DictionaryKnowledge Based Classifier for Wetland Features Extraction Using MODIS Data: A Case Study in Gujarat

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1 Sengupta, M. and Dalwani, R. (Editors) Proceedings of Taal2007: The 12 th World Lake Conference: DictionaryKnowledge Based Classifier for Wetland Features Extraction Using MODIS Data: A Case Study in Gujarat Reshu Agarwal 1 and J.K. Garg EFD/AFEG/RESA, Space Applications Centre (ISRO), Ahmedabad Corresponding author: reshuagr@hotmail.com ABSTRACT Wetlands have received considerable interest globally during recent years due to their ecological significance and biodiversity values. Mapping of wetlands is carried out using remotely sensed data from various sensors data and techniques. Present paper describes a knowledge-based classifier to identify the wetlands in Gujarat state using Moderate Resolution Imaging Spectroradiometer (MODIS) sensor data onboard Terra satellite. Spectral knowledge of Blue, Red and NIR bands in conjunction with Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) have been used to define the thresholding ranges for wetland classes (water, mud flats, aquatic vegetation and salt flats). A comparative study of the performance of this classifier in comparison to two traditional classifiers namely Minimum distance to mean, and Maximum Likelihood classifiers was carried out for a small area. Results of the classification indicates better performance of knowledge based classifier in terms of overall as well as kappa statistic which are 84 and 0.79 respectively. The results indicate the feasibility of the development of a generalized knowledge based classifier for automated extraction of wetlands at regional scale using coarse spatial resolution data by using optical as well as thermal characteristics of the targeted area. Keywords: Land Surface Temperature, Normalized Difference Vegetation Index, spectral knowledge, thermal bands. INTRODUCTION Wetlands are important parameters for environmental modeling and inputs to earth system process including biogeochemical cycling studies. A number of classifiers and techniques have been attempted to classify the wetlands using satellite remote sensing data. The choice of a particular classifier depends on the nature of the input data and the desired output. Proper addition of ancillary data to a spectral data can lead to better class distinction (Strahler et al., 1978; Hutchinson, 1982; Trotter, 1991; Jensen, 1996). Use of ancillary data for classification does not incorporate any additional data to the actual classification system but increases the of classification. Butera (1983) used Landsat and aircraft MSS data to delineate Roseau cane from mangrove and Landsat MSS only for extraction of wetlands. Knowledge base system by Zukar and Mohammad (1978) comprises 29 rules of image context and 23 rules of geographic context of the pixel. Their method resulted in a classification improvement of 13.2 percent compared with that of maximum likelihood classification algorithm. Study by Lambin and Ehrlich (1996) based on analysis of AVHRR data of 10 years has indicated that combined use of surface temperature (T s ) and NDVI improves the mapping and monitoring of land cover at broad scale. Spatial knowledge coupled with spectral knowledge has been used to improve the classification for mangroves mapping using high-resolution SPOT data by Gao et al (2004). Villeneuve and Julie (2005) have developed a suitability model to delineate the wetlands using four sub models i.e. hydrology, soil, vegetation and high altitude. Zambon et al., (2006) utilized Classification Tree Analysis (CTA) using S-Plus statistical software to construct decision tree by incorporating training data as input. Three data types (Landsat 7 ETM+, IKONOS and PROBE-1) have been classified using splitting rules (available in CTA) including Gini, class probability, towing and entropy. All splitting rules basically segregate the data. It has been observed that for LANDSAT and IKONOS datasets class probability rule give overall high while for hyperspectral image (PROBE-1) Gini rule is optimal method for classification. Most of the work summarized above involves use of high-resolution data and may be used for local and regional inventories. This paper describes a knowledge-based classifier to extract wetland features using thermal and optical coarse resolution remotely sensed MODIS data. STUDY AREA The state of Gujarat has been chosen for investigation due to covering diverse classes of wetlands (water, aquatic vegetation, salt flats, mud flats). Gujarat is situated in the west coast of India between 20º2 and 24º41 N latitude and 68º8 and 74º23 E longitude. It is the seventh largest State in India with 1,95,980 sq.km

2 area out of which sq.km are covered by wetlands (Garg et al., 1998). Figure 1 shows the location of the study area in India. Mud Flats from Salt Flats Mud Flats from High Moisture Salt Flats from High Moisture Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes METHODOLOGY Preprocessing of MODIS data, LST and NDVI Estimation Figure 1. Study area DATA USED MODIS (Moderate Resolution Imaging Spectroradiometer) and IRS-P6 AWiFS (Advanced Wide Field Sensor) data acquired on October 6, 2005 over Gujarat, India have been used in the present research. Visible bands (BLUE, RED and NIR) and two thermal bands with 1km resolution of MODIS data have been used for classifier development. IRS AWiFS data (three visible bands: GREEN, RED and NIR) of a small area was used to evaluate the performance of the classifiers. Detailed characteristics of the sensors and data used are given in Table 1. Table1. Detailed Characteristics of data used Classes/Layers LST NDVI RED NIR BLUE Water from aq. Yes Yes No Yes No veg Water from Mud Flats No Yes Yes Yes Yes Water from Salt Yes No Yes Yes Yes Flats Water from High Moisture Yes Yes No Yes No Aq. veg from Yes No Yes Yes Yes Mud Flats Aq. veg from Salt Flats No Yes Yes Yes Yes Aq. veg from Yes No No No No High Moisture MODIS provides images of daylight reflection and day/night emission of the earth surface using a whiskbroom scanner to achieve 2330 km wide swath with a maximum scan angle of ± 55 0 on either side of orbital path. This very large angle causes the instantaneous field of view to increase from 1x1 km at nadir to almost 2x5 km at maximum scan angle. The increase in IFOV produces an overlap of adjacent scan angle (bowtie effect), which causes repetition of features at every 10 th scan line. To correct this effect bowtie correction has been applied on the MODIS image using ENVI 4.2 software. Subsequently, data was georeferenced using geographic Lat/Long projection with WGS 84 Spheroid. Land Surface Temperature (LST), one of the major parameters in the classifier development, has been estimated using split window method of first order approximation (Agarwal and Garg, 2006). This method estimates the temperature of land surface in two thermal bands 31(10.78µm 11.28µm) and 32 (11.77µm 12.27µm) using the complete range of emissivities for all land cover types. Normalized Difference Vegetation Index (NDVI) has been calculated using Red and NIR bands of MODIS data as a second parameter for the classifier development. Knowledge Based Classifier Development Knowledge based classifier is a decision tree classifier which does not presuppose any assumption related to the distribution of the data. This classifier uses the additional knowledge which can be of any type related to digital elevation model, hydrological information, spatial data, thermal data etc. depending on the interest of the researcher. Using the information contained in the added ancillary layer(s), decision rule can be developed to classify the classes of interest. The main emphasis of the present study is to extract the wetland classes from coarse resolution (MODIS) data. Major wetland classes, included in the study are water, aquatic vegetation/ marshes, mud flats and salt flats. As is well known spectral characteristics of any object play a key role to identify that object. Accordingly, reflectances in Blue, Red and NIR bands have been used along with LST and NDVI to extract 648

3 wetland features. In this regard threshold ranges of LST, NDVI and radiances in Blue, Red and NIR bands were finalized by trial and error method using known targets and classifier developed. Figure 2 gives complete decision tree showing the ranges for all wetland classes. Table 2: Separation between classes using LST, NDVI, reflectance in RED, NIR and Interpretation) BLUE bands (By Visual Characteristics MODIS IRS-AWiFS Altitude 705 km 817 km Bands 36 4 Spatial Resolution 250 meter (band 1 and 2) 56 m 500 meter (band 3-7) 1000 meter (band 8-36) Radiometric Resolution 12 bits 10 bit Temporal resolution 1-2 day 24 days revisit and 5 days repetivity Swath dimension 2330 km 370 km 20 C< X <30ºC 25 C< X <30ºC (-0.4)< X ij2 < 0.3< X ij 2 <0.6 Water 500< X <1000 Aq.Veg 800< X < < X ij 4 < < X ij4 < < X < < X < C< X <28ºC 25ºC< X <30ºC 0.25< X ij2 <0.4 X 0.1< <0.3 ij2 Mud Flats 2000< X <3000 Salt Flats 5000< X < < X ij4 < < X ij4 < < X < < X <1200 Figure 2. Essential conditions for classification Performance evaluation The performance of the knowledge based classifier was evaluated with respect to two other classifiers viz: Maximum Likelihood Claasifier (MLC) and Minimum distance to mean ( MDM) using Blue, Red and NIR bands. For this, a small area was selected. IRS AWiFS data was taken as the refernce data to delineate training sites for various wetland classes. The performance of the clasifiers were evaluated for these sites. Separability analysis has been carried out by the analysis of the discrimination capabilities of all the input layers. Accuracy was evaluated in terms of producer s and user s and Kappa value. RESULTS AND DISSCUSSION Knowledge based classifier has been developed for extracting wetland features using MODIS data incorporating LST, NDVI and spectral radiances in three bands (RED, NIR and BLUE). Figure 2 shows the decision tree representing the essential ranges of all input 649

4 parameters for the wetland features classified. It is clear from the decision tree that water has temperature within 20 C to 30 C while aq.veg/marshes and salt flats have temperature ranges of 25 C - 30 C while mudflats have temperature range of 22 C - 28 C. Variability of NDVI is also noticeable for all the classes. Similarly, Blue, Red and NIR bands also play a vital role in classifier development. It should be mentioned here that though performance of SWIR band in water body s delineation is good but it has not been found promising for extraction of other wetland features. Hence, it was not included in the classifier. Table 3. Confusion matrix for Knowledge based classifier of training sites Classes Reference data Water Aq. Mud Salt Veg Flats flats Others Total Water Aq.Veg/Swa mp Mud Flats 7 7 Salt flats Others Total Figure 3 shows the pattern of radiance in RED, NIR and BLUE bands of all concerned wetland features. It is seen in Figure 3 that reflectance of water, salt flats and mud flats is highest in Blue band while for aquatic vegetation/marshes, it is highest in NIR band. Graphs in Figure3 give the pictorial representation of the separability capabilities and Table 2 gives the tabular interpretation of separability for five layers Bands Salt Flats Mud Flats Aq.Veg/Sw a mp Water Figure 3. Radiances of Water, Mud Flats, Aquatic Vegetation and salts flats in RED, NIR and BLUE bands. A separability analysis has been performed using visual interpretation to see the contribution of all factors in the classification (Table 2). It is clear from Table3 that LST is capable of separating all the classes except water from mud flats and aquatic vegetation from salt flats. These classes are separated by NDVI. Some of the classes, which are not differentiated by NDVI, can be separated by the combined use of RED, NIR and BLUE band coupled with LST. Similarly, NIR has been observed as an important layer, which can classify all layers except water from high moisture and aquatic vegetation from high moisture. These classes can be extracted using LST and NDVI, LST and NDVI, RED and BLUE respectively. Likewise, the classes that are not classified using reflectance in BLUE channel can be classified using information from other layers. On the basis of the separability performance, it has been found that the separability of NIR band is highest followed by LST. Once classifier has been developed, classified results were compared with that of the same dataset classified by Minimum distance to mean classifier and Maximum Likelihood classifier and results are shown in Figure 4. Minimum Distance to mean classifier and MXL classifiers are well known and widely used supervised classifiers (Lillesand and Kiefer, 1999) to classify remotely sensed data. Comparative analysis of classified images shows that minimum distance to mean classifier and MXL classifiers are not able to extract water and aquatic vegetation due to pixel mixing while knowledge based classifier improves this classification due to LST and NDVI variations. Similarly, water and mud flats appear merged in classified image generated using supervised classification while Knowledge based classifier differentiated both the classes. In case of salt flats, minimum distance classifier is not extracting mud flats from salt flats and classifies this class as others while MXL classifier extracts this class as only mud flats. Knowledge based classifier clearly improves the classification by extracting both classes separately. This is due to the high spectral variation in all five layers for both classes. Once classifications were performed, their accuracies were subsequently estimated. Table 3 gives the confusion matrix which shows the distribution of the selected pixels into the concerned wetland classes by knowledge based classifier. According to this matrix it is clear that out of 10 pixels of water and aquatic vegetation, 9 are classified correctly while in case of mud flats 7 pixels are labeled accurately and two go in water. For salt flats 7 pixels are allocated to salt flats and 2 as others. Producer s and user s accuracies of all the three classifiers were also calculated (Table 4). 650

5 Table 4. Producer s and User s accuracies of all classifiers Classes Mean classifier MXL classifier Knowledge based classifier Producer s User s Producer s User s Producer s User s Water Aq.Veg Mud Flats Salt flats Others (a) (b) (c) (d) Figure 4. (a) MODIS FCC image of study area and Classified MODIS images of Gujarat using (b) Minimum Distance to Mean classifier (c) Maximum Likelihood Classifier and (d) Knowledge Based Classifier Table 5: Overall Accuracies and Kappa coefficients (for all Classifiers) Classifiers Overall classification () Overall Kappa statistic Min distance to mean classifier MXL classifier Knowledge Based classifier Overall of wetland features classified using minimum distance classifier is 76 (Table 5) which means that 76 of the image area has been correctly classified. Similarly, overall for MXL classifier and Knowledge based classifier are 74 and 84 respectively (Table 5). Kappa coefficient of Knowledge based classifier is Table 5 shows the kappa statistic for all classifier. Overall, producer s and user s accuracies along with kappa coefficient have been compared for all the features using all the four classifiers. Results obtained from knowledgebased classifier have been found very promising as compared to other classifiers used in the present work and also in other studies. 651

6 CONCLUSION This work has been carried out for development of a knowledge-based classifier to extract wetland features in Gujarat using MODIS data. Spectral reflectance knowledge in two visible bands (Blue and Red), one Infrared (NIR) band, NDVI and LST have been incorporated as the input parameters to finalize the conditions for wetlands differentiation. Subsequently, separability analysis has been carried out to check the differentiating capability of the classifier. Four wetland classes (water, aquatic vegetation, mud flats, salt flats) have been extracted from the MODIS data of October 6, 2005 using minimum distance, MXL and knowledge based classifiers. Accuracies and Kappa statistic obtained using all classifiers have been compared. It has been observed that overall and kappa coefficient for knowledge classifier is highest (84 and 0.79 respectively). Wetlands are dynamic features on the landscape and frequent monitoring using high-resolution remote sensing data is prohibitive and time consuming. In this regards on the basis of thermal and spectral properties it is possible to classify the wetland features. This study has shown that knowledge based classification can be a promising methodology for large area application of coarse resolution remote sensing data for wetland feature extraction and monitoring. However, further study using temporal data is required to study the robustness of the classifier. ACKNOWLEDGEMENTS Authors express their thanks to Dr. Ranganath R. Navalgund, Director Space Applications Centre, Ahmedabad for his keen interest and encouragement. Thanks are also due to Dr. Jai Singh Parihar, Deputy Director, Remote Sensing Applications Area and Dr. S. Panigrahy, Group Director, Agriculture, Forestry and Environment Group for guidance and critical evaluation. REFRENCES Agarwal, Reshu and Garg, J.K. (2006). A semi automated empirical model for estimation of methane emission from wetlands using coarse resolution thermal data" ISPRS & SIS Vol.36, Part 4, Geospatial databases for Sustainable Development, Goa, India, 2006, pp: Butera M.K. (1983). Remote sensing of wetlands. IEEE Transactions on Geosciences and Remote Sensing, GE- 21, pp: Gao, J., Huifen Chen, Ying Zhang and Yong Zha (2004). Knowledge Based approaches to accurate mapping of Mangroves from satellite data. Photogrammetry Engineering and Remote Sensing, Vol.70, No.11, pp: Garg, J.K., Singh, T.S., and Murthy, T.V.R. (1998). Wetlands of India. Project Report: RSAM/SAC/RESA/PR/01/98.239p. Space Applications center (ISRO), Ahmedabad. Hutchinson, C.F. (1982). Techniques for combining Landsat and ancillary data for digital classification improvement. Photogrammetry Engineering and Remote Sensing, Vol.48, pp: Jensen, J.R. (1996). Introductory Digital Image Processing: A remote sensing Perspective, Second Edition, Prentice Hall, Upper Saddle River, New Jersey.pp:316. Lambin, E.F. and Ehrlich, D. (1996). The surface temperature vegetation index space for land cover and land cover change analysis. Int. Journal of Remote Sensing, Vol.17, No. 3, pp: Lillesand, Thomas M. and Ralph W. Kiefer (1999). Remote Sensing and Image Interpretation. John Willey & Sons, fourth edition. Strahler, A.H., Logan, T.L. and Bryant, N.A. (1978). Improving forest cover classification from Landsat by incorporating topographic information. Proceedings of 12 th International Symposium on Remote Sensing of Environment, April, Manila Philippines. pp: Trotter, C.M. (1991). Remotely sensed data as an information source for geographical information systems in natural resource management: A review. Int. J. of Geographic Information System, Vol.5, pp: Villeneuve and Julie (2005). Delineatig wetland using GIS and RS technologies. Master Thesis, Texas A& M University: Zambon, M., Lawrence, R., Bunn, A. and Powell, S. (2006). Effect of alternative splitting rules on image processing using classification tree analysis. Photogrammetry Engineering & Remote Sensing, Vol: 72, No. 1, pp: Zucker, S. W., and Mohammad, J. (1978). Analysis of probabilistic relaxation labeling processes. Proceedings of the I.E.E.E. Conference on Pattern Recognition and Image Processing, Chicago, IL (New York: I.E.E.E.), pp: