Investigating the Use of High Resolution Multi-spectral Satellite Imagery for Crop Mapping in Nigeria - Crop and Landuse Classification using WorldView-3 High Resolution Multispectral Imagery and LANDSAT8 Data

Tunrayo Alabi, Michael Haertel, Sarah Chiejile

Abstract

Imagery from recently launched high spatial resolution WorldView-3 offers new opportunities for crop identification and landcover assessment. Multispectral WorldView-3 at 1.6m spatial resolution and LANDSAT8 images covering an extent of 100Km² in humid ecology of Nigeria were used for crop and landcover identification. Three supervised classification techniques (maximum likelihood(MLC), Neural Net clasifier(NNC) and support vector machine(SVM)) were used to classify WorldView-3 and LANDSAT8 into four crop classes and seven non-crop classes. For accuracy assessment, kappa coefficient, producer and user accuracies were used to evaluate the performance of all three supervised classifiers. NNC performed best with an overall accuracy(OA) of 92.20, kappa coefficient(KC) of 0.83 in landcover identification using WorldView-3. This was closely followed by SVM with an OA of 91.77%, KC of 0.83. MLC performed slightly lower at an OA of 91.25% and KC of 0.82. Classification of crops and landcover with LANDSAT8 was best with MLC classifier with an OA of 92.12% , KC of 0.89. Cassava at younger than 3 months old could not be identified correctly by all classifiers using WorldView-3 and LANDSAT8 products. In summary WorldView-3 and LANDSAT8 data had satisfactory performance in identifying different crop and landcover types though at varying degrees of accuracies.

