ISSN 10642293, Eurasian Soil Science, 2015, Vol. 48, No. 10, pp. 1159–1169. © Pleiades Publishing, Ltd., 2015. Published in Russian in Pochvovedenie, 2015, No. 10, pp. 1277–1288.

DEGRADATION, CONSERVATION, AND REHABILITATION OF SOILS

Impacts of Soil Sealing on Potential Agriculture in Egypt Using Remote Sensing and GIS Techniques1 Elsayed Said Mohamed*, Abdelaziz Belal and Adel Shalaby National Authority for Remote sensing and Space Sciences (NARSS), 23 Joseph Brows Tito, ElNozha Elgedid, P.O. Box:1564 Alfmaskan, Cairo, Egypt email: [email protected]; [email protected] Received March 7, 2014

Abstract—This paper highlights the impacts of soil sealing on the agricultural soils in Nile Delta using remote sensing and GIS. The current work focuses on two aims. The first aim is to evaluate soil productivity lost to urban sprawl, which is a significant cause of soil sealing in Nile Delta. The second aim is to evaluate the Land Use and Land Cover Changes (LU LC) from 2001 to 2013 in ElGharbia governorate as a case study. Three temporal data sets of images from two different sensors: Landsat 7 Enhanced Thematic Mapper (ETM+) with 30 m res olution acquired in 2001 and Landsat 8 acquired in 2013 with 30 m resolution, and Egypt sat acquired in 2010 with 7.8 m resolution, consequently were used. Four different supervised classification techniques (Maximum Likelihood (ML), Minimum Distance, Neural Networks (NN); and Support Vector Machine (SVM) were applied to monitor the changes of LULC in the investigated area. The results showed that the agricultural soils of the investigated area are characterized by high soil productivity depending on its chemical and physical prop erties. During 2010–2013, soil sealing took place on 1397 ha from the study area which characterized by soil productivity classes ranging between I and II. It is expected that the urban sprawl will be increased to 12.4% by 2020 from the study area, which means that additional 3400 ha of productive soils will be lost from agriculture. However, population growth is the most significant factor effecting urban sprawl in Nile Delta. Keywords: land use, land cover, change detection, soil productivity DOI: 10.1134/S1064229315100075 1

INTRODUCTION

Agriculture in Egypt represents a corestone in the national economy and soils in Nile delta are the most suitable for agricultural in Egypt. Only approximately 4% of Egypt’s total area are agricultural land. The remaining 96% of the land is arid desert. Seen from this perspective, the need for reclamation of the desert appears inevitable in light of continuing population growth and increasing congestion in the longsettled lands in the Nile valley and the delta [1]. The govern ment aims also to redirect the loss of agricultural land driving forces away from the old and highly productive agricultural land of the Nile Delta by applying an effective horizontal urban expansion and reclaim more land along the desert areas and near the fringes of the Nile Delta [3]. Land degradation and urban sprawl are the most common issues that threatens the ongoing agricultural activities and prohibits further reclamation expansions [13, 14, 19]. Soil sealing is the permanent covering of land by impermeable or semi impermeable artificial materials. Due to further land take of settlement and transport areas and its related conversion of mostly agricultural land into builtup Areas [17]. Soil sealing is a degradation problem that 1 The article is published in the original.

involves different factors, and the loss of land resources in addition, remote sensing became one of the most important tools for studying soil sealing based on coregistered multitemporal remote sensing data, therefore it can identify changes between two or more dates that is uncharacteristic of normal variation [2, 18]. The synoptic capability of remote sensing pro vides a useful reconnaissance tool to target more detailed field surveys. The availability of Landsat images given the opportunity to observe changes in land use and land cover, Landsat satellite data is the most widely used data types for land cover mapping and has provided earth observation data to meet a wide range of information needs [6, 10, 12]. Several changes detection techniques have been developed and used to perform, monitor, identify, describe, and quantify changes in LULC using remotely sensed data such as postclassification, comparison (PCC), image differencing, principle components analysis, and veg etation index [27]. The accuracy of the postclassifica tion comparisons of land cover is dependent on the accuracy of single initial classifications through time [5]. Soil productivity is defined as the capability of the soil for producing a specified quantity of plant produce per unit area and the ability to produce sequences of crops under a specified system of management. The

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Fig. 1. Location map of the studied area.

land quality is a complex attribute of land which acts in a manner distinct from the actions of other land qualities in its influence on the suitability of land for a specified kind of use as well as the ability of the land to achieve specific requirements for the land use types [7, 11, 26]. Productivity is the balance between the inher ent land resources with crop requirements, paying spe cial attention to the optimization of resource use towards achievement of sustained productivity over a long period [20, 21, 22]. The aim of this study is assess ing the consequences of the Egyptian revolution (2011) for agricultural soils in at ElGharbia Gover norate through: a—Assessment of the soil productivity lost to urbanization. b—Monitoring the urban sprawl before and after the Egyptian revolution using remote sensing tech niques. MATERIALS AND METHODS Location of Study Area and Data The investigated area is located in the Middle of Nile Delta in Egypt between longitudes 30°45′ and 31°20′ Е and latitudes 30°35′ and 31°15′ N. It is bor dered by the Governors of Kafr El Shiekh at the north and Monufiya to the south, and it is aligned with Damietta and Rosetta (Nile branches) at the east and west, respectively, as shown in Fig. 1.

Digital Image Processing Digital image processing was carried out using Multispectral Scanner Landsat 7 Enhanced Thematic Mapper (ETM+) acquired in May 2001, Landsat 8 with spatial resolution 30 m, acquired in July 2013 and Egypt sate acquired in May 2010 with spatial resolu tion 7.8 m. The scenes were selected to be geometri cally corrected and calibrated. Digital Elevation Model (DEM) of the study area has been generated from SRTM 30 m, and elevation points (recorded dur ing the field survey by GPS. Land Use Land Cover Changes Change detection of land use and land cover of the study area during the period from 2001 to 2013 was assessed by specification of joint models for multitem poral classification. Post classification change detection technique was applied as the most accurate method. To determine land use and land cover (LU LC) changes four classification methods were selected: (Maximum Likelihood (ML), Minimum Distance, Neural Net works (NN) and Support Vector Machine (SVM). These classification methods were applied on the images taken in 2001, 2010 and 2013 respectively. Com parison between the different methods was done to determine the most accurate method that can then be applied to monitor land use and land cover (LU LC) changes. EURASIAN SOIL SCIENCE

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Maximum Likelihood Classification Maximum likelihood classification was applied by calculating the following discriminant functions for each pixel in the image [15]. p(ωi/x) = p(x/ωi)p(ωi)p(x), where: p(ωi/x) is the desired and the available p(x/ωi) estimated from training data, are related; p(ωi) = probability that class ωi occurs in the image and is assumed the same for all classes; p(x) is the probability of finding a pixel with measurement vector х in the image, from any class. Minimum Distance Classification The minimum distance classifier calculates the dis tance to each mean vector, μck ck from each unknown pixel (BVijk). It is possible to calculate this distance using Euclidean distance based on the Pythagorean Theorem. The computation of the Euclidean distance from point to the mean of Class1 measured at band relies on the equation [15]. Dist =

2

2

( BV ijk – μ ck ) + ( BV ijl – μ cl ) ,

Where μck and μcl represent the mean vectors for class c measured in bands k and l. Many minimumdistance algorithms let the analyst specify a distance or thresh old from the class means beyond which a pixel will not be assigned to a category even though it is nearer to the mean of that category. Artificial Neural Network Classifier Artificial Neural Network (ANN) is used in this study. ANN technique uses standard backpropagation for supervised classification. Several hidden layers were selected to use and choose between a logistic or hyperbolic activation function. Learning occurs by adjusting the weights in the node to minimize the dif ference between the output node activation and the output. The error is backpropagated through the net work and weight adjustment is made using a recursive method [8]. Support Vector Machines (SVMs) SVMs have been used in many remote sensing based applications, for example, land use and land cover, forest and agriculture tasks. SVMs classifier turned out to be an effective method of handling not only the complex distributions of the heterogeneous land cover classes that characterized the study area but also in various spatial resolution scales. SVM is a clas sification system derived from statistical learning the ory that provides good classification results from com plex and noisy data. The SVM classifier provides four types of kernels: linear, polynomial, radial basis func EURASIAN SOIL SCIENCE

