Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages

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

The very etymology of Ecology, from the greek 'Qikos', 'the household', implies that ecologists should devote some attention to the 'house' or habitat of the population or community they are studying. However, as Charles Elton (1966) has so forcibly pointed out, 'definition of habitats, or rather lack of it, is one of the chief blind spots in Zoology'. Elton himself has provided us with a qualitative classification of habitats, while another past President, Alex Watt (1947) highlighted the dynamic nature of habitats by his phrase, 'pattern and process'. Elton referred to the need to quantify habitat characteristics. In this Address I will attempt some quantification;however, you will all be aware that in doing this I will not be able to emulate those former Presidents who have been able to provide a definitative synthesis of a field or of their own studies, my offering can be but a small beginning, an indication of the type of characteristics we should quantify. In considering ecosystem patterns and environment R. M. May (1974) writes 'it is to be emphasized that although patterns may underlie the rich and varied tapestry of the natural world, there is no single simple pattern. Theories must be pluralistic'. Indeed, the complexity of the subject is daunting and in any attempt to formulate some type of general framework, one is continually beset with exceptions. In stressing the need for a framework I am echoing a plea of my predecessor Amyan Macfadyen (1975) who cited K. E. F. Watt's (1971) vivid image 'if we do not develop a strong theoretical core that will bring all parts of ecology back together we shall all be washed out to sea in an immense tide of unrelated information'. In some ways I think we may see ourselves at a similar point to the inorganic chemist before the development of the periodic table; then he could not predict, for example, how soluble a particular sulphate would be, or what was the likelihood of a particular reaction occurring. Each fact had to be discovered for itself and each must be remembered in isolation. It is noteworthy that from Dobereiner's early efforts in 1816 it took more than fifty years before Mendeleeff ormulated his Periodic Law (1869) and even after this there were various attempts at rearrangement. Another parallel may be drawn with astronomy before the development of the Hertzsprung-Russell diagram that relates the evolution and the properties of stars. Again in our own subject biology, the situation is somewhat analagous to that before the formulation of the Linnean system of classification; but now from this system of classification, we are able to organize our knowledge of, for example, the functional morphology of organisms and we can even make assumptions, with a high probability

Habitat, the template for ecological strategies?

Conservation biology has two threads: the small-population paradigm which deals with the erect of smallness on the persistence of a population, and the declining-population paradigm which deals with the cause of smallness and its cure. The processes relevant to the small-population paradigm are amenable to theoretical examination because they generalize across species and are subsumed by an inclusive higher category: stochasticity. In contrast, the processes relevant to the declining-population paradigm are essentially humdrum, being not one but many. So far they have defied tight generalization and hence are of scant theoretical interest. The small-population paradigm has not yet contributed significantly to conserving endangered species in the wild because it treats an erect (smallness) as if it were a cause

Directions in conservation biology

Stochastic models in biology.

A conceptual basis for the physics of the interaction of solar radiation with a vegetation canopy is presented, yarious factors which affect the reflectance from such a canopy are delineated. An overview of various canopy reflectance models which attempt to incorporate the physics of interaction through these factors is given. These models can be divided into four basic categories: geometrical models, turbid medium models for homogeneous canopies, hybrid (of geometrical and turbid medium) models for heterogeneous canopies, and computer simulation models. Techniques for estimating important biophysical parameters like leaf area index, leaf angle distribution, and canopy architecture, through inversion of these models are reviewed. Some directions for future research are suggested.

Models of Vegetation Canopy Reflectance and Their Use in Estimation of Biophysical Parameters From Reflectance Data

Stochastic models in biology

A detailed investigation of various aspects of the populations of interacting biological species is made, using Volterra's and other models. For a system with two species, a detailed analysis is possible, and we plot the time variation of the population of two species for a set of values of the parameters. For a many-species system, it is shown that the time average of the population ${N}_{i}$ of the $i\mathrm{th}$ species is equal to its steady state value ${q}_{i}$. For time averages of other functions of ${N}_{i}$ and ${N}_{i}{N}_{j}$, only the equations satisfied by them are derived. The population growth of a species is studied by assuming that the effect of other species is to introduce a random function of time in the growth equation. The resulting Fokker-Planck equation is shown to have the same form as the Schr\"odinger and Bloch equations. The "potential" of the Schr\"odinger equation depends upon the form of the number-dependent term in the growth function. For the Gompertz form, it is a simple harmonic oscillator potential and, for the Verhulst form, the Morse potential. For both the forms, the Fokker-Planck equation is explicitly solved to obtain the probability distribution $P(N, t)$ of the population as a function of time. It is shown that considerable simplification is achieved in the calculation of the steady state concentrations (${q}_{i}'\mathrm{s}$) of the component species if Pfaffians are used. Since ${q}_{i}$ is equal to the time average of the population of the $i\mathrm{th}$ species, a great deal can be said about the stability of the population in the sense of the rarity of explosion or extinction of one or more species. If the interactions between various species and their growth coefficients (in the absence of interactions) are known, we show that a priori one can determine whether the population will be stable or not and, if not, which of the species will disappear. Also, the stability of the population is discussed when several new species are introduced. We show that the stability is dependent on how the newly introduced species interact with each other and with the population into which they are introduced. If the information about the detailed interactions between various species is absent, as is usually the case, then a statistical mechanical treatment of the population is desirable. We show that for small deviations from steady state populations, the necessary and sufficient condition for such a treatment is that the number of species is large. For arbitrary deviations the latter is a necessary condition and may not be sufficient. This statistical mechanical treatmetn provides an empirical method for calculating the interaction between two species and the stability of the population. A measure of the stability of an ecology is defined which could be used to compare the relative stabilities of two ecologies with the same macroscopic properties. The effect of changes in the interactions between var\'{\i}ous species, due to changes in temperature, humidity, age distribution, etc., is studied by assuming the rate constants to be random. A master equation for the probability distributions of ${N}_{i}'\mathrm{s}$ is derived. It is shown that the stationary population distribution is Poisson only if the variation in the rate constants is not too rapid. A brief outline of another stochastic model for the population growth in terms of the probabilities of birth and death of the individuals is given. Since the members of the population do not react instantaneously to any change in the environment, the prey-predator interaction does not affect the population of either prey or predator instantaneously, and the egg is not converted into an adult instantaneously, the effects of the time lags in the above processes on the behavior, in particular the stability, of the population are studied. Further generalizations of the Volterra model are discussed, and a brief review of other systems of interacting species, e.g., systems of biochemical oscillators, nervous systems, multimode lasers, systems of simultaneously growing bacteria, etc., is given. Finally, a sampling of the experiments which throw some light on the validity of Volterra's model and the statistical mechanical treatment is given.

On the Volterra and Other Nonlinear Models of Interacting Populations

Vegetation Indices (VIs) are often used to estimate important biophysical parameters, like LAI and Fpar, from vegetation canopy reflectance data. In this study, a three‐dimensional model (Diana) is utilized to generate architecturally realistic tree and crop canopies in various development stages and to calculate bidirectional reflectance factors in the principal plane. We investigate the influence of various factors like soil brightness, optical properties of canopy elements, leaf angle distribution, spacing distance between plants and solar and view geometries on relationships between VI and Fpar, LAI and percentage ground cover (GC) in order to determine optimal VI and viewing conditions for the estimation of Fpar and LAI/GC. These simulation studies suggest that: (1) in most cases, Vis using off‐nadir reflectances are more informative and useful than those based on nadir reflectances; (2) the optimal VI and sun/view geometries are usually different for inferring different parameters, dependin...

Narendra S. Goel

Papers

Stochastic models in biology.

Models of Vegetation Canopy Reflectance and Their Use in Estimation of Biophysical Parameters From Reflectance Data

Stochastic models in biology

On the Volterra and Other Nonlinear Models of Interacting Populations

Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation