Journal of Environment and Waste Management Vol. 7(1), pp. 326-338, June, 2020. © www.premierpublishers.org, ISSN: 0274-6999

Research Article

Assessment of Land Use Land Cover Classification through Geospatial Approach: A Case Study of Mysuru Taluk of State, India

*Manjunatha M.C.1, Basavarajappa H.T.2 1Department of Civil Engineering, Maharaja Institute of Technology, Thandavapura, Mysuru-571 302, India 2Department of Studies in Earth Science, Centre for Advanced Studies in Precambrian Geology, University of , Mysuru-570 006, India

Earth's land use/land cover (LC/LU) classification provides valuable information particularly on natural resources, mapping and its monitoring. There is a significant change on LC/LU across the globe due to the climatic changes, rapid increase in population and over demand of economic natural resources. Remote Sensing (RS) satellite data with its synoptic view and multispectral data provides essential information in proper planning of LU/LC conditions of larger areas. The study aims to map and monitor the existing LU/LC classification scientifically using geospatial tools in database generation, analyses and information extraction. Thematic maps of the study area are prepared using satellite images in conjunction with collateral data Survey of India (SoI) toposheets, forest and wasteland maps. An attempt have been made to delineate the Level-I, Level-II and Level-III LU/LC classification system through NRSC guidelines (2011) using both Digital Image Processing (DIP) and Visual Image Interpretation Techniques (VIIT) by GIS software’s with limited Ground Truth Check (GTC). More accurate classification is observed in case of digital technique as compared to that of visual technique in terms of area statistics. The final results highlight the potentiality of geospatial technique in optimal and sustainable land use planning of natural resource and its management.

Keywords: Geospatial technology; LU/LC Classification; IRS-1D, LISS-III Image;Mysuru taluk.

INTRODUCTION

LULC classification provides vital inputs required for al., 1996). LU/LC exposes considerable influence on the socio-ecological concerns and optimal use of land various hydrological aspects such as interception, resources for the developing countries like India (Sharma infiltration, catchment area, evaporation and surface flow et al, 2018). The main resource controlling primary (Sreenivasalu and Vijay Kumar, 2000). productivity for terrestrial ecosystems can be defined in terms of land: the area of land available, land quality, Indian Remote Sensing (IRS) has been extensively moisture regime and edaphic character (Sharma et al, utilized for Satellite data acquisition at periodic intervals to 2018). Changes in LU/LC affect global systems or occur monitor the land resources and to evaluate the land use/ in a localized fashion in enough places to have a land cover classification & its impact on natural land significant effect (Meyer and Turner, 1992).LU/LC resources (NRSA, 1995). The spatial information of agro provides a better understanding on the cropping pattern ecosystem modeling (Lenz-Wiedemann et al, 2010) yields and spatial distribution of fallow lands, forests, grazing estimation (Vibhute and Gawali, 2013) subsidy control lands, wastelands and surface water bodies, which is vital for urban developmental planning and management *Corresponding Author: Manjunatha M.C, Department (Philip and Gupta, 1990). Despite successful substitution of Civil Engineering, Maharaja Institute of Technology, of land-based resources with fossil fuels and mineral Thandavapura, Mysuru-571 302, India. resources, land remains of prime importance (Darwin et Email: [email protected]

Assessment of Land Use Land Cover Classification through Geospatial Approach: A Case Study of Mysuru Taluk of Karnataka State, India

Manjunatha and Basavarajappa 327

(Schmedtmann and Campagnolo, 2015) and retrieval of scheme today, consists of multiple levels of classification biophysical plant parameters on regional scales is not designed to be compatible with different levels of details sufficient (Zhao et al, 2015). For numerous agricultural (Tammy and James, 2015). It is comprised of a hierarchal applications and differentiate crops is a demanding task, grouping of three levels, allowing for applicability at as different crop types have similar reflection properties in multiple resolutions. Level-I can be used when finer remote sensing images for some periods of the year details are not needed, such as for national or regional (Waldhoff et al, 2012). Increasing human interventions scales, and is more appropriate for land cover and unfavorable bio-climatic environment has led to identification. Yet, Level-II and Level-III is available when transformation of large tracts of agricultural land into finer detail is needed at a local scale and can be more wasteland (Basavarajappa et al, 2019). Due to the lack of readily described as land uses (Tammy and James, appropriate land cover data, many assessments have 2015). Without a standard classification framework, it is used models to delimit potential land cover (Alexandratos, difficult to identify changes occurring over time, compare 1995). Information describing current land cover is an between places, and to avoid duplication of efforts. important input for planning and modeling, but the quality of such data defines the reliability of the simulation outputs (Townshend, 1992). Proper land management and STUDY AREA development should be initiated to increase the land productivity, restoration of soil degradation, reclamation of It is located in between 12007’05” to 12027’13” N latitudes wastelands, and increase in environmental qualities and and 76027’12” to 76050’10” E longitudes with the general to meet the needs of rapidly growing population of the elevation of 770 mts above MSL covering an area of country (Manjunatha and Basavarajappa, 2015b). 805.63 km2 (Figure 1). Tourism is the major industry alongside the traditional industries in Mysuru taluk. The Land use classification is the systematic assessment and study area lends its name to various art forms and culture, alternatives for optimal land use to improve socio- such as , Mysore Painting; economic conditions (FAO, 2017). Land-use classification (sweet dish), Mysore Masala Dosa; brands such at appropriate scales increase their productivity, support as , Mysore Ink; and styles and sustainable agriculture & food systems, promotes cosmetics such as Mysore Peta (a traditional silk turban) governance over land and water resources and meets the and Mysore Silk sarees. The taluk has four hoblis namely needs of society (FAO, 2017). There are a variety of Kasaba, Ilavala, Varuna and Jayapura. The climate is methods have been introduced for LULC classification semiarid tropical and the average annual rainfall of 798 with the development and advances in remote sensing mm with 55 rainy days (CGWB, 2012). The temperature technology and satellites (Mishra et al., 2014). Anderson ranges from 120 to 350 C. classification system is the most widely used classification

