Getis-Ord's hot- and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree data

Computers and Electronics in Agriculture

Volumn 111, Issue , 2015, Pages 140-150

  Peeters, Aviva ^a,b   Zude, Manuela ^c   Käthner, Jana ^c   Ünlü, Mustafa ^d   Kanber, Riza ^d   Hetzroni, Amots ^a   Gebbers, Robin ^c   Ben Gal, Alon ^a  

^a VOLCANI CENTER   (Israel)

^b HEBREW UNIVERSITY OF JERUSALEM   (Israel)

^c LEIBNIZ INSTITUTE FOR AGRICULTURAL ENGINEERING AND BIOECONOMY ATB   (Germany)

^d CUKUROVA UNIVERSITY   (Turkey)

Author keywords

GIS; Hot spot analysis; K means clustering; Management zones; Precision agriculture; Spatial clustering

Indexed keywords

AGRICULTURE; CLUSTERING ALGORITHMS; FARMS; GEOGRAPHIC INFORMATION SYSTEMS; MULTIVARIANT ANALYSIS; ORCHARDS; TREES (MATHEMATICS);

HOT SPOT; K-MEANS CLUSTERING; MANAGEMENT ZONES; PRECISION AGRICULTURE; SPATIAL CLUSTERING;

CLUSTER ANALYSIS;

AGRICULTURAL MANAGEMENT; ALGORITHM; CULTIVATION; DATA SET; ECOLOGICAL MODELING; GEOGRAPHICAL REGION; GIS; HOT SPOT; MULTIVARIATE ANALYSIS; ORCHARD; PRECISION AGRICULTURE; STATISTICAL ANALYSIS;

EID: 84921382042     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2014.12.011     Document Type: Article

References (60)

