The Optimal Leaf Biochemical Selection for Mapping Species Diversity Based on Imaging Spectroscopy

Zhao, Yaolong; Zeng, Yuan; Zhao, Dan; Wu, Bingfang; Zhao, Qian

doi:10.3390/rs8030216

Cited by 15 publications

(11 citation statements)

References 66 publications

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“…Notably, our simulation results also show that spectral diversity may be limited by spectral convergence when the species richness exceeds a certain number (e.g., 14), such as in forest ecosystems [44,45], because the spectral features of the same genera or families are often similar. Furthermore, spectral convergence resulted in underestimations in cases of a high species richness, whereas overestimations in cases of a low species richness may have been a result of large intraspecific spectral variations.…”

Section: Spectral Diversitymentioning

confidence: 67%

“…To estimate the spectral diversity, the average coefficient of variation (CV) was calculated from the leaf spectral data set (n = 298) for each wavelength (n = 2000) with different species richness (SR), which was simulated by randomly selecting different numbers of species out of the 17 species 5000 times. To analyze the spectral diversity, principal component analysis (PCA) and hierarchical cluster analysis with the Ward's minimum variance method were applied based on the average spectra of each species [45,51], respectively. Through clustering, species with similar spectral signatures were grouped by visual assessment into 'spectral species'.…”

Section: Discussionmentioning

confidence: 99%

“…To determine the relationships among the species diversity, functional trait diversity, and spectral diversity, the Monte-Carlo technique was applied to simulate the average variations in functional traits and spectral values with increasing species diversity [44,45]. Firstly, one species was randomly selected from the total population of 17 species (n = 51).…”

Section: Discussionmentioning

confidence: 99%

“…The relationships among species diversity, spectral diversity, and functional trait diversity (or biochemical diversity) have been explored in tropical forests (e.g., in the Amazon and Hawaii) [42,44] and in the subtropical forest of Shennongjia [10,45]. However, the suitability of these links for grassland ecosystems needs to be further determined.…”

Section: Introductionmentioning

confidence: 99%

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The Potential of Mapping Grassland Plant Diversity with the Links among Spectral Diversity, Functional Trait Diversity, and Species Diversity

et al. 2021

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Mapping biodiversity is essential for assessing conservation and ecosystem services in global terrestrial ecosystems. Compared with remotely sensed mapping of forest biodiversity, that of grassland plant diversity has been less studied, because of the small size of individual grass species and the inherent difficulty in identifying these species. The technological advances in unmanned aerial vehicle (UAV)-based or proximal imaging spectroscopy with high spatial resolution provide new approaches for mapping and assessing grassland plant diversity based on spectral diversity and functional trait diversity. However, relatively few studies have explored the relationships among spectral diversity, remote-sensing-estimated functional trait diversity, and species diversity in grassland ecosystems. In this study, we examined the links among spectral diversity, functional trait diversity, and species diversity in a semi-arid grassland monoculture experimental site. The results showed that (1) different grassland plant species harbored different functional traits or trait combinations (functional trait diversity), leading to different spectral patterns (spectral diversity). (2) The spectral diversity of grassland plant species increased gradually from the visible (VIR, 400–700 nm) to the near-infrared (NIR, 700–1100 nm) region, and to the short-wave infrared (SWIR, 1100–2400 nm) region. (3) As the species richness increased, the functional traits and spectral diversity increased in a nonlinear manner, finally tending to saturate. (4) Grassland plant species diversity could be accurately predicted using hyperspectral data (R2 = 0.73, p < 0.001) and remotely sensed functional traits (R2 = 0.66, p < 0.001) using cluster algorithms. This will enhance our understanding of the effect of biodiversity on ecosystem functions and support regional grassland biodiversity conservation.

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Section: Spectral Diversitymentioning

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Potential of Mapping Grassland Plant Diversity with the Links among Spectral Diversity, Functional Trait Diversity, and Species Diversity

et al. 2021

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“…Garroutte and Hansen [22] evaluated the quality of grasslands for elk habitat in the Yellowstone River Basin using seasonal MODIS EVI and NDVI. Zhao et al [23] address the optimal detection of biochemical indicators for species mapping, and two papers show the potential of mapping foliar traits related to ecosystem functionality. Chadwick and Asner [17] used airborne imaging spectroscopy to map leaf mass area (LMA) and the foliar concentrations of nitrogen, phosphorus, calcium, magnesium and potassium for dominant trees in the Peruvian wet tropics, and McManus et al [15] address the relationships between foliar reflectance spectra and the phylogenetic composition of a tropical forest on Barro Colorado Island, Panama.…”

