Implications of patterns of carbon pools and fluxes across a semiarid environmental gradient

Landscape scale environmental gradients present variable spatial patterns and ecological processes caused by climate, topography and soil characteristics and, as such, offer candidate sites to study environmental change. Data are presented on the spatial pattern of dominant species, biomass, and carbon pools and the temporal pattern of fluxes across a transitional zone shifting from Great Basin Desert scrub, up through pinyon-juniper woodlands and into ponderosa pine forest and the ecotones between each vegetation type. The mean annual temperature (MAT) difference across the gradient is approximately 3 degrees C from bottom to top (MAT 8.5-5.5) and annual precipitation averages from 320 to 530 mm/yr, respectively. The stems of the dominant woody vegetation approach a random spatial pattern across the entire gradient, while the canopy cover shows a clustered pattern. The size of the clusters increases with elevation according to available soil moisture which in turn affects available nutrient resources. The total density of woody species declines with increasing soil moisture along the gl-adient, but total biomass increases. Belowground carbon and nutrient pools change from a heterogenous to a homogenous distribution on either side of the woodlands. Although temperature controls the: seasonal patterns of carbon efflux from the soils, soil moisture appears to be the primary driving variable, but response differs underneath the different dominant species, Similarly, decomposition of dominant litter occurs faster-at the cooler and more moist sites, but differs within sites due to litter quality of the different species. The spatial pattern of these communities provides information on the direction of future changes, The ecological processes that we documented are not statistically different in the ecotones as compared to the: adjoining communities, but are different at sites above the woodland than those below the woodland. We speculate that an increase in MAT will have a major impact on C pools and C sequestering and release processes in these semiarid landscapes. However, the impact will be primarily related to moisture availability rather than direct effects of an increase in temperature. (C) 1998 Elsevier Science B.V.

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors

The use of appropriate features to represent an output class or object is critical for all classification problems. In this paper, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of image-objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSF) of a pulse coupled neural network (PCNN), which is invariant to rotation, translation and small scale changes. The proposed method is first evaluated in a rotation and scale invariant texture classification using USC-SIPI texture database. It is further evaluated in an application of vegetation species classification in power line corridor monitoring using airborne multi-spectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective to represent spectral-texture patterns of objects and it shows better results than classic color histogram and texture features.

Quantitative electroencephalographic comodulation : an investigation of patterns in chronic fatigue syndrome

Introduction. This is a pilot study of quantitative electro-encephalographic (QEEG) comodulation analysis, which is used to assist in identifying regional brain differences in those people suffering from chronic fatigue syndrome (CFS) compared to a normative database. The QEEG comodulation analysis examines spatial-temporal cross-correlation of spectral estimates in the resting dominant frequency band. A pattern shown by Sterman and Kaiser (2001) and referred to as the anterior posterior dissociation (APD) discloses a significant reduction in shared functional modulation between frontal and centro-parietal areas of the cortex. This research attempts to examine whether this pattern is evident in CFS. Method. Eleven adult participants, diagnosed by a physician as having CFS, were involved in QEEG data collection. Nineteen-channel cap recordings were made in five conditions: eyes-closed baseline, eyes-open, reading task one, math computations task two, and a second eyes-closed baseline. Results. Four of the 11 participants showed an anterior posterior dissociation pattern for the eyes-closed resting dominant frequency. However, seven of the 11 participants did not show this pattern. Examination of the mean 8-12 Hz amplitudes across three cortical regions (frontal, central and parietal) indicated a trend of higher overall alpha levels in the parietal region in CFS patients who showed the APD pattern compared to those who did not have this pattern. All patients showing the pattern were free of medication, while 71% of those absent of the pattern were using antidepressant medications. Conclusions. Although the sample is small, it is suggested that this method of evaluating the disorder holds promise. The fact that this pattern was not consistently represented in the CFS sample could be explained by the possibility of subtypes of CFS, or perhaps co-morbid conditions. Further, the use of antidepressant medications may mask the pattern by altering the temporal characteristics of the EEG. The results of this pilot study indicate that further research is warranted to verify that the pattern holds across the wider population of CFS sufferers.