Post on 31-Oct-2014
transcript
1
1. INTRODUCTION
The term land cover signifies various attributes of the Earth’s surface including
forest, agriculture, settlements, urban landscapes and so on. However, the characteristics
of land cover have impacts on climate, hydrology, biogeochemical cycles, Diversity of
biological species and distribution of resources. More importantly, the impact of land
covers and its changes are getting importance day by days. The predominant land cover
class, which has direct implications over the biodiversity richness, is the forest cover. The
forest cover assessment, thus directly gives an idea of the carbon load and biological
species diversity of an area.
Biological diversity (referred to as biodiversity in short) is a part of our daily life
and livelihood, and constitutes resources, upon which humanity depends. It is not
uniformly distributed on the earth. Increasing human interventions on the ecosystems
have accelerated the process of biodiversity loss. As, it is fundamental to the fulfillment
of human needs. An environment, which is rich in biological diversity, offers the broadest
array of options for sustaining human welfare and for adapting to changes. Loss of
biodiversity has serious economic and social costs for any country. From the experiences
of the past, few decades have shown that, as industrialization and economic development
take place, the patterns of consumption, production and needs change, strain, alter and
even destroy ecosystems. If, one traces the linkages of biodiversity, it is observed that
human population and its interventions have been the major factors for enhancement in
the extinction process. If one overlay the global disturbance map on the biological
richness map of the world, one can see that Asia is the most threatened continent (Roy
P.S., 2002).
Biodiversity is the totality of genes, species, ecosystems and habitats in a region.
Biodiversity has direct consumptive value in agriculture, medicine and industry. Russian
scientist Nikolai I. Vavilov estimated that about 80,000 edible plants have been used at
one time or the other in human history, of which about 150 have even been cultivated on
a large scale. Today, merely 10 to 20 plant species provide 80-90% food requirements of
the world. In India, rural communities, particularly the tribal, obtain a considerable part
of their daily food from wild plants (Roy P.S., 2002). In this regard, the tropical forests
area including the mangroves is the highest valued ecosystem. In India, the mangrove
area used mostly for Non timber forest products (NTFP) like fuel, food, fodder,
construction materials for houses, net and cage preparation materials, honey, different
types of grasses for mat or baskets manufacture and also boat construction materials.
Another important use of mangrove woods in countries outside India like Sarawak
estuary of Indonesia is charcoal preparation (Bennet & Reynolds, 1993).
However, other than resource exploitation, the encroachment and reclamation of forest
land for agricultural fields and settlements to keep pace with the rapidly increasing
population pressure on the coastal zones, threats the mangroves of the world to a great
extent. The deforestation of tropical forests in South-East Asia was estimated as 1.4% per
year during 1980-1990 (Rudel et. al, 2000). On the same tract with this South East Asian
scenario, India lost 40 percent of its mangrove areas in the last century. The National
Remote Sensing Agency (NRSA) recorded a decline of 7 000 ha of mangroves in India
2
within the six-year period from 1975 to 1981. The government of India also reported a
loss of nearly 1,91,300 ha mangrove area in India during 1987 and 1997 (Kumar Rajiv,
2000). Though, the government reports showed an increase in mangrove vegetation in
Orissa coast (FSI, 1997 & 1999), several other research communications have reported a
reduction in the last decade due to huge population and developmental pressures along
with development of prawn farming aquaculture.
Coastal vegetation develops certain unique characteristics as the biota are constantly
under physiological stress caused by extreme environmental conditions, such as saline
ambience, periods of inundation and exposure, etc. The most dominating coastal
vegetation is the mangroves in tropical regions and marsh vegetations in temperate
countries. Today, mangrove forest comprise 15.8 million hectares, roughly less than half
of the original mangrove forest cover and are first declining further at an assumed rate of
2-8% per year, or 0.6% of all island forest in the world (Nayak & Bahuguna, 2001).
Bhitarkanika mangroves of Orissa, is the second largest mangrove patch of India at the
estuary of Bramhini and Baitarani river and extended between 20º33’50’’N & 20º48’N
latitude, and 86º46’50’’E & 87º05’E longitudinal extent under administrative jurisdiction
of Rajnagar Community Development block of Kendrapara district, Orissa. The
resourceful forest on Orissa coast and its adjacent areas had been designated as Wildlife
sanctuary in 1975 and National Park in 1998. The nearby seacoast, Gahirmatha, which
host Arribadas of endangered Olive Ridley turtle, has also been declared as Gahirmatha
(Marine) sanctuary for its majestic faunal biodiversity (Nayak A.K., 2002). The
mangrove has also been declared as a wetland of international importance or Ramsar Site
in 2002 for its wide range of avifauna population (Anon, GOI, 2003).
The climate of Bhitarkanika is tropical humid with average temperature range 33º to 18º
C and a mean humidity of 61 to 77%. The region receives a rainfall of 2000-2500 mm
annually which is distributed mostly between May and November. The district is situated
directly on the track of cyclonic storms, which frequently cross Orissa coast. These
storms occur between June and October. The estuarine vegetation of Bhitarkanika is
nourished mainly by the rivers Dhamra, Maipura, Patsala and Hansua, which are the
important distributaries of Brahmini and Baitarani Rivers. The fast flowing rivers become
slow while approaching the delta regions and deposit their silt content along the gradient.
As a result the riverbeds are raised and they break into many distributaries that radiate
across the plain. This topography is very conducive for luxuriant growth of mangroves
and associated fauna. The sanctuary has mangroves, tropical forest, scrub land, grass
lands, inter tidal mud flats, sandy beach, sand dunes and off shore islands (Kanvinde H.
S., 1997).
The macro level land use/ land cover assessment in the present day condition is done by
Remote Sensing techniques. The term ‘Remote Sensing’ can be defined as the technique
of sensing anything without having any physical contact with the sensed object. Such a
technique may refer to the use of different types of sensing procedures such as optical
sensing, microwave sensing, sonic sensing etc. However, of late, the most valued Remote
3
Sensing technique, especially in macro level studies is the Satellite remote sensing, where
the optical and long wave sensing technique is predominantly involved.
Since the launch of the first polar orbiting satellite Landsat in 1970s the satellite, Remote
Sensing has proved its efficiency and necessity mainly in the fields of geographical
mapping and reconnaissance surveys. The first Indian contribution in this field was the
launch of IRS 1A in 1988 with a multi-spectral Linear Image Scanning System known as
LISS I sensor having nearly 73 mts. resolution on ground. Since then, the country has
made huge advancement in this field with subsequent satellites like IRS 1B, IRS 1C, IRS
1D, IRS P4 (Ocean sat), IRS P6 (Resource sat) etc. and has reach a resolution as good as
nearly 1meter for panchromatic data and 5.5 mts. for multi-spectral data. The recent
advancements in international scenario has made a multi-spectral satellite imaging in 1
mt resolution from IKONOS satellite.
Earlier studies at the School of Oceanographic Studies, Jadavpur University by the
process of visual interpretation revealed significant rate of forest destruction at the
Bhitarkanika mangroves in last 30 years, possibly to cope up with the rapid increase in
local population (Mitra R., 2004). Further study with vegetation index calculations, to
assess particular vegetation distribution pattern of the forest, showed transitions in
mangrove forest cover of the area, which was witnessed by the presence of non-
mangrove vegetations within a dense mangrove patch. This study reported true mangrove
cover much lesser than that was estimated earlier (Mitra et. al. 2004).
The present study is thus, aimed in extension of the earlier one, to assess the pattern of
changes in mangrove cover of Bhitarkanika in a time scale of 12 years from 1989 to
2001. Here, to trace out the stages of transformation, Normalized Difference Vegetation
Index has been used for forest zonation.
4
2. Aims & Objectives
Remote sensing has been used for the reconnaissance survey for a long time. However,
multi-spectral data is proved to be the most useful incase of vegetation cover study,
because of the feasibility of band ratio study with Green, Red and Infrared, as vegetation
gives distinct reflectance patterns to these three bands.
Bhitarkanika being the second largest mangrove patch of India, on Orissa coastal area,
supports growths of mangrove and associate faunal diversity. However, in recent years,
with the increasing pressure on coastal zones through out the subcontinent, Bhitarkanika
is also facing severe threat of forest destruction. Apparently, though the forest cover was
not razed up vigorously, but a trend of forest transformation from dense mangroves to the
mixed-jungle and non-mangrove patches has been reported in earlier study using remote
sensing tools at the School of Oceanographic Studies (Mitra et. al. 2004).
