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1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 5, July – August (2013), © IAEME 126 LANDUSE/LANDCOVER AND NDVI ANALYSIS FOR HALIA CATCHMENT Dr.C.Sarala Associate Professor, Centre for Water Resources, Institute of Science and Technology, Jawaharlal Nehru Technological University Hyderabad, Kukatpally, Hyderabad- 500085, Andhra Pradesh, India ABSTRACT In India though sufficient water is available, its distribution in time and space leaves much to be desired. The water in India is not available at the right place, at the right time, in the right quantity and with the right quality. Added to this, there are severe problems with respect to availability of data for hydrological studies. Landuse and landcover change is scalar dynamic. The change is land cover occurs even in the absence of human activities through natural processes where as landuse change is the manipulation of landcover by human being for multiple purposes- food, fuelwood, timber, fodder, leaf, litter, medicine, raw materials and recreation. So many socio-economic and environmental factors are involved for the change in land use and land cover. Landuse and landcover change has been reviewed from different perspectives in order to identify the drivers of landuse and landcover change, their process and consequences. The primary objective of this paper is to study the landuse/landcover and cropped area changes in a catchment area. To obtain the changes in landuse/ landcover and Normalized Difference Vegetation Index, medium to high spatial resolution multi spectral data provided by remote sensing satellites with sensors MSS, TM and ETM whose acquisition periods are November 1975, November 1989 and November 2001 for the drainage area are obtained and processed with ERDAS Imagine software. Keywords: Cropped area, Landuse, Landcover, Normalized Difference Vegetation Index, Remote sensing satellites. 1. INTRODUCTION Land-use and land-cover change, as one of the main driving forces of global environmental change, is central to the sustainable development debate. Landuse and land-cover changes have impacts on a wide range of environmental and landscape attributes including the quality of water, land and air resources, ecosystem processes and function, and the climate system itself through greenhouse gas fluxes and surface albedo effects . Identification and delineation of Land use/Land INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 4, Issue 5, July – August 2013, pp. 126-133 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2013): 5.8376 (Calculated by GISI) www.jifactor.com IJARET © I A E M E
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 5, July – August (2013), © IAEME 127 cover, location, extent and their spatial distribution patterns are possible because of the synoptic view provided by the Satellites and their ability to resolve both macro and micro details on a single imagery. It provides periodic coverage of the same area thus enabling to obtain Multi-temporal data useful for monitoring the dynamic aspects of Land use/Land cover. It provides data both in analog and digital form. Such data is amenable for both visual interpretation and digital analysis for extracting thematic information. It is relatively fast, cost effective and economical for inventorying several details of LU/LC than the most of the other methods of surveying. The different Multi- spectral and Spatial data can be merged with other satellite data for optimizing the LU/LC identification and discrimination. Application of remotely sensed data made possible to study the changes in land cover in less time, at low cost and with better accuracy  in association with Geographical Information System (GIS) that provide suitable platform for data analysis, update and retrieval [3,4]. Space borne remotely sensed data may be particularly useful in developing countries where recent and reliable spatial information is lacking. Remote sensing technology and geographic information system (GIS) provide efficient methods for analysis of land use issues and tools for land use planning and modeling. By understanding the driving forces of land use development in the past, managing the current situation with modern GIS tools, and modeling the future, one is able to develop plans for multiple uses of natural resources and nature conservation. The change in any form of landuse is largely related either with the external forces and the pressure builtup within the system . Satellite remote sensing has typically been used to assess regional environmental change in two ways: 1) by post classification analysis of landcover change (LCC); and 2) through image time series analysis with respect to spectral indices like the normalized difference vegetation index (NDVI). The former approach is effective for documenting distinct, abrupt anthropogenic impacts on the land surface like deforestation and urbanization [6, 7]. The latter approach has been used to look for effects of climate change on land surface phenology. However, no attempt has been made to merge these very different but complementary ways of monitoring regional environmental change, despite the fact that both LCC and NDVI trend analyses can be derived from the same underlying remote sensing data. The purpose of this paper is to quantify the land use/cover changes that have happened over the past 25 years on the Halia catchment, to identify the drivers of the land use/cover changes and assess implications of the changes by the environment. 2. STUDY AREA The Halia river is one of the tributary of river Krishna which is flowing in the Nalgonda district. The length of the flow of the river is 112 km. The study area chosen is located in Nalgonga district between 170 N - 17 151 N latitude and 780 451 E - 790 151 E longitude covering an area of 1,510 km2 . The study area covers eight mandals of Nalgonda district namely Marriguda, Chandur, Kangal, Narayanpur, Chityal, Munugode, part of Choutuppal and Nalgonda. The average annual rainfall in the Halia catchment is 637 mm. The South West Monsoon sets in by middle of June and withdraws by the middle of October. About 90% of annual rainfall is received during the Monsoon months, of which more than 70% occurs during July, August and September. The study area falls in the Survey of India toposheet Nos. 56K, 56L, 56O and 56P. The location map of the study area is shown in Fig.1.
