Remote sensing of biodiversity: Soil correction and data dimension reduction methods improve assessment of α-diversity (species richness) in prairie ecosystems

Gholizadeh, Hamed; Gamon, John A.; Zygielbaum, A. I.; Wang, Ran; Schweiger, Anna K.; Cavender‐Bares, Jeannine

doi:10.1016/j.rse.2017.12.014

Cited by 101 publications

(101 citation statements)

References 64 publications

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“…Recent advances in sensor technology, particularly increased spectral resolution, have led to a variety of approaches to calculate spectral α‐diversity (Rocchini et al ). This includes metrics such as the standard deviation or coefficient of variation of spectral indices (Oindo & Skidmore ), or spectral bands among pixels (Hall et al ; Gholizadeh et al ; Wang et al ), the convex hull volume of pixels in spectral feature space (Dahlin ), the mean distance of pixels from the spectral centroid (Rocchini et al ), the number of spectrally distinct clusters or spectral species in ordination space (Féret & Asner ), and diversity metrics based on dissimilarity matrices among species spectra or image pixels (Schweiger et al ). Of these, our method is most similar to the mean distance to the spectral centroid (Rocchini et al ).…”

Section: Discussionmentioning

confidence: 99%

“…Spectral diversity is sometimes called spectral heterogeneity or spectral variability (Rocchini et al 2010), and has been defined as spatial variation in spectral reflectance (Rocchini et al 2010;Ustin & Gamon 2010;Gholizadeh et al 2018;Wang & Gamon 2019). Intuitively, spectral diversity can be conceptualised as multivariate dispersion, for which there are various statistical measures highlighting different aspects of spectral diversity.…”

Section: Introductionmentioning

confidence: 99%

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Partitioning plant spectral diversity into alpha and beta components

2019

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Plant spectral diversity – how plants differentially interact with solar radiation – is an integrator of plant chemical, structural, and taxonomic diversity that can be remotely sensed. We propose to measure spectral diversity as spectral variance, which allows the partitioning of the spectral diversity of a region, called spectral gamma (γ) diversity, into additive alpha (α; within communities) and beta (β; among communities) components. Our method calculates the contributions of individual bands or spectral features to spectral γ‐, β‐, and α‐diversity, as well as the contributions of individual plant communities to spectral diversity. We present two case studies illustrating how our approach can identify 'hotspots’ of spectral α‐diversity within a region, and discover spectrally unique areas that contribute strongly to β‐diversity. Partitioning spectral diversity and mapping its spatial components has many applications for conservation since high local diversity and distinctiveness in composition are two key criteria used to determine the ecological value of ecosystems.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Partitioning plant spectral diversity into alpha and beta components

2019

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“…For the BioDIV data collected with the leaf clip, we used spectra of 12 randomly selected individuals per plot resulting in a total of 30 communities used for analysis. For the BioDIV data collected with the imaging spectrometer, we randomly extracted 30 pixels per plot; spectra were corrected for soil effects following (Gholizadeh et al ., 2018). For FAB, we used spectra of nine randomly selected individuals per plot, resulting in a total of 68 communities used for analysis.…”

Section: Methodsmentioning

confidence: 99%

Coupling spectral and resource-use complementarity in experimental grassland and forest communities

Schweiger

Cavender‐Bares

Kothari

et al. 2020

Preprint

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24• Plants' spectra provide integrative measures of their chemical, morphological, anatomical, and 25 architectural traits. We posit that the degree to which plants differentiate in n-dimensional 26 spectral space is a measure of niche differentiation and reveals functional complementarity. 27• In both experimentally and naturally assembled communities, we quantified plant niches using 28 hypervolumes delineated by either plant spectra or 10 functional traits. We compared the niche 29 fraction unique to each species in spectral and trait spaces with increasing dimensionality, and 30 investigated the association between the spectral space occupied, plant growth and community 31 productivity. 32• We show that spectral niches differentiated species better than their functional trait niches. The 33 amount of spectral space occupied by individuals and plant communities increased with plant 34 growth and community productivity, respectively. Further, community productivity was better 35 explained by inter-individual spectral complementarity than by productive individuals 36 occupying large spectral niches. 37 • The degree of differentiation in spectral space provides the conceptual basis for identifying 38 plant taxa spectrally. Moreover, our results indicate that the size and position of plant spectral 39 niches reflect ecological strategies that shape community composition and ecosystem function, 40 with the potential to reveal insight in niche partitioning over large areas with spectroscopy. 41 42 Keywords 43 complementarity, functional traits, ecosystem function, Hutchinsonian niche, plant species 44 identification, spectroscopy, spectral space, remote sensing 45 3

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“…Soil provides the basis for plant growth, which among others is controlled by the physical, chemical, and biological properties of the soil. Soil-related factors like nutrient availability, carbon content, or soil structure affect biodiversity on the one hand, but biodiversity also determines the spatial soil heterogeneity on the other hand [48,99]. As a consequence, there are strong interactions between biodiversity and soil characteristics.…”

Section: Pedologymentioning

confidence: 99%

“…Castaldi et al [113] recently showed that narrowband, hyperspectral imagers provide significantly higher potential for the quantitative estimation of soil variables compared to multispectral sensors because broadband instruments cannot resolve diagnostic spectral features of the soil spectrum. On the other hand, recent works show the potential of the Sentinel-2 sensor for soil organic carbon mapping [99,113]. For both laboratory and imaging spectroscopy, mostly non-parametric regression algorithms are applied [114].…”

Section: Pedologymentioning

confidence: 99%

Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

Lausch

Baade

Bannehr³

et al. 2019

Remote Sensing

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In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits.

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Remote sensing of biodiversity: Soil correction and data dimension reduction methods improve assessment of α-diversity (species richness) in prairie ecosystems

Cited by 101 publications

References 64 publications

Partitioning plant spectral diversity into alpha and beta components

Partitioning plant spectral diversity into alpha and beta components

Coupling spectral and resource-use complementarity in experimental grassland and forest communities

Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

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