Ganges and Indus river basin land use/land cover (LULC ... and Indus river basin land use/land cover...

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Remote Sensing of Environm

Ganges and Indus river basin land use/land cover (LULC) and irrigated

area mapping using continuous streams of MODIS data

Prasad S. ThenkabailT, Mitchell Schull, Hugh Turral

International Water Management Institute (IWMI), P.O. Box 2075, Colombo, Sri Lanka

Received 7 September 2004; received in revised form 8 December 2004; accepted 11 December 2004

Abstract

The overarching goal of this study was to map irrigated areas in the Ganges and Indus river basins using near-continuous time-series (8-

day), 500-m resolution, 7-band MODIS land data for 20012002. A multitemporal analysis was conducted, based on a mega file of 294

wavebands, made from 42 MODIS images each of 7 bands. Complementary field data were gathered from 196 locations. The study began

with the development of two cloud removal algorithms (CRAs) for MODIS 7-band reflectivity data, named: (a) blue-band minimum

reflectivity threshold and (b) visible-band minimum reflectivity threshold.

A series of innovative methods and approaches were introduced to analyze time-series MODIS data and consisted of: (a) brightness-

greenness-wetness (BGW) RED-NIR 2-dimensional feature space (2-d FS) plots for each of the 42 dates, (b) end-member (spectral angle)

analysis using RED-NIR single date (RN-SD) plots, (c) combining several RN-SDs in a single plot to develop RED-NIR multidate (RN-

MDs) plots in order to help track changes in magnitude and direction of spectral classes in 2-d FS, (d) introduction of a unique concept of

space-time spiral curves (ST-SCs) to continuously track class dynamics over time and space and to determine class separability at various

time periods within and across seasons, and (e) to establish unique class signatures based on NDVI (CS-NDVI) and/or multiband reflectivity

(CS-MBR), for each class, and demonstrate their intra- and inter-seasonal and intra- and inter-year characteristics. The results from these

techniques and methods enabled us to gather precise information on onset-peak-senescence-duration of each irrigated and rainfed classes.

The resulting 29 land use/land cover (LULC) map consisted of 6 unique irrigated area classes in the total study area of 133,021,156 ha

within the Ganges and Indus basins. Of this, the net irrigated area was estimated as 33.08 million hectares26.6% by canals and 73.4z5 by

groundwater. Of the 33.08 Mha, 98.4% of the area was irrigated during khariff (Southwest monsoonal rainy season during JuneOctober),

92.5% irrigated during Rabi (Northeast monsoonal rainy season during NovemberFebruary), and only 3.5% continuously through the year.

Quantitative Fuzzy Classification Accuracy Assessment (QFCAA) showed that the accuracies of the 29 classes varied from 56% to

100%with 17 classes above 80% accurate and 23 classes above 70% accurate.

The MODIS band 5 centered at 1240 nm provided the best separability in mapping irrigated area classes, followed by bands 2 (centered at

859 nm), 7 (2130 nm) and 6 (1640 nm).

D 2005 Elsevier Inc. All rights reserved.

Keywords: MODIS; Reflectance; Irrigated areas; Land use; Land cover (LULC); Ganges; RED-NIR; Change vector analysis; Spiral curve; Two-band

vegetation indices

1. Background and rationale

The World Summit on Sustainable Development

(WSSD) in Johannesburg (2002) declared water to be the

0034-4257/$ - see front matter D 2005 Elsevier Inc. All rights reserved.

doi:10.1016/j.rse.2004.12.018

T Corresponding author. Tel.: +94 1 2787404; fax: +94 1 2786854.E-mail address: [email protected] (P.S. Thenkabail).

most critical resource in the twenty-first centurywith

increasing demands and decreasing supplies. Irrigation is

estimated to consume about 60% of the worlds diverted

freshwater resources. In response to continued population

growth (projected to rise from 6 billion now to 8.3 billion

in 2030) and increased calorific intake of food (to 3000

calories per day per person from the current 2100; FAO,

2003), the demand for water for irrigation is forecast to

ent 95 (2005) 317341

P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341318

grow. This is neither feasible, due to shortage of water

resources in many parts of the globe nor desirable because

of the negative environmental impacts of irrigation

schemes.

Improved water accounting is required to track

agricultural and nonagricultural water use, particularly in

irrigation. This will require mapping LULC and irrigated

area classes on a near-continuous (e.g., every 8-day)

basis. Most of the LULC classification efforts in the past

three decades used single or a selected few remote

sensing images (see Foody, 2002). Such classifications

provide little or no information on the temporal dynamics

of LULC classes, highly limiting their use in applications

such as hydrological modeling and evapotranspiration

estimations (DeFries & Los, 1999). In recent years,

AVHRR pathfinder time-series images (e.g., DeFries et

al., 1998; Loveland et al., 2000) have been used to

capture temporal dynamics of LULC at global level.

However, it is only recently that near-continuous (e.g., 8-

day composites) time series images from sensors such as

Moderate Imaging Spectrometer (MODIS) on board

NASAs Terra and Aqua satellites have allowed assess-

ment of LULC dynamics and quantitative landscape

characteristics (e.g., biomass, leaf area index) (Huete et

al., 2002) in near real time. For example, using these

datasets, vegetation in continuous streams are currently

produced (http://glcf.umiacs.umd.edu/data/modis/vcf/).

MODIS data are also known to provide a significant

improvement in terms of quality relative to the heritage

AVHRR data (Friedl et al., 2000). The advances in

spectral, spatial, radiometric, and temporal resolutions of

MODIS datasets () are further complimented by advances

in cloud/haze removal algorithms, time compositing, and

normalization of data into reflectance. It is well estab-

lished that LULC and irrigated area maps of the present

day require capturing quantitative dynamics over space

and time (DeFries & Los, 1999; Foody, 2002; Huete et

al., 2002) in order to enable them to be used more

productively in studies such as hydrological modeling

(Foody, 2002), drought assessments (Thenkabail et al.,

2004b), impact on biodiversity (Chapin et al., 2000),

human habitability and climate change (Skole et al.,

1994), global warming (Penner et al., 1992) and soil

erosion (Douglas, 1983).

The research described in this paper falls within the

framework of the Global Irrigated Area Mapping (GIAM)

project at IWMI (Droogers, 2002; Turral, 2002). The

principal objective of GIAM is to map irrigated areas at

different levels (global to local) and at different scales

using satellite sensor data from various eras. Global LULC

are essential to advancing most global change research

objectives (Loveland et al., 2000). Regional and local

LULC efforts must aim for a greater number of discrete

classes of relevance to a wide variety of users (Thenkabail,

1999; Thenkabail & Nolte, 2003). Irrespective of the level

at which the classes are mapped, it is essential to establish

acceptable levels of accuracy (Thenkabail et al., 2004a) to

avoid serious implications of land cover misclassification

on, for example, global land surface models (DeFries &

Los, 1999).

In order to achieve this goal, GIAM uses datasets that

include AVHRR (1 km to 10 km), SPOT Vegetation (1

km), MODIS (250500 m), ASTER (1590 m), ETM+

(1530 m), TM (30 m), and IRS (523.5 m). Irrigated

classes form part of some of LULC mapping efforts (e.g.,

Loveland et al., 2000), but no special focus or

importance was given to them, leading to a large

percentage of mixed classes with natural vegetation.

Primarily, there are non-remote sensing based studies on

irrigated areas (e.g., CBIP, 1989, 1994; Siebert, 1999).

The Food and Agriculture Organization (FAO, 2003;

Framji et al., 19811983; Siebert, 1999) of the United

Nations estimates that about 20% of the arable land is

irrigated at present with various scenarios of projected

increases in the future, but provides no spatial map of

where these areas are. Current estimated trends in

irrigation development are generally derived from

national agricultural statistics with many uncertainties

about their accuracy.

With the overall scope of the GIAM project as

discussed in the previous paragraph in mind, we focus

on mapping LULC with particular interest on irrigated

areas in the Ganges and Indus river basin using MODIS

data for year 20012002. The study will use multi-date,

near-continuous, MODIS data, and adopt a series of

innovative methods and proceduresthe N-dimensional

change vector analysis (CVA), new space-time spiral-curve

techniques to assess subtle and not-so subtle quantitative

changes over time and space, and evaluate the study using

fuzzy classification accuracy assessment. Through these

measures we plan to demonstrate a unique set of data,

methods, procedures, and protocols for mapping irrigated

areas. The Ganges and Indus basins (referred to as Indo-

Gangetic) was selected for this study because it is one of

the most densely populated and intensively cultivated areas

of the world with irrigation forming a key role in food

production.

2. Study area and the MODIS data

The study area (see non-hatched area within the basin

boundary in Fig. 1) covers 63% (133,071,400 ha) of the

Indo-Gangetic plain (total area=211,224,444 ha). The

study area was chosen based on the importance of the

area for agriculture and irrigation and a need to map this

area (Droogers, 2002; Turral, 2002). The Ganges river

basin originates in the Himalayan glaciers named Gang-

otri, about 4270 m above sea level. It has one of the most

fertile lands and has a very high population density of

about 530 persons per square kilometer. The river flows

through 29 cities each with a population of over 100,000,

http://glcf.umiacs.umd.edu/data/modis/vcf/

SNDVI

255

204

153

102

51

0

600

N0

N20

E80 E100

600 1200KilometersScale 1:40 000 000

0

Fig. 1. The study area within the Ganges and Indus basins. The un-hatched portion of the Ganges and Indus basins shown on an AVHRR image. The Z-scale

shows scaled normalized difference vegetation index (SNDVI) for October 1990.

