Tundra vegetation change and impacts on permafrost

Nature Reviews Earth & Environment, Journal Year: 2022, Volume and Issue: 3(1), P. 68 - 84

Published: Jan. 11, 2022

Language: Английский

---

Winters are changing: snow effects on Arctic and alpine tundra ecosystems

Arctic Science, Journal Year: 2022, Volume and Issue: 8(3), P. 572 - 608

Published: Feb. 18, 2022

Snow is an important driver of ecosystem processes in cold biomes. accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season’s start end, plant access to nutrients. Here, we review current knowledge snow cover’s role for vegetation, plant-animal interactions, permafrost microbial processes, biogeochemical cycling. We compare studies natural gradients with experimental manipulation assess time scale difference these approaches. The number tundra has increased considerably recent years, yet still lack a comprehensive overview how altered conditions will affect ecosystems. Specifically, found mismatch timing snowmelt when comparing manipulations. that achieved by addition removal manipulations (average 7.9 days advance 5.5 delay, respectively) were substantially lower than temporal variation over spatial within given year (mean range 56 days) or among years 32 days). Differences between study approaches need be accounted projecting dynamics their impact on ecosystems future climates.

Language: Английский

---

Remote sensing of phenology: Towards the comprehensive indicators of plant community dynamics from species to regional scales

Journal of Ecology, Journal Year: 2022, Volume and Issue: 110(7), P. 1460 - 1484

Published: April 22, 2022

Abstract Remote sensing of vegetation phenology has long been used to characterize ecosystem functions and responses climate at spatial temporal scales unfeasible field surveys. However, the potential remote elucidate mechanistic drivers underlying plant community processes such remains under‐discussed. This review synthesizes possibilities advance this knowledge using multi‐temporal discusses remaining challenges progress in instruments analytical tools. Recent evidence indicates that, besides documenting seasonality climate, can help meet emerging needs for indicators diversity, structure change. Responses phenological metrics stressors over large, heterogeneous regions may provide clues on ecological resilience manifested asynchronies, recovery cycles stable microrefugia. At same time, important barriers persist relation choosing among estimation methods paradigms, characterizing events beyond changes photosynthetically active biomass, interpretation patterns. Synthesis . Increasing frequency products, opportunities multi‐sensor data fusion, advances historically less available hyperspectral, microwave lidar promise navigate these enable more comprehensive assessments seasonality. Progress customizable local platforms as unoccupied aerial vehicles phenocams further enrich ground‐level understanding validate satellite‐based assessments. analyses alone are insufficient phenology, which be challenged by artefacts sensitivity estimated landscape resolution inputs. Robust informative call rigorous collaborations with studies, strategic selection ancillary environmental geographic data, wider adoption causal inference approaches address support novel explorations ecology.

Language: Английский

---

Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data

Environmental Research Letters, Journal Year: 2021, Volume and Issue: 16(5), P. 055006 - 055006

Published: April 21, 2021

Abstract The Arctic is warming twice as fast the rest of planet, leading to rapid changes in species composition and plant functional trait variation. Landscape-level maps vegetation distributions are required expand spatially-limited plot studies, overcome sampling biases associated with most accessible research areas, create baselines from which monitor environmental change. Unmanned aerial vehicles (UAVs) have emerged a low-cost method generate high-resolution imagery bridge gap between fine-scale field studies lower resolution satellite analyses. Here we used spectroscopy data (400–2500 nm) UAV multispectral test spectral methods identification water chemistry retrieval near Longyearbyen, Svalbard. Using Random Forest analysis, were able distinguish eight common High tundra 74% accuracy. partial least squares regression (PLSR), predict corresponding water, nitrogen, phosphorus C:N values ( r 2 = 0.61–0.88, RMSEmean 12%–64%). We developed analogous models using (five bands: Blue, Green, Red, Red Edge Near-Infrared) scaled up results across 450 m long nutrient gradient located underneath seabird colony. At level, map three groups (mosses, graminoids dwarf shrubs) at 72% accuracy chemistry. Our show clear marine-derived fertility gradient, mediated by geomorphology. explore two upscaling content wider landscape Sentinel-2A imagery. pertinent for high resolution, mapping Arctic.

Language: Английский

---

Seeing the System from Above: The Use and Potential of Remote Sensing for Studying Ecosystem Dynamics

Ecosystems, Journal Year: 2022, Volume and Issue: 25(8), P. 1719 - 1737

Published: Sept. 7, 2022

Abstract Remote sensing techniques are increasingly used for studying ecosystem dynamics, delivering spatially explicit information on the properties of Earth over large spatial and multi-decadal temporal extents. Yet, there is still a gap between more technology-driven development novel remote their applications dynamics. Here, I review existing literature to explore how addressing these gaps might enable recent methods overcome longstanding challenges in ecological research. First, trace emergence as major tool understanding Second, examine developments field that particular importance Third, consider opportunities emerging open data software policies suggest at its most powerful when it theoretically motivated rigorously ground-truthed. close with an outlook four exciting new research frontiers will define ecology upcoming decade.