Figure1: Location and Survey of India Topomap of Mysuru taluk

Assessment of Land Use Land Cover Classification through Geospatial Approach: A Case Study of Mysuru Taluk of Karnataka State, India J. Environ. Waste Manag. 328

METHODOLOGY National Remote Sensing Agency (NRSA), Hyderabad. The methodology adopted consists of meaningful iii. GIS software’s: Erdas Imagine v2011 and Arc GIS information extraction from Remote Sensing Satellite v10. image, data preparation, interpretation (on-screen visual), iv. GPS: Garmin 12 is used to mark exact boundaries Ground Truth Check (GTC), map finalization and and to check the conditions of the land use/land cover database organization (NRSC, 2011). LU/LC maps are patterns during field visits. prepared using satellite image in conjunction with collateral data like SoI topomaps on 1:50,000 scale by Satellite data: Data Collateral considering permanent features such as major roads, IRS-1D, Source data: drainages, power-lines, railways, settlements, co- Pan+LISS-III, i. SoI ordinates and village boundaries (Manjunatha et al, 2 Seasons toposheets 2015a). On‐screen Visual Image Interpretation Geocoded data Base Map ii. Forest Map Techniques (VIIT) are extracted manually and compared with digitally extracted vector layers in delineating land use land cover categories. Multi-temporal Resourcesat-1 of LISS III data of 2002 acquired during kharif (Aug –Nov), Classification Image Image and rabi seasons (Jan- Mar) are acquired to estimate the System Analysis Interpretation spatial distribution variability of cropping pattern (NRSC, 2011). Preliminary interpreted LULC features from satellite images are updated by limited field visits & information and the final thematic details are transferred Preliminary Interpreted onto the base maps. Maps

Supervised classification Ground Truth Check Supervised classification analyses are carried out on multispectral IRS-1D, LISS-III FCC with medium scale through ArcGIS v10 (Figure 3). The LU/LC patterns are Post field digitized based on the standard schemes developed by correction/ Modification National Remote Sensing Agency (NRSA, 1995). Maximum Likelihood Classification (MLC) scheme is one of the most widely used image classification technique Final Land Use/Land Cover adopted on LISS-III images for mapping all the land Classification Maps use/cover classes. Before the selection of training Figure 2: Flow chart of showing the methodology samples, empirical analysis of satellite imagery and adopted in Land Use/Land Cover analysis specific features on the toposheets are investigated carefully. For most of the classes, a minimum number of training samples were 100. Selecting training samples for water was tough because of the dense canopy of thick trees along with the river channel and lack of water in the river channels since the acquisition date of imagery was in mid-January and at that time most of the rivers carry less water as compared to the monsoon season. Changes in land surface conditions can affect the volume, timings and quality of run-off water. Different LU/LC are delineated and classified based on the key elements of image characteristics like tone, texture, shape, shadow, pattern, association, background etc (Table 2).

Materials used i. Base map: Survey of India toposheets of 57D/7, 57D/8, 57D/11, 57D/12, 57D/15 and 57D/16 in 1:50,000 scale (Figure 1). Source: Survey of India (SoI) Office, Govt. of India, Bengaluru. ii. Satellite Data: IRS-1D LISS-III of 23.5m Resolution th and PAN of 5.8m (Nov-2001 & Jan-2002). Source: Figure3: IRS-1D, LISS-III (12 Jan 2002) Satellite Image of Mysuru taluk

Assessment of Land Use Land Cover Classification through Geospatial Approach: A Case Study of Mysuru Taluk of Karnataka State, India Manjunatha and Basavarajappa 329

Table 1: Descriptions of Land Use and Land Cover Classification Scheme – NRSC (2011) S/N Description Description LULC Description LULC Level-III of LULC Level-II Level-I 1. Agriculture Crop land Kharif, Double Cropped Plantation Plantation – Agricultural, Horticultural, Agro Horticultural Fallow land Current and Long Fallow 2. Built-up land Urban Residential, Mixed built-up, Public/ Semi Public, Communication, Public utilities/ facility, Commercial, Transportation, Rural Village Mining Mine/ Quarry, Abandoned Mine Pit, Land fill area 3. Forest Deciduous Dense/ Closed and Open category of Deciduous Forest Plantation Forest Plantation Scrub Forest Scrub Forest, Forest Blank, Current & Abandoned Shifting Cultivation 4. Water bodies Reservoir/ River/ Perennial & Dry River/ Stream and line & Unlined Canal/ Drain Stream/ Canals Lakes/ Tanks Kharif, Rabi & Zaid extent of lake/ pond and reservoir and tanks 5. Wastelands Scrub land Dense/ Closed and Open category of Scrub land Salt Affected land Slight, Moderate & Strong Salt Affected land Barren rocky Barren rocky/ Stony waste/ Sheet rocks

