OIF Based Indeces Oriented Ecological Classification Using LANDSAT TM Digital Data – A Case Study on

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International Journal of Remote Sensing Applications Volume 4 Issue 1, March 2014 doi: 10.14355/ijrsa.2014.0401.06

OIF Based Indeces Oriented Ecological Classification Using LANDSAT TM Digital Data – A Case Study on Beluchary and Dhulibasan Island Groups, Sunderban, West Bengal, India *Ratnadeep Ray*1, Ashis Kr Paul2, Balen Basu3 Department of Remote sensing and GIS, 2Department of Geography and Environmental Management, 1,2Vidyasagar University, 3Department of Oceanography, Jadabpur University 1,2Midnapore (West), 3Kolkata, West Bengal, India 1

*1

ratnadiprsgis@gmail.com; 2akpaul_geo2007@yahoo.co.in; 3balenbasu@gmail.com

Abstract The classification of vegetation from remotely sensed data has long attracted the attention of remote sensing community as the results are fundamental sources for many environmental applications. There are different approaches and techniquesto improve the classification accuracy.

However, different uncertainty or errors may be introduced into classification due to many factors like complexity in the landscapes under investigation, selected remotely sensed data, image processing approaches, the availability of reference data etc. So much efforts should be devoted to identify these major factors in the image classification processes and then to improve them. In the present study, different vegetation indices (VIs) have been adopted for the betterment of vegetation classification accuracy. The analysis of correlation and standard deviation of each VI was used to identify the best combination for the separability analysis.

The selection of the best combination was done using Optimum Index Factor technique based on the total variance within bands and correlation coefficient between bands. The OIF technique was applied to all the calculated seven VIs. A number of twenty one colour combinations were produced and analyzed using OIF. The combination having the highest OIF value has been selected for the classification in which a distinct spectral dissimilarity has been observed, which is very helpful for information extraction. Finally overcoming the spectral self similarity, after classification five ecological classes has been got from the Beluchari and Dhulibasan islands. Finally the technique of OIF has been successful in conclusively deriving the five ecological classes in Beluchari and Dhulibasan Islands by overcoming the spectral self similarly.

Keywords Optimum Index Factor (OIF); Vegetation Indices; LANDSAT TM;

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Image Classification

Introduction Landsat TM imagery is the most common data source for land-cover classification, and much previous research has explored methods to improve classification performance, including the use of advanced classification options such as neural network, extraction and classification of homogeneous objects (ECHO), object oriented classifiers, decision tree classifier, and subpixel-based methods (Lu et al.

2004a, Lu and Weng 2007; Blaschke 2010). However, the role of vegetation indices and textural images in improving land-cover classification performance is still poorly understood, in particular in moist tropical vegetated regions such as the Sundarban mangrove forest areas. Therefore, a mangrove dominated Beluchari and Dhulibasan islands group of the Sundarban was selected in this present study.

About the Study Area In this present study, mangrove dominated Dhulibasan and Beluchary island groups have been selected for the application. These are the part of Buffer zone of the Sundarban Biosphere Reserve (SBR) located at the Thakuran and Matla estuarine section.

The co-ordinate location of these island groups is 21045’ N to 21050’ N and 88031’ E to 88045’ E. Both of overlapping and non overlapping nature of mangroves are noticeable over here. The Sonneratia sp. maintains a unique identity of its own by its

International Journal of Remote Sensing Applications Volume 4 Issue 1, March 2014

appearance in the lowest intertidal zone and it exhibits a distinct non-overlapping character from the major cluster of other mangroves. Species diversity occurs in different parts of the islands, Excoecaria- AvicenniaPhoenix combination in the upper section of the island, Avicennia – Aegialitis- Rhizophora in the middle section and Avicennia- Ceriops- Rhizophora combination in the lower section of the islands.

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OIF = Max (

∑ in=1σ (i ) ) ∑ nj =1 r ( j )

Where, OIF is the optimum index factor. σ (i) is the standard deviation of k band. r (j) is the correlation matrix value.

The combinations having the highest OIF value have been selected to generate signature set for the classification (table: 4). But in spite of this signature mixing defects the feature extraction accuracy though visually it is fantastic. So to get the distinction between the features a ratio has been calculated between NDVI and TC2 and from this ratio image 5 masks have been picked up for respective 5 ecological classes. Finally these masks are used for signature generation during the classification.

