1,721,034 research outputs found
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
TGRASS: temporal data processing with GRASS GIS
Full text linkGRASS GIS is a Free and Open Source geographic information system (GIS) with support for raster, 3D raster and vector data processing. It provides more than 450 core modules, plus hundreds of Add-ons, to run any kind of geographical analysis. GRASS GIS offers a useful graphical interface to work as in any other desktop software. However, the highest power of GRASS GIS resides in that it can be used also like a backend tool to run analysis in an automatic way, not only a personal computer, but also into HPC systems or via web services (WPS or scripting in several languages). The workshop will show the basics about GRASS GIS, it will be an intuitive mix of theoretical and hands-on sections. In the former, we will introduce the participants to some specific concepts of GRASS GIS, like its database structure, location, mapset and region and will show how simple the approach really is. During the hands-on part, the participants will learn how to use common geographical data formats in GRASS GIS, starting from simple actions like adding data into the GRASS GIS environment and displaying it. Finally, the participants will run simple analyses like map algebra calculations for raster maps and vector buffering and visualize and export the results
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
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
Benefits of hyperspectral remote sensing for tracking plant invasions
No full textAim We aim to report what hyperspectral remote sensing can offer for invasion ecologists and review recent progress made in plant invasion research using hyperspectral remote sensing. Location United States. Methods We review the utility of hyperspectral remote sensing for detecting, mapping and predicting the spatial spread of invasive species. We cover a range of topics including the trade-off between spatial and spectral resolutions and classification accuracy, the benefits of using time series to incorporate phenology in mapping species distribution, the potential of biochemical and physiological properties in hyperspectral spectral reflectance for tracking ecosystem changes caused by invasions, and the capacity of hyperspectral data as a valuable input for quantitative models developed for assessing the future spread of invasive species. Results Hyperspectral remote sensing holds great promise for invasion research. Spectral information provided by hyperspectral sensors can detect invaders at the species level across a range of community and ecosystem types. Furthermore, hyperspectral data can be used to assess habitat suitability and model the future spread of invasive species, thus providing timely information for invasion risk analysis. Main conclusions Our review suggests that hyperspectral remote sensing can effectively provide a baseline of invasive species distributions for future monitoring and control efforts. Furthermore, information on the spatial distribution of invasive species can help land managers to make long-term constructive conservation plans for protecting and maintaining natural ecosystems
Fuzzy and boolean forest membership: on the actual separability of land cover classes
No full textForests are among the most important habitats of the Earth for several ecological reasons and their management is a prior task when dealing with landscape conservation. Thematic maps and remote sensing data are powerful tools to be used in landscape planning and forest management; nevertheless, most of the European and Mediterranean forest monitoring and conservation programs do not take into account the continuity of the variation of habitats within the landscape but they only rely on boolean classification methods. The utilisation of a classification method that applies a continuity criterion is fundamental because it is expected to better represent the ecological gradients within a landscape. The aim of this paper is to assess the amount of classification uncertainty related to crisp (boolean) classes, particularly focusing on forest identification uncertainty. Forest fuzzy membership of the Tuscany region (Italy) derived from a Landsat ETM+ image scene was compared with the widely used crisp datasets in European forests management and conservation practices, i.e. the European JRC Forest/Non-Forest map, the CORINE Land Cover 2000 (levels 1 and 2), as well as the Global Land Cover 2000, in order to qualitatively and quantitatively assess the separability of crisp classes with respect to forest fuzzy membership. A statistically significant (p < 0.001) forest fuzzy membership separability among the considered crisp classes was found. Despite the crisp dataset and hierarchical level taken into account, both forest and non-forest crisp classes showed a high degree of forest fuzzy membership variability. Therefore, given the intrinsic mixture of crisp land cover classes, ecological studies on forestal ecosystems should rigorously take into account the classification uncertainty related to a crisp view of ecological entities which are being mapped. RI Neteler, Markus/C-6328-200
Korcak dimension as a novel indicator of landscape fragmentation and re-forestation
No full textIn spatial ecology, habitat fragmentation is an important problem since its increase may create habitat remnants threatening species survival. There are several descriptors to characterize the processes leading to fragmentation. Some of them are model-dependent, while others suffer from the combined error of the perimeter and area measurements of the fragmented patches. In this article – using a theoretical model and a worked example – we would like to show that the Korcak-plot (and the corresponding fractal dimension, the Korcak-dimension) is not only a proper way to describe patchiness, but also applicable to detect secondary processes, like re-forestation, following the primary fragmentation
Landscape complexity and spatial scale influence the relationship between remotely sensed spectral diversity and survey-based plant species richness
No full textQuestions: Species rarefaction curves have long been used for estimating the expected number of species as a function of sampling effort. Nonetheless, sampling species based on standard plant inventories represents an effort-intensive approach. Hence, rarefaction based on remotely sensed information can provide a rapid tool for identifying regions with exceptional richness and turnover. The aim of this paper is to examine (i) if the rates of spectral and species accumulation are positively correlated with one another at different spatial scales, and (ii) if the strength of this correlation differs between regions of varying landscape complexity. Location: Switzerland, Europe. Methods: The plant species data were derived from the Swiss “Biodiversity Monitoring” programme. Seven Landsat ETM+ images covering the whole study area were acquired. We applied species and spectral rarefaction for five biogeographical areas ranging from flat to mountainous zones. The relative increments (rates) of the species and spectral rarefaction curves were compared using Pearson correlation together with locally weighted scatterplot smoothing (LOWESS). Results: The biogeographic regions differed from one another in both their spectral and species diversity. The relationship between spectrally- and species-derived rates of accumulation was non-significant in simple landscapes, but we observed a significant positive correlation in complex landscapes over fine-to-intermediate spatial scales. Conclusions: Spectral rarefaction represents a powerful tool for measuring landscape diversity and potentially predicting species diversity at regional to global spatial scales. Based on remotely sensed information, more efficient diversity-based monitoring programmes can be developed
Big geodata management and analysis using GRASS GIS
No full textEarth Observation (EO) big data are nowadays easily accessible and consist of long time-spanning data series which are often used to perform different kinds of analysis in several fields of application. GRASS GIS is a Free and Open Source geographic information system (GIS) with support for raster, 3D raster and vector data processing. During this workshop we will show how to manage and analyze big EO data in a simple way with GRASS GIS using its temporal framework (TGRASS). The workshop will start showing the basics about GRASS GIS and it will continue processing data with the TGRASS. The workshop will be an intuitive mix of theoretical and hands-on sections using MODIS and Copernicus Sentinel-2 data
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