Land Use/Land Cover Change Assessment of Ysr Kadapa District, Andhra Pradesh, India Using Irs Resourcesat-1/2 Liss Iii Multi-Temporal Open Source Data

Home , Duvvuru, Kadapa

International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019 Land use/Land Cover Change Assessment of Ysr Kadapa District, Andhra Pradesh, India using Irs Resourcesat-1/2 Liss Iii Multi-Temporal Open Source Data

C.Venkata Sudhakar, G.Umamaheswara Reddy

Abstract: LULC change assessment by the Remote sensing re-visit capabilities, extensive earth coverage area, semi- Technology helps in understanding land dynamics effectively automated processing and analysis, achieved better image compared to conventional field inspection methods. This study interpretation by enhancing and manipulating the data and provides the spatio-temporal dynamics of LULC classes in the accurate mapping. The objective of this study is to quantify Kadapa District, Andhra Pradesh, India. IRS Resourcesat-1/2 YSR Kadapa District LULC change by the open source LISS III Multi-Temporal remote sensed data, from Bhuvan - Indian Geo-platform of ISRO is intended to assessment the remotely sensed data. changes in the study area during the years 2005-2006, 2011-2012 and 2015-2016 over tenure of 10 years. At the end the study area II. STUDY AREA claasified into five major classes namely Built-up, Agriculture, Kadapa District was ruled by Mouryans in BC era and Forest, Wastelands and waterbodies using ERDAS Imagine sathavahanas in 3rd century AD. The District (Cuddapah) 2015. The results indicate net change and rate of change of th LULC classes over the period of 10 years. Net change in Built-up was first formed in early 19 century (1808) during the land is 89.91%( 167.12 km2), Net change (decreased) in British rule and it was renamed as YSR kadapa district in Agriculture land is 3.76% (256.05km2), Net increment in the 2010 as a mark of tribute to the former chief minister of Forest land is 2.39 %( 114.83km2), Wastelands decreased by 2.92 Andhra Pradesh Sri Dr.Y.S.Rajasekhara Reddy. %( 79.09km2), and waterbodies increased by 6.28% (52.9 km2). Kadapa district was located between the latitudes 13º 43’ to 15º 14’N and Longitudes 77º 51’ to 79º 29’E. It was Keywords: LULC classes, Resourcesat-1/2 LISS III open data surrounded by Nellore, Anantapuramu, Kurnool, and source, Change analysis. Chittoor on the East, West, North and South respectively. The study area occupied 15,359 km2 contributing 5.58% of I. INTRODUCTION the total country area (figure 1). As per 2011census district LULC classes change analysis is a vital phenomenon for population is 28, 84,524 it is about 3.40% of the state understanding inhabitant of the earth [1]. At present population [3, 4, 20]. monitoring of LULC classes change and its consequences The district was rich in minerals; Minerals are the back- on the environment and socio-economic impact on the land bone for economic growth and industrial development of the is critical for global and regional sustainable development country [2]. Minerals are extracted by the mining process, [6]. LULC change monitoring with Conventional approach which significantly disturb the premises Vegetation land, is slow and expensive. Now a day’s remote sensing is the Soil, and Terrain. It forms pits, transit sites, solid waste and only cost effective and precise method for LULC change miscellaneous mining elements. When these elements are assessment, change analysis and environmental studies for superimposed with geo-environment an alternations occur in sustainable development as compared to traditional methods the LULC, Topography, Land Surface Temperature (LST) because of the following advantages [6, 19]. Obtained data and environment [5]. continuously, up-to-date information obtained with regular

Published By: Retrieval Number C6067098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijrte.C6067.098319 8139 & Sciences Publication

Land use/Land Cover Change Assessment of Ysr Kadapa District, Andhra Pradesh, India using Irs Resourcesat-1/2 Liss Iii Multi-Temporal Open Source Data

Figure 1: Geographical location map of the study area [3]

Published By: Retrieval Number C6067098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijrte.C6067.098319 8140 & Sciences Publication International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019

A. Mineral Classes The district was rich in minerals. The major minerals are Barytes, Asbestos and Limestone. The minor minerals are Laterite, Pyrophyllite, Ochere Quartzite, Shale, Steatite, Quartz & Silica Sand, and Calcite etc [3, 4, 20]. Details of minerals availability is illustrated in the Table I.

