03SPATIO-TEMPORAL ANALYSIS OF LAND USE AND LAND COVER IN DELHI-NCR
USING SATELLITE DATASant Prasad1*, R. B. Singh2 and B. W. Pandey3
1

Research Scholar, 2,3 Professor,
Department of Geography, Delhi School of Economics, University of Delhi, Delhi, India.
Email: rbsgeo@hotmail.com, bwpdsegeo@gmail.com*Corresponding author Email: santprsd1@gmail.com

How to cite this paper:

Prasad, Sant; Singh, R.B.
and Pandey, B.W. (2021)
Spatio-Temporal Analysis ofLand Use and Land Cover in
Delhi-NCR Using Satellite
Data, Journal of Global
Resources, Vol. 07 (02)DOI:
10.46587/JGR.2021.v07i02.003

Received: 21 March 2021
Reviewed: 10 April 2021
Revised: 26 April 2021
Final Accepted: 23 May 2021

Abstract: This paper provides a comparative analysis of land
use and land cover (LULC) changes among the study area of
two decades (1999-2019). Landsat images (i.e., Landsat-5
and Landsat-8) acquired during the years of 1999, 2009, and2019 were used to examine LULC changes in Delhi-NCR. A
classification composed of five classes- i.e., Crop Land,
Barren Land, Forest, Water, and Built-up were design for thisstudy. This research indicates the necessity to implementchange detection at multiple scales for better understanding
the mechanism of LULC change patterns. The result indicatesthat the significant changes in LULC have occurred over time,with a rise in built-up, water, and cropland, and a decrease inforest and barren land. Built-up area has increased by 89.29
percent and water by 29.34 percent and crop land by 1.85
percent, while the forest and barren land have decreased by 81.51 and -9.02 percent respectively.
Keywords: Satellite Imagery, Land Use and Land Cover,
Change Pattern, Delhi-NCR

Freely available Online

www.isdesr.org

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Introduction
Due to India’s rapid population and economic growth, land use and management practices are
rapidly evolving, having a significant effect on the urban environment (Wilken et al., 2017).
Urbanization is causing massive changes in land cover (Singh, 1998; Prasad & Singh, 2018),
which effects the surface’s sensible and latent heat fluxes. In comparison to vegetative areas,
concrete impervious surface areas have higher sensible heat, resulting in higher temperaturesof urban built-up surfaces (Hang & Rahman, 2018). It has been described as one of the mostvisible manifestations of human alteration of the earth, and as a result, it has become a key
component of global environmental studies (Stemn & Agyapong, 2014; Higgins, 2019). Rapid
urbanization, driven by population and economic development, places additional demands onnatural resources, resulting in land use changes (Singh & Kumar, 2012; Liaqat et al., 2021;
Zerga et al., 2021; Wang et al., 2021), especially in megacities (Mohan et al., 2011). Rapid
urbanization results in unplanned and disorganised development of towns and cities, and it
may be the most strong and noticeable anthropogenic force responsible for profound changes
in land cover and landscape pattern around the world (Das & Das 2017; Nishit, Pandey, Anandand Kumar, 2021; Pandey, Mishra and Pathak, 2020).
The LULC monitoring in rapidly urbanizing environments using remote sensing and
GIS for quantifying and analysing urban landscape change dynamics and managing healthyurban ecosystems (Mugiraneza, Ban & Haas, 2019). It is critical to reveal the temporal andspatial shifts in built-up land expansion and population growth in order to ensure the city’s longterm viability (Luo et al., 2018). The key cause of LULC changes is the transfer of other formsof land to uses related to population development and economic activities. As a result, LULC
are complex in nature and must be controlled on a regular basis to ensure long-term
development. So, land use refers to how humans use the land and its resources, such as
agriculture, grazing, logging, etc. (Mukherjee et al., 2009). LULC adjustment has become acritical component of emerging natural resource management and environment andenvironmental monitoring strategies (Pandey, 2005; Pandey, Prasad, Mishra, & Godara, 2017;
Kaul & Sopan, 2012; Wang, Munkhnasan, & Lee, 2021; Rasool, Fayaz, ul Shafiq, Singh, &Ahmed, 2021).
Geospatial technology is critical for spatial and temporal monitoring of land use andland cover change (Kafy et al., 2021; Tadese, Kumar, Koech, & Kogo, 2020), as well as
developing a relationship between land use and land cover change and their effect on theurban environment over time, which is crucial for environmental management (Chaudhuri,
Singh & Rai, 2018). Remote sensing and GIS techniques have emerged as new methods formonitoring land use changes (Goyal, Sharma & Singh, 2019). By characterising andquantifying changes in land use and land cover, remote sensing technology is one validated
technique for better documenting and understanding urbanization (Wentz et al., 2008).
Monitoring and quantifying urban development aids decision-making for the avoidance ofnegative environmental and socio-economic effects (Sapena & Ruiz, 2019). Advances inremote sensing have provided useful tools to effectively track and examine complex changes
in land use and land cover changes (Chaudhuri & Mishra, 2016). Landsat 4 and 5 (TM) havea spatial resolution of 120 m, while Landsat 7 (ETM+) has a finer resolution thermal data (60m) (Hazaymeh & Hassan, 2015). The Landsat-8 satellite (30 m) has two sensor payloads: The
Operational Land Image (OLI) and the Thermal Infrared Sensor (TIRS) (Roy et al., 2014).
The key goal of the NCR was to relieve congestion in Delhi by redistributing populationpressure to ring towns. Therefore, the NCR come into existence in 1985, when somesurrounding districts of neighbouring states become part of it (Saroj, Sharma & Prawasi, 2015).

