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  • 1
    Call number: AWI Bio-22-94840
    Description / Table of Contents: Vegetation change at high latitudes is one of the central issues nowadays with respect to ongoing climate changes and triggered potential feedback. At high latitude ecosystems, the expected changes include boreal treeline advance, compositional, phenological, physiological (plants), biomass (phytomass) and productivity changes. However, the rate and the extent of the changes under climate change are yet poorly understood and projections are necessary for effective adaptive strategies and forehanded minimisation of the possible negative feedbacks. The vegetation itself and environmental conditions, which are playing a great role in its development and distribution are diverse throughout the Subarctic to the Arctic. Among the least investigated areas is central Chukotka in North-Eastern Siberia, Russia. Chukotka has mountainous terrain and a wide variety of vegetation types on the gradient from treeless tundra to northern taiga forests. The treeline there in contrast to subarctic North America and north-western and central Siberia is represented by a deciduous conifer, Larix cajanderi Mayr. The vegetation varies from prostrate lichen Dryas octopetala L. tundra to open graminoid (hummock and non-hummock) tundra to tall Pinus pumila (Pall.) Regel shrublands to sparse and dense larch forests. Hence, this thesis presents investigations on recent compositional and above-ground biomass (AGB) changes, as well as potential future changes in AGB in central Chukotka. The aim is to assess how tundra-taiga vegetation develops under changing climate conditions particularly in Fareast Russia, central Chukotka. Therefore, three main research questions were considered: 1) What changes in vegetation composition have recently occurred in central Chukotka? 2) How have the above-ground biomass AGB rates and distribution changed in central Chukotka? 3) What are the spatial dynamics and rates of tree AGB change in the upcoming millennia in the northern tundra-taiga of central Chukotka? Remote sensing provides information on the spatial and temporal variability of vegetation. I used Landsat satellite data together with field data (foliage projective cover and AGB) from two expeditions in 2016 and 2018 to Chukotka to upscale vegetation types and AGB for the study area. More specifically, I used Landsat spectral indices (Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI) and Normalised Difference Snow Index (NDSI)) and constrained ordination (Redundancy analysis, RDA) for further k-means-based land-cover classification and general additive model (GAM)-based AGB maps for 2000/2001/2002 and 2016/2017. I also used Tandem-X DEM data for a topographical correction of the Landsat satellite data and to derive slope, aspect, and Topographical Wetness Index (TWI) data for forecasting AGB. Firstly, in 2016, taxa-specific projective cover data were collected during a Russian-German expedition. I processed the field data and coupled them with Landsat spectral Indices in the RDA model that was used for k-means classification. I could establish four meaningful land-cover classes: (1) larch closed-canopy forest, (2) forest tundra and shrub tundra, (3) graminoid tundra and (4) prostrate herb tundra and barren areas, and accordingly, I produced the land cover maps for 2000/2001/2002 and 2016/20017. Changes in land-cover classes between the beginning of the century (2000/2001/2002) and the present time (2016/2017) were estimated and interpreted as recent compositional changes in central Chukotka. The transition from graminoid tundra to forest tundra and shrub tundra was interpreted as shrubification and amounts to a 20% area increase in the tundra-taiga zone and 40% area increase in the northern taiga. Major contributors of shrubification are alder, dwarf birch and some species of the heather family. Land-cover change from the forest tundra and shrub tundra class to the larch closed-canopy forest class is interpreted as tree infilling and is notable in the northern taiga. We find almost no land-cover changes in the present treeless tundra. Secondly, total AGB state and change were investigated for the same areas. In addition to the total vegetation AGB, I provided estimations for the different taxa present at the field sites. As an outcome, AGB in the study region of central Chukotka ranged from 0 kg m-2 at barren areas to 16 kg m-2 in closed-canopy forests with the larch trees contributing the highest. A comparison of changes in AGB within the investigated period from 2000 to 2016 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. 