Table 2: Image Characteristics of various land use/land cover categories as seen in FCC (Dinakar, 2005) Sl/N LU/LC Tone/ Color Size Shape Texture Pattern Category 1. Barren rocky/ Greenish blue to Varying in Irregular, Coarse to medium Linear to contiguous and Sheet rock yellow to size discontinuous dispersed brownish 2. Built-up land Dark bluish Small to big Irregular Coarse Clustered to scattered green 3. Crop land Bright red to red Varying in Regular to Medium to smooth Contiguous to non-contiguous size irregular 4. Deciduous Red Varying in Irregular, Smooth to medium Contiguous to non-contiguous forest size discontinuous (depends on crown density) 5. Fallow land Yellow to Varying in Irregular, Course to medium Contiguous to non- greenish blue size discontinuous Contiguous 6. Forest Light red to red Varying in Regular to Smooth to medium Contiguous to non-contiguous plantation size irregular 7. Kharif crops Bright red Varying in Regular to Medium to Smooth Contiguous to non- size Irregular Contiguous 8. Land with Light yellow to Varying in Irregular, Coarse to mottled Contiguous dispersed scrub brown to size discontinuous greenish blue 9. Mining/ Light bluish to Small to Irregular in shape Mottled texture Contiguous dispersed Industrial black dark gray medium in area size 10. Salt affected White to light Small to Irregular, Smooth to mottled Dispersed, non-contiguous land blue medium discontinuous 11. Scrub Forest Light Red to dark Varying in Irregular, Course to medium Contiguous to non- brown size discontinuous (depends on crown Contiguous density) 12. Water bodies Light blue to Small, Regular to Smooth to mottled Non-contiguous dispersed dark blue medium, Irregular (Subject to large depth, weeds)

Assessment of Land Use Land Cover Classification through Geospatial Approach: A Case Study of Mysuru Taluk of Karnataka State, India J. Environ. Waste Manag. 330

RESULT ANALYSIS Wastelands

Level-I Classification These are described as degraded land which can be brought under vegetative cover with reasonable effort and Agricultural land which is currently under utilized and land which is deteriorating for lack of appropriate water and soil The agricultural land use is a function of land productivity management or on account of natural causes (NRSC, and land utilization practices over a period of time (NRSC, 2011). Wastelands can result from inherent/ imposed 2011). These covers farming, fallow, plantations, disabilities such as locations, environment, chemical and production of food, fiber and other commercial/ physical properties of the soil/ financial/ management horticultural crops including land under crops (irrigated constraints (NWDB, 1987). The total aerial extent of and un-irrigated) etc. This category covers an area of wasteland covers about 10.78 km2 (1.33%) (Figure 5, 600.81 km2 (74.57%) (Figure 5, Table 3). Table 3).

Built-up land Others

It is an area of human habitation comprised of intensive This can be treated as miscellaneous due to their nature use of land activities such as cities, towns, shopping of occurrence, physical appearance and other centers, industrial & commercial complexes, institutions, characteristics (Basavarajappa et al, 2017) in the villages, highways, transportation, power lines, integrated thematic layer noticed in eastern and western communications and other facilities in association with parts covering an area of 9.88 km2 (1.22%) (Figure 5, water, vegetation and vacant lands (Anderson et al, Table 3). 1976). Collectively any man-made constructions due to non-agricultural use are included under this category (Basavarajappa et al, 2013). The total aerial extent of built-up land is 126.01 km2 (15.64%) (Figure 5, Table 3).

Forest

Area within the notified forest boundary bearing an association predominantly of trees, other vegetation types, timber and other forest products (Manjunatha et al, 2018). The term forest is used to refer to land with a tree canopy cover of more than 10 percent and area of more than 0.5 ha. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 mts (FAO, 2017). Satellite data has become useful tool in Figure.4. Level-I LU/LC Classified map of Mysuru mapping the different forest types and density classes taluk with reliable accuracy through visual as well as digital techniques (Madhavanunni, 1992; Roy et al, 1990; Table 3: Level-I Land Use /Land Cover Classification Sudhakar et al, 1992). The total forest cover measures an of Mysuru taluk 2 area of 19.86 km (2.46 %) (Figure 5, Table 3). S/N Land pattern Area Percentage (km2) (%) Water bodies 1. Agricultural 600.8149 74.57

land This category comprises areas with surface water in the 2. Built-up land 126.0137 15.64 form of ponds, lakes, tanks and reservoirs (NRSC, 2011). 3. Forest land 19.8649 2.46 This class comprises areas of surface water, either 4. Water bodies 36.0077 4.46 impounded in the form of ponds, lakes and reservoirs or flowing as streams, rivers, canals, etc (Dinakar, 2005). 5. Wastelands 10.7862 1.33 These are clearly observed on standard FCC in different 6. Others 9.8859 1.22 shades of blackish blue to light blue color depending on Total 803.3733 99.68 the depth of water bodies (Manjunatha and Total Geographical 805.6362 Basavarajappa, 2015b). The area occupied by this Area category is 36 km2 (4.46%) (Figure 5, Table 3).

Assessment of Land Use Land Cover Classification through Geospatial Approach: A Case Study of Mysuru Taluk of Karnataka State, India Manjunatha and Basavarajappa 331

areas appear in bright red toned in color with varying shape and size in a contiguous to non-contiguous pattern. They are widely distributed in different terrains; prominently appear in the irrigated areas irrespective of the source of irrigation. This category covers an area of 539.81 km2 (67%) (Figure 7, Table 5).

