Computer Assisted Vegetation Analysis

There are many books and computer programs dealing look ahead rather than pondering the past. This is a with data analysis. It would be easy to count at least a manual of recent views that evolved in the study of hundred, yet few of these would show applications in vegetation. This book is intended to emphasize the new vegetation science. Today in the face of environmental acquisitions which we believe significantly affect the degradation caused by anthropogenic pressures on the future of vegetation analysis: biosphere there is added urgency to study vegetation 1. Vegetation is a 'fuzzy' system, it must be treated as processes and dynamics in order to understand their role such at the set level, where the idea ofconceptualized in regulating the water, oxygen and the carbon cycles, in patterns must drive the research design. relation to global warming and ozone layer depletion. It 2. Vegetation cannot be seen only in the perspective of a is well known that ecology was developed first in vegeta traditional taxonomy based on the species concept; tion studies (see Acot 1989) but after an active period character sets of ecological value must enter into marked by intensive phytoclimatic and synecological consideration and a hierarchical analysis of patterns studies, vegetation science entered in a rather dormant and processes should be the basis of comparisons. period. Other ecological disciplines such as animal popu 3.

The ability to manipulate spatial data in different forms and to extract additional meaning from them is at the heart of GIS, yet genuine spatial analysis tools are rarely incorporated into commercial software, thus seriously limiting their usefulness. The future of GIS technology wil depend largely on the incorporation of more powerful analytical and modelling functions - and there is agreement within the GIS community of the urgent need to address these issues. This text attempts this task. It presents the latest information on incorporating spatial analysis tools into GIS, and includes concepts and applications from both the environmental and socio-econimc sciences.

Modern landscape research uses a panoply of techniques to further our understanding of our changing world, including mathematics, statistics and advanced simulation techniques to combine empirical observations with known theories. This book identifies emerging fields and new challenges that are discussed within the framework of the ‘driving forces’ of Landscape Development. the book addresses all of the ‘hot topics’ in this important area of study and emphasizes major contemporary trends in these fields.

The 3rd edition of this popular textbook introduces the reader to the investigation of vegetation systems with an emphasis on data analysis. The book succinctly illustrates the various paths leading to high quality data suitable for pattern recognition, pattern testing, static and dynamic modelling and model testing including spatial and temporal aspects of ecosystems. Step-by-step introductions using small examples lead to more demanding approaches illustrated by real world examples aimed at explaining interpretations. All data sets and examples described in the book are available online and are written using the freely available statistical package R. This book will be of particular value to beginning graduate students and postdoctoral researchers of vegetation ecology, ecological data analysis, and ecological modelling, and experienced researchers needing a guide to new methods. A completely revised and updated edition of this popular introduction to data analysis in vegetation ecology. Includes practical step-by-step examples using the freely available statistical package R. Complex concepts and operations are explained using clear illustrations and case studies relating to real world phenomena. Emphasizes method selection rather than just giving a set of recipes.

A review and evaluation of the analysis methods for studying spatial pattern in vegetation.

The development of effective methodologies for the analysis of multi-temporal data is one of the most important and challenging issues that the remote sensing community will face in the next few years. The relevance and timeliness of this issue are directly related to the ever-increasing quantity of multi-temporal data provided by the numerous remote sensing satellites that orbit our planet. The synergistic use of multi-temporal remote sensing data and advanced analysis methodologies results in the possibility of solving complex problems related to the monitoring of the Earth's surface and atmosphere.This book brings together the methodological aspects of multi-temporal remote sensing image analysis, real applications and end-user requirements, presenting the state of the art in this field and contributing to the definition of common research priorities. Researchers and graduate students in the fields of remote sensing, image analysis, and environmental monitoring will appreciate the interdisciplinary approach thanks to the articles written by experts from different scientific communities.

The development of effective methodologies for the analysis of multi-temporal data is one of the most important and challenging issues that the remote sensing community will face in the next few years. The relevance and timeliness of this issue are directly related to the ever-increasing quantity of multi-temporal data provided by the numerous remote sensing satellites that orbit our planet. The synergistic use of multi-temporal remote sensing data and advanced analysis methodologies results in the possibility of solving complex problems related to the monitoring of the Earth's surface and atmosphere. This book brings together the methodological aspects of multi-temporal remote sensing image analysis, real applications and end-user requirements, presenting the state of the art in this field and contributing to the definition of common research priorities. Researchers and graduate students in the fields of remote sensing, image analysis, and environmental monitoring will appreciate the interdisciplinary approach thanks to the articles written by experts from different scientific communities. Contents:Image Analysis and Algorithms:Digital Change Detection Methods in Natural Ecosystem Monitoring: A Review (P Coppin et al.)Adaptive Reduction of Registration-Noise Effects in Unsupervised Change Detection (L Bruzzone & R Cossu)Image Thresholding for Landslide Detection by Genetic Programming (P L Rosin & J Hervás)Shape Change Detection by Fuzzy Measures (P C Smits & S G Dellepiane)The Use of SOM-Encoded Texture Spectrum for Change Detection (J C W Chan & K-P Chan)A Temporal Extension to Traditional Empirical Orthogonal Function Analysis (A A Nielsen et al.)Monitoring and Management of Natural Resources:Using Temporal Change of the Land Cover Spectral Signal to Improve Burnt Area Mapping (D Stroppiana & J-M Grégoire)MODIS 250m and 500m Time Series Data for Change Detection and Continuous Representation of Vegetation Characteristics (J R G Townshend et al.)Suspended Matter in the North Sea: A Multi-Temporal Analysis based on SEAWIFS Imagery in 2000 (R Pasterkamp et al.)The Use of Multi-Temporal SAR Images for Detecting Main Hydrological Parameters (M Bianchini et al.)Land-Cover Dynamics:Multi-Temporal Remote Sensing Analytical Approaches for Characterizing Landscape Change (R S Lunetta)An Application of Kalman Filtering for Monitoring Forest Growth Aided by Satellite Image Time Series (S Joyce)Fourier Decomposition of an AVHRR NDVI Time Series for Seasonal and Interannual Land Cover Change Detection (M E Jakubauskas et al.)Multi-temporal Analysis Using NOAA-pathfinder AVHRR Land Imagery for the Study of the Land Cover Dynamic in the Mediterranean Basin (J A Sobrino et al.)and other papers Readership: Researchers and academics in computer science, computer engineering, electrical and electronic engineering, and environmental science. Keywords: