The relationship between the spectral diversity of satellite imagery, habitat heterogeneity, and plant species richness

Warren, Steven D.; Alt, Martin; Olson, Keith D.; Irl, Severin D. H.; Steinbauer, Manuel J.; Jentsch, Anke

doi:10.1016/j.ecoinf.2014.08.006

Cited by 47 publications

(40 citation statements)

References 42 publications

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“…Locally, raw PC values representing spectral variation and raw physical landscape properties were consistently better able to balance model fit and complexity than other textural or diversity measures. This is consistent with the findings of Warren et al (2014), who found spectral Fig. 6 A map of the residuals (observed-fitted plant species richness) for 1993 illustrating the spatial variability of the relationship between plant species richness with landscape complexity.…”

Section: Discussionsupporting

confidence: 80%

Quantifying spatiotemporal drivers of environmental heterogeneity in Kruger National Park, South Africa

et al. 2016

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Context Environmental heterogeneity is considered an important mechanism of biodiversity. How environmental heterogeneity is characterised by the compositional, structural and functional variation of biotic and abiotic components is a central research theme in conservation. Objectives We explore how environmental heterogeneity relates to the underlying physical landscape template and how that relationship changes over space and time. We examine how, in some areas, environmental heterogeneity may also be driven by dynamic ecological processes, and how this relates to patterns of plant species richness. Method We use local geographically weighted regression to spatially partition environmental heterogeneity, measured as Landsat spectral variance, into the portion explained by stable physical landscape properties (R 2 ) and the portion unexplained (1-R 2 ) which we term landscape complexity. We explore how this relationship varies spatially and temporally as a function of dynamic ecological processes such as rainfall and season in Kruger National Park, as well as plant species richness at landscape scales. Results The significance and direction of relationships varied over space and time and as a function of rainfall and season. R 2 values generally decreased in higher rainfall summer months and revealed patterns describing the importance of known stable factors relative to unknown dynamic factors. Landscape complexity (1-R 2 ) explained over 70 % of variation in species richness. Conclusions Rainfall and seasonality are important drivers of environmental heterogeneity. The spatial arrangement and magnitude of model agreement helped disentangle the relative influence of the physical landscape template on environmental heterogeneity. Given the high correlation with species richness, landscape complexity provides complementary guidance to biodiversity research and monitoring prioritization.Electronic supplementary material The online version of this article

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Section: Discussionsupporting

confidence: 80%

Quantifying spatiotemporal drivers of environmental heterogeneity in Kruger National Park, South Africa

et al. 2016

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“…For example, Viedma [43] used methods based on spectral texture data, while Heumann [44] applied an approach using measures of statistical dispersion to represent spectral diversity. Warren [45] compared two categories of spectral heterogeneity metrics-one category calculated with the help of principle component analysis (PCA) and one category developed from semivariogram descriptors-and found that both types of metrics performed equally well as predictors of species diversity. Oldeland [17] and Rocchini [46] also used spectral heterogeneity calculated with the help of PCA to model species diversity.…”

Section: Calculating Mean Spectral Reflectance and Spectral Heterogenmentioning

confidence: 99%

Airborne Hyperspectral Data Predict Fine-Scale Plant Species Diversity in Grazed Dry Grasslands

