Abstract: The land use and land cover (LULC) pattern of an area is determined by natural process 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 and land 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 of different land use and land cover classes were attempted. Landsat-5 Thematic Mapper and Sentinel-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

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 the land is used, like residential, commercial, agricultural, managed forest and rangeland. Together land use and land cover information suggest specific characteristics of the land surface, such as imperviousness, solar reflectivity, vegetation type and building morphology, this can be incorporated into environmental models as distributed or bulk parameterizations (Burian et al., 2002). Since the land is changing by natural process and human interferences, the information on land use and land cover is important for planning and management of resources needed for human 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 useful for the mapping of land use and land cover (Jeffrey and Jonathan, 2003). The easy availability of coarse spatial resolution satellites is useful in the land use and land cover classification for environmental studies. The Sentinel-2 satellite by ESA (European Space Agency) is ideal for classification 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 of electromagnetic information with wide coverage and good spatial and temporal resolution (Zheng et al., 2017). The recent studies using Sentinel satellites (Gordana, 2018; Rao and Kumar, 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 were used 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, maximum likelihood classifier, artificial neural network classifier, object-based image analysis and supportive 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 machine learning algorithms for classification of high dimensional data (Huang et al., 2002). This algorithm was widely adopted for remote sensing data analysis. It is a nonparametric supervised classification algorithm that is proved to be a robust method used for pattern recognition (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 from SVM 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.

Read More


MA Mohammed Aslam, Lalitha M. and B. Mahalingam
Department of Geology, Central University of Karnataka, Kalaburagi (Karnataka), India
Department of Geology, Government College, Kasaragod (Kerala), India
Department of Geography, Central University of Karnataka, Kalaburagi, (Karnataka), India

Leave a Comment

Your email address will not be published. Required fields are marked *