LAND USE AND LAND COVER CHANGE ANALYSIS OF KOTLA SUB-WATERSHED OF PUNJAB USING REMOTE SENSING AND GIS

LAND USE AND LAND COVER CHANGE ANALYSIS OF
KOTLA SUB-WATERSHED OF PUNJAB USING REMOTE
SENSING AND GIS
Dr. Arun Kaushal, Professor
SWE, PAU, Ludhiana
Dr. D.C Loshali, Head
Forestry & Land use Division
PRSC, Ludhiana
Presented by
Amritpal Digra
L-2018-AE-208-M
M.Tech. Remote Sensing & GIS
Final Oral Examination (Thesis Seminar)
Co-Major Advisor
Major Advisor

CONTENTS
1. INTRODUCTION
2. REVIEW OF LITERATURE
3. MATERIALS AND METHODS
4. RESULTS AND DISCUSSION
5. CONCLUSIONS
6. REFERENCES
3

INTRODUCTION
• Land use and land cover (LU/LC) are two terms arranged together
but have different meanings.
• Land use generally reflects the human’s activities while land
cover refers to the physical condition of the land (Singh et al
2010).
• LU/LC can be observed by the remote sensing sensors directly
and can be represented at different scales.
• Land use and land cover are directly proportional to each other
(Rawat and Kumar 2015).
4

• Watershed is the part of land which is defined as the hydrological unit
and an area that generally drains the surface water to a common outlet
and used for managing the naturally available resources (Chang 2008).
• Information about different parameters that can affect the watershed
behavior can be collected through remote sensing technology
(Chowdary et al 2009).
• There are several factors which are playing important role to make
changes in LU/LC in watershed area. Therefore, it is important to
manage and conserve the watershed with proper strategies (Soni et al
2015; Sakthivel et al 2010).
• Geo-informatics application and satellite data of selected area is
helpful to analyze the changes in that particular area.
5

• Remote sensing is used to obtain the information about the features
present on the surface of earth without making any physical contact.
• Geographic Information System (GIS) is the system which is used to
record, store, manipulate, manage, analyze and the spatial
representation of data (Khorram et al 2012; Sadoun and Rawashdeh
2009).
• Changes in the pattern of the land use and land cover provides the
essential information to understand the situations on the earth’s surface
and this information is helpful for natural resource management and
it’s planning.
6

• Combined remote sensing and GIS technologies have been proved
very important tool which is used by researchers for various
projects like water resource development and its management,
watershed characterization and prioritization (Chowdary et al
2009).
• Keeping all these aspects in mind the present study on LU/LC
change analysis of Kotla sub-watershed of Punjab had been
planned with following objectives:
7

OBJECTIVES
1. To generate land use and land cover map of Kotla sub-
watershed, Rupnagar district of Punjab using remote
sensing and Geographic Information System.
2. To detect the land use and land cover change in the study
area over a decade using remote sensing and GIS
technique.
8

REVIEW OF LITERATURE
1. Land use and Land cover classification using Remote
Sensing and GIS.
2. Land use and Land cover change analysis using Remote
Sensing and GIS techniques.
3. Accuracy Assessment of land use/land cover classification
using Remote Sensing and GIS.
4. Application of Remote Sensing and GIS for watershed
management.
10

1. LAND USE AND LAND COVER CLASSIFICATION
USING REMOTE SENSING AND GIS
Rawat et al (2013) applied geospatial techniques for LU/LC
classification of Ramnagar, Uttarakhand using LANDSAT (TM) of
1990 and 2010 data. Result showed that in 1990 built-up, vegetation,
agricultural, water bodies and sandbar areas covered 3.91%, 32.26%,
50.36%, 7.22% and 6.25% respectively, while in 2010 it covered
12.79%, 22.85%, 49.67%, 4.46% and 10.23% area respectively.
Shafiq et al (2016) analyzed LU/LC map of Hamal watershed of North-
western Himalaya’s, Kashmir using LISS-III (2012) satellite data. The
results showed that forest land covered 29% and crop land covered 56%
of the total area in 2012.
11

Singh (2016) mapped land use land cover classes for Samastipur
District (India) using LISS III satellite data of three crop growing
seasons Kharif, Rabi and Zaid. The result of classified maps showed
that agricultural land occupied 85.02% (2276.58 k𝑚2
) which was
higher than other LU/LC classes.
Worako (2016) classified LU/LC maps in Akaki river basin using
Landsat data for the years of 1985, 2011 and 2015. The result showed
that waterbody covered 5.11 k𝑚2
(1985), 5.91 k𝑚2
(2011) and 5.03
k𝑚2(2015), forest 156.34 k𝑚2(1985), 146.45 k𝑚2(2011) and 191.75
k𝑚2(2015), agriculture 1035.94 k𝑚2(1985), 1001.19 k𝑚2(2011) and
907.12 k𝑚2
(2015) and built-up 414.35 k𝑚2
(1985), 458.17 k𝑚2
(2011)
and 507.83 k𝑚2(2015).
12