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confidence: 99%

Preface: Remote Sensing of Biodiversity

Ustin¹

2016

Remote Sensing

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Since the 1992 Earth Summit in Rio de Janeiro, the importance of biological diversity in supporting and maintaining ecosystem functions and processes has become increasingly understood [1]. Biodiversity "connects the web of life," that is, biodiversity represents the diversity of species in an ecosystem, landscape, region and globe. It is their combined interactions, with each other and their environment that alters biogeochemical cycles and the climate system. In recent years, biodiversity has come to broadly include diversity in terms of taxonomic, systematic and genetic attributes, morphological and structural attributes, and their ecological and functional traits. Human actions are driving climate change and land use changes throughout the globe, causing major losses of biodiversity [2,3]. These actions impact the climate, influencing magnitude and the timing of disturbance events, e.g., drought and wildfires, create pollution and contamination in water, air, and soil, and cause many other impacts that accelerate species losses, altering ecosystem functions and their services. The loss of biological diversity impacts ecological processes at scales comparable to other drivers of global environmental change [4] and likely interacts synergistically with climate change [5]. Biodiversity is recognized as a key factor in the maintenance of healthy ecosystems and for the sustainability of conservation efforts. Losses of biodiversity reduce the stability and resilience of ecosystems through the loss of functional traits associated with resource capture and decomposition [1]. The rapid pace of global change requires increased knowledge about species composition, numbers of species, and the states of health and the conditions for global ecosystems in order to respond effectively. Tittensor et al. (2014) [6] show that many of the 20 Aichi indicator targets from the Convention on Biological Diversity are unlikely to be met by 2020. The development of large plant trait databases like TRY [7,8] that have data on thousands of vascular plant species (46,085 species) still remain significantly under-sampled, particularly outside the northern temperate zone [9]. Remote sensing provides the only feasible way to measure and monitor biodiversity changes at the scales necessary. Nonetheless, until recently, there has been little success in monitoring ecologically meaningful aspects of diversity (e.g., alpha, beta, and gamma diversity). Today, there are an increasing number of remote sensing satellites and aircraft instruments that can provide a wide range of observational capability, in terms of spatial, temporal, and spectral resolutions, especially when combined with "big data" computational capacity and in situ monitoring systems. Similarly, significant progress in image processing algorithms has increased the potential for the successful characterization of biodiversity at various scales.We are pleased to present this special issue of 18 state-of-the-science papers [10-27] covering a wide range biological diversity issues ass...

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Assessing plant trait diversity as an indicators of species α‐ and β‐diversity in a subalpine grassland of the Italian Alps

Imran,

Sakowska,

Gianelle

et al. 2023

Remote Sens Ecol Conserv

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As the need for ecosystem biodiversity assessment increases within the climate crisis framework, more and more studies using spectral variation hypothesis (SVH) are proposed to assess biodiversity at various scales. The SVH implies optical diversity (also called spectral diversity) is driven by light absorption dynamics associated with plant traits (PTs) variability (which is an indicator of functional diversity) which is, in turn, determined by biodiversity. In this study, we examined the relationship between PTs variability, optical diversity and α‐ and β‐diversity at different taxonomic ranks at the Monte Bondone grasslands, Trentino province, Italy. The results of the study showed that the PTs variability, at the α scale, was not correlated with biodiversity. On the other hand, the results observed at the community scale (β‐diversity) showed that the variation of some of the investigated biochemical and biophysical PTs was associated with the β‐diversity. We used the Mantel test to analyse the relationship between the PTs variability and species β‐diversity. The results showed a correlation coefficient of up to 0.50 between PTs variability and species β‐diversity. For higher taxonomic ranks such as family and functional groups, a slightly higher Spearman's correlation coefficient of up to 0.64 and 0.61 was observed, respectively. The SVH approach was also tested to estimate β‐diversity and we found that spectral diversity calculated by Spectral Angle Mapper showed to be a better proxy of biodiversity in the same ecosystem where the spectral diversity approach failed to estimate α‐diversity. These findings suggest that optical and PTs diversity approaches can be used to predict species diversity in the grasslands ecosystem where the species turnover is high.

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The Optimal Leaf Biochemical Selection for Mapping Species Diversity Based on Imaging Spectroscopy

Cited by 15 publications

References 66 publications

The Potential of Mapping Grassland Plant Diversity with the Links among Spectral Diversity, Functional Trait Diversity, and Species Diversity

The Potential of Mapping Grassland Plant Diversity with the Links among Spectral Diversity, Functional Trait Diversity, and Species Diversity

Preface: Remote Sensing of Biodiversity

Assessing plant trait diversity as an indicators of species α‐ and β‐diversity in a subalpine grassland of the Italian Alps

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