Degraded forest

Forest cover with less than 10% is called as degraded Figure 5: Pie-chart depicting Percentage of Level-I forest. The degradation is brought about by maltreatment LU/LC categories of Mysuru taluk meted out by repeated felling, grazing and forest fires (Manjunatha et al, 2015a). On the contrary, if further Level-II Classification ravaged it, ultimately degrades into thorny type and ultimately dry grass prevails and naked boulders are Agricultural plantations exposed. These are notified in the south-western corner of the taluk with an aerial extent of 5.05 km2 (0.62%) These are the areas under agricultural tree crops exhibit (Figure 7, Table5). a dispersed or contiguous pattern planted adopting agricultural management techniques (NRSC, 2011). Use Fallow land of multi-season data will enable their separation in a better way. It includes agricultural plantation (like tea, coffee, The lands which are taken up for cultivation but are rubber etc.) horticultural plantation (like coconut, temporarily allowed to rest, un-cropped for one or more arecanut, citrus fruits, orchards, fruits, ornamental shrubs season, but not less than one year (NRSC, 2011). These and trees, vegetable gardens etc) and agro-horticultural are particularly devoid of crops at the time; when the plantation (NRSC, 2011). Differentiation of plantation from imagery is taken from both seasons. On FCC, fallow land cropland is possible by multi-temporal data of period shows yellow to greenish blue tone, irregular shape with matched harvesting time of inter-row crop/flowering of the varying size associated with amidst crop land as plantation crops. Overall, Rabi season data is found to be harvested agriculture field (Basavarajappa et al, 2017). better discrimination of plantations from croplands. The The total area under this category is 7.00km2 (0.86%) total area under this category is 53.98km2 (6.7%) (Figure notified around the city boundary limits (Figure 7, Table5). 7, Table5). Forest plantations Barren rocky/Stony Waste These are the areas of tree species of forestry As the area is exposed to the direct action of sun and importance, raised and managed especially in notified wind, most of the area remains barren (Dinakar, 2005). forest areas. The species mainly constitute teak, Sal, These are the lands characterized by exposed massive eucalyptus, casuarinas, bamboo etc (NRSC, 2011). rocks, sheet rocks, stony pavements or land with These are artificially planted areas with tree cover, either excessive surface, accumulation of stones that render in the open spaces or by clearing the existing forests for them unsuitable for production of any green biomass. economically inferior species (Dinakar, 2005). New and Such lands are easily discriminated from other categories young plantations can be readily separated from of wastelands due to their characteristic spectral response contiguous forested areas (Pushpavathi, 2010). The area (Basavarajappa et al., 2017). On FCC, they appears as occupied by this class is about 1.23km2 (0.15%) observed greenish blue to yellow to brownish in tone with varying in south-western corner of the study area (Figure 7, Table size associated with steep isolated hillocks, hill slopes and 5). eroded plains. These are notified as linear forms within the plain land mainly due to varying lithology in northern Lakes/ Tanks and southern parts of the taluk (NRSC, 2011) covering an area of 1.17km2 (0.14%) (Figure 7, Table 5). It is the natural course of water flowing openly on the land surface along a definite channel occupied either as Crop lands seasonal or perennial river systems (Basavarajappa et al, 2017). Rivers and tanks are the major water sources in It includes kharif, rabi and zaid croplands along with area the taluk. 23 major and 62 minor lakes and tanks have under double or triple cropping patterns (NRSC, 2011) been extracted effectively from LISS-III image based on including irrigated and un-irrigated, fallow, plantation etc the color/ tonal variation from dark to light blue (Satish et (NRSA, 1989). The area under crops have digitized based al, 2008). This covering an area of 10.12 km2 (1.25%) on the standing crops as on the date of satellite image (Figure 7, Table 5).Mysore has the Biggest 'Walk-Through acquisition using both Kharif & Rabi seasons. Cropped Aviary' called in India.

Assessment of Land Use Land Cover Classification through Geospatial Approach: A Case Study of Mysuru Taluk of Karnataka State, India J. Environ. Waste Manag. 332

Land with scrub of irrigation, domestic and industrial purposes in the study area (CGWB, 2012). Krishna Raja Sagara (KRS) dam is Scrub lands are observed along the ridges, valley built across River Cauvery in north-western part and the complex, linear ridges and steep slope areas. Most of outlet water flow from western to eastern direction in these areas are characterized by the presence of thorny northern boundary of the taluk. The reservoir along with scrub, herb species, many hillocks of steep and domal its stream and canals stores an aerial extent of 25.88 km2 shaped are associated with poor vegetal cover (3.20%) of water in the study area (Figure 7, Table 5). (Basavarajappa et al, 2014). This category covers an Artificial water course of canals are constructed for KRS aerial extent of 9.4 km2 (1.16%) noticed majorly in water outlet in use of irrigation and to drain out excess southern, southern-western parts and in northern parts of water from agricultural lands (NRSC, 2011). the taluk (Figure 7, Table 5). Rural (Villages) Mining/ Industrial Wastelands These are the built-up areas, smaller in size, mainly Mining areas encompass area under surface mining associated with agriculture and allied sectors and non- operations. Industrial areas include a wide array of land commercial activities. They can be seen in clusters non- uses from light manufacturing to heavy manufacturing contiguous or scattered (NRSC, 2011). Land used for Plants (Anderson et al, 1976). These are areas of human settlement of size comparatively less than the stockpile of storage dump of industrial raw material or urban settlement of which more than 80% of people are slag/effluents or waste material or quarried/ mixed debris involved in agricultural activities (Pushpavathi, 2010). from earth's surface (NRSC, 2011). This category covers Villages can be clearly noticed from toposheet & satellite an area of 1.00 km2 (0.12%) (Figure 7, Table 5). images with number of houses, inter spread with trees and Magnesite mine is observed in Karya village in southern agriculture fields especially in south western parts of study part of the taluk covering an area of 15.89 hectares area occupied by deciduous forest of Chikkanahalli including overburden and mine wastes. TVS is one of the (Basavarajappa et al., 2017). The area occupied by this major industrial lands noticed in Kadakola village with an class is about 11.36 km2 (1.41%) (Figure 7, Table 5). aerial of 58.82 hectares. Salt-affected land Moist & Dry Deciduous Forest The land that has excess salt in the soils with patchy Moist deciduous forests are more pronounced in the growth of grasses (NRSC, 2011). These are found in river regions which record rainfall between 100-200 cms with plains and in association with irrigated lands and main species of Teak, sal, sandalwood and other adversely effecting the growth of most of the plants due to (NCERT, 2019). Dry deciduous forest covers vast areas the action or presence of excess soluble or high of the country, where rainfall ranges between 70 -100 cms exchangeable sodium. The areas are delineated based and interspersed with patches of grass. As the dry season on white to light blue tone and its situation (Dinakar, begins, the trees shed their leaves completely and the 2005). Salt affected lands are observed near Sindhuvalli forest appears like vast grassland with naked trees all village with an extent of 0.04 km2 (Figure 7, Table 5) around (NCERT, 2019). Multi-temporal data, particularly noticed in southern part of the taluk. during October and March/April seasons help in their discrimination from other forest types. On FCC, it appears Scrub Forest as dark red to red tone mainly due to rich in timber trees like Teakwood, Bamboo, Eucalyptus plantations etc. Forest blanks which are the openings amidst forest areas, Chikkanahalli is one of the state reserved moist-dry devoid of tree cover, observed as openings of assorted deciduous forests identified in the southern-western part size and shapes as manifested on the imagery are also of the study area through LISS-III satellite image. This included in this category (NRSC, 2011). Scrub forest of category covers an area of 5.83 km2 (0.72%) (Figure7, Chamundi hill is noticed in central part of the taluk having Table 5). canopy density less than 10% during extreme summer conditions (FAO, 2017). They appear as light red to dark Reservoir/ River brown tone on standard FCC due to canopy covers. This category covers an area of 7.73 km2 (0.96%) (Figure 7, A reservoir is an artificial lake created by construction of a Table 5). Chamundi hill is observed at the fringes of forest dam across the river specifically for the generation of cover and settlements, where there is biotic and abiotic irrigation, water supply, hydro-electric power for domestic/ interferences occurs. industrial uses and flood control (Dinakar, 2005). The introduction of a huge reservoir would be disturbing the Tree groves delicate balance between soil, water and plants through rise in groundwater table (water-logging), (Piyoosh These are clump of trees that doesn't have much Rautela et al, 2002). River Cauvery is the primary sources undergrowth and occupies a contained area such as a

Assessment of Land Use Land Cover Classification through Geospatial Approach: A Case Study of Mysuru Taluk of Karnataka State, India Manjunatha and Basavarajappa 333

small orchard planted for the cultivation of fruits or nuts which more than 80% of the work forces are involved in (Basavarajappa et al., 2019). A group of trees that grow non-agricultural activities is termed as urban land use close together are noticed extensively towards eastern (Pushpavathi, 2010). Most of the land covered by building and western parts of the study area, generally without structures is parks, institutions, playgrounds and other many bushes or other plants underneath. This category open space within built up areas. Urban land occupies an covers an area of 9.88 km2 (1.22%) (Figure 7, Table 5. area of 113.80 km2 (14.12%) (Figure 7, Table 5). This class usually occurs in combination with, vegetated areas Urban (Towns and Cities) that are connected to buildings that show a regular pattern, such as vegetated areas, gardens, industrial It includes residential areas, mixed built-up, recreational and/or other areas (FAO, 2017). Mysuru is the second places, public/ semi-public utilities, communications, largest city after Bengaluru in Karnataka State having the public utilizes/ facility, commercial areas, reclaimed areas, population of 8,87,446 as per 2011 provisional census vegetated areas, transportation, industrial areas and their figures and it increased from 7,85,800 in 2001 (Shankar dumps, and ash/cooling ponds (NRSC, 2011). Land used and Vidhya, 2013). for human settlement of population more than 5000 of

Figure 6: Level-II LU/LC Classified map of Mysuru taluk

Table.4. General Conditions of LU/LC patterns during Ground Truth Check (GTC) S/N Location Name Latitude Longitude Field Conditions 1. Agrahara 12°17'14.33" 76°39'11.36" Mysore betel leaves garden and coconut plantations 2. Visveshwara Nagara 12°15'55.34" 76°39'23.59" Area exposed with barren/sheet rocky land 3. Dadada Kalla halli 12°23'28.84" 76°30'4.89" Crops like Maize, jowar, bajra are grown village 4. Bettadabeedu village 12° 9'41.04" 76°30'16.27" Reserved forest without scrub due to livestock grazing 5. Hootagalli 12°20'42.66" 76°35'16.68" Temporary rested/ un-cropped area for 1 season 6. Chikkanahalli 12°11'17.02" 76°32'7.60" Observed Coconut plantations within forest boundary 7. Karanji lake 12°18'9.13" 76°40'23.93" Biggest 'Walk-Through Aviary' (Karanji) Lake in India 8. Elivala 12°20'45.75" 76°31'50.44" Domal shaped associated with poor vegetal cover 9. Karya village 12° 9'58.12" 76°38'39.36" Abandoned Magnesite mine observed with 75hectares 10. Bettadabeedu village 12°11'13.20" 76°29'54.07" Moist & dry deciduous forest covers 3.08 km2 area here 11. Krishna Raja Sagara 12°25'35.15" 76°30'56.08" Back water of KRS dam built across river Cauvery in NW parts 12. Chikkanahalli 12°11'1.29" 76°31'39.50" Scattered cluster of human settlement associated with more than 80% of agricultural activities 13. Sindhuvalli village 12°11'24.63" 76°37'23.14" Excess salt in the soil with patchy grasses observed 14. Chamundi hill 12°16'22.81" 76°40'10.68" Chamundi forest covered with lush green vegetation 15. K.R. Circle-City Centre 12°18'31.53" 76°39'11.03" Mysore is the third most populated city in Karnataka 16. Hosur village 12°13'35.14" 76°30'13.40" Rural organic farms had noticed

Assessment of Land Use Land Cover Classification through Geospatial Approach: A Case Study of Mysuru Taluk of Karnataka State, India J. Environ. Waste Manag. 334

Table 5: Level-II Land Use/Land Cover Classification of Mysuru taluk S/N Land pattern Area Percentage (km2) (%) 1. Agricultural Plantation 53.9898 6.70 2. Barren rocky/ Stony waste 1.1750 0.14 3. Crop land 539.8172 67.00 4. Degraded Forest 5.0567 0.62 5. Fallow land 7.0078 0.86 6. Forest plantations 1.2368 0.15 7. Lakes/ Tanks 10.1235 1.25 8. Land with scrub 9.4097 1.16 9. Mining/ Industrial 1.0067 0.12 Figure 9: Google Earth image of Barren/ sheet rocky wasteland near visveshwara nagara 10. Moist & Dry Deciduous 5.8340 0.72 Forest 11. Reservoir/ River/ Stream 25.8840 3.20 12. Rural 11.3618 1.41 13. Salt Affected land 0.0424 0.00 14. Scrub Forest 7.7373 0.96 15. Tree Groves 9.8859 1.22 16. Urban 113.8040 14.12 Total 803.3726 99.62 Total Geographical Area 805.6362

Figure10: Crop land near Dadadakalla halli village

Figure 7: Pie-chart depicting Level-II LU/LC categories of Mysuru taluk

Figure11: Fallow land near Hootagalli

Figure 8: Betel leaf garden near Agrahara

Figure 12: Coconut plantation near Chikkanahalli

Assessment of Land Use Land Cover Classification through Geospatial Approach: A Case Study of Mysuru Taluk of Karnataka State, India Manjunatha and Basavarajappa 335

Level-III Classification

Double Cropped (Kharif + Rabi)

The main cropping season, kharif, starts from May and ends by September. The cropping intensity is very high due to physical factors such as flat terrain, fertile soil and irrigated from canal system. Most of the double crop areas are concentrated adjacent to the rivers flowing in the study

Figure 13: Karanji lake area (Pushpavathi, 2010). On FCC, the double crop show a dark red tone with square pattern representing soil covers with higher amount of moisture near the streams (Basavarajappa et al, 2017). The cultivated lands at elevated zones represent bright red tone representing less amount of moisture and deeper levels of groundwater prospect zones. This category has been identified and mapped using the two season satellite images which covers an area of 63.90km2 (7.93%) (Figure 19, Table6).

Kharif

Figure 14: Abandoned Magnesite Mine near Karya These are the standing crops from June to September village associated with rainfed crops under dry land farming and limited irrigation. Kharif crops are depicted by red tone on standard FCC image. The major kharif crops grown area maize, jowar, bajra, cotton, sugarcane, pulses grown under rainfed condition, whereas paddy are grown under irrigated conditions (CGWB, 2012). The land occupies an area of 475.9km2 (59.07%) (Figure 19, Table 6).

Table 6: Level-III Land Use/Land Cover Classification of Mysuru taluk S/N Land pattern Area (km2) Percentage (%) Figure 15: Back water of Krishna Raja Sagara 1. Double (Kharif + 63.9086 7.9326 Rabi) crops 2. Kharif crops 475.9086 59.0723 Total 539.8172 67.0049 Total Geographical Area 805.6362

Figure 16: Bird view of Chamundi hill

Figure 18: Level-III LU/LC Classified map of Mysuru taluk

Figure 17: Bird view of built-up area from Chamundi hill

Assessment of Land Use Land Cover Classification through Geospatial Approach: A Case Study of Mysuru Taluk of Karnataka State, India J. Environ. Waste Manag. 336

management practices, future food security and decision making in land use planning & its policy. Geospatial approach provides wide range of digital databank information in a synoptic, spatial and temporal manner for mapping and monitoring of land use/land cover in most time and cost-effective manner.

