landuse landcover and ndvi analysis for halia catchment

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 [1]. Identification and delineation of Land use/Land

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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 [2] 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 [5].

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 15

1 N latitude and 78

0 45

1E - 79

015

1 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.



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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



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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.



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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



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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



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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%.



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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.

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