References

  1. Brewster, C.C., Allen, J.C. and Kopp, D.D., 1999. IPM from space: using satellite imagery to construct regional crop maps for studying crop insect interaction. American. Entomologist, 45, 105-117.
  2. Buechel, S.W., Philipson, W.R. and Philpot, W.D., 1989. The effects of a complex environment on crop separability with Landsat TM. Remote Sensing of Environment, 27, 261-272.
  3. Campbell, J.B., 1996. Introduction to Remote Sensing, 2nd Edition. Guilford Press; 2nd edition, 622 pp.
  4. Carfagna, E., Gallego, J.F., 2005. Using remote sensing for agricultural statistics. Int. Stat. Rev. 2005,73, 389- 404.
  5. Duda, R.O, Hart, P.E., 1973. Pattern classification and scene analysis. New York, NY: Wiley.
  6. Ehrlich, D., Estes, J.E., Scepan, J. and McGwire, K.C., 1994. Crop area monitoring with an advanced agricultural information system. Geocarto International, 9, 31-42.
  7. Fauquet C., Fargette D., 1990. "African Cassava Mosaic Virus: Etiology, Epidemiology, and Control" (PDF). Plant Disease 74 (6): 404- 11. doi:10.1094/pd-74-0404.
  8. Foley, J.A., Ramankutty, N., Brauman, K.A, Cassidy, E.S., Gerber, J.S., Johnston, M., Mueller, N.D., O'Connell, C., Ray, D.K., West, P.C., 2011. Solutions for a cultivated planet. Nature 2011, 478, 337-342.
  9. Foody G.M, Mathur A. 2004. A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing. 42: 1335-1343.
  10. Hanna, R., Allah, M., Berry, A. and Sharobeem, Y., 2004. Crop estimation using satellite based and groundbased surveys (comparative study). In Proc. ASAE Annual International Meeting, (St. Joseph, Michigan: American Society of Agricultural Engineers).
  11. Hsu, C. W., Chang, C. C., Lin, C. J., 2010. A practical guide to support vector classification. Department of Computer Science National Taiwan University, Taipei 106, Taiwan http://www.csie.ntu.edu.tw/cjlin.
  12. Jia, K, Wu, B.F., Li Q.Z. 2013. Crop classification using HJ satellite multispectral data in the North China Plain. Journal of Applied Remote Sensing. 7: 073576.
  13. Jia, K., We, X., Gu, X., Yao, Y., Xie, X., Li, B., 2014. Land cover classification using LANDSAT8 Operational Land Imager data in Beijing, China. Geocarto International. Taylor & Francis. Vol. 29 No. 8, 941-951, http://dx.doi.org/ 10.1080/10106049.2014.894586.
  14. Lippmann, R.P., 1987. An introduction to computing with neural nets. ASSP Magazine, IEEE 4 (2), 4-22.
  15. Low, F., and Duveiller, G., 2014. Defining the Spatial Resolution Requirements for Crop identification Using Optical Remote Sensing; Remote Sens. 2014, 6, 9034- 9063; doi: 10.3390/rs6099034.
  16. Lulla K, Duane Nellis M, Rundquist B. 2013. The LANDSAT8 is ready for geospatial science and technology researchers and practitioners. Geocarto International. 28: 191-191.
  17. Murmu S., Biswas S., 2015. Application of Fuzzy logic and Neural Network in Crop Classification: A Review. International conference on water resources, coastal and ocean engineering (ICWRCOE 2015). Aquatic Procedia 4 (2015) 1203 - 1210, Available online at www.sciencedirect.com.
  18. Omkar S.N., Senthilnath J., Mudigere D., and Kumar M.M., 2008, Crop Classification using Biologicallyinspired Techniques with High Resolution Satellite Image, Journal of Indian Society of Remote Sensing, (36) 175-182.
  19. Ozdarici-Ok, A.; Ok, A.O.; Schindler, K., 2015. Mapping of Agricultural Crops from Single High-Resolution Multispectral Images-Data-Driven Smoothing vs Parcel-Based Smoothing. Remote Sens. 2015, 7, 5611- 5638; doi: 10.3390/rs70505611.
  20. Pal, M., Mather, P.M., 2005. Support vector machines for classification in remote sensing. Int. J. Remote Sensing. 2005, 26, 1007-1011.
  21. Phongaksorn N., Tripathi, N. K., Kumar, S., Soni, P., 2012. Inter-Sensor Comparison between THEOS and LANDSAT 5 TM Data in a Study of Two Crops Related to Biofuel in Thailand. Remote Sens. 2012, 4, 354-376; doi: 10.3390/rs4020354.
  22. Richards, J. A., Jia, X., 2006. Remote Sensing Digital Image Analysis, An Introduction, Springer, Fourth Edition.
  23. Rodriguez, J.R., Miranda, D. and Alvarez, C.J., 2006. Application of satellite images to locate and inventory vineyards in the designation of origin "Bierzo" in Spain. Transactions of the ASABE, 49(1), 277-290.
  24. Sandoval, G., Roberto A., Vazquez, P. G., and Jose A., 2014. Crop Classification Using Different Color Spaces and RBF Neural Networks. Intelligent Systems Group, Faculty of Engineering, La Salle University Benjamin Franklin 47, Condesa, Mexico, DF, 06140. L. Rutkowski et al. (Eds.): ICAISC 2014, Part I, LNAI 8467, pp. 598-609, 2014. Springer International Publishing Switzerland 2014.
  25. Sonneveld B.G. J.S., 2005. Compilation of a soil map of Nigeria: A nation-wide soil resource and landform inventory. Nig. J. Soil Res. Vol. 6: 2005 71 - 83.
  26. Srestasathiern P .and Rakwatin, P., 2014. Oil Palm Tree Detection with High Resolution Multi-Spectral Satellite Imagery. Remote Sens. 2014, 6(10), 9749- 9774; doi:10.3390/rs6109749.
  27. Vapnik, V., 1979. Estimation of Dependences Based on Empirical Data. Nauka, Moscow, pp. 5165-5184, 27 (in Russian) (English translation: Springer Verlag, New York, 1982).
  28. Vassilev V., 2013 Crop investigation using high-resolution worldview-1 and quickbird-2 satellite images on a test site in Bulgaria. Bulgarian Academy of Sciences. Space Research and Technology Institute. Aerospace Research in Bulgaria. 25, 2013, Sofia.
  29. Xavier, B., Vanhalleb, L., and Defournya, P., 2005. Efficiency of crop identification based on optical and SAR image time series. Remote Sensing of Environment, 96, 352-365.
  30. Yang C., James H. E., Reginald S. F., Dale, M., 2007. Using high resolution QuickBird imagery for crop identification and area estimation. Geocarto International Vol. 22, No. 3, September 2007, 219- 233.
  31. Yuan H., Van Der Wiele C. F., Khorram, S., 2009. An Automated Artificial Neural Network System for Land Use/Land Cover Classification from LANDSAT TM Imagery. Remote Sens. 2009, 1, 243-265; doi: 10.3390/rs1030243.
  32. Zhu, G., Blumberg, D.G., 2002. Classification using ASTER data and SVM algorithms; The case study of Beer Sheva, Israel. Remote Sensing of Environment 80 (2), 233-240.
Download


Paper Citation


in Harvard Style

Alabi T., Haertel M. and Chiejile S. (2016). Investigating the Use of High Resolution Multi-spectral Satellite Imagery for Crop Mapping in Nigeria - Crop and Landuse Classification using WorldView-3 High Resolution Multispectral Imagery and LANDSAT8 Data . In Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-188-5, pages 109-120. DOI: 10.5220/0005767301090120


in Bibtex Style

@conference{gistam16,
author={Tunrayo Alabi and Michael Haertel and Sarah Chiejile},
title={Investigating the Use of High Resolution Multi-spectral Satellite Imagery for Crop Mapping in Nigeria - Crop and Landuse Classification using WorldView-3 High Resolution Multispectral Imagery and LANDSAT8 Data},
booktitle={Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2016},
pages={109-120},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005767301090120},
isbn={978-989-758-188-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Investigating the Use of High Resolution Multi-spectral Satellite Imagery for Crop Mapping in Nigeria - Crop and Landuse Classification using WorldView-3 High Resolution Multispectral Imagery and LANDSAT8 Data
SN - 978-989-758-188-5
AU - Alabi T.
AU - Haertel M.
AU - Chiejile S.
PY - 2016
SP - 109
EP - 120
DO - 10.5220/0005767301090120