Assessment of land use and land cover mapping using object-based classification techniques for the eastern districts of Tamil Nadu

A karthikkumar; R Kumaraperumal; S Pazhanivelan; D Muthumanickam; K P Ragunath; S Selvakumar; S Kamaleshkanna

doi:10.14719/pst.6260

Authors

A karthikkumar Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0009-0004-1025-8336
R Kumaraperumal Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0000-0001-6659-9587
S Pazhanivelan Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0000-0002-3596-3232
D Muthumanickam Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0000-0002-9852-1095
K P Ragunath Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0000-0002-7851-4437
S Selvakumar Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0009-0007-3494-9598
S Kamaleshkanna Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0009-0003-8971-9221

DOI:

https://doi.org/10.14719/pst.6260

Keywords:

eCognition, land use, land cover, sentinel-2 data, segmentation

Abstract

LULC (Land use and land cover) mapping is crucial for understanding environmental monitoring, supporting sustainable development, and managing natural resources. This study evaluated the accuracy of object-based LULC classification using Sentinel-2 data and machine learning classifiers in the Ariyalur, Perambalur, and Mayiladuthurai districts of Tamil Nadu during the kharif season of 2023. OBIA (Object-based image analysis) clusters pixels based on their spectral and spatial characteristics, utilizing segmentation to generate masks that effectively represent the image content. The OBIA methodology involves multiresolution segmentation using eCognition software to delineate homogeneous image objects based on spectral, spatial, and contextual characteristics. Several widely used machine learning algorithms, including Random forest (RF), Support vector machine (SVM), Decision Tree (DT), Naive bayes (NB) and k-nearest Neighbor (k-NN), were evaluated to improve classification accuracy. The classification results varied across the districts, with the RF algorithm consistently demonstrating high performance. The Perambalur and Mayiladuthurai RF achieved an overall accuracy of 88 %, with a kappa coefficient of 0.76 and 83 % and a kappa coefficient of 0.66. In Ariyalur, the DT model was used, with an accuracy of 85 % and a kappa coefficient of 0.70. The NB and k-NN classifiers achieved lower accuracies in all districts. In contrast, the RF algorithm was the most reliable for LULC classification in these areas, highlighting its strength and efficiency in accurately identifying complex land cover patterns.

Downloads

Download data is not yet available.

References

Vizzari M, Hilal M, Sigura M, Antognelli S, Joly D. Urban-rural-natural gradient analysis with CORINE data: An application to the metropolitan France. Landsc Urban Plan. 2018;171:18–29. https://doi.org/10.1016/j.landurbplan.2017.11.005

Shalaby A, Tateishi R. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Appl Geogr. 2007;27(1):28–41. https://doi.org/10.1016/j.apgeog.2006.09.004

Carranza-García M, García-Gutiérrez J, Riquelme JC. A framework for evaluating land use and land cover classification using convolutional neural networks. Remote Sens. 2019;11(3):274. https://doi.org/10.3390/rs11030274

Selvaraj R, Amali DGB, Bessie G. Assessment of object-based classification for mapping land use and land cover using google earth. Glob NEST J. 2023;25(7):131–38.

Yang X. Parameterizing support vector machines for land cover classification. Photogramm Eng Remote Sens. 2011;77(1):27–37. https://doi.org/10.14358/PERS.77.1.27

Kulkarni AD, Lowe B. Random forest algorithm for land cover classification. Int J Recent Innov Trends Comp Comm. 2016;4 (3):58 - 63

Breiman L. Random forests. Mach Learn. 2001;45:5–32. https://doi.org/10.1023/a:1010933404324

Pal S, Talukdar S. Assessing the role of hydrological modifications on land use/land cover dynamics in Punarbhaba river basin of Indo-Bangladesh. Environ Dev Sustain. 2020;22(1):363–82. https://doi.org/10.1007/s10668-018-0205-0

Dutta D, Rahman A, Paul SK, Kundu A. Changing pattern of urban landscape and its effect on land surface temperature in and around Delhi. Environ Monit Assess. 2019;191(9):551. https://doi.org/10.1007/s10661-019-7645-3

Bhungeni O, Ramjatan A, Gebreslasie M. Machine-learning algorithms for mapping LULC of the uMngeni catchment area, KwaZulu-Natal. Remote Sens. 2024;16(12):2219. https://doi.org/10.3390/rs16122219

Ahmadi K. Assessment of the accuracy of various machine learning algorithms for classifying urban areas through google earth engine: A case study of Kabul city, Afghanistan. Eur J Artif Intell Mach Learn. 2024;3(3):1–8. https://doi.org/10.24018/ejai.2024.3.3.40

Aziz N, Minallah N, Hasanat M, Ajmal M. Geographic object-based image analysis for small farmlands using machine learning techniques on multispectral Sentinel-2 data. Proc Pak Acad Sci Phys Comput Sci. 2024;61(1):41–49. https://doi.org/10.53560/PPASA(60-1)795

Junior C, Araki H, Macedo CR. Object-Based Image Analysis (OBIA) and Machine Learning (ML) applied to tropical forest mapping using Sentinel-2. Can J Remote Sens. 2023;49(1):2259504. https://doi.org/10.1080/07038992.2023.2259504

Ecosystem CDS. Copernicus data space ecosystem Europe’s eyes on earth [Internet]. 2023. Available from: https://browser.dataspace.copernicus.eu.

Vuolo F, ?ó?tak M, Pipitone C, Zappa L, Wenng H, Immitzer M, et al. Data service platform for Sentinel-2 surface reflectance and value-added products: System use and examples. Remote Sens. 2016;8(11):938. https://doi.org/10.3390/rs8110938

Chaudhary D, Vasuja ER. A review on various algorithms used in machine learning. Int J Sci Res Comput Sci Eng Inf Technol. 2019;5(2):915–20. https://doi.org/10.32628/cseit1952248

Yimer SM, Bouanani A, Kumar N, Tischbein B, Borgemeister C. Comparison of different machine-learning algorithms for land use land cover mapping in a heterogenous landscape over the Eastern Nile river basin, Ethiopia. Adv Space Res. 2024;74(5):2180–99. https://doi.org/10.1016/j.asr.2024.06.010

Rudrapal D, Subhedar M. Land cover classification using support vector machine. Int J Eng Res. 2015 Sep 24;V4. https://doi.org/10.17577/ijertv4is090611

Adugna T, Xu W, Fan J. Comparison of random forest and support vector machine classifiers for regional land cover mapping using coarse resolution FY-3C images. Remote Sens. 2022;14(3):574. https://doi.org/10.3390/rs14030574

Saini R, Ghosh SK. Crop classification on single date Sentinel-2 imagery using random forest and suppor vector machine. Int Arch Photogramm Remote Sens Spat Inf Sci. 2018;5:683–88. https://doi.org/10.5194/isprs-archives-xlii-5-683-2018

Smith HL, Biggs PJ, French NP, Smith ANH, Marshall JC. Out of (the) bag—encoding categorical predictors impacts out-of-bag samples. PeerJ Comput Sci. 2024;10:e2445. https://doi.org/10.7717/peerj-cs.2445

Bhungeni O, Ramjatan A, Gebreslasie M. Evaluating machine-learning algorithms for mapping lulc of the umngeni catchment area, KwaZulu-Natal. Remote Sens. 2024;16(12):2219. https://doi.org/10.3390/rs16122219.

Phinzi K, Ngetar NS, Pham QB, Chakilu GG, Szabó S. Understanding the role of training sample size in the uncertainty of high-resolution LULC mapping using random forest. Earth Sci Inform. 2023;16(4):3667–77.https://doi.org/10.1007/s12145-023-01117-1

Wei C, Huang J, Mansaray LR, Li Z, Liu W, Han J. Estimation and mapping of winter oilseed rape LAI from high spatial resolution satellite data based on a hybrid method. Remote Sens. 2017;9(5):488. https://doi.org/10.3390/rs9050488

Maselli F, Chirici G, Bottai L, Corona P, Marchetti M. Estimation of Mediterranean forest attributes by the application of k?NN procedures to multitemporal Landsat ETM+ images. Int J Remote Sens. 2005;26(17):3781–96. https://doi.org/10.1080/01431160500166433

Sun D, He M, Zhang D. Land use type identification on the basis of drone imagery and object-oriented methods. In: Bilal M, Tosti F, editors. In:Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023). Qingdao, China: SPIE; 2023. p. 101. https://doi.org/10.1117/12.3007579

Prabowo NW, Siregar VP, Agus SB. Klasifikasi habitat bentik berbasis objek dengan algoritma support vector machines dan decision tree menggunakan citra multispektral spot-7 di pulau harapan dan pulau kelapa. J Ilmu Dan Teknol Kelaut Trop. 2018;10(1):123–34. https://doi.org/10.29244/jitkt.v10i1.21670

Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens Environ. 2011;115(5):1145–61. https://doi.org/10.1016/j.rse.2010.12.017

Gr?bczewski K. Techniques of decision tree induction. In: Krzysztof Gr?bczewski K, editor. Meta-learning in decision tree induction. Cham: Springer International Publishing; 2014. p. 11–117. https://doi.org/10.1007/978-3-319-00960-5_2

Coronel TY, Benitez-Paez F, Padró JC, Drenkhan F, Timaná ME, FallaValdez V. Optimization of remotely sensed variables selection for land cover mapping. SSRN; 2024. https://doi.org/10.2139/ssrn.4853907

Veenadhari S, Bharat Mishra Dr, Singh DrC. Soybean productivity modelling using decision tree algorithms. Int J Comput Appl. 2011;27(7):11–15. https://doi.org/10.5120/3314-4549

Pradhan R, Ghose MK, Jeyaram A. Land cover classification of remotely sensed satellite data using bayesian and hybrid classifier. Int J Comput Appl. 2010;7(11):1–4. https://doi.org/10.5120/1295-1783

Johnson BA, Iizuka K. Integrating OpenStreetMap crowdsourced data and Landsat time-series imagery for rapid land use/land cover (LULC) mapping: Case study of the Laguna de Bay area of the Philippines. Appl Geogr. 2016;67:140–49. https://doi.org/10.1016/j.apgeog.2015.12.006

Comber AJ, Law AN, Lishman JR. A comparison of Bayes’, Dempster–Shafer and Endorsement theories for managing knowledge uncertainty in the context of land cover monitoring. Comput Environ Urban Syst. 2004;28(4):311–27. https://doi.org/10.1016/S0198-9715(03)00013-9

Dr?gu? L, Tiede D, Levick SR. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int J Geogr Inf Sci. 2010;24(6):859–71. https://doi.org/10.1080/13658810903174803

Gao Y, Marpu P, Niemeyer I, Runfola DM, Giner NM, Hamill T, Pontius Jr RG. Object-based classification with features extracted by a semi-automatic feature extraction algorithm–SEaTH. Geocarto Inter. 2011;26(3):211?26. https://doi.org/10.1080/10106049.2011.556754

Darwish A, Leukert K, Reinhardt W. Image segmentation for the purpose of object-based classification. In: 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings, Toulouse, France; 2003. p. 2039?41. https://doi.org/10.1109/IGARSS.2003.1294332

Nussbaum S, Niemeyer I, Canty MJ. Seath - a new tool for automated feature extraction in the context of object-based image analysis. In: 1st International Conference on Object-based Image Analysis (OBIA 2006), Salzburg, Austria. OISPRS Archives; p. 1?6.

Tilahun A. Accuracy assessment of land use land cover classification using google earth. Am J Environ Prot. 2015;4(4):193. https://doi.org/10.11648/j.ajep.20150404.14

Czaplewski RL. Accuracy assessment of maps of forest condition: Statistical design and methodological considerations. In: Wulder MA, Franklin SE, editors. Remote sensing of forest environments: Concepts and case studies. Boston (MA):Springer; 2003. p. 115–40. https://doi.org/10.1007/978-1-4615-0306-4_5

Congalton RG. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ. 1991;37(1):35–46. https://doi.org/10.1016/0034-4257(91)90048-B

Basheer S, Wang X, Farooque AA, Nawaz RA, Liu K, Adekanmbi T, et al. Comparison of land use land cover classifiers using different satellite imagery and machine learning techniques. Remote Sens. 2022;14(19):4978. https://doi.org/10.3390/rs14194978

Singh R, Singh P, Drews M, Kumar P, Singh H, Gupta KA, et al. A machine learning-based classification of LANDSAT images to map land use and land cover of India. Remote Sens Appl Soc Environ. 2021;100624. https://doi.org/10.1016/j.rsase.2021.100624

Tassi A, Gigante D, Modica G, Di Martino L, Vizzari M. Pixel vs. object-based landsat 8 data classification in google earth engine using random forest: the case study of Maiella national park. Remote Sens. 2021;13(12):2299. https://doi.org/10.3390/rs13122299

Jaber HS, Shareef MA, Merzah ZF. Object-based approaches for land use-land cover classification using high resolution quick bird satellite imagery (a case study: Kerbela, Iraq). Geod Cartogr. 2022;48(2):85–91. https://doi.org/10.3846/gac.2022.14453

Garafutdinova L, Kalichkin V, Fedorov D. Object-oriented classification of remote sensing earth images using machine. Bull NSAU Novosib State Agrar Univ. 2024;37–47. https://doi.org/10.31677/2072-6724-2024-71-2-37-47

Shafizadeh-Moghadam H, Khazaei M, Alavipanah SK, Weng Q. Google earth engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors. GIScience Remote Sens. 2021;58(6):914–28. https://doi.org/10.1080/15481603.2021.1947623

Sitthi A, Nagai M, Dailey M, Ninsawat S. Exploring land use and land cover of geotagged social-sensing images using naive bayes classifier. Sustain. 2016;8(9):921. https://doi.org/10.3390/su8090921

Atef I, Ahmed W, Abdel-Maguid RH. Modelling of land use land cover changes using machine learning and GIS techniques: a case study in El-Fayoum Governorate, Egypt. Environ Monit Assess. 2023;195(6):637. https://doi.org/10.1007/s10661-023-11224-7

Sundar KSP, Deka PC. Spatio-temporal classification and prediction of land use and land cover change for the Vembanad lake system, Kerala: a machine learning approach. Environ Sci Pollut Res. 2022;29(57):86220–36. https://doi.org/10.1007/s11356-021-17257-0

Sathyaseelan M, Ghosh SK, Ojha C. Satellite-based LULC change with special emphasis on SDG using machine learning – A case study on the upper Ramganga Basin. In: IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. Athens, Greece: IEEE; 2024. p.4159–63. https://doi.org/10.1109/IGARSS53475.2024.10640733

Khan S, Bhardwaj A, Sakthivel M. Accuracy assessment of land use land cover classification using machine learning classifiers in google earth engine; a case study of Jammu district. Int arch photogramm remote sens spat Inf Sci. 2024;XLVIII-4–2024:263–68. https://doi.org/10.5194/isprs-archives-XLVIII-4-2024-263-2024

Zafar Z, Zubair M, Zha Y, Fahd S, Nadeem A. Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data. Egypt J Remote Sens Space Sci. 2024;27(2):216–26. https://doi.org/10.1016/j.ejrs.2024.03.003

Thanh Noi P, Kappas M. Comparison of random forest, k-nearest neighbor and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors. 2017;18(1):18. https://doi.org/10.3390/s18010018

Jasim BS, Jasim OZ, AL-Hameedawi AN. Evaluating land use land cover classification based on machine learning algorithms. Eng Technol J. 2024;42(5):557–68. https://doi.org/10.30684/etj.2024.144585.1638