2. LAND USE AND LAND COVER CHANGE ANALYSIS
USING REMOTE SENSING AND GIS TECHNIQUES
Oinam et al (2005) analyzed LU/LC changes for the years 1991-2001
in Jahlma watershed of the Lahaul valley using IRS-1A and IRS-1D
satellite data. The result showed that the cultivated land had been
increased from 54.87 % in 1991 to 56.89 % in 2001, while there was
decrease in grassland from 31.41 % in 1991 to 29.81 % in 2001.
Prakasam (2010) detected LU/LC change of Kodaikanal over 40
years’ period (1969-2008) by using Survey of India map (1969) and
LANDSAT imageries of 2003 and 2008. Result showed that: Forest
area decreased from 70 % (1969) to 33 % (2008) while built-up land
increased from 3 % to 21 % for the study area.
13

Tiwari and Saxena (2011) detected LU/LC changes in an around
Mandideep and Obedullaganj between 1967 and 2003 using Survey of
India toposheet, LANDSAT-5, LISS-III, PAN IRS ID imageries of the
year 1967, 1992 and 2003. The results showed that built-up, agriculture
and wasteland area had been increased by 38.31%, 0.9%, 74.96%
respectively, while dense forest had been decreased by 57.81% form
1967-2003.
Sushanth et al (2018) conducted a study on temporal land use change in
Patiala-Ki-Rao watershed of Shivalik foot-hills situated in the Mohali
district of Punjab using Landsat data for the years 2006 and 2016. Study
concluded that due to urbanization the agricultural and forest land had
been decreased by 64.57 ha and 194.90 ha respectively. 14

3. ACCURACYASSESSMENT OF LAND USE/LAND
COVER CLASSIFICATION USING REMOTE SENSING
AND GIS
Yuan et al (2005) examined accuracy assessment of LU/LC classification
of the Twin Cities (Minnesota) Metropolitan area by using Landsat (TM)
data for the years 1986, 1991, 1998, and 2002. The overall accuracy i.e.
user’s accuracy and producer’s accuracy of LU/LC classification ranged
from 80% to 90% for all the years.
Soni et al (2015) analysed accuracy assessment of LU/LC classification in
the Chakrar watershed, Madhya Pradesh using LANDSAT imageries of
the years 1990, 2000, 2005, 2011 and 2013. The results showed the
overall accuracy of LU/LC classification for all the years was 91% and
kappa (K) was 0.89. 15

Rwanga and Ndambuki (2017) analyzed accuracy assessment of
LU/LC classification using satellite data of Landsat 8 OLI/TIS of 2015.
The result showed an overall accuracy of LU/LC classification as
81.7% with Kappa (K) coefficient 0.72.
Chowdhury et al (2018) assessed the accuracy of LU/LC classification
of Halda watershed, Bangladesh, for 40 years (1978-2017) using
LANDSAT-2 (MSS) for 1978, LANDSAT-5 (TM) for 1999 and
LANDSAT-8 (OLI/TIRS) for 2017 data. The results showed an overall
accuracy of LU/LC as 88%, 88.64% and 89.22% with Kappa (K) value
as 0.78, 0.80 and 0.84 for the years 1978, 1999 and 2017 respectively.
16

4. APPLICATION OF REMOTE SENSING AND GIS FOR
WATERSHED MANAGEMENT
Kar et al (2009) studied soil hydro-physical properties and morphometric
analysis of a rainfed watershed as a tool for sustainable land use planning
in eastern India Bahasuni watershed, Dhenkanal, Orissa. Satellite data of
IRS-P6, LISS IV and ground truth information had been used for planning
of sustainable land use. The result showed the circulatory ratio about 0.56
which indicated the shape of the basin and area not prone to flood.
Kanth and Hussan (2010) prioritized watersheds in Wular catchment
situated in Sopore, Bandipore and Sonawari tehsils for sustainable
development and management of natural resources. There were 8
watersheds which had been fall under high priority zone, 8 were under
medium and 3 were under low priority zone. The highest priority level had
been attained by the 1EW2b Watershed. 17