1. Agam N., Segal E., Peeters A., Dag A., Yermiyahu U., Ben-Gal A. Spatial distribution of water status in irrigated olive orchard by thermal imaging. Precision Agric. 2014, 15(3):346-359.
2. Aggelopoulou K.D., Wufsohn D., Fountas S., Gemtos T.A., Nanos G.D., Blackmore S. Spatial variation in yield and quality in a small apple orchard. Precision Agric. 2010, 11:538-556.
3. Aggelopoulou K., Castrignano A., Gemtos T., Benedetto D. Delineation of management zones in an apple orchard in Greece using a multivariate approach. Comput. Electron. Agric. 2013, 90:119-130.
4. Anselin L. Local indicators of spatial association - LISA. Geogr. Anal. 1995, 27(2):93-115.
5. Ball, G., Hall, D., 1965. ISODATA, a novel method of data analysis and pattern classification. Technical report NTIS AD 699616.
6. Calinski T., Harabasz J. A dendrite method for cluster analysis. Commun. Stat. 1974, 3:1-27.
7. Castrignanò A., Boccaccio L., Cohen Y., Nestel D., Kounatidis I., De Papadopoulos N.T., Benedetto D., Mavragani-Tsipidou P. Spatio-temporal population dynamics and area-wide delineation of Bactrocera olea monitoring zones using multi-variate geostatistics. Precision Agric. 2012, 13(4):421-441.
8. Cauvin C., Escobar F., Serradj A. Thematic Cartography, Cartography and the Impact of the Quantitative Revolution 2010, ISTE Ltd. and John Willey & Sons Inc., NJ, USA.
9. Chopin P., Blazy J.M. Assessment of regional variability in crop yields with spatial autocorrelation: banana farms and policy implications in Martinique. Agric. Ecosyst. Environ. 2013, 181:12-21.
10. Chun Y., Griffith D.A. Spatial Statistics & Geostatistics 2013, SAGE Publications Inc., London.
11. Cohen, Y., Sharon, R., Sokolsky, T., Zahavi, T., 2011. Modified hot-spot analysis for spatio-temporal analysis: a case study of the leaf-roll virus expansion in vineyards. In: Spatial2 Conference: Spatial Data Methods for Environmental and Ecological Processes. Foggia, Italy.
12. Cohen S., Cohen Y., Alchanatis V., Levi O. Combining spectral and spatial information from aerial hyperspectral images for delineating homogenous management zones. Biosyst. Eng. 2013, 114(4):435-443.
13. Cooper M.C., Milligan G.W. The effect of measurement error on determining the number of clusters in cluster analysis. Data, Expert Knowledge and Decisions. An Interdisciplinary Approach with Emphasis on Marketing Applications 1988, 319-328. Springer-Verlag, Berlin. W. Gaul, M. Schader (Eds.).
14. Córdoba M., Bruno C., Costa J., Balzarini M. Subfield management class delineation using cluster analysis from spatial principal components of soil variables. Comput. Electron. Agric. 2013, 97:6-14.
15. Corwin D.L., Plant R.E. Applications of apparent soil electrical conductivity in precision agriculture. Comput. Electron. Agric. 2005, 46(1-3):1-10.
16. Cressie N., Wikle C.K. Statistics for Spatio-temporal Data 2011, John Willey & Sons Inc., NJ.
17. Davatgar N., Neishabouri M.R., Sepaskhah A.R. Delineation of site specific nutrient management zones for a paddy cultivated area based on soil fertility using fuzzy clustering. Geoderma 2012, 173-174:111-118.
18. ESRI, 1984. ArcGIS® Desktop Help for Version 10.0, Copyright© 1999-2010 ESRI Inc.
19. Fleming K.L., Westfall D.G., Wiens D.W., Brodahl M.C. Evaluating farmer defined management zone maps for variable rate fertilizer application. Precision Agric. 2000, 2:201-215.
20. Fraisse C.W., Sudduth K.A., Kitchen N.R. Delineation of site-specific management zones by unsupervised classification of topographic attributes and soil electrical conductivity. Trans. ASAE 2001, 44(1):155-166.
21. Fridgen J.J., Kitchen N.R., Sudduth K.A., Drummond S.T., Wiebold W.J., Fraisse C.W. Management zone analyst (MZA): software for subfield management zone delineation. Agron. J. 2004, 96:100-108.
22. Fu Q., Wang Z., Jiang Q. Delineating soil nutrient management zones based on fuzzy clustering optimized by PSO. Math. Comput. Modell. 2010, 51:1299-1305.
23. Gebbers R., Lück E., Dabas M., Domsch H. Comparison of instruments for geoelectrical soil mapping at the field scale. Near Surf. Geophys. 2009, 7:179-190.
24. Getis A., Ord J.K. The analysis of spatial association by use of distance statistics. Geogr. Anal. 1992, 24(3):189-206.
25. Getis A., Ord J.K. Local spatial statistics: an overview. Spatial Analysis: Modeling in GIS Environment 1996, 261-278. John Wiley & Sons Inc., New York, USA. P. Longley, M. Batty (Eds.).
26. Gordon A.D. A survey of constrained classification. Comput. Stat. Data Anal. 1996, 21(1):17-29.
27. Guastaferro F., De Castrignanò A., Benedetto D., Sollitto D., Troccoli A., Cafarelli B. A comparison of different algorithms for the delineation of management zones. Precision Agric. 2010, 11(6):600-620.
28. Haining R. Spatial Data Analysis. Theory and Practice 2003, Cambridge University Press, Cambridge, UK.