It seemed necessary to study, whether, the mangrove distribution was as it is now in
recent past or there is a transformation going on? The only way to assess that fact was
thus, to study the distribution in time scale framework, and that too with the similar types
of records from past to the present. Therefore, the project was designed with the use of
IRS multi-spectral (LISS) data from 1989 to 2001, with an intermediate data at 1998.
The main aim of this study was thus set to assess the pattern of forest cover change with
special reference to the mangrove distribution at Bhitarkanika mangroves at Orissa, using
Remote Sensing techniques, such as vegetation index determination.
5
3. Review of Literatures
3.1. Biodiversity assessment:
The term Biodiversity can be defined as the existence of a wide number of species
or other taxa of plants, animals and microorganisms in a natural community or habitat or
of communities within a particular environment (Anon, OUP, 2000). The initiative for
biodiversity assessment was taken long back in 1991 with the UNEP Biodiversity
Country Studies Project (consisting of bilateral and Global Environmental Facility funded
studies in developing countries) implemented in cooperation from donor countries and
UNDP.
The agreed text of the Convention on Biological Diversity was adopted by 101
governments in Nairobi in May 1992, signed by 159 governments and the European
Union at the United Nations Conference on Environment and Development (UNCED)
held at Rio de Janeiro in June 1992. At present 120 governments are parties to this
convention. More than scores of nations and regions are engaged in developing their own
National Biodiversity Strategies or Action Plans.
Global Biodiversity Assessment (UNEP, 1995) estimates the total number of animal and
plant species to be between 13 and 14 million. It further records that so far only 1.75
million species have been described and studied. Ecosystem diversity has not been even
reasonably explored as yet. Hence, there seems to be wide gap of knowledge at global,
regional and local levels.
The Indian sub-continent is known for its diverse bioclimatic regions supporting
one of the richest floras and fauna. In a most recent attempt to map biogeographical
regions, Rodgers and Pawar (1988) attempted to define the biogeographical regions of
India. The sub-continent has ten biogeographical zones, viz., Trans-Himalayan,
Himalayan, Indian desert, Semi-Arid, Western Ghats, Deccan Peninsula, Gangetic Plains,
North East India, Islands and Coasts and not yet defined zones for aquatic (freshwater
and marine) ecosystems have been mapped. India is rich in endemic flora and fauna.
According to an estimate (BSI, 1983) about 30 percent plant species are endemic to India.
Recently, Ministry of Environment and Forests, Government of India has
launched a project viz., National Biodiversity Strategy and Action Plan (NBSAP), which
envisages the assessment and stock taking of biodiversity related information at various
levels, including distribution of endemic and endangered species and site-specific threats.
India, a mega-biodiversity country, while following the path of development, has been
sensitive to the needs of conservation. India’s strategies for conservation and sustainable
utilization of biodiversity in the past have comprised of by providing special status and
protection to biodiversity rich areas by declaring them as national parks, wildlife
sanctuaries, biosphere reserves and ecologically fragile and sensitive areas. It has helped
in reducing pressure from reserve forests by alternative measures of fuel wood and fodder
need satisfaction, by afforestation of degraded areas and wastelands, creation of ex situ
conservation facilities such as gene banks and eco-development. The challenges before
India are not only to sustain the efforts of the past but also further add to these efforts by
involving people in the mission.
6
3.2. The mangroves and forest cover of India
Mangrove resources are available in approximately 117 countries, covering an
area of 190,000 to 240,000 km2. Countries like Indonesia, Nigeria and Australia have the
largest mangrove areas. These ecosystems harbour 193 plant species, 397 fishes, 259
crabs, 256 molluscs, 450 insects and more than 250 other associated species. Mangrove
ecosystem has the highest level of productivity among natural ecosystems, and performs
several ecosystem services. According to one estimate, the mangrove forest cover in
India has reduced from 6000 km2 in 1953 to 2000–3000 km
2 in 1989 (Upadhyay et. al.,
2002).
The total length of Indian coastline is 7516.6 km, including the island territories,
but the Indian mainland coastline length is extended to 5700 km. Among the length, only
3979 km2 in covered with mangroves and nearly 62954.1 km
2 are landforms like mudflat,
beach/spit, shore/bar, coral reef, marsh vegetation, paleo-mudflat, lagoons / backwaters,
sand dunes, salt pans, flood prone areas etc. Most interestingly, these data on the basis of
Landsat TM / IRS LISS 11 FCC / SPOT FCC on 1:250000 scale do not tally with the
data generated from simply ground land survey on India coastal areas and particularly on
the mangrove zones. However, it is reported that in comparison to the total Indian
mangals, west coast mangals are sparse, less extended, patches and with very stunted
growth, which may be due to less fresh water supply from the upstream and soil of these
zones are not much silty-clay in comparison to the deltaic lands of the Ganga and the
Mahanadi (Naskar & Mandal, 1999). Moreover, the maximum mangrove reduction has
been reported to be as high as 44% in the west coast in comparison with that of the all
India estimate of 30% (Jagtap et al, 1994).
The mangrove ecosystem of east coast of India is comprises of five major deltas
and estuaries in four maritime states like Tamil Nadu, Andhra Pradesh, Orissa, West
Bengal. Among the five estuaries, Sundarbans at the estuary of Hooghly river in West
Bengal and Bhitarkanika at the Mahanadi delta of Orissa stand first and second largest
mangrove patch of India respectively. According to the biological diversity, Bhitarkanika
is known to the richest mangrove of India with nearly 71 mangrove and mangrove
associate species (Banerjee & Rao, 2001) with the dominating species being Avicennia
marina, A. officinalis, Rhizophora apiculata, Excoecaria agallocha, Phoenix and
Sonneratia sp. Paddy cultivation, salinity increase and slow regeneration of mangroves
have caused considerable damage. Mangroves are being cut down for fuel and building
material. Creeks have been blocked, there by resulting in the sweetening of the water
upstream killing the mangroves. Satellite data has revealed damage and cutting of
mangroves for agriculture, aquaculture etc. (Nayak & Bahuguna, 2001).
With the rapid decrease of mangrove vegetation (from 2210 km2 in 1975 to 1635
km2 in 1990-91) on the east coast of India, Bhitarkanika is also facing sever problem of
forest clearance (Jagtap et. al., 1993). While Mohanty et. al. (2001) reported tremendous
pressure on coastal zone of Orissa for the development of fisheries, aquacultures, port &
harbours and urban settlements, Das (1998) reported more than 30 sq.kms of
Bhitarkanika mangrove forest has been razed and the rest is threatened due to extensive
7
prawn farming in recent years. To be precise, an estimated 25,000 illegal settlers and
prawn farms from the sanctuary area are threatening the forest cover leading to a drastic
reduction in an average rate of 3 km2 per annum along with pollution of coastal water of
the favored habitat of the Olive Ridley.
It has been an accepted truth that the pressure on coastal zones and mangroves are
increasing with the increase in population over years. The main threats over the
mangrove forests are mostly related to human activities like over exploitation of food,
fuel and fodder forest resource, over fishing, grazing and land reclamation for agriculture
and aquaculture activities (Kathireson & Rajendran, 2003; Mitra & Pal, 2003). Mohanti
(1998) reported a 15% loss of mangrove wetland during a span of 18 years between 1975
and 1993 due to human interference.
Some efforts have been made to raise mangrove plantations in degraded forests in
Orissa, West Bengal, etc. by the respective Forest Departments. With adequate
protection, mangroves regenerate and cover the exploited area in a short period. This
process needs to be augmented with human intervention. For example, harvesting of
mangroves on sustained yield basis with defined period of rotation should be mandated to
ensure sufficient regeneration. Clear felling of mangroves promotes fast growing of
unwanted weed species, which inhibit mangrove regeneration. Natural regeneration and
recovery can also be promoted by reducing the anthropogenic pressure due to fuel-wood
demand on natural populations. There is an urgent need to undertake massive
afforestation programmes with improved fuel-wood yielding trees in the buffer areas, to
sustain the requirements of the local people. These plantations will act as a major energy
source to local communities in future. Plantation of fast-growing species should also be
taken up in the villages, which will reduce the dependence of coastal communities for
conventional firewood from mangrove forests (Upadhyay et. al., 2002).
3.3. Application spectral characteristics in Remote Sensing:
Remote sensing is the practice of deriving information about the Earth’s land and
water surfaces using images acquired from an overhead perspective, using
electromagnetic radiation in one or more regions of the electromagnetic spectrum,
reflected or emitted from the earth surface (Campbel J.B., 1996).
