Chapter 3 MATERIALS AND METHODS

Transcription

1 Chapter 3 MATERIALS AND METHODS

2 MATERIALS AND METHODS 3.1 Study Area The details of location, geology, and landforms, climatic condition, soil association and land use/land cover of the study area are presented below Location The study area, Phewa Lake watershed, is located in the south-western part of Kaski district covering both rural and urban area. Phewa Lake watershed extends between 28 11'39" North to 28 17'25" North latitude and 83 47'51" East to 83 59'17" East longitude (Figure 1). Most of the rural area of the watershed is sited on the hilly areas whereas the urban areas are situated on the valley floor of Pokhara. The study area covers Km 2 with its geometrical east-west length of km and north-south width of 9.53 km. Phewa Lake itself covers about 4.83 km² (483 ha) areas. The variation of altitude is from ( m above msl) in the west at Panchase, the highest summit of the watershed area. Phewa Lake is silted up by 180,000 cu m annually due to rapid change of anthropogenic factors (JICA/SILT, 2002). This watershed covers seven wards of Pokhara Sub-metropolitan city (2, 4, 5, 6, 7, 8, and 17) and spread fully or partially of six VDCs (Sarangkot, Kaskikot, Dhikurpokhari, BhadaureTamagi, Chapakot and PumdiBhumdi). The watershed consists of 19 streams that encompasses north of Harpan/Andheri khola, viz. Phirke, Balandi, Orlang, Khapaundi, KhahareKhola, Betani, Lanrak, Thotne and SudheriKhola. Most of streams made semi-agricultural sub-watersheds that belong to the midhill mountain ecosystem Geology The geology of Phewa Lake watershed realm is extremely complicated as mentioned by (Yamanaka et al., 1982) in their geological explanations of the Annapurna range, the Seti River and the Pokhara valley. The hill slopes are mainly made up of phyllite and quartzite of the Precambrian to Paleozoic period, consisting of acidic, moderately fine-textured and non-stony clay (LRMP, 1986). The southern part of the Phewa lake watershed realm possesses more talerich, red phyllitic schist (Mulder, 1978) of metamorphism than the northern part (Fleming, 1978). The lowland area has gentle slope with southward gradient. Compared to sloppy parts, its morphology is resembled to valley terrain in origin. 29

3 Figure 1: Location Map of the Study Area 30

4 The terrain is rugged and comprised of several folds of steeping hills. The anticlines form a series of parallel strike-ridges with their gentler slopes facing south whereas north facing slopes are characterized by rugged with pronounced spur-development and steep slopes. Moreover, the westernmost part of the watershed has some quartzite schist and soft grey talc schist mainly in Nagdanda and Andherikhola areas of Paundur and Deurali (Lamichhane, 2000) Landforms & Relief The Phewa Lake watershed is a micro region of the hills of Nepal. The dominant landforms of the watershed consist of Ancient Lake and River Terraces (tars) erosion; Alluvial Plains and Fans (Depositional); Moderately to Steeply Sloping Mountainous Terrain; Steeply to Very Steeply Sloping Mountainous Terrain; and Phewa Lake (Figure-2). The areal extents of different land forms are presented in Table -1. It occupies the ample topographical disparities that begins from the highest peak named Panchase ( m) and reaches nearly the south-western sectors of the Pokhara valley attaining the average elevation of 789m from mean sea level. This micro region is divided into two parts; the hill and the plain. Figure 2: Landforms Map The area statistics of landforms types of the study area are presented in Table 1. 31

5 Table - 1: Proportion of Area by Landform Types S.N. Particulars Area (ha) % 1 Ancient lake and River Terraces (tars) erosion Alluvial Plains and Fans (Depositional) Moderately to Steeply Sloping Mountainous Terrain Steeply to Very Steeply Sloping Mountainous Terrain Phewa Lake Total (Source: Department of survey, Government of Nepal.) Phewa Lake is surrounded by hills in its three sides (north, south and west) and by the Pokhara plain on the other side in the east. Panchase, Sarangkot and Kaskikot are the other prominent peaks situating in the north. The southern slopes of the northern hills are settled as well as cultivated whereas most of the northern slopes of the southern hills are covered with the natural vegetation. In the hilly region, the principal source stream of the lake named Harpan and its tributaries have cut the deep valleys besides making potholes, waterfall, rapids, terraces etc. The main depositional landform made by the Harpankhola and its tributaries is the lacustrine plain where each of the meanders has been formed at several places. Alluvial fans have also been made at the foot of the hill. Phewa Lake borders the Pokhara plain in the eastern sectors Drainage Phewa Lake is the main drainage basin into which all the streams run to join from its watershed area. Harpan Khola drainage basin which joins the lake at the lacustrine plain is the target river system. The source of water of Phewa Lake is river and rivulets. These are contributed by surface runoffs and ground water recession. The annual mean surface inflow is 9.2 m 3 /sec and minimum 1.0 m 3 /sec. Harpan Khola and its 54 tributaries together contribute about 70% of total inflow into Phewa Lake, Phirke and Buladi Khola are two most important inflows beside Harpan System. The drainage system of Phewa Lake watershed area can be divided into five sub-systems, named as Harpan System, Andheri System, Mid sub System, South Flowing System and North Flowing System. Area statistics of the sub-watersheds are presented in Table -2 32

