Journal of Global Resources
doi.org/10.46587/JGR.2020.v06i02.008

Volume 6 (02)
July 2020
Page 49-55
ISSN: 2395-3160 (Print), 2455-2445 (Online)

08
ACCURACY ASSESSMENT AS A TOOL OF VALIDATION OF REMOTELY SENSED DATA
OF LAND USE/LAND COVER OF NATIONAL CAPITAL TERRITORY OF DELHI, INDIA
Sandesh Yadav1* and Shams Perwaiz2
Freelance Researcher, New Delhi, India
2
Teacher, Sr. High School, Jokihat, Araria (Bihar) India
Corresponding author’s Email: sandesh_official@yahoo.in
1

Abstract: Accuracy assessment is a tool of validation of remotely sensed data and other spatial
information and analyses the ‘goodness factor’ of the maps derived from satellite images. If wetalk about the remotely sensed data of land use/land cover then accuracy assessment helps inanalyzing the spatial details of particular landscape which in lieu serve the various purposes ofpolicy making and administrative planning. The data related to land use/land cover is of utmost
importance and helps in environmental modelling specifically models dealing with climatechange at all levels viz. micro level, meso level and macro level. The present research study
aims to analyze the accuracy assessment of remotely sensed data of land use/land cover ofnational capital territory of Delhi, India. Three different sets of Landsat images for the years 1990,
2000 and 2015 have been evaluated by level of overall accuracy, producer’s accuracy,
consumer’s accuracy and kappa coefficient.
Key words: Accuracy Assessment, Validation, Land Use/Land Cover, Kappa Coefficient.
Introduction
Accuracy assessment or validation of spatial data facilitates the decision making process. The
estimates based on accuracy assessment are more accurate, efficient and effective. Mapping
related to different issues viz. forestation/deforestation, wetlands and estimation of land use/landcover will be inappropriate and incomplete without accuracy assessment or validation. This
investigates validity of map with all possible error. The present study of land use/land cover ofNCT of Delhi has been discussed in the light of quantitative accuracy assessment.
Study Area
The study looks at the city of Delhi, national capital of India (Figure 1) due to its high degree ofurbanization, dominant grey infrastructure and high density of population. It is located between
the latitude 28°24´17´´ N and 28°53´00´´ N and longitude 76°50´2´´ E and 77°20´37´´ E. It has anarea of about 1483 km2 with maximum length of 51.90 km and maximum width of 48.48 km. The
study employs Landsat-4, 5, TM for 7 November 1990; Landsat-7 ETM+ for 16 November 2000
and Landsat-8 OLI for 9 November 2015 (see Table 1). All images were obtained from thearchives of United States Geological Survey (USGS). The Three scenes fell within the path 147
and row 40 of the WRS-2 (Worldwide Reference System) from which the data for the location
under the study could be extracted. All bands 1-5 and 7 have spatial resolution of 30 m and thethermal infrared band (band 6) has a spatial resolution of 60m for Landsat 7 and 120m forLandsat 5. For Landsat OLI, thermal infrared band (10, 11) has a spatial resolution of 100m.
Objective of the Study
The objectives of the present study are as follows:
1. To identify the land use/land cover classes (1990-2015) through the Anderson’s satelliteimage classification in the National Capital Territory of Delhi, India.
2. To analyze the accuracy assessment of land use/land cover classification (1990-2015) ofNational Capital Territory of Delhi, India.
49

Figure 01: Location Map of Study Area

Methodology
The present research study involves steps viz. pre-image processing, Image processing,post-image processing, and generation of LU/LC map and accuracy assessment. The
description/ definition of land use/land cover terms has been adopted from Manual of National
wide land use/Land Cover Mapping using multi-temporal Satellite data of NRSC (2006).Work ofGoodchild (1989) on accuracy of space derived data and Janssen (1904) on accuracy of land
use/land cover data, are also considered to derive results.
Figure 02: Pathway for the Methodology adopted
Landsat Data Sets 1990,
2000 and 2015
Layer Stacking
Pre-image processing

Band Combination

Geometric Correction

Data Pre-processing andExtraction of Study Area

Radiometric Correction

Training Data Collection
Image Processing

Supervised Image Classification
1990, 2000 and 2015
Re-coding – thematic editing

Post-Image Processing
Validation - Google Earth
Overall Accuracy

Accuracy Assessment

User Accuracy
Producer Accuracy
Kappa Co-efficient50

LULC Maps of 1990,
2000 and 2015

Data Set and Data Sources
In the present research study, the land surface biophysical types retrieved from Landsat thematicmapper, enhanced thematic mapper and operational land imager images of Delhi for 1990, 2000
and 2015 were analyzed. Table 1 shows the details of satellite images used in the present
research study. The satellite images of the National Capital Territory of Delhi for three different
periods that is 1990, 2000, 2015 were obtained from the United States Geological Survey(USGS) and National Remote Sensing Centre (NRSC), Hyderabad, India.
Table 01: Landsat Meta-Data for the Study Area
City

Path/Row Satellite Platform
147/40Delhi

Landsat-4,5 TM
Landsat-7 ETM+
Landsat-8 OLI

Band
Date Acquired
SpectralThermal
1-5 & 7607/11/1990
1-5 & 7, 8
6
16/11/2000
1-910, 11
09/11/2015

Resolution
30m/120m
30m/60m
30m/100m

Source: United States Geological Survey, 2016

Tool of Analysis
For accuracy assessment a discrete multivariate technique was used called KAPPA Index(Cohen, 1960). The result of performing a KAPPA analysis is a KHAT statistic (an estimate ofKAPPA), which is another measure of agreement or accuracy. The KHAT statistic is computedas:𝑁 ∑𝑟𝑖=1 𝑥𝑖𝑖 − ∑𝑟𝑖=1(𝑥𝑖 +∗ 𝑥+𝑖 )∆=𝐾
𝑁 2 − ∑𝑟𝑖=1(𝑥𝑖 +∗ 𝑥+𝑖 )Where,

is the number of rows in the matrix,

is the number of observations in row

and column ,andare the marginal totals of row I and column I, respectively, andis the total number of observations (Bishop et. al., 1975). Congalton et al., 1993 originallyproposed the use of this statistics for remotely sensed data to clear up the confusion caused by atopological error. KHAT equation assumes a multinomial sampling model and that the variance isderived using the delta method. Cohen's (1960) kappa index is used to measure the accuracy.
Hudson (1987) worked on correct formation of kappa coefficient. The first mention of a
kappa-like statistic is attributed to Galton (1892) and Stehman (1995).
Software and Platforms
The following software and platforms are involved in the present research study:
1. ArcGIS ver. 10.1
2. Statistical Package for the Social Sciences (SPSS).
3. Office 365.
Results and Discussion
Image Interpretation
Pre-image processing is the first stage in dealing with spatial data and involves steps like layer
stacking, band combination, geometric correction and radiometric correction. Here, geometriccorrection involves removal of aberrations of image while on the other hand; radiometriccorrections involve atmospheric correction, noise correction and shadow correction. Image
processing begins with the defining of training sites (AOI) and these training sites were selectedon the basis of clearly identified area in satellite image through the on-screen digitized features.
This digitization process is followed by the extraction of signatures in order to obtain informationof each pixel on the image. The final step in image processing is the supervised classification ofimage to obtain different classes of land use/land cover. During this process, non-parametric rule
was used and image was classified into six class’s viz. Water body, vegetation, crop land, fallow
land, river bed and built-up. Post-image processing step involves thematic editing using the51re-coding step of supervised classification and map was generated through the layoutprocessing in ArcGIS 10.4.
Figure 03: Land Use and Land Cover, 1990, 2000 and 2015, NCT of Delhiththth

Source: Landsat Imagery, 7 November 1990, 16 November 2000 and 9 November 2015.

Land Use/Land Cover Changes during the Period 1990-2015
Before carrying out interpretation of maps, we need to understand the colour scheme adopted in
the maps (Fig. 3) to understand the changes in land use/land cover during the period 1990-2015
in the study area. As per the colour scheme, water body and vegetation cover are depicted bytheir natural colors that is blue and green respectively while built-up and cropland are depictedby red and yellow colour respectively. The other categories viz. Fallow land and barren land arerepresented by pink and magenta colour respectively. The prevalence of red colour on the LULC
map of 1990 shows the dominance of built-up with variations in built-up density at the core andperipheral regions of the study area. At the core, northern part, north-eastern part, eastern part,south-eastern part, southern part and north-western part are regions of high built-up density whileon the other hand, the rarefied concentration of the built-up exist in the peripheral areas ofnorthern part, south-eastern part, south-western part, western part and north-western part of thestudy area. The LULC category of cropland has its limits in peripheral areas of northern part,south-western part and north-western part of the study area. The vegetation cover was present in
the core and peripheral areas of the eastern part and south-eastern part of the study area. The
fallow land has been seen in the peripheral areas of northern part, south-western part andnorth-western part of the study area. The LULC map for the year 2000 confirms an increase in thedensity of built-up both at the core and periphery of the study area. During the period 1990-2000,
the built-up expansion took place in the south-eastern part, south-western part, western part andnorth-western part of the periphery while the built-up became denser at the core of the study
area. The LULC map for the year 2015 shows shrinkage in areal expansion of cropland,
vegetation cover and fallow land due to built-up expansion. During the period 2000-2015, thecropland experienced slight decrease in area in the peripheral parts of south-western part,western part and north-western part of the study area. On the other hand, the vegetation cover
got reduced in area drastically with huge losses at the periphery of the study area.
The study area has an area of 1,48,413.0 hectare (Table 3). The categories occupying major
share include built-up area (42.4 percent), vegetation (30.4 percent), and cropland (17.8 percent)while the minor share occupying categories include fallow land (7.4 percent), water bodies (1.3percent) and riverbed (0.8 percent). During the period 1990-2000, built-up experienced
tremendous increase from 42.4 percent (1990) to 49.7 percent (2000). These categories include
vegetation [30.4 percent (1990) and 25.1 (2000)], crop land [17.8 percent (1990) and 16.9
percent (2000)], fallow land [7.4 percent (1990) and 6.4 percent (2000)], river bed [0.8 percent52

(1990) and 0.7 percent (2000)] and water bodies [1.3 percent (1999) and 1.2 percent (2000)].
During the period 2000-2015, the similar trend was observed in land use/land cover changes.
The built-up showed drastic increase in its area while the other categories (except river bed)
followed similar declining trend as in the year 2000. Consequently, the built-up increased from49.7 percent (2000) to 58.2 percent (2015) and the categories which experienced decline in area
includes water bodies [1.2 percent (2000) and 1.1 percent (2015)], vegetation [25.1 percent(2000) and 21.3 percent (2015)], fallow land [6.4 percent (2000) and 3.6 percent (2015)],
riverbed [0.7 percent (2000 and 2015)] did not shown any change.
Table 03: Trend and Magnitude of Land Use and Land Cover, 1990-2015, NCT of Delhi
LULC Classes

1990
Hectares

2000

Percent

Hectares2015

Percent

Hectares

Percent

1990-2000

2000-2015

Percent

Percent

Water Bodies
1899.4
1.3
1819.8
1.2
1583
1.1
-0.05
Vegetation
26402.8
30.437225.5
25.1
31675.3
21.3-5.31
Cropland
45118.8
17.825012.4
16.922468.7
15.1-0.936
Fallow land
10980.9
7.4
9519.66.4
5308.03.6-0.989Riverbed
1114.40.8
1016.20.7
1001.90.68-0.066
Built-up
62897.342.473819.5
49.7
86375.6
58.2+7.539Source: Calculated and compiled by the scholar from Landsat imagery of 1990, 2000 and 2015

-0.15
-3.73
-1.71
-2.83
-0.009+8.46

Accuracy Assessment
A confusion matrix scheme is used for quantitative accuracy assessment of image classification
for all the years. The verification and original classification pixels are along x-axis and y-axis
respectively in the resultant matrix. Producer’s accuracy and User’s accuracy, has been
calculated for each class.
Table 04: Accuracy Assessment Land Use and Land Cover Classification, 1990Waterbody
451211050Vegetation
030010031

Cropland
123801042

Fallow
land210281133

Riverbed
120438146

Builtup
1002045
48

Row
total
50
3640
36
4147
250

User’s
Accuracy
90.00
83.33
95.00
77.78
92.68
95.74

Water body
Vegetation
Cropland
Fallow landRiverbed
Built-up
Column total
Producer’s
Overall
Accuracy
90.00
96.77
90.48
84.85
82.61
93.75
89.60
Accuracy
Source: Calculated and Compiled by the Scholar from Landsat images of 1990, 2000 and 2015

For the year 1990 (Table 4), The producer’s accuracy, 1990, for water body, vegetation,crop land, fallow land, riverbed, built up is 90.00 percent, 96.77 percent, 90.48 percent, 84.85percent, 82.61 percent, 93.75 percent respectively. The user’s accuracy for water body,vegetation, and crop land, and fallow land, riverbed, built up is 90.00 percent, 83.33 percent,95.00 percent, 77.78 percent, 92.68 percent, and 95.74 percent respectively. For the year 2000
(Table 5), the producer’s accuracy, 2000, for water body, vegetation, crop land, fallow land, riverbed, built up is 90.00 percent, 93.75 percent, 86.36 percent, 83.78 percent, 80.85, 86.54 percent
respectively. The user’s accuracy for water body, vegetation, and crop land, and fallow land,riverbed, built up is 90.00 percent, 81.08 percent, 86.36 percent, 77.50 percent, 88.37 percent,and 93.75 percent respectively.
For the year 2015 (Table 6), The producer’s accuracy, 2015, for water body, vegetation,
crop land, fallow land, riverbed, built up is 89.58 percent, 97.22 percent, 90.48 percent, 87.50percent, 84.00 percent, 93.75 percent respectively. The user’s accuracy for water body,53

vegetation, and crop land, and fallow land, riverbed, built up is 89.58 percent, 85.37 percent,95.00 percent, 81.40 percent, 93.33 percent, and 95.74 percent respectively. The classified
images of 1990, 2000 and 2015 have an overall accuracy of 89.6 percent, 86.64 percent, 90.15percent respectively. On the other hand, the Kappa statistics of the classified images of 1990,
2000, 2015 were calculated as 0.89, 0.86 and 0.91 respectively (Table 7).
Table 05: Accuracy Assessment Land Use and Land Cover Classification, 2000
Water
Vegetation Crop Fallow
Riverbed
BuiltRow
User’s
LULC
body
land
landupTotal
Accuracy
Classes
Water body
45
0
12
1
150
90.00
Vegetation
130
2
12
137
81.08
Cropland
2138
11
144
86.36
Fallow land
11131
4
240
77.50
Riverbed
10
1
138
243
88.37
Built-up
00
1
1145
48
93.75
ColumnTotal
50
3244374752
262
Producer’s
OverallAccuracy
90.00
93.75
86.36
83.78
80.8586.5486.64
Accuracy
Source: Calculated and Compiled by the Scholar from Landsat images of 1990, 2000 and 2015

Table 06: Accuracy Assessment Land Use and Land Cover Classification, 2015
LULC Classes

Waterbody

Vegetation

Cropland

Fallow
landRiver bd

Built upRowTotal

User’s
Accuracy

Water body
43
0
12
1
148
89.58
Vegetation
135
2
12041
85.37
Cropland
2038
00040
95.00
Fallow land
11035
424381.40
Riverbed
101142045
93.33
Built Up
000114547
95.74
Column Total
483642405048
264
Producer’s
OverallAccuracy
89.58
97.22
90.48
87.50
84.00
93.75
90.15 Accuracy
Source: Calculated and Compiled by the Scholar from the Landsat images of 1990, 2000 and 2015

Table 07: Summary of Accuracy (percent) and Kappa Statistics
LULC Classes

1990 Accuracy
2000 Accuracy
2015 Accuracy
Producer’s
User’s
Producer’s
User’s
Producer’s
User’sWater body
90.0
90.00
90.00
90.00
89.58
89.58
Vegetation
96.77
83.33
93.75
81.08
97.22
85.37
Cropland
90.48
95.00
86.36
86.36
90.48
95.00
Fallow land
84.85
77.78
83.85
77.50
87.50
81.40
Riverbed
82.61
92.6880.85
88.37
84.00
93.33
Built-Up
93.75
95.74
86.54
93.75
93.75
95.74
Overall Accuracy
89.60
86.64
90.15
Kappa Statistics
0.89
0.86
0.91
Source: Calculated and Compiled by the Scholar from the Landsat images of 1990, 2000 and 2015

Conclusion
Accuracy assessment or validation justifies the ‘goodness factor’ of maps and facilitate thedescriptive and analytical evaluation of the spatial data. Irrespective of user, whether novice orexpert, accuracy assessment need to be made necessary and sufficient part while carrying out
interpretation of spatial data.

54

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