Detecting prairie biodiversity with airborne remote sensing

Gholizadeh, Hamed; Gamon, John A.; Townsend, Philip A.; Zygielbaum, A. I.; Helzer, Christopher J.; Hmimina, Gabriel; Yu, Rong; Moore, Ryan; Schweiger, Anna K.; Cavender‐Bares, Jeannine

doi:10.1016/j.rse.2018.10.037

Cited by 87 publications

(102 citation statements)

References 63 publications

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“…Grassland communities on a heavy clay soil supported relatively fewer equally-common species but exhibited greater spatial turnover in species composition and abundances than did those on an upland, silty clay soil. Our results support the view that grassland diversity is correlated with spatial variability in canopy structure and function at small-to moderately-sized spatial grains (pixels) but, consistent with previous investigations (e.g., [10,17]), demonstrate that spectral models developed for a given spatial grain cannot reliably be applied to data collected at differing grains.…”

Section: Discussionsupporting

confidence: 89%

“…Wang et al [13], for example, demonstrated that the Shannon Index of species diversity was strongly correlated with an optical index derived by averaging the coefficient of variation (CV) of spectral reflectance in space across measured wavebands. On the other hand, diversity at small spatial scales (several m 2 ) can be detected by using among-waveband differences in reflectance, rather than spatial variation in reflectance, provided that optical properties of species or functional groups are well-differentiated in the reflectance signal [15][16][17].More complex techniques often are employed to link remote sensing signals to β diversity, defined generally as spatial turnover of species [18]. Methods that employ spectral distances or differences are required to explain 'differences' in diversity among communities (one metric of β diversity; [18][19][20]).…”

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Spectral Heterogeneity Predicts Local-Scale Gamma and Beta Diversity of Mesic Grasslands

et al. 2019

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Plant species diversity is an important metric of ecosystem functioning, but field assessments of diversity are constrained in number and spatial extent by labor and other expenses. We tested the utility of using spatial heterogeneity in the remotely-sensed reflectance spectrum of grassland canopies to model both spatial turnover in species composition and abundances (β diversity) and species diversity at aggregate spatial scales (γ diversity). Shannon indices of γ and β diversity were calculated from field measurements of the number and relative abundances of plant species at each of two spatial grains (0.45 m2 and 35.2 m2) in mesic grasslands in central Texas, USA. Spectral signatures of reflected radiation at each grain were measured from ground-level or an unmanned aerial vehicle (UAV). Partial least squares regression (PLSR) models explained 59–85% of variance in γ diversity and 68–79% of variance in β diversity using spatial heterogeneity in canopy optical properties. Variation in both γ and β diversity were associated most strongly with heterogeneity in reflectance in blue (350–370 nm), red (660–770 nm), and near infrared (810–1050 nm) wavebands. Modeled diversity was more sensitive by a factor of three to a given level of spectral heterogeneity when derived from data collected at the small than larger spatial grain. As estimated from calibrated PLSR models, β diversity was greater, but γ diversity was smaller for restored grassland on a lowland clay than upland silty clay soil. Both γ and β diversity of grassland can be modeled by using spatial heterogeneity in vegetation optical properties provided that the grain of reflectance measurements is conserved.

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Section: Discussionsupporting

confidence: 89%

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confidence: 99%

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confidence: 99%

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Spectral Heterogeneity Predicts Local-Scale Gamma and Beta Diversity of Mesic Grasslands

et al. 2019

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“…Large-scale simulation studies through field spectroscopy can be found in other ecosystem settings (i.e., grasslands), where studies suggested that the detection of α-diversity declined with lower spatial resolution and the optimal pixel size for α-diversity prediction approximates the size of an individual plant leaf or crown [57]. Yet again, in such ecosystem types, other experimental studies present findings indicating a controversy regarding the optimal pixel size [58]. In forest areas, the challenge is aggravated by the fact that optimal spatial resolution is not constant, being primarily affected by the spatial and structural parameters of the forest stands [59].…”

Section: Discussionmentioning

confidence: 99%

A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

et al. 2020

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Earth observation data can provide important information for tree species diversity mapping and monitoring. The relatively recent advances in remote sensing data characteristics and processing systems elevate the potential of satellite imagery for providing accurate, timely, consistent, and robust spatially explicit estimates of tree species diversity over forest ecosystems. This study was conducted in Northern Pindos National Park, the largest terrestrial park in Greece and aimed to assess the potential of four satellite sensors with different instrumental characteristics, for the estimation of tree diversity. Through field measurements, we originally quantified two diversity indices, namely the Shannon diversity index (H’) and Simpson’s diversity (D1). Random forest regression models were developed for associating remotely sensed spectral signal with tree species diversity within the area. The models generated from the use of the WorldView-2 image were the most accurate with a coefficient of determination of up to 0.44 for H’ and 0.37 for D1. The Sentinel-2 -based models of tree species diversity performed slightly worse, but were better than the Landsat-8 and RapidEye models. The coefficient of variation quantifying internal variability of spectral values within each plot provided little or no usage for improving the modelling accuracy. Our results suggest that very-high-spatial-resolution imagery provides the most important information for the assessment of tree species diversity in heterogeneous Mediterranean ecosystems.

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“…Essentially, one can assess plant traits, vegetation structure and functioning, and phenology at an aggregated pixel level integrating signals from all individual plants and species present in the pixel [14]. Given that remote sensing measurements resolve these properties in space and time, they are key for investigating changes in plant diversity, ecosystem functioning, and services, globally and in near real time [15][16][17][18][19].…”

Section: What Do Satellites Measure?mentioning

confidence: 99%

Monitoring Plant Functional Diversity Using the Reflectance and Echo from Space

Migliavacca

Wirth

et al. 2020

Remote Sensing

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Plant functional diversity (FD) is an important component of biodiversity. Evidence shows that FD strongly determines ecosystem functioning and stability and also regulates various ecosystem services that underpin human well-being. Given the importance of FD, it is critical to monitor its variations in an explicit manner across space and time, a highly demanding task that cannot be resolved solely by field data. Today, high hopes are placed on satellite-based observations to complement field plot data. The promise is that multiscale monitoring of plant FD, ecosystem functioning, and their services is now possible at global scales in near real-time. However, non-trivial scale challenges remain to be overcome before plant ecology can capitalize on the latest advances in Earth Observation (EO). Here, we articulate the existing scale challenges in linking field and satellite data and further elaborated in detail how to address these challenges via the latest innovations in optical and radar sensor technologies and image analysis algorithms. Addressing these challenges not only requires novel remote sensing theories and algorithms but also urges more effective communication between remote sensing scientists and field ecologists to foster mutual understanding of the existing challenges. Only through a collaborative approach can we achieve the global plant functional diversity monitoring goal.

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Detecting prairie biodiversity with airborne remote sensing

Cited by 87 publications

References 63 publications

Spectral Heterogeneity Predicts Local-Scale Gamma and Beta Diversity of Mesic Grasslands

Spectral Heterogeneity Predicts Local-Scale Gamma and Beta Diversity of Mesic Grasslands

A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

Monitoring Plant Functional Diversity Using the Reflectance and Echo from Space

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