Remote sensing for biodiversity science and conservation

Turner, Woody; Spector, Sacha; Gardiner, Ned; Fladeland, Matthew; Sterling, Eleanor J.; Steininger, Marc K.

doi:10.1016/s0169-5347(03)00070-3

Cited by 1,123 publications

(889 citation statements)

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“…Several authors have pointed out that the spatial scales provided by remote sensing systems and those used in ecological studies do not match (Turner et al 2003;Kerr and Ostrovsky 2003). This paper is an example of how interdisciplinarity between remote sensing and landscape ecology can enhance our understanding of ecological patterns.…”

Section: Discussionmentioning

confidence: 96%

“…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%

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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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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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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“…Thus, there has been a trend for research to develop away from the mapping of broad land cover classes towards a focus on specific classes, often detailed classes such as tree species. Indeed, the latter application has become a focus of considerable attention and made use of data acquired by a range of remote sensing systems (Franklin, 2000;Nagendra, 2001;Brandtberg, 2002;Haara and Haarala, 2002;Brandtberg et al, 2003;Holmgren and Persson, 2003;Sanchez-Azofelfa et al, 2003;Turner et al, 2003;Carleer and Wolff, 2004;Wang et al, 2004;Goodwin et al, 2005;Boschetti et al, 2007 van Aardt andWynne, 2007). The derived information can aid both the assessment of biodiversity and its conservation Wilson et al,2004), especially as the spatial distribution of a species influences its ability to reproduce, compete and disperse as well as suffer damage or death.…”

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confidence: 99%

An overview of recent remote sensing and GIS based research in ecological informatics

Boyd

Foody

2011

Ecological Informatics

118

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This article provides an overview of some of the recent research in ecological informatics involving remote sensing and GIS. Attention focuses on a selected range of issues including topics such as the nature of remote sensing data sets, issues of accuracy and uncertainty, data visualization and sharing activities as well as developments in aspects of ecological modeling research. It is shown that considerable advances have been made over recent years and foundations for future research established. BackgroundGeotechnology has been couched as one of the three "mega-technologies" of the 21 st century, along with nanotechnology and biotechnology (Gewin, 2004). On the cusp of the second decade of the 21 st century it is evident that recent developments in the geotechnologies of Geographic Information Science (GIS) and remote sensing have had a substantial impact on ecological research, providing spatial data and associated information to enable the further understanding of ecological systems (Rundell et al., 2009). In a digital society there is sometimes an expectation that information is just a click away and this may apply to spatial information in the domain of ecological informatics. Indeed, it has been in the last five years that developments in and relating to the fields of GIS and remote sensing, such as those seen in internet technologies (e.g., Web 2.0; high performance communication networks; Brunt et al., 2007), sensing technology (e.g., quantum well infrared photodetectors; Krabach, 2000), data standards and interoperability (e.g., OGC® KML 2.2) and spatially explicit modeling (e.g., Osborne et al., 2007), have resulted in the revelation of previously unobservable phenomena and posing of what might be termed second generation of ecological questions. During this time we have also seen more support for the open exchange of data and software and infrastructure for location based services. This paper serves as a review of some of the recent developments in GIS and remote sensing that have been or promises to be of benefit to ecological informatics. The subjects addressed are necessarily selective and limited in scope, drawing on topics of interest to us and, we hope, to you.

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“…For example, sphagnum bogs, wetlands, savannas, salt marshes and coral reefs create habitats that provide food sources, micro-environments and protection for a whole community of species that have specificity to these habitat types (see, for example, Jones et al, 1994). The identification and classification of these macro structures by remote sensing is possible (e.g., Yang and Prince 2000;Ozesmi and Bauer 2002;Silvestri et al 2003;Mumby et al 2004;Harris and Bryant 2009) and quantitative assessments of biodiversity populations have also been made using the species-area relationship and extent of habitat derived from remote sensing (Turner et al 2003;Jha et al 2005). Other examples of the link between specific species and landscape indicators have been reported by Dormann et al (2007) and Hendrickx et al (2007).…”

Section: Developing Indicators For Ecosystem Service Monitoringmentioning

confidence: 99%

The role of remote sensing in the development of SMART indicators for ecosystem services assessment

2016

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Remote sensing for biodiversity science and conservation

Cited by 1,123 publications

References 25 publications

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

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

An overview of recent remote sensing and GIS based research in ecological informatics

The role of remote sensing in the development of SMART indicators for ecosystem services assessment

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