130,925 research outputs found
Explicitly Accounting for Pixel Dimension in Calculating Classical and Fractal Landscape Shape Metrics
No full textDifferent summarized shape indices, like mean shape index (MSI) and area weighted mean shape index (AWMSI) can change over multiple size scales. This variation is important to describe scale heterogeneity of landscapes, but the exact mathematical form of the dependence is rarely known. In this paper, the use of fractal geometry (by the perimeter and area Hausdorff dimensions) made us able to describe the scale dependence of these indices. Moreover, we showed how fractal dimensions can be deducted from existing MSI and AWMSI data. In this way, the equality of a multiscale tabulated MSI and AWMSI dataset and two scale-invariant fractal dimensions has been demonstrated. RI Rocchini, Duccio/B-6742-2011; Imre, Attila/E-9016-201
Rarefy: Rarefaction Method
No full textRarefy includes functions for the calculation of spatially and non-spatially explicit rarefaction curves using different indices of taxonomic, functional and phylogenetic diversity. The user can also rarefy any biodiversity metric as provided by a self-written function (or an already existent one) that gives as output a vector with the values of a certain index of biodiversity calculated per plot (Ricotta, C., Acosta, A., Bacaro, G., Carboni, M., Chiarucci, A., Rocchini, D., Pavoine, S. (2019) ; Bacaro, G., Altobelli, A., Cameletti, M., Ciccarelli, D., Martellos, S., Palmer, M. W., . . . Chiarucci, A. (2016) ; Bacaro, G., Rocchini, D., Ghisla, A., Marcantonio, M., Neteler, M., & Chiarucci, A. (2012)
Space-Ruled ecological processes: introduction to the special issue on spatial ecology
No full textThis special issue explores most of the scientific issues related to spatial ecology and its integration
with geographical information at different spatial and temporal scales. Papers are mainly relatedchallenging aspects of species variability over space and landscape dynamics, providing a benchmark
for future exploration on this theme
Is spectral distance a proxy of beta diversity at different taxonomic ranks? A test using quantile regression
No full textBeta diversity represents a powerful indicator of ecological conditions because of its intrinsic relation with environmental gradients. In this view, remote sensing may be profitably used to derive models characterizing or estimating species turnover over an area. While several examples exist using spectral variability to estimate species diversity at several spatial scales, most of these have relied on standard correlation or regression approaches like the common Ordinary Least Square (OLS) regression which are problematic with noisy data. Moreover, very few attempts were made to derive beta diversity characterization models at different taxonomic ranks. In this paper, we performed quantile regression to test if spectral distance represents a good proxy of beta diversity considering different data thresholds and taxonomic ranks. We used plant distribution data from the North and South Carolina including 146 counties and covering a variety of vegetation formations. The dissimilarity in species composition at different taxonomic ranks (using Sorensen distance) among pairs of counties was compared with their distance in NDVI values derived from 23 yearly MODIS images. Our results indicate that (i) spectral variability represents a good proxy of beta diversity when appropriate statistics are applied and (ii) a lower taxonomic rank is important when changes in species composition are examined spatially using remotely sensed data. (C) 2009 Elsevier B.V. All rights reserved. RI Rocchini, Duccio/B-6742-201
Does using species abundance data improve estimates of species diversity from remotely sensed spectral heterogeneity?
No full textDifferent approaches for the assessment of biodiversity by means of remote sensing were developed over the last decades. A new approach, based on the spectral variation hypothesis, proposes that the spectral heterogeneity of a remotely sensed image is correlated with landscape structure and complexity which also reflects habitat heterogeneity which itself is known to enhance species diversity. In this context, previous studies only applied species richness as a measure of diversity. The aim of this paper was to analyze the relationship of richness and abundance-based diversity measures with spectral variability and compare the results at two scales. At three different test sites in Central Namibia, measures of vascular plant diversity was sampled at two scales - 100 m(2) and 1000 m(2). Hyperspectral remote sensing data were collected for the study sites and spectral variability, was calculated at plot level. Ordinary least square regression was used to test the relationship between species richness and the abundance-based Shannon Index and spectral variability. We found that Shannon Index permanently achieved better results at all test sites especially at 1000 m(2), Even when all sites where pooled together, Shannon index was still significantly related with spectral variability at 1000 m(2). We suggest incorporating abundance-based diversity measures in studies of relationships between ecological and spectral variability. The contribution made by the high spectral and spatial resolution of the hyperspectral sensor is discussed. (C) 2009 Elsevier Ltd. All rights reserved. RI Rocchini, Duccio/B-6742-2011; Oldeland, Jens/A-1587-201
Ecological Status and Change by Remote Sensing
No full textEvaluating ecological patterns and processes is crucial for the conservation of ecosystems [1]. In this view, remote sensing is a powerful tool for monitoring their status and change. This involves several tasks like biodiversity estimate, landscape ecology, and species distribution modeling, to name a few [2]. Due to the difficulties associated with field-based data collection [3], the use of remote sensing for estimating ecological status and change is promising since it provides a synoptic view of an area with a high temporal resolution [4]. Of course in some cases remote sensing should be viewed as a help to plan a field survey rather than a replacement of it. Further, its improper use may lead to pitfalls and misleading results. [...
Introduction to the Special Issue: Geospatial Monitoring and Modeling of Environmental Change
No full textGeospatial modeling is an approach to apply analysis to monitor environmental change over time considering different fields of re-search, including computer science, remote sensing, ecology, environmental science, life science, geography (see [1,2] for a critique). The special issue was instigated to publish straightforward research on the matter in order to stimulate further discussion on the potential of geospatial modelling. Both theoretical and empirical papers are part of the issue with the support of the International Society for Photogrammetry and Remote Sensing, promoting an advanced forum for the science and technology of geographic information
Spectral Distance Decay: Assessing Species Beta-diversity by Quantile Regression
No full textRemotely sensed data represents key information for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance may allow us to quantitatively estimate how beta-diversity in species changes with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological datasets are characterized by a high number of zeroes that can add noise to the regression model. Quantile regression can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this paper, we used ordinary least square (OLS) and quantile regression to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.05) considering both OLS and quantile regression. Nonetheless, OLS regression estimate of mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when spectral distance approaches zero, was very low compared with the intercepts of upper quantiles, which detected high species similarity when habitats are more similar. In this paper we demonstrated the power of using quantile regressions applied to spectral distance decay in order to reveal species diversity patterns otherwise lost or underestimated by ordinary least square regression. RI Nagendra, Harini/A-9103-2009; Rocchini, Duccio/B-6742-201
Earth Observation for Ecosystems Monitoring in Space and Time: A Special Issue in Remote Sensing
No full textThis Editorial introduces the papers published in the special issue “Earth Observation for Ecosystems Monitoring in Space and Time” which includes the most important researchers in the field and the most challenging aspects of the application of remote sensing to study ecosystems
Teaching Mathematics Creatively: Book Review
No full text“Creative teachers remember the importance of fun” (Pound and Lee, 2011, page 138)
Thinking about mathematics in a positive way is a crucial step for catching its beauty and power. Linda Pound and Trisha Lee developed a book permeated by positive concepts like creative teaching, positive feelings, motivation, playful teaching which lead the reader through a relaxed trip into teaching mathematics creatively.
Quoting Joubert (2001, page 21) cited by Teresa Cremin, the Editor of this series of books related to “Learning to teach in the primary school”, “Creative teaching is an art. One cannot teach teachers didactically how to be creative; there is no fail safe recipe or routines. Some strategies may help to promote creative thinking, but teachers need to develop a full repertoire of skills which they can adapt to different situations.
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