However, in most of the satellite remote sensing, electromagnetic spectrums are
used. The major divisions of the electromagnetic spectrum are in essence, arbitrarily
defined. Generally, the whole useful spectral range is divided as ultraviolet, visible
spectrum and Infrared spectrum and also the microwave energy.
In multi-spectral Remote Sensing, the sensors at satellites, receive spectral bands
at different ranges for building up an image. For IRS satellites in LISS Sensors mostly
Green, Red, near Infrared and Mid Infrared bands are used. This gives a wide scope of
data analysis, as the band properties vary enormously, for features like mangroves, non-
mangroves, dry sands or lands, water bodies etc. (Figure 3.1 & 3.2). the Blue bands,
however, are excluded using appropriate filters, as the high scattering potentials of short
wavelength blue bands may create noise in data.
8
020406080100120140160180
Band 1Band 1Band 1Band 1 Band 2Band 2Band 2Band 2 Band 3Band 3Band 3Band 3 Band 4Band 4Band 4Band 4MangroveMangroveMangroveMangrove Non-mangroveNon-mangroveNon-mangroveNon-mangrove Dry sandDry sandDry sandDry sand WaterWaterWaterWater
Figure 3.1 : Spectral characteristics of different earth signatures
0
20
40
60
80
100
120
140
160
180
MangroveMangroveMangroveMangrove Non-mangroveNon-mangroveNon-mangroveNon-mangrove Dry sandDry sandDry sandDry sand WaterWaterWaterWaterRefl
ecta
nce v
alu
e
Band 1Band 1Band 1Band 1 Band 2Band 2Band 2Band 2 Band 3Band 3Band 3Band 3 Band 4Band 4Band 4Band 4
Figure 3.2: The Characteristics of different spectral bands for different features.
9
3.4. Remote Sensing in Forestry Applications
Forestry is concerned with the management of forests for wood, forage, water,
wildlife and recreation. Forest of one type or another cover nearly one third of the Earth’s
land area. They are distributed unevenly and their resource values vary widely. Visual
image interpretation provides a feasible means of monitoring many of the world’s forest
conditions. We will be concerned principally with the application of visual image
interpretation, to tree species identification, studying harvested areas, timber cruising and
the assessment of disease and insect manifestations. The visual interpretation process of
tree species are more complex than for agricultural crop identification, as a given area of
forest land is often composed of more heterogeneous mixture of different species. In
contrast, the crop distributions in agricultural lands are more homogeneous for a wide
area.
The forestry application for the tree species identification and forest zonation has
been possible by the use of Aerial Photographs and even by high-resolution panchromatic
images. But, it is always better to use multi-spectral satellite imagery in forest cover
assessment or zonation. However, the lower resolution of the data imparts an additional
burden in distinguishing vegetation patches with different species composition. The
image characteristics of shape, pattern, shadow, tone and texture are used for
interpretation of tree species as individual tree species have their own characteristic
crown shape and size.
Importance of remotely sensed data for inventorying, mapping, monitoring and
recently for the management and development planning of the optimum sustainable
utilization of natural resources has been well established. Remote Sensing data has
proved to be extremely useful in providing information on various components of the
coastal environment viz., coastal wetland conditions, density wise mapping of
mangroves, coastal landforms and shoreline changes, tidal boundary, brackish water
areas, suspended sediment dynamics etc. The use of remote sensing in forestry
applications and vegetation cover assessment is getting so much of pace now a days, a
special Vegetation Sensor was launched on-board SPOT-4 satellite in March, 1998.
Though basic principle of the sensor is more or less similar to that of AVHRR, but it
differs a little on having a push-broom system of sensing and Short Wave Infra Red
(SWIR) band, which permits the study of vegetation water content. Study with one and
half years weekly data from the SPOT-4 Vegetation data enabled the researchers in broad
scale classification of dense humid forest, dry deciduous forest, mangroves, savanna etc.
with an accuracy of 87.8% (Philippe et. al., 2000).
The polar orbiting Indian Remote Sensing Satellite (IRS) has also been proved
useful in forestry application since its launch in 1988. The presence and absence of
mangroves and its aerial extent can be mapped at 1: 2,50,000 scale using a course
resolution satellite data such as LISS I data from IRS 1A/1B. High-resolution data
however, helps in getting information on the dominant community zones of mangroves.
Use of those data enabled investigators to classify mangrove forest of Indian coast on the
basis of crown density. Mangroves with >40% crown density are termed as dense (closed
10
forest), those having 10-40% crown density are termed as sparse (open forest) and
density <10% regarded as degraded forest, in density wise classification, which gives an
idea about the conditions of mangrove habitat also (Nayak & Bahuguna, 2001). However,
the property of stocking levels generally required at the working level is of 20% crown
cover intervals. Adoptions of spectral based stock maps appear get saturated beyond 40%
crown cover. However, in certain areas of Madhya Pradesh it is found, based on tone and
texture associated with spectral separability as a function of time has been found suitable
to provide 40-60% stocking level in addition. This study indicated greater scope for
future exploitation and utilization of various algorithms for optimizing time window and
spectral band may be feasible to explore satellite based forest stock map preparation. The
studies using NDVI as a function of stocking level needs review as it provides
information pixel level on the vigor of the canopy with less degree structural relationship
canopy closure. However, development of algorithm using textural function might proved
to be an effective method for quick assessment of stocking levels (Dutta et. al., 1994).
More recently, the IRS-P4 Ocean Colour Monitor (OCM) data with a spatial resolution
360mt x 236mt are being used to study the impact of different natural catastrophes like
cyclone on coastal environment, mangroves as well as on concentration and distribution
of Chlorophyll-a and suspended particles. The OCM of the IRS-P4 is optimally designed
for estimation of chlorophyll in coastal and oceanic waters, detection and monitoring of
phytoplankton blooms, studying the suspended sediment dynamics and other coastal
processes in respect to the time and space. Study with NDVI images of OCM data,
successfully traced the impact of Super Cyclone on coastal mangroves and its change
dynamics near Paradip port of Orissa in 1999 (Nayak et. al, 2001).
However, the accuracy of remote sensing change detection study is influenced by Sensor
systems, Environmental Characteristics and Geodetic controls. Accurate spatial
registration of the images is most essential for most of the change detection methods,
though precise geometric registration of image is often difficult to achieve when there is a
lack of accurate ground points (Singh A., 1989). Moreover, extremely wet or dry
conditions on one or more of the dates can result in serious change detection problems,
which can be mitigated, to some extent by referring the subsequent precipitation records
(Forse et al., 1997).
For the forestry application, as it has been already stated, the preferred data are
the multi-spectral images, which, in case of Indian satellite is available from the LISS
sensors. As, in a LISS III data with a spatial resolution of nearly 23.5 mts, a single pixel
may cover an array of several features and represents a cumulative band value, any
particular shape, size or pattern for a defined tree is hardly possible to identify. Thus, in
spite of the species identification, the species association study is preferred. The
differential band combination values for different vegetations due to higher absorbance
range of Red and high reflectance range of Infrared band has enabled the interpreter to
design various Vegetation Indices for proper and most authentic zonation of vegetation
associations. Normalized Difference Vegetation Index (NDVI), which was developed by
Rouse et. al. (1973), is the most widely used vegetation index (Forse et al., 1997).
In different studies, significant difference were found between the forest areas and other
crops with the help of NDVI and RVI, which helped in better enhancement of forest areas
(Md. Seeni & Salleh, 1992).
11
Though the index has been proved useful for forest cover assessment, Forse et al.
reported that the general land cover types appear to be mostly unrelated to the changes in
the NDVI. The NDVI values are describing changes in moisture content of the vegetation
rather than changes in land cover. The only exception appears to be the drop in NDVI
over time, which correlates with intrusion of shrimp farms into the marsh, and the
increasing NDVI values, which corresponds with the increasing area of mangroves in and
around the marsh. It is interesting to note the stability of the ‘dense humid forest’ and the
high NIR reflectance of the secondary complex denoting a very high photosynthetic
activity. The mangroves show a similar behavior to the ‘dense humid forest’ (Philippe et
al., 2000).
Any classification scheme based on NDVI values require revalidation of the
classification, which can be done using a confusion matrix following the processes
adapted by Congalton (1991). Using such confusion matrix, during assessment of
community-based restoration of Pichavaram mangrove was found useful in measurement
of accuracy of visual classification (Selvam et al. 1993).
