Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification Export

@article{citeulike:1860238, abstract = {Terrestrial Ecosystem Mapping provides critical information to land and resource managers by incorporating information on climate, physiography, surficial material, soil, and vegetation structure. The main objective of this research was to determine the capacity of high spatial resolution satellite image data to discriminate vegetation structural stages in riparian and adjacent forested ecosystems as defined using the British Columbia Terrestrial Ecosystem Mapping (TEM) scheme. A high spatial resolution QuickBird image, captured in June 2005, and coincident field data covering the riparian area of Lost Shoe Creek and adjacent forests on Vancouver Island, British Columbia, was used in this analysis. Semi-variograms were calculated to assess the separability of vegetation structural stages and assess which spatial scales were most appropriate for calculation of grey-level co-occurrence texture measures to maximize structural class separation. The degree of spatial autocorrelation showed that most vegetation structural types in the TEM scheme could be differentiated and that window sizes of 3 x 3 pixels and 11 x 11 pixels were most appropriate for image texture calculations. Using these window sizes, the texture analysis showed that co-occurrence contrast, dissimilarity, and homogeneity texture measures, based on the bands in the visible part of the spectrum, provided the most significant statistical differentiation between vegetation structural classes. Subsequently, an object-oriented classification algorithm was applied to spectral and textural transformations of the QuickBird image data to map the vegetation structural classes. Using both spectral and textural image bands yielded the highest classification accuracy (overall accuracy = 78.95\%). The inclusion of image texture increased the classification accuracies of vegetation structure by 2-19\%. The results show that information on vegetation structure can be mapped effectively from high spatial resolution satellite image data, providing an additional tool to ongoing aerial photograph interpretation.}, author = {Johansen, Kasper and Coops, Nicholas C. and Gergel, Sarah E. and Stange, Yulia}, citeulike-article-id = {1870247}, citeulike-linkout-0 = {http://www.sciencedirect.com/science/article/B6V6V-4NCK2CB-1/2/4de7902dcd2e2aba6dce87e007a2e0cf}, doi = {10.1016/j.rse.2007.02.014}, journal = {Remote Sensing of Environment}, keywords = {bibtex-import, classification, forestry, highres, lib\_ecohydrology, lib\_landscape, lib\_obia, lib\_spatial, quickbird, riparian}, month = {September}, number = {1}, pages = {29--44}, posted-at = {2007-11-05 21:23:05}, priority = {2}, title = {Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification}, url = {10.1016/j.rse.2007.02.014}, volume = {110}, year = {2007} }

TY - JOUR ID - citeulike:1860238 L3 - citeulike-article-id:1870247 N2 - Terrestrial Ecosystem Mapping provides critical information to land and resource managers by incorporating information on climate, physiography, surficial material, soil, and vegetation structure. The main objective of this research was to determine the capacity of high spatial resolution satellite image data to discriminate vegetation structural stages in riparian and adjacent forested ecosystems as defined using the British Columbia Terrestrial Ecosystem Mapping (TEM) scheme. A high spatial resolution QuickBird image, captured in June 2005, and coincident field data covering the riparian area of Lost Shoe Creek and adjacent forests on Vancouver Island, British Columbia, was used in this analysis. Semi-variograms were calculated to assess the separability of vegetation structural stages and assess which spatial scales were most appropriate for calculation of grey-level co-occurrence texture measures to maximize structural class separation. The degree of spatial autocorrelation showed that most vegetation structural types in the TEM scheme could be differentiated and that window sizes of 3 x 3 pixels and 11 x 11 pixels were most appropriate for image texture calculations. Using these window sizes, the texture analysis showed that co-occurrence contrast, dissimilarity, and homogeneity texture measures, based on the bands in the visible part of the spectrum, provided the most significant statistical differentiation between vegetation structural classes. Subsequently, an object-oriented classification algorithm was applied to spectral and textural transformations of the QuickBird image data to map the vegetation structural classes. Using both spectral and textural image bands yielded the highest classification accuracy (overall accuracy = 78.95%). The inclusion of image texture increased the classification accuracies of vegetation structure by 2-19%. The results show that information on vegetation structure can be mapped effectively from high spatial resolution satellite image data, providing an additional tool to ongoing aerial photograph interpretation. IS - 1 JF - Remote Sensing of Environment EP - 44 TI - Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification VL - 110 SP - 29 KW - bibtex-import KW - classification KW - forestry KW - highres KW - lib_ecohydrology KW - lib_landscape KW - lib_obia KW - lib_spatial KW - quickbird KW - riparian AU - Johansen, Kasper AU - Coops, Nicholas AU - Gergel, Sarah AU - Stange, Yulia PY - 2007/September// UR - http://www.sciencedirect.com/science/article/B6V6V-4NCK2CB-1/2/4de7902dcd2e2aba6dce87e007a2e0cf ER -