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The NCR is characterized by a surge of physical and economic growth of Delhi and underdevelopment of the area outside it. This has been changing the land use and land cover of the
NCR with profound regional environmental implications and a concomitant de-stabilization of
the rural economy and food demand of the region, as evident from a range of localized studies
across many agricultural areas within (Suzanchi & Kaur, 2011; NCRPB, 2020).
Study Area
The National Capital Region (NCR) is located between 27.60oN to 29.30oN and 76.20o E to78.40 o E with a population density about 800 people per square kilometre, and spans fourstates: Delhi, Haryana, Rajasthan and Uttar Pradesh with a total 23 districts (NCRPB, 2020).
The NCR covered a total area of 58, 332 km2 during the study period, with Haryana occupying
the largest portion (28,545 km2), followed by Uttar Pradesh (14,826 km2), Rajasthan (13,447
km2) and Delhi (1483 km2), (NCRPB, 2020) (Figure 1).

Figure 01: Location of the Study Area
(Source: Prepared by authors, 2020; NCRPB, 2020)

Objective
The primary goal is to determine the spatial-temporal patterns of land use/cover change overthe last two decades (i.e., 1999 – 2019).

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Sources and Methods
Landsat-5 and Landsat-8 satellite data derived from the United States Geological Survey(http://globis.usgs.gov) for three periods were used. The flow diagram depicts the techniqueused to detect changes in land use and land cover in the figure 2.

LandsatSatellite Data

Band Stacking at 30-meter
resolution (NIR, Blue,
Green, Red)

Layer
stacked Image

Ground Tagged Plots

Google Earth
Signature
Reference Data

Mosaicking

Supervised MAXLClassification Algorithm

LU/LC Classified maps

Testing Plots

Accuracy Assessment

Resultant Maps &Statistics

Figure 02: Methodology Framework(Source: Prepared by Authors, 2020)

For this analysis, a maximum likelihood classifier has been used (Maselli, Conese,
Petkov, & Resti, 1992), which determines the probability that a given pixel belongs to aparticular class based on the assuming that the statistics for each class in each band arenormally distributed. The class with the highest probability is allocated to each pixel (that is,
the maximum likelihood) (Paola, & Schowengerdt, 1995). The Erdas accuracy assessment
tool is used for validation and accuracy assessment, which creates random points on theclassified image with the aid of classified classes and reference classes, and assigns values.
After that, an error matrix is created to verify overall accuracy, and the kappa coefficient isdetermined which represents the proportionate reduction in error caused by a classification
system relative to random classification. The overall accuracy is determined by multiplying thenumber of correctly classified values by the total number of values. The correctly categorised
values are found on the uncertainty matrix’s upper-left to lower-right diagonal. The total numberof values in either the truth or predicted-value arrays is the total number of values. The kappa
coefficient is a calculation of how well classification and truth value agree. Perfect agreement
is represented by a kappa value of 1, while no agreement is represented by a kappa value of
0. The kappa coefficient is computed as follows:

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Where:• i is the class number• N is the total number of classified values compared to truth values• mi, i is the number of values belonging to the truth class i that have also been classified
as class i (i.e., values found along the diagonal of the confusion matrix)• Ci is the total number of predicted values belonging to class i• Gi is the total number of truth values belonging to class i
RESULT AND DISCUSSIONSLand Use/ Cover Classification and Change Detection Accuracy
Overall accuracies for season-classified images of land use and land cover for the years 1999
and 2019 were 92.00 percent and 90.00 percent respectively, with kappa statistics of 90.00
percent. Though overall accuracy for 2009 was 88.00 percent with kappa statistics 85.00
percent, overall accuracy for 2019 was 86 percent with kappa statistics 82.50 percent. The
classification of images was performed with great care, but the classified images revealed
some inconsistency between built-up and open space (This is due to the fact that reflectancevalue are similar). Repeated classifications with more ground truths/training sites did notenhance classification accuracy. We increased the number of classes for built-up and barren
land, but mixing was observed due to similar reflectance. After applying the “haze reduction”
filter to the consolidated data, the two sections were classified separately using the above
mentioned “ordered clustering” method. The two independently classified sections were thenmosaicked to create a single season (haze-free) classified image with enhanced overall
accuracy and kappa statistics. A comparison of classified satellite image-based gross and net
shown area estimates with corresponding estimates from independent ground-based land use
inventories was used to assess the accuracy of satellite-based land use inventories. The
results of this study, which included the base (1999) and most recent (2019) years, revealed
that the discrepancies between the satellites were important.
Land Use /Land Cover Change Statistics
Despite the fact that land use/land cover classification maps were created for all three years,
it is found that increasing the number of classes for the training site improves the overall
accuracy of LULC classified maps, individual class area, and land use/land cover and change
statistics for season classified table 1 and figure 3 summarise these numbers, respectively(Figure 4-6).
Table 01: Summary of Land Use/ Land Cover Classification Area Statistics for 1999,
2009 & 2019Land UseClasses

1999
Area
(km2)

Percent

2009
Area
(km2)

Percent

2019 Area(km2)

Percent

Relative Change
Percentage (20191999)

Crop
Land
BarrenLand
ForestWater
Built up28289.33

64.83

30931.01

70.88

28812.42

66.03

1.85

11446.94

26.23

8568.0919.64

10413.88

23.87

-9.02

1680.03171.552048.35

3.85
0.39
4.69

535.95110.853490.3

1.23
0.25
8.00

310.62221.883877.4

0.71
0.51
8.89

-81.51
29.34
89.29

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Relative change percentage (1999-2019)
Built up

LULC Classes

Water

ForestBarren Land/ OpenLand

Crop Land
-100

-80

-60

-40

-20
0
20
Percentage Change

40

60

80

Figure 03: Relative percentage change of LU/LC (1999-2019)(Source: Prepared by Authors, 2020)

Suggestions and Recommendations
1. It’s important note that the forest area is declining, which is a major cause of pollution
control concern. Consequently, it is expected that the value of agricultural land
converted to built-up land will increase during the urban development process, making
it suitable for urban development.
2. Land use/cover planning, as well as strategic regional planning in general, are critical
tools for directing long-term urban and environmental development. Land regeneration,
despite this, is a very elegant and economically viable choice for urban growth andredevelopment.
3. Land redevelopment would help to balance the benefits and disadvantages of urban
planning.
4. In order to achieve sustainability, industrial growth is economically advantage, thedevelopment of spatial data infrastructure is needed, which will play a critical role in thedecision-making process for the spread of economic activities and the spatial
expansion of urban centres necessary for the environment’s and urban development’s
long-term sustainability.
5. Appropriate governmental policies are needed for the construction of basic
infrastructure

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Figure 4-6: LU/LC pattern of Delhi-NCR(Source: Prepared by Authors, 2020)
Conclusion
It is clearly showing that between 1999 and 2009, croplands increased by 6.82 percent, whilebarren land and water decreased by 6.76 percent and 2.33 percent, respectively. Matrixes ofland cover adjustments from 1999 to 2019 were also produced to help assess the effects ofJournal of Global Resources, July 2021, Volume 07 (02) Sant Prasad, R.B. Singh and B.W. Pandey Page 29 | 9land cover conversions. The major rise was 89.29 percent in built-up areas, owing to a 52.2
percent increase in urban population between 1989 and 1998. It’s worth noting that, in contrast
to previous years where the rural population was lower than the urban population, between
1991 and 2001, the urban population was around 29.3 percent higher than the rural population.
Conversion of 8.9 percent of desert lands, 5.5 percent of ridge and forest lands, and 3.8
percent of croplands led to the overall increase 26.81 percent in built-up land.
Acknowledgements: The lead author is thankful to University Grant Commission (UGC) for
providing funding. The authors also acknowledge to National Institute for the Humanities(HINDAS, University of Hiroshima, Japan). The authors also grateful to the all reviewers fortheir valuable suggestions, which lead to significant improvement of the Manuscript.
Conflicts of Interest: The authors declare that there is no financial or other conflict of interest.
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