Iqbal and Sajjad (2014) prioritized watershed using morphometric and LU/LC
parameters of Dudhganga catchment Kashmir Valley. Different morphometric
parameters had been determined for each watershed and assigned them ranks.
LU/LC changes had been analyzed using remote sensing data of Landsat TM of
1991 and 2010. The result showed significant changes in LU/LC and watershed
was classified into three categories i.e. high, medium and low for natural
resource conservation and management.
Biswas and Chakraborty (2016) prioritized watershed based on
geomorphometry and land use parameters for watershed development and
management using Remote sensing and GIS in Neora watershed, Darjeeling and
Jalpaiguri districts, West Bengal. Detailed study of linear, relief and shape
aspects of morphometric parameters and LU/LC analysis in 13 sub-watersheds
and their prioritization for watershed management had been done.
18

Place of Research Work
Place of Research work was Punjab Remote Sensing
Centre (PRSC), Ludhiana and Department of Soil and
Water Engineering, Punjab Agricultural University,
Ludhiana.
20

Location of Study Area
• The study area is Kotla sub-watershed of Punjab which is situated in
Anandpur Sahib block of Rupnagar district of Punjab. The study area
lies in Latitude 31° 11' 36" N - 31° 16' 40" N and Longitude 76° 30' 51"
E - 76° 36' 57" E.
• Temperature of the study area varies between 4° C in winter to 45° C in
summer season with average rainfall varying from 700mm-800mm.
• Soil texture of the study area varies from loam to silt clay loam
(rupnagar.nic.in).
21

Details of Remote Sensing (Satellite) Data
Satellite imageries which have been used in the present study i.e. Cartosat-1 and
LISS-IV. It is provided by Punjab Remote Sensing Centre (PRSC), Ludhiana. The
details of data are as follows:
Satellite Sensor Bands
Band Wavelength
(µm)
Resolution
(m)
Swath
(km)
Year
IRS-P5 Cartosat-1
PAN
(Panchromatic)
PAN = 0.5-0.85 2.5 30 2009
IRS-P6 LISS-IV
Green (B2)
Red (B3)
NIR (B4)
B2 = 0.52-0.59
B3 = 0.62-0.68
B4 = 0.77-0.86
5.8 23.9 -70
2009
and
2018
23

Satellite image from IRS-P5 Cartosat-1 of Kotla sub-watershed in 2009
24

25
False color composite of satellite image from IRS-P6 LISS-IV of Kotla sub-watershed in 2009

False color composite of satellite image from IRS-P6 LISS-IV of Kotla sub-watershed in 2018
26

1. ArcGIS Version 10.4.1
2. Digital camera to capture the pictures of different LU/LC
classes in study area during ground truthing.
3. GPS to take geo-coordinates at the time of ground truth
data collection.
4. Google earth to verify randomly selected points.
27
Software and Tools used

1. Image Interpretation
1) All three satellite imageries Cartosat-1 (2009), LISS-IV (2009) and
LISS-IV (2018) were opened (one image at a time) in ArcGIS software
to classify LU/LC classes by visual interpretation on the basis of image
interpretation keys (photo-elements) such as shape, size, tone, texture,
shadow, site, association and pattern (Satyawan et al 2015; Singh
2016).
2) Clipped the area of interest (AOI) i.e. Kotla sub-watershed by using
extract by mask tool which is in-built tool in ArcGIS software.
3) New vector shape file was created to demarcate the different features
from study area in the form of polygons.
29

4) There were total ten LU/LC classes: agriculture, built-up, canal, degraded
forest, dense forest, moderate dense forest, drainage, transport, wasteland
and waterbody, and transport network: canal side road, district road,
minor road and railway line have been demarcated using on-screen visual
image interpretation technique for all three satellite imageries.
5) The LU/LC maps were prepared using all three satellite imageries.
6) Area was calculated (ha) for each class for all three classified imageries.
7) LU/LC maps then exported in JPEG format.
30

2. LU/LC Change Analysis
1)Vector layer of LISS-IV 2009 was overlaid over vector layer of LISS-IV
2018 to analyze the changes over time (Singh and Khanduri 2011; Tiwari
and Saxena 2011).
2)Both the layers were merged by using UNION tool which is in-built tool
in ArcGIS (Version 10.4.1) software.
3)Extracted those areas where changes have been occurred by comparing
both the classified shape files of LISS-IV 2009 with LISS-IV 2018.
4) Area of all the categorized LU/LC classes have been exported and
calculated (ha) in MS-Excel.
31

5) Areas which have been calculated for particular class was then subtracted
with each other (LISS-IV 2018 with LISS-IV 2009) and percentage of
changes were calculated.
6) Change Matrix table was then prepared.
32

3. Comparison of LU/LC using different Satellite
Sensors
1) Area of LU/LC classification of Cartosat-1 and LISS-IV for year
2009 were compared with each other to check the quality of
information derived from satellite data.
2) Area of all the LU/LC classes for both satellite data were exported to
MS-Excel worksheet to check the differences in areas (ha) under
different LU/LC classes.
33

4. Ground Truth Data Collection Procedure
1)A point feature shape file was created by randomly selecting 111 points
in ArcGIS software and major areas of changes as well as other areas
were identified in the study area.
2)Transport network shape file was prepared and overlaid over the satellite
image to make route map to visit study area.
3)Visited the Kotla sub-watershed and located the 111 randomly selected
points with the help of GPS by recording geo-coordinates (Latitudes and
Longitudes) and captured the pictures from different directions with the
help of digital camera at those locations during ground truthing.
34

Selection of Ground Truth Points
35

Location of Ground Truth data collected from Study Area
36

4) The necessary corrections have been done as per ground truth data in
LU/LC map.
Ground truth data collected during field visit:
Depicting Agriculture area in sub-watershed, (Lat. 31.210382, Long. 76.54352)
Village-Baddal, Tehsil- Anandpur Sahib, District- Rupnagar, Kotla sub-
watershed
37

Depicting Religious Place in sub-watershed (Lat. 31.229029, Long. 76.555365)
Village-Lakher, Tehsil- Anandpur Sahib, District- Rupnagar, Kotla sub-
watershed
38

Depicting Residential Place in sub-watershed (Lat. 31.269321, Long. 76.555757)
Village-Dharot, Tehsil- Anandpur Sahib, District- Rupnagar, Kotla sub-
watershed
39

Depicting Canal in sub-watershed (Lat. 31.211457, Long. 76.552255)
Village-Kotla, Tehsil- Anandpur Sahib, District- Rupnagar, Kotla sub-
watershed
40

Depicting Canal in sub-watershed (Lat. 31.211457, Long. 76.552255)
watershed
41

Depicting Drainage in sub-watershed (Lat. 31.2130742, Long. 76.5543588)
watershed
42

Depicting Degraded Forest in sub-watershed (Lat. 31.219414, Long. 76.550921)
watershed
43

Depicting Dense Forest in sub-watershed (Lat. 31.217634, Long. 76.540727)
Village-Thappal, Tehsil- Anandpur Sahib, District- Rupnagar, Kotla sub-
watershed
44

5. Accuracy Assessment Procedure
1)Ground truth data which had been collected was incorporated with
LU/LC classified imageries for verification (Chowdhury et al 2020).
2)The classified imageries were validated or verified by viewing historical
satellite imageries for the years 2009 and 2018 on google earth by
following the procedure as explained by Rwanga and Ndambuki (2017).
3)Accuracy assessment of LU/LC classification of Cartosat-1 2009, LISS-
IV 2009 and LISS-IV 2018 was done using ground truth data.
4)Error matrix tables for each LU/LC classified satellite data were
prepared.
5)Producer’s, user’s and overall accuracy along with kappa (K) coefficient
were calculated for each LU/LC classification. 45

Verification of selected points using Google Earth 46

1. Producer’s Accuracy was calculated by using formula:
𝐏𝐫𝐨𝐝𝐮𝐜𝐞𝐫′𝐬 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 =
𝐓𝐨𝐭𝐚𝐥 𝐧𝐨. 𝐨𝐟 𝐜𝐨𝐫𝐫𝐞𝐜𝐭 𝐩𝐨𝐢𝐧𝐭𝐬 𝐢𝐧 𝐞𝐚𝐜𝐡 𝐜𝐥𝐚𝐬𝐬
𝐓𝐨𝐭𝐚𝐥 𝐧𝐨. 𝐨𝐟 𝐩𝐨𝐢𝐧𝐭𝐬 𝐮𝐬𝐞𝐝 𝐟𝐨𝐫 𝐭𝐡𝐚𝐭 𝐜𝐥𝐚𝐬𝐬 (𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐝 𝐓𝐨𝐭𝐚𝐥)
2. User’s Accuracy was calculated by using formula:
𝐔𝐬𝐞𝐫′𝐬 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 =
𝐓𝐨𝐭𝐚𝐥 𝐧𝐨. 𝐨𝐟 𝐜𝐨𝐫𝐫𝐞𝐜𝐭 𝐩𝐨𝐢𝐧𝐭𝐬 𝐢𝐧 𝐞𝐚𝐜𝐡 𝐜𝐥𝐚𝐬𝐬
𝐓𝐨𝐭𝐚𝐥 𝐧𝐨. 𝐨𝐟 𝐩𝐨𝐢𝐧𝐭𝐬 𝐮𝐬𝐞𝐝 𝐟𝐨𝐫 𝐭𝐡𝐚𝐭 𝐜𝐥𝐚𝐬𝐬 (𝐑𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐓𝐨𝐭𝐚𝐥)
47