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 5, July – August (2013), © IAEME 128 Fig.1 Location Map of the Study Area 3. MATERIALS AND METHODS The landuse/landcover and NDVI maps are prepared by using the false colour composite of Indian Remote Sensing Landsat satellite with sensors MSS, TM and ETM whose acquisition periods are November 1975, November 1989 and November 2001 respectively. The details of the satellite, sensor and period of acquisition are tabulated in Table 1. Table 1 Satellite Data used for the Change Analysis Year Path/Row Satellite/Sensor 1975 154/48 Landsat Data – MSS 1989 143/48 Landsat Data – TM 2001 143/48 Landsat Data – ETM To carry out the change analysis in the cropped area, the satellite data is used. For identifying appropriate satellite data, spatial resolution and spectral resolution plays an important role. Spatial resolution is the size of the smallest object that can be discriminated by the sensor of the satellite. In fact, area coverage and resolution are interdependent and these two factors determine the scale of imagery. In case of spectral resolution, the information is collected by satellite sensors in multi-band or multi spectral format i.e., individual images have been separately recorded in discrete spectral bands. The position in the spectrum, width and number of spectral bands will determine the degree to which individual targets can be determined on the multi spectral image. The use of multispectral
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 5, July – August (2013), © IAEME 129 imagery can lead to a higher degree of discriminating power than any single band taken on its own and facilitates in identifying various surface features. Normalized Difference Vegetation Index map is prepared using medium to high spatial resolution multi spectral data by using ERDAS imagine software. The satellite data is geometrically corrected with respect to Survey of India toposheets of scale 1: 50,000. To carry out the same, Ground Control Points (GCPs) were identified on the maps and raw satellite data. The coefficients for two co-ordinate transformation equations were computed based on polynomial regression between GCPs on map and satellite data. Alternate GCPs were generated till the root mean square error was less than 0.5 pixels and then both the images were co- registered. The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that uses the visible and near-infrared bands of the electromagnetic spectrum, and is adopted to analyze remote sensing measurements and assess whether the target being observed contains live green vegetation or not. Generally, healthy vegetation will absorb most of the visible light that falls on it, and reflects a large portion of the near-infrared light. Unhealthy or sparse vegetation reflects more visible light and less near-infrared light. Bare soils on the other hand reflect moderately in both the red and infrared portion of the electromagnetic spectrum [8, 9, 10]. Since we know the behavior of plants across the electromagnetic spectrum, we can derive NDVI information by focusing on the satellite bands that are most sensitive to vegetation information (near-infrared and red). Therefore the bigger the difference between the Near-Infrared (NIR) and the Red Reflectance (RED), the more the vegetation there has to be. The NDVI algorithm subtracts the red reflectance values from the near- infrared and divides it by the sum of near-infrared and red bands. This formulation allows us to cope with the fact that two identical patches of vegetation could have different values if one were, for example in bright sunshine, and another under a cloudy sky. The bright pixels would all have larger values, and therefore a larger absolute difference between the bands. This is avoided by dividing by the sum of the reflectance. Theoretically, NDVI values are represented as a ratio ranging in value from -1 to 1 but in practice extreme negative values represent water, values around zero represent bare soil and values over 0.6 represent dense green vegetation. The water bodies, kharif and forest categories in landuse/landcover classification are categorized as water bodies, vegetation and forest in NDVI analysis respectively. Similarly, the remaining ten landuse/landcover categories namely land with or without scrub, fallow land, barren land, gullied