Integrating GIS and remote sensing in environment impact ... · Integrating GIS and remote sensing in environment impact assessment of ... needed to characterize natural and human-induced

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES

Volume 7, No 1, 2016

© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0

Research article ISSN 0976 – 4380

Submitted on May 2016 published on August 2016 43

Integrating GIS and remote sensing in environment impact assessment of

Ewaso Nyiro Mega dam in Kenya

Daniel Maina Mukiri1, Charles N. Mundia1

1-Dedan Kimathi University of Technology, Institute of Geomatics, GIS and Remote Sensing

[email protected]

ABSTRACT

GIS is increasingly being used worldwide within Environmental Impact Assessment (EIA);

However, the extent has not been well documented and therefore not well appreciated in the

scientific fraternity. In this case study the National Water Conservation and Pipeline

Corporation (NWCPC) on behalf of the Kenyan government engaged a consultant to identify

a suitable site for a mega dam to supply water to the proposed Isiolo resort city. The

consultant decision in siting the dam was not based on a comprehensive scientific analysis, as

required of such a project. This could be one of the many other projects being implemented in

the wrong premise courtesy of politics, and influence of leaders. The objective of this study

was to review the E.I.A process carried out by the consultant to determine whether any

scientific technique was employed on the identification of the ideal site for the dam. The

Ewaso Nyiro basin was identified and the relevant data on dam siting collected and processed.

Spatial analysis was used to inform the location of the dam based on weights realized through

the Analytical Hierarchical Process (AHP). The analysis realized three suitable dam sites that

were further subjected to analysis based on the local conditions to finally settle at the most

suitable site for the Mega-Dam. The site slightly differs from the one fronted by the

consultant in terms of capacity, inundation area, and dam crest length. The study has revealed

that the most ideal site was within vicinity to the site identified by the contractor. Decisions

taken regarding the length of the dam wall were noted to be misleading since no scientific

approach was adopted. There is danger of implementing very noble, gigantic and capital

intensive projects in the wrong premise when wrong decisions are made based on unscientific

means.

Keywords: AHP, Dam Siting, EIA, Spatial Analysis, Remote Sensing and GIS.

1. Introduction

The principles of sustainable development are aimed at balancing between economical, social

and environmental issues. This balance is achieved by conducting a critical examination on

the effect of the project on the three issues. Environment Impact Assessment (E.I.A) is one

method of enhancing sustainable development. Effective EIA study is used as an integral

strategy for building control and assurance throughout the project. Project proponents can

failed to scientifically determine the suitable site as required. If the E.I.A is not properly

conducted it would not be possible to successfully predict the likely changes on the

environment as a result of a development. It would also not be possible to select the best

alternatives from the available options. The compatibility of the project with the environment

would also not be determined. Without proper E.I.A, it would not be possible to propose

mitigation measures due to impacts on the Environment. Dam construction has a potentially

serious effect on the floodplain forests and their resources which indirectly affect the

discharge downstream (Adams, 1989).There are three main steps of working in a project i.e.

Investigation of the site, design and construction. This case study mainly dwelt on the

Integrating GIS and remote sensing in environment impact assessment of Ewaso Nyiro Mega dam in Kenya

Daniel Maina Mukiri et al.,

International Journal of Geomatics and Geosciences

Volume 7 Issue 1, 2016 44

investigation part of the project. The investigation includes the meteorological, hydrological,

topographical and geological investigations on the watershed basin being considered for dam

construction (Narita, 2000). A dam project is determined by considering various factors

associated with topography and geology of the dam site. Detailed information regarding the

topography, drainage network, channel length, water divide, geo-morphological and

geological set up of the area for proper watershed management and implementation plan for

water conservation measures are evaluated (Singh et.al, 2014).