TABLE 1 VEGETATION INDICES

VIs PVI1

Formula {(b*NIR)-RED+a}/sqrt{(b*b)+1} 0.304TM1 + 0.279TM2 + 0.474TM3 + 0.559TM4 + 0.508TM5 + 0.186TM7 −0.285TM1 − 0.244TM2 − 0.544TM3 + 0.704TM4 + 0.084TM5 − 0.180TM7 (TM4 − TM3)/(TM4 + TM3) (TM4 +TM5−TM2−TM3)/(TM4+TM5+TM2+TM3) (TM4+TM2−TM5−TM7)/(TM4+TM2+TM5+TM7) (2 × TM4 − TM5 − TM7)/(TM4 + TM5 + TM7)

TC1 TC2 FIG. 1 LOCATION OF THE STUDY AREA

Materials and Methodology of The Study In this present study, LANDSAT TM digital data (P/R 138/04) was used which has been processed in the TNT mips Pro environment. Conventionally, it is seen that in case of LANDSAT TM digital data for digital classification 4-3-2 band combination is used to get the Standard False Colour Combination. But sometimes using this combination, the spectral self similarity of the objects results is inaccurate in the classification. So if an optimum band combination can be selected, that kind of happening can be avoided. In the present study for classifying the image ecologically, seven types of VIs (table: 1) like Perpendicular Vegetation Index 1(PVI1); Tasseled cap Transformation Index 1, 2 (TCT 1,2); Normalized Difference Vegetation Index (NDVI); Landsat TM band 4,5-2,3, 4,2-5,7,and 4-5,7 related Indices have been calculated and those indices have been considered as individual bands. From those indices each, firstly some general statistics like S.D. and Mean have been calculated (table: 2) and then a correlation coefficient matrix has been generated (table: 3). After these approaches, mathematical ‘combination’ concept has been used to get different combinations like 1-2-3, 2-1-3, 3-2-1 etc. For these combinations, lastly, the OIF has been calculated to get the best combination (table: 4). The OIF can be calculated using the following formula:

NDVI 45_23 42_57 4_57

TABLE 2 GENERAL STATISTICS OF SELECTED RASTERS

sl no 1 2 3 4 5 6 7

index pvi1 tc2 tc1 ndvi 45_23 42_57 4_57

sd 6.93749 4.00115 6.61635 0.107335 0.0912387 0.0902415 0.160982

Mean 52.0341 12.6509 86.3202 0.376318 0.238147 0.296068 0.634766

TABLE 3 CORRELARION MATRIX OF SELECTED RASTERS

VIs 1 2 3 4 5 6 7

1 1 -0.93 -0.74 0.21 -0.17 0.97 0.46

2 -0.93 1 0.79 -0.27 0.09 -0.91 -0.85

3 -0.74 0.79 1 0.17 0.50 -0.71 -0.50

4 0.21 -0.27 0.17 1 0.91 0.26 0.60

5 -0.17 0.09 0.50 0.91 1 -0.13 0.24

6 0.97 -0.91 -0.71 0.26 -0.13 1 0.92

7 0.46 -0.85 -0.50 0.60 0.24 0.92 1

Result and Discussion The products of vegetation vis-s-vis forest cover mapping derived from remote sensed images should be objectively verified and communicated to users so that they can make informed decisions on whether and how the products can be used. Result evaluation, a procedure also called accuracy assessment, is often employed to determine the degree of ‘correctness’ of 57

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International Journal of Remote Sensing Applications Volume 4 Issue 1, March 2014

the classified vegetation groups compared to the actual ones. A land cover map (ecologically classified) derived from image classification is considered accurate if it provides a true representation of the region it portrays. Four significant stages have been witnessed in accuracy assessment methods (Congalton 1994). Accuracy assessment in the first stage was done by visual inspection of derived maps. This method is deemed to be highly subjective and often not accurate.

The second stage used a more objective method by which comparisions of the area extents of the classes in the derived thematic maps (e.g. the percentage of a specific vegetation group in area) were made with the corresponding extents on ground or in other reference dataset. However, there is a major problem with this non-site-specific approach since the correct proportions of vegetation groups do not necessarily mean the correct locations at which they locate. In the third stage, the accuracy metrics were built on a comparison of the class labels in the thematic map with the ground data for the same locations. Measures such as the percentages of cases correctly (and wrongly) classified were used to evaluate the classification accuracy. The accuracy assessment at the fourth stage made further refinements on the basis of the third stage. The obvious characteristic of this stage is the wide use of the confusion or error matrix, which describes the fitness between the derived classes and the reference data through using the measures like overall accuracy and kappa coefficient. Additionally, a variety of other measures is also available or can be derived from the error matrix.

TABLE 4 WORKSHEET FOR CALCULATING

Combinations(according to sl.no) 321 421 431 432 521 531 532 541 542 543 721 731 732 741 742 743 751 752 753 754 426

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∑sd 17.55 11.04 13.66 10.72 11.02 13.64 10.70 7.13 4.19 6.81 11.09 13.71 10.77 7.20 4.26 6.88 7.18 4.25 6.86 0.35 4.19

Using the best combinations of vegetation indices on the basis of MXL classifier after classification, five ecological compartments were acquired(figure 2). The basic aim of this OIF based indices oriented classification is to get the discrimination between mangrove and salt marsh species. As per the principle of OIF, the combination 7-5-3 (4_57-45_23-tc1) has been selected for having high ∑sd and ∑r ratio value i.e. 28.09433603 (table: 4). High OIF values indicate bands (informational rasters i.e. the vegetation indices in the present study) that contain much "information" (e.g. high standard deviation) with little "duplication" (e.g. low correlation between the bands or informational rasters) by which it is easier to discriminate the near spectral similarities that are obvious in general classification using arbitrary band combinations. By using the OIF method, three band (informational rasters) colour composites can be evaluated on their effectiveness for display.

OIF

∑r OIF=(∑sd/∑r) -0.88 -19.86 -0.99 -11.14 -0.35 -38.57 0.69 15.47 -1.01 -10.84 -0.42 -32.03 1.39 7.70 0.95 7.48 0.73 5.74 1.58 4.28 -1.31 -8.45 -0.77 -17.62 -0.55 -19.39 1.29 5.581 -0.52 -8.13 0.27 24.85 0.53 13.42 -0.51 -8.32 0.24 28.09 1.76 0.20 -0.92 -4.51

FIG. 2 DIGITAL CLASSIFICATION OF ECOLOGICAL COMPARTMENTS

Table 5 represents the result of supervised classification from OIF based best combination. The Khat statistics indicates substantial agreement by a

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value of 97.55 percent and overall accuracy with 98.18 percent. In this classification, all the classes have shown the balanced producer’s and user’s accuracy.

TABLE 5 ERROR MATRIX

Name T1 T2 T3 T4 T5 Total U. A. C1 1747 5 0 0 0 1752 99.71% C2 18 697 26 0 0 741 94.06% C3 0 11 708 9 0 728 97.25% C4 0 0 8 924 6 938 98.51% C5 0 0 0 4 530 534 99.25% Total 1765 713 742 937 536 4693 99.71% P. A. 98.98% 96.76% 95.42% 98.61% 98.88% 1752 94.06% Overall Accuracy = 98.15% Khat Statistics = 97.55%

In this table 5 C1, C2....... C5 is the land cover classes, T1, T2......... T5 is the training sites for the land cover classes, U.A. is the user’s accuracy and P.A. is the producer’s accuracy.

Conclusion For any kind of environmental applications from remote sensing digital dataset, band selection is an important issue. The band selection technique is of two types: statistical band selection and target oriented band selection technique. In this present study, those two approaches have been combined, from which the target spectral signature relevant spectral ranges have been localized firstly and after that the most informative three band groups have been selected for colour composition by applying statistical technique and by this the final objective of deriving the ecological classes was successfully derived. The OIF is an important statistical technique to select the low correlation band combination having much information. In this present study, the combination having the OIF value of 28.09433603 has been selected to avoid the spectral self similarity vis-àvis for ecological classification and using this best combination, after classifying the composite 98.15% overall accuracy has been derived. After all, it can be said that this method can be successfully used and there is chance to have class similarity by mathematically removing the natural fuzziness.

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Paul, Ashis Kr,Coastal Geomorphology and Environment – Sundarban

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Subarnarekha Delta Plain. ACB Publications, P757 Block – A, Lake Town, Kolkata 89 Ren, D. and M.G. Abdelsalam, 2001. Optimum Index Factor (OIF) for ASTER data: examples from the Neoproterozoic Allaqi Suture, Egypt. In: Proceeding of the Geological Society of America (GSA), Annual Meeting, Boston, USA,Paper No. 123-0, November 5-8.

RATNADEEP RAY has received M.sc Degree in Remote Sensing and GIS from Vidyasagar University, Midnapore (West), West Bengal. Now he is doing Ph.D research work and engaged in teaching in department of Remote Sensing and GIS, Vidyasagar University. Mr. Ray has published several research articles in international journals and a book

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International Journal of Remote Sensing Applications Volume 4 Issue 1, March 2014

named “An introduction to Photogrammetry” on behalf of Mitram Publication, Kolkata. Mr. Ray may be reached at the e-mail id ratnadiprsgis@gmai.com.

DR. ASHIS KR. PAUL has received M.A. degree in geography from University of Calcutta and Ph.D. degree from the same university. He is an eminent Coastal scientist.

He has published ample amount of research articles on coastal geomorphology and environment in different national and international journals and published an important book on coastal geomorphology named “Coastal geomorphology and Environment” on behalf of ACB publication, Kolkata. He can be reached in akpaul_

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geo2007@yahoo.co.in.

DR. BALEN BASU has received his M.Sc. Tech degree from ISM, Dhanbad in Geo-Physics, and Ph.D. degree from department of Geology, Jadavpur University, Kolkata. He has a great expertise in the field of Remote Sensing and GIS. Now he is the visiting faculty of department of geology and oceanography, Jadavpur University, Kolkata and the CEO of OPSIS SYSTEM, Kolkata, which deals with Remote Sensing and GIS related software and products. He has ample amount of national and international publications on Remote Sensing and GIS and Geo-Physics. He can be reached at balenbasu@gmail.com.