The spectral variability hypothesis does not hold across landscapes

Schmidtlein, Sebastian; Fassnacht, Fabian Ewald

doi:10.1016/j.rse.2017.01.036

Cited by 88 publications

(93 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…). Our results generally support this conclusion, though only at pixel resolutions coarse enough to adequately incorporate sub‐pixel heterogeneity and at ground scales large enough to subsume local diversity (Rocchini , Schmidtlein and Fassnacht ). These findings likewise suggest the role of phenological status in determining the strength of the SVH.…”

Section: Discussionsupporting

confidence: 72%

“…In this regard, the spectral variation hypothesis (SVH) posits a positive relationship between spectral variation due to foliar biochemical diversity and phylogenetically-conserved trait variation among taxa that can aid remotely-sensed biodiversity modeling efforts (Palmer et al 2002, Cavender-Bares et al 2016. Our results generally support this conclusion, though only at pixel resolutions coarse enough to adequately incorporate sub-pixel heterogeneity and at ground scales large enough to subsume local diversity (Rocchini 2007, Schmidtlein andFassnacht 2017). These findings likewise suggest the role of phenological status in determining the strength of the SVH.…”

Section: Remotely-sensed Predictors Of Vascular Plant Richnesssupporting

confidence: 54%

See 1 more Smart Citation

Mapping multi‐scale vascular plant richness in a forest landscape with integrated LiDARand hyperspectral remote‐sensing

Hakkenberg

Zhu

Peet

et al. 2018

Ecology

View full text Add to dashboard Cite

The central role of floristic diversity in maintaining habitat integrity and ecosystem function has propelled efforts to map and monitor its distribution across forest landscapes. While biodiversity studies have traditionally relied largely on ground-based observations, the immensity of the task of generating accurate, repeatable, and spatially-continuous data on biodiversity patterns at large scales has stimulated the development of remote-sensing methods for scaling up from field plot measurements. One such approach is through integrated LiDAR and hyperspectral remote-sensing. However, despite their efficiencies in cost and effort, LiDAR-hyperspectral sensors are still highly constrained in structurally- and taxonomically-heterogeneous forests - especially when species' cover is smaller than the image resolution, intertwined with neighboring taxa, or otherwise obscured by overlapping canopy strata. In light of these challenges, this study goes beyond the remote characterization of upper canopy diversity to instead model total vascular plant species richness in a continuous-cover North Carolina Piedmont forest landscape. We focus on two related, but parallel, tasks. First, we demonstrate an application of predictive biodiversity mapping, using nonparametric models trained with spatially-nested field plots and aerial LiDAR-hyperspectral data, to predict spatially-explicit landscape patterns in floristic diversity across seven spatial scales between 0.01-900 m . Second, we employ bivariate parametric models to test the significance of individual, remotely-sensed predictors of plant richness to determine how parameter estimates vary with scale. Cross-validated results indicate that predictive models were able to account for 15-70% of variance in plant richness, with LiDAR-derived estimates of topography and forest structural complexity, as well as spectral variance in hyperspectral imagery explaining the largest portion of variance in diversity levels. Importantly, bivariate tests provide evidence of scale-dependence among predictors, such that remotely-sensed variables significantly predict plant richness only at spatial scales that sufficiently subsume geolocational imprecision between remotely-sensed and field data, and best align with stand components including plant size and density, as well as canopy gaps and understory growth patterns. Beyond their insights into the scale-dependent patterns and drivers of plant diversity in Piedmont forests, these results highlight the potential of remotely-sensible essential biodiversity variables for mapping and monitoring landscape floristic diversity from air- and space-borne platforms.

show abstract

Section: Discussionsupporting

confidence: 72%

Section: Remotely-sensed Predictors Of Vascular Plant Richnesssupporting

confidence: 54%

Mapping multi‐scale vascular plant richness in a forest landscape with integrated LiDARand hyperspectral remote‐sensing

Hakkenberg

Zhu

Peet

et al. 2018

Ecology

View full text Add to dashboard Cite

show abstract

“…We aimed to create three classes, because existing vegetation maps predefine three main community types in the study region: succulent scrub, pine forest and subalpine scrub. The K ‐means algorithm has been used before to test the SVH (Schmidtlein & Fassnacht, ). We then conducted MANOVA (Anderson, ) to estimate how K ‐means classification on RS variables fits to the β‐diversity.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, spectral variation is associated with α‐ and β‐diversity (Palmer et al., ; Rocchini, Chiarucci, & Loiselle, ). However, the SVH does not apply to all ecosystems and depends on the extent of RS and in‐situ data as well as the spatial, temporal and spectral resolution of RS data (Schmidtlein & Fassnacht, ).…”