Table I: Major and Minor Mineral in Y.S.R. District [2, 3, 4, 7]. Sl.No. Mineral Mineral raw sample(s) Applications Availability Classes Major Mineral 1. Iron Ore In metal products, 1.42 million tonnes hematite (Fe2O3), industrial machinery, in Chitvel, magnetite (Fe3O4) transportation Pendlimarri, goethite(FeOOH) equipment, Muddanur, instruments, magnets Verapunayunipalli etc.

2. Limestone Primarily used in the Jammalamadugu, calcium carbonate Cement Industry. Mylavaram, (CaCO3) with Also used in Glass, Yerraguntla, Dolomite Ceramic, Paper, Kamalapuram, CaMg(CO3)2 or Textile, sugar magnesite (MgCO3) refining and bleaching powder.

3. Black Limestone Used for flooring, Yerraguntla Mandal (Napa slabs) and building Sugumanchupalli material. areas of Jammalamadugu Mandal.

4. Chrysotile Asbestos Used in fabrics, Pulivendula, Lingala (Mg3(Si2O5)(OH)4) resistance to heat, mandals with acids alkalies and estimation of 2.5LT cements. up to depth of 200m

5. Barytes As a weighting agent with an estimation (BaSO4) in the heavy drilling of 70 MT reserve in muds and in oils Mangampeta

6. White Clay or ceramic ware, Rajampeta, Kaolinite sanitary wares, Rayachoty, (Al2Si2O5(OH)4) detergent industries Duvvuru, Simhadripuram, Pulivendula

Minor mineral

Published By: Retrieval Number C6067098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijrte.C6067.098319 8141 & Sciences Publication

Land use/Land Cover Change Assessment of Ysr Kadapa District, Andhra Pradesh, India using Irs Resourcesat-1/2 Liss Iii Multi-Temporal Open Source Data 7. Uranium Military Tumalapalli village (U3O8) applications, located in Kadapa Power generation Industries,

8. Fullerene Used in aerospace, Mangampet in (C60, C70) Nano Technology, Obulavanipalli solar power and mandal synthesis of Artificial diamonds.

B. Industrial Profile India occupied second place in cement production of the world after China followed by United States, Iran. As of August 2015, Cement production accounted for around 6.7 per cent of global cement production. Cement production capacity increases from 323MT to 390MT during the year 2011 to 2015 respectively. The Production capacity estimated 6.1 % during the period 2011 to 2020 and reaches 550 million tons (figure 2) (http://eaindustry.nic.in/ cement/default .asp) [4].

Figure 2: Cement production projection rate towards 2020 At present, Andhra Pradesh has 40 large cement plants of 210 cement plants of India. The Major cement plant clusters locations are Yerranguntla (YSR kadapa, Andhra Pradesh), Nalgonda (Andhra Pradesh) Gulbarga (Karnataka), Satna (Madhya Pradesh) and Chandoria (Rajasthan). The major cement industries which are impact the LU/LC classes in the study area are illustrated in the Table II. Table II: List of Major cement plants located in the district S.No. Plant Name Plant outlook Description 1. The India Cements Ltd. The plant was commissioned in 1998.The plant was expanded in the (previously Coromandal year 2010. Ordinary Portland Cement (OPC), Portland Pozzolona Cements), Chilamakur Cement (PPC) and cement clinker are the plant productions. http://www.indiacements.co.in/

2. The India Cements Ltd. The plant was commissioned in 1999.The plant was expanded in the Yerraguntla year 2010. Clinker production capacity is 1.485 to 5.15 MTPA and Cement Production capacity is 1.65 MTPA. http://www.indiacements.co.in/

Published By: Retrieval Number C6067098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijrte.C6067.098319 8142 & Sciences Publication International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019

3. Zuari cements, Plant was commissioned in March 1985 with production capacity Yerraguntla 23LTPA and expanded its production capacity to 2.3 MTPA in the year 2010. The plant productions are OPC 43 Grade and 53 Grade and PPC. http://www.zuaricements.com/#

4. Bharathi Cement Plant commissioned in 2006 with cement production capacity 2.75 Corporation Private Ltd. Lakh MTPA. At present it producing Portland Cement, Cement (BCCPL) Blended, and Cement Slag. Nallalingayapalli, http://www.bharathicement.com/bharati-cement.php Kamalapuram

5. Dalmia Cement (Bharat) The plant was commissioned in 2008 with a commercial cement Ltd., Chinnakomarla, production of the 2.50 MTPA Jammalamadugu https://www.dalmiacement.com/contact-us.html