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 within the 15 years from 2002 to 2017. In the third manuscript, potential future AGB changes were estimated based on the results of simulations of the individual-based spatially explicit vegetation model LAVESI using different climate scenarios, depending on Representative Concentration Pathways (RCPs) RCP 2.6, RCP 4.5 and RCP 8.5 with or without cooling after 2300 CE. LAVESI-based AGB was simulated for the current state until 3000 CE for the northern tundra-taiga study area for larch species because we expect the most notable changes to occur will be associated with forest expansion in the treeline ecotone. The spatial distribution and current state of tree AGB was validated against AGB field data, AGB extracted from Landsat satellite data and a high spatial resolution image with distinctive trees visible. The simulation results are indicating differences in tree AGB dynamics plot wise, depending on the distance to the current treeline. The simulated tree AGB dynamics are in concordance with fundamental ecological (emigrational and successional) processes: tree stand formation in simulated results starts with seed dispersion, tree stand establishment, tree stand densification and episodic thinning. Our results suggest mostly densification of existing tree stands in the study region within the current century in the study region and a lagged forest expansion (up to 39% of total area in the RCP 8.5) under all considered climate scenarios without cooling in different local areas depending on the closeness to the current treeline. In scenarios with cooling air temperature after 2300 CE, forests stopped expanding at 2300 CE (up to 10%, RCP 8.5) and then gradually retreated to their pre-21st century position. The average tree AGB rates of increase are the strongest in the first 300 years of the 21st century. The rates depend on the RCP scenario, where the highest are as expected under RCP 8.5. Overall, this interdisciplinary thesis shows a successful integration of field data, satellite data and modelling for tracking recent and predicting future vegetation changes in mountainous subarctic regions. The obtained results are unique for the focus area in central Chukotka and overall, for mountainous high latitude ecosystems.
    Type of Medium: Dissertations
    Pages: 149 Seiten , Illustrationen, Diagramme
    Language: English
    Note: Dissertation, Potsdam, Universität Potsdam, 2022 , Contents Abstract Zusammenfassung Contents Abbreviations Motivation 1 Introduction 1.1 Scientific background 1.2 Study region 1.3 Aims and objectives 2 Materials and methods 3.1 Section 4 - Strong shrub expansion in tundra-taiga, tree infilling in taiga and stable tundra in central Chukotka (north-eastern Siberia) between 2000 and 2017 3.2 Section 5 - Recent above-ground biomass changes in central Chukotka (NE Siberia) combining field-sampling and remote sensing 3.3 Section 6 - Future spatially explicit tree above-ground biomass trajectories revealed for a mountainous treeline ecotone using the individual-based model LAVESI 4 Strong shrub expansion in tundra-taiga, tree infilling in taiga and stable tundra in central Chukotka (north-eastern Siberia) between 2000 and 2017 Abstract 1 Introduction 2 Materials and methods 2.1 Field data collection and processing 2.2 Landsat data, pre-processing and spectral indices processing 2.3 Redundancy analysis (RDA) and classification approaches 3 Results 3.1 General characteristics of the vegetation field data 3.2 Relating field data to Landsat spectral indices in the RDA model 3.3 Land-cover classification 3.4 Land-cover change between 2000 and 2017 4 Discussion 4.1 Dataset limitations and optimisation 4.2 Vegetation changes from 2000/2001/2002 to 2016/2017 Conclusions Acknowledgements Data availability statement References Appendix A. Detailed description of