Remote Sensing Techniques for Classification and Mapping of Sugarcane Growth

  • M. K. Villareal School of Engineering, University of San Carlos, Philippines | College of Engineering and Design, Silliman University, Philippines
  • A. F. Tongco School of Engineering, University of San Carlos, Philippines
Volume: 10 | Issue: 4 | Pages: 6041-6046 | August 2020 |


This study aimed to apply remote sensing technologies in delineating sugarcane (Saccharum officinarum) plantations and in identifying its growth stages. Considering the growing demand for sugarcane in the local and global markets, the need for a science-based resource inventory emerges. In this sense, remote sensing techniques’ unique ability is vital to monitor crop growth and estimate crop yield. Object-Based Image Analysis (OBIA) concept was employed by utilizing orthophotos and Light Detection And Ranging (LiDAR) datasets. Specifically, the study applied the Support Vector Machine (SVM) algorithm to generate the resource map, validated by a handheld Global Positioning System (GPS). The classification result showed an accuracy of 98.4%, delineating a total of 13.93 hectares of sugarcane plantation in the study area. The height information from LiDAR datasets aided in developing the rule-set that can further classify the sugarcane according to its growth stages. Results showed that the area distribution of sugarcane at establishment, tillering, yield formation, and ripening stage were 6.65%, 11.61%, 13.89%, and 17.90% respectively. GPS validation points of the growth stages verified the accuracy of SVM. The accuracy results for growth stages, i.e. establishment, tillering, yield formation, and ripening are 88%, 94.4%, 96.3%, and 91.7% respectively. The results proved the usefulness of SVM as a remote sensing classification technique which led to an exact mapping of the sugarcane areas as well as the practical use of LiDAR height information in estimating the growth stages of the mapped resource, both of which can provide valuable aid in estimating the potential sugarcane yield in the future.

Keywords: remote-sensing technologies, crop growth, remote images


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R. Candare, M. Japitana, J. E. Cubillas, and C. B. Ramirez, "Mapping of High Value Crops Through an Object-Based SVM Model Using LiDAR Data and Orthophoto in Agusan del Norte, Philippines," ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. III-7, pp. 165-172, Jun. 2016. DOI:

"Asia - Agriculture," Encyclopedia Britannica. (accessed Jul. 11, 2020).

"Sugar industry of the Philippines," Wikipedia. Mar. 19, 2020, Accessed: Jul. 11, 2020. [Online]. Available:

S. Solomon, M. Swapna, X. Vo-Tong, and Y. Mon, "Development of Sugar Industry in ASEAN Countries," Sugar Tech, vol. 18, Nov. 2016. DOI:

H. Lin, J. Chen, Z. Pei, S. Zhang, and X. Hu, "Monitoring Sugarcane Growth Using ENVISAT ASAR Data," IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 8, pp. 2572-2580, Aug. 2009. DOI:

M. S. Hassan and S. Mahmud-ul-islam, "Rapid Identification of the Sugarcane Cultivation Area and Crop Growth Condition at Ishwardi Upazila, Bangladesh Using Landsat Imagery," International Journal of Research and Review, vol. 2, no. 4, pp. 162-168, 2015.

E. M. Abdel-Rahman and F. B. Ahmed, "The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: a review of the literature," International Journal of Remote Sensing, vol. 29, no. 13, pp. 3753-3767, Jun. 2008. DOI:

B. F. T. Rudorff, D. A. Aguiar, W. F. Silva, L. M. Sugawara, M. Adami, and M. A. Moreira, "Studies on the Rapid Expansion of Sugarcane for Ethanol Production in São Paulo State (Brazil) Using Landsat Data," Remote Sensing, vol. 2, no. 4, pp. 1057-1076, Apr. 2010. DOI:

M. El Hajj, A. Bégué, S. Guillaume, and J.-F. Martiné, "Integrating SPOT-5 time series, crop growth modeling and expert knowledge for monitoring agricultural practices - The case of sugarcane harvest on Reunion Island," Remote Sensing of Environment, vol. 113, no. 10, pp. 2052-2061, Oct. 2009. DOI:

M. A. Vieira, A. R. Formaggio, C. D. Rennó, C. Atzberger, D. A. Aguiar, and M. P. Mello, "Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas," Remote Sensing of Environment, vol. 123, pp. 553-562, Aug. 2012. DOI:

C. Palaniswami, P. Gopalasundaram, and A. Bhaskaran, "Application of GPS and GIS in Sugarcane Agriculture," Sugar Tech, vol. 13, no. 4, pp. 360-365, Dec. 2011. DOI:

T. Blaschke, S. Lang, D. Tiede, M. Papadakis, and A. Györi, "Object-Based Image Analysis Beyond Remote Sensing - the Human Perspective," ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 41B7, pp. 879-882, Jun. 2016. DOI:

U. C. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen, "Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 58, no. 3, pp. 239-258, Jan. 2004. DOI:

D. Flanders, M. Hall-Beyer, and J. Pereverzoff, "Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction," Canadian Journal of Remote Sensing, vol. 29, no. 4, pp. 441-452, Jan. 2003.

M. Baatz and A. Schäpe, "Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation," presented at the Beutrage zum AGIT-Symposium, Salzburg, Heidelberg, 2000.

R. Devadas, R. J. Denham, and M. Pringle, "Support Vector Machine Classification of Object-based Data for Crop Mapping, Using Multi-temporal Landsat Imagery," in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia, Sep. 2012, vol. XXXIX-B7, pp. 185-190. DOI:

G. Mountrakis, J. Im, and C. Ogole, "Support vector machines in remote sensing: A review," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, no. 3, pp. 247-259, May 2011. DOI:

M. V. Japitana, J. E. D. Cubillas, and A. G. Apdohan, "Coupling LiDAR Data and LANDSAT 8 OLI in Delineating Corn Plantations in Butuan City, Philippines," presented at the 36th Asian Conference on Remote Sensing, Quezon City, Metro Manila, Philippines, Oct. 2015.

W. S. Lee, V. Alchanatis, C. Yang, M. Hirafuji, D. Moshou, and C. Li, "Sensing technologies for precision specialty crop production," Computers and Electronics in Agriculture, vol. 74, no. 1, pp. 2-33, Oct. 2010. DOI:

J. R. Rosell et al., "Obtaining the three-dimensional structure of tree orchards from remote 2D terrestrial LIDAR scanning," Agricultural and Forest Meteorology, vol. 149, no. 9, pp. 1505-1515, Sep. 2009. DOI:

A. P. Charaniya, R. Manduchi, and S. K. Lodha, "Supervised Parametric Classification of Aerial LiDAR Data," in 2004 Conference on Computer Vision and Pattern Recognition Workshop, Jun. 2004, pp. 30-30.

Y. Huang et al., "Toward automatic estimation of urban green volume using airborne LiDAR data and high resolution Remote Sensing images," Frontiers of Earth Science, vol. 7, no. 1, pp. 43-54, Mar. 2013. DOI:

J. Carter et al., Lidar 101: An Introduction to Lidar Technology, Data, and Applications. Charleston, SC, USA: National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center, 2012.

FAS/Nairobi Staff, "Sugar Annual Kenya Sugar Annual Report," USDA FAS, Nairobi, Kenya, Apr. 2012.

T. Blaschke, "Object based image analysis for remote sensing," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 65, no. 1, pp. 2-16, Jan. 2010. DOI:

P. S. Thenkabail and Z. Wu, "An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data," Remote Sensing, vol. 4, no. 10, pp. 2890-2918, 2012. DOI:

D. C. Duro, S. E. Franklin, and M. G. Dubé, "A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery," Remote Sensing of Environment, vol. 118, pp. 259-272, Mar. 2012.

J. M. Peña-Barragán, M. K. Ngugi, R. E. Plant, and J. Six, "Object-based crop identification using multiple vegetation indices, textural features and crop phenology," Remote Sensing of Environment, vol. 115, no. 6, pp. 1301-1316, Jun. 2011. DOI:

C. Conrad, S. Fritsch, J. Zeidler, G. Rücker, and S. Dech, "Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data," Remote Sensing, vol. 2, no. 4, pp. 1035-1056, Apr. 2010. DOI:

O. Nevalainen et al., "Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging," Remote Sensing, vol. 9, no. 3, p. 185, Mar. 2017. DOI:

G. M. Foody and A. Mathur, "Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification," Remote Sensing of Environment, vol. 93, no. 1, pp. 107-117, Oct. 2004. DOI:

T. M. Susantoro, K. Wikantika, A. B. Harto, and D. Suwardi, "Monitoring Sugarcane Growth Phases Based on Satellite Image Analysis (A Case Study in Indramayu and its Surrounding, West Java, Indonesia)," HAYATI Journal of Biosciences, vol. 26, no. 3, pp. 117-117, Dec. 2019.

D. Pylarinos and I. Pellas, "Incorporating Open/Free GIS and GPS Software in Power Transmission Line Routine Work: The Case of Crete and Rhodes," Engineering, Technology & Applied Science Research, vol. 7, no. 1, pp. 1316-1322, Feb. 2017. DOI:

M. V. Japitana and M. E. C. Burce, "A Satellite-based Remote Sensing Technique for Surface Water Quality Estimation," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 3965-3970, Apr. 2019. DOI:


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