6 Table - 2: Sub Watershed Wise Area Statistics of Phewa Lake Watershed Sub Watershed Area (ha) Harpan System Andheri System Mid sub system South Flowing Independent System North Flowing Independent System Excluded area Total (Source: Topographical Map, Survey Department, Kathmandu) Harpan System (HS): 35 tributaries flow to join the Harpan Khola. The Harpan system has altogether 54 tributaries among which the major tributary is the Anaheri. Beside this, the other tributaries are Tora Khola, Thiri Khola, Marshe Khola, Chisa Khola, Birim Khola, Betyani Khola. It contains ha area. Andheri System (AS): Andheri stream had a separate drainage system in the northwestern section of the water area. On the whole, it has 10 tributaries among which Saureni Khola, Thitne Khola, Dhandh Khola are important. South Flowing System (SFS): There are eight south flowing independent systems out of which Phirke Khola, Sedi Khola, Maryangdi Khola and Bagguwa Khola are notable. Moreover there are some single systems of stream, such as Dumre Khola, Kamini Khola, and Chisa Khola. Mid sub-watershed (MS): The mid sub-watershed in the north-south section of water flow. It has 22 tributaries from which Danda khola, Marse khola, Tora Khola, Birim Khola, Mure Khola etc are important. North Flowing System (NFS): There are about 15 north facing streams. Among them, three streams have the considerable length and the rest 12 are short. The sizeable north facing streams are Tarikhet Khola, Sasarko Khola and Muhede Khola. Harpan, Andheri, Mid, South and North Flowing System sub-watershed contained ha, ha, ha, ha and ha area, respectively. The digital topographic map of the study area at 1:25000 scales was digitized in ARC GIS 9.3 considering topographic parameters derived contour lines and drainage system for the preparation of sub-watersheds map which is shown in Figure 3. 33

7 Figure 3: Sub-watershed System of the Study Area Climate The Phewa Lake watershed, locating centrally in the subtropical climate, possesses moderate subtropical to the cool temperature type of climate. This extreme climate is owing to its topographical variation. Mean annual rainfall data is presented in Table -3 and monthly mean maximum and minimum temperature recorded are presented in Table-4. The climate of Phewa Lake watershed can be subdivided into three micro types. Moderate Subtropical Monsoon Climate: This type of climate extends up to the altitude of 1200m. The principal localities of such a climate found in Pokhara Sub metropolitan city with the southern lower portion and eastern bordering area of Kaski and Dhikurpokhari VDCs the eastern lower portions of BhadaureTamagi VDC and northern lower are of Chapakot and Pumdi Bhumdi VDCs. Warm Temperature Monsoon Climate: This type of climate has been observed from the altitudes of 1200m to around 1700m, In fact, high altitudes of Sarangkot and Kaskikot VDC s fall on this type of climate condition. This climatic condition includes 70 percentage of eastern and southern slope of high altitudes Bhadaure Tamagi VDC followed by Dhikurpokhari VDC. In 34

8 the same way northern and eastern slope of Chapakot and PumdiBhumdi VDC come under this climatic zone. Cool Temperature Monsoon Climate: This type of climate is found above from 1700m up to the highest altitude of m mainly western and south-western location of the watershed area. The definite places of this climate are Paundurkot, Deurali, Panchase and its surrounding peaks and some upper peaks of PumdiBhumdi VDC, like Thaple and Bhumdi. Table - 3: Mean Annual Precipitation ( ) Year Rainfall (mm) (Source: Department of Hydrological and Metrology, Pokhara, 2012) The data in the Table 3 showed that highest recorded of rainfall accounted for 5970 mm in 2005 and the lowest record accounted for 3074mm in Maximum rain (about 90%) occurs in the rainy season between the months May to September, local convection rainfall in the from a hailstone occurs in spring. The winter season remains more or less dry. The highest amount of rainfall occurs in July (1041mm) and minimum in January (19mm) at which the total amount of rainfall is 3411mm. Maximum rainfall ranges between May to September. Table - 4: Mean Monthly Temperature and Precipitation (2011) Month Jan Feb March April May June July Aug Sep Oct Nove Dec Rainfall (mm) Max( C) Min( C) (Source: Department of Hydrological and Metrology, Pokhara, 2012) According to the air temperature data in Table - 4, the minimum mean temperature is the lowest in January (5.4 C). The highest mean maximum temperature is the June (30.9 C). 35