Landsat acquisitions Appendix B. MODIS NDVI time series from 2000 to 2018 Appendix C. Landsat Indices values for each analysed vegetation site Appendix D. Fuzzy c-means classification for interpretation of uncertainties for land-cover mapping Appendix E. Validation of land-cover maps Appendix F. K-means classification results Appendix G. Heterogeneity of natural landscapes and mixed pixels of satellite data Appendix H. Distribution of land-cover classes and their changes by study area 5 Recent above-ground biomass changes in central Chukotka (NE Siberia) combining field-sampling and remote sensing Abstract 1 Introduction 2 Materials and methods 2.1 Study region and field surveys 2.2 Above-ground biomass upscaling and change derivation 3 Results 3.1 Vegetation composition and above-ground biomass 3.2 Upscaling above-ground biomass using GAM 3.3 Change of above-ground biomass between 2000 and 2017 in the four focus areas 4 Discussion 4.1 Recent state of above-ground biomass at the field sites 4.2 Recent state of above-ground biomass upscaled for central Chukotka 4.3 Change in above-ground biomass within the investigated 15–16 years in central Chukotka 5 Conclusions Data availability statement Author contributions Competing interests Acknowledgements References Appendix A. Sampling and above-ground biomass (AGB) calculation protocol for field data 6 Future spatially explicit tree above-ground biomass trajectories revealed for a mountainous treeline ecotone using the individual-based model LAVESI Abstract 1 Introduction 2 Materials and methods 2.1 Study region 2.2 LAVESI model setup, parameterisation, and validation 2.2.4 LAVESI simulation setup for this study 2.2.5 Validation of the model’s performance 3 Results 3.1 Dynamics and spatial distribution changes of tree above-ground-biomass 3.2 Spatial and temporal validation of the contemporary larch AGB 4 Discussion 4.1 Future dynamics of tree AGB at a plot level 4.2 What are the future dynamics of tree AGB at the landscape level? 5 Conclusions Data availability Acknowledgements References Appendix B. Permutation tests for tree presence versus topographical parameters Appendix C. Landsat-based, field, and simulated estimations of larch above-ground biomass (AGB). 7 Synthesis 7.1 What changes in vegetation composition have happened from 2000 to 2017 in central Chukotka? 7.2 How have the above-ground biomass (AGB) distribution and rates changed from 2000 to 2017 in central Chukotka? 7.3 What are the spatial dynamics and rates of tree AGB change in the upcoming centuries in the northern tundra-taiga from 2020 to 3000 CE on the plot level and landscape level? References Acknowledgements
    Location: AWI Reading room
    Branch Library: AWI Library
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  • 4
    Publication Date: 2023-11-01
    Description: Field investigations were performed in four areas: a treeless mountainous tundra (16-KP-04; Lake Rauchuagytgyn area), tundra-taiga transition zone (16-KP-01, Lake Ilirney area) and a northern taiga (18-BIL-01, 18-BIL-02). In total, 39 sites were investigated. The sites were placed to cover different vegetation communities that characterise central Chukotka. Fifteen-meter radius sample plots (sites) were demarcated in the most homogeneous locations. Heterogeneity was accommodated by roughly assorting vegetation into two to three vegetation types per sampling plot. Within each area of roughly estimated vegetation types we selected one 0.5 x 0.5 m subplot for representative ground-layer above-ground biomass (ABG) harvesting (major taxa and other). For moss and lichen AGB harvesting inside 0.5 x 0.5 m subplots representative 0.1 x 0.1 m subplots were chosen. All harvested AGB samples were weighed fresh in the field. In general, AGB samples with a weight of more than 15 g were subsampled. All samples were oven dried (60 °C, 24 h for ground-layer and moss and lichen samples, 48 h for shrub and tree branch samples) and weighed again. This dataset contains the raw data of dry weight for each sub-ground vegetation type sampling plot. All data was collected by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia. The AGB data calculations for the plot area including tree and tall shrubs can be found at https://doi.org/10.1594/PANGAEA.923719.