3. Overall accuracy was calculated by using formula
𝑶𝒗𝒆𝒓𝒂𝒍𝒍 𝑨𝒄𝒄𝒖𝒓𝒂𝒄𝒚 =
𝑻𝒐𝒕𝒂𝒍 𝒏𝒐. 𝒐𝒇 𝒄𝒐𝒓𝒓𝒆𝒄𝒕 𝒑𝒐𝒊𝒏𝒕𝒔
𝑻𝒐𝒕𝒂𝒍 𝒏𝒐. 𝒐𝒇 𝒔𝒆𝒍𝒆𝒄𝒆𝒅 𝒑𝒐𝒊𝒏𝒕𝒔
48

4. Kappa Coefficient was calculated by using the formula
𝑲 =
𝑵 𝒊=𝟏
𝒌
𝒙𝒊𝒊+ − 𝒊=𝟏
𝒌
(𝒙𝒊+ ∗ 𝒙+𝒊)
𝑵𝟐 − 𝒊=𝟏
𝒌
(𝒙𝒊+ ∗ 𝒙+𝒊)
Where, 𝒙𝑖+ and 𝒙+𝑖 are the marginal totals for row I and column i
𝒙𝑖𝑖+number of observations in row I and column i
N is total number of observations
49

Flow Chart of Methodology
SELECTION OF STUDY
AREA
COLLECTION OF SATELLITE DATA OF STUDY
AREA (CARTOSAT-1 2009, LISS-IV 2009 and LISS
IV-2018)
DIZITIZATION USING ON SCREEN
VISUAL IMAGE INTERPRETATION
TECHNIQUE
GENERATION OF LU/LC MAPS FOR CARTOSAT-1
2009, LISS-IV 2009 and LISS-IV 2018
COLLECTION OF GROUND
TRUTH DATA
ACCURACY
ASSESSMENT
OVERLAY ANALYSIS
OF LU/LC OF LISS-IV
2009 WITH LISS-IV
2018
GENERATION OF
CHANGE ANALYSIS
MAP
COMPARISON OF LU/LC
CLASSIFICATION OF CARTOSAT-1
2009 WITH LISS-IV 2009
ACCURACY ASSESSMENT
OF SATELLITE DATA
CHANGE MATRIX TABLE
RESULTS AND DISCUSSION
ERROR MATRIX TABLE
50

Classes Demarcated under LU/LC of Kotla sub-watershed
S NO. LU/LC Classes
1 Agriculture
2 Built-up
3 Canal
4 Degraded Forest
5 Dense Forest
6 Drainage
7 Moderate Dense Forest
8 Transport
9 Wasteland
10 Waterbody
52

LU/LC Classification of Kotla sub-watershed
1. LU/LC Classification in 2009 using Cartosat-1
2. LU/LC Classification in 2009 using LISS-IV
53

1. LU/LC Classification in 2009 using Cartosat-1
54

S No LU/LC Classes Area (ha) Area in %
1 Agriculture 1409.62 40.02
2 Built-up 51.56 1.46
3 Canal 38.25 1.08
4 Degraded Forest 116.79 3.32
5 Dense Forest 1353.91 38.44
6 Drainage 64.84 1.84
7 Moderate Dense Forest 426.98 12.12
8 Transport 35.55 1.01
9 Wasteland 18.98 0.54
10 Waterbody 6.07 0.17
Grand Total 3522.55 100 55
Area under LU/LC Classes in 2009 using Cartosat-1

56

S No LU/LC Classes Area (ha) Area in %
2 Built-up 49.69 1.41
3 Canal 38.82 1.10
5 Dense Forest 1366.55 38.79
6 Drainage 67.34 1.91
Grand Total 3522.55 100 57
Area under LU/LC Classes in 2009 using LISS-IV

58

S No LU/LC Class 2018 Area (ha) Area in %
2 Built-up 107.08 3.04
3 Canal 38.82 1.10
5 Dense Forest 1416.08 40.20
6 Drainage 77.99 2.21
Grand Total 3522.55 100 59
Area under LU/LC Classes in 2018 using LISS-IV

Accuracy Assessment of LU/LC Classification of Kotla Sub-
Watershed
1. Accuracy assessment of LU/LC Classification in 2009 using Cartosat-1
2. Accuracy assessment of LU/LC Classification in 2009 using LISS-IV
3. Accuracy assessment of LU/LC Classification in 2018 using LISS-IV
60