9 3.1.6 Vegetation and Wildlife Plant associations of tropical to temperate varieties are found in different elevation of the watershed. The Phewa lake watershed area has a great variation of altitude from 789m to m. Therefore, numerous plant species have flourished. The lower part the watershed have dominance of Sal (Shorea robusta), Katus (Castonopsis indica), Chilaune (Schima wallichii), Tooni (Cedrela toona), Sisoo (Dalbergia sissoo), Pipal (Ficus religiosa), Simal (Bombax ceiba) and Bans (Dandroclamus strictus) etc. and in upper part Laligurans (Rhododendron aroboratum), Salla (Pinus species), Bamboo (Dendrocalamus species) etc are the common species found. Shorea robusta occurs in the tropical belt and Schima wallichii, Castanopsis indica predominate on the sub-tropical hills. Forests of oak and rhododendron are common in the northern highlands. Natural grasslands are found on the Seti river terraces and these sometimes overlap with riverrine Acacia catechu and Dalbergia sissoo. Shifting cultivation, overgrazing, fire and lopping have resulted in the depletion of forests. In the sub-tropical belt, forests are most vulnerable to destruction due to human encroachment. Mostly forests have been left only on the steeper slopes. The area is rich in wildlife. The major wild lives are rabbit, monkey, wild goat, leopard, etc. similarly, major birds are fowl, eagle, green pigeon, dove, etc (FRI, 2002; JICA/SILT, 2002) Soil The main soil types are a) Ustochrepts Haplustalfs b) Ustochrepts Dystrochrepts Haplumbrepts c) Typic and Rhodic Haplustalfs Ustochrepts and d) Lithic Subgroup of Typic Ustorthents. The soils in the study area exhibit wide variation due to their texture, depth, stoniness, color, drainage, moisture, organic matter, capacity exchange etc. The forest soil is mature however; immaturity is the chief characteristic of the soils in the study area. The brown forest soil is loamy in texture and fairly fertile. The red earth on the low hills with varying degree of cauterization necessitates managing for profitable farming. The preferred soils for cultivation are the recent alluvia of sandy loam with good internal drainage. Soils on the plain derived from the underlying gravels have a thin humus layer and their water-holding capacity is poor. Four types of dominant surface soil texture are found in the watershed (LRMP, 1986). The area statistics of soil types and texture of the study area is presented in Tables-5 and 6, and the maps of soil types and texture of the study area are presented in Figures 4 and 5. 36

10 Figure 4: Soil Types of Phewa Lake Watershed Table - 5: Soil Type of the Study Area Types of Soil Area (ha) % Ustochrepts Haplustalfs Ustochrepts Dystrochrepts Haplumbrepts Typic and Rhodic Haplustalfs Ustochrepts Lithic Subgroups of Typic and Ustorthents Phewa Lake Total (Source: LRMP, 1986) Figure 5: Soil Texture of Phewa Lake Watershed 37

11 Table - 6: Area Statistics of Surface Soil Texture in Phewa Lake Watershed S.N. Types of Texture Area (ha) Percentage 1 Silt clay loam Loam Sandy loam Silt loam Phewa Lake Total (Source: LRMP, 1986) Land use land cover Forest, Agriculture, Bush/Scrub, Waste Land and Built-up Land are the main land use categories of the Phewa Lake watershed. The forest land use basically consists of community forestry whereas Agriculture Land consists of field crop cultivation on both terrace and valley. The most common gregarious natural vegetation types under tropical to temperate monsoon climates are Schima wallichii, Castonopsis indica, Alnus nepalensis, Juniper and Pinus roxburghii (FRI, 2002; JICA/SILT, 2002). However, the watershed is interspersed by a number of patches of rural settlement and agricultural fields. Agricultural lands are allotted to wet and dry crops cultivation depending upon prevailing local climatic conditions Population The population of Phewa Lake watershed is in which percent are male and percent are female. Population distribution in VDC and city is uneven. In VDC Dhikurpokhari, Pumdi and Sarangkot, Kaskikot share highest population and Bhadaure, Chapakot lowest population share. The study area covers one municipality and six VDCs. The average density of population is per Km 2 but including only six VDCs the populations come to be person/ Km 2. Pokhara sub-metropolitans city include total population with households, 5.19 family size, 1.04 sex ratio and person per Km 2.In VDCs, the highest population was remain of Dhikurpokhari is 10119, Sarangkot 8701, Pumdi 7947 and Kaskikot The highest household size in Dhikurpokhari is 1970 with 5.13 family sizes, 1.05 sex ratios and Km 2 per person density and second Sarangkot have 1699 household at

12 family sizes with 1.00 sex ratio and per person Km family sizes on Pumdi have 1568 hh (household) with 0.91 sex ratio and per person Km 2. Similarly, Bhadaure and Chapakot VDCs consists nearly total population 3831 at Bhadaure 3567 at Chapakot. Bhadaure has 762 hh and Chapakot has 653 total household, at family size 5.02 and 5.47, respectively. The population status of the study area is presented below in Table 7. Table -7: Population Status of the Study Area VDC Total Population Female Male Total hh Family size Sex ratio Pop Density Km 2 Bhadaure Chapakot Dhikirpokhari Kaskikot Pokhara Pundi Sarangkot (Source: DDC Resource Mapping Profile, Kaski 2011) 3.2 Data Used and Methodology Data Sources Primary Sources Primary data have been collected through field observation. Global Positioning System (GPS) Garmin 24 channel navigation device was used for collecting the ground truths primary data. Satellite images and topographical maps information were verified by using ground truth data collected in the field Secondary Sources Most of the secondary information is available from maps, satellite image and physical, socio economic records. Two types of secondary information are used in the study, namely satellite data and ancillary data. 39

