Journal of Global Resources
Volume 6 (01)August 2019-January 2020
Page 01-07
UGC–CARE Listed Journal in Group D
ISSN: 2395-3160 (Print), 2455-2445 (Online)

01
THE ENVIRONMENTAL IMPLICATIONS OF LAND USE AND LAND COVER CHANGESAROUND A MINED AREA AT MADAYIPARA IN KANNUR DISTRICT, KERALA STATE, INDIA
1*

MA Mohammed Aslam, 2Lalitha M. and 3B. Mahalingam
Department of Geology, Central University of Karnataka, Kalaburagi (Karnataka), India
2
Department of Geology, Government College, Kasaragod (Kerala), India
3
Department of Geography, Central University of Karnataka, Kalaburagi, (Karnataka), India
Email: maslam.in@gmail.com
1

Abstract: The land use and land cover (LULC) pattern of an area is determined by naturalprocess and human utilization over time and space. Human interventions of mining activities
and its surrounding area cause changes in landcover and land use pattern. The land use andland cover changes in Madayipara area of Kannur district, Kerala State, India, which contains
the area of clay mining has been studied. The spatial distribution and temporal changes ofdifferent land use and land cover classes were attempted. Landsat-5 Thematic Mapper andSentinel-2 satellite imageries were used to study the changes in vegetation pattern, land
modification and changes in water bodies. Distinct increase in area of mining area was not
visible, as the excavation was mostly happened vertically in the same areal coverage. This
study is helpful in the better management of environmental sustainability.
Key words: Mining Area; Land cover; Land use; Environment
Introduction
Land cover indicates the physical appearance of the land surface, such as grasslands, forest,
bare soil, exposed rock, developed land. Whereas, land use indicates the purpose for which theland is used, like residential, commercial, agricultural, managed forest and rangeland. Together
land use and land cover information suggest specific characteristics of the land surface, suchas imperviousness, solar reflectivity, vegetation type and building morphology, this can beincorporated into environmental models as distributed or bulk parameterizations (Burian et al.,2002). Since the land is changing by natural process and human interferences, the informationon land use and land cover is important for planning and management of resources needed forhuman welfare (Opeyemi, 2006). Remote sensing data plays a major role for mapping earth
surface features (Mohammed-Aslam et. al, 2006; Reis, 2008; Mohammed-Aslam et. al, 2010).
It is used as a powerful tool to monitor the Earth’s surface, particularly in producing land use
and land cover (LULC) classifications (Christoph et al., 2016). Remotely sensed data are usefulfor the mapping of land use and land cover (Jeffrey and Jonathan, 2003). The easy availabilityof coarse spatial resolution satellites is useful in the land use and land cover classification forenvironmental studies. The Sentinel-2 satellite by ESA (European Space Agency) is ideal forclassification of land use and land cover as it is having 13 high quality radiometric bands(Borràs, et al., 2017). Sentinel-2 is a satellite owned by ESA, provides long-range ofelectromagnetic information with wide coverage and good spatial and temporal resolution(Zheng et al., 2017). The recent studies using Sentinel satellites (Gordana, 2018; Rao andKumar, 2017; Steinhausen et al., 2018; Nicola et al., 2017) for land use and land cover
mapping have demonstrated that Sentinel satellite imageries are highly suitable for land use
and land cover mapping. Several land use and land cover studies were conducted using
Landsat 5 satellite and it was found as suitable data for land use and land cover analysis study.
The study conducted by Abdel Rahman in 1996 to map the LULC using Landsat 5 for East Nile
Delta, Egypt, showed the capability of Landsat 5 data for land use and land cover classification.
Landsat 5 Thematic Mapper for the year 2000 and Sentinel data for 2017 respectively wereused in this study. An attempt has been made to map the land use and land cover of present
study area using Sentinel-2 and Landsat satellite images.
Since the land use and land cover classification provides the base for many
applications, the selection of appropriate classification algorithm and accuracy is very important(Ustuner et al., 2015) in this process. Supervised, unsupervised, hybrid classification, maximum1likelihood classifier, artificial neural network classifier, object-based image analysis andsupportive vector machine are the techniques used for classification purposes (Abburu and
