Maps represent powerful tools to show the spatial variation of a variable in a straightforward manner. A crucial aspect in map rendering for its interpretation by users is the gamut of colours used for displaying data. One part of this problem is linked to the proportion of the human population that is colour blind and, therefore, highly sensitive to colour palette selection. The aim of this paper is to present a function in R - \texttt{cblind.plot} - which enables colour blind people to just enter an image in a coding workflow, simply set their colour blind deficiency type, and immediately get as output a colour blind friendly plot. We will first describe in detail colour blind problems, and then show a step by step example of the function being proposed. While examples exist to provide colour blind people with proper colour palettes, in such cases (i) the workflow include a separate import of the image and the application of a set of colour ramp palettes and (ii) albeit being well documented, there are many steps to be done before plotting an image with a colur blind friendly ramp palette. The function described in this paper (\ttt{cblind.plot}), on the contrary, allows to (i) automatically call the image inside the function without any initial import step and (ii) explicitly refer to the colour blind deficiency type being experienced, to further automatically apply the proper colour ramp palette.
Bioclimate envelope models are often used to predict changes in species distribution arising from changes in climate. These models are typically based on observed correlations between current species distribution and climate data. One limitation of this basic approach is that the relationship modelled is assumed to be constant in space; the analysis is global with the relationship assumed to be spatially stationary. Here, it is shown that by using a local regression analysis, which allows the relationship under study to vary in space, rather than conventional global regression analysis it is possible to increase the accuracy of bioclimate envelope modelling. This is demonstrated for the distribution of Spotted Meddick in Great Britain using data relating to three time periods, including predictions for the 2080s based on two climate change scenarios. Species distribution and climate data were available for two of the time periods studied and this allowed comparison of bioclimate envelope model outputs derived using the local and global regression analyses. For both time periods, the area under the receiver operating characteristics curve derived from the analysis based on local statistics was significantly higher than that from the conventional global analysis; the curve comparisons were also undertaken with an approach that recognised the dependent nature of the data sets compared. Marked differences in the future distribution of the species predicted from the local and global based analyses were evident and highlight a need for further consideration of local issues in modelling ecological variables.
This article provides an overview of some of the recent research in ecological informatics involving remote sensing and GIS. Attention focuses on a selected range of issues including topics such as the nature of remote sensing data sets, issues of accuracy and uncertainty, data visualization and sharing activities as well as developments in aspects of ecological modelling research. It is shown that considerable advances have been made over recent years and foundations for future research established.
In order to inform the development of a remote sensing drought monitoring system over Sabah, Borneo, this paper explored how a relationship between remotely sensed data and rainfall developed at one site in Sabah transferred to four other sites in Sabah. By way of developing relationships between rainfall statistics collected at five Sabah rainforest sites and remotely sensed data (acquired by NOAA AVHRR) processed to MIR reflectance, the VI3 index, the Ts/NDVI index and Ts/VI3 index, two points were concluded. The first was that the relationship between remotely sensed data and rainfall is non-stationary across Sabah during the 1997/1998 ENSO and the second was that the index Ts/VI3 is an effective means of using the remotely sensed data available.
No abstract is provided for this article.
Ground reference data error is a major source of bias in the estimation of land cover change and of change detection accuracy. This paper explores the magnitude and direction of some of the main biases introduced into studies of land cover dynamics by remote sensing based upon the popular binary change detection error matrix. It is shown that substantial mis-estimation may arise through the use of imperfect reference data, even if of a very high accuracy. The magnitude and direction of the bias is a function of the size and nature of the errors. One key issue, however, is that it may sometimes be possible to reduce the effects of imperfect reference data and so derive more accurate estimates. These various issues are discussed with the use of simulated data to control for other potential error sources.
No abstract is provided for this article.