13 Satellite data: The satellite data (digital) used for Land use/land cover analysis are listed in Table - 8. Table - 8: Satellite Data Specification Year Satellite Resolution Path/row Band Date of (m) combination acquisition/pass 1995 Landsat, TM / Nov Landsat, TM / Nov Landsat, TM / Nov Landsat, TM / Nov-2010 (Source: Global Land Cover Network) The main purpose of this study was quantifying the land use /land cover change of the study area and evaluating the dynamics between the different LULC classes for the study periods. To quantify the magnitude and rate of the change as well as the dynamics of major land use/land cover types in the study area, the main data used in the study included temporal satellite data of Landsat TM of the years 1995, 2000, 2005 and 2010 (15 years with 5 years interval) for LULC mapping (Table 8). All the images were of the month of November. The Landsat satellite data provided by Global Land Cover Network (GLCN) was radiometrically and geometrically (orthorectification with UTM/WGS 84 projection) corrected. Ancillary Data: Various kinds of ancillary data used in this study are topographic maps at 1:25,000 scale and digital topographic data with contour interval at 20 m published by the Survey Department, Government of Nepal; Land System map at 1:50000 scale obtained by Survey Department, Nepal; Socio-economic data from household survey as well as from Village Secretary and District Development Cooperation; Community forest operational plan from District Forest Office (DFO) Kaski; Annual Report of Nepal Agriculture Research Council (NARC); District Profile Kaski district; Soil map and plant parameter and LRMP (1986 and Morgan, 2001); Meteorological data from the office of the meteorological stations, Pokhara, Nepal; and Demographic data of the Central Bureau of Statistics Nepal. 40

14 3.2.2 Instruments / Field Equipment Global Positioning System (GPS) (Garmin 12 channel), Magnetic Rangers compass, Binocular, Camera, Blume Leiss Hypsometer, etc were used for collecting the ground truth. Garmin GPS was employed to collect Ground Control Points (GCPs) to aid different steps of image processing and classification for change detection. Hardware and Software: The details of hardware and software used in this study are listed in Table - 9. Table - 9: Hardware and Software Used for the Study Hardware Laptop Dell, 10GB RAM Scanner Printer Software Arc GIS 9.3 ERDAS IMAGINE 9.2 ILWIS Academic Idrisi Taiga Microsoft office Arc Map 9.3 Uses Data storage, software and internet support. To convert pictures or maps to digital form. To produce high quality, hardcopy computer output viz. literature, thesis, and report. Uses Digitizing, Mapping Registration and processing of topographic map and satellite data, change detection, Image classification Morgan Morgam Finney Soil erosion modelling LULC prediction modelling Data entry, Non-spatial data analysis, Thesis compilation and Presentation To prepare map composition Methods The methods applied in the study can be categorized into three activities viz., prefieldwork, fieldwork and post-fieldwork. The flow chart of overall methodology is presented in Figure 6. 41

15 Figure 6: Overall Methodology of the Study Pre-fieldwork The main activities in this stage were literature review and collection of secondary data, topographic map, imageries and creation of base map. The field survey questionnaire was prepared for social-economic data collection. Field observation / ground truth sites were selected based on visual interpretation of the Rapid Eye; Landsat images and topographic maps at scale 1: Pre-processing Pre-processing of data includes geometric correction of temporal satellite data. Satellite data was geometrically rectified using topographic map at 1:25000 scale. Composite layers were made by stacking all band layers of each image of study periods. It enables the spectral enhancement of the images to easily distinguish for extracting the land use/land cover classes. 42

16 The four periods spectrally enhanced images were used for digital classification for preparing maps of the land use and land cover Spatial Data Base Generation Base map, soil and slope maps were created by GIS aided processing. The study area (Phewa Lake watershed) was outlined by considering the drainage network using topographic map at 1:25000 scale from Survey Department of Nepal in conjunction with satellite images. In the base map the prominent cultural features, such as road, river, canals and settlement were marked. GIS was utilized for creation of base map and soil map. The steps involved for database creation are summarized below: Creation of Spatial Data: Software, such as ARC GIS 9.3 and ERDAS IMAGINE 9.2 were used for creating spatial data, geo-referencing, sub-setting and mosaic of the image. Creation of Vector Layers: In the present study, linear features, such as roads, drainage, contours considered as a line features whereas base map, soil map, watershed and subwatersheds boundary were treated as polygon features. Settlement, rainfall and rain day maps were considered as point maps. Topology is defined as the mathematics of connectivity or adjacency of points or lines that determines spatial relationships in GIS. The topological data structure logically determines exactly how and where points and lines connect on a map. In the present study all the digitized vector layers were cleaned and built in Arc GIS software. The steps of cleaning and building are important so as to remove the data redundancy and maintain spatial relations. Finally, vector layers of the thematic maps were generated. Generation of digital elevation model (DEM): DEM was generated in Arc GIS 9.3, 3D software using contours, spot height and drainage of the study area with 30 m pixel size. The DEM was used to generate slope and aspect. The height variation in the study area is from 789m to 2508m. The DEM and slope maps were imported in ILWIS 3.3 Academic and IDRISI Taiga for soil erosion and LULC change predictive modeling Fieldwork In this stage, primary and secondary data were collected. The field work phase begun with GPS point collection of the LULC classes. Soil, Climatic and Socio-economic data were collected from the offices of respective Govt. Departments of Nepal. Field data was collected 43

