High-resolution mapping of aboveground shrub biomass in Arctic tundra using airborne lidar and imagery

Greaves, Heather E.; Vierling, L. A.; Eitel, Jan U. H.; Boelman, Natalie T.; Magney, Troy S.; Prager, Case M.; Griffin, Kevin L.

doi:10.1016/j.rse.2016.07.026

Cited by 78 publications

(71 citation statements)

References 51 publications

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“…Given the substantial cloud cover of these arctic ecosystems, previous studies (including Langford et al [47] have used imagery and ground truthing data collected in different years. Ideally the use of airborne hyperspectral imagery [19] or unmanned aerial vehicles (UAV) with very high spatial resolution (sub-centimeter for UAV), collected at the same time of ground data will improve the accuracy of vegetation mapping, especially at the patch scale [94]. In addition, airborne LiDAR [47] might also be used to improve classification accuracies by providing data of vegetation structure and biomass profiles, in addition to terrain features that may influence landscape temperature and moisture conditions and consequently vegetation characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…Many of these models assume uniform vegetation composition within a cell footprint, yet in reality Arctic landscapes have high spatial heterogeneity [16]. A better understanding of the role of vegetation community composition on greenhouse gas exchange is needed to guide scaling from plot levels to coarser resolution footprints [17][18][19]. For example, Shaver et al [20] note that changes in vegetation community composition could lead to higher ecosystem productivity, alongside increased long-term sequestration of carbon [1].…”

Section: Introductionmentioning

confidence: 99%

“…Although coarse spatial resolution imagery (>100 m) may be suitable for determining broad-scale ecosystem distributions and regional change, it is insufficient to capture the high spatial heterogeneity occurring across many Arctic tundra landscapes [16,32,33]. High resolution satellite data (<5 m) such as WorldView-2 may be able to capture this spatial heterogeneity, as tundra vegetation communities low stature may make it difficult to identify and quantify using coarser products [19]. However, there can be a trade-off between fine and coarser spatial resolution satellite data through temporal sampling frequency.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Arctic Tundra Vegetation Communities Using Field Spectroscopy and Multispectral Satellite Data in North Alaska, USA

et al. 2016

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Abstract:The Arctic is currently undergoing intense changes in climate; vegetation composition and productivity are expected to respond to such changes. To understand the impacts of climate change on the function of Arctic tundra ecosystems within the global carbon cycle, it is crucial to improve the understanding of vegetation distribution and heterogeneity at multiple scales. Information detailing the fine-scale spatial distribution of tundra communities provided by high resolution vegetation mapping, is needed to understand the relative contributions of and relationships between single vegetation community measurements of greenhouse gas fluxes (e.g.,~1 m chamber flux) and those encompassing multiple vegetation communities (e.g.,~300 m eddy covariance measurements). The objectives of this study were: (1) to determine whether dominant Arctic tundra vegetation communities found in different locations are spectrally distinct and distinguishable using field spectroscopy methods; and (2) to test which combination of raw reflectance and vegetation indices retrieved from field and satellite data resulted in accurate vegetation maps and whether these were transferable across locations to develop a systematic method to map dominant vegetation communities within larger eddy covariance tower footprints distributed along a 300 km transect in northern Alaska. We showed vegetation community separability primarily in the 450-510 nm, 630-690 nm and 705-745 nm regions of the spectrum with the field spectroscopy data. This is line with the different traits of these arctic tundra communities, with the drier, often non-vascular plant dominated communities having much higher reflectance in the 450-510 nm and 630-690 nm regions due to the lack of photosynthetic material, whereas the low reflectance values of the vascular plant dominated communities highlight the strong light absorption found here. High classification accuracies of 92% to 96% were achieved using linear discriminant analysis with raw and rescaled spectroscopy reflectance data and derived vegetation indices. However, lower classification accuracies (~70%) resulted when using the coarser 2.0 m WorldView-2 data inputs. The results from this study suggest that tundra vegetation communities are separable using plot-level spectroscopy with hand-held sensors. These results also show that tundra vegetation mapping can be scaled from the plot level (<1 m) to patch level (<500 m) using spectroscopy data rescaled to match the wavebands of the multispectral satellite remote sensing. We find that developing a consistent method for classification of vegetation communities across the flux tower sites is a challenging process, given the spatial variability in vegetation communities and the need for detailed vegetation survey data for training and validating classification algorithms. This study highlights the benefits of using fine-scale field spectroscopy measurements to obtain tundra vegetation classifications for landscape analyses and use in carbon flux scaling studies. Improved...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping Arctic Tundra Vegetation Communities Using Field Spectroscopy and Multispectral Satellite Data in North Alaska, USA

et al. 2016

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“…These types of issues were also highlighted by Parmentier et al [82] who used a maximum likelihood classifier with GeoEye-1 data over Siberia. Other data products, such as aerial imagery obtained through the use of drones or LiDAR-derived digitial elevation models (DEMs), could be used to resolve this issue [86,87] as they could also account for variability within the local microtopography. However, Bartsch et al [43] note that even coarse resolution maps can have value, especially in carbon/upscaling studies, and drones or LiDAR-derived digitial elevation models (DEMs) are expensive to collect and cannot be easily collected over large areas.…”

Section: Tundra Vegetation Mappingmentioning

confidence: 99%

Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems

et al. 2017

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Abstract:Arctic tundra ecosystems are a major source of methane (CH 4 ), the variability of which is affected by local environmental and climatic factors, such as water table depth, microtopography, and the spatial heterogeneity of the vegetation communities present. There is a disconnect between the measurement scales for CH 4 fluxes, which can be measured with chambers at one-meter resolution and eddy covariance towers at 100-1000 m, whereas model estimates are typically made at thẽ 100 km scale. Therefore, it is critical to upscale site level measurements to the larger scale for model comparison. As vegetation has a critical role in explaining the variability of CH 4 fluxes across the tundra landscape, we tested whether remotely-sensed maps of vegetation could be used to upscale fluxes to larger scales. The objectives of this study are to compare four different methods for mapping and two methods for upscaling plot-level CH 4 emissions to the measurements from EC towers. We show that linear discriminant analysis (LDA) provides the most accurate representation of the tundra vegetation within the EC tower footprints (classification accuracies of between 65% and 88%). The upscaled CH 4 emissions using the areal fraction of the vegetation communities showed a positive correlation (between 0.57 and 0.81) with EC tower measurements, irrespective of the mapping method. The area-weighted footprint model outperformed the simple area-weighted method, achieving a correlation of 0.88 when using the vegetation map produced with the LDA classifier. These results suggest that the high spatial heterogeneity of the tundra vegetation has a strong impact on the flux, and variation indicates the potential impact of environmental or climatic parameters on the fluxes. Nonetheless, assimilating remotely-sensed vegetation maps of tundra in a footprint model was successful in upscaling fluxes across scales.

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“…These changes in plant AGB and shrub dominance are affecting wildlife populations (for example, moose, caribou) that provide important sustenance for northern communities (Tape et al 2016b, Fauchald et al 2017. Plant AGB and shrub dominance will likely continue to increase in this region if warming trends persist (Epstein et al 2008, Pearson et al 2013, yet few studies , Greaves et al 2016 have mapped current plant or shrub AGB in this region or other parts of the Arctic tundra biome.…”

Section: Introductionmentioning

confidence: 99%

Tundra plant above-ground biomass and shrub dominance mapped across the North Slope of Alaska

Berner

Jantz

Tape

et al. 2018

Environ. Res. Lett.

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High-resolution mapping of aboveground shrub biomass in Arctic tundra using airborne lidar and imagery

Cited by 78 publications

References 51 publications

Mapping Arctic Tundra Vegetation Communities Using Field Spectroscopy and Multispectral Satellite Data in North Alaska, USA

Mapping Arctic Tundra Vegetation Communities Using Field Spectroscopy and Multispectral Satellite Data in North Alaska, USA

Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems

Tundra plant above-ground biomass and shrub dominance mapped across the North Slope of Alaska

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