Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data

Meaningful relationships between forest structure attributes measured in representative field plots on the ground and remotely sensed data measured comprehensively across the same forested landscape facilitate the production of maps of forest attributes such as basal area (BA) and tree density (TD). Because imputation methods can efficiently predict multiple response variables simultaneously, they may be usefully applied to map several structural attributes at the species-level. We compared several approaches for imputing the response variables BA and TD, aggregated at the plot-scale and species-level, from topographic and canopy structure predictor variables derived from discrete-return airborne LiDAR data. The predictor and response variables were associated using imputation techniques based on normalized and unnormalized Euclidean distance, Mahalanobis distance, Independent Component Analysis (ICA), Canonical Correlation Analysis (aka Most Similar Neighbor, or MSN), Canonical Correspondence Analysis (aka Gradient Nearest Neighbor, or GNN), and Random Forest (RF). To compare and evaluate these approaches, we computed a scaled Root Mean Square Distance (RMSD) between observed and imputed plot-level BA and TD for 11 conifer species sampled in north-central Idaho. We found that RF produced the best results overall, especially after reducing the number of response variables to the most important species in each plot with regard to BA and TD. We concluded that RF was the most robust and flexible among the imputation methods we tested. We also concluded that canopy structure and topographic metrics derived from LiDAR surveys can be very useful for species-level imputation.     

Symmetry conditions of Boolean functions in complex Hadamardtransform

A method to identify symmetries in Boolean functions using a complex Hadamard transform is proposed. It is shown that only the half spectrum is needed to detect symmetries in Boolean functions     

Studies on a long-term use of rapeseed products in diets for boars. Pathomorphological changes in the reproductive system, liver and thyroid gland

Three feeding groups were used: the control (SOY) was fed diets without rapeseed products, and the two experimental groups were fed with either 10% rapeseed meal (RSM) or with 12% OO rape seeds (PFRS). Half of the boars from each group were slaughtered after 1 or 2 years. In RSM and PFRS boars steroid-3-beta-ol-dehydrogenase activity was high, whilst Leydig cells were not numerous after 1 year. Degeneration and necrosis of seminiferous epithelium resulting in atrophy of seminiferous tubules appeared in RSM boars after 2 years. In the PFRS group the lesions were stronger and proliferation of Leydig cells with high steroid-3-beta-ol-dehydrogenase activity was observed. In 1-year-old RSM and PFRS boars there were foci of necrosis in the epididymal epithelium. Thyroid weight in RSM boars and liver weight in PFRS boars were distinctly higher only during the first year. In these thyroid glands flattening of glandular epithelium and enlargement of colloid masses were observed, while in the livers, parenchymatic degeneration and structural transformation appeared. Testis weight increased after 2 years in RSM and PFRS boars; however, this had little effect on semen production.     

Efficient calculation of Gray code-ordered Walsh spectra throughalgebraic decision diagrams

An extension of the symbolic computation for the calculation of non-Kronecker based transforms is presented. The formulation is illustrated by calculation of a Gray code-ordered Walsh transform. To further exploit the power of symbolic algorithms, a new operator on matrices called the pseudo-Kronecker product is introduced     

Complex decision diagrams

Complex decision diagrams to represent integer-valued functions in the form of complex Hadamard transforms and spectra are introduced. With the distinctive and unique properties of the transform, the novel complex decision diagrams could be further simplified by reduction rules and a half-spectra theorem that will lead to a more compact representation     

Classification of Landsat images based on spectral and topographic variables for land-cover change detection in Zagros forests

Detection of land-cover changes through time can be complicated because of sensor-specific differences in spatial and spectral resolutions; classified land-cover changes can be due to either real changes on the ground or a switch in sensors used to collect data. This study focused on two objectives: (1) selecting the best predictor variables for the classification of semi-arid Zagros forests given the characteristics of the study area and available data sets and (2) evaluating the application of the random forest (RF) algorithm as a unified technique for the classification of data sets acquired from different sensors. Three images of the same study area were acquired from the Landsat-5 Thematic Mapper (TM) sensor in 2009, the Landsat-7 Enhanced Thematic Mapper (ETM+) sensor with Scan Line Corrector (SLC) in 1999 and the Landsat-2 Multispectral Scanner (MSS) sensor in 1975. Following image preprocessing, the RF algorithm was applied for variable selection and classification. A test of equivalence was used to compare the overall accuracy of the classified maps from the three sensors. Slope, normalized difference vegetation index (NDVI) and elevation were determined to be the most important predictor variables for all three images. High overall classification accuracies were achieved for all three images (97.90% for MSS, 95.43% for TM and 95.29% for ETM). The ETM- and TM-derived maps had equivalent overall accuracy and even significantly higher overall accuracy was obtained for the MSS-derived map. The post-classification comparison showed an increase in agriculture and a decrease in forest cover. The selected predictor variables were consistent with ecological reality and showed more details on the changes of the land-cover classes across biophysical variables of the study area through time.     

Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability

The lack of maps depicting forest three-dimensional structure, particularly as pertaining to snags and understory shrub species distribution, is a major limitation for managing wildlife habitat in forests. Developing new techniques to remotely map snags and understory shrubs is therefore an important need. To address this, we first evaluated the use of LiDAR data for mapping the presence/absence of understory shrub species and different snag diameter classes important for birds (i.e. ≥ 15 cm, ≥ 25 cm and ≥ 30 cm) in a 30,000 ha mixed-conifer forest in Northern Idaho (USA). We used forest inventory plots, LiDAR-derived metrics, and the Random Forest algorithm to achieve classification accuracies of 83% for the understory shrubs and 86% to 88% for the different snag diameter classes. Second, we evaluated the use of LiDAR data for mapping wildlife habitat suitability using four avian species (one flycatcher and three woodpeckers) as case studies. For this, we integrated LiDAR-derived products of forest structure with available models of habitat suitability to derive a variety of species-habitat associations (and therefore habitat suitability patterns) across the study area. We found that the value of LiDAR resided in the ability to quantify 1) ecological variables that are known to influence the distribution of understory vegetation and snags, such as canopy cover, topography, and forest succession, and 2) direct structural metrics that indicate or suggest the presence of shrubs and snags, such as the percent of vegetation returns in the lower strata of the canopy (for the shrubs) and the vertical heterogeneity of the forest canopy (for the snags). When applied to wildlife habitat assessment, these new LiDAR-based maps refined habitat predictions in ways not previously attainable using other remote sensing technologies. This study highlights new value of LiDAR in characterizing key forest structure components important for wildlife, and warrants further applications to other forested environments and wildlife species.     

Electromodulation of monomer and excimer phosphorescence in vacuum-evaporated films of platinum (II) complexes of 1,3-di(2-pyridyl)benzenes

Electric field-modulated photoluminescence (EML) measurements are presented for vacuum-evaporated films of cyclometallated Pt (II) complexes of 1,3-di(2-pyridyl) benzenes used as triplet emitters in organic light-emitting diodes (OLEDs). The excimer phosphorescence is quenched by the external electric field of 2.5 MV/cm up to 25% but the same effect on monomer phosphorescence is one order of magnitude smaller. The higher quenching effect for triplet excimers than triplet monomers in solid films of Pt complexes is rationalized assuming excimers to be populated within excimer-active domains of the films through an intermediate stage of geminate (e–h) pairs derived from dissociated monomer excitons. The EML data for excimers are successfully described in the framework of Sano–Tachiya–Noolandi–Hong (STNH) theory of geminate (e–h) pair recombination where the final recombination step (e–h capture) proceeds on a sphere of finite radius (a) with a finite speed. The conventional Onsager theory (a = 0) is sufficient to explain the EML quenching effect for monomers. The results are important for explaining the decrease of electroluminescence quantum efficiency observed in OLEDs working under high electric fields.     

Metal-Organic Frameworks as Single-Site Solid Catalysts for Asymmetric Reactions

This review documents the evolution of chiral MOF catalysts from simple Lewis acid and Lewis base catalysts that afford limited selectivity to more recent advancements that exhibit high stereo- and regioselectivities. MOF-based catalysts provide unprecedented opportunities for interrogating structure-activity relationships at high structural precision and for controlling catalytic activity by using external stimuli. The most notable disadvantage of MOF-based catalysts has been the inherent instability of the materials. This hurdle, which effectively limits the reaction scope of MOF catalysts, is slowly being overcome by the advent of new binding moieties and secondary building units.