Language: Английский

---

Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges

Journal of Geophysical Research Biogeosciences, Journal Year: 2022, Volume and Issue: 127(2)

Published: Feb. 1, 2022

Abstract Observing the environment in vast regions of Earth through remote sensing platforms provides tools to measure ecological dynamics. The Arctic tundra biome, one largest inaccessible terrestrial biomes on Earth, requires across multiple spatial and temporal scales, from towers satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale using IS derived advances our understanding vegetation communities their interaction with environment. To best leverage ongoing forthcoming resources, including National Aeronautics Space Administration’s Surface Biology Geology mission, we identify series opportunities challenges based intrinsic spectral dimensionality analysis review current data literature that illustrates unique attributes biome. These include thematic mapping, complicated by low‐stature plants very fine‐scale surface composition heterogeneity; development scalable algorithms retrieval canopy leaf traits; nuanced variation growth complicates detection long‐term trends; rapid phenological changes brief growing seasons may go undetected due low revisit frequency or be obscured snow cover clouds. recommend improvements future field campaigns satellite missions, advocating research combines multi‐scale spectroscopy, lab studies satellites enable frequent continuous monitoring, inform statistical biophysical approaches model

Language: Английский

---

Landsat‐based greening trends in alpine ecosystems are inflated by multidecadal increases in summer observations

Ecography, Journal Year: 2024, Volume and Issue: 2024(12)

Published: Aug. 27, 2024

Remote sensing is an invaluable tool for tracking decadal‐scale changes in vegetation greenness response to climate and land use changes. While the Landsat archive has been widely used explore these trends their spatial temporal complexity, its inconsistent sampling frequency over time space raises concerns about ability provide reliable estimates of annual indices such as maximum normalised difference index (NDVI), commonly a proxy plant productivity. Here we demonstrate seasonally snow‐covered ecosystems, that greening derived from NDVI can be significantly overestimated because number available observations increases time, mostly magnitude overestimation varies along environmental gradients. Typically, areas with short growing season few experience largest bias trend estimation. We show conditions are met late snowmelting habitats European Alps, which known particularly sensitive temperature present conservation challenges. In this critical context, almost 50% estimated explained by bias. Our study calls greater caution when comparing magnitudes between different snow observations. At minimum recommend reporting information on observations, including per year, long‐term studies undertaken.

Language: Английский

---

The value of hyperspectral UAV imagery in characterizing tundra vegetation

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 308, P. 114175 - 114175

Published: May 15, 2024

The fine-scale spatial heterogeneity of low-growth Arctic tundra landscapes necessitates the use high-spatial-resolution remote sensing data for accurate detection vegetation patterns. While multispectral satellite and aerial imaging, including uncrewed vehicles (UAVs), are common approaches, hyperspectral UAV imaging has not been thoroughly explored in these ecosystems. Here, we assess added value relative to modelling plant communities oroarctic heaths Saariselkä, northern Finland. We compare three different spectral compositions: 4-channel broadband images, 5-channel images 112-channel narrowband images. Based on field plot data, estimate vascular aboveground biomass, leaf area index, species richness, Shannon's diversity community composition. topographic information compile 12 explanatory datasets random forest regression classification. For biomass highest R2 values were 0.60 0.65, respectively, variables most important. In best models biodiversity metrics richness index 0.53 0.46, with hyperspectral, topographic, having high importance. 4 floristically determined clusters, both classifications fuzzy cluster membership regressions conducted. Overall accuracy (OA) classification was 0.67 at best, while estimated an 0.29–0.53. Variable importance heavily dependent composition, but multispectral, all selected composition models. Hyperspectral generally outperformed ones when excluded. With this difference diminished, performance improvements from limited 0–10 percentage point increases R2, largest occurring lowest R2. These results suggest that can outperform mostly sufficient practical applications heaths.

Language: Английский

---

Evaluation of spatial and temporal variability in Sentinel-2 surface reflectance on a rice paddy landscape

Agricultural and Forest Meteorology, Journal Year: 2025, Volume and Issue: 363, P. 110401 - 110401

Published: Feb. 8, 2025

Language: Английский

---

Remote sensing of vegetation trends: A review of methodological choices and sources of uncertainty

Remote Sensing Applications Society and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 101500 - 101500

Published: Feb. 1, 2025

Language: Английский

---