vegetation cover change analysis from multi-temporal...

Indian Journal of Geo-Marine Sciences Vol. 42 (3), June 2013, pp. 331-342

Vegetation cover change analysis from multi-temporal satellite data in Jharkhali

Island, Sundarbans, India

1Sudip Manna*, 2Partho Protim Mondal, 3Anirban Mukhopadhyay, 4Anirban Akhand, 5Sugata Hazra & 6Debashis Mitra

1, 3, 4 & 5: School of Oceanographic Studies, Jadavpur University, Jadavpur, Kolkata-700 032, India. 2 & 6: Indian Institute of Remote Sensing, (Indian Space Research Organization),

Government of India, 4 Kalidas Road, Dehradun-248001, India.

[E-mail: [email protected] , [email protected]]

(Received 12 September 2011; revised 25 April 2012)

Present study intends to quantify change of natural vegetation cover (mainly of mangrove forest) in Sundarbans Island between the time span of 2004-2010, when sustained efforts of a forestation and conservation has been in vogue. Vegetation indices like Normalized Difference Vegetation Index (NDVI), Global Environmental Monitoring Index (GEMI), Optimized

Soil Adjusted Vegetation Index (OSAVI) and Transformed Difference Vegetation Index (TDVI) have been used to decipher the measure of vegetation cover in this island and its changes during the period. Radiometric normalization technique is used to nullify various imaging condition anomalies while comparing multi-temporal data for change detection analysis. TDVI has been found to be more effective in vegetation cover change detection in such deltaic environment. Present study shows an overall net increase of vegetation cover in the island as a result of sustained conservation and plantation efforts.

[Key words: Vegetation, Conservation, Sundarbans, Mangrove, Spatiotemporal]

Introduction

Jharkhali being part of Sundarban group of islands (Delta of Ganges) is undergoing continuous changes.

Indian part of Sundarbans, the largest Mangrove

Forest on earth with an area of 9,630 km2, lies

between 21°32′–22°40′ N and 88°05′–89°00′ E.1 It

hosts a wide and diverse range of flora and fauna. The

Island is dominated by mangroves at periphery and also in the creeks with regular tidal influxes; mostly

comprised of Aveccenia sp., Aegiceras sp., Aegialites

sp., Bruguiera sp. etc. Along with mangroves, various

back mangroves and xerophytes e.g. Exoecaria sp., Thespesia populnia etc. cover the vegetated area.

Jharkhali island, previously part of the Namkhana

Reserve Forest, is presently habited by fishermen and farmers. A strong population of 1, 28,802

2 in the year

2001 might have increased to 1, 58,092 by the year

2011 in Jharkhali. Anthropogenic impact caused by incessantly increasing population might caused

around 16 km2

mangrove forest deforestation3, for the

purpose of settlement, agriculture and construction of

bheries (shallow water bodies for brackish water aquaculture). In spite of a significant reduction in land

area (around 86 km2) in Sundarban island system

during the past three decades4 the island Jharkhali

remained quite immune to coastal erosion, being 50 km inland from the southern sea front and

protected from all sides by other land masses.

Sustained conservation effort was taken at both government and community level in the form of

wide-spread plantation, reclamation of land by

natural succession and sincere effort to stop further degradation. There has been a reverse trend in

Jharkhali Island regarding declining forest cover

during the time period of 2008-2010 where the

mangrove area increased by 265 ha by plantation in and around the island, and in the Indian part

of Sundarbans 1610 ha of mangroves were planted

at 70 locations during 2007-2010.5 Though being

young plantations the increase doesn’t contributes

much to vegetation cover. Also an increase of

16 km2

of mangrove cover in entire West Bengal during 2005–2007 is reported by Forest Survey

of India6.