61
Classified
Reference Agriculture Built-up Canal
Degraded
Forest
Dense
Forest
Drainage
Moderate
Dense
Forest
Transport Wasteland Waterbody
Row
Total
Agriculture 22 1 23
Built-up 22 22
Canal 7 7
Degraded
Forest
11 1 1 13
Dense Forest 4 1 1 10 1 1 1 3 22
Drainage 7 7
Moderate
Dense Forest
5 1 6
Transport 3 3
Wasteland 3 3
Waterbody 5 5
Column
Total
26 24 7 12 11 8 6 3 4 10 111
Error Matrix Table of LU/LC Classification in 2009 using Cartosat-1

LU/LC Classes Producers Accuracy (%) Users Accuracy (%)
Agriculture 84.62 95.65
Built-up 91.67 100
Canal 100 100
Degraded Forest 91.67 84.66
Dense Forest 90.91 45.46
Drainage 87.5 100
Moderate Dense Forest 83.33 83.33
Transport 100 100
Wasteland 75 100
Waterbody 50 100
Overall Accuracy = 85.58%
kappa Coefficient = 0.83 62
Producer’s and user’s accuracy of LU/LC in 2009 using Cartosat-1

Error Matrix Table of LU/LC Classification in 2009 using LISS-IV
63
Degraded
Forest
Dense
Forest
Drainage
Moderate
Dense
Forest
Row
Total
Agriculture 12 2 1 5 2 1 23
Built-up 6 12 2 2 22
Canal 7 7
Degraded
Forest
10 2 1 13
Dense Forest 1 16 1 1 3 22
Drainage 1 6 7
Moderate
Dense Forest
5 1 6
Transport 3 3
Wasteland 3 3
Waterbody 1 4 5
Column
Total
18 14 7 13 26 6 10 3 4 10 111
Classified

Built-up 85.71 54.55
Canal 100 100
Degraded Forest 76.92 76.92
Dense Forest 61.54 72.73
Drainage 100 85.71
Moderate Dense Forest 50 83.33
Transport 100 100
Wasteland 75 100
Waterbody 40 80
kappa Coefficient = 0.66 64
Producer’s and user’s accuracy of LU/LC in 2009 using LISS-IV

Degraded
Forest
Dense
Forest
Drainage
Moderate
Dense
Forest
Row
Total
Agriculture 22 1 2 1 26
Built-up 22 22
Canal 7 7
Degraded
Forest
9 9
Dense Forest 4 1 11 1 1 18
Drainage 7 7
Moderate
Dense Forest
1 6 7
Transport 3 3
Wasteland 2 2
Waterbody 10 10
Column
Total 26 24 7 12 11 8 6 3 4 10 111
65
Error Matrix Table of LU/LC Classification in 2018 using LISS-IV
Classified

Built-up 91.67 100
Canal 100 100
Degraded Forest 75 100
Dense Forest 100 61.11
Drainage 87.5 100
Moderate Dense Forest 100 85.71
Transport 100 100
Wasteland 50 100
Waterbody 100 100
kappa Coefficient = 0.87
66
Producer’s and user’s accuracy of LU/LC in 2018 using LISS-IV

Accuracy assessment of LU/LC classification
• The result showed the Overall accuracy for LU/LC classification for
Cartosat-1 2009, LISS-IV 2009 and LISS-IV 2018 were 85.58%,
70.27% and 89.19% respectively with kappa coefficient 0.83, 0.66 and
0.87 respectively.
67

Land Use and Land Cover change detection of Kotla sub-
watershed from 2009-18
• The results obtained from LU/LC classification data of Kotla sub-
watershed from the year 2009 and 2018 by using satellite data of
LISS-IV 2009 and LISS-IV 2018 showed in coming graph and
table.
• The highest amount of LU/LC changes were observed in built-up
and degraded forest, while there was no any change occurred in the
areas of canal and transport from the year 2009 to 2018.
68

Changes in LU/LC from 2009 to 2018
0
200
400
600
800
1000
1200
1400
1600
Agriculture Built-up Canal Degraded
Forest
Dense Forest Drainage Moderate
Dense Forest
Area
(ha)
LU/LC Classs
Area (ha) in 2009 Area (ha) in 2018
69

-2.80
115.47
0.00
93.39
3.62
15.81
-60.06
0.00
-4.25
3.70
-80.00
-60.00
-40.00
-20.00
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
Area
(ha)
LU/LC Classes
Area in % Agriculture
Built-up
Canal
Degraded Forest
Dense Forest
Drainage
Moderate Dense Forest
Transport
Wasteland
Waterbody
Percentage of changes occurred LU/LC from 2009 to 2018
71