    Keywords: 16-KP-01-EN18001; 16-KP-01-EN18002; 16-KP-01-EN18003; 16-KP-01-EN18004; 16-KP-01-EN18005; 16-KP-01-EN18006; 16-KP-01-EN18007; 16-KP-01-EN18008; 16-KP-01-EN18009; 16-KP-01-EN18011; 16-KP-01-EN18012; 16-KP-01-EN18013; 16-KP-01-EN18014; 16-KP-01-EN18015; 16-KP-01-EN18016; 16-KP-01-EN18017; 16-KP-01-EN18018; 16-KP-01-EN18019; 16-KP-01-EN18021; 16-KP-01-EN18022; 16-KP-01-EN18023; 16-KP-01-EN18024; 16-KP-01-EN18025; 16-KP-01-EN18026; 16-KP-01-EN18027; 16-KP-04-EN18051; 16-KP-04-EN18052; 16-KP-04-EN18053; 16-KP-04-EN18054; 16-KP-04-EN18055; 18-BIL-01-EN18028; 18-BIL-01-EN18029; 18-BIL-02-EN18030; 18-BIL-02-EN18031; 18-BIL-02-EN18032; 18-BIL-02-EN18033; 18-BIL-02-EN18034; 18-BIL-02-EN18035; Aboveground biomass; Aconogonon tripterocarpum, biomass, dry mass; Alnus fruticosa, biomass, dry mass; Andromeda polifolia, biomass, dry mass; Area; AWI Arctic Land Expedition; Betula exilis, biomass, dry mass; Carbon in Permafrost / Kohlenstoff im Permafrost; Cassiope tetragona, biomass, dry mass; Chukotka; Chukotka 2018; Date/Time of event; Dryas octopetala, biomass, dry mass; Empetrum nigrum, biomass, dry mass; Equisetum arvense, biomass, dry mass; Event label; Field measurements; Identification; KoPF; Latitude of event; Ledum palustre, biomass, dry mass; Longitude of event; Moss and lichen, biomass, dry mass; Pinus pumila, biomass, dry mass; Plants, other, biomass, dry mass; Pyrola sp., biomass, dry mass; Rosa arctica, biomass, dry mass; Rubus sp., biomass, dry mass; RU-Land_2018_Chukotka; Salix spp., biomass, dry mass; Sample area; Siberia; Site; Treeline; Vaccinium uliginosum, biomass, dry mass; Vaccinium vitis-idaea, biomass, dry mass; Vegetation; Vegetation, area; Vegetation, cover; Vegetation survey; VEGSUR
    Type: Dataset
    Format: text/tab-separated-values, 3649 data points
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  • 5
    Publication Date: 2023-11-03
    Description: Lakes cover large parts of the climatically sensitive Arctic landscape and respond rapidly to environmental change. Freshwater diatoms dominate the primary producer community in these lakes and can be used to detect biotic responses to climate and environmental change. We used specific diatom metabarcoding on sedimentary DNA, combined with next-generation sequencing and diatom morphology, to assess diatom diversity in five glacial and 15 thermokarst lakes within the easternmost expanse of the Siberian treeline ecotone in Chukotka, Russia. We obtained 163 verified diatom sequence types and identified 176 diatom species morphologically. Raw and resampled read counts of diatom DNA sequences retrieved from our approach are and its taxonomic classifications are listed in the spread sheet: Huang_16KP_diat_JOPL.xlsx. The retrieved diatom sequences are listed in the fasta file: 16KP_diat_sequences.fasta. Raw and resampled valve counts of morphologically identified diatom taxa are summarized in the spreadsheet Huang_16KP_diat_JOPL.xlsx.
    Keywords: 16-KP-01-L01; 16-KP-01-L02; 16-KP-01-L03; 16-KP-01-L04; 16-KP-01-L05; 16-KP-02-L07; 16-KP-02-L08; 16-KP-02-L09; 16-KP-03-L10; 16-KP-03-L11; 16-KP-03-L13; 16-KP-03-L14; 16-KP-03-L15; 16-KP-03-L16; 16-KP-03-L17; 16-KP-03-L18; 16-KP-04-L19; 16-KP-04-L20; 16-KP-04-L21; 16-KP-04-L22; Achnanthes spp.; Achnanthidium minutissimum; Actinella punctata; Acutodesmus obliquus; Amphora fogediana; Amphora ovalis; Amphora pediculus; Amphora spp.; Amphora staurosiroides; Anomoeoneis spp.; Arctic; Asterionella formosa; Aulacoseira alpigena; Aulacoseira ambigua; Aulacoseira distans; Aulacoseira lirata; Aulacoseira muzzanensis; Aulacoseira perglabra; Aulacoseira spp.; Aulacoseira subarctica; Aulacoseira tethera; Aulacoseira valida; AWI_Envi; AWI Arctic Land Expedition; Brachysira brebissonii; Brachysira vitrea; Brachysira zellensis; Caloneis baccilum; Caloneis silicula; Camphilodiscus spp.; Campylodiscus hibernicus; Cancris inaequalis; Caracomia arctica; Cavinula cocconeiformis; Cavinula jaernefeltii; Cavinula pseudoscutiformis; Cocconeis placentula; Counting, diatoms; Cyclotella iris; Cyclotella spp.; Cyclotella tripartita; Cymbella cesattii; Cymbella naviculiformis; Cymbella spp.; Diatomella balfouriana; diatoms; Diploneis elliptica; Diploneis finnica; Diploneis marginestriata; Diploneis modica; Diploneis oblongella; Diploneis ovalis; Drymyomma elegans; Elevation of event; Encyonema alpinum; Encyonema cespitosum; Encyonema hebridicum; Encyonema mesianum; Encyonema minutum; Encyonema muelleri; Encyonema paucistriatum; Encyonema silesiacum; Epithemia sorex; Eriophorum gracile; Eucocconeis flexella; Eunotia arculus; Eunotia arcus; Eunotia bilunaris; Eunotia diadema; Eunotia diodon; Eunotia faba; Eunotia fallax; Eunotia incisa; Eunotia monodon; Eunotia mucophila; Eunotia nymanniana; Eunotia paludosa; Eunotia parallela; Eunotia pectinalis; Eunotia praerupta; Eunotia septentrionalis; Eunotia spp.; Eunotia sudetica; Eunotia tetraodon; Eunotia triodon; Event label; Fragilaria acus; Fragilaria capucina; Fragilaria constricta; Fragilaria lata; Fragilaria spp.; Fragilaria tenera; Fragilariforma virescens var. subsalina; Frustulia rhomboides; Geissleria ignota var. palustris; Geissleria schoenfeldii; Gomphonema acuminatum; Gomphonema affine; Gomphonema angustum; Gomphonema clavatum; Gomphonema gracile; Gomphonema insigne; Gomphonema lagerheimii; Gomphonema olivaceum var. fonticola; Gomphonema parvulum; Gomphonema spp.; Gyrosigma attenuatum; Gyrosigma spp.; Hannaea inaequidentata; Hantzschia amphioxys; Hippodonta costulata; Humidophola contenta; Karayevia laterostrata; Karayevia suchlandtii; Keperveem_2016; lakes; Latitude of event; Lindavia bodanica; Lindavia ocellata; Longitude of event; Luticola mutica; Martyana schulzii; Melosira moniliformis; Meridion circulare; Navicula libonensis; Navicula radiosa; Navicula spp.; Navicula vulpina; Neidium bisulcatum; Neidium hitchcockii; Neidium iridis et forma vernales; Neidium spp.; Nitzschia acicularis; Nitzschia alpina; Nitzschia amphibia; Nitzschia spp.; Peronia fibula; Peroniopsis heribaudii; Pinnularia balfouriana; Pinnularia borealis; Pinnularia braunii; Pinnularia brevicostata; Pinnularia dactylus; Pinnularia gentilis; Pinnularia gibba; Pinnularia hemiptera; Pinnularia interrupta; Pinnularia microstauron; Pinnularia nodosa; Pinnularia obscura; Pinnularia schwabei; Pinnularia stomatophora; Pinnularia viridis; Placoneis elginensis; Planothidium calcar; Planothidium lanceolatum; Planothidium oestrupii; Pliocaenicus spp.; Polar Terrestrial Environmental Systems @ AWI; Psammothidium chlidanos; Psammothidium rossii; Pseudostaurosira brevistriata; Pseudostaurosira parasitica; Pseudostaurosira pseudoconstruens; Rhoicosphenia abbreviata; Rossithidium pusillum; RU-Land_2016_Keperveem; Sedimentary DNA; Sellaphora bacillum; Sellaphora pupula; Siberia; Sosane gracilis; Station label; Stauroneis anceps; Stauroneis phoenicenteron; Stauroneis smithii; Staurosira construens; Staurosira construens var. exigua; Staurosira venter; Staurosirella pinnata; Stephanodiscus spp.; Stephanodiskus rotula; Surirella amphioxys; Surirella angusta; Surirella linearis var. helvetica; Surirella robusta; Synedra cyclopum; Tabellaria fenestrata; Tabellaria flocculosa; Tetracyclus glans; Tryblionella angustata; Tschukotka, Sibiria, Russia; Ulnaria ulna; Water sampler, UWITEC; WSUWI
