Subcategories Multiple Uses of Land Recognized by Land Use and Land Cover Classification System Using LISS-III and NRSC Enumerate Data: Case Study at Medchal Mandal, Hyderabad, India

1. INTRODUCTION

As per population records, India is the second country in the world (1.32 billion). Telangana State stands at the 12^th place for the population in India. Year by year the urban population increases because more people prefer to live in urban cities like Hyderabad. The development scenario in the fields of industrialization, infrastructure, transportation, accommodation, with high-income sources is only in such of these cosmopolitan cities (Zhang and Ro, 2017). The rapid and incessant growth of migrants in urban slum areas of large cities is creating continuous pressure on urban cities. It insists on major changes in ecosystems from purities to impurities. Modifications of land features/subtypes have accelerated after the industrial revolution, driven to obtain more resources to supply to a continuously growing population (Cihlar, 2000; Rosenfield and Fitzpatrick-Lins, 1986).

The land is referred in a holistic way as upper layer of the earth’s crust combined with the troposphere (plants, human), minerals, hydrology (lakes, rivers, ocean) and sediments (Anuraga et al., 2006; De Leeuw et al., 2006; Foody, 2002). The human settlement pattern and physical features are the results of past and present human activity, terracing, water storage or drainage structures, roads, buildings, etc. (Abdulhakim, 2019).

Land use defines the manual activities and settlement on the earth’s surface. It is managed and changed the environmental aspects. Land cover explains about vegetation, rocks, soils, surface water bodies. Land use is concluded based on the land cover only, land cover and land use both are similar but they are indistinguishable (Hermosilla et al., 2018; Di Gregorio and Jansen, 2004). Agricultural and animal flourishing put pressure on land increased by human demand. Vigorous action on the available land accordingly and continuous monitoring of land resources is necessary to know not just land features, but also the optimal study of land subtypes increases based on growing human population (Yeh and Li, 1998).

A GIS (Geographic Information System) is a discipline that helps to identify topographical features and is useful in many applications such as land use/land cover classification (LULC), determination of plantation in the forest area, marine features, urban planning, drainage network and transportation, etc. (Butusov, 2003; Houghton et al., 1999; Vitousek et al., 1997; Meyer and Turner, 1992).

Land use data analysis, preparation of thematic maps on different aspects such as road networks, agricultural, plantation and build-up land, will help to understand, problems to resolve environmental prospects of present and past. People migrate from rural to urban regions. Usually population, economy, basic amenities and resources increase in an urban region. Thematic map of build-up land explains urban sprawl development and direction (Hasse and Lathrop, 2003; Epstein et al., 2002; Torrens and Alberti, 2000). Cities have good infrastructure, industries, parks, theatres, etc., with good facilities that attracted more people from the last decade, most of the rural people were transported and settled in cities and accumulated a place (Zhao et al., 2019; Jat et al., 2008; Taubenbock, 2008). Town planning engineers have to prepare and design a town-planning map for future generations but a sudden increase in the urban population creates a lot of discrimination in vision and planning (Jaiswal et al., 2003). Advanced survey techniques of remote sensing will have to analyze census data and segregate the people as per vision (Oleksandr and Matthias, 2013; Weng and Lu, 2007).

The land which is outside the forest boundary and not utilized for cultivation is the land with or without shrubs is usually associated with shallow, stony, rocky other non-bearable lands (Chi et al., 2008). There are different techniques of classification used for detection of LULC. The methods are image comparison, classified images, raster classification, and system for automated geo-scientific analyses (SAGA) classification (Green et al., 1994).

2. METHODOLOGY

Integration of LULC of Medchal Mandal compiles onscreen interpretation of thematic maps such as Transportation map (Fig. 1), drainage networks and water bodies map (Fig. 2) which are produced LULC thematic map by using GIS software, IRS_P6 LISS III (Path 24 and Row 61) imagery and Survey of India Toposheets (56 K/6 and 56 K/10 on 1 : 50,000 scale) are used for visual interpretation and ASTER DEM maps used to determine the drainage network, waterbodies, land elevation and geology of the area. NRSA standard LULC classification is used for the preparation of LULC map.