LU/LC Classes Area (ha) in 2009 Area (ha) in 2018 Changed Area (ha) Changed Area in %
Agriculture 1360.72 1322.59 -38.13 -2.80
Built-up 49.69 107.08 57.38 115.47
Canal 38.82 38.82 0 0
Degraded Forest 174.58 337.63 163.05 93.39
Dense Forest 1366.55 1416.08 49.53 3.62
Drainage 67.34 77.99 10.65 15.81
Moderate Dense Forest 402.86 160.89 -241.97 -60.06
Transport 38.07 38.07 0 0
Wasteland 17.62 16.87 -0.75 -4.25
Waterbody 6.30 6.53 0.23 3.70
Statistical analysis of LU/LC Changes from 2009 to 2018
71

LU/LC
Classes
Year 2018 Change Matrix Table
Year 2009 Agriculture
Built-
up
Canal
Degraded
Forest
Dense
Forest
Drainage
Moderate
Dense Forest
Grand
Total
Agriculture 1312.98 47.74 1360.72
Built-up 49.69 49.69
Canal 38.82 38.82
Degraded
Forest
1.78 120.55 35.96 1.72 14.05 0.52 174.58
Dense
Forest
8.13 4.23 64.12 1261.80 10.54 16.82 0.26 0.64 1366.55
Drainage 0.39 1.90 64.98 0.08 67.34
Moderate
Dense
Forest
1.48 2.99 151.72 116.05 0.51 129.72 0.40 402.86
Transport 38.07 38.07
Wasteland 0.64 0.14 0.23 16.59 0.01 17.62
Waterbody 0.86 0.23 0.22 0.02 4.97 6.30
Grand
Total
1322.59 107.08 38.82 337.63 1416.08 77.99 160.89 38.07 16.87 6.53 3522.55
72

Areas where major changes occurred from 2009-18
73

S/N Changes from 2009 to 2018 Area (ha) S/N Changes from 2009 to 2018 Area (ha)
1 Agriculture-Built-up 47.74 17 Moderate Dense Forest-Agriculture 1.48
2 Degraded Forest-Built-up 1.78 18 Moderate Dense Forest-Built-up 2.99
3 Degraded Forest-Dense Forest 35.96 19 Moderate Dense Forest-Degraded Forest 151.72
4 Degraded Forest-Drainage 1.72 20 Moderate Dense Forest-Dense Forest 116.05
5 Degraded Forest-Moderate Dense Forest 14.05 21 Moderate Dense Forest-Drainage 0.51
6 Degraded Forest-Waterbody 0.52 22 Moderate Dense Forest-Waterbody 0.40
7 Dense Forest-Agriculture 8.13 23 Wasteland-Built-up 0.64
8 Dense Forest-Built-up 4.23 24 Wasteland-Dense Forest 0.14
9 Dense Forest-Degraded Forest 64.12 25 Wasteland-Drainage 0.23
10 Dense Forest-Drainage 10.54 26 Wasteland-Waterbody 0.01
11 Dense Forest-Moderate Dense Forest 16.82 27 Waterbody-Degraded Forest 0.86
12 Dense Forest-Waterbody 0.90 28 Waterbody-Dense Forest 0.23
13 Dense Forest-Wasteland 0.26 29 Waterbody-Moderate Dense Forest 0.22
14 Drainage-Degraded Forest 0.39 30 Waterbody-Wasteland 0.02
15 Drainage-Dense Forest 1.90 GRAND TOTAL 484.64
16 Drainage-Moderate Dense Forest 0.08
74
Areas where major changes occurred

Comparison of LU/LC Classification of Cartosat-1 2009
and LISS-IV 2009
• This particular objective was conducted to determine the quality of
information derived from LU/LC classification of satellite imageries.
• Area of all the LU/LC classes of Cartosat-1 (2009) and LISS-IV (2009)
were compared with each other and area statistics have been generated
and represented in the Table shown in the coming slide.
• The area under LU/LC classes showed variability when compared to
each other. In such case it becomes important to compare the two
datasets for better understanding of the accuracy.
75

