International Journal of Pure and Applied Mathematics Volume 119 No. 15 2018, 331-339 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl.eu/hub/ Special Issue http://www.acadpubl.eu/hub/

Integrated Geoinformatics techniques for erosion studies and development of management plan – A case study

Ronnie Rex K and Sivakumar.R, Mail id [email protected] and [email protected]

 Abstract—Soil erosion is one among the important hazards in population which in turn cause over fertilization that effect the land use and land cover patterns, reservoir inducing undesirable algae growth leading to sedimentation and further impact on hydal power projects. It eutrophication. requires to understand the potential erosion prone areas of Quantitative and qualitative characteristics of surface any catchment and remedial measures to be suggested. Due to water bodies can be acquired in an opportune and cost some disadvantages in the conventional methods, remote sensing is playing very significant role in demarcating effective manner using remote sensing has an optical potential erosion zone especially in inaccessible areas. The standard for monitoring water quality. Remote sensing image processing technique is well established for such techniques have an upper hand over conventional water studies. Similarly GIS playing a major role in change quality measuring methods which are expensive in process detection analysis. In the present research an attempt has and are time consuming [2]. It involves the measurement of been made to study multi dated satellite data of 1988 and 2017 to demarcate catchment erosion zones through image properties of components by quantifying the level of classification techniques. Further field verification was radiation absorbed, emitted and reflected at different carried out during the period September 2017 to February wavelengths of electromagnetic spectrum which provide a 2018 to increase the accuracy of interpretation. Subsequently wider class in research for the development of an GIS based vector data on erosion zones were demarcated and environmental measure for assessing water quality to make the comparison of erosion zone for various years. especially over inaccessible locations [3]. Remote sensing in Accordingly catchment management plan is suggested to minimize the erosion and reduce the siltation in reservoirs. terms of water quality involves correlation of parameters Keywords: Soil Erosion, GIS, Sedimentation, Siltation, with strong spectral characteristics like turbidity, Remote Sensing concentration of algal chlorophyll, suspended sediments, dissolved organic matter, colored dissolved organic matter to establish an empirical relation between spectral and I. INTRODUCTION physical characteristics of the surface waterbodies to ANAGING water quality in surface water bodies have identify the potential impact constraints involved in the been taken up as an important area of interest for following [4]. Mdetailed studies in the recent years due to critical over The study aims to assess the erosion potential in the exploitation and intensive multi objective demands which catchment and changes in water quality in selected arise on limited amount of resources. Hence water quality reservoirs of sub watersheds in Chalakudy river basin investigation attracts significant amount of attention for the through geoinformatics, and also to study the impacts and better conservation and sustainable development of natural causes for changes in water quality with the development of resources. Reservoirs are surface water bodies which are a management model for conservation of water resources technically found in domains which face an excessive [5]. The area of study is a predominant destination for proportion of water scarcity or for controlled facility of environmental diversity and has been promoted by the water which may be required for agricultural and hydral tourism which receive tourist throughout the year, technological advancements. They are the outcomes of Hence, this makes it crucial to study the effect on reservoir human activity which are precisely modified to provide a which is leading to the reduction in the catchment area and reliable use of water resources which include important uses detrition of the watershed due to various effects like erosion, like controlled water supply, flood control, hydropower, deforestation, sedimentation etc for the need of conservation navigation, fish and wild life conservation and recreational of natural inland surface waterbodies and sustainable facilities. Catchment erosion induces by land degradation is development of environmental resources [6]. a major threat which has been effecting the drainage area in The scope of the study involves understanding the trend reservoirs [1]. Important concerns include excessive flow of of catchment erosion and change in water quality in the nutrients which drain into the reservoir as outcomes of reservoir especially due to eutrophication and siltation with intense agricultural practices, forestry and drastic increase the help of muti-dated image analysis [7]. The concurrent effects involving the adjacent reservoirs surrounding the study area are also considered as a rising advantage to study

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its influence on the catchment area and the considered India. Poringalkuthu is the first hydroelectric power project watershed. This finally helps in development of a built across the Chalakudy river and spreads across an area management plan for the minimizing of erosion and of 2.82sq.km and catchment area of 512sq.km enhance the potential water storage in the catchment area (Fig.2). [8].

