A Case Study of Simly Watershed, Islamabad, Pakistan

ARTICLE IN PRESS The Egyptian Journal of Remote Sensing and Space Sciences (2015) xxx, xxx–xxx

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RESEARCH PAPER Land use change mapping and analysis using Remote Sensing and GIS: A case study of Simly watershed, Islamabad, Pakistan

Amna Butt, Rabia Shabbir, Sheikh Saeed Ahmad *, Neelam Aziz

Department of Environmental Sciences, Fatima Jinnah Women University, Rawalpindi, Pakistan

Received 21 March 2015; revised 19 June 2015; accepted 22 July 2015

KEYWORDS Abstract Evaluation of watersheds and development of a management strategy require accurate Change detection; measurement of the past and present land cover/land use parameters as changes observed in these Land cover/use change; parameters determine the hydrological and ecological processes taking place in a watershed. This Supervised classification; study applied supervised classification-maximum likelihood algorithm in ERDAS imagine to detect Simly watershed land cover/land use changes observed in Simly watershed, Pakistan using multispectral satellite data obtained from Landsat 5 and SPOT 5 for the years 1992 and 2012 respectively. The watershed was classified into five major land cover/use classes viz. Agriculture, Bare soil/rocks, Settlements, Vegetation and Water. Resultant land cover/land use and overlay maps generated in ArcGIS 10 indicated a significant shift from Vegetation and Water cover to Agriculture, Bare soil/rock and Settlements cover, which shrank by 38.2% and 74.3% respectively. These land cover/use transfor- mations posed a serious threat to watershed resources. Hence, proper management of the watershed is required or else these resources will soon be lost and no longer be able to play their role in socio- economic development of the area. Ó 2015 Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).