ACKNOWLEDGEMENT

Figure 19: Pie-chart depicting Level-III LU/LC map of Mysuru taluk The authors are indepthly acknowledged Prof. P.Madesh, Chairman, Department of Studies in Earth Science, CAS in Precambrian Geology, Manasagangothri, University of DISCUSSION Mysore, Mysore; Dr. Y.T. Krishne Gowda, Principal, MIT, Thandavapura, Mysore; Dr. Pushpavathi K.N, Senior Scientific assessment of our land resources is a Geologist, Department of Mines & Geology, Mysuru; prerequisite for optimal planning of natural resources of CGWB, Bengaluru; Survey of India, Bengaluru, ISRO- the country (NRSC, 2011). Remotely sensed data has NRSC, Hyderabad. made it possible to study changes in land cover and its monitoring in less time, at low cost and better accuracy (Anji Reddy, 2001; Kachhwaha, 1985). The present study CONFLICTS OF INTEREST reveals that 5 major classes in Level-I (Figure 4; Table 3) 16 classes in Level-II (Figure 6; Table 4) and 2 classes in The authors declare no conflicts of interest. Level-III have been effectively generated by IRS-1D, PAN+LISS-III satellite images (Figure 8; Table 5). Kharif crops are dependent mainly of rainfall and occupy the REFERENCES maximum areal extent of 475.9 km2 (59.07%) that indirectly reflect that groundwater dependent crops are Alexandratos N (1995). World Agriculture: Towards 2010: less. Double crops are noticed adjacent to the perennial An FAO Study, Food and Agriculture Organization of rivers Cauvery and in its drainage patterns which provide the United Nations, Rome/ Wiley and Sons, well developed canal system for irrigation purpose Chichester, XXVI: 488. (Basavarajappa et al, 2014). The major crops grown are Anderson J.R, Hardy E.E, Roach J.T and Witmer R.E cotton, ragi, vegetables and mango practiced in large (1976). A land use and land cover classification system agricultural fields (CGWB, 2012). LU/LC provides for use with remote sensor data, Department of the necessary tasks to enhance human occupation to the Interior, No. 964, Washington, DC. changing social, economic and natural environmental Anji Reddy M (2001). A Textbook of Remote Sensing and conditions. The area occupied by built-up land is 126.01 geographical information system”, Second edition, BS km2 (15.64%) and further increase in population can Publications, Hyderabad. negatively impacts on biodiversity and also disturbs Basavarajappa H.T, Dinakar S, Satish M.V, Nagesh D, natural land cover, increase in soil erosion into streams Balasubramanian A and Manjunatha M.C (2013). and lakes (Manjunatha et al, 2015a). The purpose of land- Delineation of Groundwater Potential Zones in Hard use planning is to support decision makers in selecting the Rock Terrain of Kollegal Shear Zone (KSZ), South best practice for specific lands that meet the needs of India, using Remote Sensing and GIS, International people while safeguarding natural resources & ecosystem Journal of Earth Sciences and Engineering (IJEE), services for current and future generations (FAO, 2017). Cafet-Innova, Hydrology & Water Resource Management, 6(5): 1185-1194. Basavarajappa H.T, Dinakar S and Manjunatha M.C CONCLUSION (2014). Analysis on Land use/ Land cover classification around Mysuru and Chamarajanagara district, The study highlights the capability of Geospatial Karnataka, India using IRS-1D, PAN+LISS-III Satellite technology in extracting meaningful and valuable Data, International Journal of Civil Engineering and information which is extremely important in monitoring Technology (IJCIET),5(11): 79-96. and management of dynamic LULC features. Precise and Basavarajappa H.T, Pushpavathi K.N and Manjunatha timely interpretation of LULC classification data will be an M.C (2017). Land Use Land Cover Classification effective tool in addressing the spatial changes, analysis in Chamarajanagara taluk, Southern tip of environmental & socio-economic concerns, growing Karnataka state, India using Geo-informatics, Journal demand for economic natural resources, risks related to of Environmental Science, Computer Science and public health, cropping patterns, vulnerability to certain Engineering & Technology, 6(3): 209-224.

Assessment of Land Use Land Cover Classification through Geospatial Approach: A Case Study of Mysuru Taluk of Karnataka State, India Manjunatha and Basavarajappa 337