17 during November - January The techniques used for collection of various field data are described below: Reconnaissance Survey: The reconnaissance survey has been carried out at the beginning of fieldwork in order to familiarize with the study area and selecting sites for ground truth collection. Collection of Ground Truth Data: Extensive field survey was conducted using GPS to collect information of existing land use/land covers of the study area. Past LULC information were collected by interviewing official of stake holders as well as local people residing near the ground truth sites. Finally, ground truth training sites of different LULC were marked on satellite image (FCC False Color Composite) hard copy print as well as on topographical map. Different land uses and FCD (Forest canopy density) classes (<40%, 40-70% and %) were identified and location of ground truth sites were found out with the help of the GPS device. The land uses type was recorded as well as major plant species were enumerated. The tonal variation representing the different land uses were correlated with various features. Different type of statistical data and other important ancillary information were taken from different organizations viz. DOF, DDC, VDC office etc. Based on the preliminary reconnaissance survey, visual interpretation of the satellite data and topographic map, report from DOF, District profile of Kaski district and relative study report the following LULC classes which were identified for LULC classification in the study area. Table - 10: Forest/Land use classification scheme Land Uses Dense Forest Medium to Fairly Dense Forest Open Forest Terrace Agriculture Description Forest composed of tree of two or more than species consist greater than 70% in the main canopy composition. The main species are Schima wallichii, Castonopsis indica, Alnus nepalensis, Pinus roxburghii, Juniper Dendrocalamus species, Dalbergia sissoo, Cedrela toona, etc. Forest having (40-70%) in the canopy cover with tree of two or more than species. Forest having less than 40% in the canopy cover. Cultivation in sloping mountainous areas in terraced fields. The major crops are Wheat (Triticum vulgare), Paddy (Oryza sativa), Millet (Pennisetum glaucum), Soybean (Glycine max), maize (Zea mays), Potato (Solanum tuberosum) etc. 44

18 Valley Agriculture Bush/Scrub Grass Land Waste Land Water Body Wetland Built-up land In the downstream of study area mainly variety of paddy is growing and irrigation is good. The major crops are Wheat (Triticum vulgare), Paddy (Oryza sativa), maize (Zea mays), Potato (Solanum tuberosum) etc. It includes areas covered with Shrubs and Bushes with isolated trees. The tree density is generally low i.e. less than 10 percent tree density and more than 20 percent Shrub and Bush. Grassland is an open and continuous, which are found naturally in the study area. Main source is for grazing animals. Few grass land are found in study area. Waste land is mostly unfertile barren landscape. These types of area are found in the river bed side and long term agriculture practice is absent. Quality of soil is poor. The land covered under natural drainage system like river, streams as well as manmade linear drainage system like canals (used or unused) and natural or manmade linear reservoir or ponds and lakes. Mainly some area near the lake found swampy. This type of area remains waterlogged conditions most of the period but some agriculture practice was done occasionally. Urban and rural human settlement areas. Other Data Collection: Detailed rainfall data is required for soil erosion modeling. This includes rainfall total amount, intensity and number of rainy days. These data were collected from the meteorological station at Pokhara. Forest plantation, compartment map and timber production data were also obtained from Forest Department in Kaski, Pokhara, Nepal. Soil map and other important documents related to soil is taken from Soil Conservation Department of Kaski, Pokhara, Nepal Post-fieldwork After the fieldwork, post field work was carried out, viz.; field data compilation, data was processed for digital supervised classification, LULC Mapping, Modeling of LULC change; soil erosion Modeling and data analysis was followed. Aiming at the getting of desired results and outputs, it is essential to use a variety of computer software as presented above in Table -9 for effective handling and analyzing of the large amount of data in proper manner Processing of Data All the hard copy maps were scanned using scanner and transformed into computer readable digital data sets. These digital maps were imported to ERDAS IMAGINE 9.2 and the rectification processes were done. These digital maps were geometrically rectified using 45

19 topographical maps. The rectified digital maps were consequently imported to ARC GIS 9.3 in order to digitize and generate the various theme layers viz.; extent of the study area, road network map, settlements map, drainage map, contour map, settlements map, etc. LULC maps of the years of 1995, 2000, 2005 and 2010 were generated from the digital classification of satellite data with maximum likelihood classifier (MLC) technique. Accuracy assessment of classified LULC map was done using ground truth. DEM derivatives, such as slope and aspect maps were generated from DEM. Thematic layers, such as road and settlements maps were rasterized and buffering was carried out from roads and settlements as the center of increasing distance from 100m to 1400m and used as drivers of LULC change in the present study. Finally, these spatial thematic data bases were used for further analysis as per the objectives of this study. The data processing steps for database creation are illustrated in Figure 7. Figure 7: Methodology of Data Processing for Database Creation Image Classification and Accuracy Assessment Essentially, digital image classification is the process of assigning pixel to classes. The term classifier refers to loosely to a computer program those implement specific procedures for image classification. Supervised classification can be defined as the process of using samples of 46

