REMOTE SENSING OF VEGETATION, PLANT SPECIES RICHNESS, AND REGIONAL BIODIVERSITY HOTSPOTS

By W. Gould. 2000

386 citations recorded by the source corpus.

Subfields: Ecology, Nature and Landscape Conservation, Plant Science

View Paper: https://www.semanticscholar.org/paper/4b86fb25f218c6d6b6c57024a2317c556f837f8f

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In Ecology

In Nature and Landscape Conservation

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Citations in the corpus

Distance decay in spectral space in analysing ecosystem β‐diversity (2007, background): “Spectral heterogeneity has been demonstrated to have a sort of predictive power with respect to species richness within a given site, at different spatial scales (Gould 2000, Oindo and Skidmore 2002, Rocchini et al. 2004).”
Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches (2011, background): “These models can also help to improve our understanding of biodiversity patterns and community assembly processes (Gioia & Pigott, 2000; Gould, 2000; Moser et al., 2005; Thuiller et al., 2006; NoguesBravo et al., 2008).”
Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges (2010, methodology): “Examples include dispersion measures based on vegetation indices such as NDVI (e.g. Gould, 2000; Oindo and Skidmore, 2002; Gillespie, 2005; Lassau et al., 2005; Levin et al., 2007) or on each band or on one principal component (e.g. Carter et al., 2005; Lucas and Carter, 2008; see also “7.”
Precipitation mediates the effect of human disturbance on the Brazilian Caatinga vegetation (2017, background): “We also assessed landscape-scale vegetation cover (VC) – an important indicator of acute disturbance (Gould 2000; Fahrig 2013).”
Linking variability in species composition and MODIS NDVI based on beta diversity measurements (2009, background): “Introduction field or plot data to assess the spatial and temporary changes An important issue in terrestrial vegetation study is to identify the distribution patterns of floristic composition of a given site and to determine the factors controlling the distribution and diversity of species.”
Modelling butterfly distribution based on remote sensing data (2002, methodology): “Remote sensing data may be used to parameterize statistical models explaining observed variation in the occurrence and diversity of species (Griffiths et al., 1993; Scott et al., 1993; Gould, 2000; Griffiths et al., 2000).”
Predicting species distributions across the Amazonian and Andean regions using remote sensing data (2008, background): “Direct measurements of the Earth’s hydrological and biophysical characteristics, its geological features and its climate from space have provided new data layers with spatial and temporal resolutions relevant to landscape-scale habitat characteristics and ecological processes (Gould, 2000; Nagendra,”
Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks (2014, background): “Selected authors have also presented reviews on the potential and contribution of remote sensing for assessing plant species diversity (Gould 2000; Griffiths, Lee, and Eversham 2000; Nagendra 2001).”
Extent and fragmentation of suitable leopard habitat in South Africa (2013, background): “The NDVI represents various factors that contribute to suitable leopard habitat including the abundance of prey, water and vegetation cover (Gould, 2000).”
Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery (2006, methodology): “Environmental factors, such as elevation, slope, and soil moisture, are widely used ancillary data (Gould, 2000; Dymond and Johnson, 2002; McIver and Friedl, 2002).”
Does climate determine broad‐scale patterns of species richness? A test of the causal link by natural experiment (2003, neutral): “Remotely sensed NDVI is a measure of the absorbance of light by foliage, and it has been related both to plant biomass and to plant productivity (Lo Seen Chong et al., 1993; Gould, 2000).”
Lightweight unmanned aerial vehicles will revolutionize spatial ecology (2013, background): “In particular, a strong focus has been placed on the use of data obtained from spaceborne remote-sensing instruments because these provide regional-to global-scale observations and repeat timeseries sampling of ecological indicators (eg Gould 2000).”
Measuring and modelling biodiversity from space (2008, methodology): “…studies have related species richness or diversity to information on the land-cover mosaic of test sites derived from satellite imagery (Nagendra and Gadgil, 1999a; 1999b; Gould, 2000; Griffi ths et al., 2000; Kerr et al., 2001; Oindo et al., 2003;Gottschalk et al., 2005; Leyequien et al., 2007).”
Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert of New Mexico (2009, background): “Texture of NDVI, as opposed to mean NDVI only, also accounts for up to 65% of the variability in plant species richness in the Canadian Arctic (Gould 2000).”