Section: Introductionmentioning

confidence: 99%

Remote sensing of β‐diversity: Evidence from plant communities in a semi‐natural system

Hoffmann

Schmitt

Chiarucci

et al. 2018

Applied Vegetation Science

View full text Add to dashboard Cite

Question: Do remote sensing signals represent β-diversity? Does β-diversity agree with community types? Location: UNESCO Man and the Biosphere Reserve, La Palma, Canary Islands. Methods: We recorded perennial, vascular plant species abundances in 69 plots (10 m × 10 m) in three pre-defined community types along an elevational gradient of 2,400 m: succulent scrubland, Pinus canariensis forest and sub alpine scrubland. The remote sensing data consists of structural variables from airborne Light Detection and Ranging (LiDAR) and multispectral variables from a time series of Sentinel-2 (S2)images. Non-metric Multidimensional Scaling was used to assess β-diversity between plots. K-means unsupervised clustering was applied to remote sensing variables to distinguish three community types. We subsequently quantified the explanatory power of S2 and LiDAR variables representing β-diversity via the Mantel test, variation partitioning and multivariate analysis of variance. We also investigated the sensitivity of results to grain size of remote sensing data (20, 40, 60 m). Results:The β-diversity between the succulent and pine community is high, whereas the β-diversity between the pine and sub alpine community is low. In the wet season, up to 85% of β-diversity is reflected by remote sensing variables. The S2 variables account for more explanatory power than the LiDAR variables. The explanatory power of LiDAR variables increases with grain size, whereas the explanatory power of S2 variables decreases. Conclusion:At the lower ecotone, β-diversity agrees with the pre-defined community distinction, while at the upper ecotone the community types cannot be clearly separated by compositional dissimilarity alone. The high β-diversity between the succulent scrub and pine forest results from positive feedback switches of P. canariensis, being a fire-adapted, key tree species. In accordance with the spectral variation hypothesis, remote sensing signals can adequately represent β-diversity for a large extent, in a short time and at low cost. However, in-situ sampling is necessary to fully understand community composition. Nature conservation requires such interdisciplinary approaches.

show abstract

“…The reasoning behind this approach is that environmental heterogeneity and high biological diversity are interconnected, because heterogeneous areas are likely to support more species due to a higher number of available ecological niches (Gaston, ). Limitations of this approach have been identified (Schmidtlein & Fassnacht, ), particularly related to coarse spatial grains. In an earlier study, Schmidtlein, Feilhauer, Bruelheide, and Rocchini () developed a model using PLSR by regressing canopy reflectance against field data on the distribution of plant strategies according to the CSR model (competitive strategists, stress tolerators, ruderals) of Grime (, ).…”

Section: Sensors: Technical Principles and Recent Research Findingsmentioning

confidence: 99%

Remote sensing as a tool to assess botanical composition, structure, quantity and quality of temperate grasslands

Wachendorf

Fricke

Möckel

2017

Grass and Forage Science

116

100

View full text Add to dashboard Cite

Grassland systems frequently exhibit small‐scale botanical and structural heterogeneity with pronounced spatio‐temporal dynamics. These features present particular challenges for sensor applications, in addition to limitations posed by the high cost and low spatial resolution of many available remote‐sensing (RS) systems. There has been little commercial application of RS for practical grassland farming. This article considers the developments in sensor performance, data analysis and modelling over recent decades, identifies significant advances in RS for grassland research and practice and reviews the most important sensor types and corresponding findings in research. Beside improvements of single sensor types, the development of systems with complementary sensors is seen as a very promising research area, and one that will help to overcome the limitations of single sensors and provide better information about grassland composition, yield and quality. From an agronomic point of view, thematic maps of farm fields are suggested as the central outcome of RS and data analysis. These maps could represent the relevant grassland features and constitute the basis for various farm management decisions at strategic, tactical and operational levels. The overarching goal will be to generate low cost, appropriate and timely information that can be provided to farmers to support their decision‐making.

show abstract

The spectral variability hypothesis does not hold across landscapes

Cited by 88 publications

References 38 publications

Mapping multi‐scale vascular plant richness in a forest landscape with integrated LiDARand hyperspectral remote‐sensing

Mapping multi‐scale vascular plant richness in a forest landscape with integrated LiDARand hyperspectral remote‐sensing

Remote sensing of β‐diversity: Evidence from plant communities in a semi‐natural system

Remote sensing as a tool to assess botanical composition, structure, quantity and quality of temperate grasslands

Contact Info

Product

Resources

About