Fig. 1.

Transport network and Settlement Locations of Medchal Mandal, Medchal-Malkajgiri District, Telangana State.

Fig. 2.

Drainage and Surface water bodies of Medchal Mandal, Medchal-Malkajgiri District, Telangana State.

Preparation of LULC thematic map of Medchal Mandal. Collected the SOI map at Survey of India office, Uppal, Hyderabad, and satellite data solicit from National Remote Sensing Centre, Hyderabad. The Topo-sheets scanned copies and satellite images were converted into digital mode. Geo-referenced Topo-sheets overlap on the satellite imagery and then distinct layers for the LULC is created with layers such as boundary, water bodies, transport, built-up land (Fig. 3). In the transport map, roads and road networks, drainage networks and streams are drawn by line command. Lakes, reserve forest, natural forest, and buildings are drawn by using polygon command.

Fig. 3.

Data processing methodology for Medchal Mandal LULC classification.

2. 1 Land Use/Land Cover of Medchal Mandal

Medchal Mandal is located in NNE direction to Hyderabad with distance of 34 km in Telangana state (Fig. 4). The Medchal Mandal has 196.31 sq km of geographical area, for the analysis and segregation of LULC of the Medchal Mandal. Remote sensing techniques are the best method, in regards to LULC map prepared by Quantum geographical information system software. Ground truth data is also included for on-screen interpretation. In this scenario, the Medchal Mandal contains the 11 units of the LULC characteristics. Each category has an individual color determination for clear visibility.

Fig. 4.

Location map of Medchal Mandal, Medchal-Malkajgiri District, Telangana State.

LULC of Medchal Mandal is classified based on the Manual of National LULC mapping prepared by the National Remote Sensing Centre (NRSC, 2014). Using IRS-_P6 LISS-III satellite data and analyzed the data in Quantum GIS software. LULC of Medchal Mandal comprises the 11 broad categories (Fig. 5) such as

• 1. Core-urban
• 2. Hamlets and dispersed household
• 3. Agricultural land - crop land
• 4. Agricultural land - plantation
• 5. Forest
• 6. Forest plantation
• 7. Wastelands - Barren Rocky/Stony
• 8. Wastelands - Scrub land - Open scrub
• 9. Mining/Industrial
• 10. Waterbodies - Reservoir/Tanks
• 11. Waterbodies - River/Stream

Fig. 5.

Land use/Land cover map of Medchal Mandal, Medchal-Malkajgiri District, Telangana State.

2. 2 Core-urban

Core-urban division includes municipality, corporation, and cantonment, which are notified town areas and all other areas, which has 5000 minimum population, and out of which 75% of the male working population is nonagricultural (Maktav et al., 2005; Chen and Stow, 2003). Kistapur, Ravalkole, Atevelle, Gundlapochampalle, Medchal, Pudoor, Goudavelly, Kajiguda villages are in this category. This unit is observed near Ravalkole, Dabilpur, Pudoor, Sree Rangavaram, and Nuthankal, villages and has an aerial extent of 4736 acres which is 10 per cent to the total geographical area of the study area (Fig. 6). It comprises regions with widely used land resources covered by intensive use and structures. It includes residential, industrial, transportation, power, communications and isolated places such as mills, and quarries (Figs. S1a, S1b, S2a, and S2b). Shopping centers, parks, playgrounds, open spaces, and institutions.

Fig. 6.

Graphical representations of land use/land cover of Medchal-Malkajgiri District, Telangana State.

2. 3 Hamlets and Dispersed Households

This category has occupied the less portion in this Medchal Mandal (Table 1). It includes all the requisite conditions and is basically an open land. Such areas are found either lying idle or associated with buildings and other related infrastructure facilities for recreation and sport (Yin et al., 2010; Herold et al., 2003). They include playgrounds, gardens, and parks areas. However, forest, water, wetlands, and barren lands are not within the parks or recreation areas. Kandlakoya village has a park name as “Oxygen Park” nearby the national highway ring road.