    Type: Dataset
    Format: text/tab-separated-values, 3540 data points
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  • 6
    Publication Date: 2023-11-03
    Description: Field investigations were performed in four areas: a treeless mountainous tundra (16-KP-04; Lake Rauchuagytgyn area), tundra-taiga transition zone (16-KP-01, Lake Ilirney area) and a northern taiga (18-BIL-01, 18-BIL-02). In total, 39 sites were investigated. The sites were placed to cover different vegetation communities that characterise central Chukotka. Fifteen-meter radius sample plots (sites) were demarcated in the most homogeneous locations. Heterogeneity was accommodated by roughly assorting vegetation into two to three vegetation types per sampling plot. Within each area of roughly estimated vegetation types we selected one 0.5 x 0.5 m subplot for representative ground-layer above-ground biomass (ABG) harvesting (major taxa and other). For moss and lichen AGB harvesting inside 0.5 x 0.5 m subplots representative 0.1 x 0.1 m subplots were chosen. All ground-layer vegetation AGB assessments were calculated for the fifteen-meter radius plot in g m^2 for each sample plot. Tree (Larix cajanderi) AGB was assessed using partial harvesting of three representative individual trees per sample plot, specifically developed for the study area allometric equations and measurements of all trees' heights on the fifteen-meter radius plot. AGB of tall shrubs (Alnus fruticosa, Pinus pumila and Salix spp. (non-creeping)) was assessed from harvested subsamples and projective cover on the fifteen-meter radius sample plot. All harvested AGB samples were weighed fresh in the field. In general, AGB samples with a weight of more than 15 g were subsampled. All samples were oven dried (60 °C, 24 h for ground-layer and moss and lichen samples, 48 h for shrub and tree branch samples, up to one week for tree stem discs) and weighed again. All data was collected by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia.
    Keywords: 16-KP-01-EN18001; 16-KP-01-EN18002; 16-KP-01-EN18003; 16-KP-01-EN18004; 16-KP-01-EN18005; 16-KP-01-EN18006; 16-KP-01-EN18007; 16-KP-01-EN18008; 16-KP-01-EN18009; 16-KP-01-EN18011; 16-KP-01-EN18012; 16-KP-01-EN18013; 16-KP-01-EN18014; 16-KP-01-EN18015; 16-KP-01-EN18016; 16-KP-01-EN18017; 16-KP-01-EN18018; 16-KP-01-EN18019; 16-KP-01-EN18020; 16-KP-01-EN18021; 16-KP-01-EN18022; 16-KP-01-EN18023; 16-KP-01-EN18024; 16-KP-01-EN18025; 16-KP-01-EN18026; 16-KP-01-EN18027; 16-KP-04-EN18051; 16-KP-04-EN18052; 16-KP-04-EN18053; 16-KP-04-EN18054; 16-KP-04-EN18055; 18-BIL-01-EN18028; 18-BIL-01-EN18029; 18-BIL-02-EN18030; 18-BIL-02-EN18031; 18-BIL-02-EN18032; 18-BIL-02-EN18033; 18-BIL-02-EN18034; 18-BIL-02-EN18035; above-ground biomass; Above-ground vegetation survey; Aconogonon tripterocarpum, biomass, dry mass; Alnus fruticosa, biomass, dry mass; Andromeda polifolia, biomass, dry mass; AWI_Envi; AWI Arctic Land Expedition; Betula