Detecting and mapping Gonipterus scutellatus induced vegetation defoliation using WorldView-2 pan-sharpened image texture combinations and an artificial neural network

Abstract. Defoliation induced by the weevil Gonipterus scutellatus is causing significant damage to South Africa’s eucalyptus plantations. Therefore, the ability of remote sensing to detect and map G. scutellatus defoliation is essential for monitoring the spread of the weevil so that precautionary measures are set in place. In our study, an integrated approach using image texture in various processing combinations and an artificial neural network (ANN) were developed to detect and map G. scutellatus induced vegetation defoliation. A 0.5-m WorldView-2 pan-sharpened image was used to compute texture parameters from the gray-level occurrence matrix and gray-level co-occurrence matrix, using optimal moving windows for specific levels of G. scutellatus induced vegetation defoliation. In order to improve the accuracy of detecting and mapping G. scutellatus induced vegetation defoliation, a method involving a three-band texture processing combination was proposed and tested. Using a sequential forward selection algorithm allowed for the selection of optimal texture combinations, which were subsequently input into a backpropagation ANN. The results showed an improvement in detecting vegetation defoliation using single texture bands [R2  =  0.82, root mean square error (RMSE) = 0.95 (1.82% of the mean measured defoliation)] when compared to single spectral reflectance bands [R2  =  0.60, RMSE = 1.79 (3.43% of the mean measured defoliation)], two-band spectral reflectance combination model [R2  =  0.74, RMSE = 1.48 (2.83% of the mean measured defoliation)], and the three-band spectral reflectance combination model [R2  =  0.80, RMSE = 1.35 (2.59% of the mean measured defoliation)]. Further improvements were obtained using the two-band texture combination model [R2  =  0.85, RMSE = 1.05 (2.01% of the mean measured defoliation)] and the most promising result was obtained using the proposed three-band texture combination model [R2  =  0.90, RMSE = 0.85 (1.63% of the mean measured defoliation)]. Overall, our study highlights the potential of image texture combinations in improving the detection and mapping of vegetation defoliation.

[1]  O. Mutanga,et al.  Estimating the road edge effect on adjacent Eucalyptus grandis forests in KwaZulu-Natal, South Africa, using texture measures and an artificial neural network , 2012 .

[2]  Onisimo Mutanga,et al.  Predicting Thaumastocoris peregrinus damage using narrow band normalized indices and hyperspectral indices using field spectra resampled to the Hyperion sensor , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[3]  Amanda Huerta-Fuentes,et al.  Susceptibility of eucalyptus species to Gonipterus scutellatus and electrophoretic profiles of adult marker proteins , 2008 .

[4]  R. Hall,et al.  Incorporating texture into classification of forest species composition from airborne multispectral images , 2000 .

[5]  F. Tooke,et al.  The Eucalyptus Snout.beetle, Gonipterus scutellatus Gyll. A Study of its Ecology and Control by biological Means. , 1955 .

[6]  W. Cohen,et al.  Aerial and satellite sensor detection and classification of western spruce budworm defoliation in a subalpine forest , 1995 .

[7]  G. D. Tribe.  The present status of Anaphes nitens (Hymenoptera: Mymaridae), an egg parasitoid of the Eucalyptus snout beetle Gonipterus scutellatus, in the Western Cape Province of South Africa , 2005 .

[8]  Onisimo Mutanga,et al.  Optimising the spatial resolution of WorldView-2 pan-sharpened imagery for predicting levels of Gonipterus scutellatus defoliation in KwaZulu-Natal, South Africa , 2016 .

[9]  Ana Reis,et al.  Efficiency of biological control of Gonipterus platensis (Coleoptera: Curculionidae) by Anaphes nitens (Hymenoptera: Mymaridae) in cold areas of the Iberian Peninsula: implications for defoliation and wood production in Eucalyptus globulus , 2012 .

[10]  S. Franklin,et al.  Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia , 2001 .

[11]  M. Matsuki,et al.  Effects of defoliation by Eucalyptus weevil, Gonipterus scutellatus, and chrysomelid beetles on growth of Eucalyptus globulus in southwestern Australia , 2010 .

[12]  Philip A. Townsend,et al.  Estimating the effect of gypsy moth defoliation using MODIS , 2008 .

[13]  Ranga B. Myneni,et al.  The interpretation of spectral vegetation indexes , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Janet E. Nichol,et al.  Improved Biomass Estimation Using the Texture Parameters of Two High-Resolution Optical Sensors , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[15]  C. W. Mally,et al.  The eucalyptus snout beetle (Gonipterus scutellatus Gyll.). , 1924 .

[16]  David A. Clausi,et al.  Preserving boundaries for image texture segmentation using grey level co-occurring probabilities , 2006, Pattern Recognit..

[17]  R. Meakins,et al.  Inter- and intra-specific variation in the susceptibility of eucalypts to the snout beetle Gonipterus scutellatus Gyll. (Coleoptera: Curculionidae). , 1986 .

