1. Spatial Distribution of Vegetation Using
Normalized Difference Vegetation index
A Case Study of Delhi
Submitted for partial fulfillment of the degree of Master of Science
in Geo- informatics semester IV
Under the Supervision:
Professor Mehtab Singh Submitted By:
Neeraj Rani
1159775
Deptt. Geography
MDU ROHTAK
2. Introduction
Recent advances in precision agriculture technology have led to the development of ground based
active remote sensors (or crop canopy sensors) that calculate NDVI readings. Previously this index was
determined using passive sensors via airborne or satellite imagery which had several limitations
including expense and weather related issues such as cloud cover that could greatly limit the
effectiveness of these sensing techniques. Active sensors have their own source of light energy and
allow for the determination of NDVI at specific times and locations throughout the growing season
without the need for ambient illumination or flight concerns.
Remote Sensing has developed as a powerful tool in environmental studies because it can provide
calibrated, objective, repeatable and cost effective information for large areas and it can be
empirically related to collected field data. One of the most common applications of remote sensing is
land/canopy cover monitoring and assessment via remote sensing indices which combine reflectance
measurements from the bands of remote sensing instruments. Remote sensing indices derived from
satellite data are one of the primary sources of information for operational monitoring of the land’s
vegetative and other land covers.
3. Vegetation plays an important role either harvested for purposes like biomass conversion to energy
products (e.g. electricity) or seen as a key position in storing atmospheric carbon sources. The diversity
of species in different vegetation types influences the potential of such possible uses. Various types of
vegetation are present ranging from highly diverse forests in the tropics to less species-rich steppes or
savannahs to human influenced ecosystems in the 9 Temperate zone like semi-natural grasslands.
About 70% of the Earth’s land surface is covered by vegetation with perennial or seasonal
photosynthetic activity. The NDVI is the example of the most common vegetation indices to analyze
the green cover of photosynthetic vegetation in image processing.
4.
5. •NDVI values range from +1.0 to -1.0.
•Low NDVI values (for example, 0.1 or
less).
•Areas of barren rock, sand, or snow.
• Moderate NDVI values (approximately
0.2 to 0.5).
•Sparse vegetation such as shrubs and
grasslands or senescing crops.
•High NDVI values (approximately 0.6 to
0.9)
•Dense vegetation such as that found
in temperate and tropical forests or
crops at their peak growth stage.
NDVI = (NIR - Red) / (NIR + RED)
6. Transforming raw satellite data into NDVI values give rough measures
●Vegetation types
●Amount of vegetation
●Conditions on land surfaces around the world
●Change in vegetation over time
•NDVI is useful for continental- to global-scale vegetation monitoring because it can
compensate for
●Changing illumination conditions
●Surface slope
●Viewing angle.
•Most well-known and used index to detect live green plant canopies in multispectral
remote sensing data.
7. Review of literature:
Remote Sensing has strong tools for NDVI. There are many sources from which one can do the
NDVI studies. The main focus of the study is to represent the present status and scope of
mapping, planning, and management of the selected NDVI area with the help of available satellite
data. Some of the studies are discussed here:
Teillet et al., (1997) remotely sensed spectral data used to derive vegetation indices (VI) have
become one of the primary information sources to characterize the surface of the Earth and
employed as a measure of green vegetation density.
Muraliet.al. (1998) offer a method for classifying the vegetation at tree, shrub and herb layers
utilizing GIS and other statistical tools. This method views the forest a continuous stretch of
land and not as discrete patches. Their studies suggest that the spatial dynamics of vegetation at
one layer may not reflect on others. Also mapping the diversity of the forest ecosystem could
be possible.
8. Delhi also known as the National Capital Territory of India is the capital of India. Delhi is
bounded by Uttar Pradesh on the East and by Haryana on the North, South and West. Delhi is
located between the 28º24´17"N latitudes and 28º53´00"N latitudes and 76º 45´ 30" E longitude
and 77º 21´ 30" E longitude fig.2.1 . The geographical area of Delhi is 1,483 km² which is 0.05
percent of India. Delhi is approximately 213 meters to 305 meters above the mean sea level. The
NCT and its urban region have been given the special status of National Capital Region (NCR)
under the Constitution of India's 69th amendment act of 1991.
A union territory, the political administration of the NCT of Delhi today more closely resembles
that of a state of India, with its own legislature, high court and an executive council of ministers
headed by a Chief Minister. New Delhi is jointly administered by the federal government of India
and the local government of Delhi, and is the capital of the NCT of Delhi. In all there are 9
districts in Delhi.
Study Area
Introduction:
9.
10. Delhi was the site of ancient Indraprastha (Khandavprastha), the ancient capital of the Pandavas
during the Mahabharata.
Delhi is the capital of India and most populated state. During the time of 1961 census, Delhi had
only one district and one Tehsil. From 1971-1991 Census, Delhi revenue district was divided into
two Tehsils, known as Delhi Tehsil and Mehrauli Tehsil. The situation changed in 1996, as shown in
Delhi was divided into 9 revenue districts and 27 sub-divisions coterminous with Tehsils . This was
the administrative set up that prevailed during the 2001 census, and stands unchanged.
Geology:
Delhi is bounded by the Indo-Gangetic alluvial plains in the North and East, by Thar Desert in the
West and by Aravalli hill ranges in the South .
The development of any area mostly depends on the quality as well as quantity of ground water.
Yamuna River has a big influence on the availability of sweet ground water in most part of the capital.
In NCT of Delhi, 90 per cent of the fresh water is available up to 60 m depth and the quality and
quantity of water is also good .
History:
Administrative Set-up:
Hydrology:
11. Delhi features a typical version of the humid subtropical climate (Köppen Cwa). Delhi has an
extreme climate. It is very hot in summer (April - July) and cold in winter (December - January).
According to the 2011 census of India, the population of Delhi is 16,753,235. The
corresponding population density was 11,297 persons per km2, with a sex ratio of 866 women
per 1000 men, and a literacy rate of 86.34 percent.
The largest commercial center in northern India is Delhi. The most important sector of the
economy of Delhi is the service sector. In fact, this sector employs the most amounts of people in the
city. The manufacturing sector remains an important aspect as well, but the agricultural sector is
longer significant. The majority of the work force participates in trade, finance, or public
administration. The per capita income of Delhi is currently the highest in the whole country. Also,
the work force makes up about 33 percent of the population and has been continuing to increase over
the years.
Climate:
Demography:
Economy of Delhi:
12. DATA SOURCE AND METHODOLOGY
Methodology is the central part of the any research work which helps in scientific description and
explanation of reality. The present study entitled “Spatial Distribution of Vegetation Using Normalized
Difference Vegetation Index: A Case Study of Delhi ” .The study is mainly based on the description,
interpretation and analysis of maps. NDVI has found a wide application in vegetative studies as it has
been used to estimate crop yields, pasture performance, and rangeland carrying capacities among others.
It is often directly related to other ground parameters such as percent of ground cover, photosynthetic
activity of the plant, surface water, leaf area index and the amount of biomass.
Generally the term data means group of information that represent the qualitative or quantitative
attributes of a variable or set of variables. Data are typically the results of measurements and can be
the basis of graphs, images or observations of a set of variables. Everything in the real world is turned
into a feature on a map in GIS and each of those features has data that can be used to depict or analyze
them.
Data:
introduction
13. Data sources, as the name implies provides data via data site. Data in stores an organization s
database, data files including non- automated. Mainly two types data is used i.e. Primary and
Secondary data. A primary data uses first hand information while secondary data is collect by
someone other than user. The present study is based on secondary data sources.
Data Sources:
Secondary data is the data that have been already collected by and readily available from other
sources. Such data are cheaper and more quickly obtainable than the primary data and also may
be available when primary data cannot be obtained at all. Secondary data is classified in terms of
its source – either internal or external. Internal, or in- house data, is secondary information
acquired within the organization where research is being carried out. External secondary data is
obtained from outside sources. The sources of secondary data are: Censuses, surveys,
organizational records and data collected through qualitative methodologies or qualitative
research etc.
Secondary Data Sources:
14. Satellite Type Sensor Number of Bands Resolutions(m)
IRS-IC, ID LISS-III(2002) 4 23.5
IRS-IC, ID LISS-III(2010) 4 23.5
Present study use LISS- III images of year 2002 and 2010. LISS III (Linear imaging Self Scanning
Sensor) sensor is optical sensor working in four spectral bands (green, red, near infrared and short waves
infrared). It covers a 141 km – wide swath with a resolution of 23.5 meters in all spectral bands. Present
study used secondary source data. Remote Sensing data to support this. I have used conventional data else
i.e. topo-sheet etc. A brief description of satellite data used in the study is given in this Table 1.1
Analysis of Remote Sensing Data:
Software used:
Erdas Imagine (9.0)
Arc Map (10.1)
Ms – Office (2007)
15. RESEARCH METHODOLOGY
DATA UTILIZATION (LISS-III IMAGE 2002, 2010 AND TOPOSHEET
GEO- REFERENCING OF SATELLITE DATA
CREATION OF THE SUBSET OF THE STUDY AREA
VISUAL INTERPRETATION
NORMALIZED DIFFERENCE VEGETATION INDEX
GROUND TRUTH
MODIFICATION AND CORRECTION
FINAL MAP PREPARATION
REPORT WRITING
Flow Chart Showing the
General Methodology
16. ANALYSIS AND CONCLUSION
Remote sensing studies use data gathered by satellite sensors that measure wave length of light
absorbed and reflected by green plants certain pigments in plants leaves strongly absorbs wavelengths
of visible infrared light near infrared light which is invisible to human eyes. As a plant canopy
changes from early spring growth to late season maturity and senescence, these reflectance properties
also change. Many sensors carried aboard satellites measure red and near-infrared light waves
reflected by land surfaces. Using mathematical formulas (algorithms), scientists transform raw
satellite data about these light waves into indices i.e. A vegetative indices is an indicator that describes
the greenness the relative density and health of vegetation for each picture element, or pixel, in a
satellite image.
Introduction:
17. Comparative Analysis of vegetation using NDVI: DELHI 2002- 2010
This fig. shows the spatial distribution of vegetation using NDVI on LISS- III image. The NDVI of
Delhi (2002) gives the value in the range of -32 to 0.573. It is seen that value -32 (dark green areas)
corresponds to high dense built-up area on the eastern side of river Yamuna, CBD (Central Business
District) of Delhi and old Delhi on the northern side of CBD. High NDVI 2002 values (dark red areas)
are observed in the central ridge (forest), north-west and south-west part of city. Medium NDVI
values (green areas to yellow areas) are observed over agricultural croplands, in the northern part of
the study area.
Delhi officially the National Capital Territory of Delhi, is a city and a union territory of India. It is
bordered by Haryana on three sides and by Uttar Pradesh to the east. It is the most expansive city in
India area 1,483 square kilometres (573 sq miles). It has a population of about 25 million, making it
the second most populous city after Mumbai and most populous urban agglomeration in India and 3rd
largest urban area in the world. Urban expansion in Delhi has caused it to grow beyond the National
Capital Territory (NCT) to incorporate towns in neighbouring states. At its largest extent, there is a
population of about 25 million residents as of 2014.
18. The NDVI of Delhi during 2010 estimated the value in the range of -0.184 to 0.452. It is seen that lower
NDVI value 0.184 is representing (green areas) high dense built up area on the eastern side of river
Yamuna, CBD (Central Business District) of Delhi and Old Delhi on the northern side of CBD.
The 2010 NDVI or vegetative greenness in Delhi is maximum in New Delhi and Central Delhi
whereas North, Northwest and East Delhi having highly concentration and, therefore, have the least
NDVI values. The NDVI values in North, Northwest and East Delhi are below except in a few
patches, which are the green areas in the dense built-up zones, which also act as breathing room. The
southwest corner of the city has low NDVI owing to the presence of agricultural land. Some red
areas are visible along the drain lines and around the agricultural fallow land. Further in the East is
the dense amalgamation of apartments and buildings, where the tree cover along the roads, highways
and open land in Delhi is more dominating than the forest cover. The international airport in the
south records the lowest NDVI. On the other hand, most areas in central and New Delhi have very
high NDVI, reflecting the healthy tree cover in the city. The Delhi Ridge, popularly known as the
lungs of the city and the adjoining areas of India Gate, Rashrapati Bhawan, and others have the
highest NDVI. Along the banks of the River Yamuna, also, the NDVI values are relatively high,
owing to the presence of agricultural land.
