Feasibility of tundra vegetation height retrieval from Sentinel-1 and Sentinel-2 data

Bartsch, Annett; Widhalm, Barbara; Leibman, Marina; Ermokhina, Ksenia; Kumpula, Timo; Skarin, Anna; Wilcox, Evan J.; Jones, Benjamin M.; Frost, G. V.; Höfler, Angelika; Pointner, Georg

doi:10.1016/j.rse.2019.111515

Cited by 54 publications

(54 citation statements)

References 76 publications

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“…Grassland, shrubland, Arctic tundra, savanna and proglacial montane landscapes are often more sensitive and respond faster to changes in climate than forests 6 , but have received less systematic research attention 1,7,8 . Non-destructive measurements of canopy height and biomass are fundamental requirements for plant science [9][10][11] to understand the roles of these ecosystems in climate change mitigation, sustainable food production and land management [12][13][14] . However, measuring biomass with in situ measurements is labour intensive and thus prone to undersampling, particularly in ecosystems that are spatially heterogeneous and/or temporally dynamic, putting on (and losing) biomass rapidly 1,[15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

“…However, measuring biomass with in situ measurements is labour intensive and thus prone to undersampling, particularly in ecosystems that are spatially heterogeneous and/or temporally dynamic, putting on (and losing) biomass rapidly 1,[15][16][17] . Gaps in available data mean that biomass dynamics are not being captured in many important ecosystems across the globe, hindering the calibration and validation of vegetation models and products derived from satellite observations 7,14 . The lack of accurate biomass data limits our ability to track changes and predict future responses in globally relevant non-forest ecosystems.…”

Section: Introductionmentioning

confidence: 99%

“…Improving the accuracy of biomass data in non-forest biomes requires approaches that are: (i) sensitive to small differences in AGB, (ii) sufficiently inexpensive to be adopted worldwide, and allow (iii) spatially continuous sampling across (iv) representative areas at (v) temporal frequencies appropriate for dynamic ecosystems 14,16 . The most accurate non-destructive estimates of AGB are generally obtained from in situ measurements of attributes such as plant cover, height and stem diameters, using allometric functions fitted to harvested biomass observations.…”

Section: Introductionmentioning

confidence: 99%

“…Globally-available biomass products from space-based sensors such as LiDAR, synthetic-aperture radar or vegetation optical depth are either insensitive and/or poorly calibrated and validated in low-biomass (<20 Mg ha -1 ) ecosystems 1,7,8,14,24 .…”

Section: Introductionmentioning

confidence: 99%

“…Lightweight and inexpensive drones enable vegetation sampling at temporal intervals appropriate for highly dynamic ecosystems 16,32 and can be used for detailed surveys over extents of 1-10 ha, covering more representative areas of heterogeneous ecosystems 15,27 that allow spatially explicit comparison with other biomass estimates 1,14,32 .…”

Section: Introductionmentioning

confidence: 99%

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Drone-derived canopy height predicts biomass across non-forest ecosystems globally

Cunliffe

Anderson

Boschetti

et al. 2020

Preprint

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Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, yet are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely-sensed biomass products and are undersampled by in-situ monitoring. Current global change threats emphasise the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we assess whether canopy height inferred from drone photogrammetry allows the estimation of aboveground biomass (AGB) across low-stature plant species sampled through a global site network. We found mean canopy height is strongly predictive of AGB across species, demonstrating standardised photogrammetric approaches are generalisable across growth forms and environmental settings. Biomass per-unit-of-height was similar within, but different among, plant functional types. We find drone-based photogrammetry allows for monitoring of AGB across large spatial extents and can advance understanding of understudied and vulnerable non-forested ecosystems across the globe.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Drone-derived canopy height predicts biomass across non-forest ecosystems globally

Cunliffe

Anderson

Boschetti

et al. 2020

Preprint

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The High–Low Arctic boundary: How is it determined and where is it located?

Ermokhina,

Terskaia,

Ivleva

et al. 2023

Ecology and Evolution

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Geobotanical subdivision of landcover is a baseline for many studies. The High–Low Arctic boundary is considered to be of fundamental natural importance. The wide application of different delimitation schemes in various ecological studies and climatic scenarios raises the following questions: (i) What are the common criteria to define the High and Low Arctic? (ii) Could human impact significantly change the distribution of the delimitation criteria? (iii) Is the widely accepted temperature criterion still relevant given ongoing climate change? and (iv) Could we locate the High–Low Arctic boundary by mapping these criteria derived from modern open remote sensing and climatic data? Researchers rely on common criteria for geobotanical delimitation of the Arctic. Unified circumpolar criteria are based on the structure of vegetation cover and climate, while regional specifics are reflected in the floral composition. However, the published delimitation schemes vary greatly. The disagreement in the location of geobotanical boundaries across the studies manifests in poorly comparable results. While maintaining the common principles of geobotanical subdivision, we derived the boundary between the High and Low Arctic using the most up‐to‐date field data and modern techniques: species distribution modeling, radar, thermal and optical satellite imagery processing, and climatic data analysis. The position of the High–Low Arctic boundary in Western Siberia was clarified and mapped. The new boundary is located 50–100 km further north compared to all the previously presented ones. Long‐term anthropogenic press contributes to a change in the vegetation structure but does not noticeably affect key species ranges. A previously specified climatic criterion for the High–Low Arctic boundary accepted in scientific literature has not coincided with the boundary in Western Siberia for over 70 years. The High–Low Arctic boundary is distinctly reflected in biodiversity distribution. The presented approach is appropriate for accurate mapping of the High–Low Arctic boundary in the circumpolar extent.

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Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems

Cunliffe

Anderson

Boschetti

et al. 2021

Remote Sens Ecol Conserv

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Feasibility of tundra vegetation height retrieval from Sentinel-1 and Sentinel-2 data

Cited by 54 publications

References 76 publications

Drone-derived canopy height predicts biomass across non-forest ecosystems globally

Drone-derived canopy height predicts biomass across non-forest ecosystems globally

The High–Low Arctic boundary: How is it determined and where is it located?

Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems

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