196,534 research outputs found
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)
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
Surface temperatures at the continental scale: tracking changes with remote sensing at unprecedented detail
No full textTemperature is a main driver for most ecological processes, and temperature time series provide key environmental indicators for various applications and research fields. High spatial and temporal resolutions are crucial for detailed analyses in various fields of research. A disadvantage of temperature data obtained by satellites is the occurrence of gaps that must be reconstructed. Here, we present a new method to reconstruct high-resolution land surface temperature (LST) time series at the continental scale gaining 250-m spatial resolution and four daily values per pixel. Our method constitutes a unique new combination of weighted temporal averaging with statistical modeling and spatial interpolation. This newly developed reconstruction method has been applied to greater Europe, resulting in complete daily coverage for eleven years. To our knowledge, this new reconstructed LST time series exceeds the level of detail of comparable reconstructed LST datasets by several orders of magnitude. Studies on emerging diseases, parasite risk assessment and temperature anomalies can now be performed on the continental scale, maintaining high spatial and temporal detail. We illustrate a series of applications in this paper. Our dataset is available online for download as time aggregated derivatives for direct usage in GIS-based application
Spectral rank-abundance for measuring landscape diversity
No full textInvestigation of the diversity of a landscape implies finding appropriate measures coupling information on richness and equitability. Most of the papers dealing with remotely sensed images have relied on the richness of digital numbers (DNs) or on Shannon entropy or Pielou evenness indices for measuring their heterogeneity. Instead, based on ecological theory, we will show that rank–abundance diagrams may be profitably used in remote sensing to take into account both spectral richness and spectral equitability at the same time, by using a unique approach. After a theoretical introduction to the problem, we will empirically test the proposed method by extractingDNabundances derived froma Landsat Enhanced Thematic Mapper Plus (ETM+) image representing contrasting landscapes (test sites), plotting the relative abundance of each DN value versus its rank (rank–abundance diagrams) and interpreting statistically and ecologically the achieved results. We do not propose rank–abundance diagrams as a replacement of existing measures of spectral diversity, but as a parallel method to encompass (at the same time) both richness and evenness of remotely sensed images
Why Geomatics matter
No full textWhy Geomatics matter The boundaries between surveying, mapping, geographic information system (GIS), remote sensing and global navigation satellite systems (GNSS) are blurring making it easier to integrate them under the umbrella of geomatics
Commentary on Krishnaswamy et al. - Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance surrogate
No full textRemote sensing is a powerful tool for characterizing, estimating or modelling species diversity. Differences in environmental properties of different habitats should lead to differences of spectral responses, which can be detected by satellite imagery. Hence, spectral distance may be related to species diversity. Based on previous studies, Krishnaswamy et al. [Krishnaswamy, J., Bawa, K, S., Ganeshaiah, K. N.. & Kiran, M. C. (2009). Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance Surrogate. Remote Sensing of Environment.] used spectral distance to estimate species diversity. Since a noisy scatterplot of species versus spectral diversity is expected, the commonly used Ordinary Least Square regression may fail to detect trends which occur across other quantiles than the mean. Krishnaswamy et al. [Krishnaswamy, J., Bawa, K. S., Ganeshaiah, K. N., & Kiran, M. C. (2009). Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance surrogate. Remote Sensing of Environment.] proposed a quantile-quantile plot method as an alternative to conventional regression based approaches which are inappropriate for dependent pair-wise dissimilarity or similarity data. By this commentary I demonstrate the utility of a quantile regression technique to complement the Krishnaswamy et al. (Krishnaswamy, J., Bawa, K. S., Ganeshaiah, K. N., & Kiran, M. C. (2009). Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance Surrogate. Remote Sensing of Environment.] graphical approach in terms of a predictive model. (C) 2009 Elsevier Inc. All rights reserved
Analisi spaziale in ambiente open source
No full textThe idea of Free and Open Source (FOSS) software has been around for almost as long as software development (Neteler and Mitasova, 2008). The famous “four freedoms” paradigm, developed by Richard Stallman (1985, 1997) in his seminal work, proclaims that FOSS is defined as (i) the freedom to run the program for any purpose, (ii) the freedom to study how the program works and adapt it to one’s own needs, (iii) the freedom to redistribute copies, and (iv) the freedom to improve the program and release such improvements to the public. This guarantees that the whole community benefits from software development
Let the four freedoms paradigm apply to ecology
No full textIn 1985, Richard Stallman, one of the most brilliant minds in computer science, founded the Free Software Foundation and launched the concept of 'copyleft', the opposite of copyright. The aim, outlined in the GNU Manifesto http://www.gnu.org/gnu/manifesto.html, [1]), was to make software programs “free” as in “freedom”. The famous “four freedoms” expounded by Stallman [1] are: i) the freedom to run the program for any purpose, ii) the freedom to study how the program works and adapt it to one's own needs, iii)
the freedom to redistribute copies, and iv) the freedom to make improvements to the program and release them to the public. Thus the whole (scientific) community benefits from software development. These freedoms are also inherent in several free software licenses, the GNU General Public License
(GPL) being one of the most popular. Approximately a quarter of a century after Stallman put forward his ideas, William K. Michener and Matthew B. Jones, in an article in TREE [2] focusing on analysis of ecological data, stated: “analytical processes are fundamental to most published results in ecology”. Explicit reference to the analytical procedures adopted in generating scientific results is crucial for reproducibility, yet these processes are rarely documented in published ecological papers [2]. Scientific workflow applications
such as Kepler (https://kepler-project.org) attempt to address the problem [2], but are only partially successful since the underlying algorithms may still be opaque
Spatial land cover pattern analysis
No full textIn the previous chapters we introduced land cover classifications, fractional cover and time-series analysis. All these approaches aimed to extract ecological relevant information based on the spectral signal. However differentiating a tree plantation (spatially regularly planted trees of same species, age, height) from a natural forest based on the spectral signal only might be quite challenging since the spectral signals might be quite similar but their spatial heterogeneity is different. A tree plantation will not have a high spatial variation in its spectral signal due to the same age and height of the trees while a natural forest will have different tree heights with casting shadows or even tree fall gaps, hence such a forest will show up with a higher spatial variation. Such information can be retrieved using texture metrics based on remote sensing data sets e.g. the NDVI
Biodiversity, roads, & landscape fragmentation: two Mediterranean cases
No full textThe most pervasive threats to biological diversity are directly or indirectly linked to the road networks. For this reason, over the last few decades, interest in the study of the ecological characteristics of the edges associated with roads has increased. This work aims to investigate the effect of roads as a human-induced disturbance on the plant diversity in two managed Mediterranean forest sites, focusing on the responses of plants species richness, evenness, composition and taxonomic diversity.
A stratified random sampling was performed in two protected areas located in Tuscany, Central Italy. The species richness, composition and abundance were measured in 53 20x20 m plots. Ordinary Least Square and quantile regressions were used to study the effect of the roads on species richness, evenness and taxonomic distinctness, and redundancy analysis was used to examine the species composition. Generalized linear models in conjunction with an Information Criterion-based approach to model selection were used to test the role of road distance in the structure of forest plant biodiversity.
Our findings indicated a clear relationship between road distance and different plant biodiversity facets, which showed its maximum effect in the first 0-20 m forest-to-road segment and a mitigation after the 200 m threshold. Furthermore, the presence and abundance of many key forest species, such as Fagus sylvatica and Abies alba, were influenced more by the road distance than by other environmental gradients. The few remnants of core forest habitats in the Mediterranean basin highlight the need to recognize that road construction and maintenance have several ecological implications and accordingly require long-term monitoring programs
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