et al. 2016

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Semi-natural grasslands with grazing management are characterized by high fine-scale species richness and have a high conservation value. The fact that fine-scale surveys of grassland plant communities are time-consuming may limit the spatial extent of ground-based diversity surveys. Remote sensing tools have the potential to support field-based sampling and, if remote sensing data are able to identify grassland sites that are likely to support relatively higher or lower levels of species diversity, then field sampling efforts could be directed towards sites that are of potential conservation interest. In the present study, we examined whether aerial hyperspectral (414-2501 nm) remote sensing can be used to predict fine-scale plant species diversity (characterized as species richness and Simpson's diversity) in dry grazed grasslands. Vascular plant species were recorded within 104 (4 mˆ4 m) plots on the island of Öland (Sweden) and each plot was characterized by a 245-waveband hyperspectral data set. We used two different modeling approaches to evaluate the ability of the airborne spectral measurements to predict within-plot species diversity: (1) a spectral response approach, based on reflectance information from (i) all wavebands, and (ii) a subset of wavebands, analyzed with a partial least squares regression model, and (2) a spectral heterogeneity approach, based on the mean distance to the spectral centroid in an ordinary least squares regression model. Species diversity was successfully predicted by the spectral response approach (with an error of ca. 20%) but not by the spectral heterogeneity approach. When using the spectral response approach, iterative selection of important wavebands for the prediction of the diversity measures simplified the model but did not improve its predictive quality (prediction error). Wavebands sensitive to plant pigment content (400-700 nm) and to vegetation structural properties, such as above-ground biomass (700-1300 nm), were identified as being the most important predictors of plant species diversity. We conclude that hyperspectral remote sensing technology is able to identify fine-scale variation in grassland diversity and has a potential use as a tool in surveys of grassland plant diversity.

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“…The vegetation affects both the micro-climate to which ticks are exposed and the host communities that ticks feed on [19,29]. The larger the number of potential ecological niches present in heterogeneous landscapes, the higher species diversity there may be [50,67]). In mosaic croplands, land cover heterogeneity is expected to be higher, and thus likely associated with arthropod abundance and diversity [68][69][70][71].…”

Section: Discussionmentioning

confidence: 99%

Looking for Ticks from Space: Using Remotely Sensed Spectral Diversity to Assess Amblyomma and Hyalomma Tick Abundance

Clercq

Tordoni

et al. 2019

Remote Sensing

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Landscape heterogeneity, as measured by the spectral diversity of satellite imagery, has the potential to provide information on the resources available within the movement capacity range of arthropod vectors, and to help predict vector abundance. The Spectral Variation Hypothesis states that higher spectral diversity is positively related to a higher number of ecological niches present in the landscape, allowing more species to coexist regardless of the taxonomic group considered. Investigating the landscape heterogeneity as a proxy of the resources available to vectors may be relevant for complex and continuous agro-forest mosaics of small farmlands and degraded forests, where land cover classification is often imprecise. In this study, we hypothesized that larger spectral diversity would be associated with higher tick abundance due to the potentially higher number of hosts in heterogeneous landscapes. Specifically, we tested whether spectral diversity indices could represent heterogeneous landscapes, and if so, whether they explain Amblyomma and Hyalomma tick abundance in Benin and inform on their habitat preferences. Benin is a West-African country characterized by a mosaic landscape of farmland and degraded forests. Our results showed that both NDVI-derived and spectral predictors are highly collinear, with NDVI-derived predictors related to vegetated land cover classes and spectral predictors correlated to mosaic landscapes. Amblyomma abundance was not related to the predictors considered. Hyalomma abundance showed positive relationships to spectral diversity indices and negative relationships to NDVI-derived-ones. Though taxa dependent, our approach showed moderate performance in terms of goodness of fit (ca. 13–20% R2), which is a promising result considering the sampling and scale limitations. Spectral diversity indices coupled with classical SRS vegetation indices could be a complementary approach for providing further ecological aspects in the field of disease biogeography.

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The relationship between the spectral diversity of satellite imagery, habitat heterogeneity, and plant species richness

Cited by 47 publications

References 42 publications

Quantifying spatiotemporal drivers of environmental heterogeneity in Kruger National Park, South Africa

Quantifying spatiotemporal drivers of environmental heterogeneity in Kruger National Park, South Africa

Airborne Hyperspectral Data Predict Fine-Scale Plant Species Diversity in Grazed Dry Grasslands

Looking for Ticks from Space: Using Remotely Sensed Spectral Diversity to Assess Amblyomma and Hyalomma Tick Abundance

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