exilis, biomass, dry mass; Carbon in Permafrost / Kohlenstoff im Permafrost; Cassiope tetragona, biomass, dry mass; Chukotka; Chukotka 2018; Code; Dryas octopetala, biomass, dry mass; ELEVATION; Empetrum nigrum, biomass, dry mass; Equisetum arvense, biomass, dry mass; Event label; KoPF; Larix cajanderi, biomass, dry mass; LATITUDE; Ledum palustre, biomass, dry mass; LONGITUDE; Moss and lichen, biomass, dry mass; Pinus pumila, biomass, dry mass; Plants, other, biomass, dry mass; Plot radius; Polar Terrestrial Environmental Systems @ AWI; Pyrola sp., biomass, dry mass; Rosa arctica, biomass, dry mass; Rubus sp., biomass, dry mass; RU-Land_2018_Chukotka; Salix spp., biomass, dry mass; Siberia; Site; subarctic vegetation; Vaccinium uliginosum, biomass, dry mass; Vaccinium vitis-idaea, biomass, dry mass; Vegetation survey; VEGSUR
    Type: Dataset
    Format: text/tab-separated-values, 897 data points
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  • 7
    Publication Date: 2023-11-03
    Description: Tree and tall shrub above ground biomass (AGB) samples were taken in five areas: a treeless mountainous tundra (16-KP-04; Lake Rauchuagytgyn area), tundra-taiga transition zone (16-KP-01, Lake Ilirney area; 18-BIL-00) and a northern taiga (18-BIL-01, 18-BIL-02). In total, 31 sample plots with 15-m radius were investigated for tree and tall shrub AGB. The only present tree species there is Larix cajanderi Mayr. By tall shrubs we mean Pinus pumila (Pall.) Regel, Alnus viridis ssp. fruticosa (Rupr.) Nyman and Salix spp. L. Three living trees (the lowest, a tree with the average height and the highest) per each site were cut down. From each individual tree certain representative samples were taken: samples of branches, needles, cones and tree stem discs. Sampled branches were divided into four categories: 1) big (first order, connected to the stem), 2) medium (second order, connected to the big branches), 3) small (third order, connected to the medium branches), 4) dead (including dead cones). Needles are typically found on the third order branches. Cones were divided by colour (red, brown and grey). Tree stem discs were taken at the base of a tree (0 cm, disc A), breast height (130 cm, disc B) and top/close to the top of a tree (260 cm, disc C). To estimate each tree's stem biomass, the stem was assumed to have a cone shape. Dead trees were also sampled, but irregularly (not at every sample plot). In most cases, they did not have branch and needle material, so the samples of dead trees mostly consist of tree discs' samples. Tall shrubs were representatively sampled similarly to trees – three individuals per site. Samples included branch, leaves/needles and cones/catkin biomass. All harvested AGB samples were weighed fresh in the field and subsampled. All subsamples were oven dried (60 °C, 48 h for shrub and tree branch samples, up to one week for tree stem discs) and weighed again. Protocol for total tree and shrub AGB estimation can be found enclosed as a separate file. All data were collected by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia.” All data were collected during the “Chukotka 2018” expedition, that has been supported by the German Federal Ministry of Education and Research (BMBF), which enabled the Russian-German research programme “Kohlenstoff im Permafrost KoPf” (grant no. 03F0764A) and by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852).