P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341 319

23 cities each with a population between 50,000 and

100,000, and about 48 towns (Aitken, 1992; Ilich, 1996).

The source of the Indus River is in Western Tibet in the

Mount Kailas region at an altitude of 5500 m above sea

level. The Indus basin comprises the Indus river, its five

major left bank tributariesthe Jhelum, Chenab, Ravi,

Beas and Sutlej riversand one major right bank

tributary, the Kabul (Khan, 1999). The catchments contain

some of the largest glaciers in the world outside the Polar

Regions (Meadows, 1999). The southwest monsoon or

khariff season (June to October) is followed by northeast

monsoon or Rabi season (November to February). The

mean annual rainfall is about 2000 mm, of which

approximately 70% occurs during the khariff season.

The dry season (MarchMay) the highest temperatures

vary between 40 and 45 8C.In order to enable the study of the characteristics of

land use and irrigation on a near- continuous basis, the 8-

day composite MODIS images of year 2001, a rainfall

normal year, and year 2002, which experienced rainfall

deficit in terms of amount and distribution, were selected).

One of the main goals of the study was to establish crop

calendar for irrigated area crops as precisely as possible.

The goal was to determine onset-duration-magnitude of the

peak-senescence for each irrigated area class. As a result

we need to use as frequent images as possible-leading us

to use 8-day composites and apply cloud removal

algorithm rather than use 32-day images with significantly

lesser cloud issues. About 95% of the Ganges basin (total

area 95,111,154 ha) and 37% of the Indus basin

(116,113,290 ha), were covered by 3 MODIS tiles

(h24v06, h25v06, and h26v06; each tile of 10001000km). The three tiles were mosaicked into a single

contiguous tile by running batch scripts in ERDAS

Imagine 8.6 from which the areas within the Ganges and

Indus basins were delineated (Fig. 1).

3. Methods and techniques

3.1. Mega file: multitemporal MODIS data for Ganges and

Indus river basins

In this study, we use the MOD09 product, with 7 of the

36 MODIS 500 m bands. The MOD09 is computed from

MODIS level 1B land bands 17 (centered at 648 nm, 858

nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm).

The product is an estimate of the surface reflectance for

each band as it would have been measured at ground level

if there was no atmospheric scattering or absorption

(Vermote et al., 2002). The original MODIS data are

acquired in 12-bit (04096 levels), and are stretched to 16-

bit (065,536 levels). Dividing these data by 100 will

make them comparable to laboratory spectra in the 0

100% range.

The long time series analysis of MODIS data requires

construction of mega datasets that involve hundreds of

bands. Altogether 294 bands (42 images7 bands) from21 images from year 2001 and 2002 were formulated

into a single mega file of approximately 7 GB. A

separate 42-band NDVI mega file (one NDVI band for

each date) was also created. The single mega file

facilitate (a) analyzing the time series in their entirety

(e.g., they perform unsupervised classification of 294-

band data and determine how classes change in

magnitude and direction over space and time) and (b)

tracking quantitative changes at any level in near-

continuous mode (e.g., NDVI variations at pixel or


entire study-area level in 8-day time interval). Performing

analysis on 10 s or 100 s of images of individual dates

is too cumbersome, leads to repetitive work, hard to

keep track of class number changes for a given pixel,

and just leads to chaos of handling too many files. In

comparison mega file offers a single file of data, a

single file of output, and provide temporal variations for

every pixel in quantitative terms (e.g., NDVI dynamics

over time).

3.2. Cloud removal algorithm

The Indo-Gangetic basin is subject to the effects of the

oscillating Sub-Tropical Convergence Zone (www.srh.

weather.gov). These effects include the monsoon (June

September), which brings extensive cloud cover and heavy

rains. During this season, there is a great change in the

vegetation cover, rapid change in its dynamics and biomass

accumulation. In order to retain the maximum number of

time series images during this period we (a) retained all

images with b5% cloud cover and (b) developed a cloud-

masking algorithm so as to eliminate areas of cloud cover

and retain the rest of the image as is. Of 42 images, 8 images

had 2540% cloud cover which also implies that 6075% of

133 million hectare study area is cloud-free. Our attempts to

use MODIS quality control layers and flags were not

successful and resulted in several difficulties. These include:

(a) cloud vs. snow vs. desert sand vs. aerosol confusion: as a

result of this often Himalayan seasonal snow was removed

as cloud; (b) over-correction issue: over correction by

quality control flags lead to significantly low reflectance

values which in turn effected temporal NDVI profiles; and

(c) bblockyQ effects: applying quality flags lead to bblockyQeffects in the images probably as a result of original quality

flags being performed at 1-km pixel size which seemed to

cause bblocky/noisyQ effects in 500-m pixels (four 500-mpixels in one 1-km pixels). In fact, we were able to establish

a more consistent, smooth, and stable NDVI profiles from

the MODIS cloud removal algorithm specially developed in

this study rather than use MOD09 QC layers.

3.2.1. Cloud algorithm: statistical characteristics

Clouds have unique spectral characteristics with consis-

tently high reflectivity in all visible and NIR wavebands, but

are quite often mixed with snow and desert backgrounds

the other two highly reflective classes. To establish clear

statistical characteristics for clouds, we obtained sample

spectra from 350 locations for clouds, 240 locations for

snow and 180 locations for deserts. When the means,

minima and maxima of spectra for the clouds, snow and

desert were plotted the results showed there were 2 excellent

possibilities for separating most of the clouds.

3.2.1.1. Blue band minimum reflectivity threshold for cloud.

When we use minimum blue band reflectivity of 21% or

above (a) all clouds get removed, (b) much of snow gets

removed and (c) none of the desert gets removed. A simple

algorithm for cloud removal in ERMapper (ERMapper,

2004) was:

If i3N21% then null else I 1

Where, i3 is MODIS band 3 (blue band). The algorithm

assigns null values to all cloud areas.

3.2.1.2. Visible band minimum reflectivity threshold for

cloud. The minimum reflectivity of clouds in the MODIS

visible bands (bands 3, 4, and 1), provide the best

separability in which almost all clouds gets removed.

The algorithm for cloud removal, using this approach

with MODIS visible bands 3 (blue), 4 (green), and 1

(red) was

If i1N22 and i3N21 and i4N23 then null else I 2

However, when using this approach much of snow and

a significant portion of the desert also get removed. This is

not a problem, since we have several other time series

images where snow and desert data exist in their entirety.

So clouds were removed using Eq. (2), but snow and desert

areas were retained in their entirety, based on non-cloudy

images.

The results of cloud removal have been illustrated before

and after images in Fig. 2.

3.3. Normalization of temporal variability

The MODIS reflectance product has gone through a

rigorous atmospheric correction scheme based on the 6S

radiative transfer code for normalizing for molecular

scattering, gaseous absorption and aerosols that affect

the top of the atmosphere (TOA) signal (see inter alia

Vermote et al., 2002). Aerosol effects are known to

remain uncorrected even after long compositing periods

(e.g., a month) (Vermote et al., 2002) so such effects in

8-day time intervals are significant. It would be desirable

to do further corrections for these effects, for which we

found a time-invariant location in the Rajasthan desert,

calculated mean values of each band for each of the 42

images for this time-invariant location, determined the

calibration coefficient of each band for each date by

dividing its reflectance by the mean, and then normalized

images of each date by multiplying using calibration

coefficients.

3.4. Image processing and interpretation

A summary of the image processing and interpretation

undertaken in this research is provided in Fig. 3. The

basis of the work stems from unsupervised classification

of all bands in the mega file, followed by various

innovative refinements in class membership using techni-

ques derived from RED-NIR and time series signatures,

which are discussed in more detail in the section on

http:www.srh.weather.gov

Before Cloud Removal Algorithm Day 153 2001

After Cloud Removal Algorithm Day 153 2001

Before Cloud Removal Algorithm Day 185 2001

After Cloud Removal Algorithm Day 185 2001

E70

N25

500 0 500 1000Kilometers

RGB

TCC;RGB1,4,3648, 555, 470 nmDay 153 2001

Scale 1:16 500 000

E75 E80 E90 E95 E100E85 E70

N25


RGB

TCC;RGB1,4,3648, 555, 470 nmDay 185 2001

Scale 1:16 500 000

E75 E80 E90 E95 E100E85

E70

N25


RGB

TCC;RGB1,4,3648, 555, 470 nmDay 153 2001

Scale 1:16 500 000

E75 E80 E90 E95 E100E85 E70

N25


RGB

TCC;RGB1,4,3648, 555, 470 nmDay 185 2001

Scale 1:16 500 000

E75 E80 E90 E95 E100E85

Fig. 2. MODIS images before and after application on cloud removal algorithm. An algorithm was developed to remove cloud from MODIS data. The figures

above show the cloud removal capability of the algorithms.


Results and discussion. Ground-truth data have therefore

been a crucial element in the process and are described in

the next section.

Fig. 3. Ground truth data point distributions in the study area. Precise location of t

MODIS RED-NIR image. Color key: red: dry, cyan/green/yellow: green, blue/li

legend, the reader is referred to the web version of this article.)

We adopted a hierarchical classification system based on

modified Anderson classification (Anderson et al., 1976).

For example, if a class does not belong to rice (class A) or

he 9090 m ground truth locations spread across the study area shown on aght blue: wet. (For interpretation of the references to colour in this figure


sugarcane (class B) it will fall into a higher category of

irrigated croplands (classes A and B).

4. Ground-truth data

Ground truthing was conducted during October 122,

2003 to coincide with the peak khariff (monsoonal rainy

season from June to October) conditions. For such a large

area as the Ganges and Indus basins, random or systematic

sampling is unrealistic and costly (Muchoney & Strahler,

2002). Therefore, the sampling was stratified by access

through roads and foot paths and randomized by locating

sites every few minutes of the drive.

The MODIS data require a minimum sampling unit of

500 m500 m, which in itself is inadequate. A largersampling unit is desired, but was quite impractical in the

field. The approach we adopted was to look for contiguous

areas of homogeneous classes within which to sample (see

Thenkabail, 2003, for sampling LAI), taking a representa-

tive area of 90 m90 m. Class labels were assigned in thefield, using a system that allows merging to a higher class or

breakdown into a distinct class, based on the land cover

percentages taken at each location.

In all, about 6500 km were covered to gather data from

196 sample locations (Fig. 4). The precise locations of the

samples were recorded by GPS in the Universal Transverse

Mercator (UTM) and the latitude/longitude coordinate

system with a common datum of WGS84. The sample size

per class varied from 8 to 37 and the ideal target of 50

Cloud Removal Algorithms (CRAs): (A) Blue band minimum reflectivity threshold, (B) Visible band minimum reflectivity threshold

Class assignment

Mega-fof 294 b42 MOD

End Member Analysis (EMA) : Brightness-greenness-Wetness (BGW) 2-dimensional Feature Space (BGW

Class refinement

NIR-RED Single Dates (NR-SDs)

NIR-RED Multi Dates (NR-MDs)

Class simplification

Extraction of Irrigated pixels

Class Signatures Multi-band Reflectivity (CS-MBR)

Calculate Statistics

Space-Time Spiral-Curve (ST-SCs) from multi-date

tasseled cap

2-band spectral plots 2:1 & 6:7

Mega Classes

Fig. 4. Methods and techniques workflow diagram. Flow chart showing methods an

of MODIS data.

samples (Congalton, 1988) was infeasible due to limitations

in resources.

At each location (e.g., Fig. 5), the following data were

recorded:

1. LULC classes: levels I, II and III of the Anderson

approach.

2. Land cover types (percentage): trees, shrubs, grasses,

built-up area, water, fallow lands, weeds, different crops,

sand, snow, rock, and fallow farms.

3. Crop types, cropping pattern and cropping calendar: for

khariff, rabi (second main cropping period from Novem-

ber to March) and interim seasons.

4. Source of water: irrigated, rain-fed, supplemental

irrigation.

5. 311 digital photos hot linked @ 196 locations.

The data were organized in proprietary image processing

and GIS formats with accompanying metadata so that they

could be co-located with the unsupervised classification

(e.g., Fig. 4).

5. Results and discussion

5.1. Unsupervised classification

To begin with, unsupervised classification was per-

formed on the mega file (UC-MF) using an ISODATA

statistical clustering algorithm for multidimensional data

ile (MFC) for time-series analysis ands for 2001 and 2002 IS 500-m 7-band images

Net irrigated area

Sub-pixel

composition

Multidate-multiband unsupervised classification

(MD-MB UC)

Ground truth

Class Signature based on NDVI (CS-NDVI): time-series

Quantitative Fuzzy Classification Accuracy Assessment

(QFCAA)

Kharif, Rabi, and Continuous irrigated

areas

d techniques of LULC and irrigated area mapping using continuous streams

Fig. 5. Photographs illustrating irrigated area classes and forest cover land use and land cover (LULC) classes. At each ground truth point, 2 photographs were

taken apart from other ground truth data. Illustrated here are representative photos (ae) of 6 unique irrigated area classes (classes 2126) and representative

photos (fh) of 3 forest classes (classes 2729).


(ERDAS, 2004). Initially, 100 classes were obtained as a

starting block for further refinement and analysis. The

UC-MF provides a substantial within-class variance

(Friedl et al., 2000; McIver & Friedl, 2002) that is

essential to map classes within a theme (e.g., different

types of irrigated-area classes). The sample size of the

field-plot data was insufficient for certain classes to make

the supervised classification robust. Hence unsupervised

approach backed by RED-NIR plots (Sections 5.2 and

5.3), ground truth data (Sections 4 and 5.4), temporal NDVI

plots (Section 5.9), and space-time spiral curves (Section

5.10) were used.

5.2. RED-NIR Plots for single dates (RN-SDs), class

identification and labeling

The spectral properties of the 100 classes obtained

through UC-MF were analyzed, based on their distribution


in brightnessgreennesswetness (BGW) RED-NIR feature

space. The distributions of a selection of the 100 unique

spectral classes for May 2001 are illustrated in Fig. 6.

All classes were identified and labeled, based on their

position in the BGW RED-NIR feature space, use of higher-

resolution images (Geocover Landsat TM MrSid images),

NDVI thresholds at different time periods and ground truth

information (e.g., Figs. 4 and 5).

All of the information was used in the hierarchical class

labeling process that led to the reduction of the 100 classes

to the final 29 classes. The pure pixels (the brightest, the

greenest and the wettest) are at the edges of the triangle

(spectral angle). Most pixels are some combination, linear or

nonlinear of these purest pixels. Brightness is represented by

albedo (approximately the mean of the red and NIR

reflectances) and the greenness by the difference between

NIR and red bands. The brightnessgreenness space is just a

458 clockwise rotation of the red and NIR space. Treecanopies and hills have deeper shadows compared with

crops making tree classes to cluster in the wetnessgreen-

Snow type 1

Snow type 2

Snow Type 3 Barren Type 8Very Bright Soils

Barren Type 7Moist Soils

Forest Type 6

Forest Type 1

Forest Type 2

Forest Type 3

Forest Type 4

Forest Type 5

Barren

Water Type 1

Water type 3

Wa

Mixed: irrigated crops

Mixed: Natural Veg. (open)/dry rain fed ag

CroCrop type 3

Mixed: grasslands (floodplain)/Irrigated crops (moist)

Crop type 7

Natural Vegetation (floodplain) Crop type 6

Mixed: Natural Veg. / cropsAgriculture (floodplains)

Mixed: open forest/ crops

Mixed: Forest/ sugarcane & rice

Crop type 1Wetlands

MODIS band 1 Vs. MODIS band

MODIS ban

MO

DIS

ban

d 2

ref

lect

ance

(%

)

0

0

15

25

35

5

10

20

40

30

10155

Fig. 6. RED-NIR single dates (RN-SDs) plot of 100 unsupervised classes. The 10

band 1 (red) and band 2 (NIR). The classes are shown in brightnessgreennessw

further investigations during ground truthing. Similar to figure shown above RN-

ness areas compared to the crop classes clustering in the

brightnessgreenness area (see Fig. 6).

5.3. RED-NIRs for multi-dates (RN-MD)

The 42 separate TC SDs, one for each MODIS image,

were plotted together to observe and interpret classes. We

found that it was more useful to juxtapose RED-NIR plots

of multiple dates (RN-MDs) in a single plot (e.g., Fig. 7) in

order to arrive at the final 29 classes (Table 1). The TC MDs

capture both the direction and magnitude of change in time

and space. The change angle (h) and change magnitude (M)were computed using equations (Zhan et al., 2002):

h arctan Dkred=DkNIR 3

M Sqrt Dkred 2 DkNIR 2h i

4

where h=change direction or angle; M=change magni-tude; Dkred=red reflectance at time 2-red reflectance at

Type 6

ter Type 2

Barren Type 1

Barren Type 2

Seasonal Snow Type 1

Barren Type 3

Barren Type 5

Barren Type 4

Mixed: Barren/Irrigated crop

Mixed: Barren/rain fed crop

/ riparian vegetation

Mixed: barren/ fallow crops

Mixed: Natural veg. (open)/ supplemental ag.Mixed: Riparian vegetation (moist), wetlands/built-up

.

Crop type 5p type 4

Mixed: Natural Veg. / Irrigated crops

Mixed: water / barren land

Mixed: Rangelands & open areas/ rain fed crops

Soil line2 mean reflectance values: May 9, 2001

d 1 reflectance (%)20 30 40

3525

0 unsupervised classes are plotted taking mean class reflectance in MODIS

etness (BGW) feature space and their preliminary class names identified for

SDs were plotted for each of the 42 dates.

0 10 20 30 40

MODIS Band 1 Reflectance (%)

0

10

20

30

40

MO

DIS

Ban

d 2

Ref

lect

ance

(%)

1

2

45

6

7

8

9

1011

12

13

14

1516

1718

19

20

21

22

2324

25

26

27

28

29

12

4

5

6

8

9

1011

12

13

14

15

16

17

18

1920

2122

232425 2627

28

29

1

4

5 6

8

9

1011

12

1314

15

16

17

181920

21

2223

24

25

26

27

2829

MODIS band 1 Vs. MODIS Band 2 mean refelectance values: Jan. 1, 2002(green);May 9, 2002 (red); Sept. 6, 2002 (blue)

Soil Line

Fig. 7. RED-NIR multi dates (RN-MDs) Change vector analysis of 29 unsupervised classes. First the 100 unsupervised classes shown in Fig. 6 are reduced to

29 classes after a rigorous analysis including RN-SDs, ground truth, vegetation index signatures, RN-MDs, and others (e.g., geo-cover TM images). Here, we

illustrate the magnitude and direction of change of each of the 29 LULC classes over time using RN-MDs taking a driest month (May), a wettest monsoon

month (September), and a second Rabi cropping month (January) during year 2002. RN-MDs were also initially plotted for all 100 classes. These plots are also

done for year 2001 and for other dates in both years.


time 1; DkNIR=NIR reflectance at time 2-NIR reflectanceat time 1; arctan=arc tangent; Sqrt=square root.

We investigated the dynamics of the classes in three key

seasons: rabi peak in January, summer in May, and

monsoonal peak in September (Fig. 7). The connectivity

of the vectors of three distinct classes during the three

dates is illustrated in Fig. 7. Class 8 is barren land and

remains near the soil line during all three seasons (Fig. 7).

In contrast, class 17 is rain-fed agriculture with rangelands

and is close to soil line on the bright side of BGW feature

space between khariff and rabi seasons. During the khariff

peak (September) and rabi peak (January), class 17 is in

the greennessbrightness area. Class 22 is irrigated and has

high greenness during September, mid-way in the green-

nesswetness feature space in January, and only comes

anywhere nearer the soil line during the summer month of

May.

The availability of time series images has provided an

opportunity to define irrigated areas and other LULC

classes (e.g., rainfed agriculture) based on their seasonal

or multi-seasonal dynamics. The phenological informa-

tion contained in these multi-temporal images signifi-

cantly contributes to land cover classification, further

confirming the similar results by Dymond et al. (2002),

Jensen (2000) and Schriever and Congalton (1995).

5.4. LULC classes and their linkage with land cover (LC)

percentages: class labeling and area calculations

A total of 29 LULC classes (Table 1, Fig. 8) were mapped

which showed clear spectral separability on one or more

single dates (e.g., Fig. 6), and/or one or more multiple dates

(e.g., Fig. 7), and/or over a near-continuous time interval

(e.g., Fig. 9a and b). The total study area within the Ganges

and Indus basins was 133,021,156 ha (Table 1) where there

was a high degree of irrigation (e.g., see classes 2126 in Fig.

8 and Table 1). Class 30 was data noise that amounted to

0.5% of the total study area and, hence, was negligible.

The LULC name is based on predominance of a particular

land cover. For example, the name for class 27 is bForests(Himalayan): Mature.Q The land cover (LC) of this class isdominated by mature forests (31.7%, see Table 1), which

occur along the Himalayan mountains. The trees were 20+

years and hence classified as mature. Similarly, class 18 was

labeled brain-fed cropsQ since this was an intensely croppedarea class that is heavily dependent on seasonal rains.

Table 1

LULC and irrigation area the study area in Ganges and Indus from MODIS time series images of 2001 and 2002

Class

(#)

Class name (name) MODIS

LULC

area (ha)

MODIS

LULC

percent (%)

Watering method

(irrigation

type/rainfed)

Ground

truth LC %

of tree

Ground

truth LC %

of shrubs

Ground

truth LC %

of grass

Ground

truth LC %

of cultivated

all the LCs within a LULC class (%)

1 Water: Lakes and Rivers 133883 0.1 NA NA NA NA NA

2 Water: Marshland or

estuary

36449 0.0 NA NA NA NA NA

3 Water: Glacial Lakes 23570 0.0 NA NA NA NA NA

Water Total 193901 0.1

4 Wetlands: Natural

vegetation


5 Wetlands: Agriculture 1059235 0.8 NA NA NA NA NA

Wetlands Total 1145850 0.9

6 Snow: Seasonal 2830150 2.1 NA NA NA NA NA

7 Snow: Year round 1507185 1.1 NA NA NA NA NA

Snow Total 4337335 3.3

8 Barren lands: Himalayas

with bright tones, river

beds and built-up

1649611 1.2 NA

9 Barren lands: Himalayas

with bright tones


Barren lands Total 2509085 1.9

10 Desert lands: Lower

NDVI

7779006 5.8 NA NA NA NA NA

11 Desert lands: Higher

NDVI

9752495 7.3 NA NA NA NA NA

Desert lands Total 17531501 13.2 NA NA NA NA NA

12 Mixed: Marshlands and

Himalayan barren lands

with dark tones

625817 0.5 NA

13 Mixed: Rice, other crops,

and wetlands

2731665 2.1 wetlands 1.0 0.3 20.0 75.3

14 Mixed: Rice, other crops,

shrubs, and young

secondary forest

22823167 17.2 rainfed+

supplemental

2.9 12.7 11.9 54.1

Mixed classes and crops

rice dominant Total

26180649 19.7

15 Mixed: Rangelands,

open areas, rainfed

crops, and sub-urban

built-up

4158052 3.1 rainfed 6.1 7.0 41.1 25.1

16 Mixed: Shrublands, fallow

lands, built-up, and others

3411039 2.6 rainfed 1.0 20.4 7.7 33.7

Rangelands and

shrublands Total

7569091 5.7

17 Rainfed Crops and

Rangelands

7584546 5.7 rainfed 1.4 5.3 29.5 43.5

18 Rainfed Crops 5347864 4.0 rainfed 0.0 5.0 0.0 95.0

Rainfed Total 12932411 9.7

19 Forests (open): mix with

rice and other crops

1822605 1.4 rainfed 1.5 0.0 13.8 66.8

20 Forests (open): mix with

rice and natural vegetation

2719730 2.0 rainfed 5.3 6.7 20.0 61.3

Forests (open) Total 4542335 3.4

21 Irrigated: Rice, sugarcane,

other crops

3150636 2.4 Canal+tube

well

3.8 0.5 2.0 91.3

22 Irrigated: Rice,

sugarcane, agroforests,

other crops


well

11.2 8.4 7.5 61.6

23 Irrigated: Other crops,

fallow farms, rice

16212207 12.2 tube well 1.4 1.5 1.8 90.8


Ground

truth LC %

of others

Ground truth

LC % of rice

only LC %

Actual tree area

of class (LULC

area of classtree

Actual shrub

area of class

(LULC area of

Actual grass area

of class (LULC

area of classgrass

Actual cultivated

area of class

(LULC area of

Actual other

areas of class

(LULC area of

Actual rice

area within

cultivated area

cover % of class)

(ha)

classshrubcover % of

class) (ha)

cover % of class)

(ha)

classcultivatedcover % of class)

(ha)

classothercover % of

class) (ha)

(LULC area

of classricecover % of

class) (ha)

NA NA NA NA NA NA NA NA











NA NA NA

NA NA NA NA NA NA

3.5 70.0 28000 6829 546333 2055578 94925 1912165

18.3 28.6 667578 2906784 2724833 12345432 4178542 6526792

20.6 1.4 255483 291064 1710741 1045453 855311 59401

37.1 12.1 34110 696827 263137 1150007 1266957 414198

20.3 0.0 103426 400602 2240958 3302656 1536905 0

0.0 0.0 267 267126 0 5080471 0 0

18.0 45.5 27795 0 250608 1216589 327613 829285

6.7 46.7 145052 181315 543946 1668101 181315 1269207

2.5 39.3 118149 15753 63013 2874955 78766 1236625

11.4 31.6 674781 504877 450459 3727321 688991 1910369

4.6 25.2 227480 241377 287188 14714662 741500 4083160

(continued on next page)


Table 1 (continued)

Class

(#)

Class name (name) MODIS

LULC

area (ha)

MODIS

LULC

percent (%)

Watering method

(irrigation

type/rainfed)

Ground

truth LC %

of tree

Ground

truth LC %

of shrubs

Ground

truth LC %

of grass

Ground

truth LC %

of cultivated

all the LCs within

a LULC class (%)

24 Irrigated: Water logged

crops (Indus), rice, shrubs


well

0.5 16.3 3.3 28.8

25 Irrigated: Rice with

wetlands

5607387 4.2 tube well 0.9 0.2 7.8 65.3

26 Irrigated: Rice and

other crops

6762875 5.1 tube well 1.6 0.3 5.5 87.4

Irrigated Total 45402568 34.1

27 Forests (Himalayan):

Mature

2412553 1.8 NA 31.7 15.3 33.3 0.0


Young and wetlands

6010237 4.5 floodplain/

tube well

19.2 8.0 10.0 16.5


Young

1635143 1.2 NA 25.0 1.0 60.0 0.0

Forests Total 10057933 7.6

30 Striping: Noise 618497 0.5 noise noise noise noise noise

Total Area from all

classes (ha)

133021156 100.0

Total area of particular LC from all LULC classes

Total % area of particular LC from all LULC classes

26.6

73.4

A total of 62.9% of the Ganges Indus basins is covered in this study. The actual LULC class areas are determined by multiplying LULC areas obtained from

MODIS images with LC percentages of each class determined during ground-truthing.


The ability to map a large number (29) of classes

(Fig. 8) even at 500 m spatial resolution, demonstrates

the strength of the 7-band near continuous MODIS data

and attests to the improved sensitivity of this instru-

ment compared to earlier sensors. Even within an

irrigated area class, 6 distinct classes (classes 2126 in

Table 1) were spectrally and temporally differentiated

(Fig. 9b).

Ground

truth LC %

of others

Ground truth

LC % of rice

only LC %

Actual tree area

of class (LULC

area of classtree

Actual shrub

area of class

(LULC area of

Actual grass area

of class (LULC

area of classgrass

Actual cultivated

area of class

(LULC area of

Actual other

areas of class

(LULC area of

Actual rice

area within

cultivated area

cover % of class)

(ha)

classshrubcover % of

class) (ha)

cover % of class)

(ha)

classcultivatedcover % of class)

(ha)

classothercover % of

class) (ha)

(LULC area

of classricecover % of

class) (ha)

51.3 22.5 38115 1238743 247749 2191623 3906805 1715183

25.8 53.8 50778 9657 438934 3662870 1445148 3018332

5.3 60.2 107618 17348 369018 5913105 355786 4072427

19.7 0.0 763975 369925 804184 0 474469 0

46.3 15.8 1151962 480819 602025 991689 2783742 951621

14.0 0.0 408786 16351 981086 0 228920 0

noise noise

ha. 4803355 7645397 12524211 61940511 19145695 27998764

% 3.6 5.7 9.4 46.6 19145695.4 21.0

Irrigated: canal ha 8793899 classes 21, 22,

and 24 in area

irrigated: tube

wells

ha 24290637 classes 23, 25,

and 26 in area

Irrigated (Total:

canal+tube well)

ha 33084536 classes 21 to

26 in area

% 24.9 classes 21 to

26 in %

Irrigated (Khariff

Total: canal+tube

well)

32555183 98.4 % of NET

Irrigated (Rabi

Total: canal+tube

well)

30603195 92.5% of

NET

Irrigated (Continuous Khariff-summer-Rabi Total:

canal+tube well)

1157959 3.5% of

NET

Irrigated (Gross from kariff, Rabi, continuous Total: canal+tube well) 64316337

Rainfed: Total ha 13463277 classes 15 to

20 in area

% 10.1 classes 15 to

20 in %

Rainfed+supplemental: Total ha 12345432 class 14 in area

% 9.3 class 14 in %

Wetland cultivation: Total ha 3047267 classes 13 and

28 in area

% 2.3 classes 13 and

28 in %

Cultivated: Total from all classes ha 61940511 Classes 13

to 29 in ha.

% 46.6 Classes 13 to

29 in %


5.5. Irrigated area, rice area and cropped area estimates

Each LULC class is a composite of several LC types

(see Table 1). For example, in class 22, cultivated areas

(61.6%) dominate but there are significant other LC types

that include other land cover (11.4%), trees (11.2%),

shrubs (8.4%) and grasses (7.5%) (Table 1). Of the

cultivated areas, 31.6% is rice cropthe single major

Fig. 8. The 29 LULC and irrigated area classes in the study area. Final 29 classes were mapped using 294 band MODIS data (42 MODIS images, each of 7

bands, during 2001 and 2002). The study area covers 63% of the Ganges and the Indus basins.


crop of the class. Sugarcane was the next major crop

although statistics of sugarcane are not shown in Table 1.

Precise estimates of various thematic areas within classes

were calculated as follows (see Table 1):

Tree area in class 22

LULC class area for class 22 LC percentage of tree for class 22

6; 046; 429 11:2=100 674; 781 ha 11:2%

Using the same approach, there were 504,877 ha (8.4%)

of shrubs, 450,489 ha (7.5%) of grasses, 372,7321 ha

(61.6%) of cultivated areas and 688,991 ha (11.4%) of other

areas. The rice crop alone totaled 1,910,369 ha (31.6%).

We propose the above approach to area calculations since

it takes into account sub-pixel composition of the pixels. Let

us take the example of irrigated area class 21 which has a

total area of 3,150,616 ha (Table 1). Every pixel of this class

is irrigated, but at different degreesome pixels are 100%

irrigated and some 50% and some others a different pro-

portion. In order to calculate exact area under irrigation for

this class, we will need to perform sub-pixel decomposition.

We adopt a fairly straightforward approach based on land

cover (LC) composition for the class based on ground truth

data. The accuracy of this approach increases with sample

size for the class. Since we have fairly large sample size

locations for each class we feel confident that our area

estimates are reasonable. Normally, most studies take non-

decomposed pixel areas as actual areas of a particular land

use class.

Field data on bwatering sourceQ (column 5 of Table 1)was used to define classes as irrigated, rain-fed, rain-fed

with supplemental irrigation and flooded or wetland

cultivated. Classes 21, 22 and 24 were canal irrigated and

classes 23, 25 and 27 were tube-well irrigated. The same

approach described in the previous paragraph was used to

estimate the irrigated areas in each class wherein the total

area is multiplied by LC percent for crops in class 21

through 27 (since these classes are exclusive irrigated

agriculture). For example, the irrigated area resulting in a

total irrigated area of 33,084,536 ha (24.9% of the total

study area). Of this, canal irrigated area was 8,793,899 ha

(6.6% of the total area of the study of 133,021,156 ha)

compared the tube-well supplied area of 24,290,637 ha

(18.3% of the total area). The cropland LCs of classes 23,

25, and 27 were exclusively tube-well irrigated. The

cropland LCs of classes 21, 22, and 24 were overwhelm-

ingly canal irrigated, but has some very minor tube well

irrigated mix that we ignore.

There were 12,345,432 ha (9.3% of the total area of the

study) of rain-fed areas with substantial supplemental

irrigation of one sort or another. A significant portion,

3,047,267 ha (2.3%) incorporated wetland cultivation.

All Class type biomass fluctuation for 2001 and 2002

-0.2

0

0.2

0.4

0.6

0.8

1

1 41 57 73 89 113

129

185

249

345

361 33 49 65 81 10

512

115

320

931

335

3

Julian Date

ND

VI V

alu

e

class 18-Rainfed Crops class 21- Irrigated: Rice, sugarcane, other crops

class 27- Forests (Himalayan): Mature class 5- Wetlands: Agriculture

class 7- Snow: Year round class 8- Barren lands: Himalayas with bright tones, river beds and built-up

class 10 Desert lands: Lower NDVI class 15- Mixed : Rangelands, open areas, rainfed crops, and sub-urban built-up

Irrigated crop biomass fluctuation for 2001 and 2002

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 33 41 49 57 65 73 81 89 105

113

121

129

153

185

209

249

313

345

353

361 1 33 41 49 57 65 73 81 89 105

113

121

129

153

185

209

249

313

345

353

361

Julian Date

ND

VI V

alu

e

class 21- Irrigated: Rice, sugarcane, other crops class 22- Irrigated: Rice, sugarcane, agroforests, other crops

class 23- Irrigated: Other crops, fallow farms, rice class 24- Irrigated: Water logged crops (Indus), rice, shrubs

class 25- Irrigated: Rice with wetlands class 26- Irrigated: Rice and other crops

a

b

Fig. 9. MODIS NDVI signatures over time. With the availability of near-real-time MODIS data it is possible to develop LULC spectral signatures over time. (a)

Illustrates MODIS NDVI signatures for 8 spectrally distinct classes over 2 years. The classes are spectrally separable, distinctly, from each other at one time or

the other. (b) Illustrates MODIS NDVI signatures for 6 spectrally close irrigated area classes over 2 years. Time series MODIS data enables separability even

within close classes at one time or the other.


Purely rain-fed dryland cropping was estimated at

13,463,277 ha (10.1%).

Rice is grown not only in the 6 irrigated classes 2126,

but also in other classes albeit only to some extent. Specific

LC percentages of rice crop within LULC classes 1329

were used to estimate the total rice area of 27,998,763 ha

(21% of the total study area) (Table 1).

The total cultivated land area in the study region is

61,940,511 ha (46.6% of the total area). Classes 112 have

almost no area under cultivation or irrigation and occupy

18.6% of the total area. LC percentages were not measured on

the ground, as these classes are relatively pure (e.g., LULC

classes composed one predominant LC type) and also rather

inaccessible. These classes were identified, based on their

spectral characteristics, GeoCover Landsat TM images, their

geographic location and from numerous other sources of data

(e.g., USGS LULC classification, Loveland et al., 2000).

It is important to note that the precise class areas pre-

sented in Table 1 were applicable to the khariff season only,

as LC percentage data were not available for other seasons.

We adopted a unique strategy to determine intensity of

irrigation in different seasons. The maximum monthly

NDVI composite images for different seasons were masked

out taking the spatial extent of irrigated areas of classes 21

Table 2

Cropping pattern for different classes in the study area in Ganges and Indus river basins

MODIS LULC class # Khariff crops Summer crops Rabi crops

crop-1 crop-2 crop-3 crop-4 crop-1 crop-2 crop-3 crop-4 crop-1 crop-2 crop-3 crop-4

1 NA NA NA NA NA NA NA NA NA NA NA NA












13 Rice maize Moong arhar Rice maize moong arhar Wheat Gram Barley Mustard

14 Rice Jawar Basra Moong Rice Jawar Basra Moong Wheat Barley Gram Mustard

15 Jawar Soybean Basra Rice Jawar Soybean Basra Rice Wheat Barley Sugarcane Mustard

16 Jawar Urd Moong Rice Jawar Urd Moong Rice Wheat Barley Gram Mustard

17 Jawar Basra Gowar Jawar Basra Gowar Wheat Barley Gram Mustard

18 Jawar Basra Arhar Jawar Basra Arhar Wheat Barley Gram Mustard

19 Jawar Rice Basra Maize Jawar Rice Basra Maize Wheat Barley Mustard Maize

20 Rice Jawar Basra Vegetables Rice Jawar Basra Vegetables Sugarcane Wheat Barley Potato

21 Rice Jawar Sugarcane Vegetables Rice Jawar Sugarcane Vegetables Wheat Barley Sugarcane Berseem

22 Jawar Rice Mango Vegetables Jawar Rice Mango Vegetables Wheat Sugarcane Barley Mustard

23 Jawar Basra Arhar Rice Jawar Basra Arhar Rice Wheat Barley Gram Mustard

24 Jawar Rice Vegetables Jawar Rice Vegetables Wheat Sugarcane Vegetables

25 Jawar Rice Arhar Maize Jawar Rice Arhar Maize Wheat Sugarcane Mustard Vegetables

26 Rice Jawar Basra Maize Rice Jawar Basra Maize Wheat Barley Gram Mustard

27 NA NA NA NA NA NA NA NA Wheat Mustard Maize Sugarcane

28 Jawar Rice Basra arhar Jawar Rice Basra arhar NA NA NA NA


30 noise noise noise noise noise noise noise noise noise noise noise noise

The cropping pattern are given for different seasons.

P.S.Thenkabailet

al./Rem

ote

Sensin

gofEnviro

nment95(2005)317341

332

Table

3

Irrigated

area

comparisonsbetweendifferentstudiesforstudyarea

inGanges

andIndus

Studyarea

Totalarea

ofthis

studyin

hectares

relative

tototal

basin

area

(ha)

Totalarea

ofthis

study

inpercent

relative

tototal

basin

area

(%)

Irrigated

area

USGSusing

AVHRR1000

m19921993

36im

ages

each

of1NDVI

band(ha)

Irrigated

area

USGSusing

AVHRR1000

m19921993

36im

ages

each

of1NDVI

band(%

)

Irrigated

area

GLC2000

usingSPOT

1000m

2000

36each

of1

NDVIband

(ha)

Irrigated

area

GLC2000

usingSPOT

1000m

2000

36each

of1

NDVIband

(%)

Irrigated

area

thisstudyusing

MODIS

500-m

20012002

42

images

each

of

7-bands(ha)

Irrigated

area

thisstudyusing

MODIS

500-m

20012002

42

images

each

of

7-bands(%

)

Irrigated

area

withsupplemental

thisstudyusing

MODIS

500-m

2001200242

images

each

of

7-bands(ha)

Irrigated

area

with

supplementalthis

studyusing

MODIS

500-m

2001200242

images

each

of

7-bands(%

)

Ganges

andIndusbasins

133021156

63

40046229

30.1

72614135

54.6

33084536

24.9

45429968

34.2

Ganges

basin

90221264

95

32255630

35.8

56466954

62.6

26873934

29.8

37602567

41.7

Irrigated

area

inthisstudybased

onMODIS

dataof20012002relativeto

irrigated

area

byUSGS1993:studyarea

inGanges

andIndus

(%)

13.4

increase

Irrigated

area

inthisstudybased

onMODIS


irrigated

area

byUSGS1993:Ganges

basin

(%)

16.6

Irrigated

area

inthisstudybased

onMODIS


irrigated

area

byGLC2000:studyarea

inGanges

andIndus

(%)

37.4

decrease

Irrigated

area

inthisstudybased

onMODIS


irrigated

area

byGLC2000:Ganges

basin

(%)

33.4

Irrigated

areasmapped

usingdifferentstudiesiscompared

withthisstudy.


through 26. The masked areas for different months in the

seasons were then classified and areas irrigated and fallow

were determined based on their seasonal NDVI dynamics.

The classes will cluster based on NDVI dynamics. A class

with high degree of irrigation (say 90100% of pixel areas

are irrigated) will have a higher NDVI threshold over 24

months of a growing season relative to a class with a low

degree of irrigation (say 2030% of pixel areas are

irrigated). Based on this approach, we determined 98.4%

of this area during Khariff (JuneOctober), 92.5% during

Rabi (NovemberFebruary), but only 3.5% all through the

year or continuous (apart from Khariff and Rabi, also in

MarchMay) cropping.

5.6. Cropping pattern

The cropping pattern of classes 1329 are given in Table

2 for khariff and rabi. In some cases, there is a short interim

season between rabi and khariff when summer crops are

grown if water is available, and according to ground survey

these are the same combinations as for khariff. The irrigated

area classes, 2126, have either rice or sorghum as main

crops during khariff and, where applicable, in summer. The

cropping mix in rabi is generally wheat-sugarcane or wheat-

barley. The main rain-fed crops, classes 17 and 18, have

sorghum-millet in khariff, but change to wheat-barley in rabi

(Table 2). During the field work, the authors were

accompanied by highly knowledgeable local agricultural

experts from the Indian National systems (see Acknowl-

edgements) who were instrumental in determining rabi and

summer crops at each field plot location, at times involving

interview with local farmers.

5.7. Irrigated area comparison with other studies

The results of this study were compared with: (a) USGS

study using monthly AVHRR 1-km NDVI time series from

April 1992 to March 1993 (Loveland et al., 2002), and (b)

global land cover (GLC) for year 2000 using monthly SPOT

1-km data (Belward et al., 2003). In the GLC2000 study,

data from the 4 spectral bands of the SPOT sensor were

used: blue (0.430.47 Am), red (0.610.68 Am), infrared(0.780.89 Am) and shortwave infrared (1.581.75 Am).

In the entire study area, the combined irrigated and

supplemental irrigated areas mapped using 20012002

MODIS data in this study showed an increase of 13.4%

to 45,429,968 ha, compared with the USGS figure of

40,046,229 ha (Table 3). The GLC2000 irrigated areas

(72,614,135 ha) did not tally with our study. This is

because GLC has 2 irrigated area classes (class 32 and 33)

with a contrasting definition. Class 32 is irrigated with

intensive agriculture, which is similar to our irrigated area

classes. Almost all of the spatial distribution of this class

fell within our irrigated-area classes. However, the

GLC2000 class number 33 (irrigated agriculture) with

38.4 million hectares is a predominantly rain-fed with some


irrigated. Almost all of our rain-fed classes, 17 and 18, fall

within this class 33. In addition, classes we identified as

rangelands and some of the forests are also labeled irrigated

agriculture by GLC200.

The comparisons are only indicative to show how the

irrigated areas are estimated for Ganges river basin by

various studies using remote sensing datasets of wide

range of characteristics. The results of this study

performed at 500-m were compared with AVHRR and

SPOT classifications performed at 1-km scale. The USGS

AVHRR study use 2 broad bands (band 1 and 2), The

GLC SPOT study used 4 broad bands (2 visible and 2

SWIR). This study used 7 narrow bands (4 visible, 2

NIR and 2 SWIR). Thereby, differences in LULC or

irrigated areas are often as a result of factors such as

data types, class definitions, level at which the classes

are mapped, analysis methods and techniques, and

resources spent on analysis and classification schemes

rather than change per se.

5.8. Tree cover, shrub and grass cover in the basin

The tree shrubs and grasses are predominant in classes 27

to 29. But often, other classes have a significant percentage

of one of these cover types. For example, 11.16% of the

land cover in irrigated-area class 22 was trees as a result of

agroforests forming part of the cropping system. Using the

tree, shrub and grass percentages of all classes we found

there was 4,803,355 ha (or 3.6% of the total area) of trees,

7,645,397 ha (5.7%) of shrubs, and 12,524,211 ha (9.4% of

grasses) in the study area.

5.9. Class signatures, NDVI-reflectivity thresholds, and

onset-peak-senescence-duration of crops

The class signatures of NDVI (CS-NDVI) are unique

time series of a class using NDVI or spectral reflectivity

in individual wavebands. It is not possible to have

temporal signatures when single date or a few date

images are used as is often the case with most LULC

studies. The set of NDVI class signatures is shown in Fig.

9a and b for classes mapped in Fig. 8. Threshold NDVIs

and NDVI signatures over time help us determine the

onset and duration of cropping seasons (rabi and khariff),

the intensity of cropping in drought and normal years and

the end of a cropping season.

MODIS CS-NDVI signatures are presented and dis-

cussed for a set of distinct classes (Fig. 9a) and

thematically similar classes (Fig. 9b). The NDVI of forest

class 27 never falls below 0.5 on any date throughout a

year and across years (Fig. 9a). The agricultural lands in

wetlands (class 5) have a moderately high NDVI

throughout the year as a result of continuous soil moisture

availability. The rainfed agriculture (class 18 in Fig. 9a)

shows the dramatic differences in NDVI dynamics during

the normal year (2001) vs. drought year (2002). During

the normal year, the NDVI for Khariff season steeply

raises from Julian day 160, reaches peak NDVI of 0.25

and then starts falling reaching low values again around

Julian day 300. In contrast, during 2002 the NDVI never

rose above 0.2 and near complete crop failure is obvious

relative to NDVI dynamics of 2001. Rangeland class 15

has a sharp NDVI increase from about 0.25 during the

driest period to little over 0.6 during the monsoon from

June to October. During khariff, this is a class with rise in

NDVI almost similar to that of irrigated-area class 21.

However, the 2 classes are distinctly separate during other

periods. As expected, the desert class has a near-flat

NDVI across the year.

The temporal signatures of the six irrigated classes are

plotted in Fig. 9b. Irrigated class 21 peaks on day 49 (rabi

crop peak), reaches the lowest biomass around day 129 (dry

season, low), and reaches peak again around day 249

(khariff, crop peak). The cycle is remarkably similar for

both 2001 and 2002 (see Fig. 9a). For example, the rabi crop

peak green period or critical growth phase was around

Julian day 57 during 2001 and day 49 during 2002.

Senescence begins around day 89 during year 2001 and

day 81 during year 2002. Based on these results, the

nominal crop duration from sowing to harvest during khariff

is (Fig. 9a) 180 days (Julian day 153333 days), rabi is 142

days (from day 333 of 1 year to the next year Julian day

110), and a short dry season of no cropping for 43 days

(days 110153).

The six irrigated-area classes are identified by subtle

differences between these classes. Most of these classes

were dominated by rice and other irrigated crops during

khariff. Crop vigor, biomass levels and percent area

cultivated are comparable at certain times of the year,

but not at other times (Fig. 9b). In spite of many

similarities, the classes often provide significantly different

NDVI signatures (Fig. 9b) at one time or another during a

year. There are several reasons for this. The first is the

type of land cover within and between these classes. Class

22, for example, is found mainly along the Indus river

basin, is heavily irrigated and flooded (31% water) or

moist throughout the year, suppressing NDVI substan-

tially. The presence of flooding or wet soils may result in

substantial absorption in near-infrared leading to low

NDVI throughout the season. Irrigated land accounts for

85% of all cereal grain production (mainly rice and

wheat), all sugar production and most of the cotton

production (Khan, 1999). Class 21, for example, is

basically dryland that is irrigated whereas other classes

like 22 exhibit higher moisture levels. The NIR reflec-

tance in drier lands with vigorous vegetation is substan-

tially higher than the NIR reflectance in irrigated areas

with substantial moisture or water logging. The second is

that differences occur in LC percentages within and

between classes. Class 26, for example, has about 20%

more rice than class 21 (Table 1). Class 21 has greater

percentage of other crops including sugarcane. The third is


that the differences in irrigated classes 2126 can also be

attributed to differences in the cropping calendar between

these classes.

The threshold MODIS NDVI limits of the class are

time-dependent (e.g., Fig. 9a and b). For example, in

class 21, the threshold NDVI range during Julian days 1

to 81 was (a) 0.62 to 0.75 during 2001 and (b) 0.53 to

0.77 during 2002. No other class has such a high

threshold MODIS NDVI. But these fall drastically in

summer when the NDVI thresholds for Julian days of 113

to 153 were (a) 0.23 to 0.27 during 2001 and (b) 0.2 to

0.24 during 2002. The pattern of very high NDVI during

crop growing seasons and low NDVIs between crop

seasons is a characteristic of the irrigation classes in the

Ganges and Indus basins. Classes 22 and 25 are the only

classes with relatively high NDVI during summer as a

result of the presence of significant agroforests (11.2%,

listed under forests in Table 1) in class 22 and significant

summer cropping (17.5%, listed under other land covers

Table 4

Fuzzy classification accuracy assessment (FCAA)

MODIS LULC

class (#)

Sample

size

Fuzzy

classification

accuracy

TOTAL

Correct

(%)

Fuzzy

classification

accuracy

TOTAL

Incorrect

(%)

Fuzzy

classification

accuracy

(absolutely

correct) (100 %

correct) (%)

Fuzzy

classification

accuracy (mo

correct) (75 %

and above

correct) (%)

1 10 70 30 30 40

2 10 70 30 30 20

3 10 100 0 70 30

4 8 63 38 13 13

5 10 100 0 50 40

6 10 90 10 70 20

7 10 100 0 100 0

8 10 90 10 40 30

9 10 100 0 50 50

10 10 80 20 60 20

11 10 100 0 80 10

12 8 75 25 38 25

13 10 100 0 50 20

14 40 85 15 48 25

15 10 80 20 60 0

16 10 60 40 30 20

17 13 69 31 46 15

18 8 88 13 63 25

19 8 88 13 25 25

20 8 88 13 0 50

21 8 100 0 50 38

22 20 75 25 30 15

23 37 84 16 49 24

24 9 56 44 44 11

25 19 79 21 37 11

26 23 100 0 52 30

27 8 75 25 13 50

28 8 63 38 25 0

29 8 63 38 38 25

30 NA NA NA NA NA

Total (%) 82.3 17.7 44.4 23.5

The quantitative FCAAwas performed on all MODIS derive LULC classes were d

images during field visit, geo-cover Landsat TM images, and, rarely, land use ma

in Table 1) in class 25 as a result of the availability of

water or moisture. All classes have a distinct cropping

calendar, onset-peak-senescence cycle and the biomass

magnitudes.

In stark contrast to irrigated-area classes, the rain-fed

class 18 (Fig. 9a), has a MODIS NDVI around 0.15

throughout the year 2001 with an NDVI between 0.2 and

0.28 during days 185249 with a peak around day 209

(Tables 3 and 4). During 2001, NDVI of rain-fed class 17

rises to a peak of 0.46 on day 209 with values of 0.35 on

day 185 and 0.39 on day 249. During the rest of the year,

the NDVI of class 17 is between 0.2 and 0.3. During

2002, the rains failed and as a result the NDVI of classes

17 and 18 never rose above 0.33 and 0.17, respectively,

indicating a severe drought situation in rain-fed areas. The

irrigated classes were not affected by the drought of 2002,

hence a similar pattern of MODIS NDVI magnitudes,

durations, and onset-peak-senescence cycles was main-

tained as in 2001 (Fig. 9b).

stly

Fuzzy

classification

accuracy

(correct) (51 %

and above

correct) (%)

Fuzzy

classification

accuracy

(incorrect)

(51 % and above

incorrect) (%)

Fuzzy

classification

accuracy

(mostly incorrect)

(75 % and above

incorrect) (%)

Fuzzy

classification

accuracy

(absolutely

incorrect) (100 %

incorrect) (%)

0 20 10 0

20 20 10 0

0 0 0 0

38 38 0 0

10 0 0 0

0 0 10 0

0 0 0 0

20 10 0 0

0 0 0 0

0 10 10 0

10 0 0 0

13 13 13 0

30 0 0 0

13 10 5 0

20 10 10 0

10 30 10 0

8 23 0 8

0 0 0 13

38 0 0 13

38 0 13 0

13 0 0 0

30 25 0 0

11 3 8 5

0 11 22 11

32 16 5 0

17 0 0 0

13 13 0 13

38 13 13 13

0 25 0 13

NA NA NA NA

14.4 9.9 4.8 3.0

etermined using ground truth point data, observations marked on maps and

ps from other sources.

Soil Line

0

10

20

30

40

1

33

41

4957

6573 81

89

105113121

129

153185

209

249

313

345

353361

133

4149

57 6573

8189

105

113

121

129

153

185

209249

313

345

353

361

133

41

49

576573 81

89

105

113 121

129

153

185

209

249

313345

353361

1

33

41

49

57

657381

89105

113121

129153

185

209

249

313

345353

361

1

33

4149

57

65

73 8189105

113121

129153

185 209

249

313345

353361

LULC ClassesWater: Lakes and Rivers (class 1)-2001Mixed: Marshlands and Himalayan barren lands with dark tones (class 12)-2001Rainfed Crops (class 18)-2001Irrigated: Rice and other crops (class 26)-2001Forests (Himalayan): Mature (class 27)-2001

Soil Line

LULC Classes

Irrigated: Rice, sugarcane, agroforests, other crops (class22)-2001

Irrigated: Water logged crops (Indus), rice, shrubs (class 24)-2001

Irrigated: Rice with wetlands (class 25)-2001

0 10 20 30 40

Mean Reflectance (%) MODIS Band 1

0 10 20 30 40


0

10

20

30

40

Mea

n R

efle

ctan

ce (%

) MO

DIS

Ban

d 2

Mea

n R

efle

ctan

ce (%

) MO

DIS

Ban

d 2

1

49

73105

129209

345

1

49

73

105

129

209

3451

4973 105

129

209

345

a

b

Fig. 10. Space-time spiral curves (ST-SCs) to study subtle and not-so-subtle changes in LULC spectral separability. The ST-SCs are a unique and powerful

representation of observing subtle and not so subtle changes over time mapped in 2-dimensional feature space. MODIS band reflectance in band 1 (red) and

band 2 (NIR) are used to plot ST-SCs for: (a) 5 spectrally distinct LULC classes and (b) 6 spectrally similar irrigated area classes. As the spectral properties of

classes change over time, we can observe dates on which 2 or more classes spectral intersect (no spectral separability) or stay spectrally separate highlighting

the near-continuous interval multi-temporal data in LULC studies.



5.10. Space time spiral curves (SC-STs)

The space-time spiral curves (ST-SCs) (Fig. 10a and b)

are introduced as an innovative approach to represent and

track near continuous changes in class behavior over time

and space. The dynamics of five classes are shown in a 2-

dimentional feature space using MODIS class reflectivity in

Fig. 10a. In most cases, classes have their own bterritoryQand mostly move around within it. In Fig. 10a the rain-fed

class is in bbrightness territory,Q the irrigated class inbgreenness territoryQ and the water class in bwetnessterritory.Q ST-SCs depict change over time and capture themagnitudes of change no matter how subtle (e.g., an

incremental increase in leaf area) or dramatic (e.g., clear-

cut forests), temporary (e.g., senescing crop) or more

permanent (e.g., built-up areas in place of agricultural

lands). The classes shown in Fig. 10a rarely overlap one

another, providing an excellent opportunity to separate

classes on most dates. SC-STs tell us when two classes

have similar spectra and when they are most separable. In

contrast, the irrigated-area classes significantly overlap one

another on most dates (Fig. 10b) since these classes are

spectrally close to one another. However, there are one or

more dates when a given class is separable spectrally from

other classes. For example, on day 209 all six classes have a

unique place in the 2-d FS as shown in SC-ST (Fig. 10b)

and, similarly, on day 153.

