Phenology-based Vegetation Mapping with IRS-WiFS

1. Phenology based Classification Model for Vegetation Mapping using IRS-WiFS Shefali Agrawal, Sarnam Singh, P.K.Joshi and P.S.Roy Indian Institute of Remote Sensing, 4 Kalidas Road, Dehradun Introduction The establishment and implementation of procedures for vegetation classification has a long history using remotely sensed data from various sensors such as Landsat-MSS and TM, SPOT-XS, IRS-LISS-III and NOAA AVHRR. The techniques used for NOAA AVHRR based land cover classification are similar to those by other digital multispectral image, the only difference being the analysis uses a high frequency multi temporal data set. The earlier demonstrations of the suitability of AVHRR data set for large area land cover mapping have been reported by Tucker et al.(1985) for land cover classification of Africa and by Townshed et al. (1987) for south America land cover classification. These investigations showed that resampled AVHRR Global Area Coverage (GAC) data were an alternative to Landsat data for large area land cover mapping due to lower data volume, cost and higher temporal frequency. As a result a number of studies have been reported using coarse resolution AVHRR data to map on national to continental scales by using different classification algorithms viz. supervised and unsupervised methods on multi temporal data sets. Multi temporal remote sensing data are widely acknowledged as having significant advantages over single date imagery (Townshed et al., 1985). Mapping of land cover can be improved by using variations in phonological patterns of vegetation. Phenological differences are also very useful in the detection of large-scale vegetation disturbances. The use of multitemporal images not only results in higher classification accuracy but also gives consistent accuracy in all classes. Use of multitemporal data is especially advantageous in areas where vegetation or land use changes rapidly. This offers many opportunities for more complete vegetation description than could be achieved with a single image. For example, the differences between evergreen and deciduous trees can be highlighted by the fact that the former may appear quite uniform throughout the year, whereas the latter varies widely between leaf-on and leaf-off periods. The discriminant power of multitemporal observations is based on their characterization of seasonal dynamics of vegetation growth (phenology).

2. Classification Methods Traditional classification methods such as supervised and unsupervised methods of multispectral image classification depends on spectral reflectance. Land cover classification using remote sensing is based on the assumption that different types of land cover have distinct reflectance properties. The unique spectral properties of a land cover class is governed by canopy geometry, leaf densities, colors, optical properties and moisture content, shadow components, transpiration rates, and non-vegetated reflectances. These factors contribute to the reflectance in each pixel, and the total number of pixels within each class offer a set of mean values and variances for classification. These mean values and variances represent the central tendency and spread of a class respectively and together are referred as spectral signature. The signatures for each class are collected and subjected to a statistical classifiers (supervised and unsupervised), which assign each pixel on the image to one of the classes according to some form of best-match algorithm. Vegetation indices (VIs) and derived metrics have been extensively used for monitoring and detecting vegetation and land cover change (deFries et al. 1995). The development of vegetation indices is based on differential absorption, transmittance, and reflectance of energy by the vegetation in the red and near-infrared regions of the electromagnetic spectrum (Jensen 1996). Various studies have indicated that only Normalised Difference Vegetation Index (NDVI) is least affected by topographic factors. Vegetation indices condense the data from two (or more) spectral bands into one level of information. Vegetation indices are especially advantageous with multi date data sets. Various multidate vegetation indices are clustered to classify broad areas (usually at continental scale) according to the seasonalities of their greenup/senescence sequences. Therefore compared to land cover classification using single date data, multitemporal datasets are often found to improve the accuracy of classification. Further the classification can also be improved by using the phenological metrices derived from NDVI viz. maximum, minimum, amplitude , average and time integrated NDVI, which can be used as a layer or added band for classification in combination with a rule-based approach for determining cover types. Maximum NDVI is the maximum measurable NDVI recorded during the year and is normally associated with the peak of green during the growing season and the corresponding lowest NDVI value recorded during the year is referred as the minimum NDVI. Mean NDVI is the maximum NDVI value obtained for each recording period during the growing season divided by the total number of periods. The Principal Component Analysis (PCA) is one of the best the best known data reduction techniques where in multispectral imagery is transformed into a lesser number of principal component image bands. PCA reduces the dimensionality of a data set containing large number of interrelated variables, while still retaining as much as possible the variation present in the data set. This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. Therefore, Principal Component images always contain most of the original input image variance in a lesser number of bands.

3. On applying unstandardized PCA of time series NDVI it was observed that Principal Component 1 could be interpreted as the time-integrated NDVI over the entire three year period; representing the typical greenness of the continent (Eastman, 1992). PrincipalComponent 2 is interpreted as a change component, representing winter/summer seasonal effect. Principal Components 3 and 4 are also essentially seasonal, but represent areas where the timing of greenup is different than that for component 2. Higher order components are interpreted as sensor artifacts or relatively short-term meteorological effects. As is common in PCA, the interpretation of higher order components becomes progressively difficult and it is not clear how many components are significant in terms of information (Jackson, 1993). Present Methodology In the study an attempt has been made to classify vegetation over northeastern part of India using distinct phenological growth stages and spectral characteristic at mesoscale. Multi date IRS Wide field Sensor (WiFS) data has been used for this purpose. IRS-WIFS data with two spectral bands red (0.62-0.68m) and infrared (0.77-0.86m) at a spatial resolution of 188m and temporal resolution for 3-5 days meets the requirement of vegetation mapping at regional and continental scale using phenological variability in vegetation. In the present analysis temporal vegetation characteristics over a five month period at different phonological stages are analysed by considering three datasets corresponding to maturity (December, January and February), senescent (March) and leaf fall (April) periods. The satellite data was first corrected for atmospheric effects due to scattering using dark pixel subtraction technique. The data was then geometrically rectified using control points and all the images from different months were co-registered. To use the different aspect of vegetation phenology for classification, the multidate data sets was subjected to various analytical procedures viz. Vegetation Indices and Principal Component Analysis. Maximum, minimum, mean and amplitude NDVI were calculated on different season data. The resulting NDVI images were subjected to grey level scaling in order to segregate vegetation types into broad categories based on NDVI values. The multidate data set of February, November and December was compressed into three principal components. Land use/cover characterization was attempted first by using unsupervised classification technique on the raw data layers in combination with the maximun NDVI data. K-means algorithm was run on maximum NDVI value and the raw bands of November. Each cluster was assigned a preliminary cover type label taking care of the spatial pattern and spectral or multi temporal statistics of each class on comparison with ancillary data and extensive ground truth. Ancillary data included descriptive land cover information, NDVI profiles and class relationships to the other land cover classes. The classes were then grouped into broad classes using a convergence of evidence approach. The snow and cloud classes were masked out. The unsupervised classification was followed by post classification refinement for the coherent set of classes.