3.5. Vegetation Indices
Vegetation indices are quantitative measures, based on digital values, that attempt
to measure biomass or vegetative vigor. Usually, a vegetation index is formed from
combinations of several spectral values that are added, divided, multiplied in a manner
designed to yield a single value that indicates the amount or vigor of vegetation within a
pixel. For large area vegetation monitoring, primarily the vegetation index was calculated
over the Advanced Very High Resolution Radiometer (AVHRR) data. Typically the
spectral bands used for this purpose have been the channel 1 visible band (0.58 to 0.68
µm) and channel 2, near-Infra Red band (0.73 to 1.10 µm). Various mathematical
combinations of AVHRR channel 1 and 2 data have been found to be sensitive indicators
of the presence and condition of green vegetation. These mathematical quantities are thus
referred to as vegetation indices. Two such indices have been calculated from AVHRR
data – a simple vegetation index (VI) and a normalized difference vegetation index
(NDVI). These indices are computed from the equations:
VI = Ch2 – Ch1
NDVI = Ch2 – Ch1 / Ch2 + Ch1
Where Ch1 and Ch2 represent data from AVHRR channels 1 and 2, preferably expressed
in terms of radiance or reflectance.
12
High values of the vegetation index identify pixels covered by substantial
proportions of healthy vegetation. Band ratios are quotients between measurements of
reflectance in separate portions of the spectrum. Ratios are effective in enhancing or
revealing latent information when there is an inverse relationship between two spectral
responses to the same biophysical phenomenon.
For living vegetation, the ratio strategy can be especially effective because of the inverse
relationship between vegetation brightness in the Red and Infra Red region. That is,
absorption of Red light (R) by chlorophyll, and strong reflection of Infra Red (IR)
radiation by Mesophyll tissues ensures that the red and near infrared values will be quite
different and that the Ratio (IR/R) will be high. Plant pigments such as chlorophyll
strongly absorbs much of the light at the wavelengths longer than 1.4 mm. This contrasts
with strong reflectance in the near infrared in the range from 0.75 mm through about 1.4
mm, at which plant materials are relatively transparent. Much attention in the Remote
Sensing of green vegetation is focused on the strong reflectance contrast between the
visible red and near infrared, which forms a strong step in the spectrum of green
vegetation which is often referred to as the “Red Edge” (Anon, CIT, 1999). Studies on
the photosynthetic efficiency of four rhizophoracean mangroves, Rhizophora apiculata,
R. mucronata, Bruguiera cylindrica and Ceriops decandra, randomly collected from
Pichavaram mangrove forest at southeast coast of India revealed that Bruguiera
cylindrica had higher stomatal conductance where the net CO2 fixation was also high. It
was concluded that the chlorophylls present in reaction centre and light harvesting
complex could be referred as "membrane-bound chlorophyll"; and also could be used as
an index to measure the photosynthetic productivity of mangrove species (Moorthy &
Kathiresan, 1999).
The high ‘penetrating’ capability of the near-infrared band through forested
canopies was the dominant factor in vegetation index sensitivity and performance. We
found that indices with higher weighing coefficients in the “near-infrared” to be the best
approach in extending vegetation index performance over forested and dense vegetated
canopies. (Huete et. al., 1997)
There have been two general approaches taken to develop indices for measuring
green vegetation cover based on the characteristics of the tasseled cap. The first approach
is to measure the distance between where the pixel plots in the tassel cap point from the
soil line. The soil line is used, because it is generally easier to find than the 100%
vegetation point. The Perpendicular Vegetation Index (PVI) of Richardson and Wiegand
(1977) assumes that the perpendicular distance of the pixel from the soil line in linearly
related to the vegetation cover. This index is calculated as follows:
PVI NIR red = - sin a (NIR) cos a (red)
Where, NIR is the near-infrared reflectance, red is the red reflectance and a is the angle
between the soil line and the near-infrared axis.
13
The above equation means that the isovegetation lines (lines of equal vegetation) would
all be parallel to the soil line. A special case of this is the simple Vegetation Index or
Vegetation Index (VI) described by Lillesand and Kiefer (1987) which has more recently
been christened deference vegetation index (DVI) by Richardson and Everitt (1992):
VI = DVI = NIR –Red
This case occurs when the soil line has a slop of 1.0.
The next possibility is to assume that the isovegetation lines all intersect at a
single point. As the first approximation, Jordan (1969) developed the ratio vegetation
index:
RVI = NIR / R
RVI itself is no longer generally used in remote sensing. Instead, an index known
as the normalized difference vegetation index (NDVI) is used. This index is functionally
identical to the RVI, and it can be written as:
NDVI = NIR - red / NIR +red = RVI-1 / RVI +1
Both RVI and NDVI basically measure the slope of the line between the origin of
red – NIR space and the red-NIR value of the image pixel. The only difference between
RVI and NDVI is the range of values of the two indices. The range from –1.0 to + 1.0 for
NDVI is easier to deal with than the infinite range for RVI (Anon, CIT, 1999).
Non-vegetated surfaces, including open water, manmade features, bare soil, and
dead or stressed vegetation, will not display this specific spectral response, and the ratios
will decrease in magnitude. Thus, the IR/R ration can provide a measure of the
importance of vegetative reflectance within a given pixel, which provides a tool of better
visualization of the image (Herndez-Cruz et. al., 2003)
The IR/R ratio is only one of many related measures of vegetation vigor and
abundance. The Green/Red (G/R) ratio is based on the same concepts as used for the
IR/R ratio, although it is considered less effective. Although ratios can be applied with
digital values from any Remote Sensing system, much of the research on this topic has
been conducted using Landsat MSS data. In this context, the IR/R ratio is implemented
for Landsat 4 & 5 as (MSS 4 / MSS 2), although some have preferred to use MSS 3 in
place of MSS 4.
Thus, the NDVI calculations with the MSS data can be formulated as:
NDVI = IR –R / IR + R = MSS4 – MSS2 / MSS4 + MSS2
This index in principle conveys the same kind of information as the IR/R and G/R ratios
but is defined to produce desirable statistical properties in the resulting values. Studies of
14
Tucker et. al.(1979) and Perry and Lautenschlager (1984) suggested that in practice there
are few differences between the many vegetation indices that have been developed.
Meza Diaz and Blackburn (2000) found major differences in the behavior of the different
spectral indices when correlated with mangrove leaf area index (LAI). NDVI, SAVI,
TSAVI, RVI and SAVI2 were the indices that were most strongly correlated with
mangrove LAI, for both the three individual species (Avicennia germinans, Rhizophora
mangle, Laguncularia racemosa) and for all species combined. The rest of the indices
showed very poor correlation with mangrove LAI, especially the three derivative-based
indices. It has been found that the background in this environment has a strong influence
on the spectral properties of the mangrove canopy, which affected the performance of the
spectral indices investigated in different ways.
Mangroves are intertidal, often grow in dense stands and have complex aerial root
systems, which make extensive sampling impractical with the difficulty of moving
through dense mangrove stands and the general inaccessibility of many mangrove areas
posing a major logistic problem. Luckily field measurements indicated that there is a
linear relationship between mangrove Leaf Area Index and NDVI, which can be obtained
from remotely sensed data. This means that a relatively modest field survey campaign
can be conducted to obtain LAI measurements in more accessible mangrove areas and
these used to establish a relationship to NDVI using regression analysis. Once this
relationship is known then NDVI values for the remainder of the mangrove areas can be
converted to LAI (Anon, 1999). Although we lack quantitative biomass data, by
observation it is difficult to qualitatively associate any component with increasing
biomass or a characteristic NDVI over the whole year. For instance, while mangroves
and dry prairie areas were strongly positive in the first component image, the
pine/hardwood complex just to the north, home of the endangered Florida panther, was
strongly positive in the third component image (Roberts, 1994).
Although NDVI has been accepted by most of the scientific schools as one of the best
vegetation index world wide, a few limitations of the index have also been revealed.
NDVI has been reported to be unable to highlight subtle differences in canopy density. It
has been found to improve by using power degree of the infrared response. The index
thus calculated has been termed as advanced vegetation index (AVI). It has been more
sensitive to forest density and physiognomic vegetation classes. AVI has been calculated
using following equation
AVI = {(B4 +1) x (256-B3) x B4/3}1/3
However, the biophysical spectral indices may require several steps of indices calculation
steps like Normalization of Landsat TM band, Temperature calibration, calculation of
Bar Soil Index, Canopy Shadow Index etc (Roy et. al., 1997).
15
3.6. Image Classification
Digital classification can be defined as the process of defining pixels to class or
more precisely land cover classes. Normally, multi-spectral data are used to perform the
classification indeed the spectral pattern present within the data for each pixel is used as
numerical basis of categorization. That is different feature types manifest different
combinations of DN values based on their inherent spectral reflectance and emitance
properties. These classes form regions on a map or an image; after classification, thus,
digital image is presented as a mosaic of uniform parcels, each identified by a colour or
symbol.
Image classification has an important role in remote sensing data analysis, as it depends
on the spectral properties of each pixel or a cluster of pixels. For a work, like forest cover
change assessment, two types of classification schemes can be followed, such as
Unsupervised and Supervised classification.
3.6.1. Unsupervised classification:
Unsupervised classification can be defined as the identification on natural groups,
or structures, within multi-spectral data. The notion of existence of natural, inherent
grouping of spectral values within a scene may not be intuitively obvious, but it can be
demonstrated that remotely sensed images are usually composed of spectral classes that
internally are reasonably uniform in respect to brightness in several spectral channels.
Unsupervised classification identifies spectrally homogenous classes within the data,
thus, these classes does not necessarily corresponds to the informal categories that the
analyst is intend to have. Spectral properties of specific information changes over time in
respect of seasonal or even diurnal variations, but being the classification scheme without
a knowledge support, unsupervised classification separates same feature information as
different class. Moreover, such classification provides little scope to the analyst to
analyze the data using spectral characteristics (Campbell J. B., 1996 and Lillesand &
Kiefer, 2000).
3.6.2. Supervised classification:
Supervised classification can be defined informally as the process of using
samples of known identity to classify pixels of unknown identity. The analyst in these
cases defines training areas (sample pixels located within a defined region) by identifying
regions on the image that can be clearly matched to areas of known identity on the image.
In supervised classification the analyst have several controls over the classes and thus,
can manipulate the data according to the interest of his or her study. Moreover, a
supervised classification considers the seasonal variations, as the training sets are
assigned manually, which includes the knowledge base. Thus, the classification possesses
more accuracy and truthfulness in practice (Campbell J. B., 1996 and Lillesand & Kiefer,
2000).
16
3.6.3. Accuracy assessment and confusion matrix:
the standard form for reporting site-specific error is the error matrix, sometimes referred
to as the confusion matrix because it identifies not only overall errors for each category
but also misclassification (due to confusion between categories) by category.
Compilation of an error matrix is required for any serious study of accuracy. The matrix
consists of n x n array, where ‘n’ represents the number of categories. It reveals the
results of comparison of evaluated and reference images. Thus, inspection of the matrix
shows how the classification represents actual areas on the landscape (Campbel J.B.,
1996).
However, the accurate classes, i.e. the frequency of the match evaluated and reference
classes are situated as the diagonal position in the matrix. In several studies this
confusion matrix has cropped huge success in measurement of classification accuracy
and also in identifying the ground features, which represent same characteristics of each
other.
17
4. METHODOLOGY
Mangrove forest is regarded as one of the most productive ecosystem of the world. Due
to presence of high water and chlorophyll content it gives a higher value of Normalized
Difference Vegetation Index (NDVI) than other coastal vegetations like Casuarina
plantations or woodlots. However, using this property, several forest cover change
detection studies have been done successfully with the help of Multi-spectral satellite
imageries.
4.1. Data used
The present forest cover change detection study has been done on the basis of forest
community assessment by NDVI values. All the satellite imageries used, were taken by
Indian Remote Sensing (IRS) satellite with Linear Image Scanning System (LISS)
sensors. Pre-monsoon data of 1989 (21st February), 1998 (13
th February) and 2001 (19
th
March) from IRS-1A (LISS I), IRS-1C (LISS III) and IRS-1D (LISS III) respectively,
have been used for the forest cover assessment.
4.2. Data processing, Classification procedures and analysis
The digital data supplied by the National Remote Sensing Agency (NRSA), Hyderabad
were either in Binary or Super structure format. Those data were first imported as ‘.img’
format and then analyzed and processed by the Remote Sensing software ERDAS
IMAGING 8.5. As the digital data did not have any real earth co-ordinates, all the three
data were geometrically corrected using ground control points such as road–road
intersection, jetty, canal–road intersection, etc. taken from the toposheet using the same
image- processing package.
The area of interest was chosen and scooped out of the whole image by a ‘sub-set’
operation on the same. Only the mangrove areas and villages adjacent to the forested
areas were considered as study area, as man-mangrove relationships are most
predominant at this part and it is the perfect area for studying any trend of forest
degradation.
Reconnaissance survey of Mangrove wetland of 1989, 1998 and 2001 were done by on-
screen visual interpretation method using different enhancement techniques like
histogram equalization, available with the software. Different classes of the mangroves
and adjacent areas, such as dense mangroves, degraded mangroves, young mangrove
stands, sand dune associated with mangrove wetlands, vegetation associated with sand
dunes, water body and dry land were then identified using visual interpretation keys such
as colour, tone, texture, pattern, size and shape. However, prior knowledge of the ground
reality was necessary for such type of identification process (Table 4.1).
18
As the author was frequenting to the area of study and it was very much possible to
gather ground reality from several on field points, Supervised classification was chosen
for the study. The software was made familiar to the tones and their combinations in
clusters of pixels by a few informal sets of defined pixel with known ground signature,
known as ‘Training sets’. The Training sets were made on the basis of field experience
and at a few ground truth points. For the data of past, as a ground truth verification was
not possible, the researcher had to rely on the visual interpretations on the basis of the
field experience. Primarily, total 42 different classes were defined and classified
accordingly. But, after ward, those were clustered together as per the need of this present
study following the vegetation index calculation.
During the classification, the software was instructed to calculate the minimum and
maximum values of each band of reflectance among all the pixels of a given ‘Training
set’, and then to ascertain an average with standard deviations, which will be the assigned
value for a certain class. The NDVI was calculated with the average values of each of
those 42 classes using the formula discussed in chapter 4.
Area of each class was calculated digitally by software itself. The total area for each of
the categories, however were calculated afterwards by simply adding the areas of each
class.
4.3. Radiometric correction
The data supplied to us were mostly corrected for haze and other atmospheric
disturbances on the reflectance. Further, radiometric correction was done during the data
processing and classification. The radiometric correction is done on the assumption that,
in a full scene there must be at least, one absolutely dark pixel, which does not have any
reflectance value for any band of the useful spectrum. Because of the atmospheric
scattering the imaging system records a non-zero DN value at this supposedly dark
shadowed pixel location. This represents the DN value that must be subtracted from a
particular spectral band to remove the first order scattering component (Singh et al,
1999). Thus, the darkest point on the scene is identified and processed accordingly.
19
Class Colour Shape Features No. of
training
sets
Mangroves Bright red Irregular Mangroves & new mangroves
11
Degraded
mangroves
Light brown
to pink
Irregular Dry and scrubby and sparse
mangrove
4
Succession Brownish red Irregular Acanthus sp., Poteratia sp. &
Excoecaria sp.
1
Mixed
jungle
Mixed red
and brown
Irregular Mangroves & non-mangroves
like Acacia sp., Zizypus, Ficus,
Prosopis sp. etc.
2
Non-
mangroves
Pink,
purplish red
Irregular
and
linear
Bank vegetations with weed
and grass, scrubs,
embankment Acacia sp.
Prosopis sp. etc.
3
Marshy
vegetation
Dark blue-
brownish red
Irregular Wet drench vegetation
predominantly with Sueda sp.
and Nalia grass.
1
Swamp
Greenish blue Irregular Wetlands with marshy
vegtations
1
Agricultur
al land
White to
greenish blue
& pink
Flat
irregular
Dry and wet agricultural lands
and lands with sparse weeds
and grasses
3
Sand
White(dry) &
light greenish
blue (wet)
Rough/s
mooth
Dry sand and wet sand 2
Water
body
Light to dark
blue and
black
Irregular,
regular
& linear
River, Channels, Sea,
potholes, aquaculture, shallow
water and sediment load.
8
Table 4.1: Details of classification and the description different classed and their ground
features.
20
5. Results and Discussion
Tree canopy is essential to environmental and economic health, providing
additional cooling, reducing energy needs, increasing property values, improving
air/water quality, reducing the cost of storm water control, and contributing to a more
beautiful, friendlier, and livable community. In lieu of assessing the canopy cover of
mangroves in Bhitarkanika, the study was designed in such a way, so that, a proper
assessment of mangrove and non-mangrove floral distribution can be done, even in the
inaccessible areas.
Analysis of satellite data has been carried out using different digital analytical
procedures, as it has been described previously. In the view of minimizing the seasonal
variations and the atmospheric disturbances cloudless pre-monsoon multi-spectral images
were used for all the three years data. However, the radiometric corrections were also
done to nullify the reflectance values from aerosols and hazes. Although, the band
combinations and spatial resolution were same for the IRS 1C and IRS 1D LISS III data
of 1998 and 2001 respectively, but it differed in case of 1989 data from IRS 1A satellite
by LISS I sensor (Ref. Annexure 1). With the use of various enhancement techniques
and classification schemes, that too on basis of pixel properties and the most widely used
band ratio indexing method like Normalized Difference Vegetation Index (NDVI), the
mapping procedure is expected to be quite efficient.
Supervised classification technique based on ground truth observation points and
revalidation of the classified areas represented significant variations in NDVI values of
mangroves, degraded mangroves, mixed jungle with both the mangrove and non-
mangrove plant species and non-mangrove patches. While, dense mangrove forest cover
gave NDVI at a higher side, the degraded forest area represented a NDVI value at the
lowest range (Table 5.1) among all the vegetation signatures. The other land use, land
cover classes, such as, marshy vegetations, swamp, agriculture, aquaculture, Sands,
Mudflats and water bodies were classified differently (Table 5.2). During ground truth
verification and revalidation at Bhitarkanika, it was found that patchy distribution of non-
mangrove plants like Prosopis juliflora, Acacia sp., Zizypus jujuba, Ficus sp. etc. are
present in close association with mangroves to form a mixed vegetation type (Table 5.3),
which may be indicative of an undesired transformation of mangrove ecosystem.
However, the NDVI study could distinctly separate such patches from the dense
mangrove covers. It was most interesting to note that in close observation of the
classified map, such mixed patches were found to be present at the areas nearer to the
forest edges, where human interactions are more (See Map 1, 2, & 3).
The NDVI values show significant differences for Mangroves and non-mangroves. While
the mangroves represented average NDVI between 0.398272±0.044 and 0.475683±0.029
for the three respective years, the maximum NDVI for mixed vegetations with
comprising of both the mangrove and non-mangrove species composition were reported
to be 0.301818 among all the data. The NDVI of non-mangroves including woodlot
plants, coconut and palm trees and typical river bank scrubs ranged between 0.023793
21
and 0.23811 with average values between 0.089342± 0.035 and 0.146132± 0.047 for the
three years. But most interestingly, the marshy land vegetations predominated by Saueda
sp. and mangrove grasses showed NDVI in the higher side and the highest value was
obtained from the young mangrove patches at the second or third sere of succession, for
all the data. Such clear trends in NDVI values for various signatures indicate the
efficiency of the process. Estimated accuracy level of the classified data, using Confusion
matrix process was more than 86% (Table 5.6). The highest amount of errors was
observed in cases of the class succession and degraded forest, probably due to their
similarity in ground characteristics i.e. muddy soil base with scattered vegetations.
However, merging of those two classes as a single signature resulted in an increased
accuracy of nearly 92%.
Area calculation for different land cover classes was done digitally in a composite way.
As, during classification, 12 different signatures were designated as mangroves to
enhance the accuracy of classification through NDVI calculation, all those classes
together have been classified as Mangrove and the composite area of those were
designated as the dense mangrove area. The same schemes were followed in case of other
features also. However, 10.53 sq.km. (>10%) decrease in dense mangrove cover has been
reported during 1989 and 2001. Perhaps there was a conversion towards the agricultural
fields, which has increased 9.9 sq.km during the same time scale. On the other hand, the
cultural lands as a whole has shown an increase perhaps in expense of the vegetation
covers and forestland. Most surprisingly, the pattern of changes, particularly in case of
forest cover, varied in nature for the two different time scale of 1989-1998 and 1998-
2001(Figure 5.1 & 5.2). While during the first nine years of 1990s, changes in land
coverage indicated towards a trend of forest destruction, in contrast, the forest health
were found to be revived during the last three years (Table 5.4 & 5.5). In addition, in
some areas with mixed or degraded mangroves in 1998, restoration of dense mangroves
has been reported. The observation, however, supports the claim of Forest department
regarding their honest efforts to protect and restore the mangrove forest cover following
establishment of Bhitarkanika National Park in 145 sq.kms, within the premises of
Wildlife sanctuary.
Trend analysis of the aforesaid observations set different relations of diverse land cover
types, especially in forested areas. Other than the general trend of forest to cultural land
(agriculture and aquaculture) conversion, several trends of forest conversion were also
found. Decreasing trends of dense mangrove were accompanied by an increase in mixed
or non-mangrove vegetations and vice-versa. On the other hand, the trends of changes in
degraded mangroves, agriculture and aquaculture were nearly parallel (Figure 5.2).
Setting these trends on an analytical purview it seems that the forest goes through a series
of changes before it gets wiped away. The forest degradation, invasion of non-mangroves
within dense mangrove, growth of non-mangroves and marshy vegetations are a few
probable intermediate stages of a forest transformation and subsequent clearance process.
22
23
2001 1998 1989
Class NDVI Area NDVI Area NDVI Area
Mangrove 1 0.45002 260.4717 0.43895 1276.916 0.404239 645.76
Mangrove 2 0.41912 151.9236 0.4903 541.8657 0.345439 933.12
Mangrove 3 0.37351 867.7287 0.47457 899.2944 0.419951 309.76
Mangrove 4 0.37046 1422.7164 0.48466 773.3961 0.404784 1242.88
Mangrove 5 0.33783 1511.0712 0.421 2374.718 0.356539 2011.52
Mangrove 6 0.41674 486.3888 0.47099 428.7249 0.452721 126.72
Mangrove 7 0.40035 306.6174 0.45798 404.0847 0.452164 146.56
Mangrove 8 0.34141 1450.9287 0.50479 89.8857 0.412148 343.68
Mangrove 9 0.42646 297.3591 0.48147 276.4368 0.342803 866.56
Mangrove 10 0.42796 541.1367 0.4653 86.6781 0.383513 1527.04
Mangrove 11 0.34365 1362.7197 0.48093 767.4912 0.412366 817.92
New mangrove 0.47177 86.9697 0.53725 293.6412 0.4204 828.16
Deg. mangrove 1 0.26583 496.3761 0.23529 329.7267 0.223732 798.08
Deg. mangrove 2 0.26589 1217.3571 0.18635 355.2417 0.17724 107.52
Deg. mangrove 3 0.1826 448.8453 0.21184 471.5172 0.157641 544
New Succession 0.14677 603.4662 0.17522 85.4388 0.215504 975.36
Mangrove scrub 0.1904 476.037 0.23113 1105.456 0.205841 673.28
Mixed jungle 1 0.22394 1244.9862 0.30182 1619.911 0.280815 862.08
Mixed jungle 2 0.26501 501.7707 0.25421 755.8272 0.21284 808.32
Bank vegetation 0.1091 395.9928 0.02379 953.8965 0.060694 359.04
Embankment vegetation 0.15743 783.675 0.16931 2650.644 0.096727 1141.76
Woodlots 0.20849 732.9366 0.23811 966.3624 0.136476 714.24
Floodplain vegetation 0.10951 1481.8383 0.09357 1255.192 0.063472 468.48
Dune vegetation 0.0618 515.1114 0.2722 250.776 0.225589 256
Marshy vegetation 0.28055 469.2573 0.36853 156.4434 0.304193 82.56
Swamp 0.01261 1618.0155 -0.0028 628.1064 0.057518 584.32
Dry Agricultural land 0.00105 2182.1886 -0.0114 1892.703 0.012335 1112.32
Agricultural land 0.00086 4771.4508 -0.0207 3640.991 0.047834 5767.68
Weedy Agriculture 0.06047 1915.3017 0.0485 2623.015 0.098678 1000.96
Dry sand -0.09346 308.8044 -0.0745 249.6096 -0.03664 853.12
Wet sand -0.204 932.5368 -0.1424 391.3272 -0.19837 380.16
Wetland -0.09124 289.8504 -0.0635 232.7697 -0.03906 424.32
Aquaculture 1 -0.23942 239.6952 -0.1817 54.6021 0 0
Deposition -0.17468 511.1019 -0.469 1692.446 -0.35039 2928
Sediment flow -0.39144 3268.6173 -0.259 1174.273 -0.3496 1615.36
Creeks/ mud 0.16873 251.7237 0.27172 957.3228 0.148289 645.76
Wet channel -0.02124 979.4844 -0.084 1056.758 0.041783 2903.68
River 1 -0.18939 2011.2381 -0.4462 5483.174 -0.19355 3414.4
River 2 -0.26862 2970.7479 -0.2769 1073.161 -0.03397 684.8
Shallow water -0.1631 520.3602 -0.1786 602.7372 -0.10634 850.56
Coastal sea -0.30075 5876.6877 -0.4078 6826.283 -0.32387 4679.04
Sea -0.32278 2010.7278 -0.3621 2105.498 -0.34107 4030.08
Table 5.2: Details of classification signatures with their NDVI values and area in Hector.