Table 1.

Percentage of land use/land cover data of Medchal Mandal, Medchal-Malkajgiri District, Telangana State.

In satellite imagery playgrounds appear in white to yellowish color, regular in shape, dispersed in pattern and are mostly associated with residential areas and/or institutions (Madhu et al., 2017; Food, 2008). Gardens/parks appear in red to brick red in color, small to medium in size, generally regular in shape, dispersed and associated with residential areas, adjacent to roads.

Educational institutions such as schools (Figs. S3 and S4), colleges, universities religious places, health centers, cantonment areas, government buildings, etc. (Figs. S5a, S5b, and S5c). All buildings and grounds that comprise the facility are included within the institutional unit. In education building point of view, Medchal Mandal has many institutes like Engineering colleges, Degree colleges, High Schools, Primary Government Schools, and Medical Colleges. Vegetation land has a different plantation in the Medchal Mandal (Figs. S6, S7, S8a, and S8b). This unit is observed near Ravalkole, Pudoor, Dabilpur, Raja bollaram, and Nuthankal villages and has an aerial extent of 2580 acres which is 5 percent to the total geographical area of the study area.

Transporting goods and material as a measure of accessibility and connectivity is called transportation. It includes railways, roads, etc. (Figs. S9 and S10). The linear features listed above appear in small width for roads, and narrow in case of the railway line, dark bluish green to light yellow in most of the cases and at times are seen associated with vegetation along with them. They are more prominently seen in plain areas, across water bodies, agricultural lands connecting settlements. The regions are well connected by bituminous roads. Southeastern railway lines are passing through in Medchal Mandal. Nehru outer ring road passes through (on NH - 65 towards Mumbai) Kandlakoya (on NH - 44 towards Nagpur) to Masireddipalle village.

2. 5 Agricultural Land - Plantation

In Telangana State, two crops dominate in agriculture. Kharif and Rabi, in that Kharif crop, is spread from June to October, with precipitation merely for growing the plants in dry-land farming, with limited or no irrigation and areas of rain-fed paddy and other dry crops. Second largest cultivation season is Rabi, extending between November/December-February/March. Black cotton soil is a major source for the growth of plantation in Rabi months. The cultivation depends on mainly groundwater resources. Some of the fields are cultivated on rainwater and canal (Rao et al., 2015; Oleksandr et al., 2012).

Medchal Mandal has 9% agricultural plantation, in that Ravalkole village (1006 acres land), Pudoor village (705 acres), Gundlapochampalle (512 acres), and Atevelle (500 acres), remaining villages have less land acquisition (Table 2). The main crops are, maize (Ravalkole, Raja bollaram, Gaudavelly), millets (Dabilpur, Ravalkole, and Bandamadharam) redgram (Raja bollaram and Ravolkole) pulses (Raja bollaram and Ravolkole) foodgrains (Dabilpur, Ravalkole and Pudoor) mangoes (Ravalkole, Pudoor and Raja bollaram) lime (Pudoor and Nuthankal) orange (Pudoor) grapes (Yadaram, Railpur and Pudoor) guava (Konaipalle, Medchal and Pudoor) papaya (Raja bolaram) pomegranate (Pudoor) Railapur and Ravalkole) dry fruits (Pudoor, Railapur and Ravalkole) brinjal (Gundlapochampalle, Pudoor) green leafy vegetables (Pudoor) cabbage (Gundlapochampalle) tomato (Pudoor and Gosaiguda) mirchi (Rayalapur and Kandlakoya) grass (Pudoor, Dabilpur, Ravalkole and Medchal). This unit is observed near Ravalkole, Pudoor, Gundlapochampalle, Atevelle, and Raja bollaram villages and has an aerial extent of 4654 acres which is 10 percent to the total geographical area of the study area.

Table 2.

Village wise land use land cover distribution of Medchal Mandal, Medchal-Malkajgiri District, Telanagana State.

3. RESULTS AND DISCUSSION

Medchal Mandal has divided into eleven categories of LULC areas, using remote sensing and GIS techniques. It provides a good understanding of the topographic features. LULC combined with GIS produces high-quality maps that will be helpful for future generations through easy to understand recent scenarios (Goodin et al., 2015; DeFries and Chan, 2000).

Built-up land includes hospitals, houses, shops, complexes, and apartments, etc. The buildings in Medchal Mandal are constructed with concrete material instead of sand. The availability of sand is very less portion in this Medchal Mandal for that civil engineers preferred to use stone dust. Stone dust mines also are available in this Medchal Mandal and it’s surroundings.

Medchal Mandal has 48,510 acres of land and in that cropland occupies 12,312 acres. Pudoor and Ravalkole villages have a major portion of cropland, and Velgalkunta, Akbarjapet, Kaziguda, and Suthariguda villages have less portion. Medchal Mandal has cropland of 26% and is shown in the graphical representation. People have to acquire awareness to develop cropland.

Uncultivated lands, barren land, boulder region and wastelands are about 3272 acres in this Medchal Mandal. Some of the wastelands converted into anthropogenic activity areas. Dangerous toxic chemicals have accumulated in anthropogenic area, therefore natural resources especially aquifers are polluted at a high level. Anthropogenic activity villages are Girmapur, Gosaiguda, Ghanpur, Ravalkole, and Kandlakoya.

Medchal Mandal has an adverse stage of surface water resources. Most of the lake’s water is filled with rainwater only. The lakes become dry when scarce rainfall takes place. Few of the region’s lakes have been taken over for building apartments, cultivation lands, and other purposes. Reservoir and lakes have 5,136 acres land, 112 lakes are located in and around the national highway ring road and lakes are interconnected with each other, while water flows in the northeast direction depending on the slope. Village wise LULC classification categorised by Cluster analysis, Cluster-I 4 villages are more dominated in all sectors, Cluster-II and Cluster-III villages have near by equal priority at lowest level division, Cluster-V villages less below dominate compare to Cluster-I viallages and Cluster-IV villages have moderate by remain clusters (Fig. 7).

Fig. 7.

Clustrer division fo village wise Medchal Mandal LULC Classification.

4. SUMMARY AND CONCLUSION

Medchal Mandal is located in Medchal-Malkajgiri District of Telangana State. It is present in the northeastern part of Hyderabad region. The study area has 196 sq km area at a distance of 35 km from Hyderabad city. Medchal Mandal has an annual average rainfall of 848 mm. The population in this Madal is rapidly growing as most business people are interested to invest and establish the industries in this region.

The geology of the area has hard compacted nature with peninsular gneissic complex of Archean age. This rock type has predominantly granite and alkali feldspar granite rock. Medchal Mandal lies in the North East direction of Hyderabad, it is an Industrial sub hub and District headquarters’ of the Medchal-Malkajgiri region Telangana State, India. Major industries, mining areas, Medical colleges, and Engineering colleges have existed here. As such from the observation of water conditions the quality of water in 60% of the villages is good but in 40% of the villages is in a very bad condition.

Medchal Mandal normal rainfall is 835.7 mm. Rain drained from southwest monsoon and it’s covers the area between June and September months. It starts from the north Kerala State and enters into the Hyderabad regions.

Recharge of groundwater is essential for Medchal Mandal because of groundwater level. Rainfall and surface water bodies are not sustained the socio-economical values. Each individual houses, industries, parks, commercial buildings, and education institutes have to implement rainwater harvesting structures for recharge of groundwater or collection of water.

LULC of Medchal Mandal comprises the 11 broad categories, which covers 196.3 sq.km. in this five division are occupied large area, followed by build-up land, cropland, forest, industries, and water bodies, with percentage 13%, 26%, 9%, 10%, and 13%. The study area dominated by the industries. The highest contribution is cropland and the lowest contribution is forest land. Cluster-I division have more dominated villages such as Ravalkole, Gundlapochampally, Medchal, and Sreerangavam villages. Cluster-V have 7 villages in second place remain 3 clusters have low to moderate in all LULC sectors.

Medchal Mandal has an adverse stage of water resources. Most of the lake’s water is filled with rainwater only. Medchal Mandal has different lakes and tributaries which are interconnected. The lakes become dry when scarce rainfall takes place. Few of the region’s lakes have been taken over for building apartments, cultivation lands, and other purposes. Suggestion from in this study, that the government initiates mobile education, higher education institutes, and small-scale industries in villages Muraharipalle, Gosaiguda, Maisereddipalle, Konaipalle, Shahajadiguda, Somaram, Ghanpur, Suthariguda, Railapur, Yadaram, Bandamadharam, Muneerabad, Nuthankal, Girmapur, and Yellampet for healthy development.

CONFLICTS OF INTEREST

The authors declare that they have no conflict of interest.

References

• Abdalla M.F., Abdulaziz M.A. (2012) Monitoring land-use change-associated land development using multitemporal Landsat data and geoinformatics in Kom Ombo area, South Egypt. International Journa of Remote Sensing, 33(22), 7024-7046. [https://doi.org/10.1080/01431161.2012.697207]
• Abdulhakim M.A. (2019) Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience and Remote Sensing, 57(1), 1-20. [https://doi.org/10.1080/15481603.2019.1650447]
• Amee, K.T., Venkappayya, R.D., Ajay, Patel., Madhukar, B.P. (2017) Post-classification corrections in improving the classification of Land Use/Land Cover of arid region using RS and GIS: The case of Arjuni watershed, Gujarat, India. The Egyptian Journal of Remote Sensing and Space Scinece, 20(1), 79-89. [https://doi.org/10.1016/j.ejrs.2016.11.006]
• Anuraga, T.S., Ruiz, L., Mohan Kumar, M.S., Sekhar, M., Leijnse, A. (2006) Estimating groundwater recharge using land use and soil data: a case study in South India. Agriculture Water Management, 84, 65-76. [https://doi.org/10.1016/j.agwat.2006.01.017]
• Butusov, O.B. (2003) Textural classification of forest types from Landsat 7 imagery. Mapping Sciences and Remote Sensing, 40, 91-104. [https://doi.org/10.2747/0749-3878.40.2.91]
• Chen, D., Stow, D.A. (2003) Strategies for integrating information from multiple spatial resolutions into land‐use/land-cover classification routines. Photogrammetric Engineering and Remote Sensing, 69, 1279-1287. [https://doi.org/10.14358/PERS.69.11.1279]
• Chi, M., Feng, R., Bruzzone, L. (2008) Classification of Hyperspectral Remote-sensing Data with Primal SVM for Small-sized Training Dataset Problem. Advances in Space Research, 41(11), 1793-1799. [https://doi.org/10.1016/j.asr.2008.02.012]
• Cihlar, J. (2000) Land cover mapping of large areas from satellites: status and research priorities. International Journal of Remote Sensing, 21(6-7), 1093-1114. [https://doi.org/10.1080/014311600210092]
• Clevers, J.G.P.W., Bartholomeus, H., Mücher, C.A., Wit, A.J.W. De. (2004) Land Cover Classification with the Medium Resolution Imaging Spectrometer (MERIS). EARSeL eProceedings, 3(3), 354-362. https://library.wur.nl/WebQuery/wurpubs/fulltext/139566
• Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E. (2004) Digital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing, 25(9), 1565-1596. [https://doi.org/10.1080/0143116031000101675]
• DeFries, R.S., Chan, J.C. (2000) Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data. Remote Sensing of Environment, 74, 503-515. [https://doi.org/10.1016/S0034-4257(00)00142-5]
• De Leeuw, J., Jia, H., Yang, L., Liu, X., Schmidt, K., Skidmore, A.K. (2006) Comparing Accuracy Assessments to Infer Superiority of Image Classification Methods. International Journal of Remote Sensing, 27(1), 223-232. [https://doi.org/10.1080/01431160500275762]
• Di Gregorio, A., Jansen, L.J.M. (2004) Land Cover Classification System. Classification Concepts and User Manual, version 2, United Nations Food and Agriculture Organization, Rome. http://www.fao.org/3/y7220e/y7220e00.htm
• El-Raey, M., Fouda, Y., Gal, P. (2000) GIS for environmental assessment of the impacts of urban encroachment on Rosetta region, Egypt. Environmental Monitoring and Assessment, 60(2), 217-233. https://link.springer.com/article/10.1023/A:1006195006898 [https://doi.org/10.1023/A:1006195006898]
• Epstein, J., Payne, K., Kramer, E. (2002) Techniques for mapping suburban sprawl. Photogrammetric Engineering & Remote Sensing, 63(9), 913-918. https://home.cis.rit.edu/~cnspci/references/epstein2002.pdf
• Fernández‐Prieto, D. (2002) An iterative approach to partially supervised classification problems. International Journal of Remote Sensing, 23, 3887-3892. [https://doi.org/10.1080/01431160210133563]
• Foody, G.M. (2002) Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80, 185-201. [https://doi.org/10.1016/S0034-4257(01)00295-4]
• Foody, G.M. (2008) RVM-based multi-class classification of remotely sensed data. International Journal of Remote Sensing, 29, 1817-1823. [https://doi.org/10.1080/01431160701822115]
• Franklin, S.E. (2001) Remote Sensing for Sustainable Forest Management. Lewis Publishers, Boca Raton, 407pp. [https://doi.org/10.1201/9781420032857]
• Ganasri, B.P., Dwarakish, G.S. (2015) Study of Land use/land Cover Dynamics through Classification Algorithms for Harangi Catchment Area, Karnataka State, INDIA. Aquatc Procedia, 4, 1413-1420. [https://doi.org/10.1016/j.aqpro.2015.02.183]
• Georganos, S., Grippa, T., Vanhuysse, S., Lennert, M., Shimoni, M., Wolff, E. (2018) Very High Resolution Object-Based Land Use-Land Cover Urban Classification Using Extreme Gradient Boosting. IEEE Geoscience and Remote Sensing Letters 15(4), 607-611. [https://doi.org/10.1109/LGRS.2018.2803259]
• Goodin, D.G., Anibas, K.L., Bezymennyi, M. (2015) Mapping Land Cover and Land Use from Object-based Classification: An Example from a Complex Agricultural Landscape. International Journal of Remote Sensing, 36(18), 4702-4723. [https://doi.org/10.1080/01431161.2015.1088674]
• Green, K., Kempka, D., Lackey, L. (1994) Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric Engineering and Remote Sensing, 60, 331-337. https://www.asprs.org/wp-content/uploads/pers/1994journal/mar/1994_mar_331-337.pdf
• Hasse, J.E., Lathrop, R.G. (2003) Land resource impact indicators of urban sprawl’. Applied Geography, 23(3) 159-175. [https://doi.org/10.1016/j.apgeog.2003.08.002]
• Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W. (2018) Disturbance-informed Annual Land Cover Classification Maps of Canada’s Forested Ecosystems for a 29-year Landsat Time Series. Canadian Journal of Remote Sensing, 44(1), 67-87. [https://doi.org/10.1080/07038992.2018.1437719]
• Herold, M., Goldstein, N.C., Clarke, K.C. (2003) The spatiotemporal form of urban growth: Measurement, analysis and modeling. Remote Sensing of Environment, 86, 286-302. [https://doi.org/10.1016/S0034-4257(03)00075-0]
• Houghton, R.A., Hackler, J.L., Lawrence, K.T. (1999) The U.S. carbon budget: contribution from land-use change. Science, 285, 574-578. [https://doi.org/10.1126/science.285.5427.574]