20 known identity (i.e., pixels already assigned to informational classes) to classify pixels of unknown identity (i.e., to assign unclassified pixels to any of several informational classes. Samples of known identity are those pixels located within training areas (or training fields). The analyst defines training areas by identifying regions on the image that can be clearly matched to areas of known identity on the image. Clearly, the selection of these training data is a key step in supervised classification. At the beginning, the training sites have been developed based on the collected reference data and ancillary information. Prior to supervised classification, a classification scheme (Table 10) was developed to obtain a broad level of classification to derive various LULC classes. The four multi-temporal images (1995, 2000, 2005 and 2010) of the study area were independently classified based on LULC classification scheme proposed in Table -10. Preliminary image analysis was performed to extract meaningful information from the acquired satellite image of each year. Next to this, image enhancement was made to improve the appearance of the imagery to assist in visual interpretation and analysis. This includes spatial enhancement and spectral enhancement. Its function includes contrast stretching to increase the tonal distinction between various features in a scene, and spatial filtering to enhance (or suppress) specific spatial patterns in an image. In order to improve the visualization the image for the prospected classification, different false color composite were produced in addition to the true color composite. The application of each color composite for different land use/land cover features identification and training sites election for supervised classification were used. In this study false color composite including (4, 3, and 2) bands is employed. Finally, supervised classification procedure with maximum likelihood (MLC) algorithm in ERADAS Imagine 9.2 was used for image classification for all study periods. In MLC classification method each the image pixel is assign to each class with high probability based on the training sample. In addition to Landsat TM satellite data, Eye bird satellite of the year 2010, Google Earth, ESRI online, digital topographic map of scale 1:25000 and other ancillary layers were used as reference data for identifying ground truth sites of LULC features for digital classification. Signature editor was created to define the classes in ERDAS 9.2 software. The boundaries and number of pixels for each class were used as inputs in signature editor by using area of interest (AOI) tools for generation of training signatures. Finally, LULC maps of 1995, 2000, 2005 and 2010 periods 47

21 were generated by digital supervised classification method. The various steps followed for digital supervised classification are shown in Figure -8. Digital Satellite Data (TM) (1995, 2000, 2005 and 2010) Image Preprocessing Training Sample LULC Maps 1995, 2000, 2005 and 2010 Supervised classification Field survey & ancillary data, (Rapid eye satellite data, Topographic map, Google earth etc) Change Detection and Analysis (1995 to 2000, 2000 to 2005, 2005 to 2010 and 1995 to 2010) Accuracy Assessment Figure 8: Satellite Image Analysis Methodology For accuracy assessment of digital LULC maps, the classified maps were assessed with the test samples sites generated from ground truth data as well as high resolution satellite and ancillary reference data. The overall 115 nos. of test samples were generated. GPS points were used to validate the result of classification through the confusion matrix /error matrix. Confusion /error matrix consists of row and columns in which row and column represents classification value and fact value from the field respectively. Correctly classified pixels were represented by the diagonal line of the error matrix. The Overall accuracy was calculated from correctly classified pixel divided by total number of pixel checked. The producer accuracy index was produced by dividing the number of correctly classified pixels. Land use classes and validate points with coordinates in the text format were imported as true classes. The user s accuracy index was produced by dividing the total number of correctly classified pixels that belongs to a class by the sum of the values of the rows of the same class. Kappa statistical parameter computed from the data of confusion matrix was also used for accuracy assessment of LULC maps. 48

22 Land Use Land Covers Change Analysis LULC map derived by digital supervised classification of Landsat TM data of the four periods was analyzed for assessment of LULC change. It provided the information on the trend of conversation in terms of time. But this comparison failed to provide information about the destination and contribution of each LULC class for the change in spatial extent of the other. Thus, to understand the LULC dynamics in the study Phewa Lake watershed change and nochange matrices were employed and analyzed for each period. The LULC maps of , , and were overlaid respectively using matrix option in ERDAS IMAGINE 9.2 to find changes in each five years interval and fifteen year time intervals. Change and no-change matrices were made from each of attribute table of the overlaid images. The areas of change and no-change and percent change were also calculated using Excel. Moreover, classified images were exported to Arc Map 9.3 and LULC maps for the years 1995, 2000, 2005, and 2010 were converted into Arc GIS format. Sub-watersheds wise LULC spatial distributions for each study period were also found out by combining LULC maps and digital sub-watersheds map using spatial modelling approach using Arc Map LULC Prediction Modeling and Validation A number of LUCC models have been developed for the prediction of land use land cover which is available in different GIS software. Among the numbers of LULC modeling tools and techniques, the commonly used models which are embedded in IDRISI is Markov Chain, Cellular Automata (CA), CA-MARKOV, GEOMOD, Logistic Regression, MLP, etc. In this study, LULC prediction modeling was done using CA-MARKOV and GEOMOD models for prediction of LULC for the years 2010, 2015 and 2020 on the basis of past trends of LULC changes. For the accuracy assessment or validation of the model, the predicted LULC 2010 was compared with actual LULC map of The accuracy of predictive modeling was assessed using Kappa index parameters viz. Kno = Kappa for no information, Klocation = Kappa for location, Kquantity = Kappa for quantity and Kstandard = Kappa standard. Markov chain is a series of random values whose probabilities at a time interval depend on the value of the number at the previous time. Theoretically, a given land use may change from one category of land use to any another at any time then Markov chain prediction is based on the current situation of LULC use not the previous context. The Markov analysis uses 49

23 matrices that represent all the multi-directional LULC changes between all the mutually exclusive land use categories. Markov Chain analyzes two land cover images from different dates and produces a transition probability matrix, a transition areas matrix, and a set of conditional probability images required for further CA- MARKOV LULC change modeling. The work flow of Markov process is shown in Figure - 9. Figure 9: Methodology of Markov Chain Modeling Process The transition probability may be accurate on a per category basis, but there is no knowledge of the spatial distribution of occurrences within each land use category. Hence, Cellular Automata (CA) was used to add spatial character to the model the land use change. CA operates on a grid based cells and transition rules that are applied to determine the state of a cell. The transition potential rule is attained by different criteria/ factors with its effect and influence. The quantification of these different effects and influence has computed with different weights. These weights are argued on the basis of different LULC policies integrated with multi-criteria evaluation (MCE), analytical hierarchy process (AHP) and pair wise comparison weighting methods. The transition potential rule process is presented below in Figure 10. Factors are not hard rule like constraints. Constraints are Boolean in nature; they totally allow or block completely a certain area from change. In the case of factors, it is different and they give a degree of suitability for an area to change (mostly on distance basis). Hence, all the 50