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tion (RBF), and signed [4]. Land use /land cover was classified based on sigmoid equation as follow: Sigmoid K(xi, xj) = tan(g xTxj xj + r), where: K(xi, xj) is kernel function g is the gamma term in the kernel function for all kernel types and r is the bias term in the kernel function for the polynomial and sig moid kernels. Supervised training was adopted in this study. Groups of neighboring pixels were selected as training samples in the class signatures as agriculture, water and urban. For each class at each data set, the overall and individual Kappa coefficients are calculated for each confusion matrix to evaluate the agreement between the classification results and reference data. A random sample of 260 reference points was selected within the investigated area which covered all districts of ElGharbia Governorate. Using tenfold crossvalida tion, we split all available ground truth points into train ing (90%) and validation (10%) samples. Field Work and Laboratory Analysis Morphological description of fourteen soil profiles, which represent the different geomorphic units were carried out according to the field book for describing and sampling soils [23]. The laboratory analyses of soil samples that collected and analyzed using the soil sur vey laboratory methods manual [24]. The analyses include, particle size distribution, soil pH, organic matter concentration, free CaCO3 content, electric conductivity (dS/m), cation exchange capacity (cmol/kg soil) and exchangeable sodium percentage. Using the field work and laboratory analyses data, the soils were classified to the sub great group level on the basis of the USDA Soil Taxonomy [25]. Soil Productivity Soil productivity classes were defined to evaluate soil capability of the studied area using the rating and procedure [16] and modified by [7]. Soil productivity is evaluated based on chemical and physical properties such as texture, pH, and available waterholding capacity. Productivity ratings in general are numbers that reflect relative value of physical and chemical properties of soils and the effect of these properties on productivity for the most commonly grown crops. Rating for evaluating soil productivity, i.e., excellent I (65–100), good II (35–64), average III (20–34), poor IV (8–19) and extremely poor to nil V (0–7). The system suggests the calculation of the productivity index considering nine factors as determining soil pro ductivity, viz.: moisture (H), drainage (D), effective depth (P), texture/structure (T), soluble salt concen tration (S), organic matter content (O) mineral exchange capacity/nature of clay (A) and mineral reserve (M) [16]. An attempt has been made to evolve a mathematical formula expressing productivity as a

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S

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1

4

2

5

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6

Fig. 2. Geomorphological units of the study area: (1) decantation basin, (2) high terraces, (3) levees, (4) low terraces, (5) middle terraces, and (6) overflow basin.

resultant of the various factors, considered the follow ing formula:

terraces (12%), moderately high terraces (11%), low terraces (23%) and levee (7%) as shown in Fig. 2.

Productivity index = H*D*P*T*S *O*A*M. Each factor on scale from 0 to 100. The actual per centages being multiplied by each other. The resulting index of productivity, also lying between 0 and 100, is set against a scale, placing the soil productivity in one or other of five productivity classes. RESULTS AND DISCUSSION Landforms The geomorphic units were recognized and delin eated by analyzing the main landscape units that extracted from satellite image and DEM with the aid of the different maps and field survey. The obtained results show that flood plain is the main landscape of the investigated area that include decantation basins (27% of the total area), overflow basins (20%), high

Soil Productivity Productivity index is associated with chemical and physical characteristics, as well as crop yields. Soil tex ture classes are varied from silt, clay loam, clay loam and clay in the different mapping unit. The results of the chemical and physical analysis of the collected soil sam ples from the different geomorphic units revealed the following: Soil salinity varies between slightly to highly saline, where the EC values higher than 10 dS/m in some parts of soil terraces while the rest of the area is characterized by moderate to low soil salinity where EC values less than 8 dS/m. The soil profile depths of some parts of decantation basins and levee are less than 90 cm, and the groundwater table is near the soil sur face. Other geomorphic units are characterized by deep soils and deep groundwater table. The organic matter content is low in general in the study area. Exchange EURASIAN SOIL SCIENCE