6. Rayalaseema Thermal The project started in 1994.At present it has 5 units; each unit Power Project, power generation capacity is 210MW. The total power generation Kalamalla capacity is 1050 MW (5*210MW) https://apgenco.gov.in/home.do

C. Environmental and Social Impacts data are ideally suited for the surface feature analysis because of the long history of earth observation and rich The cement industry create an employment and business bands with ample spectral information. Landsat series opportunities to the local people but Cement production dataset from USGS (https://earthexplorer. usgs.gov/) widely disturb the landscape, Create pollution in the form of dust used for diverse applications such as LULC mapping, and gases, Noise generation with operating machinery and Disaster monitoring, urban expansion, rural development, Disruption to local biodiversity. Plant limestone quarries environment, forestry, ocean resources study and may disfigure the local environment [8]. biodiversity. Landsat images are also used for large-scale land cover mapping with a relatively fine spatial resolution III. MATERIALS AND METHODS or subtle change detection [6, 10].The landsat data can also A. Open data sources access from Global visualization viewer (GloVis) and Landsat look viewer. Remotely sensed data from Landsat TM/ ETM+/OLI, Defense Meteorological Satellites Program (DMSP) / ii). Bhuvan Indian Geo-platform of ISRO Operational Line scan System (OLS), Moderate resolution ISRO started launching operational remote sensing imaging Spectro-radiometer (MODIS), stable nighttime satellites in 1998 with IRS-1A. ISRO launched main Sun light (NTL) used for monitoring and assessment LULC synchronous orbit satellites are Resourcesat-1/ 2/ 2A, changes [6, 10] for sustainable development. Cartosat-1/ 2/ 2A/ 2B, Risat-1/2, Ocensat-2, and few i). Landsat TM/ETM+/OLI data geostationary orbit satellites are Insat-3D/3DR, Kalpana & Insat 3A. https: // bhuvan-app3.nrsc.gov.in/ data/ download/ First Landsat series satellite was launched by the NASA index. php in 1972 for earth observation. Landsat provide massive amount of data for LULC change detection. Landsat based

Published By: Retrieval Number C6067098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijrte.C6067.098319 8143 & Sciences Publication

Land use/Land Cover Change Assessment of Ysr Kadapa District, Andhra Pradesh, India using Irs Resourcesat-1/2 Liss Iii Multi-Temporal Open Source Data Table III: Satellite sensors; global prospective. Spectral Spatial Radiometric Temporal Satellite sensor Name Bands Resolution (m) Resolution (bits) Resolution (days)

Low Resolution POLDER B1to B9 6k 12 4 (> 1000 m)

B1, B2 250 Moderate MODIS B3 to B7 500 12 1 resolution B8 to 36 1K (100–1000 m) AVHRR B1to B5 1.1k at Nadir 10 1 EO-1(ALI) B1 to B10 30 16 16 B1 15 8 ASTER B2 to B9 30 16 B11 to B14 90 12 Landsat7/ Pan 15 TM/ETM+/ B1to 30 8 16 Landsat8/ OLI B5,B7,B9 High B6 60 Resolution Pan 2.5 or 5 (5-100 m) HRV/SPOT5 B1to B3 10 8 26/2.4 SWIR 20 VNIR 5.8 5 at 26° Latitude RESOURCESAT-2A VNIR, series 23.5 12-13 SWIR LISS-4/ LISS-3/ VNIR, AWiFS 56 2-3 SWIR Pan 0.82 at Nadir IKONOS 11 3 at 40° Latitude B1to B4 3.2 at Nadir Very high Pan 0.61 QuickBird 11 1 to 3.5 Resolution B1to B4 2.44 (< 5 m) Worldview Pan 0.5 at Nadir 11 1.7 to 5.9 Pan 1.41 at Nadir 2.1 to 8.3 at 40° Geoeye-1 11 B1to B4 1.65 at Nadir Latitude Source: https://www.sciencedirect.com

IV. DATA PROCESSING SOFTWARE(S) Remote sensing analysts process the data to provide solutions for the global issues like change in environment, forecasting of weather, disaster management and food security so on. Major Commercial and open source Remote sensing data processing tools are listed in Table IV. ERDAS IMAGINE is widely used for LULC applications. Table IV: Commercial and open source Digital Image processing software systems Commercial Open source ERDAS IMAGINE ILWIS https://www.hexagongeospatial.com http://www.ilwis.org/index.htm ENVI GRASS https://www.harrisgeospatial.com/Software-Technology/ENVI http://grass.osgeo.org IDRISI OSSIM https://clarklabs.org/ www.ossim.org PCI Geomatica MultiSpec https://www.pcigeomatics.com/ http://engineering.purdue.edu/~biehl/MultiSpec/index.html eCognition QGIS http://www.ecognition.com/ https://qgis.org/en/site/ MATLAB https://in.mathworks.com/products/matlab.html ArcGIS software https://www.esri.com/en-us/home TNTmips software https://www.microimages.com/products/tntmips.htm Source: https://elearning1.iirs.gov.in/spaceapplications/