1. Introduction nature of the environment of interest (Verburg et al., 2004). Ecologists pay considerable attention to the land use change Changes in land use can be categorized by the complex inter- impacts predominantly with respect to its effects on biodiver- action of structural and behavioral factors associated with sity and aquatic ecosystems (Turner et al., 2001). Changes in technological capacity, demand, and social relations that affect the land use in a watershed can affect water quality and sup- both environmental capacity and the demand, along with the ply. For instance, land use patterns change due to watershed development frequently resulting in increased surface runoff, reduced groundwater recharge and transfer of pollutants * Corresponding author. Tel.: +92 321 5167726; fax: +92 51 (Turner et al., 2001). Thus, the assessment of land use patterns 9271168. and their changes at the watershed level is crucial to planning E-mail address: [email protected] (S.S. Ahmad). and management of water resources and land use of the partic- Peer review under responsibility of National Authority for Remote Sensing and Space Sciences. ular watershed. http://dx.doi.org/10.1016/j.ejrs.2015.07.003 1110-9823 Ó 2015 Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Analysis of detected change is the measure of the distinct employed the same technique and achieved highly satisfactory data framework and thematic change information that can results including Rawat and Kumar (2015), who applied the lead to more tangible discernment to underlying process same technique to monitor land use/land cover change in involved in upbringing of land cover and land use changes Hawalbagh block, district Almora, Uttarakhand, India. (Ahmad, 2012). Change analysis of features of Earth’s surface Boori et al. (2014) analyzed the land use/land cover distur- is essential for better understanding of interactions and bance caused by tourism using a number of Remote Sensing relationships between human activities and natural phenomena. and GIS based techniques including supervised classification. This understanding is necessary for improved resource man- Rawat et al. (2013) also applied the same technique for agement and improved decision making (Lu et al., 2004; Seif Ramnagar town area, Uttarakhand, India to track the changes and Mokarram, 2012). Change detection involves applying observed in the area between the time period of 1990 and 2010. multi-temporal Remote Sensing information to analyze the The study area was selected for change detection because of historical effects of an occurrence quantitatively and thus helps being subjected to urbanization, sewage discharges without in determining the changes associated with land cover and land treatment, active water and soil erosion, over grazing, cutting use properties with reference to the multi-temporal datasets of trees, non-existence of any cooperative communal structure (Ahmad, 2012; Seif and Mokarram, 2012; Zoran, 2006). and reduced livelihood opportunities. Along with these, rapid Various studies have been conducted all over the world discharge of pesticide residues and poultry discharge in the regarding the change analysis of watersheds through different streams is also one of the major concerns faced by the Simly methods. They are important to develop effective management watershed due to the rapidly increasing agricultural activities strategies for watersheds worldwide (Ashraf, 2013; Bazgeera and number of poultry farms in the study area (IUCN, 2005; et al., 2008; Caruso et al., 2005; Dietzel et al., 2005; Fortin Mangrio et al., 2011). The rapid urban development taking et al., 2003; Gajbhiye and Sharma, 2012; Hu et al., 2012; place in the study area has led to environmental problems as Kearns et al., 2005; Parker and Meretsky, 2004; Stewart well, encompassing, fragmentation of aquatic habitats, soil et al., 2004; Wang et al., 2004). Watershed management is erosion, and water pollution due to deforestation and dis- necessary because a watershed is not merely a hydrological charge of municipal garbage and industrial waste (Hagler unit (Singh et al., 2014) but also socio-ecological being which Bailly, 2007; Tanvir et al., 2006). plays a vital role in determining economical, food and social Therefore, the main objective of the present research was to security and provision of life support services to local residents utilize GIS and Remote Sensing applications to discern the (Wani et al., 2008). Changes in land cover/land use in water- extent of changes occurred in Simly watershed, Islamabad, shed area including urbanization and de(/re)forestation contin- Pakistan over 20 years’ time period. However the specific uously affect the water availability as well as the nature and objectives included (i) to identify and delineate different extent of surface and subsurface water interactions thus influ- LULC categories and pattern of land use change in watershed encing watershed ecosystems and the services provided by from 1992 to 2012 (ii) to examine the potential of integrating them. With proper understanding of the spatial and temporal GIS with RS in studying the spatial distribution of different variations occurring in a watershed over time and the interac- LULC changes (iii) to determine the shift in LULC categories tion of the hydrological components of a watershed with each through spatial comparison of the LULC maps produced. other, better water conservation strategies can be formulated (Ashraf, 2013). Remote Sensing (RS) has been used to classify and map 2. Materials and methods land cover and land use changes with different techniques and data sets. Landsat images in particular have served a great 2.1. Study area deal in the classification of different landscape components at a larger scale (Ozesmi and Bauer, 2002). Recently several Situated in Pothwar Plateau, the study area is geologically com- change detection techniques have been developed that make posed of Tertiary sandstone, limestone and alluvial deposits. use of remotely sensed images. A variety of change detection These sandstones apparently belong to the Sirmar and Siwalik techniques and algorithms have been developed and reviewed series of the sub Himalayan system. Climate of the area is humid for their advantages and disadvantages. Among these subtropical. May, June, and July are the warmest months with Unsupervised classification or clustering, Supervised classifica- average temperature ranging from 36 °Cto42°C with extremes tion, PCA, Hybrid classification and Fuzzy classification are sometimes as high as 48 °C. While the coldest months are the most commonly applied techniques used in classification December and January, with mean minimum temperatures (Lu et al., 2004; Rundquist et al., 2001; Zhang et al., 2000). ranging from 3 °C to 5.5 °C. Simly watershed starts at Simly A variety of supervised classification methods have been lake which is around 40 km north-east of Islamabad (at latitude applied extensively for the land use change analysis through- 33°430 N and longitude 73°190 E), near Bhara Kahu. It stores the out the world. This technique depends on a combination of perennial water flow from Murree springs/Patriata mountain background knowledge and personal experience with the study aquifer and a substantial amount of flood water of the Soan area to a greater extent than other areas. Thus per-pixel signa- River. It is the largest drinking water reservoir for the residents tures are taken and stored in signature files by using this of Islamabad and is recognized mostly as Simly Dam. Two main knowledge and the raw digital numbers (DN) of each pixel streams feed the Simly Dam catchment namely the Soan Nullah in the scene are therefore converted to radiance values and the Khad Nullah. The area surrounding the Dam has (Jensen, 2005; SCGE, 2011). Similar technique was used to esthetic value and attracts tourists by the beautifully planted detect changes observed in a nearby watershed (Rawal water- ornamental trees, resort, and picnic points (Daaira, 2012; shed, Islamabad, Pakistan) and achieved up to 95% accurate Groupin, 2011; Hagler Bailly, 2007; IUCN, 2005). The map results (Butt et al., 2015). Several other researchers have of the study area is shown in Fig. 1.

2.2. Data collection Table 1 Satellite data specifications. Data Year of Bands/color Resolution Source The data used in this research were divided into satellite data acquisition (m) and ancillary data. Ancillary data included ground truth data for the land cover/use classes, aerial imagery of watershed and Landsat 5 1992 Multi- 250 USGS its surrounding area, topographic maps. The ground truth TM spectral glovis imagery data were in the form of reference data points collected using SPOT 2012 Multi- 2.5 SUPARCO Geographical Positioning System (GPS) from March to imagery spectral October 2012 for 2012 image analysis, used for image classification and overall accuracy assessment of the classification results. Satellite data for 2 years on the other hand consisted of multi-spectral data acquired by Landsat satellite for the signatures and differentiating the watershed into five classes month of September provided by USGS glovis. on the bases of the specific Digital Number (DN) value of dif- Specifications of the satellite data acquired for change analysis ferent landscape elements. The delineated classes were are given in Table 1. Agriculture, Bare soil/rocks, Settlements, Vegetation and Water class (Table 2). For each of the predetermined land 2.3. Image pre-processing and classification cover/use type, training samples were selected by delimiting polygons around representative sites. Spectral signatures for the Satellite image re-processing prior to the detection of change is respective land cover types derived from the satellite imagery immensely needed and has a primary unique objective of estab- were recorded by using the pixels enclosed by these polygons. lishing a more direct affiliation between the acquired data and A satisfactory spectral signature is the one ensuring that there biophysical phenomena (Coppin et al., 2004). Data were pre- is ‘minimal confusion’ among the land covers to be mapped processed in ERDAS imagine 12 for geo-referencing, mosaick- (Gao and Liu, 2010). After that maximum likelihood algo- ing and subsetting of the image on the basis of Area of Interest rithm was used for supervised classification of the images. It (AOI). All satellite data were studied by assigning per-pixel is the type of image classification which is mainly controlled

Figure 1 Study area map showing Simly watershed.

