are related to the presence of dry conditions, low level of moisture content, and low vegetation density. The predicted values of the organic carbon represent the spatial pattern of the landscape, in which the drainage network shows very obvious boundaries by having higher organic matter content than the surrounding areas. The concentration in the mountains and high elevation areas reached up to 4%, while it reaches up to 0.2% in the eastern plain. The central parts characterized by a low to medium organic matter content ranges between 0.5% and 2%, with a high le-
vel of variability. These differences are explained on basis of the spatial characteristics of the landscape properties, which are in accordance with the topographic characteristics of the DEM result as shown in Fig. 3. On the other hand, concentration of soil inorganic carbon is related to the rainfall amounts and type of parent material. The spatial distribution of inorganic carbon pattern represents the surface characteristics of the landscape, in which the concentration in the western part reaches about 0.5%; it increases in the middle parts between 4% and 6%, while it reaches more than 9% for the built up and construction objects. At the mountain summits and high elevation areas there is a relatively high concentration of inorganic carbon and low concentration of organic carbon due to the presence of rocks, stones and shallow soils on the surface. In addition, low moisture availability accounts for the accumulation of carbonate on the surface and hence gives high concentrations of inorganic carbon. However, concentration of the inorganic carbon in the drainage network reach around 3%, this value corresponds to the content of well developed soil in the study area. Low concentration of inorganic carbon and high concentration of organic carbon in the water drainage network returned to the water flow paths, and water run-off areas. Investigation in the spatial distributions of both indicators indicate that the concentration of organic carbon and inorganic
Fig. 4. Spatial distribution of organic carbon content in the study area. The upper right part of the image (blue colour) is not covered with the Landsat scene. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Fig. 5. Spatial distribution of inorganic carbon content in the study area. The upper right part of the image (blue colour) is not covered with the Landsat scene. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
carbon are conjoined with each other. These results are considered as evidence that the estimation of organic and inorganic carbon concentrations as well as the performance of both model represent the actual landscape conditions. It is noticeable that spatial distributions of both indicators explain the landscape characteristics and the potential of using these results in delineating the water harvesting catchments and sub-catchments in the study area. These indicators have been integrated in order to improve the allocation and selection of water-harvesting sites based on derived landscape properties and drainage network. Run-on/run-off surface properties have been spatially determined by investigating the distribution of surface properties of organic and inorganic carbon so that the run-off areas have been spatially determined with high detailed using inorganic carbon. On the other hand, run-on areas have been determined by using spatial distribution of organic carbon of the surface. This approach can be applied on different mediterranean and dry land regions and for countries having landscape conditions that are similar to countries such as Syria and Lebanon. Remote sensing derived landscape conditions, DEM and GIS hydrological tool analysis have been integrated in order to optimize the selection of the water-harvesting sites within the different catchments. Integration of soil surface indicators and DEM are very useful in determination and selection of on-farm water
harvesting, allocating dams for sub-catchments, and improve the watershed management practices. 5. Conclusion This paper presents an integrated approach for developing water harvesting and management of sub-watersheds in Mediterranean basins using field analysis and Landsat images. Derived indicators of landscape conditions from remote sensing data, combined with DEM and GIS hydrological analysis tools have been used to optimize delineation and selection of the water-harvesting sites. The run-off areas have been spatially determined with high details using inorganic carbon, while run-on areas have been determined by using spatial distribution of organic carbon of the surface. Integration of soil surface indicators and DEM are very useful to determine and to select on-farm water harvesting, allocating local dams for sub-catchments, and to improve the watershed management practices. The advantage of this approach is that it takes into consideration both the physical and current landscape conditions. It considers the landscape surface properties and the effect of built up areas and human construction objects on the surface drainage network and direction of water flow. While the traditional watershed management approaches consider the physical landscape characteristics and do not consider the actual landscape conditions.
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References Baumgardner, M.F., Silva, L.F., Biehl, L.L., Stoner, E.R., 1985. Reflectance properties of soils. Advances in Agronomy 38, 1–44. CIE (International Commision on Illumination), 1931. In: Proc. 8th Session. Cambridge. Escadafal, R., Girard, M.C., Courault, D., 1988. Modelling the relationship between Munsell soil color and soil spectral properties. International Agrophysics 4, 249–261. Gangodagamage, C., Clarke, A.C., 2001. Hydrological modeling using remote sensing and GIS. In: Twenty second Asian Conference on Remote Sensing, Singapore, 5– 9 November, 2001. Gosain, K., Sandhya, R., 2004. GIS based technologies for watershed management. Current Science 87 (7), 948–953. Hill, J., Schütt, B., 2000. Mapping complex patterns of erosion and stability in dry Mediterranean ecosystems. Remote Sensing of Environment 74, 557–569. Kahinda, M.J., Boroto, R.J., Taigbenu, A.E., 2005. Developing an integrated water resources management and rainwater harvesting system in South Africa. In: 12th SANCIAHS Symposium, Midrand, 5–7 September. Korkalainen, H.J., Lauren, A.M., Kokkonen, T.S., 2007. A GIS-based analysis of catchment properties within a drumlin field. Borel Environment Research 12, 489–500. Kumar, M.G., Agarwal, A.K., Rameshwar, B., 2008. Delineation of potential sites for water harvesting structures using remote sensing and GIS. Journal of the Indian Society of Remote Sensing 36, 323–334. Makhamreh, Z., 2006. Evaluation of Soil Quality and Development Stage Using Spectral Reflectance of Soils: a Case Study in Eastern Mediterranean Region. Scientific Workshop of AK BoGeo and Desert Net: Soil and Desertification. Hamburg, Germany.
Mbilinyi, B.P., Tumbo, S.D., Mahoo, H.F.m., Mkiramwinyi, F.O., 2007. GIS based decision support system for identifying potential sites for rainwater harvesting. Physics and Chemistry of the Earth 32, 1074–1081. Pandey, V.K., Panda, S.N., Raghuwanshi, N.S., Sudhakar, S., 2006. Delineation and parametrization of Banikdih watershed using remote sensing and Avawat model. Journal of Indian Society of Remote Sensing 34, 144–152. Ramakrishnan, D., Durga Rao, K.H.V., Tiwari, K.C., 2008. Delineation of potential sites for water harvesting structures through remote sensing and GIS techniques: a case study of Kali watershed, Gujarat, India. Geocarto International 23 (2), 95–108. Schiettecattea, W., Ouessarb, M., Gabrielsa, D., Tanghea, S., Heirmana, S., Abdellib, F., 2005. Impact of water harvesting techniques on soil and water conservation: a case study on a micro catchments in southeastern Tunisia. Journal of Arid Environments 61, 297–313. Sekar, I., Randhir, T.O., 2007. Spatial assessment of conjunctive water harvesting potential in watershed systems. Journal of Hydrology 334, 39–52. Srinivasa, S., Govindaiah, S., Honne, H.G., 2008. Prioritization of sub-watersheds for sustainable development and management of natural resources: an integrated approach using remote sensing, GIS and socio-economic data. Current Science 95 (3), 10. Winnaar, G., Jewitt, W., Horan, M., 2007. A GIS based approach for identifying potential runoff harvesting sites in the Thukela River basin, South Africa. Physics and Chemistry of the Earth 32, 1058–1067. Ziadat, F.M., Mazahreh, S.S., Oweis, T.Y., Bruggeman, A., 2006. A GIS based approach for assessing water harvesting suitability in a Badia benchmark watershed in Jordan. In: Fourteenth International Soil Conservation Organisation Conference: Water Management and Soil Conservation in Semi-Arid Environments, Marrakech, Morocco, 14–19 May.