29. Jain A.K. Data clustering: 50 years beyond K-means. Pattern Recog. Lett. 2010, 31(8):651-666.
30. Kitchen N.R., Sudduth K.A., Drummond S.T. Soil electrical conductivity as a crop productivity measure for claypan soils. J. Prod. Agric. 1999, 12:607-617.
31. Kitchen N.R., Drummond S.T., Lund K.A., Sudduth K.A., Buchleiter G.W. Soil electrical conductivity and topography related to yield for three contrasting soil-crop systems. Agron. J. 2003, 95:483-495.
32. Kitchen N.R., Sudduth K.A., Myers D.B., Drummond S.T., Hong S.Y. Delineating productivity zones on claypan soil fields using apparent soil electrical conductivity. Comput. Electron. Agric. 2005, 46:285-308.
33. Kounatidis I., Papadopoulos N.T., Mavragani-Tsipidou P., Cohen Y., Tertivanidis K., Nomikou M., Nestel D. Effect of elevation on spatio-temporal patterns of olive fly (Bactrocera oleae) populations in northern Greece. J. Appl. Entomol. 2008, 132(9-10):722-733.
34. Lark R.M. Forming spatially coherent regions by classification of multi-variate data: an example from the analysis of maps of crop yield. Int. J. Geogr. Inform. Sci. 1998, 12(1):83-98.
35. Legendre P., Legendre L. Numer. Ecol. 2012, Elsevier, Amsterdam, The Netherlands. third ed.
36. Lloyd S. Least squares quantization in PCM. IEEE Trans. Inf. Theory 1982, 28:129-137. (Originally as an unpublished Bell laboratories Technical Note at 1957).
37. Lloyd C.D. Spatial Data Analysis 2010, Oxford University Press Inc., New York.
38. Lück E., Gebbers R., Ruehlmann J., Spangenberg U. Electrical conductivity mapping for precision farming. Near Surf. Geophys. 2009, 7:15-25.
39. MacQueen, J.B., 1967. Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, Berkeley, California, pp. 281-297.
40. Mann K.K., Schumann A.W., Obreza T.A. Delineating productivity zones in a citrus grove using citrus production, tree growth and temporally stable soil data. Precision Agric. 2010, 12(4):457-472.
41. McQueen, J., 1967. Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematics, Statistics and Probability, Berkeley, pp. 281-297.
42. Milligan G.W., Cooper M.C. An examination of procedures for determining the number of clusters in a data set. Psychometrika 1985, 50(2):159-179.
43. Mitchell A. The ESRI guide to GIS analysis. Spatial Measurements & Statistics 2005, ESRI Press, Redlands, CA, vol. 2.
44. Mucherino A., Papajorgji P., Pardalos P.M. Data mining in Agriculture 2009, Springer, New York.
45. Ord J.K., Getis A. Local spatial autocorrelation statistics: distributional issues and an application. Geogr. Anal. 1995, 27(4):286-306.
46. Orpin A.R., Kostylev V.E. Towards a statistically valid method of textural sea floor characterization of benthic habitats. Mar. Geol. 2006, 225(1-4):209-222.
47. Ortega R.A., Santibáñez O.A. Determination of management zones in corn (Zea mays L.) based on soil fertility. Comput. Electron. Agric. 2007, 58(1):49-59.
48. Pedroso M., Taylor J., Tisseyre B., Charnomordic B., Guillaume S. A segmentation algorithm for the delineation of agricultural management zones. Comput. Electron. Agric. 2010, 70(1):199-208.
49. Perry E.M., Dezzani R.J., Seavert C.F., Pierce F.J. Spatial variation in tree characteristics and yield in a pear orchard. Precision Agric. 2010, 11:42-60.
50. Ping J.L., Dobermann A. Creating spatially contiguous yield classes for site-specific management. Agron. J. 2003, 95(5):1121-1131.
51. Rud R., Shoshany M., Alchanatis V. Spatial-spectral processing strategies for detection of salinity effects in cauliflower, aubergine and kohlrabi. Biosyst. Eng. 2013, 114(4):384-396.
52. SAS Institute Inc., 2013. SAS/STAT 13.1 User's Guide.
53. Shatar, T.M., McBratney, A.B., 2001. Subdividing a field into contiguous management zones using a k-zone algorithm. In: Proceedings of the 3rd European Conference on Precision Agriculture, Agro Montpellier, Montpellier, France, pp. 115-120.
54. Steinhaus H. Sur la division des corp materials en parties. Bull. Acad. Polon. Sci. IV 1956, 12:801-804. (in French).
55. The MathWorks, 2013. MATLAB Statistical Toolbox R2014a.
56. Theodoridis S., Pikrakis A., Koutroumbas K., Cavouras D. Introduction to Pattern Recognition 2010, Academic Press, Amsterdam, The Netherlands, A MATLAB Approach.
57. Van Vitharana U.W.A., Meirvenne M., Simpson D., Cockx L., De Baerdemaeker J. Key soil and topographic properties to delineate potential management classes for precision agriculture in the European loess area. Geoderma 2008, 143(1-2):206-215.
58. Yan L., Zhou S., Feng L., Hong-Yi L. Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land. Comput. Electron. Agric. 2007, 56:174-186.
59. Zaman Q., Schumann A.W. Nutrient management zones for citrus based on variation in soil properties and tree performance. Precision Agric. 2006, 7:45-63.
60. Zhang N., Wang M., Wang N. Precision agriculture - a worldwide view. Comput. Electron. Agric. 2002, 36(3-2). 132-113.