[18]  Steven E. Franklin,et al.  Discrimination of adelgid-damage on single balsam fir trees with aerial remote sensing data , 1995 .

[19]  Ryan R. Jensen,et al.  Using remote sensing image texture to study habitat use patterns: a case study using the polymorphic white‐throated sparrow (Zonotrichia albicollis) , 2006 .

[20]  John J. A. Ingram,et al.  Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks , 2005 .

[21]  J. Huber,et al.  Redescription of anaphes nitens (girault) and description of two new species of anaphes haliday (hymenoptera: mymaridae), parasites of gonipterus scutellatus gyllenhal (coleoptera: curculionidae) in tasmania1 , 1990 .

[22]  B. Turner,et al.  Performance of a neural network: mapping forests using GIS and remotely sensed data , 1997 .

[23]  Weixing Cao,et al.  Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat , 2012 .

[24]  Yilmaz Kaya,et al.  Evaluation of texture features for automatic detecting butterfly species using extreme learning machine , 2014, J. Exp. Theor. Artif. Intell..

[25]  Kenton W. Ross,et al.  Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks , 2011 .

[26]  Michal Strzelecki,et al.  Texture Analysis Methods - A Review , 1998 .

[27]  Steven E. Franklin,et al.  Relationship between Landsat TM and SPOT vegetation indices and cumulative spruce budworm defoliation , 2008 .

[28]  O. Mutanga,et al.  Detecting and mapping levels of Gonipterus scutellatus-induced vegetation defoliation and leaf area index using spatially optimized vegetation indices , 2018 .

[29]  Matti Pietikäinen,et al.  Performance evaluation of texture measures with classification based on Kullback discrimination of distributions , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[30]  O. Mutanga,et al.  Using WorldView-2 bands and indices to predict bronze bug (Thaumastocoris peregrinus) damage in plantation forests , 2013 .

[31]  Marcus J. Byrne,et al.  The host range of the Eucalyptus Weevil, Gonipterus “scutellatus” Gyllenhal (Coleoptera: Curculionidae), in South Africa , 2011, Annals of Forest Science.

[32]  A. Skidmore,et al.  Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa , 2004 .

[33]  Lars Eklundh,et al.  Mapping insect defoliation in Scots pine with MODIS time-series data , 2009 .

[34]  J. Pulliainen,et al.  Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data , 2002 .

[35]  Anthony R. Clarke,et al.  Gonipterus scutellatus Gyllenhal (Coleoptera: Curculionidae) oviposition on seven naturally co-occurring Eucalyptus species , 1998 .

[36]  Steven E. Franklin,et al.  Relationship between airborne multispectral image texture and aspen defoliation , 2004 .

[37]  Andrew K. Skidmore,et al.  Mapping beech (Fagus sylvatica L.) forest structure with airborne hyperspectral imagery , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[38]  Onisimo Mutanga,et al.  Discriminating Sirex noctilio Attack in Pine Forest Plantations in South Africa Using High Spectral Resolution Data , 2008 .

[39]  Brad M. Potts,et al.  Eucalypt hybrid susceptibility to Gonipterus scutellatus (Coleoptera: Curculionidae) , 2003 .

[40]  Michael A. Wulder,et al.  Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters , 1998 .

[41]  Doug King,et al.  Sugar maple decline assessment based on spectral and textural analysis of multispectral aerial videography , 1991 .

[42]  Michael J. Wingfield,et al.  A new lepidopteran insect pest discovered on commercially grown Eucalyptus nitens in South Africa , 2005 .

[43]  Ruiliang Pu,et al.  Crown closure estimation of oak savannah in a dry season with Landsat TM imagery: Comparison of various indices through correlation analysis , 2003 .

[44]  Jan A. N. van Aardt,et al.  Extracting structural attributes from IKONOS imagery for Eucalyptus plantation forests in KwaZulu-Natal, South Africa, using image texture analysis and artificial neural networks , 2011 .

[45]  P. Defourny,et al.  Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery , 2006 .

[46]  Joachim M. Buhmann,et al.  Empirical evaluation of dissimilarity measures for color and texture , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[47]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[48]  A. Loch,et al.  Parasitism of the Eucalyptus weevil, Gonipterus scutellatus Gyllenhal, by the egg parasitoid, Anaphes nitens Girault, in Eucalyptus globulus plantations in southwestern Australia , 2008 .

[49]  A. Loch,et al.  Insect pests of Tasmanian blue gum, Eucalyptus globulus globulus, in south-western Australia: History, current perspectives and future prospects , 2001 .

[50]  Onisimo Mutanga,et al.  Forest health and vitality: the detection and monitoring of Pinus patula trees infected by Sirex noctilio using digital multispectral imagery , 2007 .

[51]  Volker C. Radeloff,et al.  High-resolution image texture as a predictor of bird species richness , 2006 .