Golla, 2015). Among the existing satellite image classification techniques, Supportive Vector
Machine (SVM) classification was highly used in recent time due to optimal separating
hyperplane between classes (Bahari et al., 2014). SVM is one of the best superior machinelearning algorithms for classification of high dimensional data (Huang et al., 2002). Thisalgorithm was widely adopted for remote sensing data analysis. It is a nonparametricsupervised classification algorithm that is proved to be a robust method used for patternrecognition (Sukawattanavijit and Chen, 2017). The use of SVM algorithm is getting increased
attraction due to the advantages it possesses (Martins et al., 2016). The result obtained fromSVM classification is relatively better than maximum likelihood classifier (Taati et al., 2014).
Therefore, keeping the advantages and accuracy of results, the SVM classification method has
been followed in this study.
Methodology
Land use and land cover of study area was generated using Sentinel-2 and Landsat -5 satellite
imageries, acquired on 28th December 2017 and 21 January 2000 respectively. The sentinel
image was downloaded from “scihub.copernicus.eu” and Landsat-5 image was downloaded
from “USGS Glovis” for free of cost. The Landsat-5 satellite bands 1, 2, 3, 4, 5 and 7 are having
same spatial resolution of 30m were considered for the classification LULC of study area for theyear 2000. The Sentinel satellites bands 2, 3, 4 and 8 are having same spatial resolution of
10m were considered for the classification LULC for the year 2017. The downloaded satellite
imageries were preprocessed before the classification. The land use and land coveridentification were carried out using Supportive Vector Machine (SVM) classifier technique in
ArcGIS environment. Totally five classes were chosen for classification as the study area ispredominately covered by it, such as barren land, built up land, mining land, vegetation andwaterbodies. To classify the satellite imagery using SVM technique it is necessary to create asignature file, hence, 75 samples for vegetation, 50 samples for built up land, 40 samples forbarren land, 15 samples for waterbodies and 10 samples for mining land were selected onsatellite imagery, using the representative pixels. These signature files were used as input data
to classify the entire imageries. The result of classification was subjected to accuracy
assessment to understand the precision of classified classes. Ground verification is conductedwith the help of Global Positioning System (GPS) to determine the accuracy of classification
result. The verified ground information is compared with the classification result of satellite
image and error matrices are generated (Paliwal and Katiyar, 2015; Kim, 2016). The error
matrices and Kappa coefficient are the standard way of assessing accuracy of land use andland cover classification, generated by comparing classified land use and land cover samples
with the information collected in the field using GPS (Adam et al, 2013).
Therefore, in order to access the accuracy of land use and land cover classification ofthis study, a total of 150 samples were chosen which consist 20 samples for barren land, 40
samples for built up land, 5 samples for mining land, 80 samples for vegetation and 5 samples
for waterbodies respectively. The latitude and longitude of the selected samples were loaded in
handheld GPS and field verification was carried out to compare the classification result withreal world situation. The comparison of collected satellite image information and groundinformation was analyzed using error matrix. The error matrix is an array of information whereclassified classes are shown. The information in the columns was indicated by reference dataor ground truth while the information in the rows was indicated by mapped classes of satellite
images. It has numbers as the quantity of sample unit for any particular quantity arranged inrows and columns i.e. square matrix where columns represent the referencing data while row
represents the classification data. It is useful in calculating the parameters like the producer’s
accuracy, user’s accuracy, overall accuracy and the kappa statistics (Nain and Kumar, 2016).
The error matrix and kappa coefficient are predominantly used for land use and land coverclassification derived from satellite image for accuracy determination, which is based on thecomparison of ground truth against classified result by classifier. (Upadhyay et al., 2015).
Table 1 shows the error matrix of the study area.

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Table 01: Error Matric of the Classified Image of the Study Area

Barren Land

BarrenLand
19

Built UpLand
1

Mining
Land
0

0

Water
bodies020

User
Accuracy
95

Built Up Land

1

38

0

1

0

40

95

Mining Land

0

0

5

0

0

5

100

Vegetation

0

2

0

78

0

80

97.5

Waterbodies

0

0

0

0

5

5

100

Total

20

41

5

79

5

150

Producer Accuracy

95

92.68

100

98.73

100

Classes

Vegetation

Total

The error matrix provides two kinds of accuracy namely, producer accuracy and user
accuracy. The producer accuracy pertained to waterbody on a ground was correctly classified
on the satellite image. The user accuracy of the classified waterbody on the satellite image wasfound correctly matches with ground condition. It was also clear that, the user and producer
accuracy of barren land was 95 and 95, built up land was 95 and 92.68, mining land was 100
and 100, vegetation was 97.5 and 98.73, water bodies were 100 and 100 respectively. The
overall accuracy of classification was 96.66. According to Anderson (1976), the accuracy ofinterpretation for the classes required is about equal and minimum level of land use and land
cover accuracy should be 85 per cent. The resulted classes and overall accuracy of theclassified images were well within the accepted level of accuracy in the present study. Cohen’s
Kappa analysis was also used to understand the accuracy of classified imagery. It is one of the
popular techniques to identify the accuracy of the classified image attempted by several
researchers in the field of land use and land cover mapping (Reis, 2008; Kaul and Sopan,
2012; Rawat and Kumar, 2015; Tilahun and Teferie, 2015; Liping et al., 2018). Kappa analysis
is performed using error matrix, it is a discrete multivariate calculation used for thedetermination of land use and land cover map accuracy. Kappa coefficient varies between 0
and 1, wherein 0 is indicative of the lack of agreements exists between the variables used.
Whereas 1 is indicative of the strong agreements present between the variables. The result of
Kappa coefficient can be classified into five groups based on the agreements of variables usedin the calculation. If the result of Kappa coefficient is < 0.2, it is considered as having poor
agreement. If the range is between 0.2 and 0.4, it is considered as having fair agreement. If
Kappa coefficient is fallen between 0.4 and 0.6, it is considered to have moderate agreement. If
the result is found between 0.6 and 0.8, it is considered as having good agreement between
the variables. If the Kappa coefficient between 0.8 and 1, it is considered as having strong
agreement. Equation 1 shows the formula for the computation of Kappa coefficient.

(1)
In above formula, ‘N’ indicates total number of sites in the matrix. ‘r’ indicates the number ofrows in the matrix. ‘Xii’ indicates the number in row ‘I’. ‘X+I’ indicates the total for row ‘I’. ‘X i+’
indicates the total for column ‘i’ (Jensen, 1996). The Kappa result of land use and land coverclassification of present study was ‘0.94’ which is considered as having strong agreement type.
The information obtained from the field visit was applied on the classified image and anomalies
in the data were corrected. Further spatial extension of each classified classes was identified
and mapped using GIS software.
RESULTS AND DISCUSSION
LULC Classification of 2000
The LULC classification of study area for the year 2000 was carried out by adopting Supportive
Vector Machine classification method using Landsat 5 Thematic mapper Landsat satellite
image. Landsat TM image was used owing to its free availability and in the season, as Sentinel
satellite was not launched by then. The result of land use and land cover was observed visuallyin the software and based on the field visit the errors in the result were corrected accordingly.
Totally five classes were identified that were mapped as barren, built up, mining, vegetation3

and waterbody categories. The spatial extensions of each class were identified and shown in
the Table 2, the spatial extension of identified classes was mapped and shown in the Figures 1
and 2.
Table 02: Land Use and Land Cover Classification – 2000
#
1
2
3
4
5

Class Name
Vegetation
Barren Land
Built Up Land
Mining Land
Waterbodies
Total

Area (Sq. hac.)232.06143.52
66.34
7.47
2.57
452

Area (%)
51.34
31.75
14.68
1.65
0.57
100.00Figure 01: Classified Image in the Year 2000

From the table 2 it was very clear that, during the year 2000 the larger area was covered byvegetation which is about 51.34 per cent (232.06 hac.) followed by barren land which of 31.75
per cent (143.52 hac.), built up land of about 14.68 per cent (66.34 hac.) and mining land of
about 1.65 per cent (7.47 hac.), with the lesser area of waterbodies which is about 0.57 percent (2.57 hac.).
Figure 02: Relative Occurrence of Classified Image in the Year 2000

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LULC Classification of 2017
The LULC of year 2017 was carried out by employing the Supportive Vector Machineclassification method using Sentinel satellite image. By following the categories done for 2000
LULC classification, similar five classes were used in 2017 for classifying as barren, built up,mining, vegetation and waterbody (Table 3). The field information was appropriately used
before finalizing the final classified image (Figures 3 and 4).
Table 03: Land Use and Land Cover Classification - 2017
#
1
2
3
4
5
Total

Class Name
Vegetation
Barren Land
Built Up Land
Mining Land
Waterbodies

Area (Sq. hac.)
264.26
90.33
87.12
7.48
2.81
452

Area (%)
58.46
19.98
19.27
1.65
0.62
100.00From the Table 3 it was visible that, during the year 2017 the larger area was covered byvegetation which is approximately 58.46 per cent (264.26 hac.) followed by barren land of
about 19.98 per cent (90.33 hac.), built up land of 19.27 per cent (87.12 hac.), mining land of
about 1.65 per cent (7.48 hac.), and the waterbodies of about 0.62 per cent (2.81 hac.).
Figure 03: Classified Image in the Year 2017

Figure 04: Relative Occurrence of Classified Image in the Year 2017Land Use and Land Cover Classification 2017
Waterbodies
1%

Vegetation
58%

Barren Land
20%

Built Up Land
19%
Mining Land
2%

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LULC change between 2000 and 2017
The result obtained through Landsat 5 satellite image for the year 2000 and the Sentinel
satellite image for the year 2017 was compared to identify the temporal changes in the periodof study. The spatial extension of each class in selected time periods were compared andshown in the Table 4. From the result it was clear that, 32.2 hac. of area covered by vegetation
was increased, whereas 53.19 hac. of barren land was decreased. The area covered by built
up was increased around 21 hac. and the area covered by mining land and waterbodies
increased slightly as 0.01 hac. and 0.24 hec. respectively. Distinct increase in area of mining
area was not visible much, as the mining processes took place with lots of excavation, without
much increase in areal extent. The decrease barren land was largely gained by the increase invegetation and built up land.
Table 04: Comparison of Land Use and Land Cover Classification Results#

Class Name

1 Vegetation
2 Barren Land
3 Built Up Land
4 Mining Land
5 Waterbodies
Total

LULC- 2000 Area
(Sq. hac.)232.06143.52
66.34
7.47
2.57
452

LULC-2017 Area
(Sq. hac.)
264.26
90.33
87.12
7.48
2.81
452

Changes Area
(Sq. hac.)
32.2-53.19
20.78
0.01
0.24

Conclusion
To understand the effects of geo-environmental impact on clay mining in Madayipara, land use
and land cover pattern has been studied. The spatial distribution and temporal changes ofdifferent land use and land cover (LULC) of the study area was carried out. For this purpose,
Landsat-5 Thematic Mapper and Sentinel-2 satellite imageries were used. The bands 1, 2, 3, 4,
5 and 7 of Landsat-5 were used for generating the LULC for the year 2000. Whereas, 2, 3, 4
and 8 bands of Sentinel-2 were used for the LULC classification of the year 2017. It was foundthat an increase of about 32.2 hac. of vegetation was recorded during this span of time in thisstudy. Whereas, the barren land was decreased by 53.19 hac in this time. At the same time,
the area covered by built up land have shown a considerable increase of about 21 hac. The
area covered by mining land and water bodies were slightly increased as 0.01 hac. and 0.24
hac. respectively owing to mining as well anthropogenic activities. Distinct increase in area ofmining area was not visible, as the excavation was mostly happened vertically in the same
areal coverage. The mining activity had made significant changes in the land cover of theadjoining area, which had made significant impact on the environmental conditions of the area.
Mined land should be reclaimed for future agricultural purposes or planting trees. Mined landshould be reclaimed for future agricultural purposes or planting trees.
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