5.11. Performance of MODIS spectral wavebands in

irrigated area mapping

The best wavebands for irrigated area mapping were

determined using 3 distinct methods. First, the class

7-band mean reflectance values for

0

5

10

15

20

25

30

35

40

45

MODIS band/ date

Mea

n R

efle

ctan

ce (%

)

class 21 Irrigated: Rice, sugarcane, other crops class 22 Irrigated: Rice, sug

class 24 Irrigated: Water logged crops (Indus), rice, shrubs class 25 Irrigated: Rice with

3 4 1 2

129

5 6 7 3 4 1 2

153

5 6 7 3 4 1 2

185

5 6 7

Fig. 11. Multi-band reflectivity signatures (MB-RS) is separating closely related

irrigated area classes we use the spectral strength of 7 MODIS bands. For example

sensor and this provides maximum separability in 6 closely related irrigated area

signatures based on multiple band reflectivity (CS-MBR)

of 7 MODIS bands were used to see spectral differ-

entiation of close classes such as irrigated area classes 21

26 (see Fig. 11). The greatest difference in reflectivity

between the six irrigated classes was found in MODIS

band 5. The results were consistent across dates,

confirming the utility of this band in separating spectrally

close irrigated-area classes. This band centered on 1240

nm is a unique 20 nm-wide narrow-band not found in

satellite sensors currently orbiting the globe except

MODIS and Hyperion, aboard the Earth Observing-1

(EO-1) satellite.

Second, multiband feature space plots (MB-FSP; Fig.

12ae) were used to determine class separability. Closely

related irrigated-area classes 2126 were used in the test in

order to determine whether the use of multiple wavebands

help separate them. There is good evidence that classes 23

and 25 (which were close to each other in band 1 vs. band

2 (Fig. 12a) prove to be distinct when band 2 and band 7

(Fig. 12b), band 6 vs. band 7 (Fig. 12c), and band 2 vs.

band 3 (Fig. 12d) are plotted. The results generally

support the use of multiple wavebands in irrigated-area

mapping and highlight the importance of bands 2, 7, 6 and

3 in that order.

Third, we determined the frequency of occurrence of

least redundant bands (FO-LRB) in irrigated area map-

ping. Correlations for 7 band (k1) by the 7 band (k2)matrix (see Thenkabail et al., 2004a, 2002) were

established for each class taking all 42 dates into account

and it was found that the higher the correlation between

bands greater the redundancy and vice versa. In each

correlation matrix, we plotted and looked for the lowest

R2 values (least redundancy) between two bands. Thus,

Irrigated classes: May 9-Nov. 9, 2001

arcane, agroforests, other crops class 23 Irrigated: Other crops, fallow farms, rice

wetlands class 26 Irrigated: Rice and other crops

3 4 1 2

209

5 6 7 3 4 1 2

249

5 6 7 3 4 1 2

313

5 6 7

irrigated area classes. In order to separate closely related classes such as 6

, the MODIS band 5 (centered at 1240 nm) is a unique band in any satellite

classes.

21

2223

24

25

26

21

22

23

24

2526

0 10 20 30 40


0 10 20 30 40


0 10 20 30 40


0 10 20 30 40


0

10

20

30

40

Mea

n R

efle

ctan

ce (%

) MO

DIS

Ban

d 2

0

10

20

30

40

Mea

n R

efle

ctan

ce (%

) MO

DIS

Ban

d 2

0

10

20

30

40

Mea

n R

efle

ctan

ce (%

) MO

DIS

Ban

d 2

0

10

20

30

40

Mea

n R

efle

ctan

ce (%

) MO

DIS

Ban

d 6

21

2223

24

25

26

Irrigated classes for Sept. 6, 2001 MODIS Bands 2 and 6




21

2223

24

25

26

a b

c d

Fig. 12. Multi-band bispectral plots (MB-BP) for separating closely related irrigated area classes. Spectra of different combinations of MODIS bands were

plotted in 2-dimensional feature space to evaluate class separability of 6 irrigated area classes. There is remarkable improvement in class separability of 2

classes. Some classes like 23 and 25 that were close to each other when band 1 (red) vs. band 2 (NIR) are plotted (a) show distinct separability when 2 mid-

infrared bands, band 6 vs. band 7, are used (c).


the two-band combinations providing the three lowest R2

values were recorded for all classes. Similar plots and

selections of bands for each of the 42 classes were carried

out. The NIR band 2 occurred most frequently (over 25% of

the time), followed by the two shortwave infrared (SWIR)

bands 7 and 6. This means that MODIS band 2 is most useful

in LULC classifications followed by SWIR bands 7 and 6,

further supporting the results from the previous section. The

SWIR bands are subjected to less attenuation due to

atmospheric water (Kerber & Schutt, 1986) and more

strongly correlated with biophysical properties of vegetation

than the visible and NIR wavelengths (Foody et al., 1996).

6. Fuzzy classification accuracy assessment (FCAA)

Relative classification accuracies (Table 4) were eval-

uated using a fuzzy approach (Mickelson et al., 1998;

Woodcock & Gopal, 2000). Error matrix accuracies are

deterministic (correct or wrong; yes or no) whereas the

accuracy in this study is based on a fuzzy (absolutely

correct, mostly correct, correct, incorrect, mostly incorrect,

and absolutely incorrect) approach. When there is no simple

byes/noQ answer, but a scaled approach to error/accuracyassessments, the method is referred to as bfuzzyQ.

FCAA procedure begins with overlaying the 300

independent ground-truth data points (Fig. 4) on the 29

classes (Fig. 8). Great care was taken to avoid misregistra-

tion problems between field plot data, Landsat high

resolution imagery, and coarse resolution MODIS imagery

when performing fuzzy classification approach. This was

done by collecting field plot data from highly representative

locations for each of the 29 classes. All 300 independent

points were selected with care to avoid uncertainties and

ambiguities. The sites were selected by driving in the

vehicle; marking coordinates using a GPS, and recording


distances in vehicle odometer. The 300 locations were very

large homogeneous areas of at least 33 pixel areasrepresenting one of the 29 classes.

Actual LULC data available from the 33 pixel areagathered during ground truthing were compared with

classes mapped. A quantitative approach to FCAA was

adopted. At each point, if 9 pixels out of 9 pixels in the

LULC map matched exactly with ground-truth data then we

called it babsolutely correct.Q Similarly, the criterion adoptedwas: mostly correct (7599% correct), correct (5174%

correct), incorrect (2450% correct), mostly incorrect (1

24% correct) and absolutely incorrect (0% correct). Using

this approach we established a fuzzy classification criterion

for all points within each class with sample sizes for each

class varying between 8 and 40. These results were used to

derive a final single fuzzy classification criterion for each

class (Table 4). For example, in class 26 there were 23

ground-truth locations, each of 3 by 3 pixel area for a total

of 207 pixels. Of these, 108 pixels (52%) were absolutely

correct, 63 pixels (32%) were mostly correct and 36 (17%)

correct.

Overall, the FCAA established that the 29 classes were

accurate from 56 to 100%17 classes from 80% to 100%, 6

classes from 70% to 80%, and 6 other classes from 56% to

70% accurate. A high degree of classification accuracy was

observed when a large number of classes are mapped and

this highlights the value of using time-series multiple band

MODIS data in contrast to low levels of accuracy, 1654%,

reported for AVHRR data (e.g., Friedl et al., 2000; Strahler

et al., 1999).

Fuzzy logic also allows a qualitative understanding of the

impact of misclassification (Muchoney & Strahler, 2002). A

misclassification of classes other than irrigated areas is more

acceptable in this study, since we wish to determine irrigated

area and intensity. Intermixing between forest classes or an

irrigated-area class intermixing with another irrigated-area

class is more acceptable than an irrigated-area class getting

mixed with forests. The six irrigated-area classes, 2126, had

an accuracy (in %) of 100, 75, 84, 56, 79 and 100. Only class

24 had a low accuracy of 56%, but it mixed almost only with

the other five irrigated classes. The forest classes 28 and 29

had a low accuracy, also, mainly due to intermixing between

the two classes. In the International Geosphere Biosphere

Programmes Data and Information System (IGBP-DIS)

global land cover data, nearly 60% of the problems

addressed in the post-classification process for the set arose

from confusion between natural vegetation and agriculture

(Loveland et al., 1999). Similar issues arose with MODIS

data (Friedl et al., 2000).

7. Conclusions

This study proposed and implemented a suite of

techniques and methods for mapping irrigated-area and

other LULC classes at river-basin level using near-

continuous time-series (8-day) MODIS 7-band reflectance

data. The study espoused a new unique approach to time-

series MODIS data analysis that begins with a single 7-GB

mega file dataset of 294 bands (42 images of 7-bands

each) for Ganges and Indus basins during year 2001 and

2002. The study resulted in mapping a large number (29)

of classes (Fig. 8, Table 1), yet maintaining high levels of

accuracy (56100% with most classes accurate between

80% and 100%; Table 4). The study highlighted the use of

FCAA technique in coarse resolution data accuracy

assessments.

A new powerful concept of space-time spiral curves (ST-

SCs), which quantitatively tracks subtle and not-so-subtle

changes of spectral reflectivity of irrigated and LULC

classes in the 2-dimensional feature space (2-d FS) near-

continuously over time was introduced. The ST-SCs

establish the space-time domain of each class, demarcate

the precise bterritoryQ in which a particular class roams overtime in a 2-d FS, and identify the date/s on which a class is

best separable from other classes. A s

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