Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

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Climate Change 2007: The Physical Science Basis.

Evolution of Protein Molecules

The Nature of Remote Sensing: Introduction. Remote Sensing. Information Extraction from Remote-Sensing Images. Spectral Factors in Remote Sensing. Spectral Signatures. Remote-Sensing Systems. Optical Sensors. Temporal Characteristics. Image Display Systems. Data Systems. Summary. Exercises. References. Optical Radiation Models: Introduction. Visible to Short Wave Infrared Region. Solar Radiation. Radiation Components. Surface-Reflected. Unscattered Component. Surface-Reflected. Atmosphere-Scattered Component. Path-Scattered Component. Total At-Sensor. Solar Radiance. Image Examples in the Solar Region. Terrain Shading. Shadowing. Atmospheric Correction. Midwave to Thermal Infrared Region. Thermal Radiation. Radiation Components. Surface-Emitted Component. Surface-Reflected. Atmosphere-Emitted Component. Path-Emitted Component. Total At-Sensor. Emitted Radiance. Total Solar and Thermal Upwelling Radiance. Image Examples in the Thermal Region. Summary. Exercises. References. Sensor Models: Introduction. Overall Sensor Model. Resolution. The Instrument Response. Spatial Resolution. Spectral Resolution. Spectral Response. Spatial Response. Optical PSFopt. Image Motion PSFIM. Detector PSFdet. Electronics PSFel. Net PSFnet. Comparison of Sensor PSFs. PSF Summary for TM. Imaging System Simulation. Amplification. Sampling and Quantization. Simplified Sensor Model. Geometric Distortion. Orbit Models. Platform Attitude Models. Scanner Models. Earth Model. Line and Whiskbroom ScanGeometry. Pushbroom Scan Geometry. Topographic Distortion. Summary. Exercises. References. Data Models: Introduction. A Word on Notation. Univariate Image Statistics. Histogram. Normal Distribution. Cumulative Histogram. Statistical Parameters. Multivariate Image Statistics. Reduction to Univariate Statistics. Noise Models. Statistical Measures of Image Quality. Contrast. Modulation. Signal-to-Noise Ratio (SNR). Noise Equivalent Signal. Spatial Statistics. Visualization of Spatial Covariance. Covariance with Semivariogram. Separability and Anisotropy. Power Spectral Density. Co-occurrence Matrix. Fractal Geometry. Topographic and Sensor Effects. Topography and Spectral Statistics. Sensor Characteristics and Spectral Stastistics. Sensor Characteristics and Spectral Scattergrams. Summary. Exercises. References. Spectral Transforms: Introduction. Feature Space. Multispectral Ratios. Vegetation Indexes. Image Examples. Principal Components. Standardized Principal Components (SPC) Transform. Maximum Noise Fraction (MNF) Transform. Tasseled Cap Tranformation. Contrast Enhancement. Transformations Based on Global Statistics. Linear Transformations. Nonlinear Transformations. Normalization Stretch. Reference Stretch. Thresholding. Adaptive Transformation. Color Image Contrast Enhancement. Min-max Stretch. Normalization Stretch. Decorrelation Stretch. Color Spacer Transformations. Summary. Exercises. References. Spatial Transforms: Introduction. An Image Model for Spatial Filtering. Convolution Filters. Low Pass and High Pass Filters. High Boost Filters. Directional Filters. The Border Region. Characterization of Filtered Images. The Box Filter Algorithm. Cascaded Linear Filters. Statistical Filters. Gradient Filters. Fourier Synthesis. Discrete Fourier Transforms in 2-D. The Fourier Components. Filtering with the Fourier Transform. Transfer Functions. The Power Spectrum. Scale Space Transforms. Image Resolution Pyramids. Zero-Crossing Filters. Laplacian-of-Gaussian (LoG) Filters. Difference-of-Gaussians (DoG) Filters.Wavelet Transforms. Summary. Exercises. References. Correction and Calibration: Introduction. Noise Correction. Global Noise. Sigma Filter. Nagao-Matsuyama Filter. Local Noise. Periodic Noise. Distriping 359. Global,Linear Detector Matching. Nonlinear Detector Matching. Statistical Modification to Linear and Nonlinear Detector. Matching. Spatial Filtering Approaches. Radiometric Calibration. Sensor Calibration. Atmospheric Correction. Solar and Topographic Correction. Image Examples. Calibration and Normalization of Hyperspectral Imagery. AVIRIS Examples. Distortion Correction. Polynomial Distortion Models. Ground Control Points (GCPs). Coordinate Transformation. Map Projections. Resampling. Summary. Exercises References. Registration and Image Fusion: Introduction. What is Registration? Automated GCP Location. Area Correlation. Other Spatial Features. Orthrectification. Low-Resolution DEM. High-Resolution DEM. Hierarchical Warp Stereo. Multi-Image Fusion. Spatial Domain Fusion. High Frequency Modulation. Spectral Domain Fusion. Fusion Image Examples. Summary. Exercises. References. Thematic Classification: Introduction. The Importance of Image Scale. The Notion of Similarity. Hard Versus Soft Classification. Training the Classifier. Supervised Training. Unsupervised Training. K-Means Clustering Algorithm. Clustering Examples. Hybrid Supervised/Unsupervised Training. Non-Parametric Classification Algorithms. Level-Slice. Nearest-Mean. Artificial Neural Networks (ANNs). Back-Propagation Algorithm. Nonparametric Classification Examples. Parametric Classification Algorithms. Estimation of Model-Parameters. Discriminant Functions. The Normal Distribution Model. Relation to the Nearest-Mean Classifier. Supervised Classification Examples and Comparison to Nonparametric Classifiers. Segmentation. Region Growing. Region Labeling. Sub-Pixel Classification. The Linear Mixing Model. Unmixing Model. Hyperspectral Image Analysis. Visualization of the Image Cube. Feature Extraction. Image Residuals. Pre-Classification Processing and Feature Extraction. Classification Algorithms. Exercises. Error Analysis. Multitemporal Images. Summary. References. Index.

Remote sensing, models, and methods for image processing

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

Nutrient requirements of dairy cattle , Nutrient requirements of dairy cattle , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

Nutrient requirements of dairy cattle

The scientific community that includes meteorologists, physical scientists, engineers, medical doctors, biologists, and environmentalists has shown interest in a better understanding of fog for years because of its effects on, directly or indirectly, the daily life of human beings. The total economic losses associated with the impact of the presence of fog on aviation, marine and land transportation can be comparable to those of tornadoes or, in some cases, winter storms and hurricanes. The number of articles including the word ``fog'' in Journals of American Meteorological Society alone was found to be about 4700, indicating that there is substantial interest in this subject. In spite of this extensive body of work, our ability to accurately forecast/nowcast fog remains limited due to our incomplete understanding of the fog processes over various time and space scales. Fog processes involve droplet microphysics, aerosol chemistry, radiation, turbulence, large/small-scale dynamics, and surface conditions (e.g., partaining to the presence of ice, snow, liquid, plants, and various types of soil). This review paper summarizes past achievements related to the understanding of fog formation, development and decay, and in this respect, the analysis of observations and the development of forecasting models and remote sensing methods are discussed in detail. Finally, future perspectives for fog-related research are highlighted.

Fog Research: A Review of Past Achievements and Future Perspectives

Part I Introduction 1 The Ecosystem (Reserva Biologica San Francisco) E. Beck, F. Makeschin, F. Haubrich, M. Richter, J. Bendix, C. Valerezo 2 Mountain rain forests in southern Ecuador as a hotspot of biodiversity - Limited knowledge and diverging patterns G. Brehm, J. Homeier, K. Fiedler, I. Kottke, J. Illig, N.M. Noske, F. Werner, S-W. Breckle 3 The people settled around Podocarpus National Park P. Pohle 4 Ecuador suffers the highest deforestation rate in South America R. Mosandl, S. Gunter, B. Stimm, M. Weber 5 Methodological challenges of a megadiverse ecosystem G. Brehm, K. Fiedler, Ch. Hauser, H. Dalitz Part II Gradients in ecosystem analysis 6 Why investigating gradients in ecosystem analysis K. Fiedler, E. Beck 7 The investigated gradients E. Beck, R. Mosandl, M. Richter, I. Kottke Part III The altitudinal gradient Part III.1 Gradual changes along the altitudinal gradient 8 Climate J. Bendix, R. Rollenbeck, M. Richter, P. Fabian, P. Emck 9 Soils along the altitudinal transect and in catchments W. Wilcke, S. Yasin, A Schmitt, C. Valarezo, W. Zech 10 Flora: Composition and function 10.1 Potential vegetation and floristic composition of Andean forests in South Ecuador, with a focus on the RBSF J. Homeier, F. A. Werner, S. R. Gradstein, S -W. Breckle, M. Richter 10.2 Past vegetation and fire dynamics H. Niemann & H. Behling 10.3 Forest structure along an altitudinal gradient in southern Ecuador A. Paulsch, D. Piechowski, K. Muller-Hohenstein 10.4 Vegetation structures and ecological features of the upper timberline ecotone M. Richter, K.-H. Diertl, Th. Peters, R. W. Bussman 10.5 Mycorrhizal state and new and special features of mycorrhizae of trees, ericads, orchids, ferns andliverworts I. Kottke, A. Beck, I. Haug, S. Setaro, V. Jeske, J.P. Suarez, L. Paxmino, M. Preussing, M, Nebel, F. Oberwinkler 11 Fauna: Composition and function 11.1 Bird species distribution along an altitudinal gradient in southern Ecuador and its functional relationships with vegetation structure D. Paulsch and K. Muller-Hohenstein 11.2 Seed dispersal by birds, bats and wind F. Matt, K. Almeida, A. Arguero, C. Reudenbach 11.3 Variation of diversity patterns across moth families along a tropical elevational gradient K. Fiedler, G. Brehm, N. Hilt, D. Sussenbach, and C.L. Hauser 11.4 Soil fauna M. Maraun, J. Illig , D. Sandman,V. Krashevskaya, R.A. Norton, S. Scheu Part III.2 Processes along and within the gradient 12 Water relations W. Wilcke, S. Yasin, K. Fleischbein, R. Goller, J. Boy, J. Knuth, C. Valarezo, W. Zech 13 Nutrient status and fluxes at the field and catchment scale W. Wilcke, S.Yasin, K. Fleischbein, R. Goller, J. Boy, J. Knuth, C. Valarezo, W. Zech 14 Biotic soil activities S. Iost, F. Makeschin, M. Abiy, F. Haubrich 15 Elevational changes in stand structure and biomass allocation of tropical mountain forests in relation to microclimate and soil chemistry G. Moser, M. Roderstein, N. Soethe, D. Hertel, C. Leuschner 16 Stand structure, transpiration responses in trees and vines and stand transpiration of different forest types within the mountain rainforest M. Kuppers, T. Motzer,D. Schmitt., C. Ohlemacher, R. Zimmermann, V. Horna, B.I.L. Kuppers, T. Mette 17 Plant growth along the altitudinal gradient-role of plant nutritional status, fine root activity, and soil properties N. Soethe, W. Wilcke, J. Homeier, J. Lehmann, C. Engels Part III.3 Gradient heterogeneities Part III.3.A Spatial heterogeneities 18 Spatial

Gradients in a Tropical Mountain Ecosystem of Ecuador

http://www.climate.ruhr-uni-bochum.de/fileadmin/Mitarbeiter/Cermak/papers_link/Cermak_Bendix_AR2008.pdf

A novel approach to fog/low stratus detection using Meteosat 8 data

A satellite-based climatology of fog and low-level stratus in Germany and adjacent areas

Flowering and fruiting as phenological events of 12 tree species in an evergreen tropical mountain rain forest in southern Ecuador were examined over a period of 3-4 years. Leaf shedding of two species was observed for 12 months. Parallel to the phenological recordings, meteorological parameters were monitored in detail and related to the flowering and fruiting activity of the trees. In spite of the perhumid climate of that area, a high degree of intra- and inter-specific synchronisation of phenological traits was apparent. With the exception of one species that flowered more or less continuously, two groups of trees could be observed, one of which flowered during the less humid months (September to October) while the second group started to initiate flowers towards the end of that phase and flowered during the heavy rains (April to July). As reflected by correlation coefficients, the all-time series of meteorological parameters showed a distinct seasonality of 8-12 months, apparently following the quasi-periodic oscillation of precipitation and related cloudiness. As revealed by power spectrum analysis and Markov persistence, rainfall and minimum temperature appear to be the only parameters with a periodicity free of long-term variations. The phenological events of most of the plant species showed a similar periodicity of 8-12 months, which followed the annual oscillation of relatively less and more humid periods and thus was in phase or in counter-phase with the oscillations of the meteorological parameters. Periods of unusual cold or dryness, presumably resulting from underlying longer-term trends or oscillations (such as ENSO), affected the homogeneity of quasi-12-month flowering events, fruit maturation and also the production of germinable seeds. Some species show underlying quasi-2-year-oscillations, for example that synchronise with the development of air temperature; others reveal an underlying decrease or increase in flowering activity over the observation period, influenced for instance by solar irradiance. As Ecuador suffers the highest rate of deforestation in South America, there is an urgent need for indigenous plant material for reforestation. A detailed knowledge of the biology of reproduction in relation to governing external factors (mainly climate) is thus required.

Jörg Bendix

Papers

Fog Research: A Review of Past Achievements and Future Perspectives

Gradients in a Tropical Mountain Ecosystem of Ecuador

A novel approach to fog/low stratus detection using Meteosat 8 data

A satellite-based climatology of fog and low-level stratus in Germany and adjacent areas

Seasonality of weather and tree phenology in a tropical evergreen mountain rain forest