Multi-sensor Fusion Workflow for Accurate Classification and Mapping of Sugarcane Crops
Abstract
This study aims to assess the classification accuracy of a novel mapping workflow for sugarcane crops identification that combines light detection and ranging (LiDAR) point clouds and remotely-sensed orthoimages. The combined input data of plant height LiDAR point clouds and multispectral orthoimages were processed using a technique called object-based image analysis (OBIA). The use of multi-source inputs makes the mapping workflow unique and is expected to yield higher accuracy compared to the existing techniques. The multi-source inputs are passed through five phases: data collection, data fusion, image segmentation, accuracy validation, and mapping. Data regarding sugarcane crops were randomly collected in ten sampling sites in the study area. Five out of the ten sampling sites were designated as training sites and the remaining five as validation sites. Normalized digital surface model (nDSM) was created using the LiDAR data. The nDSM was paired with Orthophoto and segmented for feature extraction in OBIA by developing a rule-set in eCognition software. A rule-set was created to classify and to segment sugarcane using nDSM and Orthophoto from the training and validation area sites. A machine learning algorithm called support vector machine (SVM) was used to classify entities in the image. The SVM was constructed using the nDSM. The height parameter nDSM was applied, and the overall accuracy assessment was 98.74% with Kappa index agreement (KIA) 97.47%, while the overall accuracy assessment of sugarcane in the five validation sites were 94.23%, 80.28%, 94.50%, 93.59%, and 93.22%. The results suggest that the mapping workflow of sugarcane crops employing OBIA, LiDAR data, and Orthoimages is attainable. The techniques and process used in this study are potentially useful for the classification and mapping of sugarcane crops.
Keywords:
image analysis, sugarcane mapping, remote sensingDownloads
References
Canadian Sugar Institute, Global Sugar Trade (WTO), available at: https://sugar.ca/International-Trade/Global-Sugar-Trade-(WTO).aspx
L. Hui, J. Chen, Z. Pei, S. Zhang, X. Hu, “Monitoring Sugarcane Growth Using ENVISAT ASAR Data”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, No. 8, pp. 2572–2580, 2009 DOI: https://doi.org/10.1109/TGRS.2009.2015769
Bureau of Agricultural Statistics, 23rd Annual Publication, 2012
E. M. Abdel-Rahman, F. B. Ahmed, “The application of remote sensing techniques to sugarcan (Saccharum spp. hybrid) production: A review of the literature”, International Journal of Remote Sensing, Vol. 29, No. 13, pp. 3753-3767, 2008 DOI: https://doi.org/10.1080/01431160701874603
G. J. Hay, G. Castilla, “Object-based Image Analysis: Strengths, Weaknesses, Opportunities and Threats (SWOT)”, in: ISPRS Archives, Vol. 36, 2006
T. Blaschke, K. Johansen, D. Tiede, “Object-Based Image Analysis for Vegetation Mapping and Monitoring”, Advances in Environmental Remote Sensing: Sensors Algorithms and Applications, pp. 241-271, CRC Press Taylor & Francis Group, 2011 DOI: https://doi.org/10.1201/b10599-13
M. V. Japitana, J. E. D. Cubillas, A. G. Apdohan, “Coupling LiDAR Data and LANDSAT 8 OLI in Delineating Corn Plantations in Butuan City, Philippines”, 36th Asian Conference on Remote Sensing 201, Quezon City, Metro Manila Philippines, October 19-23, 2015
M. Baatz, M. Schape, “Multiresolution segmentation — An optimization approach for high quality multi-scale image”, Angewandte Geographische Informations, Vol. 12, pp. 12-23, 2000
