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Change Detection Of Vegetation Cover, Using Multi- Temporal Remote Sensing Data And GIS Techniques

Adia S.Oa, Rabiu A. B (Phd)b
aDepartment of Meteorology, Federal University Of Technology, Akure Ondo State
bSpace Physics Lab, Physics Department Federal University of Technology, Akure Ondo State

Remote sensing technology in combination with geographic information system (GIS) can render reliable information on vegetation cover. The analysis of the spatial extent and temporal change of vegetation cover using remotely sensed data is of critical importance to agricultural sciences.

This paper investigates the Spatio- Temporal change of vegetation cover of Jos and its surrounding areas. For this study landsat images (TM and ETM+) of 17 Nov, 1986 and 02 Nov, 2001 were used. For recognition of vegetation reflectance, layer stacking of band 4, 3 and 2 (false color composite) for TM and ETM+ was performed. GCP were taken with a GPS device in strategic locations of the study area; this was to establish ground information on vegetation cover. The images were then classified into dense shrubs, less dense shrubs, cropland/ grassland, built-up area, bare surface and water body. The GCP were plotted on ETM+ which was used to evaluate the vegetation change in TM. Supervised classification was done and maximum likelihood operation was performed to generate vegetation cover maps. Afterwards, vegetation cover map of 1986 and 2001, were crossed to generate the map of change of vegetation cover for the respective dates and to find out the changing pattern of vegetation cover.

In addition, the use of spectral vegetation index, namely the Normalized Difference Vegetation Index (NDVI) was applied to detect areas of vegetation cover decrease. The study then reveals that vegetation cover of the area has changed significantly during 1986 to 2001.

1. Introduction
Urban growth, in population has been a major factor which has altered natural vegetation cover, due to anthropogenic activities. The results of these have left significant effects on local weather and climate. The use of remote sensing data in recent times has been of immense help in monitoring the changing pattern of vegetation. Change detection as defined by Hoffer (1978) is temporal effects as Variation in spectral response involves situations where the spectral characteristics of the vegetation or other cover type in a given location change over time. Singh (1989) described change detection as a process that observes the differences of an object or phenomenon at different times.

Vegetation indices among other methods have been reliable in monitoring vegetation change. One of the most widely used indices for vegetation monitoring is the Normalised Difference Vegetation Index (NDVI), because vegetation differential absorbs visible incident solar radiant and reflects much if the infrared (NIR), data on vegetation biophysical characteristics can be derived from visible and NIR and mid- infrared portions of the electromagnetic spectrum (EMS). The NDVI approach is based on the fact that healthy vegetation has low reflectance in the visible portion of the EMS due to chlorophyll and other pigment absorption and has high reflectance in the NIR because of the internal reflectance by the mesophyll spongy tissue of green leaf (Campbell, 1987). NDVI can be calculated as a ratio of red and the NIR bands of a sensor system.

NDVI values range from -1 to +1, because of high reflectance in the NIR portion of the EMS, healthy vegetation is represented by high NDVI values between 0.1 and 1. Conversely, non- vegetated surfaces such as water bodies yield negative values of NDVI because of the electromagnetic absorption quality of water. Bare soil areas represent NDVI values which are closest to 0 due to high reflectance in both the visible and NIR portions of the EMS (Lillesand and Kiefer, 1994). NDVI is related to the absorption of photosythetically active radiation (PAR) and basically measures the photosynthetic capability of leaves, which is related to vegetative canopy resistance and water vapour transfer (Malo et al, 1990).

Remote sensing has shown great potential in agricultural mapping and monitoring due to its advantages over traditional procedures in terms of cost effectiveness and timeliness in the availability of information over larger areas (Murthy et al, 1998). The aim of this study was to incorporate the temporal dependence of multi- temporal image data to identify the changing pattern of vegetation cover and consequently enhance the interpretation capabilities. Moreover integration of multi- sensor and multi- temporal satellite data effectively improves the temporal attribute and the reliability of multi- data. Therefore, this paper discusses methods of the detection of vegetation cover utilizing multi- temporal and multi- sensor remotely sensed data.

Study Area
The study area is located in Plateau State, Nigeria with spatial extent between 9o 34I 09.07II to 10 o 11I 14.82II N latitude and 8 o 34 I 26.60 II to 9o 11 I 30.90 II E longitude. Fig. 1 shows the map of Nigeria and Fig.2 shows the map of Plateau State and the Study area. Table 1 below shows the area cover in hectares of the different local governments of the study area. The area has attracted tourist due to its cold climate with average temperature of 17o and 25 o. The area is considerably cooler than other parts in the country and serves as a cool highland resort in a setting of scenic beauty. Over the years the study area has served as a centre of mining and smelting activities. The landscape is mostly treeless, with vast grassland/ cropland. Main crops grown are Irish potatoes, Cabbage, Maize and Groundnuts, because of the varied topography, climatic conditions and natural environment; the area offers the potential of studying vegetation states and patterns. Temperature and rainfall in the season of the satellite capture is shown in Table II.

The objectives of this study include:

  • Identification of vegetation cover and the spatial distribution
  • To analyse the Spatio- Temporal change of Vegetation cover
  • To perform NDVI calculation, showing Vegetation reflectance
  • To produce NDVI maps
  • To establish a field check system for comparing ground measurements with the processed remote sensed data.

Multi- temporal satellite data; landsat data of different sensors (TM and ETM+) and acquisition dates were used in this study. Both sensors have spatial resolution of 30m. Characteristics of both data are shown in Table III and population census data of local govt. for both periods shown in Table IV.