Published By: Retrieval Number C6067098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijrte.C6067.098319 8144 & Sciences Publication International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019

V. LULC CHANGE ASSESSMENT Rawat JS et al. (2015) [13] analyzed the LU/LC disturbance from 1990 to 2010 using Landsat TM data from A. Literature review GLCF and earth explorer. The images are analyzed by Musa Dalil et al. (2017) [8] focused on the cement plant means of supervised classification such as Maximum and its effects on LULC change using supervised Likelihood Algorithm which generates False Colour classification using ILWIS 3.3 and Arc GIS 10.2. In this Composite (FCC). This is achieved by ERDAS Imagine 9.3. paper Obajana lokoja Local government area classified into The results suggest that an enormous increase in the built-up four classes as built-up, bare surface, vegetation and area corresponds to decrease in agricultural and forest land. waterbody for the period 2005 to 2015. Rawat JS et al. (2013) [14] analyzed the LU/LC change Borana S.L et al. (2014) [11] analyzed open pit sandstone by means of GIS and Supervised classification method by mining spatial extent and temporal behavior and also LULC computing NDVI, NDWI and NDBI indices using Landsat changes around Jodhpur city, Rajasthan, India. This study TM data from 1990 to 2010. He concluded on LU/LC uses IRS-P6, Cartosat and GeoEye satellite data for 3D Map classes as built-up area was increased from 1.25 km2 to 4.08 and LANDSAT data and processed using ENVI software. km2 contributing 8.88% of the total area. The vegetation The analysis identified vegetation and agricultural areas cover was decline from 10.29 km2 to 7.29 km2. which are under risk due to mining activity, scrubland, Fanan Ujoh and group (2014) [21] carried out a research vegetation land have been converted either into mines and on Limestone Mining and production of cement impact on mine overburden, the water bodies increased due excavation vegetation about 32 years from 1996 to 2006 using Multi- of huge amount of sandstone. Environment for habitation is temporal satellite images with ILWIS and SPSS. The study also affected due to pollution of air, water and noise level. concluded that built-up area increases from 0.05 to Sreenivasulu et al. (2014) [12] analyzed LU/LC change 1.51sq.km, Vegetation declined from 4.30 to 1.51sq.km. using remote sensing and GIS technology in and around D. Lu,et. al. (2007) [15] carried out a study on remote Rajampet mandal of Kadapa District, Andhra Pradesh, sensing image classification methods and techniques for India. IRS LISS III satellite images were processed using improving classification performance. Major factors ArcGIS 9.2 and ERDAS IMAGINE 9.1 for classification of influencing the selection of remotely sensed data are user’s study area. Analysis of the study concluded as fast shrinkage requirements, economic condition, scale of study area and in waterbodies and wet areas, rapid growth in built-up land the analyst’s experience. with buildings resulting degradation in agriculture due to residential. Resourcesat-1/2 Resourcesat-1/2 Resourcesat-1/2 LISS III data in LISS III data in LISS III data in 2005-06 2011-12 2015-16

Image processing

Onscreen digitization

Built-up Agriculture Forest Barren lands Wetlands area land area

Change analysis

Accuracy assessment

Figure 3: Flow diagram showing the LU/LC change assessment using Indian satellite Resourcesat-1/2 LISS III data from Bhuvan Indian Geo-platform of ISRO.

Published By: Retrieval Number C6067098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijrte.C6067.098319 8145 & Sciences Publication

Land use/Land Cover Change Assessment of Ysr Kadapa District, Andhra Pradesh, India using Irs Resourcesat-1/2 Liss Iii Multi-Temporal Open Source Data Table V: Summary of LULC classes change assessment models [16, 17] Machine learning models Name of the Applications Merits Demerits model To study Soil property, crop yield Prediction of complex Support Vector Results are complex production urban growth, land systems, No guess on the Machine (SVM) parametrization use, evapotranspiration primary data No assumption on the primary Artificial Neural LU change, residue yield, landslide data, complex process Black box Networks ANN) study estimation Markov Chains Urban expansion, LU transform, crop Able to Predict complex (MC) yield, vegetation cover change Stationary process systems estimation Degradation in Vegetation cover , to Logistic study soil properties, Dynamics in Stationary system, Regression Simple model forest area, crop production rate auto co linearity (LR) estimation Precious knowledge extraction professional Data mining Crop production, drought independently of data nature. involvement needed Cellular Automata Scalability and Spatial precise Urban growth, Deforestation, Integrate quantitative (CA) prediction vegetation change external factors

Land cover class maps and change assessment for the B. Change Analysis years 2005-06, 2011-12 and 2015-16 shown in Figure 4a–c LULC change was analyzed for the period 2005 to 2016 and Tables VI–IX respectively. with the Vector data from Bhuvan-Inaian Geo-Platform of The built-up area increases with an industrial development, ISRO. Firstly with the digital data base LULC change expansion of Limestone, Barytes, kadapa slab mines, and matrix was prepared by cross tabulation analysis [18]. After urban/rural expansion. The built-up area is 185.87 km2 that with the LULC distribution results (Table 6) we (1.21%) for the assessment 2005-06. which sharply jumped computed Net Change, Percent Change and Rate of LULC to 328.07 km2 (2.14%) for the assessment year 2011-12. It change for LULC classes during the assessment years 2005- again increased to 352.99km2 (2.30%) for the assessment 06, 2011-12 and 2015-16. year 2015-16, it showing a net increase of 167.12km2 C. LULC percentage change and rate of change (89.91%) for the period 2006 to 2016 (Tables 6 and 7). The Agricultural land comprises nearly 45% of total study LULC class percentage change is computed with the help area. Agricultural land for the assessment year 2005-06 is of present and previous LU/LC spatial coverage [18]. 6811.56 km2 (44.35%) which is gradually decreased to 6645.99km2 (43.26%) and to 6555.51 km2 (42.68%) for the LULC percentage Change = assessment years 2011-12 and 2015-16 respectively (Table 6

*100 [18] and 7).

The forest area occupied nearly 30% of the total study 2 Open source remotely sensed data is utilized to compute the area. The forest area is decreases from 4807.75 Km (31.29%) to 4803.26 Km2 (31.26%) in the period 2006 to LULC classes rate of change during the period 2005 to 2 2016. LULC rate of change (R) is computed as [18]. 2012. But it is increased to 4922.58 Km (32.05%) for the

assessment year 2015-16. The forest cover decrease may be [18] due to new industries establishment, new open pit mines. 2 Where A1 is the area of LULC class at year t1 The net change in the forest cover is 114.83 Km (2.39%) A2 is the area of LULC class at year t2 during the study period. The barren land is decreases from 2711.39 km2 (17.65%) 2 V. RESULTS AND DISCUSSION to 2686.81km (17.48%) during the time period 2006 to 2012 subsequently to 2632.30 km2 (1.71%) for the year Study area LULC maps of three assessment years 2015-16. The net decrease of barren land is 79.09km2 depicted in Figure 4a–c. spatial distribution statistics of the (2.92%) during the 2006-2016 (Tables 6 and 7). five major LULC categories (Built-up, Agriculture, Forest, The waterbody shows rising trend from 2006 to 2012, Barren lands, and waterbodies) and they % change and Net covering the land area of 842.43km2 (5.48%) for assessment change are listed in Table 6 and Table 7 respectively for the year 2005-06 and 895.92 km2 (5.80%) for assessment year period 2005 to 2016. The rates of change of LULC class 2011-12 (Table 6). However, waterbody total area decreased during the period shown in Table 8. The detailed LULC to 895.33km2 (5.833%) for the assessment year 2015-16. classes are mentioned in Table 9 The net increase in the water body (Tables 6 and 7) is A. Spatial Distribution and LULC change observed during the period from 2006-2016 that is 52.9km2 (6.28%)

Published By: Retrieval Number C6067098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijrte.C6067.098319 8146 & Sciences Publication International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019

Table VI: Study area major LULC class spatial distribution (Total area 15,359 KM2) 2005-2006 2011-2012 2015-2016 Land use / S.No. Area Area Area Land cover % % % (km2) (km2) (km2) 1. Built-up land 185.87 1.21 328.07 2.14 352.99 2.30 2. Agriculture land 6811.56 44.35 6645.99 43.26 6555.51 42.68 3. Forest 4807.75 31.29 4803.26 31.26 4922.58 32.05 4. Barren/Wastelands 2711.39 17.65 2686.81 17.48 2632.30 17.14 5. Wetlands/Waterbodies 842.43 5.48 895.92 5.80 895.33 05.83 Source: http://bhuvan3.nrsc.gov.in/applications/bhuvanstore.php