land, forest blank, forest plantations, sandy coastal, double crop, plantation and settlements are classified under non- vegetation category in NDVI analysis. The cropped area image is generated by generating forest map of the study area and masked on the vegetation index map to get cropped area map. The forest map is digitized from the Indian Remote Sensing Landsat satellite with sensors MSS, TM and ETM images acquired during the summer months where the cropped area is minimal and the contrast between forest and non-forest area was larger in the False Color Composite of Landsat satellite. The digitized forest vector was converted into raster image. Once the forest area is masked on the vegetation index image, the remaining unmasked region represents the cropped area in the study area. The change in the cropped area between two years is obtained by comparing the current season cropped area with the pervious reference seasons cropped area of similar period. The percentage deviation of current year cropped area with previous/reference year cropped area is computed using the formula: Vegetation condition = [(current year cropped area- previous year cropped area)/ previous year cropped area] * 100 (1) 4. RESULTS The changes of landuse/landcover from 1975 to 1989, 1989 to 2001 and 1975 to 2001 in the study area are shown in the Table 2.
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 5, July – August (2013), © IAEME 130 Table 2 Landuse/Landcover Change Analysis in the Study Area S.No Landuse/Landcover Category Area in the Year 1975 (Ha) Area in the Year 1989 (Ha) Area in the Year 2001 (Ha) Percentage of change 1975- 1989 1975- 2001 1989- 2001 1 Water bodies 6823.87 7343.43 7524.28 7.61 10.26 2.46 2 Kharif 78761.71 68493.1 71043.93 -13.04 -9.8 3.72 3 Forest 6514.89 6353.58 5945.99 -2.48 -8.73 -6.42 4 Land with or without scrub 40210.5 37659.08 35055.41 -6.35 -12.82 -6.91 5 Fallow land 6921.67 5118.58 4378.35 -26.05 -36.74 -14.46 6 Barren land 1987.41 1708.85 1526.71 -14.02 -23.18 -10.66 7 Gullied land 0 0 0 0 0 0 8 Forest blank 0 0 0 0 0 0 9 Forest plantations 0 0 0 0 0 0 10 Sandy coastal 0 302.4 260.06 0 0 -14.00 11 Double crop 6206.69 13719.05 16547.69 121.04 166.61 20.62 12 Plantation 0 5190.28 4060.95 0 0 -21.76 13 Settlements 3573.25 5111.65 4656.63 43.05 30.32 -8.9 The satellite imageries and landuse/landcover classification done for various categories namely water bodies, kharif, forest, Land with or without scrub, fallow land, barren land, gullied land, forest blank, forest plantation, sandy coastal, double crop, plantation and settlements during the years 1975, 1989 and 2001 are shown in Figs. 2, 3 and 4. It is evident from the table that double crop increased inspite of the decrease in the kharif crop. It is also observed that there is a decrease in the settlements which may be due to the migration of the people to other areas. Fig.2 Satellite Imagery and Landuse/Landcover during November, 1975
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 5, July – August (2013), © IAEME 131 Fig.3 Satellite Imagery and Landuse/Landcover during November, 1989 Fig.4 Satellite Imagery and Landuse/Landcover during November, 2001 Further NDVI analysis has been carried out by using the medium to high spatial resolution data provided by remote sensing satellites, such as Landsat for the years 1975, 1989 and 2001 during kharif season. The spatial variation of the cropped area, forest, water bodies is shown in Figs. 5, 6 and 7 and the results are presented in Table 3. Fig.5 NDVI Analysis of the Study Area during November, 1975
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 5, July – August (2013), © IAEME 132 Fig.6 NDVI Analysis of the Study Area during November, 1989 Fig.7 NDVI Analysis of the Study Area during November, 2001 Table 3 Areas of Normalized Difference Vegetation Index Classes for the Study Area S.No NDVI Category Area in the Year 1975 (Ha) Area in the Year 1989 (Ha) Area in the Year 2001 (Ha) Percentage of Change 1975- 1989 1975- 2001 1989- 2001 1 Water bodies 6856.83 7393.64 7597.26 7.83 10.80 2.75 2 Vegetation 78582.05 68348.36 71149.98 -13.02 -9.46 4.10 3 Forest 6518.57 6325.51 5948.08 -2.96 -8.75 -5.97 4 Non - vegetation 59042.55 68932.49 66304.67 From the table it is evident that water bodies increased by 10.8% while there is a decrease in vegetation by 9.46% and forest area by 8.75%.
