The Richit-Richards family of distributions and its use in forestry

Johnson's SB and the logit-logistic are four-parameter distribution models that may be obtained from the standard normal and logistic distributions by a four-parameter transformation. For relatively small data sets, such as diameter at breast height measurements obtained from typical sample plots, distribution models with four or less parameters have been found to be empirically adequate. However, in situations in which the distributions are complex, for example in mixed stands or when the stand has been thinned or when working with aggregated data, then distribution models with more shape parameters may prove to be necessary. By replacing the symmetric standard logistic distribution of the logit-logistic with a one-parameter “standard Richards” distribution and transforming by a five-parameter Richards function, we obtain a new six-parameter distribution model, the “Richit-Richards”. The Richit-Richards includes the “logit-Richards”, the “Richit-logistic”, and the logit-logistic as submodels. Maximum likelihood estimation is used to fit the model, and some problems in the maximum likelihood estimation of bounding parameters are discussed. An empirical case study of the Richit-Richards and its submodels is conducted on pooled diameter at breast height data from 107 sample plots of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.). It is found that the new models provide significantly better fits than the four-parameter logit-logistic for large data sets

Compartir los datos de investigación en ciencia: introducción al data sharing

The emergence in the scientific community of an initiative known as data sharing, consisting of sharing research data among researchers and aiming to maximize efforts and resources, is analysed. First, the concept of research data and the related technical difficulties depending on the discipline are reviewed. We also examine the motivations, origins and growth of this movement, which has had an important impact on the scientific community’s behaviour through the creation of reposi- tories and data banks, raising both technical and social challenges. Then we discuss leading funding agencies’ initiatives and scientific journals’ editorial policies promoting these practices. Finally, we examine the impact these major changes in researchers’ habits have for librarians, including the emergence of new professional profiles

Components of error of maps from remotely sensed images

The concept of a “true” ground-truth map is introduced, from which the inaccuracy/error of any production map may be measured. A partition of the mapped region is defined in terms of the “residual rectification” transformation. Geometric RMS-type and Geometric Distortion error criteria are defined as well as a map mis-classification error criterion (the latter for hard and fuzzy produc-tion maps). The total map error is defined to be the sum (over each set of the map partition men-tioned above) of these three error components integrated over each set of the partition

The optimal sample allocation problem for mapped outputs

Abstract not availabl

Towards the sustainable use of Europe’s forests: a centre for European forest science! – Some missing catalytic elements?

The Symposium, “Towards the sustainable use of Europe’s forests”, with sub-title “Forest ecosystem and landscape research: scientific challenges and opportunities” lists three fundamental substantive areas of research that are involved: Forest management and practices, Ecosystem processes and functional ecology, and Environmental economics and sociology. This paper argues that there are essential catalytic elements missing! Without these elements there is great danger that the aimed-for world leadership in the forest sciences will not materialize. What are the missing elements? All the sciences, and in particular biology, environmental sciences, sociology, economics, and forestry have evolved so that they include good scientific methodology. Good methodology is imperative in both the design and analysis of research studies, the management of research data, and in the interpretation of research finding. The methodological disciplines of Statistics, Modelling and Informatics (“SMI”) are crucial elements in a proposed Centre of European Forest Science, and the full involvement of professionals in these methodological disciplines is needed if the research of the Centre is to be world-class. Distributed Virtual Institute (DVI) for Statistics, Modelling and Informatics in Forestry and the Environment (SMIFE) is a consortium with the aim of providing world-class methodological support and collaboration to European research in the areas of Forestry and the Environment. It is suggested that DVI: SMIFE should be a formal partner in the proposed Centre for European Forest Science

FALSIFICATION AND CERTAINTY REPOST

The reason that Professor Zeide (Zeide, 2010) objects so strongly with Popper’s falsification view of scientific theory (Popper, K.S., 1968) is because Professor Popper and Professor Zeide are defining, interpreting and using the terms “induction”, “verification ” and “falsification ” in different ways: they have different ontologies and metadata. There are many possible types of “induction” (logical, empirical-scientific, statistical, mathematical (which deductive, not inductive)...etc.) but Professor Zeide seems to be defining (by usage) an “induction” I do not recognise, and seems not that of Popper (1968). Consider the certain demonstration that a particular swan is black (let us assume the bird is a bird and not a fish, it a swan and not a goose, or ugly duckling) by examining every feather (and assume a black swan is defined in terms of its feather colours). This is NOT “verification ” in the inductive sense, as used by Popper (1968), or Hume (1748) or the ancient Greeks (e.g. Sextus Empiricus, 200), even though the word may be used in this way in colloquial English language (validate: “to prove that something is true”, OED(2010)). “Inductive verification ” is only meaningful in relation to the general assertio

Image classification accuracy from LANDSAT data for tropical rain forest in Sumatra: An evaluation of ERDAS imagine software

Abstract not availabl