4. Results and Discussion The unique climatic condition of northeast India supports luxuriant vegetation growth resulting in extensive forest cover. Different types of forest have been identified in northeastern region by Champion and Seth (1968). According to the latest satellite based survey report of Forest Survey of India (FSI), northeastern region has 164359 Km2 of forest, approximately 25% of the total forest cover in the country (Anonymous, 1997). However, due to human activities such as shifting cultivation have brought considerable change in the ecological status of the forests. Shifting cultivation (locally called ‘jhumming’) is the single factor responsible for forest and land degradation. About 0.45 million families in this region cultivate 10,000 sq. km forests annually affecting approximately 44,000 Km2 of forest area (Singh, 1990). In the present study, attempts were taken to stratify forest using the temporal data set and to observe the phenological variation among the different types of forest in different regions. The temporal NDVI images provide the rhythmic growth of vegetation and hence able to distinguish the same species type occurring in different biogeographical or climatic conditions. Even the abandoned shifting cultivation areas, which have attained good growth of tree species or bamboo, have been identified in the different regions using temporal data set. Four different time period NDVI images are considered as representative for seasonal changes. The NDVI values varies from –1.0 to + 1.0. However, values for land surfaces were ranging from -1.0 to + 0.992 (February), 0.357 to + 0.994 (March), - 0.352 to + 0.576 (April)- 0.449 to + 0.758 (November) and - 0.492 to + 0.748 (December). The maximum NDVI image has been computed to represent the maximum foliage cover in the study period. Temporal plots were selected for each landuse class and analyzed for the study area. NDVI values obtained from the vegetation index product/image for different cover types were assessed. The representative sites selected for each cover type indicate the internal variation of the NDVI response of the cover type. For each location area averaged NDVI value was assessed. The NDVI images showed the foliage cover in the respective season. The maximum NDVI image represents the maximum foliage cover or greenness in the study period for each forest legends (Figure 1). The coniferous locations have comparatively low NDVI values throughout the study period striking uni-modal peak in December. The broad-leaved forest is having small peak during month of December with a steep decline in values from February to March. The semievergreen forest is having moderate NDVI values with small peak during December and March. The moist deciduous types are having high photosynthetic activity during the study period with highest during March. The secondary forest (abandoned jhum >10 yrs) shows the bimodal NDVI values during the study period with high value in March. From the preliminary analyses it is apparent that different cover types exhibit characteristic NDVI curves. The non-forest classes viz. degraded grasses/shrubs and agriculture showed almost similar pattern of NDVI values throughout the year having high foliage curve/photosynthetic activity during December. The Bamboo jhum (5-10 yrs) showed high NDVI values in the month of December and decline in March. Because of high cloud cover during March, the NDVI values do not follow the trend of temporal variation i.e. phenology. The monitoring of the crop development through growing period is possible for the agricultural areas. However in case of northeastern India such an approach will be rather difficult due to recurring cloud cover.

5. In the present case the PCs of the data set consisting of February, November and December 1998 data were studied. The scenes with high cloud cover were rejected to overcome the contamination. The inverse principal component was carried out to visualise the data in RGB. The false color composite of the first three PC images was found to be informative over the raw data sets and NDVI images. The PC1 was containing information from all the bands of the three dates. The PC 2 is collective information from the IR bands of the each image hence supporting the vegetation and land cover. The PC3 was having contribution of IR and R band of November data. The FCC of PC images provides this discrimination between forest and non-forest inspite of healthy foliage cover. (Figure 2) Within the forest classes the discrimination among the forest types is also highlighted. The broad-leaved and coniferous evergreen forests were discriminated as per the NIR response. The semi evergreen patches were found to be enhanced and distinguished. The fresh jhum patches and abandoned jhum classes were identified as different classes. The maximum NDVI image gives intermixing among the northern healthy forest of Arunachal Pradesh and southern part of northeast i.e. Mizoram, which is dominated by the abandoned jhum. The discriminating loading factors of the PC2 represented the abandoned jhum and degraded forest. Within non-forest classes, the agriculture patches and tea gardens were distinguished. The seasonal and permanent water bodies were also clearly distinguished. This region is endowed with vast natural resources in the form of tropical evergreen/semi-evergreen, subtropical evergreen forest, moist deciduous, temperate broad-leaved forest, temperate conifer forest, alpine grasslands/scrub and secondary forest and fresh water streams, rivers and lakes (Figure 3). The forest cover area estimated is about 42.24% of the geographical area. The forest cover recorded by FSI is found overestimated in comparison to previous and present studies carried out using satellite remote sensing. This is attributed to the fact that the maps prepared by using visual interpretation are unable to separate abandoned jhum and is grouped with open/degraded forest. However in the present analysis this class could be separated as abandoned jhum (5-10 years). The total forest cover of northeastern region including jhum is worked out as 55.06% that is almost equal to forest cover reported by FSI i.e. 54.02%. Conclusion Accuracy assessment of the classification was performed by using confusion matrix. From the error matrix of the classified forest cover it was observed that among the various forest classes, evergreen and moist deciduous forest showed relatively low user’s accuracy. The above classes got mixed with degraded forest and patches of agriculture. The non-forest classes have shown higher accuracy except agriculture and jhum (<10 yrs). It may be due to intermixing with moist deciduous forest. The fresh jhum (<10 yrs) normally occurred in various stages of succession and various cover type combinations. The overall accuracy was 82.15% and Kappa statistics was 80.03% in agreement (Khat coefficient 0.80).