II. MATERIALS AND METHODOLOGY The study proceeds through a data base generation which include use of remote sensing and its importance in digital image processing and interpretation of image through classification with direct correlation with field verification which helps in finalization of soil erosion zones which is linked with the GIS thematic data base which help for analysis of erosion zonation and finally developing a management plane to reduce the impacts of erosion [9] (Fig.1).

Fig. 2.Study area

In the Chalakudy river basin Sholayar reservoir was formed as a part of sholayar hydroelectric project by the construction of sholayar dam across the Sholayar River which is a major tributary of Chalakudy River. It is located upstream of poringalkuthu dam and is commenced as the second power station in Chalakudy basin having a water spread area of 8.7 sq.km and a catchment area of 186.6 sq. km. The watershed boundary of the study area comprises of 3 other reservoirs of which the sholayar extension over the Tamil Nadu region enclosed by the sholayar city dam and the Parambikulam reservoir which is formed over the Parambikulam embankment dam across the Parambikulam River located in the Palakkad district in the Western Ghats of Kerala. It also has to smaller reservoirs Peruvarippalam reservoir comprising of the Peruvarippalam dam and Thunakadavu reservoir formed over the Thunakadavu dam which are all laying adjacent to the poringalkuthu reservoir

IV. WATERSHED DELINEATION CreatingFig. 1. Methodology of base map for the study area by identification of watershed through watershed delineation using Watershed which is reciprocatively know as drainage CARTOSAT DEM and topographical maps for the basin or catchment are potential land cover area which are extraction of drainage patterns. All major locations and responsible for the draining of all watercourse and rainfall transportation networks were digitized using satellite image into a prevalent exits like discharge of a reservoir, opening [10]. Landsat Satellite products which are available for the of an estuary, or any location long a waterway. All land past and present years where downloaded and processed for areas that drains water to the outflow locations act as the an accurate lookout for the study. watershed for that pertained points which mainly consist of surface water lakes, reservoirs, wetlands and all underlying ground water. Watersheds which are separated by ridges III. STUDY AREA and hills are known as drainage divide and there are Poringalkuthu and Sholayar reservoirs are situated across prominent chances of larger watersheds accommodating the Chalakudy River in Thrissur district Kerala state of many smaller watersheds.

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Study over watershed have an important priority because It primarily involves transformation of paper maps into human induced or activities not occurring in the land area digital image using a scanner which automatically over the river drainage extremity have been majorly apprehend all the map attributes including all the text and effecting the streamflow and water quality in river system symbols in the map as discrete cells technically known as [11]. The process of distinguishing a watersheds boundary pixels and generates an automated digital image in raster by plotting lines over a map typically topographical maps format. A new layer is created for the drainage pattern in using information from lines of equal elevation know as the digital image of topographical sheet by zooming the contour lines is stated as delineation. displayed digital image to a congenial level and tracing the For the study to be conducted, watershed was delineated drainage pattern using the proper projection requisite so as for the extraction of drainage pattern both form to associate geographical information which is essential topographical sheets inclusive of the consider study area [14]. All-important geographical locations and major boundary and also from digital elevation model data product transportation networks were recognized and marked from CARTOSAT DEMs data [12] (Fig.3 &Fig.4). according to its concurrent geographical co-ordinates. Three dimensional representation of the study area was acquired form terrain elevation data from CARTOSAT DEMs data from which the drainage pattern was extracted and the watershed was delineated by carrying out the following steps in ArcGIS [15] (Fig.5).

Fig. 3.Method of for watershed delineation from DEM

Drainage patterns from the topographical sheets were extracted by manual digitization which convert digital

image into suitable functional GIS environment having geographical co-coordinates incidental to each attribute on Fig. 5. Watershed delineated from DEM Data the sheet [13](Fig.4).

V. CATCHMENT EROSION ZONATION THROUGH DIGITAL IMAGE PROCESSING TECHNIQUES Digital image processing of satellite data gives good accurate information on area of erosion. It is immensely demonstrated that all erosion zones in inaccessible area are easily demarcated through various image processing technique. In the present study classification of satellite image for delineating various soil erosion area was carried out using supervised classification technique in ERDAS imagine software. Before performing supervised classification detailed field work was carried out for generating the spectral information using satellite data (Fig.6&7) Fig. 4.Drainage network The satellite imaginary was used to identify the erosion

prone area and change in area due to erosion with special reference to soil erosion. Spectral signature were created in

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by selecting polygon to classify each feature into classes. The polygons were selected the recent satellite image and the raster layer was converted to vector layer so as to have the same area of reference when classification is done for the considered years. The major principle involved in the process is generation of supervised spectral signature AOI and using the signature file as input for probable classifications. The spectral signatures are training classes which are generated by sorting of pixels into finite number of individual classes which is recognized with the knowledge of data and desired classes with respect to detailed field work carried out with special significant reference to standard features observed from topographical sheet The image dated for 2017 was used to determine the erosion prone zones through supervised classification in Fig. 7.Catchment erosion map 2017

ERDAS Supervised classification was selected and the respective signature file was inputted to with the The siltation in water bodies are due to the erosion in the corresponding satellite image which is to be classified. The catchment area and it is important to understand the erosion following classified image obtained was represented with trend in the catchment and planning of remedial measure respective legends for better visual portrayal. The generated may reduce the siltation in water bodies. The field AOI was reused as input to classify the other two images of information are incorporated in developing signature editor the year 2017 and 1988. for detailed classification of various zones with special reference to erosion. Recommendation of revegetation, check dam construction and drainage reorganization is suggested to minimize the catchment erosion and its impact on water bodies like reservoir and lakes [16]. .

VI. FIELD WORK The field work was planned in 3 phases which begin with a preliminary visit to the catchment area to study the surrounding area and the field conditions. During the time of visit September 2017 the water bodies in the catchment area was observed for full capacity due to the intensive rainfall experienced during the monsoons in Kerala. The stage two field visit was conducted in the month of December 2017 and the upstream portion of catchment and Fig.6. Catchment erosion map 1988 observation regarding erosion and deforestation was

observed and locations were measured for field references and satellite interpretation. The environmental conditions were observed and field evidences where collected as photographs and permanent features and key features identified from topographical sheet were confirmed like features like hard rock and exposed rocks strata was observed. Evidences of soil erosion were also observed in the forest region and degradation of forest region were evidently captured accordingly by observation of amputated tress which confirms the chances of occurrences of deforestation (Fig.8).

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visit in the waterbodies in the region was connected major transportation networks. Major observation made with regard to the change in environment with respect to the present climatic conditions and were observed by giving preferences the dramatic change in components like water level in reservoir and forest vegetation changes The upstream section of the waterbody was observed to have complete dry vegetation with reduced level in water catchment which had a major area of exposed banks of the waterbodies with sediment deposition and siltation was perfectly overlays the observations which was made using the satellite imagery processing. Due to the qualitative reduction in water level, the steeper slopes at the reservoir banks with deposition layer which evidently proved the Fig.8. Deforestation in poringalkuthu periodic action sedimentation was observed [17]. This was evident from the first visit during September 2017 which The field trip extended to the upper stretch of Sholayar depicted the full reservoir capacity due to the intensive reservoir both in the Kerala as well as the part extending rainfall experienced during the monsoons. In a 6 months towards the Tamil Nadu region. The Sholayar dam in the span the critical change in water level was evidently Kerala region is a prime forest region which is filled with observed which proves the reduction in catchment area with dense vegetation and the road level is situated to a intern is effecting the natural vegetation in the study area. maximum level of 30 feet above the reservoir Traces of eroded down slopes and various plantation approximately. This makes it inaccessible from the regions were observed and field evidences were collected surrounding regions through the periphery of transportation (Fig.10). Traces of agricultural plantations was the networks which run over connecting the regions. particular observation made at the Malakapara region. The field visit extended to the region where the reservoir Traces of soil erosion and reduction in catchment forest was clearly visible throughout the major road network area has also predominantly effected the vegetation in the providing accessibility towards the banks of reservoir region. (Fig.9). The reduction of water in the reservoir and have exposed the banks of the reservoir clearly evident of the bank erosion taking place throughout the years in a periodic manner. The exposure of sedimentation through flow network and soil erosion in proceeding was observed and evidently captured for field references and location measurements for satellite interpretation.

Fig. 10. Land degradation in malakapara

Fig. 9.Deposition and Erosion in sholayar VII. CATCHMENT EROSION AREA DELINEATION Using high resolution satellite image catchment erosion

To discretely study the present field conditions during the prone area was identified for the present stage of the

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watershed under study. Satellite data was used to identify topographical sheet (Fig.12&13). erosion prone area and the delineation process was adopted by digitization process after the image was processed. Sentinel 2 provides multispectral data products having 13 Erosion was observed to be concentrated the maximum in spectral bands ranging from visible and near-infrared to the parambikulam and thunakadavu, Peruvarippalam shortwave infrared wavelengths through an orbital swath of regions. This is predominately due to the change in field 290km. The reflected radiance measured by the conditions due to the climatic changes which are in turn multispectral instrument accordingly such that different induces geomorphological changes responsible for erosion. spectral resolutions are acquired relative to specific bands. Anthropogenic activities like deforestation and slope Basically the primary 4 bands blue (490nm), green cultivation have accelerated the rate of erosion in the (560nm), red (665nm), and near infrared (842nm) have 10 Poringalkuthu and Sholayar city dam region. meter resolution whereas 6 bands at 20 meter of which 4 narrow bands are used for characterization of vegetation (705nm, 740nm, 783nm, and 865nm) and 2 larger shorter VIII. RESULT AND ANALYSIS wave infrared bands (1610nm, 2190nm) which come across The satellite images obtained for years 1988 and 2017 for for distinguishing features like snow, ice and clouds. For the study area was processed and analyzed for erosion prone the delineation of soil eroded area the following steps were area zonation with special reference to physical adopted (Fig.11). interpretation which was confined to the standard topographical references and field observations made in the area. The area which is more liable to erosion was Satellite image demarcated by the process of digitization and area was identified and quantified with the help of various remote Layer stacking sensing tools. The variation in the erosion trend was Reprojection of image observed to from the year 1988 to 2017. The increase in land degradation induced by erosion is evident from the Mosaic image vector layers generated with field observation. The trend of catchment erosion can be seen to increase in Subset image specific location in the watershed and all the major observation is to correlate with the stream flow and major Digitization erosion causes due to flow gully and rill erosion was confirmed. Erosion in the banks of the water body was Erosion area map principally observed to increase in the following period of observation. The increase in silt deposition as layers was

Fig. 11 Catchment erosion delineation methodology confirmed from both field visit conducted and satellite image interpretation. The satellite image was corrected and layer stacking was The increase in bank erosion specifically in done. Further manual digitization was adopted to create Paramibikulam and Sholayar reservoir was observed to polygon of erosion zones and a layer was created critically increase in the year 2017 from 1988. The hilly exclusively for all selected area digitized. The polygons regions in Malakapara which due to intensive agricultural formed where processed along with the geo referenced activities has leaded to soil failure and land degradation. To locations and drainage pattern layer was transferred so as to the global change in climatic condition through 30 years identify how majorly bank erosion is taking place and have drastically changed in the field conditions. During watershed zone accordingly through the drainage flow prolong dry season the rate of land degradation increases to a rate which makes natural re-vegetation difficult. network extracted from the digital elevation model and The poringalkuthu region is observed for erosion in the year 1988 but the rate is increased to a higher rate in 2017. It leads to drying of streams generated from the main Chalakudy tributary. The forest regions in the Sholayar Kerala region is also tend to show a change of reduction in reservoir capacity as well as increasing patterns of soil erosion. The vectorization gives the quantified area of soil erosion prone regions from the year 1988 to 2017. The erosion prone area in 1988 was estimated as 34.635km2 and for the year 2017 it is 57.562km2. Hence the increase in erosion in

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Fig. 13.Catchment erosion map-2017 Fig. 12.Catchment erosion map-1988