Ewaso Nyiro basin falls on an arid and semi – arid landscape which is a fragile ecosystem,

sensitive to degradation and desertification. Loss of productivity results from complex

interaction between climate impacts and unsustainable land use practice. Different factors

play a greater role depending on the specific situation. There are certain parameters that apply

globally while others vary depending on the local conditions. It is therefore very necessary

that in-depth study is carried out to determine what parameters apply in prevailing conditions

and how to get the best out of such conditions. GIS and Remote sensing technology provides

economics of scale for monitoring, analyzing, and in some cases quantifying the parameters

needed to characterize natural and human-induced environmental changes (Michael, 2000).

Macharia et.al (2015) noted that expert opinion ought to be sought from different groups of

experts based on their professional knowledge. The Analytic Hierarchy Process (AHP) is a

multi-criteria decision- making approach solve complex decision process (Saaty, 1980).

2. Study area

The Area under study shown in Fig.1 covers four counties in the Kenyan Rift Valley and

Upper eastern regions of the country comprising Isiolo, Meru, Laikipia, and Samburu

counties, covering an area of 25 336.1 square Kilometers. The population in the study area

was 143,294 according to 2009 population census. Ewaso Nyiro is the largest catchment

basin in Kenya with the least population. The spatial distribution of rainfall varies from

800mm/year in the highlands to 400mm/year in the Arid and Semi Arid Lands (ASAL) areas.

The Ewaso Nyiro is the main river in the basin and the name is derived from the Maasai

name for brown or muddy water. Ewaso Nyiro is the third largest river in Kenya and has

twenty tributaries. The main economic activity for the region is pastoralism and the Ewaso

Nyiro River provides a livelihood to the community living in the region. Isiolo town where

the resort city is proposed is currently served by Isiolo water supply constructed in 1980 -

1983 to serve a population of 15,000 persons. The population has been on the increase and

has since grown to 60,000 persons according to 2009 population census (KNBS, 20`0), and

as a result the water demand has far exceeded the production capacity of the existing water

supply. It is important therefore to mitigate on the water shortfall currently being witnessed in

the region partly because of the proposed resort city and for reasons of future generation.

3. Methodology

The study has employed a number of processes to determine the suitable sites as shown in

Fig.2. Literature obtained from different sources singles out four (4) main factors that should

be considered during the process of dam siting. These are geological, topographical, climatic

and demographic factors. The Kenya counties map was sourced from survey of Kenya.

Scanning, digitizing and georeferencing were done on the hard copy to come up with the geo-

referenced county map in soft copy. The boundary outline for the four counties being

considered for the study i.e. Isiolo, Laikipia, Meru, and Samburu were used to clip all other





data sets that were included in the study. The acquired climatic data composed of rainfall and

temperature in an excel spreadsheets. The spreadsheets were converted to ‘csv’ format for

uptake in ArcGIS. Interpolation was carried out to predict values of the unmeasured locations

by using Kriging method. Kriging interpolation assumed that the interpolated surface was

homogenous across the surface and applied weighting based on the distance. Reclassification

was done by assigning zones with higher rainfall output as suitable because of water supply

to the dam, while compared with low rainfall. Zones with low temperatures were considered

prime zones for dam construction because of lower evaporation when compared with zones

of higher temperatures. Population data obtained from the Kenya National Bureau of

Statistics was in excel spreadsheet.

Figure1: Study area

A column of Population density was deduced from the total population and the areal extent.

The spreadsheet was converted to ‘csv’ and rasterized. Zones that are sparsely populated

were considered more suitable than areas that are densely populated, because of reduced mass

transfer of population during construction. Data containing soil drainage and texture was

defragmented to deduce the individual characteristics. Excessive well drained soils were

considered more suitable than poorly drained soils because of retention reasons. The

reclassification was then done. Soil texture with high concentration of clay are considered

more ideal for dam construction because they provide better permeability and water loss

retention than sandy soils.

Six (6) LandSat 8 images (accuracy of 30m) were downloaded from USGS website. The

images were uploaded to Erdas Imagine 10, where radiometric and image enhancements were

first carried out (Pre-processing). Layer stacking was done where different spectral bands

were combined for the layer; for example band 1, 4, 7 were used for the forest. The stacked

imagery mosaic was created and used to create a subset using the study area boundary map.

