Relationship between airborne multispectral image texture and aspen defoliation

A Compact Airborne Spectrographic Imager (CASI) multiresolution dataset, comprised of imagery with spatial resolutions of 60 cm, 1 m and 2 m, was used to asses the relationship between defoliation severity of aspen (Populus tremuloides Michx.) stands infested with the Bruce spanworm (Operophtera bruceata), the Leaf Area Index (LAI) of these stands and the CASI image components comprising of Normalized Difference Vegetation Index (NDVI) and image texture. The study area was located in the foothills of the Alberta Canadian Rockies. Defoliation severity, LAI, and crown closure of the stands were measured on the ground. Multiple stepwise regression methods were used to develop relationships between the field and imagery data. Image texture derived from the grey-level co-occurrence matrix of the first principle component, or ‘brightness' image, was incorporated into the discriminant analysis of defoliation severity classes. The highest spatial resolution imagery outperformed the coarser image resolutions. The characteristics of the defoliation changed the spectral response of the moderately and severely defoliated stands considerably when compared to healthy stands. The following paper demonstrates that aspen defoliation severity can be detected with CASI image data, specifically through the incorporation of imagery spatial component captured by image texture.

[1]  S. Franklin,et al.  High Spatial Resolution Optical Image Texture for Improved Estimation of Forest Stand Leaf Area Index , 1996 .

[2]  J. Innes Forest decline , 1992 .

[3]  E. Hogg,et al.  Tree-ring analysis of declining aspen stands in west-central Saskatchewan , 1999 .

[4]  Richard A. Fournier,et al.  A catalogue of potential spatial discriminators for high spatial resolution digital images of individual crowns , 1995 .

[5]  P. G. Jarvis,et al.  Productivity of temperate de-ciduous and evergreen forests , 1983 .

[6]  D. Bartos,et al.  Decline of quaking aspen in the Interior West - examples from Utah , 1998 .

[7]  M. S. Moran,et al.  Reflectance- and radiance-based methods for the in-flight absolute calibration of multispectral sensors , 1987 .

[8]  H. Hyppänen,et al.  Spatial autocorrelation and optimal spatial resolution of optical remote sensing data in boreal forest environment , 1996 .

[9]  W. Ives,et al.  Tree and Shrub Insects of the Prairie Provinces , 2002 .

[10]  S. Franklin,et al.  Aerial Image Texture Information in the Estimation of Northern Deciduous and Mixed Wood Forest Leaf Area Index (LAI) , 1998 .

[11]  Nita Bhagia,et al.  Monitoring Cropping Pattern Changes Using Multi‐temporal WiFS Data , 2002 .

[12]  Donald G. Leckie,et al.  Analysis of high resolution multispectral MEIS imagery for spruce budworm damage assessment on a single tree basis , 1992 .

[13]  J. H. Archibald,et al.  Field Guide to Ecosites of Southwestern Alberta , 2002 .

[14]  J. Brandt Forest insect and disease caused impacts to timber resources of west-central Canada: 1988-1992 , 1995 .

[15]  E. B. Peterson,et al.  Ecology, management, and use of aspen and balsam poplar in the Prairie Provinces, Canada , 1992 .

[16]  B. A. Wilson,et al.  Spectral Reflectance Characteristics of Dutch Elm Disease , 1998 .

[17]  R. Fournier,et al.  Remote sensing and the measurement of geographical entities in a forested environment. 2. The optimal spatial resolution , 1994 .

[18]  Steven E. Franklin,et al.  Multi‐layer Forest Stand Discrimination with Spatial Co‐occurrence Texture Analysis of High Spatial Detail Airborne Imagery , 2002 .

[19]  S. Running,et al.  Numerical Terradynamic Simulation Group 12-1988 Rapid Estimation of Coniferous Forest Leaf Area Index Using a Portable Integrating Radiometer , 2018 .

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

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

[22]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

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

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

[25]  Ronald J. Hall,et al.  Variability of Landsat Thematic Mapper data in boreal deciduous and mixed wood stands with conifer understory , 1995 .

[26]  F. J. Ahern,et al.  Progress toward improving aerial defoliation survey methods by using electronic imagers , 1991 .

[27]  S. Franklin Remote Sensing for Sustainable Forest Management , 2001 .