The use of digitized aerial photographs and local operation for classification of stand development classes

Abstract

The increasing capacity of modern computers has created the opportunity to routinely process the very large data sets derived by digitizing aerial photographs. The very fine resolution of such data sets makes them better suited than satellite imagery for some applications; however, there may be problems in implementation resulting from variation in radial distortion and illumination across an aerial photograph. We investigated the feasibility of using local operators (e.g., non-overlapping moving window means and standard deviations) as auxiliary data for generating stand development classes via three steps: (i) derive 6 local operators intended to represent texture for a 16 by 16 m window corresponding to a forest inventory sampling unit, (ii) apply a calibration process (e.g., accounting for location relative to a photo's principal point and solar position) to these local operators, and (iii) apply the calibrated local operators to classify the forest for stand development. Results indicate that calibrated local operators significantly improve the classification compared to what is possible using uncalibrated local operators and satellite images.

Assessment of manual and automated methods for updating stand-level forest inventories based on aerial photography

“…The most commonly used are statistical variation measures, including first order statistics, like variance of pixel values (e.g. Wang et al 1998) or a variogram (e.g. St-Onge and Cavayas 1995, Lévesque and King 1999), and second order statistics, like spatial grey-level dependence (Haralick et al 1973).…”

“…Inclusion of textural features has usually improved estimation and classification accuracy when using airborne data (e.g. Franklin and McDermid 1993, Wang et al 1998, Wulder et al 1998, Franklin et al 2000, Franklin et al 2001a, II, Tuominen and Pekkarinen 2005.…”

Error and Uncertainty Analysis for Ecological Modeling and Simulation

“…Texture analysis is widely used in image processing, classification, and mapping, and varies depending on different measure indices such as variance, standard deviation (Holopainen and Wang, 1998), and Haralick textures (Haralick et al, 1973), etc. Similar to local variance, the spatial variability of image data in terms of textures varies with spatial resolution.…”

“…The widely used methods for mapping are supervised and unsupervised classification or stratification, and methods that integrate both of the previous methods (Campbell, 1996;Holopainen and Wang, 1998;Lillesand and Kiefer, 2000;Wang, 1996;Wang et al, 1998). These methods result in homogeneous polygons or strata of pixels and, therefore, smoothing of estimates and the disappearance of spatial heterogeneity.…”

“…Recent developments include spectral mixing analysis, uses of hyper-spectral remote sensing and fine resolution images (Campbell, 1996), and data fusion from different sensors (Wang, et al, 1998). However, real breakthroughs in methodology and accuracy have not been realized.…”