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 5, July – August (2013), © IAEME 133 5. CONCLUSION It can be concluded from the Landuse/Landcover analysis that double crop area has increased significantly inspite of the decrease in the kharif crop, which implies that groundwater irrigation has increased over the years. It is also found that the decrease in runoff is attributed to the increase in double cropped area indicating substantial increase in rabi crop which must have drawn water necessarily from groundwater source depleting the water table in many of these areas considerably. Much of the rainfall water has been utilized in the study area to fill the depleting water table and could not produce appreciable runoff over the years. Moreover, in the catchment there is an increase in area of water bodies but decrease in kharif area, a paradox which is explained by the fact that the efficiency of all the surface water infrastructure (tanks, small and medium reservoirs) has gone down appreciably due to siltation and for want of maintenance making the people to resort to more on groundwater irrigation. The NDVI analysis also agrees with the Landuse/Landcover analysis is that the total vegetative area has decreased over the time. There is also a decrease in the settlements which may be due to the migration of the people to other areas. REFERENCES  E.F Lambin, M.D.A. Rounsevell, and H.J.Geist, Are agricultural land-use Models able to predict changes in land use intensity, Agriculture, Ecosystems and Environment, Vol .82, 2000, 321– 331.  Kachhwala TS., Temporal monitoring of forest land for change detection and forest cover mapping through satellite remote sensing, Proceedings of the 6th Asian Conf. on Remote Sensing. Hyderabad, 1985, 77–83.  Star JL, Estes JE, McGwire KC, Integration of geographic information systems and remote sensing, New York, Cambridge University Press, 1997.  Chilar J., Landcover mapping of large areas from satellites: status and research priorities, International Journal of Remote Sensing, 21(67), 2000, 1093–1114  Bisht, B.S. and Kothyari, B.P., Landcover Change Analysis of Garur Ganga Watershed Using GIS/Remote Sensing Technique. , Indian Soc. Remote Sensing, 29(3), 2001,165-174.  Viña A, Echavarria R, Rundquist D, Satellite change detection analysis of deforestation rates and patterns along the Colombia- Ecuador border. Ambio, 33(3), 2004, 118–125  Yuan F, Sawaya K E, Loeffelhoz B C, Bauer M E, Landcover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multi temporal Landsat remote sensing, Remote Sens Environ, 98(2–3), 2005, 317–328.  Jackson, T. J., Measuring Surface Soil Moisture using Passive Microwave Remote Sensing, Hydrology Proc., 7, 1993, 139 -152.  Tucker,C.J., Red Photographic Infrared Linear Combinations for Monitoring Vegetation, Remote Sensing of Environment, Vol. 8, 1979, 27-50.  Tucker,C.J., Remote Sensing of Leaf Water Content in the Near Infrared, Remote Sensing of Environment, Vol. 10, 1980, 23-32.  A.N.Satyanarayana, Dr Y.Venkatarami Reddy and B.C.S.Rao, “Remote Sensing Satellite Data Demodulation and Bit Synchronization”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 3, 2013, pp. 1 - 12, ISSN Print: 0976-6480, ISSN Online: 0976-6499.  Mohammed Hashim Ameen and Dr. R. K. Pandey, “Delineation of Irrigation Infrastructural, Potential and Land Use/ Land Cover of Muzaffarnagar by using Remote Sensing and GIS”, International Journal of Civil Engineering & Technology (IJCIET), Volume 4, Issue 3, 2013, pp. 1 - 11, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316.