Optimising the spatial resolution of WorldView-2 pan-sharpened imagery for predicting levels of Gonipterus scutellatus defoliation in KwaZulu-Natal, South Africa

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 112, Issue , 2016, Pages 13-22

  Lottering, Romano ^a   Mutanga, Onisimo ^a  

^a UNIVERSITY OF KWAZULU NATAL   (South Africa)

Author keywords

Artificial neural networks; Gonipterus scutellatus; Minimal variance; Spatial resolution

Indexed keywords

ANIMALS; FORECASTING; NEURAL NETWORKS; SATELLITE IMAGERY; STATISTICAL TESTS; VEGETATION;

ENHANCED VEGETATION INDEX; GONIPTERUS SCUTELLATUS; HIGH SPATIAL RESOLUTION IMAGERY; IMAGE SPATIAL RESOLUTION; MINIMAL VARIANCE; NORMALIZED DIFFERENCE VEGETATION INDEX; REMOTELY SENSED IMAGERY; SPATIAL RESOLUTION;

IMAGE RESOLUTION;

ARTIFICIAL NEURAL NETWORK; DEFOLIATION; INSECT; NDVI; QUANTITATIVE ANALYSIS; SATELLITE IMAGERY; SHRUB; SPATIAL RESOLUTION; VEGETATION MAPPING; WORLDVIEW;

KWAZULU-NATAL; SOUTH AFRICA;

EUCALYPTUS; GONIPTERUS SCUTELLATUS;

EID: 84955153196     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2015.11.010     Document Type: Article

References (43)

1. Atkinson P.M., Curran P.J. Choosing an appropriate spatial resolution for remote sensing investigations. Photogramm. Eng. Remote Sens. 1997, 63:1345-1351.
2. Barry K.M., Stone C., Mohammed C.L. Crown-scale evaluation of spectral indices for defoliated and discoloured eucalypts. Int. J. Remote Sens. 2008, 29:47-69.
3. Bocher P.K., McCloy K.R. The fundamentals of average local variance - Part I: Detecting regular patterns. IEEE Trans. Image Process. 2006, 15:300-310.
4. Boyd D.S., Foody G.M., Ripple W.J. Evaluation of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing. Appl. Geogr. 2002, 22:375-392.
5. Cho M.A., Skidmore A.K., Sobhan I. Mapping beech (Fagus sylvatica L.) forest structure with airborne hyperspectral imagery. Int. J. Appl. Earth Obs. Geoinf. 2009, 11:201-211.
6. Clarke A.R., Paterson S., Pennington P. Gonipterus scutellatus Gyllenhal (Coleoptera: Curculionidae) oviposition on seven naturally co-occurring Eucalyptus species. For. Ecol. Manage. 1998, 110:89-99.
7. de Beurs K.M., Townsend P.A. Estimating the effect of gypsy moth defoliation using MODIS. Remote Sens. Environ. 2008, 112:3983-3990.
8. Eklundh L., Johansson T., Solberg S. Mapping insect defoliation in Scots pine with MODIS time-series data. Remote Sens. Environ. 2009, 113:1566-1573.
9. ENVI Version 4.7, Exelis Visual Information Solutions 2009, ITT Industries, USA.
10. Eyles A., Barry K.M., Quentin A., Pinkard E.A. Impact of defoliation in temperate eucalypt plantations: physiological perspectives and management implications. For. Ecol. Manage. 2013, 304:49-64.
11. Franklin S.E., Fan H., Guo X. Relationship between Landsat TM and SPOT vegetation indices and cumulative spruce budworm defoliation. Int. J. Remote Sens. 2007, 29:1215-1220.
12. Franklin S.E., Waring R.H., McCreight R.W., Cohen W.B., Fiorella M. Aerial and satellite sensor detection and classification of western spruce budworm defoliation in a subalpine forest. Can. J. Remote Sens. 1995, 21:299-308.
13. Garzelli, A., Capobianco, L., Nencini, F., 2009. On the effects of pan-sharpening to target detection. In: Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009, pp. II-136-II-139.
14. Gebeyehu S., Hurley B.P., Wingfield M.J. A new lepidopteran insect pest discovered on commercially grown Eucalyptus nitens in South Africa. South Afr. J. Sci. 2005, 101:26-28.
15. He Y., Guo X., Wilmshurst J., Si B.C. Studying mixed grassland ecosystems II: optimum pixel size. Can. J. Remote Sens. 2006, 32:108-115.
16. Hegde G., Hegde N., Muralikrishna I. Measurement of quality preservation of pan-sharpened image. Int. J. Eng. Res. Dev. 2012, 2:12-17.
17. Huete A., Didan K., Miura T., Rodriguez E.P., Gao X., Ferreira L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002, 83:195-213.
18. HyppÄNen H. Spatial autocorrelation and optimal spatial resolution of optical remote sensing data in boreal forest environment. Int. J. Remote Sens. 1996, 17:3441-3452.
19. Ingram J.C., Dawson T.P., Whittaker R.J. Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks. Remote Sens. Environ. 2005, 94:491-507.
20. Ismail R., Mutanga O., Ahmed A. Discriminating Sirex noctilio Attack in Pine Forest Plantations in South Africa Using High Spectral Resolution Data. Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests 2008, 161-175. CRC Press.
21. Ismail R., Mutanga O., Kumar L., Bob U. Determining the optimal spatial resolution of remotely sensed data for the detection of Sirex noctilio infestations in pine plantations in KwaZulu-Natal, South Africa. South Afr. Geogr. J. 2008, 90:22-31.
22. Johnson B. Effects of pansharpening on vegetation indices. ISPRS Int. J. Geo-Inf. 2014, 3:507-522.
23. Loch A.D., Floyd R.B. Insect pests of Tasmanian blue gum, Eucalyptus globulus globulus, in south-western Australia: history, current perspectives and future prospects. Austral Ecol. 2001, 26:458-466.
24. Loch A.D., Matsuki M. Effects of defoliation by Eucalyptus weevil, Gonipterus scutellatus, and chrysomelid beetles on growth of Eucalyptus globulus in southwestern Australia. For. Ecol. Manage. 2010, 260:1324-1332.
25. Lottering R., Mutanga O. Estimating the road edge effect on adjacent Eucalyptus grandis forests in KwaZulu-Natal, South Africa, using texture measures and an artificial neural network. J. Spat. Sci. 2012, 57:153-173.
26. Lottering R., Mutanga O. Optimizing the spatial resolution of WorldView-2 imagery for discriminating forest vegetation at subspecies level in KwaZulu-Natal, South Africa. Geocarto Int. 2015, 1-19.
27. Marceau D.J., Gratton D.J., Fournier R.A., Fortin J.-P. Remote sensing and the measurement of geographical entities in a forested environment. 2. The optimal spatial resolution. Remote Sens. Environ. 1994, 49:105-117.
28. McCloy K., Bøcher P. Optimizing image resolution to maximize the accuracy of hard classification. Photogramm. Eng. Remote Sens. 2007, 73:893-903.
29. McGrew J.C., Monroe C.B. An Introduction to Statistical Problem Solving in Geography 2000, McGraw-Hill, Boston.
30. Menges C.H., Hill G.J.E., Ahmad W. Use of airborne video data for the characterization of tropical savannas in northern Australia: the optimal spatial resolution for remote sensing applications. Int. J. Remote Sens. 2001, 22:727-740.
31. Mutanga O., Skidmore A.K. Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa. Remote Sens. Environ. 2004, 90:104-115.
32. Mutanga O., Skidmore A.K. Narrow band vegetation indices overcome the saturation problem in biomass estimation. Int. J. Remote Sens. 2004, 25:3999-4014.
33. Nijland W., Addink E.A., De Jong S.M., Van der Meer F.D. Optimizing spatial image support for quantitative mapping of natural vegetation. Remote Sens. Environ. 2009, 113:771-780.
34. Openshaw S. The Modifiable Areal Unit Problem 1984, Geo Books.
35. Oumar Z., Mutanga O. Using WorldView-2 bands and indices to predict bronze bug (Thaumastocoris peregrinus) damage in plantation forests. Int. J. Remote Sens. 2012, 34:2236-2249.
36. Oumar Z., Mutanga O., Ismail R. Predicting Thaumastocoris peregrinus damage using narrow band normalized indices and hyperspectral indices using field spectra resampled to the Hyperion sensor. Int. J. Appl. Earth Obs. Geoinf. 2013, 21:113-121.
37. Richardson K.F., Meakins R.H. Inter- and Intra- specific variation in the susceptibility of eucalypts to the snout beetle Gonipterus scutellatus Gyll. (Coleoptera: Curculionidae). South Afr. For. J. 1986, 139:21-31.
38. Spruce J.P., Sader S., Ryan R.E., Smoot J., Kuper P., Ross K., Prados D., Russell J., Gasser G., McKellip R., Hargrove W. Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sens. Environ. 2011, 115:427-437.
39. Tooke F.G.C. The Eucalyptus Snout-beetle, Gonipterus Scutellatus Gyll: A Study of Its Ecology and Control by Biological Means 1955, The Government Printer.
40. Tran T.D.-B., Puissant A., Badariotti D., Weber C. Optimizing spatial resolution of imagery for urban form detection-the cases of France and Vietnam. Remote Sens. 2011, 3:2128-2147.
41. Woodcock C.E., Strahler A.H. The factor of scale in remote sensing. Remote Sens. Environ. 1987, 21:311-332.
42. Wulder M. Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Prog. Phys. Geogr. 1998, 22:449-476.
43. Yuhendra, Alimuddin I., Sumantyo J.T.S., Kuze H. Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data. Int. J. Appl. Earth Obs. Geoinf. 2012, 18:165-175.