Figure 4a: LULC map for the assessment year 2005-06

Figure 4b: LULC map for the assessment year 2011-12

Published By: Retrieval Number C6067098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijrte.C6067.098319 8147 & Sciences Publication

Land use/Land Cover Change Assessment of Ysr Kadapa District, Andhra Pradesh, India using Irs Resourcesat-1/2 Liss Iii Multi-Temporal Open Source Data

Figure 4c: LULC map for the assessment year 2015-16

8,000 X-axis: LULC classes 7,000 Y-axis: Area in KM2 6,000 5,000 4,000 3,000 2005-06 2,000 2011-12 1,000 2015-16 0

Figure 5: Graphical representation of LULC changes in Sq.KM during 2005-2016

Published By: Retrieval Number C6067098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijrte.C6067.098319 8148 & Sciences Publication International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019

Table VII: Major LULC classes Net Change in areal extent 2006-2012 2012-2016 2006-2016 (Net Change) S.No. LULC classes Area Area Area % % % (km2) (km2) (km2) 1. Built up land 142.2 76.51 24.92 7.60 167.12 89.91 2. Agriculture land -165.57 -2.43 -90.48 -1.36 -256.05 -3.76 3. Forest -4.49 -0.09 119.32 2.48 114.83 2.39 4. Barren/Wastelands -24.58 -0.91 -54.51 -2.03 -79.09 -2.92 5. Wetlands/Waterbodies 53.49 6.35 -0.59 -0.07 52.9 6.28

B. Rate of LULC change Table 8 shows the rate at which LULC varies with time. It can also be observed that waterbodies are increase during the period 2006-2012. The rate at which waterbodies are changing is obtained as 1.02km2/year and 0.61km2/year as the net rate of change. Built-up land maximum change rate observed during the period 2006 to 2012(i.e. 9.46km2/year) and net rate of change is 6.41km2/year. It is observed that the net rate of change of forest cover is 0.23km2/year and a negative rate of change for wastelands/Barren lands of 0.29km2/year during 2006-2016. The net rate of change of agriculture land during the period 2006 to 2016 is 0.38%

Table VIII: Rate of change of major LULC classes in the study area (km2 /year) 2006-2016 S.No. LULC classes 2006-2012 2012-2016 (Net Change) 1. Built up land 9.46 1.83 6.41 2. Agriculture land -0.04 -0.32 -0.38 3. Forest -0.001 0.61 0.23 4. Barren/Wastelands -0.1 -0.51 -0.29 5. Wetlands/Waterbodies 1.02 -0.02 0.61

Agriculture Land (Plantation) Waste Land

Forest Land Water bodies (Buggavanka Reservoir)

Published By: Retrieval Number C6067098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijrte.C6067.098319 8149 & Sciences Publication

Land use/Land Cover Change Assessment of Ysr Kadapa District, Andhra Pradesh, India using Irs Resourcesat-1/2 Liss Iii Multi-Temporal Open Source Data

Built up Land (Rural ) Built up Land (Napa slab Quarry area ) Figure 6: Field photos of LU/LC classes in the study area Table IX: LU/LC sub-classes spatial distribution in the study area 2005-06 2011-12 2015-16 S.No. LU/LC Classes Area Area Area % % % (km2) (km2) (km2) 1. Built up land  Urban 53.36 0.34 107.67 0.70 119.63 0.77  Mining 34.68 0.23 70.3 0.46 74.10 0.48  Rural 97.83 0.64 150.1 0.98 159.29 1.04 2. Agriculture land  Plantation 221.21 1.44 274.74 1.79 249.69 1.63  Crop land 4420.31 28.78 5703.18 37.13 5548.48 36.12  Fallow 2170.04 14.13 667.03 4.34 757.41 4.93 3. Forest  Deciduous 3317.9 21.60 3433.54 22.35 3546.87 23.09  Scrub Forest 1480.93 9.64 1359.47 8.85 1367.59 8.90  Forest Plantation 8.92 0.05 10.25 0.06 8.10 0.05 4. Barren/Wastelands  Gullied/Ravinous Land 12.51 0.08 12.19 0.08 10.19 0.07  Sandy area/Riverine 1.11 0.007 1.09 0.007 0.68 0.00  Scrub land 2390.35 15.56 2378.71 15.48 2356.39 15.34  Barren rocky/Stony waste 287.28 1.87 261.73 1.70 232.82 1.52  Salt Affected land 20.14 0.13 33.09 0.215 32.33 0.21 5. Wetlands/Waterbodies  River/Stream/canals 342.15 2.23 354.96 2.31 354.42 2.31  Reservoir/Lakes/Ponds 500.28 3.25 537.08 3.49 537.08 3.49  Inland Wetland - - 3.88 0.03 3.83 0.02