24
No. Scientific name Common Name
1. Ficus bengalensis Bat
2.
Ficus religiosa Aswath
3.
Syzigium cumini Jam
4.
Prosopis juliflora Kanta babla
5.
Acacia nilotica Babul
6. Thespetia pupalnea Habul
7. Strichnos potatorum Makal
8. Cocos nusifera
Coconut
9.
Mimusops elingii Bakul
10.
Borasus flabelicum Palm
11.
Pongamea glabra ---
12. Casuarina equisitifolia Jhau (on Dune)
13. Pandanus sp.
Kea (Back mangrove)
14.
Spinifix littoralis (Dune vegetation)
Table 5.3: The list of non-mangrove plants found in close association with
the mangroves in mixed jungle patches at Bhitarkanika.
25
Year Area Area Change Area Change Change
Land cover class 1989 1998 1989-98 2001 1998-2001 1989-2001
Mangrove 9799.68 8213.133 -1586.55 8746.03 532.899 -1053.65
Deg-man 2122.88 2261.941 139.061 2638.62 376.675 515.736
Succession 975.36 85.4388 -889.921 603.466 518.0274 -371.894
Mixed jungle 1670.4 2375.738 705.338 1746.76 -628.981 76.357
Non-mangrove 2683.52 5826.095 3142.575 3394.44 -2431.65 710.923
Dune vegetation 256 250.776 -5.224 515.111 264.3354 259.1114
Marsh vegetation 82.56 156.4434 73.8834 469.257 312.8136 386.697
Agriculture 7880.96 8156.708 275.748 8868.94 712.233 987.981
Aquaculture 0 54.6021 54.6021 239.695 185.0931 239.6952
Composite cover
Forest 11470.08 10588.87 -881.209 10492.8 -96.082 -977.291
Degraded/succession 3098.24 2347.38 -750.86 3242.08 894.7024 143.8422
Non-mangrove 3658.88 5911.534 2252.654 3997.91 -1913.62 339.0292
Cultural land 7880.96 8211.31 330.3501 9108.64 897.3261 1227.676
Table 5.4: The land cover area calculation on the basis of NDVI studies.
(Areas written in Hector.)
Land %
1989
Land %
1998
Change/yr
1989-98
Land %
2001
Change/yr
1998-2001
Change/yr
1989-2001
Mangrove 38.47 29.99 -0.94 32.13 0.71 -0.53
Deg-man 8.33 8.26 -0.01 9.69 0.48 0.11
Succession 3.83 0.31 -0.39 2.22 0.64 -0.13
Mixed jungle 6.56 8.68 0.24 6.42 -0.75 -0.01
Non-mangrove 10.54 21.28 1.19 12.47 -2.94 0.16
Dune veg. 1.01 0.92 -0.01 1.89 0.32 0.07
Marsh veg. 0.32 0.57 0.03 1.72 0.38 0.12
Agriculture 30.94 29.79 -0.13 32.58 0.93 0.14
Aquaculture 0 0.2 0.02 0.88 0.23 0.07
Table 5.5: The percentage of change in land cover area on the basis of NDVI studies.
26
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1989 1998 2001
% of
Area
Mangrove Deg_man Succession
Mixed jungle Non_mangrove Dune vegetation
Marsh vegetation Agriculture Aquaculture
Figure 5.1: The change in land cover area during 1989-2001 at Bhitarkanika
27
-25
-20
-15
-10
-5
0
5
10
15
20
25
1989-98 1998-2001
Ch
an
ge
in
are
a (
Sq
. k
ms
)
Forest Degraded forest Non forest Cultural land
Figure 5.2 : The profile of changes in land cover at Bhitarkanika
28
0
2000
4000
6000
8000
10000
12000
1989 1998 2001
Are
a (
ha
)
mangrove deg_man SuccessionMixed jungle Non_mangrove Marsh vegetationAgriculture
Figure 5.3: The trend of land cover changes at Bhitarkanika during 1989-2001
29
30
6. Conclusion
Bhitarkanika, the second largest mangrove patch of India, has been in the news headlines
for supporting one of the largest rookeries of endangered Olive Ridley turtles. But, it has
another matter of concern regarding its majestic floral and faunal diversity, which is
sometimes considered as one of the richest in India. Increasing human interference with
the increasing population pressure at the forest adjacent villages has been held
responsible for causing severe damages to the mangroves for its resourcefulness. Even,
the most predominant Estuarine crocodiles (Crocodylus porosus) are facing a threat of
habitat shrinkage over the rivers of and around Bhitarkanika.
The present study was designed with a goal of forest cover assessment with special
reference to mangrove and non-mangrove distribution. Time series analysis with the help
of vegetation index like Normalized Difference Vegetation Index (NDVI), revealed
yearly 0.5% loss in mangrove forest during 1989-2001. But, most interestingly, the rate
of loss was 0.9% during 1989 to 1998, which was reversed after the forest was declared
National Park. During 1998 to 2001 the mangrove regeneration rate was 0.7% a year,
which is perhaps indicating an honest effort of forest department of Orissa in preservation
of the valuable forest.
Although, the rate of forest destruction is much lower than the global average of 2%, the
reports of human interference within the forest premises and the trend of change in land
use and human lifestyle of adjacent areas are alarming for the mangroves. Mushrooming
of aquaculture ponds in expense of agricultural fields, over-fishing leading to a reduced
fish catch, unaccounted forest resource utilization by the locals etc. are driving the forest
ecosystem towards the upper limit of carrying capacity, exceeding which may create a
disastrous result for the ecosystem. An indication of such habitat threat was perceived
when 85% of crocodile population of the forest was found to be restricted in only 40%
river areas during 2000-01 crocodile census (Kar S., 2001).
However, besides the forest cover change assessment, the efficiency of Remote Sensing
techniques and supervised classification in such type of study was also assessed, which,
cropped a success. But, it has also been confirmed that, a ground truth verification and
knowledge based supervision is most important tool for such studies, which enhances the
accuracy levels a lot.
Thus, it can be concluded that use of band ratio analysis preceded by ground truth
verification and visual interpretation of imageries and followed by thorough revalidation
of the classified map with quite a large number of ground points is an useful integrated
process of such vegetation cover assessment study.
31
7. List of References:
Anon, (1999) Assessing Mangrove Leaf-Area Index (Lai) Using Casi Airborne Imagery,
Applications Of Satellite And Airborne Image Data To Coastal Management
(Session 2: Lession8), Bilko for Windows Module 7.
Anon, (1999) California Institute of Technology website (http://www.gps.caltech.Edu/~
arid/analysis/vegindex.html), Vegetation Indices, 1999.
Anon, (1999) Dictionary of Science, Oxford University Press.
Anon, (2003) Govt. of India Order on announcement of 13 new Wetlands of India to be
declared as RAMSAR sites (D.O. No. J-22012/68/89 – W (vol II); Ministry of
Environment and Forest, 16th
January.
Banerjee L.K. Rao, T.A. (2001) Flora of the Mahanadi Delta, Orissa (Flora of India
Series 4); Botanical Survey of India; pp. 307.
Bennet E. L., Reynolds C.J., (1993) The value of mangrove area at Sarawak,
Biodiversity and Conservation, 2, 359-375.
Campbell J. B., (1996) Introduction to Remote Sensing, 2nd
Ed. Taylor & Francis,
London, pp. 621.
Chadha S., Kar. C.S. (1998) Bhitarkanika: Myth and Reality; Natraj Publishers,
Dehradun.
Congalton, R. G., (1991) A review of assessing the accuracy of classifications of
remotely sensed data. Remote Sensing Environ., , 37, 34–46.
Das B.B. (1998) Present Status of Gahirmatha beach in Bhitarkanika Sanctuary, Orissa.
Marine turtle Newsletter, 79, 1-2.
Dutta C.B.S., Udayalakshmi V., Sadhasivaiah A. S., (1994) Role of Remote Sensing in
Forest Management, Proceedings of Asean Coeference on Remote Sensing -
Session Forestry.
Forest Survey of India, (1997) MoEF, Dehradun, State Forest Report,.
Forest Survey of India, (1999) MoEF, Dehradun, State Forest Report, 1999.
Forse J.L., Leach J.H., Choowaew S., Bishop I.D., (1997) Change detection analysis of
coastal land cover in Thailand using Landsat Thematic Mapper, Proceedings of
Northern Australian Geographic Information Systems (NAGIS) Conference,
Caims.