Our present study is aimed at capturing that

conservation effort in terms of increase in natural vegetation cover through multi-temporal change

detection study. Under the present circumstances

continuous spatiotemporal monitoring of the forest cover becomes a critically important element for

—————— * Corresponding author: Phone and fax no. 033-24146242

INDIAN J. MAR. SCI., 42, NO.3 JUNE 2013

332

sustainable management of such susceptible

ecosystem. Remote sensing can act as an effective

tool here, to facilitate collection of ground truth data and conducting scientific study over such relatively

inaccessible parts of the Sundarban delta. This study

is an effort to assess vegetation cover change with

special emphasis on mangrove vegetation, in the Jharkhali island of Indian Sundarbans, from the year

2004 to 2010.

Materials and Methods

The study was carried out at Jharkhali

(88°38′14.83″E to 88°47′38.74″E and 22°00′18.80″N to 22°12′29.18″N, Fig. 1) an island, part of the

Sundarbans deltaic ecosystem. For studying

the change in vegetation cover, cloud free images

of Landsat 5 TM (thematic mapper) sensor (Path 138/Row 45) dated 4

th November 2004 and

6th February 2010 were used. Satellite data used

have a spatial resolution of 30 meters (120 meters for Thermal band) with spectral resolution of

seven bands (0.45-0.52 µm Blue, 0.52-0.60 µm

Green, 0.63-0.69 µm Red, 0.76-0.90 µm Near IR, and 1.55-1.75 µm Mid-IR, 10.4-12.5 µm Thermal-IR and

2.08-2.35 µm SWIR).

Data Pre-processing

Calibration of raw sensor data to meaningful

physical units (e.g. reflectance) prior to any multi-temporal analysis was strongly recommended by

Duggin and Robinove, (1990)7. Raw images were

first calibrated to radiance and then reflectance with

the help of the satellite and sensor calibration parameters

8. For this study 2004 image was taken

as slave image and 2010 image as master. Both were

georeferenced to UTM projection with WGS 84 datum at zone north 45 and co-registered with

accuracy of 0.5 pixels. Though both the images

were taken by same sensor thus having similar

spectral response, relative radiometric normalization is necessary for multi-temporal change detection

study. Radiometric normalization is a linear first order

data transformation, which is applied to reduce the sensor and annual variability effects between multi

temporal datasets over the same geographic area9.

The 2004 image was radiometrically normalized with reference to the 2010 image following the

method of Jenson (1983)10

. Though a very old

method with certain drawbacks, but it is effective

for coastal, estuarine delta and islands where, the entire geomorphologic system is very dynamic and

permanent features are hard to find. Subsequently

Fig. 1—Study area

MANNA et al.: VEGETATION COVER CHANGE ANALYSIS FROM MULTI-TEMPORAL SATELLITE DATA

333

four vegetation indices (VIs) namely Global

Environmental Monitoring Index (GEMI),11

Normalised Difference Vegetation Index (NDVI),12,13

Transformed Difference Vegetation Index (TDVI),

14

and Optimized Soil Adjusted Vegetation Index

(OSAVI)15

were computed for assessment of

vegetation cover and various other land cover classes. All the indices used for the study were

developed based on differential spectral responses off

vegetation over Red and near Infrared spectrum. Individually they have different supremacy over

each other at different percentage vegetation cover

and soil types. GEMI reduces the effect of

atmospheric interferences and retains the information about vegetation cover with fewer or no anomalies

11.

NDVI is the most widely used VI for vegetation

cover and biophysical parameter analysis.16

OSAVI is having benefit that its formulation is simple

with no initial knowledge of soil type required and

the soil background variation is annulated17

whereas, TDVI performs better than NDVI and shows a linear

relation with vegetation cover percentage at high

canopy density.14

The mathematical formulation for them

Classification and accuracy assessment

Unsupervised classification was applied on the

vegetation indices images of both the years to

produce multiple user-defined classes. Initial

clustering was done using ISODATA (Iterative Self

Organizing Data Analysis Technique) algorithm to clump the images into 100 classes, which were

further generalized into 4 classes viz. (A) natural and

planted vegetation, (B) water, (C) mudflats, low-

lying area & inter-tidal zones and (D) settlement, agricultural field, aquaculture farms & unclassified

pixels. Classification accuracy was also estimated

using field verification data collected at 50 sites for four land-cover classes (Figure 2a). Application of a

SR normalization10

enabled estimation of the

classification accuracy of the 2004 image using 2010

ground data. An error matrix was formed calculating user’s and producer’s accuracy with an overall

accuracy for each of the four vegetation index

images for both years. Based on the classified maps, change detection matrix was generated to quantify

the change in natural vegetation cover within the

study period.

Results

Different vegetation indices (VI) calculated from the reflectance value showed different sensitivity in

demarcation of natural vegetation from other

land-use land-cover classes. The classified VI images are shown in Figure 3 and Figure 4 for year 2004

and 2010 respectively. Table 1 summarizes the

Fig. 2—a: Ground verification locations; b: Vegetation cover change map


334

Fig. 3—Classified vegetation Index images for year 2004


335

Fig. 4—Classfied vegetation Index images for year 2010


336

Table 1—Classified image based land cover composition for the

years 2004 and 2010

Class Indices 2004 AREA in ha

2010 AREA in ha

Vegetation GEMI NDVI

OSAVI TDVI

3523.05 2881.08

3287.97 3146.31

3648.78 3095.37

3393.45 3274.11

Water

GEMI NDVI

OSAVI TDVI

1481.31 1486.26 1474.02 1539.27

1281.15 1113.03 1115.28 1202.94

MUD flat, low lying

area, intertidal zones.

GEMI

NDVI OSAVI TDVI

1145.70

1197.54 1192.86 1887.3

1551.51

1155.42 1529.28 1915.65

Settlement, agricultural field, aquaculture farms and unclassified zones.

GEMI NDVI

OSAVI TDVI

11675.0 12260.2 11870.2 11248.8

11343.6 11787.0 12089.5 11432.3

Table 2—Classification accuracy and Kappa Statistics

Vegetation indices

Overall classification accuracy

Overall Kappa Statistics KHAT (K^)

GEMI 86.00 % 0.8108

NDVI 80.00 % 0.7247

OSAVI 84.00 % 0.7827

TDVI 88.00 % 0.8373

classification result for the both years. The table

shows that TDVI performs better than NDVI in

extraction of vegetation cover as NDVI tend to saturate at high density canopy cover, a typical

feature forest. The error matrix result is summarized

in Table 2. TDVI also fared superior in terms of

classification results with an overall classification accuracy of 88.00% and kappa statistics of 0.8373.

For the purpose of multi-temporal change detection

post-classification comparison approach was taken which is sometimes referred to as delta-classification.

It involves independently produced spectral

classification results of the time interval of interest, followed by a pixel-by-pixel or segment-by-segment

comparison to detect changes in cover type.

By adequately coding the classification results, a

complete matrix of change is obtained, and change classes can be defined by the analyst. As the two

dates of imagery are classified separately, thereby

minimizing the problem of radiometric calibration between dates.

18 Figure 5 (a), 5 (b), 5 (c) and 5 (d)

shows the results of change detection analysis

for the four VIs: GEMI, NDVI, OSAVI and TDVI

respectively. Change detection matrix for TDVI is given in Table 3. Though different results have been

obtained using different indices but net increase in

natural vegetation and plantations compensating

the forest loss by anthropogenic actions over the study period has been confirmed by all. A detailed

analysis of Table 3 shows that wherever vegetation

cover has decreased, it is due by conversion of mangrove forest zones into aquaculture farms and

in marginal areas by water ingression and coastal

erosion. Also in few places, vegetation cover

has decreased due to replacement by agricultural fields and human settlements indicating direct

anthropogenic intervention. Increased vegetation

cover indicates plantation and natural colonization by mangroves reclaiming swampy areas, accreted

mud flats and other sites supporting the conservation

efforts. An increase of 127.80 Ha in vegetation cover is found by change detection (TDVI). Few

areas with changes are marked in Fig. 5. A complete

change dynamic on the Island, depicted by the

vegetation in Table 4.

Discussion

Change detection study of the vegetation cover

in deltaic ecosystem is an important issue for

sustainable development with reference to the present day scenario of climatic change and

global warming. Our study is based on the central

question that whether there is any increase or decrease in vegetation cover in Jharkhali Island

within the study period. The outputs show the

varied sensitivity of different indices in

discriminating vegetation from other features. TDVI proved to be the most accurate method

for physical assessment of vegetation cover in

mangrove ecosystem due to its less sensitivity to saturation over NDVI. Soil background interference

in signature assortment was minimised by OSAVI,

hence the vegetation cover can be separated from others with good accuracy index. However,

GEMI and OSAVI seem to be over-estimating

vegetation coverage over other indices. As OSAVI

is better in delineating crop canopy cover, it might confuse agricultural land with natural mangrove

forest. GEMI tends to saturate at high LAI values

and perform poorly with respect to soil noise at low vegetation covers.

19 Its capability towards


337

Fig. 5a—GEMI based Change detection map


338

Fig. 5b—NDVI based Change detection map


339

Fig. 5c—OSAVI based Change detection map


340

Fig. 5d—TDVI based Change detection map


341

Table 3—Change detection matrixof TDVI based classification (2004-2010)

ClassesArea in ha. 2010 2004

Vegetation Water Mudflat, low lying areas, intertidal zones

Settlement, agriculture, aquaculture, unclassified

Vegetation Vegetation 929.7

Erosion and forest cover depletion 0.81

Erosion and forest cover depletion 97.29

Vegetation destruction and depletion. 2118.51

Water Accretion and natural

colonisation. 15.66 +ve

Water 1048.32

Accretion 374.58

Accretion, Anthropogenic

encroachment. 100.71

Mudflat, low lying areas, intertidal zones

Accretion and natural

colonisation. 91.53 +ve

Erosion 136.62

Mudflat, Low Lying Areas,

Intertidal Zones 863.46

Accretion, Anthropogenic encroachment. 795.69

Settlement, agriculture, aquaculture, unclassified

Natural colonisation, Plantation and

conservation. 2237.22 +ve

Unused land, unmanaged and low yielding Aq. farms. 13.86

Erosion, unmanaged and low yielding

Aq. farms. 580.32

Settlement, agriculture, aquaculture, unclassified.

8417.43

Table 4—Change in vegetation cover (2004-20100 derived from different vegetation indices

Vegetation

indices

No Change Vegetation

Area in ha.

Decreased Vegetation

cover Area in ha.

Increased Vegetation

cover Area in ha.

Net Change in Vegetation

cover. Area in ha.

GEMI 1091.34 2431.71 2557.44 + 126.00

NDVI 850.68 2030.40 2244.69 + 214.29

OSAVI 930.60 2297.79 2403.27 + 105.48

TDVI 929.70 2216.61 2344.41 + 127.80

neutralizing the atmospheric interferences is

proved effective in multi-temporal change analysis.

Result of this study shows that overall natural vegetation cover in the island has increased

offsetting the anthropogenic loss due to sustained

conservation efforts. Natural colonization of

mangroves in charlands and naturally accreted areas in and around the island is noticed.

Observations show that a good measure of mudflats

were colonised by mangroves naturally during the course and the process is continuing.

This increase is evident clearly and marked in

Figure 5d. In addition to that, plantation is done

by state forest department. Reduction of the vegetation cover in some parts of the island

during the study period was due to expansion

of aquaculture activities in violation to CRZ regulation and activities related with increasing

population pressure along with sporadic cases of

water ingression/erosion. Multi-vegetation-index approach adopted here for change detection study

of sensitive and dynamic ecosystems is found

to be appropriate as it requires relatively less amount

of field data, and similar assessment can be done on other important but inaccessible parts of

Sundarbans.