• Jaiswal, R.K., Mukherjee, S., Krishnamurthy, J., Saxena, R. (2003) Role of remote sensing and GIS techniques for generation of groundwater prospect zones towards rural development: an approach. International Journal of Remote Sensing, 24(5), 993-1008. [https://doi.org/10.1080/01431160210144543]
• Jat, M.K., Garg, P.K., Khare, D. (2008) Monitoring and modeling of urban sprawl using remote sensing and GIS techniques. International Journal of Applied Earth Observation and Geoinformation, 10(1), 26-43. [https://doi.org/10.1016/j.jag.2007.04.002]
• Khatami, R., Mountrakis, G., Stehman, S.V. (2016) A Meta-analysis of Remote Sensing Research on Supervised Pixel-based Land-cover Image Classification Processes: General Guidelines for Practitioners and Future Research. Remote Sensing of Environment, 177, 89-100. [https://doi.org/10.1016/j.rse.2016.02.028]
• Madhu, T., Kumar, D.N., Reddy, D.N., Teja, P.V.E.R., Narayana, L., Vishal, V., Subash, B., Maniratnam, A., Vasavi, Y., Shakeena, V. (2017) Spatial-Temporal Analysis of LU/LC Classification in Nirmal Mandal, Adilabad, Telangana State, India, by Using Remote Sensing and GIS. International Journal of Geosciences, 8, 1315-1331. [https://doi.org/10.4236/ijg.2017.811076]
• Maktav, D., Erbek, F.S., Jürgens, C. (2005) Remote sensing of urban areas. International Journal of Remote Sensing, 26(4), 655-659. [https://doi.org/10.1080/01431160512331316469]
• Miao, L., Shuying, Z., Bing, Z., Shanshan, L., Chang, S.W. (2017) A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information. European Journal of Remote Sensing, 47(1), 389-411. [https://doi.org/10.5721/EuJRS20144723]
• Manual for national geomorphological and lineament mapping on 1 : 50,000 scale. First edition, 2010 A project under national (natural) resources census (NRC). National remote sensing centre, Hyderabad. 1-150. https://www.researchgate.net/publication/284020063
• Mathur, A., Foody, G.M. (2008) Crop classification by support vector machine with intelligently selected training data for an operational application. International Journal of Remote Sensing, 29, 2227-2240. [https://doi.org/10.1080/01431160701395203]
• Meyer, W.B., Turner II, B.L. (1992) Human Population growth and global land-use/Cover Change. Annual Review of Ecology and Systematics, 23, 39-61. [https://doi.org/10.1146/annurev.es.23.110192.000351]
• Narasimha Rao, P.V., Sesha Sai, M.V.R., Sreenivas, K., Krishna Rao, M.V., Rao, B.R.M., Dwivedi, R.S., Venkataratnam, L. (2010) Textural analysis of IRS-1D panchromatic data for land cover classification. International Journal of Remote Sensing, 23, 3327-3345. [https://doi.org/10.1080/01431160110104665]
• NRSA (2007) Manual of National Wastelands Monitoring Using Multi‐temporal Satellite Data, Department of Space, Hyderabad. https://bhuvan-app1.nrsc.gov.in/2dresources/thematic/LULC250/0506.pdf
• NRSC (2014) Land Use/Land Cover database on 1 : 50,000 scale, Natural Resources Census Project, LUCMD, LRUMG, RSAA, National Remote Sensing Centre, ISRO, Hyderabad. https://bhuvan-app1.nrsc.gov.in/2dresources/thematic/2LULC/lulc1112.pdf
• Oleksandr, K., Matthias, L., Diana, R. (2012) Texture-based identification of urban slums in Hyderabad, India using remote sensing data, Applied Geography, 32(2), 660-667. [https://doi.org/10.1016/j.apgeog.2011.07.016]
• Oleksandr, K., Matthias, L. (2013) Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 83, 130-137. [https://doi.org/10.1016/j.isprsjprs.2013.06.009]
• Rao, C.V., Malleswara Rao, J., Senthil Kumar, A., Lakshmi, B., Dadhwal, V.K. (2015) Expansion of LISS III swath using AWiFS wider swath data and contourlet coefficients learning. GIScience and Remote Sensing, 52(1), 78-93. [https://doi.org/10.1080/15481603.2014.983370]
• Rosenfield, G.H., Fitzpatrick-Lins, K. (1986) A Coefficient of Agreement as a Measure of Thematic Classification Accuracy. Photogrammetric Engineering and Remote Sensing, 52(2), 223-227. https://www.asprs.org/wp-content/uploads/pers/1986journal/feb/1986_feb_223-227.pdf
• Smits, P.C., Dellepiane, S.G., Schowengerdt, R.A. (1999) Quality Assessment of Image Classification Algorithms for Land-cover Mapping: A Review and A Proposal for A Cost-based Approach. International Journal of Remote Sensing, 20(8), 1461-1486. [https://doi.org/10.1080/014311699212560]
• Song, K.S., Wang, Z.M., Liu, Q.F., Liu, D.W., Ermoshin, V.V., Ganzei, S.S., Zhang, B., Ren, C.Y., Zeng, L.H., Du, J. (2011) Land Use/Land Cover (LULC) Classification with MODIS Time Series Data and Validation in the Amur River Basin. Geography and Natural Resources, 32, 9-15. [https://doi.org/10.1134/S1875372811010021]
• Sreenivas, K., Roy, P.S. (2008) Land use land cover classification of Orissa using multi-temporal IRS-P6 awifs data: A decision tree approach. International Journal of Applied Earth observation and Geoinformation, 1(2), 186-193. [https://doi.org/10.1016/j.jag.2007.10.003]
• Swain, P.H., Davis, S.M. (1978) Remote Sensing: The Quantitative Approach. McGraw-Hill, New York.
• Taubenbock, H., Wegmann, M., Roth, A., Mehl, H., Dech, S. (2008) Urbanization in India - Spatiotemporal analysis using remote sensing data. Computers, Environment and Urban Systems, 33, 179-188. [https://doi.org/10.1016/j.compenvurbsys.2008.09.003]
• Torrens, P.M., Alberti, M. (2000). Measuring sprawl. Working paper no.27 Association of Collegiate Schools of Planning Conference, November 2000. https://discovery.ucl.ac.uk/id/eprint/1370/1/paper27.pdf
• Townshend, J.R.G. (1992) Land cover. International Journal of Remote Sensing, 13, 1319-1328. [https://doi.org/10.1080/01431169208904193]
• UNESCO. (1973) International Classification and Mapping of Vegetation. Paris.
• Vitousek, P.M., Mooney, H.A., Lubchenco, J., Melillo, J.M. (1997) Human domination of earth’s ecosystems. Science, 277, 494-499. [https://doi.org/10.1126/science.277.5325.494]
• Weiss, M., Jacob, F., Duveiller, G. (2020) Remote sensing for agricultural applications: a meta-review. Remote Sensing of Environment, 236, 1-20. [https://doi.org/10.1016/j.rse.2019.111402]
• Weng, D, Lu, Q. (2007) A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823-870. [https://doi.org/10.1080/01431160600746456]
• Li, X., Yeh, A.G.O. (1998) Principal component analysis of stacked multi-temporal images for the monitoring of rapid urban expansion in the Pearl River. International Journal of Remote Sensing, 19(8), 1501-1518. [https://doi.org/10.1080/014311698215315]
• Yin, J., Yin, Z., Zhong, H., Shuyuan, X., Xiaomeng, H., Jun W., Jianping, W. (2010) Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979-2009) in China. Environ Monit Assess., 177, 609-621. [https://doi.org/10.1007/s10661-010-1660-8]
• Zhang, H.K., Roy, D.P. (2017) Using the 500 M MODIS Land Cover Product to Derive a Consistent Continental Scale 30 M Landsat Land Cover Classification. Remote Sensing of Environment, 197, 15-34. [https://doi.org/10.1016/j.rse.2017.05.024]
• Zhao, B., Ping, T., Jun, Y. (2019) Land-cover classification from multiple classifiers using decision fusion based on the probabilistic graphical model. International Journal of Remote Sensing, 40(12), 4560-4576. [https://doi.org/10.1080/01431161.2019.1569785]