19. Many factors have an effect on greenness values like climate, urbanization, and deforestration. Forest
degradation has become a serious problem, especially in developing countries. In the year 2000, the
total area of degraded forest in 77 countries was estimated at800million hectares 500 million hectares
of which had changed from primary to secondary vegetation. Among other impacts the process of
forest degradation represents a significant proportion of greenhouse gas emissions. There is an urgent
need to measure and analyses it, in order to design action to reverse the process. It presents a study
carried out to identify relationships between indicators of forest functions and the Normalized
Difference Vegetation Index (NDVI), which is estimated through analysis of satellite images to give
an indication of “greenness”. The premise is that NDVI is an indicator of vegetation health, because
degradation of ecosystem or decrease in green area, would be reflected in a decrease in NDVI value.
Therefore, if a relationship between the quantity of an indicator aerial biomass in various forest
ecosystems and the NDVI can be identified, processes of degradation can be monitored.
23. Conclusion :
NDVI a ratio of the intensity of light reflected of the Earth’s surface in the visible and near-
infrared spectral wavelengths which quantifies the photosynthetic capacity of the vegetation in a
given pixel of land surface.
• NDVI is an equation that takes into account the amount of infrared reflected by plants.
• NDVI relates to vegetation
• Clouds, snow, and water have high reflectivity in the visible band, while non-vegetated soil
reflects equally in both channels.
• Surfaces containing large amounts of chlorophyll have larger reflectivity in the Near Infrared
Region (NIR) band.
24. Although there are several vegetation indices, one of the most widely used is the Normalized
Difference Vegetation Index (NDVI). NDVI values range from +1.0 to -1.0. Areas of barren rock,
sand, or snow usually show very low NDVI values (for example, 0.1 or less). Sparse vegetation such
as shrubs and grasslands or senescing crops may result in moderate NDVI values (approximately 0.2
to 0.5). High NDVI values (approximately 0.6 to 0.9) correspond to dense vegetation such as that
found in temperate and tropical forests or crops at their peak growth stage. There are various
methodologies for studying seasonal changes in vegetation through satellite images, one method of
which is to apply vegetation indices relating to the quantity of greenness (Chuvieco,1998). The NDVI
is a measurement of the balance between energy received and energy emitted by objects on Earth.
When applied to plant communities, this index establishes a value for how green the area is, that is,
the quantity of vegetation present in a given area and its state of health or vigor of growth.
25. Delhi is known as the National Capital Territory of India and is the capital of India. Delhi is bounded by
Uttar Pradesh on the East and by Haryana on the North, South and West.Delhi is most populous urban
agglomeration in India and 3rd largest urban area in the world. Forest degradation has become a serious
problem and is increase day by day in Delhi.
The NDVI of Delhi (2002) gives the values in the range of -32 to 0.573. In 2002 NDVI values were -
0.32 to 0.573 and during 2010 values -0.184 to 0.452. Highest built up area have 45
lower NDVI values and sparse vegetation, grassland have higher NDVI values. It is seen that value -32
(dark green areas) corresponds to high dense built-up area on the eastern side of river Yamuna, CBD
(Central Business District) of Delhi and old Delhi on the northern side of CBD. Higher NDVI 2002
values (dark red areas) are observed in the central ridge (forest), north-west and south-west part of city.
Medium NDVI values (green areas to yellow areas) are observed over agricultural croplands, in the
northern part of the study area.
26. The NDVI of Delhi during 2010 estimated the value in the range of -0.184 to 0.452. It is seen that
lower NDVI value 0.184 is representing (green areas) high dense built up area on the eastern side of
river Yamuna, CBD (Central Business District) of Delhi and Old Delhi on the northern side of CBD.
The 2010 NDVI or vegetative greenness in Delhi is maximum in New Delhi and Central Delhi
whereas North, Northwest and East Delhi having highly concentration and, therefore, have the least
NDVI values. NDVI is a suitable indices gives the best result .