Supervised classification was done by first identifying the specific homogeneous





representative samples of different land cover types of interest. These ‘training sites’ were

isolated and mapped during ground truthing. Signatures were created from these training sites

vide the signature editor. The classification was then done to achieve the eleven (11) classes

of the land cover i.e. woodland, towns, swamp, plantation, grassland, forest, bush land

(sparse, bush land (dense), barren land (S/G), barren land (R), agriculture (sparse), and

agriculture (dense). The supervised classified image was converted to a polygon in ArcGIS

10.3. The Catalog was used to create an inventory for the processed data. Expert opinion was

sought through a questionnaire from different sources. Pair wise comparisons were

formulated to determine the relative importance of each criterion. These comparisons were

used to obtain the weights of importance of the decision criteria, and the relative performance

measures of the alternatives in terms of each individual decision criterion.

These pair wise comparison were based on the Thomas Saaty scale of 1-9.Spatial modeling in

ArcGIS Model builder analyzed the different variables independently and collectively to

obtain the suitable site map as a weighted overlay. To pinpoint the exact position to locate the

dam, further analysis was required to identify the most suitable site guided by Food and

Agriculture Organization (FAO) guide on dam siting. A comparison between the contractor’s

site and the ideal site identified by the analysis was done to determine whether the two sites

differ in terms of elevation, capacity and crest length. The comparison highlighted the

differences and similarities between the two sites.

4. Results and discussion

4.1 Variables criteria results

The slope of the Ewaso Nyiro river basin as shown in Fig. 3 is predominantly flat with gentle

slopes covering the large section of the basin. The low lying areas (1° - 20°) are preferred

more for dam construction than the high lying areas (40° - 78°). The largest proportion of the

study area comprise of bush land 60.6% as shown in Fig. 4. The statistics on land cover

show that 1% is under swamp, 5% woodland, 10% plantation, 5% grassland and less than1%

settlement. Agriculture takes paltry 4% of the area being considered for dam construction.

This will definitely improve with irrigated agriculture from surplus water from the dam.

The soil distribution in the area under study as shown in Fig. 5 indicates that the largest

proportion of the soils is well drained and amounts to 75%. Excessively drained soils

constitutes -7%, poorly drained -5% ,moderately drained -11%, very poorly drained - 1% ,

very poorly drained and imperfectly drained soils constitutes a dismal 1% of the soil

distribution.

Well drained soils are preferred in the construction of the dam because of water retention

consideration. The soil texture distribution shown in Fig. 6 indicates that the area under

study has good clay. The statistics on soil texture demonstrates that clayey soils constitute

75.5%, very clayey 8%, loamy 6.5% and sand 10%. Soils with high concentration of clay are

considered to be good for dam construction because they provide permeability necessary for

water loss prevention. Very clayey soils in the component are therefore given more

preference than sandy soils that have poor water retention capabilities.





Figure 2: Slope Mape Figure 3: Land use cover map

Figure 4: Drainage map Figure 5: Soil texture map

Climate data obtained from the meteological department comprised of rainfall and

temperature. The mean of each component for the past thirty (30) years was used to deduce

the climatic condition for the basin. Temperature contributes to the evaporation of the water

mass and therefore must be considered. Over 90% of the basin is covered by mean

temperatures of above 24°C, however cooler zones (19°C - 24°C) constituting the 10% was

given more preference than zones of higher temperatures. Ewaso Nyiro river basin falls

generally on a semi-arid area that experiences little rainfall of not more than 360mm within

the year. Areas with prevalent higher rainfall were considered more ideal for siting the dam

than areas with less rainfall (below 50mm annually). Rainfall provides water that fills up the

dam and without rainfall rivers will not flow, and therefore makes dam construction along the

river impossible.