Acknowledgement

First author, Mr. Sudip Manna is grateful to

Department of Science and Technology for providing PURSE fellowship to carryout the research work.

References 1 Nandy, S. & Kushwaha, S. P. S., Study on the utility of IRS

1D LISS-III data and the classification techniques for

mapping of Sunderban mangroves, J. Coast. Conserv.,

15(2011) 123–137.

2 Indian Census, 2001, http://www.censusindia.gov.in/ accessed 15 June 2011.

3 Manna, S., Chaudhuri, K., Bhattacharyya, S., & Bhattacharyya, M.,. Dynamics of Sundarban estuarine

ecosystem: eutrophication induced threat to mangroves. Saline systems, 6:8 (2010) 1-16.

4 Hazra, S., Ghosh, T., Das Gupta, R., & Sen, G., Sea level changes in the Sundarbans, Sci. Cu,. 68(9-12): (2002) 309-321.

5 Sundarban Development Board (2010), Programme for regeneration of mangroves in the charlands of Sunarban by

the Social Forestry Division of Sundarban Development Board. Department of Sundarbans affairs. Government of West Bengal. Unpublished data.

6 India State of Forest Report, 2009, Forest survey of India, Ministry of Environment & Forests., Government of India.

7 Duggin, M. J., & Robinove, C. J., Assumptions implicit in

remote sensing data acquisition and analysis. Int. J. Remote.

Sens., 11 (1990) 1669–1694.


342

8 Chander, G., Markham, B. L., & Helder, D. L., Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 113(2009) 893–903.

9 Ya’allah, S.M., & Saradijian, M. R., Automatic normalization of satellite images using unchanged pixels within urban areas., Inf. Fusion. 6(2005) 235–241.

10 Jensen, J.R., Urban/Suburban Land Use Analysis. In R.N. Colwell (editor-in-chief), Manual of Remote Sensing, Second Edition, American Society of Photogrammetry, Falls Church, USA, (1983)1571-1666.

11 Pinty, B., & Verstraete, M.M., GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio. 101(1992) 15-20.

12 Rouse, J.W., Haas, R.H., Shell, J.A. & Deering, D.W., Monitoring vegetation systems in the Great Plains with ERTS. Third earth resources technology satellite-1 symposium.,1(1974) 10-14.

13 Leprieur, C., Kerr, Y. H., Mastorchio, S., & Meunier, J. C. Monitoring vegetation cover across semi-arid regions:

Comparison of remote observations from various scales, Int. J. Remote. Sens., 21(2000) 281−300.

14 Bannari, A., Asalhi, H., & Teillet, P.M., Geoscience and

Remote Sensing Symposium, IGARSS IEEE International,

5(2002) 3053 – 3055.

15 Rondeaux, G., Steven, M., & Baret, F., Optimization of Soil-Adjusted Vegetation Indices. Remote. Sens.Eenviron., 55(1996) 96-106.

16 Jiang, Z., Huete, A.R., Chen. J., Chen, Y., Li, J., Yan, G. &

Zhang, X., Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction, Remote.

Sens.Eenviron., 101 (2006) 366–378.

17 Steven, M. D., The Sensitivity of the OSAVI Vegetation Index to Observational Parameters Remote. Sens.Eenviron., 63(1998) 49–60.

18 Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B. & Lambin, E., Digital change detection methods in ecosystem monitoring: a review. Int. J. Remote. Sens., 25(2004) 1565-1596.

19 Qi, J., Kerr., Y., & Chehbouni, A., External factor consideration in vegetation index development. Proceedings

of Physical measurements and signatures in Remote sensing, Int. Soc. Photogramm. Remote. Sens., (1994)723-730.

vegetation cover change analysis from multi-temporal...

Documents