    Keywords: 16-KP-01-EN18001; 16-KP-01-EN18003; 16-KP-01-EN18004; 16-KP-01-EN18005; 16-KP-01-EN18006; 16-KP-01-EN18007; 16-KP-01-EN18008; 16-KP-01-EN18009; 16-KP-01-EN18011; 16-KP-01-EN18012; 16-KP-01-EN18013; 16-KP-01-EN18014; 16-KP-01-EN18015; 16-KP-01-EN18016; 16-KP-01-EN18017; 16-KP-01-EN18021; 16-KP-01-EN18022; 16-KP-01-EN18023; 16-KP-01-EN18024; 16-KP-01-EN18025; 16-KP-01-EN18026; 16-KP-01-EN18027; 18-BIL-00-EN18000; 18-BIL-01-EN18028; 18-BIL-01-EN18029; 18-BIL-02-EN18030; 18-BIL-02-EN18031; 18-BIL-02-EN18032; 18-BIL-02-EN18033; 18-BIL-02-EN18034; 18-BIL-02-EN18035; above ground biomass; Alnus fruticosa; AWI_Envi; AWI Arctic Land Expedition; Branches; Carbon in Permafrost / Kohlenstoff im Permafrost; Catkins/Cones; Catkins/Cones, dry mass; Catkins/Cones, fresh mass; Chukotka; Chukotka 2018; Code; Crone diameter; Diameter; Dry mass; Event label; Identification; KoPF; Larix Cajanderi; LATITUDE; Leaves/Needles, dry mass; Leaves/Needles, fresh mass; Length; LONGITUDE; Pinus pumila; Polar Terrestrial Environmental Systems @ AWI; RU-Land_2018_Chukotka; Salix; Sample code/label; Siberia; Site; Species; subarctic vegetation; Tree/shrub biomass, aboveground; Tree/shrub height; Vegetation survey; VEGSUR; Vitality; Volume; Wet mass; Width; Wood, dry mass; Wood, fresh mass; Wood density
    Type: Dataset
    Format: text/tab-separated-values, 10717 data points
    Location Call Number Expected Availability
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  • 8
    Publication Date: 2023-11-16
    Description: Hyperspectral field measurements were acquired in the central Lena Delta in August 2018. The aim was to conduct spectral surface reflectance surveys of various homogeneous vegetation areas on different permafrost landforms to establish a representative spectral reflectance database. In total, we took 28 hyperspectral field measurements of 30 m x 30 m homogeneous vegetation plots across Samoylov and Kurungnakh-Island. Four plots were measured twice with a two-week delay, therefore depicting the changes on reflectance signature. We conducted the field-spectrometry measurements with the Spectral Evolution SR-2500 with a 1.5 m Fiber Optic Cable. The instrument is calibrated to a spectral radiance range of 350 to 2.500 nm. Further technical details are provided in a separate document. We identified homogeneous vegetation plots with a size of 30 m x 30 m and acquired about 100 individual spectrometry measurements, randomly scattered across the plot. At the start and at the end of each survey the system was referenced by measuring the back reflected radiance from a Zenith Lite^TM Diffuse Reflectance Target of 50% reflectivity. Hyperspectral field measurements with the plot name SAM18 were taken on Samoylov and those with KUR18 on Kurungnakh-Island. All data was collected by scientists from the Alfred Wegener Institute (AWI) Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany.
    Keywords: AWI_Envi; AWI_Perma; Carbon in Permafrost / Kohlenstoff im Permafrost; field spectrometry; hyperspectral; KoPF; Kurungnakh; Permafrost Research; Polar Terrestrial Environmental Systems @ AWI; Samoylov; spectral evolution; Vegetation
    Type: Dataset
    Format: application/zip, 29 datasets
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  • 9
    Publication Date: 2023-11-16
    Keywords: AWI_Envi; AWI_Perma; AWI Arctic Land Expedition; Carbon in Permafrost / Kohlenstoff im Permafrost; DATE/TIME; field spectrometry; hyperspectral; KoPF; Kurungnakh; LAND; Lena 2018; Lena Delta; Number of observations; ORDINAL NUMBER; Parameter; Permafrost Research; Polar Terrestrial Environmental Systems @ AWI; Reference sample; Reflectance; Region of interest; RU-Land_2018_Lena; SAM18-SP-002; Samoylov; Sampling/measurement on land; Site; spectral evolution; Target value; Vegetation; Wavelength
    Type: Dataset
    Format: text/tab-separated-values, 3292692 data points
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  • 10
    Publication Date: 2023-11-16
    Keywords: AWI_Envi; AWI_Perma; AWI Arctic Land Expedition; Carbon in Permafrost / Kohlenstoff im Permafrost; DATE/TIME; field spectrometry; hyperspectral; KoPF; KUR18-SP-006; Kurungnakh; LAND; Lena 2018; Lena Delta; Number of observations; ORDINAL NUMBER; Parameter; Permafrost Research; Polar Terrestrial Environmental Systems @ AWI; Reference sample; Reflectance; Region of interest; RU-Land_2018_Lena; Samoylov; Sampling/measurement on land; Site; spectral evolution; Target value; Vegetation; Wavelength
    Type: Dataset
    Format: text/tab-separated-values, 2047379 data points
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