24 factors criteria were standardized to a continuous scale of suitability from 0 (least suitable) to 255 (most suitable) with standardized with the FUZZY module mainly for two reasons in the case of this study. First, all the criteria could not have the same degree of suitability. Second, all areas of studies cannot have the same continuous suitability level. Therefore, based on the knowledge of the area and LULC policy issues a Fuzzy Logic is incorporated. Figure 10: Transition Potential Rule The fuzzy module available in IDRISI, enables to standardization the whole range of the fuzzy set to membership functions type (sigmoid, J-shape and Linear) and membership function shape (monotonically increase, decrease or symmetric). Besides, it enables us the set a control points to set the limits of the standardization. Here in this study, driver s data (distance from road and distance from settlements) was integrated with terrain driver data (DEM derived slope) of the watershed through MCE (Multi-criteria Evaluation) technique for LULC change modeling. To use MCE technique, it is necessary need to develop criteria for making decision about LULC classes. Different criteria were considered to determine, which LULC classes of watershed are suitable for changing from one class to another with time including proximity from road and settlement, socio-economic drivers, and biophysical drivers (slope). In this study, these criteria were divided into different types factors can pertain either to attributes of the individual or to an entire decision. These principles generally should be based on the government policy formulated 51

25 according to environmental and socio-economic consideration. The development of Built-up areas should mostly be preferable to underutilized places but, these kinds of areas are rarely available in the cities. So, agricultural areas having relatively flat slopes are being extensively utilized nowadays for urban development. It is also supposed that the urban development takes place closest to existing road networks and developed unoccupied areas. However, as the distances of such areas increase, they are less preferred due to cost effectiveness. Nearness to Dense Forest and Water Body should also be avoided for urban development. Considering these general principles the factors with non-boolean condition of WLC approach were standardized into "fuzzy" rule, i.e. suitability of contiguous range of 0 = least suitable to 255 = most suitable using MCE in IDRISI. The CA-Markov is a combination of both CA and Markov chain. CA-MARKOV can be used to project with any number of LULC classes.ca-markov uses the output from the Markov Chain Analysis particularly transition area file to apply a contiguity filter to grow out land use from time two to a later time period. In essence, the CA will develop a spatially explicit weighting more heavily areas that proximate to existing land uses. This will ensure that land use change occurs proximate to existing like LULC classes, and not wholly random. The work flow process of CA-MARKOV model of the present study is presented in Figure -11. Figure 11: Methodology of CA Markov Modeling Process 52

26 In present study, the transition area matrix obtained from two time periods in Markov process was used as the basis for predicting the future LULC scenarios. The LULC map of the year 2000 is used as the base image while 2005 LULC map as the later image in Markov model to obtain the transition area matrix between 2000 and 2005 years for prediction of LULC in The same image of 2005 was used as base image to obtain the transition area matrix between the years 2005 and 2010 for prediction of LULC of 2015 and the image of 2000 as base image to obtain the transition area matrix between 2000 and 2010 for prediction of The actual 2010 LULC map was used as the base map for estimating future LULC scenarios for 2015 and 2020 by CA MARKOV. GEOMOD is relatively easy LULC simulation model that predicts, forwards or backwards, the locations of grid cells that changes over time. It runs only with two numbers of classes, for example, Built-up and Non Built-up etc.the GEOMOD uses the beginning time map i.e and ending 2010 time along with suitability maps are prepared as before in CA- MARKOV and the information on the number of grid cells of the LULC classes areas for the ending time i.e Modelling for the 2015 and 2020, LULC the previous year s actual LULC maps 2005 and 2010; and 2000 and 2010 are used respectively, along with the suitability maps.therefore in this study, GEOMOD modeling was employed for comparing dominant LULC classes for the predicted LULC 2010, 2015 and The work flow process of GEOMOD model is presented in Figure Figure 12: Methodology of GEOMOD Modeling Process 53

27 CA-MARKOV can be used to project with any number of land use classes, whereas GEOMOD runs only with two numbers of classes e.g. Built up and Non-built up. A collection of suitability maps needs to be created as decision rules for simulation process which can be prepared using Multi-Criteria-Evaluation (MCE). However, GEOMOD uses suitability maps along with beginning land use map, ending time as well as ending time land classes pixel quantities. Although there are different ways of validating the simulated map output, the mostly used one is VALIDATE module technique in Idrisi. In this study the same was employed for validation of predicted LULC maps. The VALIDATE module examines the agreement between two maps that show the same categorical variable i.e. comparing a pair of true ending time map versus simulated ending time map. In the comparison, the true ending time map is referred as "reference map" and the simulated map as "comparison map". Here in this study, predicted LULC 2010 from CA-MARKOV and GEOMOD model results were validated with actual LULC map of 2010 as reference image and evaluated with Kappa statistics parameters. The projected 2010 by CA-MARKOV was also assessed with ground data collected by GPS in the field for checking the accuracy. Moreover, predicted LULC maps 2015 and 2020 were exported to Arc Map 9.3 and ERDAS Imagine 9.2 in which five sub-watersheds were delineated. The identification and quantification of LULC changes has been studied both independently for the whole watershed and sub-watershed wise from actual 2010 to predicted LULC for years 2015 and On the basis of these change, the results and conclusion were derived Assessment of Soil Erosion Status The methodology used for soil erosion risk assessment was the use of Revised Morgan- Morgan and Finney (RMMF) model in a raster GIS environment (or grid-based approach) for the quantification of soil loss utilizing several input parameters, such as DEM derived slope; soil map and soil characteristics; plant parameters; Rainfall characteristics data; and Land use /Land cover maps derived by digital classification of satellite data. In this study, a cell size or pixel of 30m was chosen. The flow diagram of RMMF model is presented in Figure 13 54

