Research Article
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Determination of the Olive Trees with Object Based Classification of Pleiades Satellite Image

Year 2018, Volume: 5 Issue: 2, 132 - 139, 01.08.2018
https://doi.org/10.30897/ijegeo.396713

Abstract

Identification of fruit trees and determination of their spatial distribution is an important task for several agricultural activities including fruit yield estimation, irrigation planning, disease management and supporting agricultural policies. This research aims to determine spatial distribution of olive trees at parcel level by using geographic object based image analysis (GEOBIA) and very high resolution satellite images. A pilot area located in the Aegean region of Turkey was selected to conduct research considering the massive amount of olive production within the area. GEOBIA based decision-tree classification was applied to accurately map perennial crop parcel boundaries. After applying multi-resolution segmentation to create image objects, thresholds determined from spectral properties of image objects were integrated into the decision tree to ensure accurate mapping of olive trees. Accuracy assessment was conducted by comparing a highly accurate parcel database with classification results and efficiency of parcel identification and areal information derivation were evaluated. Our results indicated that, decision-tree oriented GEOBIA classification provided sufficient results for determination of olive trees with 90 percent classification accuracy and differentiating them from non- vegetated areas and annual crops. Area estimation and parcel detection performances of the method were also acceptable by providing 0.11 and 0.08 relative errors respectively.

References

  • AIRBUS, 2017. Pleiades Satellite Imagery. http://www.geoairbusds.com/pleiades/ (Accessed 4 April 2017).
  • Alganci, U., Sertel, E., Ozdogan, M., Ormeci, C. 2013. Parcel-Level Identification of Crop Types Using Different Classification Algorithms and Multi-Resolution Imagery in Southeastern Turkey. Photogrammetric Engineering and Remote Sensing, 79(11), 1053-1065.
  • Beach, R.H., DeAngelo, B.J., Rose, S.K., Li, C., Salas, W., Del Grosso, S.J. 2008. Mitigation potential and costs for global agricultural greenhouse gas emissions. Agricultural Economics, 38, 109-115.
  • Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M. 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58, 239–258.
  • Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 66(1), 2-16. Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37-46.
  • Duveiller, G., Defourny, P. 2010. A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing. Remote Sensing of Environment, 114, 2637–2650.
  • FAO, 2017. Agricultural Statistics of the Food and Agriculture Organization of the United Nations,http://www.fao.org/faostat/en/#data/QC. (Accessed 20 January 2017.)
  • Heller, E., Rhemtulla, J.H., Lele, S., Kalacska, M., Badiger, S., Sengupta, R., Ramankutty, N. 2012. Mapping crop types, irrigated areas, and cropping intensities in heterogeneous landscapes of southeastern India using multi-temporal medium-resolution imagery: implications for assessing water use in agriculture. Photogrammetric Engineering and Remote Sensing, 78(8), 815-827.
  • Johansen, K., Phinn, S., Witte, C., Philip, S., Newton, L. 2009. Mapping banana plantations from object-oriented classification of SPOT-5 imagery. Photogrammetric Engineering and Remote Sensing, 75(9), 1069–1081.
  • Johansen, K., Arroyo, L.A., Phinn, S., Witte, C. 2010. Comparison of geo-object based and pixel-based change detection of riparian environments using high spatial resolution multi-spectral imagery. Photogrammetric Engineering and Remote Sensing, 76(2), 123–136.
  • Kim, M., Madden, M., Warner, T.A. 2009. Forest type mapping using object-specific texture measures from multispectral Ikonos imagery: segmentation quality and image classification issues. Photogrammetric Engineering and Remote Sensing, 75(7), 819–829.
  • Mathieu, R., Aryal, J. 2005. Object-oriented classification and Ikonos multispectral imagery for mapping vegetation communities in urban areas. Proceedings of SIRC 2005 – The 17th Annual Colloquium of the Spatial Information Research Centre University of Otago, Dunedin, New Zealand November 24th-25th 2005.
  • Ozdogan, M., Woodcock, C.E. 2006. Resolution dependent errors in remote sensing of cultivated areas. Remote Sensing of Environment, 103, 203–217.
  • Peña-Barragán, J.M., Jurado-Expósito, M., López-Granados, F., Atenciano, S., Sánchez de la Orden, M., García-Ferrer, A., García-Torres, L. 2004. Assessing land-use in olive groves from aerial photographs. Agriculture, Ecosystems and. Environment, 103, 117–122.
  • Peña-Barragán, J.M., Ngugi, M.K., Plant, R.E., Six, J. 2011. Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sensing of Environment, 115, 1301–1316.
  • Story, M., Congalton, R.G. 1986. Accuracy assessment: a user's perspective. Photogrammetric Engineering and Remote Sensing, 52, 397-399.
  • Tansey, K., Chambers, I., Anstee, A., Denniss, A., Lamb, A. 2009. Object-oriented classification of very high resolution airborne imagery for the extraction of hedgerows and field margin cover in agricultural areas. Applied Geography, 29, 145–157.
  • TUIK, 2017. Crop Production statistics. http://tuikapp.tuik.gov.tr/bitkiselapp/bitkisel_ing.zul. (Accessed 29 March 2017).
  • van der Sande, C.J., de Jong, S.M., de Roo, A.P.J. 2003. A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. International Journal of Applied Earth Observation and Geoinformation, 4, 217–229.
  • Zhou, W., Troy, A. 2008. An object-oriented approach for analyzing and characterizing urban landscape at the parcel level. International Journal of Remote Sensing, 29(11), 3119–3135.
Year 2018, Volume: 5 Issue: 2, 132 - 139, 01.08.2018
https://doi.org/10.30897/ijegeo.396713

Abstract

References

  • AIRBUS, 2017. Pleiades Satellite Imagery. http://www.geoairbusds.com/pleiades/ (Accessed 4 April 2017).
  • Alganci, U., Sertel, E., Ozdogan, M., Ormeci, C. 2013. Parcel-Level Identification of Crop Types Using Different Classification Algorithms and Multi-Resolution Imagery in Southeastern Turkey. Photogrammetric Engineering and Remote Sensing, 79(11), 1053-1065.
  • Beach, R.H., DeAngelo, B.J., Rose, S.K., Li, C., Salas, W., Del Grosso, S.J. 2008. Mitigation potential and costs for global agricultural greenhouse gas emissions. Agricultural Economics, 38, 109-115.
  • Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M. 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58, 239–258.
  • Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 66(1), 2-16. Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37-46.
  • Duveiller, G., Defourny, P. 2010. A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing. Remote Sensing of Environment, 114, 2637–2650.
  • FAO, 2017. Agricultural Statistics of the Food and Agriculture Organization of the United Nations,http://www.fao.org/faostat/en/#data/QC. (Accessed 20 January 2017.)
  • Heller, E., Rhemtulla, J.H., Lele, S., Kalacska, M., Badiger, S., Sengupta, R., Ramankutty, N. 2012. Mapping crop types, irrigated areas, and cropping intensities in heterogeneous landscapes of southeastern India using multi-temporal medium-resolution imagery: implications for assessing water use in agriculture. Photogrammetric Engineering and Remote Sensing, 78(8), 815-827.
  • Johansen, K., Phinn, S., Witte, C., Philip, S., Newton, L. 2009. Mapping banana plantations from object-oriented classification of SPOT-5 imagery. Photogrammetric Engineering and Remote Sensing, 75(9), 1069–1081.
  • Johansen, K., Arroyo, L.A., Phinn, S., Witte, C. 2010. Comparison of geo-object based and pixel-based change detection of riparian environments using high spatial resolution multi-spectral imagery. Photogrammetric Engineering and Remote Sensing, 76(2), 123–136.
  • Kim, M., Madden, M., Warner, T.A. 2009. Forest type mapping using object-specific texture measures from multispectral Ikonos imagery: segmentation quality and image classification issues. Photogrammetric Engineering and Remote Sensing, 75(7), 819–829.
  • Mathieu, R., Aryal, J. 2005. Object-oriented classification and Ikonos multispectral imagery for mapping vegetation communities in urban areas. Proceedings of SIRC 2005 – The 17th Annual Colloquium of the Spatial Information Research Centre University of Otago, Dunedin, New Zealand November 24th-25th 2005.
  • Ozdogan, M., Woodcock, C.E. 2006. Resolution dependent errors in remote sensing of cultivated areas. Remote Sensing of Environment, 103, 203–217.
  • Peña-Barragán, J.M., Jurado-Expósito, M., López-Granados, F., Atenciano, S., Sánchez de la Orden, M., García-Ferrer, A., García-Torres, L. 2004. Assessing land-use in olive groves from aerial photographs. Agriculture, Ecosystems and. Environment, 103, 117–122.
  • Peña-Barragán, J.M., Ngugi, M.K., Plant, R.E., Six, J. 2011. Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sensing of Environment, 115, 1301–1316.
  • Story, M., Congalton, R.G. 1986. Accuracy assessment: a user's perspective. Photogrammetric Engineering and Remote Sensing, 52, 397-399.
  • Tansey, K., Chambers, I., Anstee, A., Denniss, A., Lamb, A. 2009. Object-oriented classification of very high resolution airborne imagery for the extraction of hedgerows and field margin cover in agricultural areas. Applied Geography, 29, 145–157.
  • TUIK, 2017. Crop Production statistics. http://tuikapp.tuik.gov.tr/bitkiselapp/bitkisel_ing.zul. (Accessed 29 March 2017).
  • van der Sande, C.J., de Jong, S.M., de Roo, A.P.J. 2003. A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. International Journal of Applied Earth Observation and Geoinformation, 4, 217–229.
  • Zhou, W., Troy, A. 2008. An object-oriented approach for analyzing and characterizing urban landscape at the parcel level. International Journal of Remote Sensing, 29(11), 3119–3135.
There are 20 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Ugur Alganci 0000-0002-5693-3614

Elif Sertel

Sinasi Kaya

Publication Date August 1, 2018
Published in Issue Year 2018 Volume: 5 Issue: 2

Cite

APA Alganci, U., Sertel, E., & Kaya, S. (2018). Determination of the Olive Trees with Object Based Classification of Pleiades Satellite Image. International Journal of Environment and Geoinformatics, 5(2), 132-139. https://doi.org/10.30897/ijegeo.396713

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