Transcript 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

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, SPOTXS, 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).
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 nonvegetated 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.
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). Principal Component 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 (0.620.68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.
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.
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/semievergreen, 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).
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
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Eastman, J.R., 1992. Time series map analysis using standardized principal components.
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D.C., pp. 195-204.
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Roberts Miles, Well Chris, Doyle, Thomas W., 1994. Component analysis for interpretation of time
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Figure 1
Maximum NDVI Image
North East India
0
60
Kilometers
Scale 1:3,000,000
N
Image = MAX (February, March, April, November, December)
Projection Lambert Conformal Conic
Figure 2
False Color Composite of Principal Components - North East India
Data Set
Path/Row
112/52, 56
112/56,
113/56
113/52,
114/57
Date
Feb’ 1998
Nov’ 1998
Dec’ 1998
Projection Lambert Conformal Conic
PC1:PC2:PC3
Figure 3
Forest Cover Map
North East India
Level II
0
60
Kilometers
Scale 1:3,000,000
N
Legends
Evergreen Forest
(Coniferous)
Evergreen Forest
(Broad Leaved)
Data Set
Path/Row
112/52, 56
112/52, 56
108/54
113/54
112/56,
113/56
113/52,
114/57
Date
Feb’ 1998
Mar’1998
Apr’ 1998
Apr’ 1998
Nov’ 1998
Dec’ 1998
Projection Lambert Conformal Conic
Semievergreen Forest
Moist Decidous Forest
Abandoned Jhum (>10 Yrs.)
Jhum (5 – 10 Yrs.)
Grassland
Degraded Forest
Agriculture
Water Body
River Channel
Shadow
Snow/Cloud