Figure 6: Temperature map Figure 7: Rainfall distribution map

The Population density for the catchment area and the entire Ewaso Nyiro river basin was

considered. Zones with scanty population (0-138 persons per Km2) would have minimal

relocation to the population to pave way for the dam construction and are therefore preferable

more than areas with dense population (415-554 persons per Km2).The Euclidean distance to

the river was considered among the other variables to determine the ideal site for the dam.

The ideal site was considered to be within a radius of one kilometer from the river.

Figure 8: Population Density Figure 9: Map showing distance to the river

4.2 Weighting

To determine the weights for use in modeling, the expert opinion extracted from the

questionnaires was analyzed, assembled and tabulated as pair wise comparison between the

eight variables considered. The independent comparisons between the eight variables were

tabulated to form the 8*8 matrix as shown in Table 1.





Table1: Tabulation of Eigen variables

Population Soil

Texture Slope Temperature Rainfall

Distance

to River Drainage

Land

Cover

Population 1 1 2 8 6 5 3 8

Soil Texture 1 1 5 3 8 4 1 6

Slope 0.5 0.2 1 5 8 8 0.2 8

Temperature 0.125 0.333 0.2 1 0.333 7 0.167 9

Rainfall 0.167 0.125 0.125 3 1 3 0.125 5

Distance to

River 0.2 0.25 0.125 0.143 0.333 1 0.125 0.5

Drainage 0.333 1 5 6 8 8 1 3

Land Cover 0.125 0.167 0.125 0.111 0.2 2 0.333 1

Weights were deduced from the normalized matrix by carrying out calculations based on the

Saaty dissertation. The outcome of the weights as shown on Table 2 indicates that the experts

considered population, soil texture and drainage to be of more importance than slope,

temperature, rainfall and distance to the river. The degree of preference is as indicated as a

percentage and the weights are derived there from. The obtained weights were then used in

the spatial modeling process in the weighted overlay of the model builder.

Table 2: Final variable weights from AHP

4.3 Modeling

The spatial analysis was executed on the model builder to achieve the suitability map shown

in Fig. 11.The suitability map shows various zones within my study area with varying

suitability outcomes. Areas covered by blue shades are more suitable for the dam than zones

covered by pink shades. The analysis demonstrated graphically in Fig.12 indicates that less

suitable sites constitute the biggest margin (53.8%) of the areas sought for dam suitability.

Moderately suitable followed with 34.8%; Suitable site managed 9.6%; Unsuitable site tailed

together with the most suitable site with 1.5% and 0.3% respectively. It was envisaged at the

beginning that the dam will be located on the most suitable site but because of the marginal

score (0.3%), it was found necessary to consider the next available option of suitability i.e.

Weights %

Population 0.232978 23%

Soil Texture 0.210137 21%

Slope 0.145848 15%

Temperature 0.10196 10%

Rainfall 0.05379 5%

Land Cover 0.015914 2%

Drainage 0.220581 22%

River

Distance 0.01879 2%

Total 1 100%





the suitable site which had a good score of 9.6%. The ideal dam site has been considered in

these two zones (i.e. most suitable-0.3% and suitable-9.6%).

Figure 9: Suitability Map Figure 11: Suitability Graph

4.3 Site analysis

The maximum crest length for the wall on sub-surface dams depends on the geological

formation and stability thereof. Based on statistics on a number of reservoirs done in Kenya,

the maximum crest length for the dam to achieve 215 Million Cubic Metres is approximated

to be 1000m.Thorough investigation on the suitable sites that can achieve this condition is

conducted by investigating the behavior of contours along the identified suitable sites. The

contours were generated within the suitable sites, and a close scrutiny on the curvature done

in view of identifying the sites where contours bounds themselves by leaving a minimal neck

of not more than 1000m.

Figure 12: Contour Polygon on TIN Figure 13: Analysis on contours





The feature polygon was created by closing the contour at the neck identified as shown in Fig.

14. The neck of the uploaded contour was identified and measured to determine whether it

meets the desired criteria in terms of length. All the contours enclosed by the outer contour

(ideal contour forming the reservoir) are selected and removed to pave way for a single

polygon. The contour line was then closed at this point to create the Dam wall and the

polygon was used in volume calculation. The best ideal dam site attained the desired volume

and even exceeded the mark by 20.5 Million cubic Metres. Three tributaries of the main river

met at the main channel at this site making the dam site more desirable for maximum storage

capacity. However, before concluding that this would be the ideal site, it was prudent to

investigate the discharge of the river at this site and determine whether the flow would meet

the demand of filling the dam within one year.

Figure 14: Volume calculation on the reservoir

Figure 15: Graph showing the mean discharge of Ewaso Narok (Depattas) and Ewaso Nyiro

River at the confluence of Ewaso Narok and the Main Ewaso Nyiro tributary.

The ratio of annual inflow of water to the storage capacity was considered according to

(Nicholas, Undated). This should not be too high or too low. The ratio should range from 1:1

to 1:5 otherwise the reservoir would never fill up or have a situation where large outflows are





witnessed on the spillway. To attain this volume a discharge capable of attaining the 235.5

Million cubic metres was required. The minimum annual discharge = (235500000/

(364*24*60*60) = 7.46 M3/Sec. To investigate this matter, discharge data from River gauge

station 5DC01 (Confluence of the Ewaso Narok and Ewaso Nyiro) and station 5BC04

(‘Depattas’) were obtained from the WARMA in full and analyzed to determine the

suitability of the site in terms of the discharge capability.

To ensure that the reservoir is filled up within one year, discharge values from 1963-2013

were investigated. The level of discharge capable of filling the reservoir was not attained in

1970, 1972-74, 1984, 1991 and in 1999-2006, as demonstrated in Fig.17. A dramatic increase

in discharge has been noted from 2007-2012, although a huge drop was witnessed in 2013,

though not significant to affect the required flow to the reservoir. The discharge between 199-

2006 was too low even in some instance below the discharge of the Ewaso Nyiro tributary at

Depattas. It is therefore evident from the flow regime of the Ewaso Nyiro River that the

required capacity can be achieved. My ideal dam site falls on the same area considered by

CAS Consultant Ltd. The consultant had considered elevation 1580m asl as the dam crest

elevation and maintained that at this level, it was possible to attain a dam crest length of

850m. The minimum crest length that can be attained at this level is however 1.14 Kms and

not 850m as alleged.

That is the reason why I have adopted contour whose elevation is lower (1570m asl) to

achieve a crest length of 785m which is below the maximum value adopted. Reducing the

crest length to the proposed 850m will mean additional cost to the project and compromise

the stability of the wall. The storage capacity on the 1580mm asl dam site proposed by the

CAS Consultant Ltd is of course higher by 76.5 million cubic meters from my proposal. It

also covers a larger area than what I had achieved for inundation (905.6 Ha.). The sites are

well elevated to provide natural flow (gravity) of water from the reservoir to the resort city.

My site offers an opportunity for the National Water and Pipeline Corporation (NWPC) to

engage an engineering firm to carry out designs and engineering works on the two sites and

immediately embark on the construction of the Mega Dam as envisaged by the Vision

2030.The dam site identified, meets all the desired criteria identified for the dam siting. The

site also offers a better opportunity because the elevation of 1400m asl is well above the

resort city. The proponent should also consider drilling wells downstream to allow people

living downstream to access water that may otherwise be lost through seepage. Fencing the

dam and reservoir may be required to prevent access to the embankment and reservoir.

Involvement of the beneficiaries in any remedial or mitigation works also engenders a sense

of responsibility in using and maintaining the water resource provided. Dam construction

disturbs the landscape around the dam and these should be kept to a minimum. It should be

part of any contract for the contractor to remove and store the topsoil of any area to be

disturbed and then return such topsoil to the site to allow normal vegetation to re-grow and

prevent any subsequent erosion.

Community participation is important for the successful implementation of the projects. The

communities downstream are not actually opposed to the project as such, but they feel

sidelined in the planning stage for this noble vision 2030 project. From the complaints raised

on this project, it is evident that the views obtained from the stakeholders were not

representative as demonstrated on the report. Representative views, not just that of land

owners but also those who will be most directly affected or benefit from the dam should be

consulted to determine their needs and views. The issue of river Ewaso- Nyiro drying up

downstream due to the construction of the dam is a non-issue because an investigation during





ground revealed that the river had dried up even before reaching Archers post bridge and the

dam is yet to be constructed.

5. Conclusion

This study sought to investigate using spatial analysis a suitable site for the dam among the

five sites speculated by project proponent. The E.I.A report did not adduce any scientific

evidence to demonstrate on how they arrived at their conclusion to locate the Mega –Dam at

‘Crocodile Jaw’ dam site. The decision could be based on past experience, influence from

powerful personalities in the society, local politicians, and administrators. There is danger

therefore if a project of such a magnitude of investment is located in the wrong location

because it would fail to attain its desired objectives. The aim of the study was to demonstrate

how GIS and Remote Sensing applications could be integrated in Environment Impact

Assessment (EIA). It has addressed the three research questions on criterion of dam siting

and whether the dam site identified by the contractor was ideal or not. Expert opinion and

literature materials were relied upon to determine the criterion to site the dam. Four primary

factors namely geological, topographical, climatic and demographic were identified as the

most critical areas to consider while siting a dam site. Data sets related to these factors were

sought and collected respectively as follows: - Soil data vector layer, topographical maps,

LandSat 8 and SRTM image, climatic and population data.

The output was applied in the Analytic Hierarchical Process (AHP) technique to derive the

weights used on the weighted overlay. Each variable was independently treated in modeling

and collectively on a weighted overlay to produce the suitability map. Among the most

suitable sites, three sites were identified and further analyzed to determine their suitability in

terms of size, volume and capacity. They were then compared with the site identified by the

project consultant. This case study has demonstrated that GIS and Remote sensing techniques

are valuable tools in decision making process for the determination of suitable site within a

given environment. Both GIS and remote sensing provides a wealth of environmental data

over a range of spatial and temporal scales and therefore play a major role in the provision of

indicators on environment conditions.GIS has a powerful visualization capability that allows

themes to be viewed in a spatial context and enable alternative viewpoints like deciding on

the contour line to adopt for the dam. The entire river flow and release in the dry season

should be further investigated to ensure the maintenance of downstream watercourse in as

natural condition as possible. The enquiry should put to end the fears of the pastoralists living

downstream.

6. References

1. Abraham Thomas (2015), Modeling of Spatially Distributed Surface run-off and

infiltration in the Olifants River catchment water management area using GIS.

International Journal of Advanced Remote Sensing and GIS, 4 (1), pp 828-862

2. Ainhoa Gonzalez Del Campo (2012), GIS in Environment Assessment: A review of

current Issues and future needs. Journal of Environmental Assessment Policy and

management, 14 (1), pp 1-23

3. Alan L. Porter and John J. Fittipaldi (1998), Environmental Methods Review:

Retooling Impact Assessment for the new century. International Association for

Impact Assessment. The press club. North Dakota (USA), pp15-173





4. Ali.R.R (2015), Scenarios of the Ethiopian Renaissance Dam altitude and relevant

reservoir by using DEM and 3D analysis. Middle East Journal for Applied Sciences,

(2), pp 151-156

5. Conway D (1997), A water balance model of the upper Nile in Ethiopia. Journal for

Hydrological Sciences, 42(2), pp265-286.

6. Fred G. Bell (2004), Water resources and the Environment. Engineering Geology and

Construction. Amazon Press. pp 406-463

7. John C. Trinder (2008), Remote Sensing for assessing Environmental Impacts based

on Sustainability Indicators. The International Archives of the Photogrammetry,

Remote Sensing and Spatial Information Sciences, 37(Part B8), pp1421-1428

8. Kailasl Narayan, Dikshit P.K.S, Dwivedi S.B (2012), GIS supported Geomorphologic

Instantaneous Unit Hydrograph (GIUH), of Varuna river basin using

geomorphological characteristics, International Journal of Advances in Earth Sciences,

1 (2), pp 68-76

9. Kurt Lonsway,Kelello, Ntoampe, Noel Kulemeka (2009), Environmental Social

ImpactAssessment on Thwake River, pp 16-38

10. Idowu Innocent Abbas and Ukoje J.A (2012), Application of Remote Sensing and

Geographic Information to Environment Impact Assessment (EIA), for sustainable

development, 2(1), pp 6-12

11. Itiator J.C, (1996), GIS and Remote Sensing integration for Environmental

Applications. International Journal of Geographic Science, 10(7), pp 877-890

12. Rorat Hillary Moitalel (2013), Dam Sitation. Student Project. Geology Department.

Nairobi University.

13. Macharia Peter M. Charles N. Mundia, (2014), GIS Analysis and spatial modeling

for optimal oil Pipeline Route location: A case study of proposed Isiolo- Nakuru

Pipeline Route, International Journal of Science and Research, 3(12), pp1227-1231

14. Michael F. Good Child. Spatial Analysis. Methods and problems in land use

management. pp 39-51

15. Michael J. Hill and Richard J. Aspinal., (2000), Spatial Information for land use

management. Gordon and Breach Science Publishers, pp 39-46

16. Mohammed El. Bastawasy, Safwat Gabr, I Hab Mohammed (2015), Assessment of

Hydrological Changes in the Nile River due to the construction of renaissance dam in

Ethiopia. The Egypt Journal of Remote Sensing and Space Sciences, 18 (1), pp 65-

75.

17. Mustafa Al- Shangiti (2009), Using GIS to evaluate environmental Impacts for Dams

construction, pp10-16





18. Narita Kunitomo (2000), Design and construction of Embankment Dams. Archi

Institute of Technology, pp 3-10

19. Nicholas Papenfus, (Undated), Criteria for siting Dams. Dams of Africa. pp5-16

20. Nicola Hartley and Christopher Wood (2005), Public Participation in environment

assessment-Implementing the Aarhus Convention. Environment Impact Assessment

review, 25 pp 319-340

21. Prayfull Singh, Ankit Gupta and Mdhulika Singh (2014), Hydrological Inferences

from Watershed analysis for water resources management using remote sensing and

GIS Techniques, The Egyptian Journal of Remote Sensing and Space Science, 17(2),

pp 111-121

22. Peter M. Macharia, Charles N. Mundia, Miriam W. Wathuo., (2015), Experts’

Responses comparison in a GIS- AHP Oil Pipeline Route Optimization: A statistical

Approach. American Journal of Geographic Information Systems, 4(2), pp53-63.

23. Ryblezuk K.Y (2001), GIS as an aid to environmental management and community

participation in the Negril watershed, Jamaica. Computers, Environment and Urban

Systems, 25(2), pp141-165.

24. Siva Bharatha Murthy. T. (Undated), Engineering Geology. Unit IV(4), pp 31-40

25. Thomas L. Saaty., (2008), Decision Making with the Analytic Hierarchy Process.

International Journal Services Sciences, 1(1), pp 83-98.

26. Tim Stephens (2010),FAO. Manual on small earth dams. A guide to siting, design and

construction. Sales and Marketing group Rome, pp 29-39.

27. Zekai Sen and Khalid al- Suba., (2012), Hydrological considerations for dam siting in

arid regions. A Saudi Arabian Study. Hydrological Sciences Journal, Taylor and

Francis, 3(2), pp132-136

Documents

Integrating GIS and remote sensing in environment impact ... · Integrating GIS and remote sensing in environment impact assessment of ... needed to characterize natural and human-induced