Flower Detection Using Object Analysis: New Ways to Quantify Plant Phenology in a Warming Tundra Biome

Rising temperatures caused by global warming are affecting the distributions of many plant and animal species across the world. This can lead to structural changes in entire ecosystems, and serious, persistent environmental consequences. However, many of these changes occur in vast and poorly accessible biomes and involve myriad species. As a consequence, conventional methods of measurement and data analysis are resource-intensive, restricted in scope, and in some cases, intractable for measuring species changes in remote areas. In this article, we introduce a method for detecting flowers of tundra plant species in large data sets obtained by aerial drones, making it possible to understand ecological change at scale, in remote areas. We focus on the sedge species E. vaginatum that is dominant at the investigated tundra field site in the Canadian Arctic. Our system is a modified version of the Faster R-CNN architecture capable of real-world plant phenology analysis. Our model outperforms experienced human annotators in both detection and counting, recording much higher recall and comparable level of precision, regardless of the image quality caused by varying weather conditions during the data collection. (K. Stanski, GitHub - karoleks4/flower-detection: Flower detection using object analysis: New ways to quantify plant phenology in a warming tundra biome. GitHub. Accessed: Sep. 17, 2021. [Online]. Available: https://github.com/karoleks4/flower-detection.

Resilience: Easy to use but hard to define

First conceptualized in the 1970s, resilience has become a popular term in the ecological literature, used in the title, abstract, or keywords of approximately 1% of papers identified by ISI Web of Science in the field of environmental sciences and ecology in 2011. However, many papers make only passing reference to the term and do not explain what resilience means in the context of their study system, despite there being a number of possible definitions. In an attempt to determine how resilience is being used in ecological studies, we surveyed 234 papers published between 2004 and 2011 that were identified under the topic “resilience” by ISI Web of Science. Of these, 38% used the word resilience fewer than three times (often in the abstract or keyword list), 66% did not define the term, and 71% did not provide a citation to the resilience literature. Studies that defined resilience most often discussed it as pertaining to an entire ecosystem under continuous rather than discrete disturbance. Given the complex nature of this concept, we believe that care should be taken to properly describe what is meant by the term resilience in ecological studies

Aboveground biomass corresponds strongly with drone-derived canopy height but weakly with greenness (NDVI) in a shrub tundra landscape

This is the author accepted manuscript. The final version is available from IOP Publishing via the DOI in this recordData accessibility: The data that support the findings of this study are openly available at the following DOI: https://doi.org/10.5285/61C5097B-6717-4692-A8A4-D32CCA0E61A9)Arctic landscapes are changing rapidly in response to warming, but future predictions are hindered by difficulties in scaling ecological relationships from plots to biomes. Unmanned aerial systems (UAS, hereafter 'drones') are increasingly used to observe Arctic ecosystems over broader extents than can be measured using ground-based approaches and facilitate the interpretation of coarse-grained remotely-sensed datasets. However, more information is needed about how drone-acquired remote sensing observations correspond with ecosystem attributes such as aboveground biomass. Working across a willow shrub-dominated alluvial fan at a focal study site in the Canadian Arctic, we conducted peak season drone surveys with a RGB camera and multispectral multi camera array to derive photogrammetric reconstructions of canopy and normalised difference vegetation index (NDVI) maps along with in situ point intercept measurements and biomass harvests from 36, 0.25 m2 plots. We found high correspondence between canopy height measured using in situ point intercept compared to drone-photogrammetry (concordance correlation coefficient = 0.808), although the photogrammetry heights were positively biased by 0.14 m relative to point intercept heights. Canopy height was strongly and linearly related to aboveground biomass, with similar coefficients of determination for point framing (R2 = 0.92) and drone-based methods (R2 = 0.90). NDVI was positively related to aboveground biomass, phytomass and leaf biomass. However, NDVI only explained a small proportion of the variance in biomass (R2 between 0.14 and 0.23 for logged total biomass) and we found moss cover influenced the NDVI-phytomass relationship. Biomass is challenging to infer from drone-derived NDVI, particularly in ecosystems where bryophytes cover a large proportion of the land surface. Our findings suggest caution with broadly attributing change in fine-grained NDVI to biomass differences across biologically and topographically complex tundra landscapes. By comparing structural, spectral and on-the-ground ecological measurements, we can improve understanding of tundra vegetation change as inferred from remote sensing.Natural Environment Research Council (NERC)Dartmouth CollegeAarhus University Research FoundationEuropean Union Horizon 202

High methane production in drained lake basin wetlands in northern Alaska

Wetlands in drained lake basins are important elements of the Arctic carbon budget. They may store large amounts of carbon while also producing substantial amounts of greenhouse gasses. After lake drainage the former lake bottom is colonized by pioneer graminoids, succeeded by mosssedge-dwarf shrub vegetation, producing a typical peat sequence. However, post-drainage organic matter dynamics are not well studied. We hypothesize that vegetation composition reflects both succession and surface wetness, which in turn determine soil organic matter content and methane production. We propose that vegetation types detected by remote sensing-based landcover classification may be used to extrapolate methane production and organic matter composition across drained lake basin landscapes. We investigated (i) plots along a temporal drainage gradient, surveying vegetation, surface sediment, and pond water. We then used (ii) landcover classification of main eco-hydrological classes to (iii) upscale from plot to basin scale. We found that vegetation and organic matter changed markedly between recently drained basins and older age classes. Overall, vegetation composition differed more between eco-hydrological classes than between age classes. Surface sediments had very high water contents (>80 %), suggesting largely anaerobic conditions favouring methane production. Methane concentrations were indeed relatively constant throughout, and particularly high in sediments beneath few centimetres of water (“wet patches”, up to 200 μmol/L) and in pond water (up to 22 μmol/L). Landcover classification yielded seven classes including five classes we also identified using statistical clustering of vegetation data plus a water class and a bare ground class. We found that 67 % of basin areas were occupied by wet patches with especially high methane production. Our study shows that remote sensing-based landcover classifications are useful for quantifying wet-vs-moist patches and high-vs-moderate methane production in Arctic drained lake basins. The study highlights the potential for future upscaling of methane emissions from these abundant wetland environments