Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges

Rocchini, Duccio; Balkenhol, Niko; Carter, Gregory A.; Foody, Giles M.; Gillespie, Thomas W.; He, Kate S.; Kark, Salit; Levin, Noam; Lucas, Kelly L.; Luoto, Miska; Nagendra, Harini; Oldeland, Jens; Ricotta, Carlo; Southworth, Jane; Neteler, Markus

doi:10.1016/j.ecoinf.2010.06.001

Cited by 315 publications

(274 citation statements)

References 166 publications

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“…In a broad sense, the selected predictors represent the environmental variability in Monte Oscuro, ratifying a positive correlation between environmental heterogeneity and species richness, which has also been found in other studies [16,18,20,35]. The variables derived from LiDAR information were the best estimators of plant diversity (Figure 4), supporting the notion that greater variability in different biotic and abiotic conditions and the larger number of niches these may provide are important factors in the estimation of diversity [46].…”

Section: Ecological Implicationssupporting

confidence: 83%

“…The conservation of biodiversity must be a priority [9,14,15]. Therefore, rapid and objective methods must be developed to assess and predict biodiversity spatially [16]; observation by remote sensing plays a fundamental role here in light of its capacity to extrapolate point information about biodiversity collected in situ to different spatial and temporal scales [17].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

et al. 2015

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Abstract:The Andes foothills of central Chile are characterized by high levels of floristic diversity in a scenario, which offers little protection by public protected areas. Knowledge of the spatial distribution of this diversity must be gained in order to aid in conservation management. Heterogeneous environmental conditions involve an important number of niches closely related to species richness. Remote sensing information derived from satellite hyperspectral and airborne Light Detection and Ranging (LiDAR) data can be used as proxies to generate a spatial prediction of vascular plant richness. This study aimed to estimate the spatial distribution of plant species richness using remote sensing in the Andes foothills of the Maule Region, Chile. This region has a secondary deciduous forest dominated by Nothofagus obliqua mixed with sclerophyll species. Floristic measurements were performed using a nested plot design with 60 plots of 225 m 2 each. Multiple predictors were evaluated: 30 topographical and vegetation structure indexes from LiDAR data, and 32 spectral indexes and band transformations from the EO1-Hyperion sensor. A random forest algorithm was used to identify relevant variables in richness prediction, and these variables were used in turn to obtain a final multiple linear regression predictive model (Adjusted R 2 = 0.651; RSE = 3.69). An independent validation survey was performed with significant results (Adjusted R 2 = 0.571, RMSE = 5.05). Selected variables were statistically significant: OPEN ACCESSRemote Sens. 2015, 7 2693 catchment slope, altitude, standard deviation of slope, average slope, Multiresolution Ridge Top Flatness index (MrRTF) and Digital Crown Height Model (DCM). The information provided by LiDAR delivered the best predictors, whereas hyperspectral data were discarded due to their low predictive power.

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Section: Ecological Implicationssupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

et al. 2015

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“…Palmer, Earls, Hoagland, White, & Wohlgemuth, 2002;Rocchini, Balkenhol, Carter, et al, 2010) which relies on the assumption that spectral heterogeneity can be used to quantify (species) diversity. The obtained species diversity patterns are in turn believed to also be related to environmental (ecosystem) heterogeneity, based on the 'portfolio effect' (Rocchini et al, 2010). The SVH has been mainly used for two purposes: (1) the mapping or detection of biodiversity hotspots (so-called α-diversity); and (2) the development of quantitative measures for species turnover between ecosystems (so-called β-diversity).…”

Section: Validation Of Remotely Sensed Proxies Of Tree Species Richnessmentioning

confidence: 99%

Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy

Somers

Asner

Martin

et al. 2015

Remote Sensing of Environment

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“…Optical remote sensing has long been recognized as a pivotal tool in estimating species diversity [31]. Advances in technology that combine high spatial and spectral resolution data allow for the reliable identification of functional groups, individual species and sometimes even individual trees [29,32,33]. For instance, Asner et al [34] used imaging spectroscopy (sometimes called 'hyperspectral') data to effectively map the distribution of invasive and native forest tree species in a Hawaiian rainforest.…”

Section: (B) Remote Sensing Of Biodiversitymentioning

confidence: 99%

“…(c) Spectral characterization of genotypic identity and genetic variation Spectra have been used to estimate canopy chemistry [21,26] and species diversity [30,33,38,117] in multiple systems, with impressive accuracy. Here, we also demonstrate that AVIRIS imagery can also discriminate genotypes within species (i.e.…”

Section: (A) Genetic Mosaics Of Ecosystem Functioningmentioning

confidence: 99%

Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

Madritch

Kingdon²,

Singh³

et al. 2014

Phil. Trans. R. Soc. B

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Fine-scale biodiversity is increasingly recognized as important to ecosystemlevel processes. Remote sensing technologies have great potential to estimate both biodiversity and ecosystem function over large spatial scales. Here, we demonstrate the capacity of imaging spectroscopy to discriminate among genotypes of Populus tremuloides (trembling aspen), one of the most genetically diverse and widespread forest species in North America. We combine imaging spectroscopy (AVIRIS) data with genetic, phytochemical, microbial and biogeochemical data to determine how intraspecific plant genetic variation influences below-ground processes at landscape scales. We demonstrate that both canopy chemistry and below-ground processes vary over large spatial scales (continental) according to aspen genotype. Imaging spectrometer data distinguish aspen genotypes through variation in canopy spectral signature. In addition, foliar spectral variation correlates well with variation in canopy chemistry, especially condensed tannins. Variation in aspen canopy chemistry, in turn, is correlated with variation in below-ground processes. Variation in spectra also correlates well with variation in soil traits. These findings indicate that forest tree species can create spatial mosaics of ecosystem functioning across large spatial scales and that these patterns can be quantified via remote sensing techniques. Moreover, they demonstrate the utility of using optical properties as proxies for fine-scale measurements of biodiversity over large spatial scales.

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Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges

Cited by 315 publications

References 166 publications

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy

Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

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