Basavarajappa H.T, Pushpavathi K.N, Manjunatha M.C the Photogrammetry, Remote Sensing and Spatial and Maruthi N.E (2019). Mapping and Land Use Land Information Sciences, XL-8: 833 – 837. Cover Classification Analysis on Gundlupete taluk, NCERT (2019). National Council of Educational Research Karnataka, India using Geoinformatics, Journal of and Training, India: Physical Environment, A Textbook Emerging Technologies and Innovative Research in Geography for Class-XI, Chapter-5, New Delhi, 1- (JETIR), 6(6): 963-973. 11. CGWB (2012). Central Ground Water Board, NRSA (1989). Manual of Nationwide land use/ land cover Groundwater Information Booklet, Mysuru district, mapping using satellite imagery, part-1, National Karnataka State, South Western region, Govt. of Remote Sensing Agency. Govt. of India, Balanagar, Karnataka, Bengaluru,1-21. Hyderabad. Darwin R, Tsigas M, Lewandrowski J & Raneses A NRSA (1995). Integrated mission for sustainable (1996). Land use and cover in ecological economics, development, Technical Guidelines, National Remote Ecological Economics,17: 157-181. Sensing Agency, Dept. of Space, Govt. of India, Dinakar S (2005). Geological, Geomorphological and Hyderabad, 1-21. Landuse/cover studies using Remote Sensing and GIS NRSC, (2011), Land Use Land Cover Atlas of India around Kollegal Shear Zone, , unpub. (Based on Multi‐temporal Satellite Data of 2005‐2006), Ph.D. thesis, Univ. of Mysore,1-191. Department of Space, ISRO, GOI, Hyderabad. FAO (2017). Land Resource Planning for Sustainable NWDB (1987). Description and Classification of land Management, Food and Agriculture Organization Wastelands, National Wastelands Development of United Nations,Rome, 14:1-68. Board. Ministry of Environmental and Forest.Govt. of Kachhwaha, T.S. (1985). Temporal monitoring of forest India. New Delhi. land for change detection and forest cover mapping Philip G and Gupta R.A., (1990). Channel migration through satellite remote sensing, In: Proceedings of studies in the middle Ganga basin, India using Remote the 6th Asian Conf. on Remote Sensing, Hyderabad, sensing data, Int. J.Remote Sensing, 10(6): 1141- 77– 83. 1149. Lenz-Wiedemann V.I.S, Klar C.W, and Schneider K Piyoosh Rautela, Rahul Rakshit, Jha V.K., Rajesh Kumar (2010). Development and test of a crop growth model Gupta and Ashish Munshi (2002). GIS and Remote for application within a Global Change decision support Sensing based study of the reservoir-induced land- system. Ecol. Model. 221: 314–329. use/land-cover changes in the catchment of Tehri dam Madhavanunni N.V (1992). Forest and ecology in Garhwal Himalaya, Uttaranchal (India), Current application of IRS-1A data, Natural resources Science, 83(3): 309-311. management – A new perspective, Publication and Pushpavathi K.N (2010). Integrated Geomorphological Public Relations Unit, ISRO-Hq, Bangalore, 108-119. study using Remote Sensing and GIS for development Manjunatha M.C, Basavarajappa H.T and Jeevan L of Wastelands in Chamarajanagar district, Karnataka, (2015a). Geoinformatics analysis on Land use/ Land India, Unpub. PhD thesis, University of Mysore, 1-201. covers classification system in Precambrian terrain of Roy P.S., Diwakar P.G., Vohra T.P.S and Bhan S.K Chitradurga district, Karnataka, India. International (1990). Forest resources management using Indian Journal of Civil Engineering and Technology Remote Sensing Satellite data, Asian-Pacific Remote (IJCIET),6(2): 46-60. Sensing J.,3(1): 11-16. Manjunatha M.C and Basavarajappa H.T (2015b). Spatial Satish M.V, Dinakar S and Basavarajappa H.T data integration of lithology, geomorphology and its (2008).Quantitative morphometric analysis of sub- impact on Groundwater prospect zones in water sheds in and around Yelandur Taluk Precambrian terrain of Chitradurga district, Karnataka, Chamarajanagara District using GIS, Remote Sensing India using Geospatial application, Global Journal of and GIS Applications, Edited Volume, University of Engineering Science and Research Management, Mysore, 1(1): 156-164. 2(8): 16-22. Schmedtmann J, Campagnolo M.L (2015). Reliable crop Manjunatha M.C, Maruthi N.E, Siddaraju M.S and identification with satellite imagery in the context of Basavarajappa H.T (2018). Temporal Mapping of common agriculture policy subsidy control, Remote Forest Resources in Hosadurga taluk of Karnataka Sens. 7: 9325–9346. State, India using Geo-informatics, Journal of Shankar B and Vidhya D (2013). Transitioning Residential Emerging Technologies and Innovative Research Neighborhoods: A Case Study of Jayalaximpuram, (JETIR), 5(11): 124-132. Mysore, India, International Journal of Recent Meyer W.B and Turner B.L. (1992). Human Population Technology and Engineering (IJRTE), 2(2): 1-5. Growth and Global Land Use/Land Cover Change, Sharma J, Prasad R, Mishra V.N, Yadav V.P and Bala R Ann. Rev. Ecol. Syst., 23: 39-61. (2018). Land Use and Land Cover Classification of Mishra V.N, Kumar P, Gupta D.K, Prasad R (2014). Multispectral Landsat-8 Satellite Imagery using Classification of various land features using risat-1 Discrete Wavelet Transform, The International dual polarimetric data, The International Archives of Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 62(5): 703-706.

Assessment of Land Use Land Cover Classification through Geospatial Approach: A Case Study of Mysuru Taluk of Karnataka State, India J. Environ. Waste Manag. 338

Sreenivasalu V and Vijay Kumar (2000). Land use/land Zhao Q, Hütt C, Lenz-Wiedemann, V.I.S, Miao Y, Yuan F, cover mapping and change detection using satellite Zhang F, Bareth G. (2015). Georeferencing multi- data – A case study of Devak catchment, Jammu and source geospatial data using multi-temporal Terra- Kashmir, Proc. of ICORG, 1, 520-525. SAR-X imagery: A case study in Qixing farm, Sudhakar S., Krishnan N., Das P and Raha A.K (1992). Northeast China. Photogramm. Fernerkund. Geoinf, Forest cover mapping of Midnapore forest division 173–185. using IRS-1A LISS-II data, Natural resources management – A new perspective, Publication and Public Relations Unit, ISRO-Hq, Bangalore, 314-319. Tammy E. Parece and James B. Campbell (2015). Land Use/ Land Cover Monitoring and Geospatial Technologies: An Overview, Springer International Publishing Switzerland, T. Younos, T.E. Parece (eds.), Advances in Watershed Science and Assessment, The Accepted 26 June 2020 Handbook of Environmental Chemistry,33, DoI: 10.1007/978-3-319-14212-8. Citation: Manjunatha MC, Basavarajappa HT (2020). Townshend J.R.G (1992). Improved global data for land Assessment of Land Use Land Cover Classification applications, IGBP Secretariat/ Royal Swedish through Geospatial Approach: A Case Study of Mysuru Academy of Sciences, Stockholm, IGBP Report, 20: 1- Taluk of Karnataka State, India. Journal of Environment 75. and Waste Management, 7(1): 326-338. Vibhute A.D and Gawali B.W (2013). Analysis and modeling of agricultural land use using remote sensing

and geographic information system: A review. Int. J. Eng. Res. Appl. (IJERA), 3: 81–91. Copyright: © 2020: Manjunatha and Basavarajappa. This Waldhoff G, Curdt C, Hoffmeister D and Bareth G (2012). is an open-access article distributed under the terms of Analysis of multi-temporal and multi-sensor remote the Creative Commons Attribution License, which permits sensing data for crop rotation mapping, ISPRS Int. unrestricted use, distribution, and reproduction in any Arch. Photogramm, Remote Sens. Spat. Inf. Sci, I-7: medium, provided the original author and source are 177–182. cited.

Assessment of Land Use Land Cover Classification through Geospatial Approach: A Case Study of Mysuru Taluk of Karnataka State, India