Mapping coconut plantation in Western Agro-Climatic zone using object-based classification and machine learning technique
DOI:
https://doi.org/10.14719/pst.4861Keywords:
Area mapping, change detection, coconut, Landsat 7, machine learning, object-based classificationAbstract
Coconut (Cocos nucifera), a key crop for over 10 million farming families in India, is vital in the agricultural economies of southern states like Tamil Nadu. However, traditional methods of monitoring coconut plantations are challenging due to the crop's geographical dispersion and seasonal variations. This study focuses on mapping coconut plantations in the Western Agro-Climatic Zone of Tamil Nadu using Object-Based Classification (OBC) and machine learning techniques. A ten-year time series of Landsat 7 optical satellite data (2012-2013 and 2022-2023) was employed, combined with ground truth surveys across the region. The study utilized Support Vector Machine (SVM) and Random Forest (RF) classifiers, with RF demonstrating superior accuracy. The RF classifier achieved an accuracy of 91.7% in 2012-2013 and 90.3% in 2022-2023, outperforming SVM, which hovered around 70%. The research also conducted a change detection analysis, revealing a net increase of 3,270 hectares of coconut plantations over the decade, with the Coimbatore district contributing the most significant growth of 2,560 hectares. This study underscores the effectiveness of integrating OBC and machine learning, mainly RF, for accurate and efficient mapping of coconut plantations using Landsat satellite data.
Downloads
References
Anand AK. Ceremonial and ritual plants of India: The Shubh-Labh Connections Between Spirituality And Science: Blue Rose Publishers; 2024.
Theerkhapathy S, Chandrakumarmangalam S. Coconut processing industries: An outlook. Global Journal of Commerce and Management Perspective. 2014;3(5):219-21.
Kannan B, Ragunath K, Kumaraperumal R, Jagadeeswaran R, Krishnan R. Mapping of coconut growing areas in Tamil Nadu, India using remote sensing and GIS. Journal of Applied and Natural Science. 2017;9(2):771-3. https://doi.org/10.31018/jans.v9i2.1272
Sivakumar K, Jagadeeswaran R, Kannan B, Pazhanivelan S. Coconut area mapping and change detection analysis of Coconut growing areas of Coimbatore and Tirupur district of Tamil Nadu, India. Eco Env & Cons. 2022; 28 (May): S202-S206.
Subbaian S, Balasubramanian A, Marimuthu M, Chandrasekaran S, Muthusaravanan G. Detection of coconut leaf diseases using enhanced deep learning techniques. Journal of Intelligent & Fuzzy Systems. 2024(Preprint):1-13. https://doi.org/10.3233/JIFS-233831
Kumar SN, Rajagopal V, Cherian V, Thomas T, Sreenivasulu B, Nagvekar D, et al. Weather data-based descriptive models for prediction of coconut yield in different agro-climatic zones of India. Indian Journal of Horticulture. 2009;66(1):88-94. https://doi.org/10.5958/0974-0112.2015.00016.X
Guhan V, Annadurai K, Easwaran S, Marimuthu M, Balu D, Vigneswaran S, et al. Assessing the impact of climate change on water requirement and yield of sugarcane over different agro-climatic zones of Tamil Nadu. Scientific Reports. 2024;14(1):8239. https://doi.org/10.1038/s41598-024-58771-8
Tanveer MU, Munir K, Raza A, Almutairi MS. Novel artificial intelligence assisted Landsat-8 imagery analysis for mango orchard detection and area mapping. Plos One. 2024;19(6):e0304450. https://doi.org/10.1371/journal.pone.0304450
Tatsumi K, Yamashiki Y, Torres MAC, Taipe CLR. Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data. Computers and Electronics in Agriculture. 2015;115:171-9. https://doi.org/10.1016/j.compag.2015.05.001
Sabthapathy M, Kaliaperumal R, Pazhanivelan S, Velmurugan S. Cashew area mapping using Sentinel-2 in Ariyalur District of Tamil Nadu, India. Eco Env & Cons. 2022; 28 (January Suppl. Issue): S512-S516
Foody GM. Ground Truth in Classification Accuracy Assessment: Myth and Reality. Geomatics. 2024;4(1):81-90. https://doi.org/10.3390/geomatics4010005
Ranjithkumar S, Anbazhagan S, Tamilarasan K. Image Processing of Landsat-8 OLI satellite data for mapping of alkaline-carbonatite complex, Southern India. Remote Sensing in Earth Systems Sciences. 2024:1-23. https://doi.org/10.21203/rs.3.rs-2646789/v1
Malinverni ES, Tassetti AN, Mancini A, Zingaretti P, et al. Hybrid object-based approach for land use/land cover mapping using high spatial resolution imagery. International Journal of Geographical Information Science. 2011;25(6):1025-43. https://doi.org/10.1080/13658816.2011.566569
Jacquin A, Misakova L, Gay M. A hybrid object-based classification approach for mapping urban sprawl in periurban environment. Landscape and Urban Planning. 2008;84(2):152-65. https://doi.org/10.1016/j.landurbplan.2007.07.006
Wang L, Sousa W, Gong P. Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International Journal of Remote Sensing. 2004;25(24):5655-68. https://doi.org/10.1080/014311602331291215
Peña JM, Gutiérrez PA, Hervás-Martínez C, Six J, Plant RE, López-Granados F. Object-based image classification of summer crops with machine learning methods. Remote sensing. 2014;6(6):5019-41. https://doi.org/10.3390/rs6065019
Priyadharshini S, Subramoniam SR, Raj KG, Anandhi V. Coconut inventory and mapping using object oriented classification. Int J Curr Microbiol App Sci. 2019;8(8):58-65. https://doi.org/10.20546/ijcmas.2019.808.007
Segar N, Kaliyaperumal R, Pazhanivelan K, Latha M. AI and machine learning tools in plantation mapping: potentials of high-resolution satellite data. Agricultural Science and Technology. 2024;16(2):3-16. https://doi.org/10.15547/ast.2024.02.012
Ok AO, Akar O, Gungor O. Evaluation of random forest method for agricultural crop classification. European Journal of Remote Sensing. 2012;45(1):421-32. https://doi.org/10.5721/EuJRS20124535
Tariq A, Yan J, Gagnon AS, Riaz Khan M, Mumtaz F. Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-Spatial Information Science. 2023;26(3):302-20. https://doi.org/10.1080/10095020.2022.2100287
Lunetta RS, Lyon JG. Remote sensing and GIS accuracy assessment: CRC press; 2004. https://doi.org/10.1201/9780203497586
Ma Z, Redmond RL. Tau coefficients for accuracy assessment of classification of remote sensing data. Photogrammetric Engineering and Remote Sensing. 1995;61(4):435-9.
Lillesand TM. Strategies for improving the accuracy and specificity of large-area, satellite-based land cover inventories. International Archives of Photogrammetry and Remote Sensing. 1994;30:23-30.
Aziz G, Minallah N, Saeed A, Frnda J, Khan W. Remote sensing based forest cover classification using machine learning. Scientific Reports. 2024;14(1):69. https://doi.org/10.1038/s41598-023-50863-1
Liu C, Frazier P, Kumar L. Comparative assessment of the measures of thematic classification accuracy. Remote Sensing of Environment. 2007;107(4):606-16. https://doi.org/10.1016/j.rse.2006.10.010
Rwanga SS, Ndambuki JM. Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences. 2017;8(04):611. https://doi.org/10.4236/ijg.2017.84033
Richards JA, Richards JA. Remote sensing digital image analysis: Springer; 2022. https://doi.org/10.1007/978-3-030-82327-6
Sajid M. Impact of Land-use Change on Agricultural Production & Accuracy Assessment through Confusion Matrix. Pakistan Journal of Science. 2022;74(4). https://doi.org/10.57041/pjs.v74i4.793
Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS journal of Photogrammetry and Remote Sensing. 2012;67:93-104. https://doi.org/10.1016/j.isprsjprs.2011.11.002
Kaplan G, Avdan U. Mapping and monitoring wetlands using Sentinel-2 satellite imagery. ISPRS Annals of the photogrammetry, remote sensing and spatial information sciences. 2017;4:271-7. https://doi.org/10.5194/isprs-annals-IV-4-W4-271-2017
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Segar VP Nithya, Kaliaperumal Ragunath, S Pazhanivelan, R Kumaraperumal, Paramanandham Latha
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright and Licence details of published articles
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Open Access Policy
Plant Science Today is an open access journal. There is no registration required to read any article. All published articles are distributed under the terms of the Creative Commons Attribution License (CC Attribution 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited (https://creativecommons.org/licenses/by/4.0/). Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