has been successfully used by various researchers in urban Table 2 Classes delineated on the basis of supervised environment due to its efficiency in detecting the location, classification. nature and rate of changes (Hardin et al., 2007). Another Sr. Class name Description technique used to obtain the changes in land cover/land use No. during the specified time period was overlay procedure. A 1 Agriculture Crop fields and fallow lands two-way cross-matrix was obtained by the application of this 2 Settlements Residential, commercial, industrial, procedure and was used to describe the main types of change transportation, roads, mixed urban in the study area. In order to determine the quantity of conver- 3 Bare soil/ Land areas of exposed soil and barren area sions from a particular land cover to other land cover category rock influenced by human influence and their corresponding area over the evaluated period, cross 4 Vegetation Mixed forest lands tabulation analysis on a pixel-by-pixel basis was conducted. 5 Water River, open water, lakes, ponds and reservoirs Thus, a new thematic layer was also produced from the two five-class maps, containing different combinations of ‘‘from– to’’ change classes. by the analyst as the analyst selects the pixels that are representative of the desired classes. 3. Results and discussion To improve classification accuracy and reduction of mis- classifications, post-classification refinement was therefore The classified LULC map of Simly watershed of years 1992 used for simplicity and effectiveness of the method (Harris and 2012 is given in Fig. 2. The achieved overall classification and Ventura, 1995). Moreover, using data having medium- accuracies were 95.32% and 95.13% and overall kappa statis- spatial resolution such as that of Landsat mixed pixels are a tics were 0.9237 and 0.9070 respectively for the classification of common problem (Lu and Weng, 2005); especially for the 1992 and 2012 images. According to Lea and Curtis (2010), urban surfaces that are a heterogeneous mixture of features accuracy assessment reporting requires the overall classifica- mainly including buildings, grass, roads, soil, trees, water tion accuracy above 90% and kappa statistics above 0.9 which (Jensen and Im, 2007). The problem of mixed pixels was were successfully achieved in the present research. addressed by visual interpretation. For the enhancement of The classification results for 1992 and 2012 are summarized classification accuracy and therefore the quality of the land in Table 3. Percentage of classes based on these results show cover/land use maps produced, visual interpretation was very the land cover/land use practices observed in watershed area important. Thus, visual analysis, reference data, as well as during 1992 and 2012. local knowledge, considerably improved the results obtained The results show that major decline with respect to area using the supervised algorithm. coverage in Simly watershed was observed in Vegetation and Water classes whereas, the area of Settlements, Bare soil/rocks 2.4. Accuracy assessment and Agriculture classes was increased. Vegetation land shrank from 69% to 43% of the total area while Water class, which Assessment of classification accuracy of 1992 and 2012 images was least area covering class in 1992, further lost area under was carried out to determine the quality of information derived its cover and reduced from 4% to 1%. The share of from the data. If the classification data are to be useful in detec- Settlements was 6% of the total area which increased up to tion of change analysis, it is essential to perform accuracy 11%. The Agriculture class was increased from a share of assessment for individual classification (Owojori and Xie, 11% to 29% and the Bare soil/rocks faced an increment in 2005). For the accuracy assessment of land cover maps the total share from 10% to 16%. extracted from satellite images, stratified random method was The comparison of each class of 1992 and 2012 showed that used to represent different land cover classes of the area. The there has been a marked land use and land cover change dur- accuracy assessment was carried out using 100 points, based ing the study period of 20 years. During the 1992–2012 period on ground truth data and visual interpretation. The compar- the percentage area covered by Agriculture class in watershed ison of reference data and classification results was carried increased by 163.7%. This increasing trend of land cover/land out statistically using error matrices. In addition, a non- use change in the watershed’s area reinforces that economic parametric Kappa test was also performed to measure the forces are commonly a major stimulus on anthropogenic extent of classification accuracy as it not only accounts for diag- change of land (Wang et al., 2008) and it is the main reason onal elements but for all the elements in the confusion matrix why the area near and around the main water-bodies and (Rosenfield and Fitzpatirck-Lins, 1986). Kappa is a measure streams in the watershed has shifted from other land covers of agreement between predefined producer ratings and user to Agriculture cover. At start the overall agricultural produc- assigned ratings. It is calculated by a formula (Eq. (1)): tion and expansion were only subjected to peripheral lands K ¼ PðAÞPðEÞ=1 PðEÞð1Þ on steep slopes until biophysical limitations like steep slopes where P(A) is the number of times the k raters agree, and P(E) and thin fertile topsoil limited further expansion and is the number of times the k raters are expected to agree only Agriculture land was pushed into formerly wooded areas. It by chance (Gwet, 2002; Viera and Garrett, 2005). has also been observed in watershed that the Settlements or Built up areas are mostly surrounded by Agricultural area, 2.5. Land use/cover change detection especially in the catchment area and by the main streams. It means the area near the population has been cleared for the Post-classification change detection technique, performed in production of crops in order to fulfill the basic necessities of ArcGIS 10 was employed by the study. Post classification life (Hagler Bailly, 2007).

Figure 2 Classiﬁed maps of Simly watershed (1992 and 2012).

Table 3 Land cover/land use classes and areas in hectares. area mainly includes lower vegetation, Patriata forest, range- Land cover/use classes 1992 2012 lands, conifer forest and dry semi evergreen forest. A complete checklist of climbers, wild trees and shrubs in the study area Area (ha) % Area (ha) % was prepared by Nasir and Akhtar (1987). This class was also Agriculture 1775 11 4681 29 replaced by Settlements and Agriculture class. In addition to Bare soil/rocks 1648 10 2691 16 deforestation, cutting of fuel wood by the local communities Settlements 1038 6 1870 11 and extensive cattle grazing have deformed the plants present Vegetation 11,342 69 7008 43 in the area to small bushes and in some areas have left only Water 603 4 155 1 barren lands (Arfan, 2008; Shafiq et al., 1997). Ali et al. (2008) reported that if the built up area in and around Rawalpindi and Islamabad continued to increase at a rapid The second class which faced an increment in the total area rate as observed now, it would result in increased surface run- was Bare soil/rocks with 63.3% increase in the overall area off and a significant decrease in forest and arable land. during the study period. This increase in the Bare soil area According to IUCN (2005) and Tanvir et al. (2006) the major was due to rapid deforestation in the area which removed accelerators of forest decline in the watershed’s area are the the Vegetation cover from the land and rendered it barren human activities like illegal forest wood cutting due to high and exposed. The losses of soil from these practices thus market value and also the rigorous use of forest wood for ful- ensued less productive lands which were ultimately abandoned filling household requirements like cooking and heating and by farmers for crop production due to economic inefficiency, also for timber production. In addition to these, ineffective resulting in a large increase in barren land area. Similar trends management of forests and forest disease also play an impor- have been observed in the study of District Swat conducted by tant role in forest decline. Qasim et al. (2011). Classification results supported the above According to the information revealed by classification mentioned facts that Vegetation area decreased over the past results, the Settlements or built up area showed 80.1% increase 20 years by 38.2% from 1992 to 2012. Vegetation in the study from 1992 to 2012. Reasons were a number of new housing

Figure 3 Major land use conversion in Simly watershed from 1992 to 2012. schemes, farmhouses and recreational pursuits that have been surface runoff. However, a study conducted on Zone IV of developed in and around area in the past 20 years. Along with Islamabad by Adeel (2010) concluded that Settlements or built these developments, there is an incline toward the construction up areas located near Simly dam have a very little further of new pavements, highways, roads and other structures to future development potential due mainly to the steep topogra- access these areas. Another developmental activity in the phy and relatively poor approachability to the area. Simly watershed area is ‘‘New Murree’’. NESPAK (2004) The area covered by Water class has also witnessed a reported that it will spread over Patriata forest and the pro- decrease from 1992 to 2012. The percentage decrease in land posed plan would remove 5–8% of the trees present in the area covered by this class was 74.3%. The land cover/land area. This would affect the snow melt and rain fall driven use changes observed in all other classes affected the Water