28 Figure 13: Modeling for Soil Erosion Status Using RMMF Soil Erosion Modeling Different models have been developed to address soil erosion. Among them, a revised Morgan, Morgan and Finney Model (MMF) with the aid of remote sensing and GIS is used in the present study. The MMF model simplified the erosion processes into two: detachment of soil particles from the soil mass by raindrop impact and the transport of those particles by runoff. The results obtained by the model are most sensitive to changes in annual rainfall and soil parameters when erosion is transport-limited and to changes in rainfall interception and annual rainfall when erosion is detachment-limited. The model input parameters for RMMF modeling are listed in Table

29 Table -11: Input Parameters for RMMF soil erosion modeling Factor Parameter Definition and remarks Rainfall R Annual or mean annual rainfall (mm) Rn Number of rain days per year I Typical value for intensity of erosive rain (mm/h); 10 for temperate climates, 25 for tropical climates and 30 for strongly seasonal climates e.g. Mediterranean type Soil MS Soil moisture content at field capacity or 1/3 bar tension (% w/w) BD Bulk density of the top soil layer (Mg/m) EHD Effective hydrological depth of soil (m); depend on vegetation/crop cover, presence or absence of surface crust, presence of impermeable layer within 0.15 m of the surface K Soil detachability index (g/j) defined as the weight of soil detached from the soil mass per unit of rainfall energy. COH Cohesion of the surface soil (kpa) as measured with a torvane under saturated conditions. Landform S Slope steepness ( ). Land Cover A Proportion between 0 and 1 of the rainfall intercepted by the vegetation or crop cover. Et/Eo Ratio of actual (Et) to potential (Eo) evapo-transpiration C Crop cover management factor; combines the C and P factors of the Universal Soil Loss Equation. CC Percentage canopy cover, expressed as a proportion between 0 and 1 GC Percentage ground cover, expressed as a proportion between 0 and 1 PH Plant height (m), representing the height from which raindrops fall from the crop or vegetation cover to the ground surface R = Rainfall; Rn = Number of rainy days per year; I = Intensity of erosive rain (mm/h); MS = Soil moisture content at field capacity (w/w); BD = Bulk Density (Mg/m³); EHD = Effective Hydrological depth of the soil (m); K = Erodibility of the soil (g/j); COH = Cohesion of the surface soil; S = Slope steepness ( ); A = Proportion between 0 and 1 of the rainfall intercepted by the vegetation; Et/Eo = Ratio of actual (Et) to potential (Eo) evaopotranspiration; C = Crop cover management factor; CC = Percentage canopy cover; GC = Percentage ground cover; PH = Height of the plant canopy (m) Model Input Parameters For running the RMMF model, soil data (detachability, moisture content at field capacity of the surface soil layer, bulk density, effective hydrological depth), rainfall data (annual rainfall, 56

30 rain intensity and number of rainy days/year), land cover data (types of Agriculture, Forest, Bush/ Scrub, Grass, Waste Land and the crop cover management practices) and topographic data (slope gradient) are required. Land use land cover information: Land use/land cover maps generated using Landsat TM/ satellite data by supervised classification for the year 1995, 2000, 2005 and 2010 were reclassified and imported into ILWIS software for further analysis. The percentage of rainfall contributing to permanent interception (A), the ratio of actual to potential evaporate transpiration (Et/E 0 ), the crop cover management factor (C), canopy cover (CC), ground cover (GC), plant height (PH) and Effective hydrological depth (EHD) were used as plant parameters and values derived using various literatures are presented in Table Table 12: Plant Parameters Used in the Model Forest/Land Use Type A GC CC PH Et/E 0 C EHD Dense Forest Medium to Fairly Dense Forest Open Forest Terrace Agriculture Valley Agriculture Bush/ Scrub Land Grass Land Waste Land (Source: Morgan et al., 1984, Shrestha, 1997 and Morgan, 2001) The C values for Terrace Agriculture and Valley Agriculture are adjusted by multiplying by 0.15 and 0.6, respectively, while EHD values for both Terrace and Valley Agriculture was added by 0.01 because of conservation measure through terracing (Morgan, 1982 and Morgan, 2001). Digital elevation model and slope gradient map: Slope gradient map is an important parameter in the RMMF model (Morgan, 2001), especially in computing soil particle detachment and transport capacity of overland flow values. Digital elevation model was generated by digitizing contour lines at 20m intervals from a topographic base map at scale 1:25000 in ARC GIS and was imported in ILWIS. The height differential filters (in X and Y directions) were 57