Harnandez-Cruz L.R., (2003) Remote Characterization of Black Mangrove areas in the
Estuarine bay of Bahia de Jobos, Puetro Rico, ADVANCE Institutional
Transformation Program, University of Puerto Rico at Humacao.
Web page: http://cuhwww.upr.clu.edu/~advance/participants%20Coloquio.htm.
32
Huete A. R., Liu H.-Q., & van Leeuwen, W. J. D. (1997) The use of vegetation indices
in forested regions: Issues of linearity and saturation. In Proceedings of IGARSS
'97 - International Geoscience and Remote Sensing Seminar (Vol. 4, 1966-1968).
Noordwijk, The Netherlands: ESA Publications.
Huete, A. R., Liu, H.-Q., & van Leeuwen, W. J. D. (1997) The use of vegetation indices
in forested regions: Issues of linearity and saturation in Proceedings of IGARSS
'97 - International Geoscience and Remote Sensing Seminar (Vol. 4).
Noordwijk, The Netherlands: ESA Publications. pp. 1966-1968.
Jagtap T. G., Untawale A. G., Inamdar S.N., (1994) Study of mangrove environment of
Maharashtra coast using Remote Sensing data, Ind. J. Mar. Sci. vol 23, June
1994. 90-93.
Jagtap T.G., A.G.Untawale, V.S.Chavan. (1993) Synopsis: Mangrove Ecosystem in
India; A need for protection. Ambio, vol. 22(4) June 1993.
Jordan, C. F. (1969) Derivation of leaf area index from quality of light on the forest
floor, Ecology_, vol. 50,. pp. 663-666.
Kanvinde H. S., (1997) Gender Dimensions in Biodiversity Management : India, Report
submitted to FAO Regional Office for Asia and Pacefic, Bangkok, Thiland. June
1997.
Kar S.K., Kar C.S., (2002) Bhitarkanika Mangrove Ecosystem and its Biodiversity – An
overview, Proceedings of National Workshop on Mangrove Conservation and
Restoration, 70-76.
Kar Sudhakar (2001) Annual Census of Salt water Crocodiles in Bhitarkanika Wildlife
Sanctuary. Crocodile Specialist Group newsletter vol. 20. No.3, July-Sept. 2001,
pp. 57-58.
Kathiresan K., Rajendran N., (2003) Conservation and management of mangrove
ecosystem in India, Seshaiyana, vol. 11, No. 1, p. 1-4.
Kumar R., (2000) Conservation and management of mangroves in India, with special
reference to the State of Goa and the Middle Andaman Islands, Unasylva (203),
Vol. 51- 2000/4.
Lillesand T.M., Kiefer R.W., (2000) Remote Sensing and Image Interpretation, 4th
Ed.,
John Wiley & Sons, Inc.
Lillesand, T. M. and Kiefer, R. W. (1987) Remote Sensing and Image Interpretation, 2nd
edition, John Wiley and Sons, New York, Chichester, Brisbane, Toronto,
Singapore, 721 p.
Md Seeni Ibrahim0, Salleh Azhar Jj., (1992) Detecting forest areas and crops using
vegetation indices, Proceedings of Asean Conference on Remote Sensing -
Session Agriculture/forestry.
33
Meza Diaz B., and Blackburn, G. A., (2000) The Relationships between Mangrove LAI
and Broadband and Derivative-Based Spectral Vegetation Indices: Evidence
from a field study, 26th
Annual Conference of Remote Sensing Agency,
University of Leicester, UK.
Mitra A., Pal S., (2002) The Oscillating Mangrove ecosystem and the Indian
Sundarbans, WWF, pp. 112.
Mitra R. (2004) Forest cover changes in the Bhitarkanika Wildlife sanctuary, Orissa,
Proceedings (Abstract) of Environmental Science section of 91st Session of
Indian Science Congress, Chandigarh. p. 13.
Mitra R., Hazra S., Santra S.C., (2004) ‘Mangrove distribution study using Remote
Sensing techniques, at Bhitarkanika National Park, Orissa’, International
Conference on Biogeochemistry of Estuaries-Mangroves and the Coastal Zone
Management, JNU, New Delhi, ENVIS Centre in Biogeochemistry Newsletter,
Vol. 9 No. 4. p. 25.
Mohanti M, (1998) Tropical coastal wetlands, Orissa coast, Eastern India and
management perspective: An overview, Proceedings of 5th
International
conference on Remote Sensing for marine and coastal environment.
Mohanty P. K., Pal, Supriyo Ranjan. Mishra Pravakar. Takashige Sugimoto, (2001),
Environmental Changes along Orissa Coast, Bay Bengal: Its monitoring and
management; Proceedings of The 5th
International Conference on the
Environmental Management of Enclosed Coastal Seas.(EMECS-Abstracts).
Moorthy P., and Kathiresan K. (1999), Photosynthetic efficiency in Rhizophoracean
mangroves with reference to compartmentalization of photosynthetic pigments.
Revista de Biologia Tropical. March/June 47(1-2): 21-25.
Naskar K R., Mandal R N., (1999), Ecology and Biodiversity of Indian Mangroves, Part
–I, Daya Pub. House, Delhi, pp. 361.
Nayak A. K., (2000) Conservation and Management of Mangrove forests of
Bhitarkanika, Orissa, Proceedings of National Workshop on Mangrove
Conservation and Restoration, pp. 30-42.
Nayak S., Bahuguna A. (2001), Application of Remote Sensing data to monitor
mangroves and other coastal vegetations of India, Indian Journal of Marine
Sciences, 30 (4), 195-213.
Nayak S.R., Sarangi R.K., Rajawat A.S., (2001) Application of IRS-P4 OCM data to
study the impact cyclone on coastal environment of Orissa, Current Science, vol.
80, No 9, pp. 1208-1212.
Philippe M., Valory G., Etienne B. (2000), Mapping the forest cover of Madagascar
with SPOT 4- VEGETATION data, Proceedings of Vegetation - 2000, Lake
Maggiore, Italy.
Richardson, A. J. , Everitt, J. H. (1992) Using spectra vegetation indices to estimate
rangeland productivity, Geocarto International_, vol. 1, pp. 63-69.
34
Richardson, A. J., Wiegand, C. L. (1977) Distinguishing vegetation from soil
background information, Photogrammetric Engineering and Remote Sensing, vol.
43, pp. 1541-1552.
Roberts M., Wells C., Doyle Thomas W. (1994), Component Analysis for Interpretation
of time series NDVI imagery, Proceedings of American Society for
Photogrammetry and Remote Sensing, Annual Convention & Exposition.
Roy P. S., (IIRS) (2002), Biodiversity characterization at landscape level in North-East
India using Satellite Remote Sensing and Geographic Information System; Jt.
Project of Dept. of Biotechnology and Dept. of Space.
Roy P.S., Miyatake S., Rikimaru A. (1997), Biophysical Spectral Response Modeling
Approach for Forest Density Stratification, Proceedings of Asean Conference on
Remote Sensing (Session Forestry).
Rudel T.K., Flesher K., Bates D., Baptista S., Holmgren P. (2000), Tropical
deforestation literature: geographical and historical patterns, Unasylva (203),
Vol. 51.
Selvam V., Ravichandran K. K., Gnanappazham L. and Navamuniyammal M. (2003),
Assessment of community-based restoration of Pichavaram mangrove wetland
using remote sensing data, Current Science, Vol. 85, No. 6, 25 Sept.2003.
Singh A. (1989) Digital change detection techniques using remotely-sensed data,
International Journal of Remote Sensing, vol. 10, 989-1003.
Singh S., Agarwal S., Joshi P.K., Roy P.S. (1999), Biome level classification of
vegetation in Western India – An application of Wide Field View Sensor (WiFS),
Proceedings of Joint Workshop of International Society for Photogrammetry and
Remote Sensing (ISPRS) Working Group I/1, I/3 & I/4 on Sensors and Mapping
from Space.
Singh, H.S. (2002) Marine Protected Areas of India: Status of Coastal wetland
conservation. World Comission on Protected Areas publication, January 2002.
Upadhyay V. P. Ranjan Rajiv, Singh J. S. (2002), Human–mangrove conflicts: The way
out, Current Science, Vol. 83, No. 11, pp.1328 – 36.
van Leeuwen W. J. D., Laing T. W., & Huete A. R. (1997) Quality assurance of global
vegetation index compositing algorithms using AVHRR data. In Proceedings of
IGARSS '97 - International Geoscience and Remote Sensing Seminar (Vol. 1,
341-343). Noordwijk, The Netherlands: ESA Publications.