Table 4 Cross-tabulation of land cover classes between 1992 and 2012 (area in ha). 1992 Agriculture Bare soil/rocks Settlements Vegetation Water Total 2012 Agriculture 560 562 378 3047 134 4681 Bare soil/rocks 748 622 278 1021 22 2691 Settlements 212 283 245 982 148 1870 Vegetation 253 180 133 6286 155 7008 Water 2 0.3 4 5 144 155.3 Total 1775 1648 1038 11,342 603 16405.3 class during two decades. Easy accessibility of water resulted in classified maps were overlaid to produce the land use and land the depletion of water and ended in dried up tributaries and its cover change map, in addition to the cross tabulation matrix replacement by either compact surfaces or barren land. between the two dates. Another reason for the shrinkage was an increased rate of sur- The cross-tabulation matrices (Table 4) show the nature of face runoff due to lack of plant roots to withhold the water. As change of different land cover classes or in other words the the runoff exceeded recharge capacity of ground water it shift in the land cover classes. Out of the 1775 ha that was resulted in the lowering of water table. Increased deforestation Agriculture area in 1992, 560 ha was still Agriculture area in rate also contributed to the increase in surface runoff and is 2012 but 748 ha was converted to Bare soil/rocks, and rest to responsible for down flow of nutrients and sediments (Ali Vegetation and Settlements. At the same time the increase of et al., 2008; Hagler Bailly, 2007; Mendoza et al., 2011). Agriculture, from 1992 to 2012, was mainly from Vegetation Moreover, the change in the total area of Simly lake was (3047 ha). Vegetation out of 11,342 ha in 1992 lost area mainly also observed. Classification indicates that the lake covered to Agriculture as mentioned before, Bare soil/rocks, 148 ha area in 1992 constituting about 0.9% of the total area Settlements and retained 6286 ha of total in 2012. of watershed and this area was reduced to 139 ha in 2012 cov- Settlements increased from 1038 ha in 1992 to 1870 ha in ering 0.8% of the total area of Simly watershed. Thus, approx- 2012. It retained 245 ha of it and was mainly replaced by imately 6% of the lake area diminished in 20 years. Agriculture and Bare soil/rocks. The class which Settlements Simly dam is one of the three major sources supplying water mainly replaced in 2012 was Vegetation (982 ha) (Table 4). to Islamabad and Rawalpindi. It not only supplies water to ever Water class retained only 144 ha of the total 603 ha in 1992. increasing population of twin cities but also supplies it for irriga- It was reduced to 155 ha and mainly replaced by Vegetation, tion purposes. Along with the loss due to agriculture and urban- Settlements and Agriculture in 2012 (Table 4). The area of ization demands, evaporation, seepage and percolation are also other land cover classes replaced by water was small. leading causes of quantitative decline of water in the reservoir This study elucidates the significance of incorporating (Ashraf et al., 2007; Hagler Bailly, 2007; Keller et al., 2000). Remote Sensing and GIS for change detection study of land Rapid expansion of urbanization and agriculture practices not cover/land use of an area as it offers crucial information about only depletes the water quantitatively but also plays a leading the spatial distribution as well as nature of land cover changes. role in polluting the water (IUCN, 2005). The extensive use of Overall 95% accuracy of the land use/land cover maps indicates herbicides and pesticides in agriculture is a toxic pollution that the integration of supervised classification of satellite source. Increase in the agricultural land use and nutrient losses imagery with visual interpretation is an effective method for associated with it, are the chief driver of water quality degrada- the documentation of changes in land use and cover of an area. tion and inherently increase the vulnerability of water that trans- ports these nutrients. Researches conducted on Simly Lake by Ahad et al. (2005) and Iram et al. (2009) concluded that the pes- 4. Conclusion ticide residues present in the lake exceed the European Union (EU) standards. Based on the results obtained by employment of GIS and RS Similarly a developmental activity, ‘‘New Murree Project’’, applications to achieve the specific research objectives, it is spread over Patriata forest, which was envisioned to be a tour- concluded that the land cover/land use practices in the study ist city of international standards by NESPAK (2004) has no area have altered significantly in 20 years. The LULC shift in facilities for waste management in and around the planned the watershed area was evident by the decline in the area of locality of New Murree. Thus, the generated waste by the local Vegetation and Water class (38.2% and 74.3% respectively) inhabitants and tourists is dumped on the slopes in the Patriata and augmentation of area covered by classes of Settlements area and as share of the runoff from Patriata ridge comes to (80.1%), Agriculture (163.7%) and barren land (63.3%). The Simly Dam and pollutes dam’s water. Thus the sewage waste haphazard expansion of Settlement and Agriculture area in along with the pesticide and fertilizer residues viz., Nitrates the watershed was mainly due to lack of proper management and Phosphates flow into the main streams and ultimately ends and land use planning since no EIA report is generated prior up in the main water body and cause an increase in algal to land development in the study area. The major impact of bloom. These causes of water pollution are verified by this expansion was subjected on Vegetation and Water class Gliessman (2006). to deforestation and water depletion respectively. GIS was used for the post-classification comparison of the Additionally, all these alterations in the land cover and land detected change, producing change map for comprehending use patterns adversely affected water quality and accessibility the spatial pattern of change between years (Fig. 3). The two by 2012 which may prove a limiting factor in the future for

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