The main goal of this study is to reveal vegetation change using multi- temporal satellite data, in order to extract changes. Digital image-processing software Erdas imagine 8.6 and Ilwis 3.3 were used for the processing, analysis and integration of spatial data to reach the objectives of the study. Erdas Imagine was used to generate the false colour composite, by combing near infrared, red and green which are bands 4, 3, 2 together for both images. This was done for vegetation recognition, because chlorophyll in plants reflects very well to near infrared than the visible. For image classification, six classes was defined which are Dense shrubs, Less dense shrubs, Cropland/ grassland, Built-up area, Bare surface and Water body. Ground control points obtained using a Global positioning System from locations in relation to the classes of the study area was plotted on landsat ETM+ image, which was used to verify the training sites (defined classes) as regards the spectral signature. Supervised classification for the various classes was performed using Ilwis 3.3 for both images. Finally maximum likelihood classification was used for the classification of the images. Calculation of NDVI was performed. NDVI can be calculated as a ratio of red and near infrared bands of a sensor system.

NDVI= (near infrared band- red band)/ (near infrared band+ red band)……… Equ. 1 NDVI values range from -1.0 to 1.0. As a result NDVI values between -1.0 and 0 represents non- vegetative features such as bare surface, built- up area and water body. Conversely, greater than 0 display vegetation covers.

To find out the changing pattern of vegetation during 1986- 2001 both images were crossed in ilwis environment.

Three main methods of data analysis were adopted in this study

  • Maximum likelihood classification
  • NDVI Calculation
  • Overlay operation

Premised on the objective of this study, vegetation cover over the study area has changed. The above mentioned methods for analysis have proved this change. Table- IV shows the spatial extent of land cover after classification according to supervised classification for both images, likewise Fig. 3 shows the histogram.

The table reflects the decrease in vegetation for cropland/ grassland and less dense shrubs from 1986 to 2001, but increase in dense shrubs. The study area has been a hive of mining activities in the 80’s till the late 90’s. The effect of these activities has left the study area with degraded land, bare surfaces today. Table- IV depicts increase in built- up area with its effect on vegetation cover change. Fig. 4 and 5- shows the spatial extent of land cover after classification according to supervised classification.

NDVI is an excellent and widely used method for crop growth and condition assessment (Rahman, Islam and Rahman, 2004). For this study NDVI calculation was performed to produce NDVI images for both periods.

Fig. 6 and 7- shows both periods of NDVI images. It shows that high reflectance of vegetation was seen in 1986 image of the study area, with increase in NDVI values. Conversely, vegetation reflectance is low in 2001 image, likewise in NDVI value.

Fig. 8- shows the changing pattern of vegetation for both periods been crossed.

So, it may be said that the vegetation change was mainly due to increase in population, as a result of anthropogenic activities.

The study has indicated the potential use of remote sensing data in studying vegetation change. GIS techniques integrated in this study has proved beyond doubt its capabilities of spatial analysis. In this study LANDSAT images were used satisfactorily for the identification of vegetation. It’s observed that the area under vegetation changed during 1986- 2001 remarkably. Decrease in vegetation has been as a result of anthropogenic activities in the study area. In conclusion for detecting changes in areas based on a subject e.g population increase, vegetation etc, over a period of years both spatial and in quantitative way, integrating remote sensing data and GIS techniques will be useful


  • Campbell JB, (1987); ‘Introduction to Remote Sensing’, New York: The Guilford Press.
  • George Xian, Mike Crane; ‘Evaluation of Urbanization Influences on Urban Climate with Remote Sensing and Climate Observations’, SAIC/ USGS National Center for Earth Resources Observation and Science, Sioux falls, SD57198.
  • Hoffer R. M (1978); ‘Biological and Physical Considerations in Applying Computer- aided analysis techniques to Remote Sensor data. In Remote Sensing: The quantitative approach, edited by P. H. Swam and S. M Davis U. S. A; McGraw- Hill.
  • James B. Campbell (1996, 2002); ‘Introduction to Remote Sensing (third edition); the Guilford press, a division of Guilford Publication, Inc 72 spring street New York. NY10012.
  • Lillesand TM and Keifer W (1994); ‘Remote Sensing and Image Interpretation’; New York: John Wiley.
  • MD. Rejaur Rahman, A. H. M. Hedayutul Islam, MD. Shareful Hassan (2004); ‘Change Detection of Winter Crop Coverage and the Use of Landsat data with GIS’; The Journal of Geo- Environment Vol.4, PP1-13.
  • Murthy C.S, Raju P.V, Jonna S, Abdul Hakeem K and Thiruvengadachari S (1998); Satellite derived Crop Calender for Canal Operation Schedule in Bhadra project command area, India; International Journal of Remote Sensing, 19, 2865- 2876.
  • Rahman, Md. Rejaur, Islam A.H.M Hedayutul and Rahman, Md. Ataur (2004); ‘NDVI Derived Sugar cane area Identification and crop condition Assessment’, planplus, Vol.2, Urban and Rural Planning Discipline, Khula University, Bangladesh.
  • Shaw, M.K Sandhov, and T. Turner (2000); ‘Modernization of the Global Positioning System GPS World; Vol.11, No.9 PP36- 40.
  • Singh .A (1989); ‘Review Article: Digital Change Detection Techniques using Remotely Sensed Data’; International Journal of Remote Sensing; Vol.10 PP 989- 1003.
  • Steven M.D (1987); ‘Ground Truth: An Under view’; International Journal of Remote Sensing, Vol.8 PP 1033- 1038.