LU/LC Classes Areas (ha) using Cartosat-1 Areas (ha) using LISS-IV
Area (ha) Dissimilarities
(Mode Value)
Agriculture 1409.62 1360.72 48.90
Built-up 51.56 49.69 1.87
Canal 38.25 38.82 0.57
Degraded Forest 116.79 174.58 57.79
Dense Forest 1353.91 1366.55 12.64
Drainage 64.84 67.34 2.50
Moderate Dense
Forest
426.98 402.86 24.12
Transport 35.55 38.07 2.52
Wasteland 18.98 17.62 1.36
Waterbody 6.07 6.30 0.23
76
Comparison of LU/LC Classification Areas for 2009 from
Cartosat-1 and LISS-IV Satellite Imageries

• Cartosat-1 has high spectral resolution of 2.5 meters as compared to
LISS-IV spectral resolution which is 5.8 meters due to which LU/LC
features in Cartosat-1 data were clearer and easier to interpreted as
compared to LISS-IV data.
Built-up Area in Cartosat-1 and LISS-IV 77
Cartosat-1 Resolution 2.5 meters LISS-IV Resolution 5.8 meters

78
Cartosat-1 Resolution 2.5 meters
Cartosat-1 Resolution 2.5 meters
LISS-IV Resolution 5.8 meters
LISS-IV Resolution 5.8 meters
Agriculture Area in Cartosat-1 and LISS-IV
Waterbody in Cartosat-1 and LISS-IV

CONCLUSIONS
1. LU/LC classification in 2009 using Cartosat-1 satellite imagery showed
area under agriculture as 1409.62 ha, built-up 51.56 ha, canal 38.25 ha,
degraded forest 116.79 ha, dense forest 1353.91 ha, drainage 64.84 ha,
moderate dense forest 426.98 ha, transport 35.55 ha, wasteland 18.98 ha
and waterbodies 6.07 ha which was 40.02%, 1.46%, 1.08%, 3.32%,
38.44%, 1.84%, 12.12%, 1.01%, 0.54% and 0.17% respectively of total
area (3522.55 ha).
2. LU/LC classification in 2009 using LISS-IV satellite imagery showed area
under agriculture as 1360.72 ha, built-up 49.69 ha, canal 38.82 ha,
degraded forest 174.58 ha, dense forest 1366.55 ha, drainage 67.34 ha,
moderate dense forest 402.86 ha, transport 38.07 ha, wasteland 17.62 ha
and waterbodies 6.30 ha which was 38.63%, 1.41%, 1.10%, 4.96%,
38.79%, 1.91%, 11.44%, 1.08%, 0.50% and 0.18% respectively of the total
area. 79

CONCLUSIONS
3. LU/LC classification in 2018 using LISS-IV satellite imagery showed
area under agriculture as 1322.59 ha, built-up 107.08 ha, canal 38.82
ha, degraded forest 337.63 ha, dense forest 1416.08 ha, drainage 77.99
ha, moderate dense forest 160.89 ha, transport 38.07 ha, wasteland
16.87 ha and waterbodies 6.53 ha which was 37.55%, 3.04%, 1.10%,
9.58%, 40.20%, 2.21%, 4.57%, 1.08%, 0.48% and 0.19% respectively
of the total area.
4. Overall accuracy for LU/LC classification for Cartosat-1 2009, LISS-
IV 2009 and LISS-IV 2018 were 85.58%, 70.27% and 89.19%
respectively with kappa coefficient value as 0.83, 0.66 and 0.87
respectively.
80

5. LU/LC change analysis (2009-18) showed that total area under
agriculture, moderate dense forest and wasteland decreased by 38.13 ha,
241.97 ha and 0.75 ha respectively which was 2.80%, 60.06% and 4.25%
respectively.
6. Area under built-up, degraded forest, dense forest, drainage and
waterbody increased by 57.38 ha, 163.05 ha, 49.53 ha, 10.65 ha and 0.23
ha respectively from 2009-18 which was 115.47%, 93.39%, 3.62%, 15.81%
and 3.70% respectively, while area under canal and transport remained
unchanged.
81
CONCLUSIONS

7. Same area under a particular LU/LC classes in 2009 decreased in 2018
for all the classes except built-up, canal and transport. The gain in
total area under built-up, degraded forest, dense forest, drainage and
waterbody in 2018 classes was from areas under different classes of
2009.
8. High spatial resolution data can be used for better accuracy.
9. The study showed that the continuous monitoring using Remote
Sensing may serve as a vital tool for assessment of temporal changes in
LU/LC on watershed basis.
82
CONCLUSIONS

LAND USE AND LAND COVER CHANGE ANALYSIS OF KOTLA SUB-WATERSHED OF PUNJAB USING REMOTE SENSING AND GIS

LAND USE AND LAND COVER CHANGE ANALYSIS OF KOTLA SUB-WATERSHED OF PUNJAB USING REMOTE SENSING AND GIS

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