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Fig. 3. Soil map of the studied area: (1) Typic Natrargids, (2) Vertic Torrifluvents, (3) Typic Torrargids, (4) Vertic Torrifluvents, and (5) Typic Haplargids.

able sodium percentage (ESP) range from 8.39 to 27.38% and bulk density of these soils from 1.27 to 1.51 mg/cm3. The calcium carbonate content ranges between 0.7 and 3.2%. The cation exchange capacity ranges between 32 to 52 cmol/kg soil, in the different layers of the investigated area, the high values of CEC referred to fine materials and organic matter contents. The soils of the study area are classified as Typic Torrar gids Typic Torrifluvents, Vertic Torrifluvent, Typic Hap largids and Typic Natrargids as shown in Fig. 3. The soil productivity was calculated using the value (V) of nine factors viz.: moisture (H), drainage (D), effective depth (P), texture/structure (T), solu ble salt concentration (S), organic matter content (O) EURASIAN SOIL SCIENCE

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mineral exchange capacity/nature of clay (A) and mineral reserve (M) by using the formula mentioned above. The results obtained indicated that soil pro ductivity in decantation basin and some parts of low terraces attributed to class III and it covers about 10% of the total area and the rest of the area is attributed to moderate to high productivity (class I and II) as shown in Fig. 4. Agricultural Potential Land use, land cover changes. Agricultural poten tial is associated with land use, land cover changes (LULC). Four classification methods are used to eval

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Fig. 4. Productivity map of the studied area: (I, II, III) soil productivity classes.

uate the changes of land use, land cover as follows: Maximum Likelihood (ML), Minimum Distance (MD), Neural Networks (NN); and Support Vector Machine (SVM). The methods were applied to the image acquired in 2013 where random samples as ref erence points are taken. Two measures of accuracy were tested, namely overall accuracy and Kappa coef

ficient. The results obtained indicated that the best classification accuracy method was Support Vector Machine where classification accuracy reached to 99.05% for Urban, 99.7% for Vegetation and 51.3% for Water bodies; the overall accuracy and Kappa coeffi cient were 97.2% and 0.94 respectively. The second best overall classification accuracy method was Neural

Table 1. Accuracy assessment results for different classification methods related to LULC in the studied area Classes Urban Vegetation Water Kappa coefficient Overall accuracy

Maximum likelihood 98.17 75.25 79.59 0.69 82.5

Minimum distance 89.48 71.84 58.01 0.58 76.6

Neural networks 99.01 99.55 5.04 0.89 95.3

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(a) 30°50′0′′

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3

Fig. 5. LULC map of the studied area in 2001: (1) urban, (2) agricultural, and (3) water bodies.

Network for all the three classes (Urban 99.01%, Veg etation 99.55% and Water bodies 50.04%) the overall accuracy recorded 95.3% and Kappa coefficient reached 0.89 as shown in Table 1. Therefore SVM classification was applied on images acquired in the years 2001, 2010 and 2013 as shown in figures 5, 6 to determine LULC of the studied area. The current study illustrates that the changes in land use, land cover that occurred between the years 2001 and 2013 revealed increasing in urban sprawl. The highest increase in urban sprawl was recorded in El Mahalla EURASIAN SOIL SCIENCE

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ElKobra followed by the Tanta district as shown in Fig. 8 and Table 2. The urban areas of ElGharbia Governorate in 2001 accounted for about 8% of the total area. This percentage increased to 10% during ten years from 2001 to 2010 whilst, during the last three years from 2011 to 2013 the urban areas were increased to 10.8% of the total area. Otherwise, the results showed a decrease in vegetation cover of the study area during thirteen years from 91% in 2001 to 88.2% of the total area due to urban sprawl as shown in Table 2.

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Fig. 6. LULC map of the studied area in 2010: (1) urban, (2) agricultural, and (3) water bodies.

Impact of Soil sealing on soil productivity. Soil seal ing occur when land is lost to housing, industrial and commercial activities, areas of health care, education, and nursing infrastructure, traffic areas, this loss led to the removal of topsoil layers also, soil function [9]. The results given in Table 3 showed that sealing soil during

2001 to 2010 recorded 3992.4 hectares, 2395 hectares of them are attributed by soil capability class ranging between I to II. Moreover the results illustrated that the percentage of soil sealing was significantly increased from 2011 until 2013; the process came along with the increasing of urban areas where, during

Table 2. Change detection area and percent for LULC in studied area Area 2001

Area 2010

Area 2013

hectares

%

hectares

%

hectares

%

Predicted Area 2020, %

15970 181657 1996 199624

8 91 1 100

19962 177665 1996 199623

10 89 1 100

21360 176268 1996 199623

10.8 88.2 1 100

12.4 86.6 1 100

Mapping unite Urban Agricultural Water bodies Total area

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31°20′0′′ E

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1 2 3

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km 8 12 16

Fig. 7. LULC map of the studied area in 2013: (1) urban, (2) agricultural, and (3) water bodies.

three years the soil sealing was recorded 1397.4 hect ares, 1090 hectares of them attributed to soil capability classes ranging between I and II. However, the total soil sealed during thirteen years recorded an area about 5389 hectare from the study area. Furthermore, the study area is considered as a one of ten governor ates, which cover the Nile Delta territory. Therefore the soil sealing problem in the Nile Delta could be about ten times of the current results. However the sealing is threatening to agricultural potentiality and food security in Egypt, especially in case of deficiency of the horizontal expansion of agriculture in the desert lands as an alternative of soil loss due to urban sprawl. The main reason of increasing soil sealing is the popu lation growth rates where, directly affected by increas ing of urban sprawl driven mainly by speculation in the housing sector. The population growth amounted to 66.97% of the total population during the period from 1976 to 2006 in the Tanta district [3]. In addition to that, the weakness of the role of the government in the EURASIAN SOIL SCIENCE

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implementation of laws to face the farmers in land encroachment especially after The Egyptian revolu tion 2011 led to increased soil sealing. Otherwise in the event of continued increase of the urban area’s growth rate it will be increased to 12.4% of the total area in 2020. This is a serious indicator of agricultural soil degradation in the Nile Delta. This will lead to increased food gap in Egypt, especially with the tre Table 3. Soil loss during 2001 to 2013 Soil productivity I II III Total loss area

Soil loss 2010–2010 hectare

%

439.2 1956.3 1597.0 3992.4

11 49 40 100

Soil loss 2010–2013 hectare 251.5 838.4 307.4 1397.4

% 18 60 22 100

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Fig. 8. Change detection map from 2001 to 2013.

mendous increase in population and lack of expansion of new agricultural soils reclamation. CONCLUSION AND RECOMMENDATION The current study illustrated that the agricultural soils in the El—Gharbia governorate are character ized by high soil productivity depending on its chemi cal and physical properties. The soil sealing threatens the agricultural soils potentiality in Nile Delta. Remote sensing and GIS are the main tools to monitor changes of the urban growth, therefore, can assess the environmental impacts on agricultural soils. Four dif ferent supervised classification techniques (Maximum Likelihood (ML), Minimum Distance (MD), Neural Networks (NN), and Support Vector Machine (SVM) were applied in the investigated area. The results indi cated that the finest one is a Support Vector Machine

followed by Neural Networks where overall accuracy reached 97.2% and 95.3% and Kappa coefficient reached 0.94 and 0.89 respectively. The study suggested to mitigate soil sealing include the following points: —Use of diverse media to increase people’s aware ness of the seriousness of urban sprawl on the Egyptian food security. —Activate the law to combat urban sprawl that increased after the Egyptian revolution in 2011, which threatens the potential agriculture in Egypt. —Increase the horizontal expansion of agriculture in the desert land as an alternative to land which have been lost due to urban sprawl and build up new vil lages, which include all facilities and services in New reclaimed areas in the nearest desert to encourage the farmers. EURASIAN SOIL SCIENCE

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