Abstract
Urban dynamics refers to a phenomenon wherein certain factors contribute to imparting changes to an urban area. These factors can aid in either growth or deterioration of the city. One important factor that acts as a threat to the urban environment is rapid urbanization. To monitor and control such urban expansion, prediction is a necessity. It will throw some light on how the city grows and how it will affect the environment and living conditions. A precise and accurate dataset on land use/land cover (LULC) is a must for such analysis. Several methods exist to classify satellite data based on spectral reflectance with advancements in satellite technology and the means to process it. One problem is that there is no single classifier that can produce accurate results on LULC predictions. Each classifier varies in its performance based on several factors. Hence this research was carried out by choosing three classifiers, namely maximum likelihood (MLC), random forest algorithm (RFA) as well as support vector machines (SVM), which are commonly used. Its performance is then evaluated for a pan-sharpened Landsat 8 data of the study area, i.e., Salem and its surrounding urban agglomerations. From the results, it was inferred that the overall accuracies of MLC, SVM and RFA are 0.885, 0.930 and 0.945, respectively. Though the MLC accuracy is acceptable, it had many misclassifications of other classes into built-up classes. SVM and RFA performances were found good overall, but SVM had fewer misclassifications compared to RFA. SVM produced results close to reality and was concluded as the best classification method for pan-sharpened Landsat 8 data for Salem and its surrounding area.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of data and material
Available on request.
Code availability
Not applicable.
References
Abbas T, Sarmadian F, Mousavi A, Pour CTH, Shahir AHE (2014) Land use classification using support vector machine and maximum likelihood algorithms by Landsat 5 TM images. Walailak J Sci Technol (WJST) 12(8):681–687. https://doi.org/10.14456/WJST.2015.33
Ahmad A, Quegan S (2012) Analysis of maximum likelihood classification on multispectral data. Appl Math Sci 6(129):6425–6436
Alparone L, Wald L, Chanussot J, Thomas C, Gamba P, Bruce LM (2007) Comparison of pansharpening algorithms: outcome of the 2006 GRS-S data-fusion contest. IEEE Trans Geosci Remote Sens 45:3012–3021
Dengsheng Lu, Mausel P, Batistella M, Batistella E (2004) Comparison of land-cover classification methods in the Brazilian Amazon basin. Photogramm Eng Remote Sens. https://doi.org/10.14358/PERS.70.6.723
Ghosh S, Biswas S, Sarkar D, Sarkar PP (2014) A tutorial on different classification techniques for remotely sensed imagery datasets. Smart Comput Rev. https://doi.org/10.6029/smartcr.2014.01.004
Inglada J, Arias M, Tardy B, Hagolle O, Valero S, Morin D, Defieu G, Sepulcre G, Bontemps S, Defourney P, Koetz B (2015) Assessment of an operational system for crop type map production using high temporal and spatial resolution satellite optical imagery. Int J Appl Eng Res 4(4):881–887 (ISSN 0973-4562)
Karan SK, Samadder SR (2018) A comparison of different land-use classification techniques for accurate monitoring of degraded coal-mining areas. Environ Earth Sci. https://doi.org/10.1007/s12665-018-7893-5
Ming D, Zhou T, Wang M, Tan T (2016) Land cover classification using random forest with genetic algorithm-based parameter optimization. J Appl Remote Sens 10(3):035021. https://doi.org/10.1117/1.JRS.10.035021
Morgan RS, Rahim IS, Abd El-Hady M (2015) A comparison of classification techniques for the land use/land cover classification. Glob Adv Res J Agric Sci 4(11):810–818 (ISSN: 2315-5094)
Nivetha S, Jensi R (2020) Classification and comparison of remote sensing image using support vector machine and k-nearest neighbor algorithms. Adv Eng Int J (ADEIJ) 2(3):31–43
Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26(1):217–222
Phan TN, Martin K (2017) Comparison of random forest, -nearest neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery. Sensors. https://doi.org/10.3390/s18010018
Rahmani S, Strait M, Merkurjev D, Moeller M, Wittman T (2010) An adaptive IHS pan-sharpening method. IEEE Geosci Remote Sens Lett 7:746–750
Rozenstein O, Karnieli A (2011) Comparison of methods for land-use classification incorporating remote sensing and GIS inputs. Appl Geogr 31:533–544. https://doi.org/10.1016/j.apgeog.2010.11.006
Saroglu E, Bektas F, Musaoglu N, Goksel C (2004) Fusion of multisensor remote sensing data: assessing the quality of resulting images. isprs.orgproceedings-XXXV-congress-414 Comission IV 2004; WG IV/7
Sunuprapto H, Danoedoro P, Ritohardoyo S (2016) Evaluation of pan-sharpening method: applied to artisanal gold mining monitoring in Gunung pani Forest area. Procedia Environ Sci 33:230–238
Tokar O, Vovk O, Kolyasa L (2018) Using the random forest classification for land cover interpretation of Landsat images in the Prykarpattya region of Ukraine. In: 13th International scientific and technical conference on computer sciences and information technologies (CSIT), Lviv, Ukraine, 11–14 September 2018. https://doi.org/10.1109/STC-CSIT.2018.8526646
Vaishnnave MP, Devi S, Srinivasan P (2019) A study on deep learning models for satellite imagery. Int J Appl Eng Res 4(4):881–887 (ISSN 0973-4562)
Zhang X, Feng X, Xiao P, He G, Zhu L (2015) Segmentation quality evaluation using region-based precision and recall measures for remote sensing images. ISPRS J Photogramm Remote Sens 102:73–84
Funding
Authors did not receive any funding.
Author information
Authors and Affiliations
Contributions
BLT is responsible for designing the framework, analyzing the performance, validating the results, and writing the article. RS, is responsible for collecting the information required for the framework, provision of software, critical review, and administering the process.
Corresponding authors
Ethics declarations
Conflict of interests
Authors do not have any conflicts.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of a Topical Collection in Environmental Earth Sciences on Deep learning for earth resource and environmental remote sensing, guest edited by Carlos Enrique Montenegro Marin, Xuyun Zhang and Nallappan Gunasekaran.
Rights and permissions
About this article
Cite this article
Theres, B.L., Selvakumar, R. Comparison of landuse/landcover classifier for monitoring urban dynamics using spatially enhanced landsat dataset. Environ Earth Sci 81, 142 (2022). https://doi.org/10.1007/s12665-022-10242-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-022-10242-x