Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling

Räsänen, Aleksi; Juutinen, Sari; Aurela, Mika; Virtanen, Tarmo

doi:10.1080/01431161.2018.1524176

Cited by 21 publications

(16 citation statements)

References 62 publications

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“…(2011) and Räsänen et al. (2019a), and it fits well with classifications presented in Chapin et al. (1996).…”

Section: Methodssupporting

confidence: 88%

“…2017; Räsänen et al. 2019a), has evident fine‐scale heterogeneity in wetness and trophic status patterns. However, the transitions between different vegetation and microforms are more gradual than in Kaamanen and the low strings dominated by Sphagnum and evergreen shrubs are only some decimeters above the wet flarks dominated by wet brown mosses and some graminoids.…”

Section: Methodsmentioning

confidence: 99%

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Detecting northern peatland vegetation patterns at ultra‐high spatial resolution

Räsänen

Aurela

Juutinen

et al. 2019

Remote Sens Ecol Conserv

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Within northern peatlands, landscape elements such as vegetation and topography are spatially heterogenic from ultra-high (centimeter level) to coarse scale. In addition to within-site spatial heterogeneity, there is evident between-site heterogeneity, but there is a lack of studies assessing whether different combinations of remotely sensed features and mapping approaches are needed in different types of landscapes. We evaluated the value of different mapping methods and remote sensing datasets and analyzed the kinds of differences present in vegetation patterns and their mappability between three northern boreal peatland landscapes in northern Finland. We utilized field-inventoried vegetation plots together with spectral, textural, topography and vegetation height remote sensing data from 0.02-to 3-m pixel size. Remote sensing data included truecolor unmanned aerial vehicle images, aerial images with four spectral bands, aerial lidar data and multiple PlanetScope satellite images. We used random forest regressions for tracking plant functional type (PFT) coverage, non-metric multidimensional scaling ordination axes and fuzzy k-medoid plant community clusters. PFT regressions had variable performance for different study sites (R 2 À0.03 to 0.69). Spatial patterns of some spectrally or structurally distinctive PFTs could be predicted relatively well. The first ordination axis represented wetness gradient and was well predicted using remotely sensed data (R 2 0.64 to 0.82), but the other three axes had a less straightforward explanation and lower mapping performance (R 2 À0.09 to 0.53). Plant community clusters were predicted most accurately in the sites with clear string-flark topography but less accurately in the flatter site (R 2 0.16-0.82). The most important remote sensing features differed between dependent variables and study sites: different topographic, spectral and textural features; and coarse-scale and fine-scale datasets were the most important in different tasks. We suggest that multiple different mapping approaches should be tested and several remote sensing datasets used when maps of vegetation are produced. Saarimaa et al. 2019), water storage and hydrology (Waddington et al. 2015) and carbon exchange (Aurela et al. 1998, 2009Loisel et al. 2017). Within peatlands, many biogeochemical cycles, such as flows of carbon, water and nutrients, are linked to vegetation (Loisel et al. 2017;Lees et al. 2018;McPartland et al. 2019). For

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“…(2011) and Räsänen et al. (2019a), and it fits well with classifications presented in Chapin et al. (1996).…”

Section: Methodssupporting

confidence: 88%

Section: Methodsmentioning

confidence: 99%

Detecting northern peatland vegetation patterns at ultra‐high spatial resolution

Räsänen

Aurela

Juutinen

et al. 2019

Remote Sens Ecol Conserv

Self Cite

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“…In the high Arctic, however, shrubs growing in extremely harsh conditions keep most of their biomass below ground and little is known about the processes of internal growth allocation. Classical measurements of tundra greening based on estimations of canopy cover changes (Atkinson and Treitz, 2013; Beck and Goetz, 2011; Berner et al, 2018; Buchhorn et al, 2013; Greaves et al, 2016; Räsänen et al, 2019) are not sufficient to answer questions regarding the complicated nature of arctic plants’ internal responses to climate change, so a frontier of high-latitude dendrochronology has been the exploration of growth allocation processes above and below ground in terms of the physiology of high Arctic shrubs (Buchwal et al, 2013; Le Moullec et al, 2019; Ropars et al, 2017). Recent studies suggest that shrub response to growing temperatures might be higher than is estimated from above ground canopy cover alone and is heterogeneous across different tundra ecosystems (Bret-Harte et al, 2002; Campioli et al, 2012; Elmendorf et al, 2012a, 2012b; Havström et al, 1993; Hill and Henry, 2011; Hudson and Henry 2009, 2010; Jones et al, 1997; Shaver et al, 2001).…”

Section: Nontraditional Speciesmentioning

confidence: 99%

New frontiers in tree-ring research

et al. 2020

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From its inception as a scientific discipline, tree-ring research has been used as a trans-disciplinary tool for dating and environmental reconstruction. Tree-ring chronologies in some regions extend back many thousands of years, opening up new potential for the study of climate, people, and ecology at annual and sub-annual resolution. As such, they are a frequently used resource for a diverse range of studies spanning the Holocene. They are also the focus of a constantly evolving array of analytical techniques and multidisciplinary approaches to research questions. This literature review discusses case studies at the cutting-edge of interdisciplinary tree-ring research, notes recent breakthroughs and limitations, and identifies key frontiers for the future of tree-ring research.

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“…Thus, the change in AGB between PFTs can be caused by changing species contributions within PFTs. However, many studies of Arctic and sub-Arctic regions present AGB state or change at a PFT level (Räsänen et al, 2018;Berner et al, 2018;Webb et al, 2017;Walker et al, 2003). Some focus only on shrub biomass of one or more species (Vankoughnett and Grogan, 2015;Berner et al, 2018), while others focus on tree biomass (Berner et al, 2012) or on species and PFT AGB of one specific community (e.g.…”

Section: Introductionmentioning

confidence: 99%

Recent above-ground biomass changes in central Chukotka (Russian Far East) using field sampling and Landsat satellite data

et al. 2021

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Abstract. Upscaling plant biomass distribution and dynamics is essential for estimating carbon stocks and carbon balance. In this respect, the Russian Far East is among the least investigated sub-Arctic regions despite its known vegetation sensitivity to ongoing warming. We representatively harvested above-ground biomass (AGB; separated by dominant taxa) at 40 sampling plots in central Chukotka. We used ordination to relate field-based taxa projective cover and Landsat-derived vegetation indices. A general additive model was used to link the ordination scores to AGB. We then mapped AGB for paired Landsat-derived time slices (i.e. 2000/2001/2002 and 2016/2017), in four study regions covering a wide vegetation gradient from closed-canopy larch forests to barren alpine tundra. We provide AGB estimates and changes in AGB that were previously lacking for central Chukotka at a high spatial resolution and a detailed description of taxonomical contributions. Generally, AGB in the study region ranges from 0 to 16 kg m−2, with Cajander larch providing the highest contribution. Comparison of changes in AGB within the investigated period shows that the greatest changes (up to 1.25 kg m−2 yr−1) occurred in the northern taiga and in areas where land cover changed to larch closed-canopy forest. As well as the notable changes, increases in AGB also occur within the land-cover classes. Our estimations indicate a general increase in total AGB throughout the investigated tundra–taiga and northern taiga, whereas the tundra showed no evidence of change in AGB.

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Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling

Cited by 21 publications

References 62 publications

Detecting northern peatland vegetation patterns at ultra‐high spatial resolution

Detecting northern peatland vegetation patterns at ultra‐high spatial resolution

New frontiers in tree-ring research

Recent above-ground biomass changes in central Chukotka (Russian Far East) using field sampling and Landsat satellite data

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