C. Land Surface Temperature VI. CONCLUSION The tropical climate of the region is manifested in hot and The Resourcesat-1/2 LISS-III Multi-Temporal satellite humid summer, moderately monsoon and mild winter data from the Bhuvan-Indian Geo-platform of ISRO taken to seasons. May month is the hottest month in the year and analyze LULC change in YSR kadapa district for the tenure December is the coldest in the year. The LST would be of 10 years (i.e. 2005-06 to 2015-16).The five major classes varied with LULC change, the variation of Temperature (Level-1) are summarized from Level-2 and Level-3 LULC. ranges in the years 2011, 2015 and 2017 is shown in the This study provides change in LULC of the study area from Table X. 2006 to 2016. From the results we observed that there is an

impact of surface Mining, urban and rural develop on Table X: LST variation in the study area agriculture land. This case study helps in decision-making Maximum Minimum Year 0 0 on land reclamation and land management in the surface Temperature( C) Temperature( C) Mining and industrial area in the study area. The overall 2017 48 14 LULC classification accuracy varies from 79% (like Agro- horticulture) to 97% (like waterbodies). 2015 41.30 19.92

2011 37.1 19.5

Published By: Retrieval Number C6067098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijrte.C6067.098319 8150 & Sciences Publication International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019

The future work of this study includes mapping of industrial 19. Haoteng Zhao et.al “Monitoring Quarry Area with Landsat Long mines and assessment of mining activity impact on the Time-Series for Socioeconomic Study” Journal of remote sensing Remote Sens. 2018, 10, 517; doi: 10. 3390 /rs 10040517. LULC classes and environment. 20. District Survey Report YSR Kadapa District prepared by Andhra Pradesh Space Applications Centre (Apsac),ITE&C Department, REFERENCES Govt. of Andhra Pradesh 2018. 21. Fanan Ujoh et.al “Multi-temporal change detection at a limestone 1. Zenebe Mekonnen, Habtamu Tadesse, Teshale Woldeamanuel, mining and cement production facility in Central Nigeria” American Zebene Asfaw and Habtemariam Kassa, “Land use and land cover Journal of Environmental protection volume 3(3), pg.113-121, June changes and the link to land degradation in Arsi Negele district, 10, 2014 Central Rift Valley, Ethiopia”, Remote Sensing Applications: Society and Environment July 2018, https:// doi.org/ 10.1016/ j.rsase. 2018. 07.012. AUTHOR PROFILE 2. V. Sunitha, B. Muralidhara Reddy and M. Ramakrishna Reddy “ C Venkata Sudhakar Received B.Tech Degree in Mineral Resources of Cuddapah Basin “An International Journal of Electronic Instrumentation and Control Engineering from Life Sciences and Chemistry, Vol. 31, No. 1, pg.226-235, 2014. S.V.University, Tirupati, Andhra Pradesh, India and 3. District Survey Report of Y.S.R. District Prepared as per M.Tech. Degree in Digital systems and Computer Environmental Impact Assessment (EIA) Notification, 2006 issued Electronics from J.N.T.U.H. Kukatpally , Hyderabad, under (S.O 141 (E) Dt: 15.01.2016) Department of Mines Telangana, India. Working as Assistant Professor in the Department of Government of Andhra Pradesh, November-2017. Elecronics and Communication Engineering in Sree Vidyanikethan 4. Online resources http://kadapa.ap.nic.in/, http:// english. kadapa. info/ Engineering College since 2011and Pursuing Ph.D. (Part Time) in the area , https:// www. mines.ap.gov.in/, http:// www.apmdc.ap.gov.in/, https: “Remotely sensed data processing” in the department of Electronics and //www.ibef. org/ industry. aspx, Communication Engineering at S.V.University Tirupati. Research interest http://eaindustry.nic.in/cement/default.asp, areas include Remote sensing data and Image Analysis, VLSI 5. Weitao Chen, Xianju Li, Haixia He, and Lizhe Wang, “A Review of Architectures for Image processing application, Sensor Signal conditioning. Fine-Scale Land Use and Land Cover Classification in Open-Pit

Mining Areas by Remote Sensing Techniques”, Remote Sens. 2018, Prof. G. Umamaheswara Reddy Received B. Tech & 10, 15; doi:10.3390/ rs10010015. M.Tech degree in Electronics and communication 6. L.Yu, Y. Xu, Y. Xue, X. Li, Y. Cheng, X. Liu, A. Porwal, E-J. engineering from S.V.University, Tirupati- 517 502, India. Holden, J. Yang, P. Gong,“Monitoring surface mining belts using Received Ph.D. degree from the S.V.University, Tirupati, multiple remote sensing datasets: a global Perspective”, Ore Geology India in the area “Denoising of ECG Signals Using Wavelet Reviews 2018 Based Threshold Methods with Grey Incidence Degree”. He has more than 7. V Sunitha1, D Venkat Reddy and P R Reddy, “Mineral Wealth of 20 years of Teaching Experience from various institutes. At present Cuddapah Basin and its Use for Sustainable Development-An working as professor in the Department of Electronics and Communication Overview”, An International Journal of Earth sciences and Engineering, SVU College of Engineering in the SV University Tirupati. Engineering, ISSN 0974-5904, Vol. 08, No. 06, December 2015. His Research Interest areas include Signal / Image Denoising, System 8. Musa Dalil*, Isaiah Omeiza Amodu and team “Effect of cement Identification, Spectral Estimation, Biomedical Signal Processing. factory on land use- land cover in Obajana Lokoja Local Government

Area, Kogi State, Nigeria” African Journal of Environmental Science

and Technology Vol. 11(7), pp. 384-392, July 2017. 9. YSR Kadapa District Survey Report prepared by Andhra Pradesh Space applications Centre (APSAC) ITE&C Department, Govt. of Andhra Pradesh 2018. 10. E. Charou , M. Stefouli , D. Dimitrakopoulos “ Using Remote Sensing to Assess Impact of Mining Activities on Land and Water Resources”, Springer-Verlag -Mine Water Environ (2010) 29:45–52 2010 .DOI 10.1007/s10230-010-0098-0. 11. Borana S.L.*, Yadav S.K and team “Impact Analysis of Sandstone Mines on Environmentand LU/LC Features Using Remote Sensing and GIS Technique : A Case Study of The Jodhpur City, Rajasthan, India” Journal of Environmental Research And Development Vol. 8 No. 3A, Pg:796-804, January-March 2014. 12. Sreenivasulu G, Jayaraju K, Lakshmi P “Land Use and Land Cover analysis using remote sensing and GIS: a case Study in and around Rajampet, Kadapa District, Andhra Pradesh, India” Indian J.Sci. Res. Vol.8(1) pg:123-129,2014. 13. Rawat JS, Manish K “Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India”. The Egyptian Journal of Remote Sensing and Space Sciences (2015) 18, 77–84. 14. Rawat JS, Vivekananda B, and Manish K “Changes in land use/cover using geospatial techniques: A case study of Ramnagar town area, district Nainital, Uttarakhand, India”. Egypt. J. Remote Sens. Space Sci. vol.16:111-117.2013. 15. D. Lu and Q. Weng, “Survey of image classification methods and techniques for improving classification performance,” Int. J.Remote Sensing, vol. 28, no. 5, pp. 823–870, Mar. 2007. 16. Eshrat Jahan Esha and Afzal Ahmed “Impacts of Land Use and Land Cover Change on Surface Temperature in the North-Western Region of Bangladesh” 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) 21 - 23 Dec 2017, Dhaka, Bangladesh. 17. Oumayma Bounouh, Houcine Essid and Imed Riadh Farah “Prediction of Land Use/ Land Cover Change methods: A Study” 3rd International Conference on advanced Technologies for Signal and Image Processing - ATSIP'2017, pg: 1-7, May 22-24, 2017, Fez, Morroco. 18. Debashri Garai, A.C. Narayana “Land use/land cover changes in the mining area of Godavari coal fields of southern India” The Egyptian Journal of Remote Sensing and Space Sciences(2018)https: // doi.org/ 10.1016 /j.ejrs. 2018. 01.002

Published By: Retrieval Number C6067098319/2019©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijrte.C6067.098319 8151 & Sciences Publication