6. The present study highlights the use of NDVI and metrices derived from it and use of other enhancement techniques like principal component analysis for land use/land cover mapping. The NDVI has been found related to green leaf activity and as such provides a useful means to monitor the vegetation cover/phenology. Its effectiveness lies in its discrimination ability among forest types and major crops and other land cover classes. The accuracy have been found to be satisfactory (accuracy ~ 80 to 87%) to perform forest cover assessment, mapping and delineation. Phenological derived metrices viz maximum, mean, minimum NDVI, integrated NDVI in combination with raw data layers on multitemporal data sets were also applied for vegetation cover mapping in other regions (Gujarat, Himachal Pradesh and Madhya Pradesh) of India and was found to be satisfactory for land cover characterization at regional and global scales. References Champion, H.G. and Seth, S.K., 1968. In A revised survey of forest types of India, New Delhi Govt. Publication. deFries, R., M. Hansen, J. Townshend, 1995. Global discrimination of land cover types from metrics derived from AVHRR pathfinder data, Remote Sensing of Environment 54(3): 209-222. Eastman, J.R., 1992. Time series map analysis using standardized principal components. ASPRS/ACSM/RT 92 Technical Papers, Vol. 1: Global Change and Education. Aug. 3-8, Wash. D.C., pp. 195-204. Jensen, J.R. 1996. Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice Hall, New Jersey, 316p Roberts Miles, Well Chris, Doyle, Thomas W., 1994. Component analysis for interpretation of time series NDVI imagery, ASPRS/ACSM. Roy, P.S., Sarnam Singh, Agrawal Shefali, Joshi, P.K., 2001 Assesment of Forest cover in North east India and Northern Myanmar- Potential of Indian Remote sensing satellite (IRS-1C WiFS) Data. IIRS-JRC Report. Singh, S., Agrawal S., Joshi, P.K., and Roy, P.S., 1999. Biome Level Classification of Vegetation in Western India- An application of Wide Field View Sensor (WiFS). Joint workshop of ISPRS Working Groups I/1,I,3 and IV/4: Sensors and Mapping from Space, Hannover(Germany) 27-30 Sept. 1999

7. Singh, G., 1990. Soil and water conservation in India, In Proceeding Symposium on Water Erosion, Settlement and Resource Conservation, March 25, Deharadun. Systems of the northeastern hill region of India. Agro-ecosystem, 7,11-25. Townshend, J.R.G., Golf, T.E., and Tucker, C.J., 1985, Multispectral Dimensionality of Images of Normalized Difference Vegetation Index at Continental Scales, IEEE Transaction on Geoscience Remote Sensing,23,888-895. Townshend, J., Justice, C., and Kalb, V., 1987, Characterization and Classification of South American Land Cover Types Using Satellite data, International Journal of Remote Sensing, 8,1189-1207. Tucker, C.J., Townshend, R.G., and Goff, T., 1985, Continental land cover classification using NOAA-7 AVHRR data, Science, 227, 369-375.

8. 0 60 Kilometers Scale 1:3,000,000 N Figure 1 Maximum NDVI Image North East India Image = MAX (February, March, April, November, December) Projection Lambert Conformal Conic

9. Data Set Path/Row Date 112/52, 56 Feb’ 1998 112/56, Nov’ 1998 113/56 113/52, Dec’ 1998 114/57 Figure 2 False Color Composite of Principal Components - North East India PC1:PC2:PC3 Projection Lambert Conformal Conic

10. 0 60 Kilometers Scale 1:3,000,000 N Legends Evergreen Forest (Coniferous) Evergreen Forest (Broad Leaved) Semievergreen Forest Moist Decidous Forest Abandoned Jhum (>10 Yrs.) Jhum (5 – 10 Yrs.) Grassland Degraded Forest Agriculture Water Body River Channel Shadow Snow/Cloud Data Set Path/Row Date 112/52, 56 Feb’ 1998 112/52, 56 Mar’1998 108/54 Apr’ 1998 113/54 Apr’ 1998 112/56, Nov’ 1998 113/56 113/52, Dec’ 1998 114/57 Figure 3 Forest Cover Map North East India Level II Projection Lambert Conformal Conic