D. Flanders, M. Hall-Beyer, 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, 2003 DOI: https://doi.org/10.5589/m03-006
U. C. Benz, G. Hofmann, I. Willhauck, M. Lingenfelder, 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. 3-4, pp. 239-258, 2004 DOI: https://doi.org/10.1016/j.isprsjprs.2003.10.002
Z. Zhou, J. Huang, J. Wang, K. Zhang, Z. Kuang, S. Zhong, X. Song, “Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost”, Plos One, Vol. 10, No. 11, p. e0142069, 2015 DOI: https://doi.org/10.1371/journal.pone.0142069
A. R. Formaggio, M. A. Vieira, C. D. Renno, D. A. Aguiar, M. P. Mello, “Object-based Image Analysis and Data Mining for Mapping Sugarcane with LANDSAT Imagery in Brazil”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 38, pp. 553-562, 2012
M. A. Viera, A. Formaggio, C. Renno, C. Atzberger, D. Aguiar, M. Mellp, “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, 2012 DOI: https://doi.org/10.1016/j.rse.2012.04.011
C. J. Cechim, J. A. Johann, J. F. G. Antunes, “Mapping of sugarcane crop area in the Parana State using Landsat/TM/OLI and IRS/LISS-3 Images”, Revista Brasileira de Engenharia Agricola e Ambiental, Vol. 21, No. 6, pp. 427-432, 2017 DOI: https://doi.org/10.1590/1807-1929/agriambi.v21n6p427-432
D. Fonseca-Luengo, A. Garcia-Pedrero, M. Lillo-Saavedra, R. Costumero, E. Menasalvas, C. Gonzalo-Martin, “Optimal Scale in a Hierarchical Segmentation Method for Satellite Images”, in: Lecture Notes in Computer Science, Vol. 8537, Springer, 2014 DOI: https://doi.org/10.1007/978-3-319-08729-0_36
C. D. Alves, T. G. Florenzano, D. S. Alves, M. N. Pereira, “Mapping Land Use and Land Cover Changes in a Region of Sugarcane Expansion Using TM and MODIS Data”, Revista Brasileira de Cartografia, Vol. 66, No. 2, pp. 337-347, 2014
C. H. W. de Souza, R. A. C. Lamparellib, J. V. Rocha, P. S. G. Magalhaes, “Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehicle (UAV) images”, Computers and Electronics in Agriculture,Vol. 143, pp. 49–56, 2017 DOI: https://doi.org/10.1016/j.compag.2017.10.006
W. S. Lee, V. Alchanatis, C. Yang, M. Hirafugi, D. Moshu, C. Li, “Sensing technologies for precision specialty crop production”, Computers and Electronics in Agriculture,Vol. 74, No. 1, pp. 2-33, 2010 DOI: https://doi.org/10.1016/j.compag.2010.08.005
J. R. Rosell, J. Llorens, R. Sanz, J. Arno, M. Ribes-Dasi, J. Masip, “Obtaining the three-dimensional structure of tree orchards from remote 2D terestrial LiDAR scanning”, Agricultural and Forest Meteorology, Vol. 149, No. 9, pp. 1505-1515, 2009 DOI: https://doi.org/10.1016/j.agrformet.2009.04.008
T. F. Canata, J. P. Molin, A. F. Colaco, R. G. Trevisan, M. Martello, P. R. Fiorio, “Measuring height of sugarcane plants through LiDAR technology”, 13th International Conference on Precision Agriculture, Missouri, USA, July 31-August 3, 2016
A. L. Hiscox, “Lidar Measurement Techniques for Understanding Smoke Plume Dynamics in Sugarcane Production”, Optical Instrumentation for Energy and Environmental Applications. Optical Society of America, 2013 DOI: https://doi.org/10.1364/E2.2013.EM2A.1
A. V. Pada, M. A. Silapan, F. Cabanlit, J. Campomanes, J. Garcia, “Mangrove Forest Cover Extraction of the Coastal Areas of Negros Occidental, Western Visayas, Philippines Using LiDAR Data”, 23th ISPRS Congress, Prague, Chech Republic, July 12-19, 2016 DOI: https://doi.org/10.5194/isprsarchives-XLI-B1-73-2016