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terms of area gives a drastic increase in erosion which gives [7] Ming-Der Yang., Carolyn J. Merry., and Robert M. Sykes.,(1999), “Integration Of Water Quality Modeling, Remote Sensing and GIS”. an increasesof 2 times in between (2017-1988=29 Environment International, Vol. 23, No. 1, pp. 103-I 14 years)with references to the year 1988 in terms of soil [8] N. Haregeweyn., J. Poesen., J. Nyssen., J. De Wit.,M. Haile., G. erosion area. Govers., S. Deckers., (2006) “Reservoirs In Tigray (Northern Ethiopia): Characteristics And Sediment Deposition Problems” Land Degradation & Development, 17: 211–2 IX. CONCLUSION [9] P.Murugan., R.Sivakumar., R.Pandiyan., M.Annadurai., (2015), “Comparison Of In Situ Hyperspectral And Landsat ETM+ Data For For the supervised classification it was observed that the Chlorophyll-a Mapping In Case-2 Water (Krishnarajapuram Lake area prone to soil erosion increased from the year 1988 to ,Bangalore)”ISRS,DOI 10.1007/s12524-015-0531-8 2017. The total watershed area was found to be 1209.59 [10] Xinhao Wang., Zhi-Yong Yin.,(1997), “Using GIS To Assess The Relationship Between Land Use And Water Quality At A Watershed sq.km. An increase of roughly 2 times in erosion area over Level”. Environmental International, vol 23,no 1,pp 103-114. the year 1988 to 2017 was observed in a span of 29 years [11] Mohammad Haji Gholizadeh., Assefa M. Melesse and Lakshmi consider. Erosion was majorly observed in areas where Reddi.,(2016), “A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques”,Sensors there was persistent flow of water i.e. along the drainages [12] Apurba K. Bera., Vishal Singh., Niteenkumar Bankar., Sagar S. observed. The field observations made during the field visit Salunkhe, J. R. Sharma., (2013), “Watershed Delineation in Flat Terrain of Thar Desert Region in North West India – A Semi Automated was authenticated with the standard observation made from Approach Using DEM”. Indian Society of Remote Sensing. the topographical sheets. All the major drainages digitized [13] Seyed Reza Hosseinzadeh.,(2011), “Drainage Network Analysis, helped to correlate the corresponding. The soil erosion Comparison of Digital Elevation Model(DEM) from ASTER with High Resolution Satellite Image and Aerial Photographs”. International prone area characterized using band combination was also Journal of Environmental Science and Development, Vol. 2, No. 3, relatively indistinguishable with the satellite image [14] P.Venkatachalam., B. Krishna Mohan., Amit Kotial., Vikas Mishra., V. Mithrama Krishnan.,Mayur Panyan.,(2001), “Automatic Delineation of processing technique used. Further discrete concentration watershed for Hydrological Application”. Center for Remote imaging, on classification is required for detailed work progress. Soil sensing and processing Singapore erosion was observed majorly in the banks of the reservoirs [15] Solomon Vimal., D. Nagesh Kumar and Indu Jaya.,(2012), “Extraction of Drainage Pattern from ASTER and SRTM Data for a River Basin visited. The confirmation was made with respect to the using GIS Tools” International Conference on Environment, Energy observed processed satellite images acquired for the study and Biotechnology, IPCBEE vol.33 area. Evidences of soil erosion by degradation and [16] Guangqian Wang., Baosheng Wu., and Zhao-Yin Wang.,(2005), “Sedimentation problems and management strategies of Sanmenxia deforestation was also observed. Erosion due to weathering Reservoir, Yellow River, China”. water resources research, vol. 41, and in the course of all the major tributaries of the w09417 [17] Herath M. Gunatilake., Chennat Gopalakrishnan., (1999) “The Chalakudy river basin was predominantly observed. Digital Economics of Reservoir Sedimentation: A Case Study of Mahaweli image processing contributed to assurances over the Reservoirs in Sri Lanka” Water Resources Development, Vol. 15, No. simultaneously field evidences collected during the study. 4, 511± 526

X. ACKNOWLEDGEMENT The authors are also thankful to SRM Institute of science and technology for providing all necessary facilities and constant encouragement for doing this research work. Also authors are thankful to BDA-HSRS/DST HYPERWATER PROJECT for providing necessary facilities and data generation

REFERENCES [1] Abdelhadi Lahlou., (1996), “Environmental and socio-economic impacts of erosion and sedimentation in north Africa”. Department of Hydraulic Management, IAHS Publ. no. 236, 1996. [2] J.A. Foster., A.T. McDonald.,(2000), “Assessing pollution risks to water supply intakes using geographical information systems (GIS)”. Environmental Modelling & Software 15 (2000) 225–234 [3] John R. Jensen., (2007) “Remote Sensing of the Environment”. Second Edition Pearson Prentice Hall, [4] Jerry C. Ritchie., Paul V. Zimba., and James H. Everitt.,(2003), “Remote Sensing Techniques to Assess Water Quality”. Photogrammetric Engineering and Remote Sensing, Research Gate. [5] Mark S. Dortch.,(1981), “Water Quality Considerations In reservoir Management” ,V108_A3 Flood Control Operation, pdf. [6] Jiahua Fan., Gregory L. Morris., (1992) “Reservoir Sedimentation: Delta And Density Current Deposits” Journal of Hydraulic Engineering, Vol. 118, No.3,

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