From space to species: ecological applications for remote sensing

Kerr, Jeremy T.; Ostrovsky, Marsha

doi:10.1016/s0169-5347(03)00071-5

Cited by 1,145 publications

(849 citation statements)

References 49 publications

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“…The leaf area index (LAI) records the total one-sided leaf area over a unit area, is positively correlated with NPP and can also be measured by satellites. LAI may be a useful productive energy metric (Kerr & Ostrovsky, 2003), but it is rarely used to investigate species-energy relationships (but see van Rensburg, Chown & Gaston, 2002).…”

Section: Key Concepts and Definitions (1 ) Energy Availabilitymentioning

confidence: 99%

Species–energy relationships at the macroecological scale: a review of the mechanisms

2005

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Correlations between the amount of energy received by an assemblage and the number of species that it contains are very general, and at the macro-scale such species-energy relationships typically follow a monotonically increasing curve. Whilst the ecological literature contains frequent reports of such relationships, debate on their causal mechanisms is limited and typically focuses on the role of energy availability in controlling the number of individuals in an assemblage. Assemblages from high-energy areas may contain more individuals enabling species to maintain larger, more viable populations, whose lower extinction risk elevates species richness. Other mechanisms have, however, also been suggested. Here we identify and clarify nine principal mechanisms that may generate positive species-energy relationships at the macro-scale. We critically assess their assumptions and applicability over a range of spatial scales, derive predictions for each and assess the evidence that supports or refutes them. Our synthesis demonstrates that all mechanisms share at least one of their predictions with an alternative mechanism. Some previous studies of species-energy relationships appear not to have recognised the extent of shared predictions, and this may detract from their contribution to the debate on causal mechanisms. The combination of predictions and assumptions made by each mechanism is, however, unique, suggesting that, in principle, conclusive tests are possible. Sufficient testing of all mechanisms has yet to be conducted, and no single mechanism currently has unequivocal support. Each may contribute to species-energy relationships in some circumstances, but some mechanisms are unlikely to act simultaneously. Moreover, a limited number appear particularly likely to contribute frequently to species-energy relationships at the macro-scale. The increased population size, niche position and diversification rate mechanisms are particularly noteworthy in this context.

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Section: Key Concepts and Definitions (1 ) Energy Availabilitymentioning

confidence: 99%

Species–energy relationships at the macroecological scale: a review of the mechanisms

2005

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“…Understanding spatial species distribution is directly linked to the ability to characterize the environmental conditions that drive species distribution. Remotely sensed data offer a unique opportunity to provide environmental information with complete coverage, at different spatial and temporal resolutions and extents, such as land cover classification (Kerr and Ostrovsky 2003) and vegetation biophysical properties (Turner et al 2003;Jacquemoud et al 2009) or structural properties (Lee and Pottier, 2009;Imhoff et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

“…The use of remotely sensed data for ecological applications has increased in recent years, for instance to predict species richness (Kerr and Ostrovsky, 2003;Levanoni et al 2011), or map plant assemblages (Betbeder et al 2014a;Pu 2009;Clark et al 2005). Most of the time, the imagery used in ecology is optical remotely sensed imagery (Kerr and Ostrovsky 2003), for instance the Normalized Difference Vegetation Index (NDVI) is used for many ecological applications (Pettorelli et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Most of the time, the imagery used in ecology is optical remotely sensed imagery (Kerr and Ostrovsky 2003), for instance the Normalized Difference Vegetation Index (NDVI) is used for many ecological applications (Pettorelli et al, 2014). NDVI provides information on vegetation distribution and dynamics and can be used to predict animal distribution, abundance etc.…”

Section: Introductionmentioning

confidence: 99%

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Assessing ecological habitat structure from local to landscape scales using synthetic aperture radar

Betbeder

Hubert‐Moy

Burel

et al. 2015

Ecological Indicators

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Ecological studies need accurate environmental data such as vegetation characterization, landscape structure and organization, to predict and explain the spatial distribution of biodiversity. Few ecological studies use remote sensing data to assess the biophysical or structural properties of vegetation to understand species distribution. To date, Synthetic Aperture Radar (SAR) data have seldom been used for ecological applications. However, these sensors provide data allowing access to the inner structure of vegetation which is a key information in ecology. The objective of this article is to compare the predictive power of ecological habitat structure variables derived from a TerraSAR-X image, an aerial photograph and a SPOT-5 image for species distribution. The test was run with a hedgerow network in Brittany and assessed the spatial distribution of the forest ground carabid beetles which inhabit these hedgerows. The results confirmed that radar and optical images can be indifferently used to extract hedgerow network and derived landscape metrics (hedgerow density, network grain) useful to explain the spatial distribution of forest carabid beetles. In comparison with passive optical remotely sensed data, VHSR SAR images provide new data to characterize vegetation structure and more particularly hedgerow canopy cover, a variable known to explain the spatial distribution of carabid beetles in an agricultural landscape, but not yet quantified at a fine scale. The hedgerow canopy cover derived from the SAR image is 2 a strong predictor of the abundance of forest carabid beetles at two scales i.e. a local scale and a landscape scale. Keywordsbiodiversity, remote sensing, forest carabid beetles, hedgerow canopy cover, landscape scale, quantitative maps

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“…Tortato and Izzo (2016) suggest that this mismatch may result from incompleteness of the version of the World Database on Protected Areas (WDPA) that was used in our global analysis (from August 2013). While we are aware that remotely sensed imagery, GIS datasets, and social media data applied at the global scale may have limitations due to issues of spatial resolution, accuracy, and completeness (Goodchild and Quattrochi 1997, Kerr and Ostrovsky 2003, Goodchild and Li 2012, we disagree with Tortato and Izzo's observation. We show below in a detailed local-scale analysis that the visitation hotpots in Brazil's Pantanal region, as identified in our global analysis (Levin et al 2015), are indeed unprotected, supporting our original paper.…”

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confidence: 61%