R. V. Peralta, R. L. Jalbuena, C. A. Cruz, A. M. Tamondong, “Development of an Object-based Classification Technique for Extraction of Aquaculture Features using LiDAR and Worldview-2 Satellite Image Data”, 13th South East Asian Survey Congress: Expanding the Geospatial Future, Singapore, July 28-31, 2015
A. Charaniya, R. Manduchi, S. Lodha, “Supervised parametric classification of aerial LiDAR data”, 2004 Conference on Computer Vision and Pattern Recognition Workshop, Washington, USA, June 27-July 2, 2004
Y. Huang, B. Yu, J. Zhou, C. Hu, W. Tan, Z. Hu, “Toward automatic estimation of urban green volume using airborne LiDAR data and high-resolution remote sensing images”, Frontiers of Earth Science, Vol. 7, pp. 43–54, 2013 DOI: https://doi.org/10.1007/s11707-012-0339-6
M. D. McCoy, G. P. Asner, M. W. Graves, “Airborne lidar survey of irrigated agricultural landscapes: an application of the slope contrast method”, Journal of Archaeological Science, Vol. 38, No. 9, pp. 2141-2154, 2011 DOI: https://doi.org/10.1016/j.jas.2011.02.033
Q. Man, P. Dong, H. Guo, “Pixel- and feature-level fusion of hyperspectral and lidar data for urban land-use classification”, International Journal of Remote Sensing, Vol. 36, No. 6, pp. 1618-1644, 2015 DOI: https://doi.org/10.1080/01431161.2015.1015657
M. B. Cadalin, J. Silapan, M. Remolador, M. R. C. Ang, “Biomass Resource Assessment on Theoritical and Available Potential of Sugarcane Using LiDAR-Derived Agricultural Land-Cover Map in Victorias City, Negros Occidental, Philippines”, 36th Asian Conference on Remote Sensing, Quezon City, Philippines, October 19-23, 2015
M. Luck-Vogel, C. Mbolambia, K. Rautenbachb, J. Adams, L. Van Niekerkab, “Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDAR”, South African Journal of Botany, Vol. 107, pp. 188-199, 2016 DOI: https://doi.org/10.1016/j.sajb.2016.04.010
L. C. G. David, A. H. Ballado, “Mapping Mangrove Forest from LiDAR Data Using Object- Based Image Analysis and Support Vector Machine: The Case of Calatagan, Batangas”, 8th IEEE International Conference Humanoid, Nanotechnology, Information Technology Communication and Control, Environment and Management, Cebu, Philippines, December 9-12, 2015
L. C. G. David, A. H. Ballado, “Object-based land use and land cover mapping from LiDAR data and orthophoto application of decision tree-based data selection for SVM classification”, IEEE Region 10 Humanitarian Technology Conference, Agra, India, Decemer 21-23, 2016 DOI: https://doi.org/10.1109/R10-HTC.2016.7906854
R. J. Candare, M. V. Japitana, J. E. Cubillas, C. B. Ramirez, “Mapping of High Value Crops Through an Object-Based SVM Model Using LiDAR Data and Orthophoto in Agusan del Norte, Philippines”, 23th ISPRS Congress, Prague, Chech Republic, July 12-19, 2016 DOI: https://doi.org/10.5194/isprsannals-III-7-165-2016
R. Devadas, R. J. Denham, M. Pringle, “Support Vector Machine Classification of Object-based Data for Crop Mapping, Using Multi-temporal Landsat Imagery”, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 39, pp. 185-190, 2016 DOI: https://doi.org/10.5194/isprsarchives-XXXIX-B7-185-2012
V. Vapnik, Statistical Learning Theory, Wiley, 1998
V. Vapnik, Estimation of Dependences Based on Empirical Data, Nauka, Springer Verlag, 2006 DOI: https://doi.org/10.1007/0-387-34239-7
B. E. Boser, I. V. V. Guyon, “A training algorithm for optimal margin classifiers”, Fifth Annual Workshop on Computational Learning Theory, Pennsylvania, USA, July 27-29, 1992 DOI: https://doi.org/10.1145/130385.130401
C. C. Chang, C. J. Lin, “LIBSVM: A library for support vector machines”, ACM Transactions on Intelligent Systems and Technology, Vol. 2, No. 3, ArticleNo 27, 2011 DOI: https://doi.org/10.1145/1961189.1961199
G. M. Foody, A. Mathur, “Toward intelligent training of supervised Image classifications: directing training data acquisition for SVM classification”, Remote Sensing of Environment, Vol. 93, No. 1–2, pp. 107-117, 2004 DOI: https://doi.org/10.1016/j.rse.2004.06.017
G. M. Foody, “Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes”, International, Journal of Remote Sensing, Vol. 25, No. 15, pp. 3091-3104, 2004 DOI: https://doi.org/10.1080/01431160310001648019
G. I. J. Mountrakis, C. Ogole, “Support vector machines in remote sensing: A review”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66, pp. 247-259, 2011 DOI: https://doi.org/10.1016/j.isprsjprs.2010.11.001
B. Mulianga, A. Begue, P. Clouvel, P. Todoroff, “Mapping Cropping Practices of a Sugarcane-Based Cropping System in Kenya Using Remote Sensing”, Remote Sensing, Vol. 7, pp. 14428-14444, 2015 DOI: https://doi.org/10.3390/rs71114428
Kenya Sugar Board, Year book of Statistics, KSB, 2012
M. T. Blaschke, “Object based image analysis for remote sensing”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 65, pp. 2-16, 2010 DOI: https://doi.org/10.1016/j.isprsjprs.2009.06.004
D. C. Duro, S. E. Franklin, M. G. Dube, “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, 2012 DOI: https://doi.org/10.1016/j.rse.2011.11.020
J. Lowry, R. D. Ramsey, K. Thomas, D. Schrupp, T. Sajwaj, J. Kirby, E. Waller, S. Schrader, S. Falzarano, L. Langs, G. Manis, C. Wallace, K. Schulz, P. Comer, K. Pohs, W. Rieth, C. Velasquez, B. Wolk, W. Kepner, K. Boykin, L. O'Brien, D. Bradford, B. Thompson, J. Prior-Magee, “Mapping Moderate-Scale Land-Cover over Very Large Geographic Areas within a Collaborative Framework: A Case Study of the Southwest Regional Gap Analysis Project (SWReGAP)”, Remote Sensing of Environment, Vol. 108, pp. 59-73, 2007 DOI: https://doi.org/10.1016/j.rse.2006.11.008
O. Nevalainen, E. Honkavaara, S. Tuominen, N. Viljanen, T. Hakala, X. Yu, J. Hyyppa, H. Saari, I. Polonen, N. N. Imai, A. M. G. Tommaselli, “Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging”, Remote Sensing Vol. 9, No. 3, ArticleNo 185, 2017 DOI: https://doi.org/10.3390/rs9030185
N. Ekhtari, M. J. V. Zoej, M. R. Sahebi, A. Mohammadzadeh, “Automatic building extraction from LIDAR digital elevation models and World View imagery”, Journal of Applied Remote Sensing. Vol. 3, No. 1, p. 033571, 2009 DOI: https://doi.org/10.1117/1.3284718
L. Zhu, H. Shimamura, K. Tachibana, Y. Li, P. Gong, “Building Change Detection Based on Object Extraction in Dense Urban Areas”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. 27B, pp. 905-908, 2008
M. A. Friedl, D. K. McIver, J. C. F. Hodges, X. Y. Zhang, D. Muchoney, A. H. Strahler, C. E. Woodcock, S. Gopal, A. Schneider, A. Cooper, A. Baccini, F. Gao, C. Schaaf, “Global land cover mapping from MODIS: algorithms and early results”, Remote Sensing of Environment, Vol. 83, No. 1–2, pp. 287-302, 2002 DOI: https://doi.org/10.1016/S0034-4257(02)00078-0
J. P. M. O'Neil-Dunne, S. W. MacFaden, A. R. Royar, K. C. Pelletier, “An object-based system for LiDAR data fusion and feature extraction”, Geocarto International, Vol. 28, No. 3, pp. 227-242, 2013 DOI: https://doi.org/10.1080/10106049.2012.689015
T. M. Lillesand, R. W. Kiefer, J. Chipman, Remote Sensing and Image Interpretation, John Wiley & Sons, 2004
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