1,721,382 research outputs found
What is up? Testing spectral heterogeneity versus NDVI relationship using quantile regression
No full textHigh resolution satellite imagery for tropical biodiversity studies: The devil is in the detail
No full textPositioning of remotely sensed spectral heterogeneity in the framework of life cycle impact assessment on biodiversity
No full textLife cycle assessment (LCA) is among the most robust, comprehensive and scientifically sound methodologies to unravel the potential causes and effects of anthropogenic impacts on the different geobiosphere compartments . In this framework, a major challenge is related to the development of consensual and operational approaches to assess the human pressure on biodiversity. This has recently brought about the attention of the larger community of ecologists and biologists by Souza et al. (2015). They thoroughly examined the practice of Life Cycle Impact Assessment (LCIA) of land use interventions (land occupation and conversion) on biodiversity, identifying several modeling gaps. Among them is the absence of a wider landscape oriented and operational procedure to evaluate the loss of biological diversity. Concerned by the widespread lack in LCIA of cross-fertilization with disciplines traditionally related to biodiversity analysis (e.g. biology and ecology), they proposed a roadmap to address current methodological limitations. They recommended developing impact characterization factors (CFs) at different spatial scales e.g. by replacing land cover maps with continuous environmental information, and including landscape aspects such as habitat fragmentation or connectivity of ecosystems
Boosting the use of spectral heterogeneity in the impact assessment of agricultural land use on biodiversity
No full textNo consensus has been yet achieved among Life Cycle Assessment (LCA) practitioners on how to assess the impact on biodiversity due to land uses and land use changes, in particular with regard to agricultural areas. In the domain of nature conservation and landscape ecology, spectral heterogeneity (SH) derived from remotely sensed imagery is considered a viable proxy for species diversity detection. The assessment rationale is based on the ‘spectral variation hypothesis’: the higher the spectral variability, the higher the ecological heterogeneity and species community diversity, occupying different niches. Our hypothesis is that SH can be effective to improve or complement current Life Cycle Impact Assessment−LCIA practice on biodiversity loss evaluation driven by land useNo consensus has been yet achieved among Life Cycle Assessment (LCA) practitioners on how to assess
the impact on biodiversity due to land uses and land use changes, in particular with regard to agricultural
areas. In the domain of nature conservation and landscape ecology, spectral heterogeneity (SH) derived
from remotely sensed imagery is considered a viable proxy for species diversity detection. The assess-
ment rationale is based on the ‘spectral variation hypothesis’: the higher the spectral variability, the
higher the ecological heterogeneity and species community diversity, occupying different niches. Our
hypothesis is that SH can be effective to improve or complement current Life Cycle Impact Asses-
smentLCIA practice on biodiversity loss evaluation driven by land use. Hence, we aim here to explore
this assumption by computing SH at a local scale of crops cultivation in Southern Alps (Trentino province,
Italy), and then combining this information with land use over 30 years. We observe and analyse the
relationships between land cover maps and habitat heterogeneity at different time and spatial resolu-
tions. This allows us to argue about the robustness of SH to be a potential surrogate of environmental
nuances for species variability detection in LCIA.
. Hence, we aim here to explore this assumptiNo consensus has been yet achieved among Life Cycle Assessment (LCA) practitioners on how to assess
the impact on biodiversity due to land uses and land use changes, in particular with regard to agricultural
areas. In the domain of nature conservation and landscape ecology, spectral heterogeneity (SH) derived
from remotely sensed imagery is considered a viable proxy for species diversity detection. The assess-
ment rationale is based on the ‘spectral variation hypothesis’: the higher the spectral variability, the
higher the ecological heterogeneity and species community diversity, occupying different niches. Our
hypothesis is that SH can be effective to improve or complement current Life Cycle Impact Asses-
smentLCIA practice on biodiversity loss evaluation driven by land use. Hence, we aim here to explore
this assumption by computing SH at a local scale of crops cultivation in Southern Alps (Trentino province,
Italy), and then combining this information with land use over 30 years. We observe and analyse the
relationships between land cover maps and habitat heterogeneity at different time and spatial resolu-
tions. This allows us to argue about the robustness of SH to be a potential surrogate of environmental
nuances for species variability detection in LCIA.
on by computing SH at a local scale of crops cNo consensus has been yet achieved among Life Cycle Assessment (LCA) practitioners on how to assess
the impact on biodiversity due to land uses and land use changes, in particular with regard to agricultural
areas. In the domain of nature conservation and landscape ecology, spectral heterogeneity (SH) derived
from remotely sensed imagery is considered a viable proxy for species diversity detection. The assess-
ment rationale is based on the ‘spectral variation hypothesis’: the higher the spectral variability, the
higher the ecological heterogeneity and species community diversity, occupying different niches. Our
hypothesis is that SH can be effective to improve or complement current Life Cycle Impact Asses-
smentLCIA practice on biodiversity loss evaluation driven by land use. Hence, we aim here to explore
this assumption by computing SH at a local scale of crops cultivation in Southern Alps (Trentino province,
Italy), and then combining this information with land use over 30 years. We observe and analyse the
relationships between land cover maps and habitat heterogeneity at different time and spatial resolu-
tions. This allows us to argue about the robustness of SH to be a potential surrogate of environmental
nuances for species variability detection in LCIA.
ultivation in Southern Alps (Trentino province, Italy), and then combining this information with land use over 30 years. We observe and analyse the relationships between land cover maps and habitat heterogeneity at different time and spatial resolutions. This allows us to argue about the robustness of SH to be a potential surrogate of environmental nuances for species variability detection in LCI
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
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
Space-Ruled ecological processes: introduction to the special issue on spatial ecology
Full text linkThis 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
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