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  • 1
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    Hyperspectral outcrop models for palaeoseismic studies (2021)
    Details
    Publication Date: 2021-11-10
    Description: The traditional study of palaeoseismic trenches, involving logging, stratigraphic and structural interpretation, can be time consuming and affected by biases and inaccuracies. To overcome these limitations, a new workflow is presented that integrates infrared hyperspectral and photogrammetric data to support field-based palaeoseismic observations. As a case study, this method is applied on two palaeoseismic trenches excavated across a post-glacial fault scarp in northern Finnish Lapland. The hyperspectral imagery (HSI) is geometrically and radiometrically corrected, processed using established image processing algorithms and machine learning approaches, and co-registered to a structure-from-motion point cloud. HSI-enhanced virtual outcrop models are a useful complement to palaeoseismic field studies as they not only provide an intuitive visualisation of the outcrop and a versatile data archive, but also enable an unbiased assessment of the mineralogical composition of lithologic units and a semi-automatic delineation of contacts and deformational structures in a 3D virtual environment. Résumé L'étude traditionnelle des tranchées paléosismiques, impliquant l'enregistrement des coupes et l'interprétation stratigraphique et structurelle, peut prendre beaucoup de temps et être entachée de biais et d'inexactitudes. Pour surmonter ces limites, une nouvelle méthodologie est présentée, intégrant des données photogrammétriques et hyperspectrales infrarouges en appui aux observations paléosismiques de terrain. Comme étude de cas, cette méthode est appliquée à deux tranchées paléosismiques creusées à travers un escarpement de faille post-glaciaire dans le nord de la Laponie finlandaise. L'imagerie hyperspectrale (HSI) est corrigée géométriquement et radiométriquement, traitée à l'aide d'algorithmes classiques de traitement d'images et d'apprentissage machine, et recalée sur un nuage de points photogrammétrique. Les modèles virtuels d'affleurements améliorés par HSI constituent un complément utile aux études paléosismiques de terrain, car ils fournissent non seulement une visualisation intuitive de l'affleurement et une archive de données facile d'emploi, mais permettent également une évaluation non biaisée de la composition minéralogique d'unités lithologiques ainsi qu'une délimitation semi-automatique des contacts et des structures de déformation dans un environnement virtuel 3D.
    Keywords: 551.22 ; geology ; hyperspectral imaging ; outcrop models ; palaeoseismology ; remote sensing ; SfM photogrammetry
    Language: English
    Type: map
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    CITATION GEO-LEO
  • 2
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    Integrated Geological and Geophysical Mapping of a Carbonatite-Hosting Outcrop in Siilinjärvi, Finland, Using Unmanned Aerial Systems (2020)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2020-09-15
    Description: Mapping geological outcrops is a crucial part of mineral exploration, mine planning and ore extraction. With the advent of unmanned aerial systems (UASs) for rapid spatial and spectral mapping, opportunities arise in fields where traditional ground-based approaches are established and trusted, but fail to cover sufficient area or compromise personal safety. Multi-sensor UAS are a technology that change geoscientific research, but they are still not routinely used for geological mapping in exploration and mining due to lack of trust in their added value and missing expertise and guidance in the selection and combination of drones and sensors. To address these limitations and highlight the potential of using UAS in exploration settings, we present an UAS multi-sensor mapping approach based on the integration of drone-borne photography, multi- and hyperspectral imaging and magnetics. Data are processed with conventional methods as well as innovative machine learning algorithms and validated by geological field mapping, yielding a comprehensive and geologically interpretable product. As a case study, we chose the northern extension of the Siilinjärvi apatite mine in Finland, in a brownfield exploration setting with plenty of ground truth data available and a survey area that is partly covered by vegetation. We conducted rapid UAS surveys from which we created a multi-layered data set to investigate properties of the ore-bearing carbonatite-glimmerite body. Our resulting geologic map discriminates between the principal lithologic units and distinguishes ore-bearing from waste rocks. Structural orientations and lithological units are deduced based on high-resolution, hyperspectral image-enhanced point clouds. UAS-based magnetic data allow an insight into their subsurface geometry through modeling based on magnetic interpretation. We validate our results via ground survey including rock specimen sampling, geochemical and mineralogical analysis and spectroscopic point measurements. We are convinced that the presented non-invasive, data-driven mapping approach can complement traditional workflows in mineral exploration as a flexible tool. Mapping products based on UAS data increase efficiency and maximize safety of the resource extraction process, and reduce expenses and incidental wastes.
    Electronic ISSN: 2072-4292
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by Molecular Diversity Preservation International
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    PAPER CURRENT
  • 3
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    Component Decomposition-Based Hyperspectral Resolution Enhancement for Mineral Mapping (2020)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2020-09-07
    Description: Combining both spectral and spatial information with enhanced resolution provides not only elaborated qualitative information on surfacing mineralogy but also mineral interactions of abundance, mixture, and structure. This enhancement in the resolutions helps geomineralogic features such as small intrusions and mineralization become detectable. In this paper, we investigate the potential of the resolution enhancement of hyperspectral images (HSIs) with the guidance of RGB images for mineral mapping. In more detail, a novel resolution enhancement method is proposed based on component decomposition. Inspired by the principle of the intrinsic image decomposition (IID) model, the HSI is viewed as the combination of a reflectance component and an illumination component. Based on this idea, the proposed method is comprised of several steps. First, the RGB image is transformed into the luminance component, blue-difference and red-difference chroma components (YCbCr) , and the luminance channel is considered as the illumination component of the HSI with an ideal high spatial resolution. Then, the reflectance component of the ideal HSI is estimated with the downsampled HSI image and the downsampled luminance channel. Finally, the HSI with high resolution can be reconstructed by utilizing the obtained illumination and the reflectance components. Experimental results verify that the fused results can successfully achieve mineral mapping, producing better results qualitatively and quantitatively over single sensor data.
    Electronic ISSN: 2072-4292
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by Molecular Diversity Preservation International
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    PAPER CURRENT
  • 4
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    Feature extraction for hyperspectral mineral domain mapping: A test of conventional and innovative methods (2021)
    Elsevier
    Details
    Publication Date: 2021-01-01
    Print ISSN: 0034-4257
    Electronic ISSN: 1879-0704
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by Elsevier
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    PAPER CURRENT
  • 5
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    Drone-based surveys of mineral deposits (2020)
    Springer Nature
    Details
    Publication Date: 2020-03-17
    Electronic ISSN: 2662-138X
    Topics: Energy, Environment Protection, Nuclear Power Engineering , Geosciences
    Published by Springer Nature
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  • 6
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    UAS-Based Hyperspectral Environmental Monitoring of Acid Mine Drainage Affected Waters (2021)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2021-02-09
    Description: The exposure of metal sulfides to air or water, either produced naturally or due to mining activities, can result in environmentally damaging acid mine drainage (AMD). This needs to be accurately monitored and remediated. In this study, we apply high-resolution unmanned aerial system (UAS)-based hyperspectral mapping tools to provide a useful, fast, and non-invasive method for the monitoring aspect. Specifically, we propose a machine learning framework to integrate visible to near-infrared (VNIR) hyperspectral data with physicochemical field data from water and sediments, together with laboratory analyses to precisely map the extent of acid mine drainage in the Tintillo River (Spain). This river collects the drainage from the western part of the Rio Tinto massive sulfide deposit and discharges large quantities of acidic water with significant amounts of dissolved metals (Fe, Al, Cu, Zn, amongst others) into the Odiel River. At the confluence of these rivers, different geochemical and mineralogical processes occur due to the interaction of very acidic water (pH 2.5–3.0) with neutral water (pH 7.0–8.0). This complexity makes the area an ideal test site for the application of hyperspectral mapping to characterize both rivers and better evaluate contaminated water bodies with remote sensing imagery. Our approach makes use of a supervised random forest (RF) regression for the extended mapping of water properties, using the samples collected in the field as ground-truth and training data. The resulting maps successfully estimate the concentration of dissolved metals and related physicochemical properties in water, and trace associated iron species (e.g., jarosite, goethite) within sediments. These results highlight the capabilities of UAS-based hyperspectral data to monitor water bodies in mining environments, by mapping their hydrogeochemical properties, using few field samples. Hence, we have demonstrated that our workflow allows the rapid discrimination and mapping of AMD contamination in water, providing an essential basis for monitoring and subsequent remediation.
    Electronic ISSN: 2075-163X
    Topics: Geosciences
    Published by Molecular Diversity Preservation International
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  • 7
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    Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm (2020)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2020-12-07
    Description: The increasing amount of information acquired by imaging sensors in Earth Sciences results in the availability of a multitude of complementary data (e.g., spectral, spatial, elevation) for monitoring of the Earth’s surface. Many studies were devoted to investigating the usage of multi-sensor data sets in the performance of supervised learning-based approaches at various tasks (i.e., classification and regression) while unsupervised learning-based approaches have received less attention. In this paper, we propose a new approach to fuse multiple data sets from imaging sensors using a multi-sensor sparse-based clustering algorithm (Multi-SSC). A technique for the extraction of spatial features (i.e., morphological profiles (MPs) and invariant attribute profiles (IAPs)) is applied to high spatial-resolution data to derive the spatial and contextual information. This information is then fused with spectrally rich data such as multi- or hyperspectral data. In order to fuse multi-sensor data sets a hierarchical sparse subspace clustering approach is employed. More specifically, a lasso-based binary algorithm is used to fuse the spectral and spatial information prior to automatic clustering. The proposed framework ensures that the generated clustering map is smooth and preserves the spatial structures of the scene. In order to evaluate the generalization capability of the proposed approach, we investigate its performance not only on diverse scenes but also on different sensors and data types. The first two data sets are geological data sets, which consist of hyperspectral and RGB data. The third data set is the well-known benchmark Trento data set, including hyperspectral and LiDAR data. Experimental results indicate that this novel multi-sensor clustering algorithm can provide an accurate clustering map compared to the state-of-the-art sparse subspace-based clustering algorithms.
    Electronic ISSN: 2072-4292
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by Molecular Diversity Preservation International
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  • 8
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    Detection of REEs with lightweight UAV-based hyperspectral imaging (2020)
    Springer Nature
    Details
    Publication Date: 2020-10-15
    Description: Rare earth elements (REEs) supply is important to ensure the energy transition, e-mobility and ultimately to achieve the sustainable development goals of the United Nations. Conventional exploration techniques usually rely on substantial geological field work including dense in-situ sampling with long delays until provision of analytical results. However, this approach is limited by land accessibility, financial status, climate and public opposition. Efficient and innovative methods are required to mitigate these limitations. The use of lightweight unmanned aerial vehicles (UAVs) provides a unique opportunity to conduct rapid and non-invasive exploration even in socially sensitive areas and in relatively inaccessible locations. We employ drones with hyperspectral sensors to detect REEs at the earth’s surface and thus contribute to a rapidly evolving field at the cutting edge of exploration technologies. We showcase for the first time the direct mapping of REEs with lightweight hyperspectral UAV platforms. Our solution has the advantage of quick turn-around times (〈 1 d), low detection limits (〈 200 ppm for Nd) and is ideally suited to support exploration campaigns. This procedure was successfully tested and validated in two areas: Marinkas Quellen, Namibia, and Siilinjärvi, Finland. This strategy should invigorate the use of drones in exploration and for the monitoring of mining activities.
    Electronic ISSN: 2045-2322
    Topics: Natural Sciences in General
    Published by Springer Nature
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  • 9
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    Tree data set from forest inventories in north-eastern Siberia, Plot data base (2022)
    PANGAEA
    Details
    Publication Date: 2024-01-10
    Keywords: 11-CH-02II; 11-CH-02III; 11-CH-06I; 11-CH-06III; 11-CH-12I; 11-CH-12II; 11-CH-17I; 11-CH-17II; 12-KO-02; 12-KO-03; 12-KO-04; 12-KO-05; 13-TY-02-VI; 13-TY-02-VII; 14-OM-02-V1; 14-OM-02-V2; 14-OM-11-V3; 14-OM-20-V4; 14-OM-TRANS1; 14-OM-TRANS2; 14-OM-TRANS3; 14-OM-TRANS4; 14-OM-TRANS5; 14-OM-TRANS6; 14-OM-TRANS6-7; 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-EN18010; 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; 16-KP-V01; 16-KP-V02; 16-KP-V03; 16-KP-V04; 16-KP-V05; 16-KP-V06; 16-KP-V07; 16-KP-V08; 16-KP-V09; 16-KP-V10; 16-KP-V11; 16-KP-V12; 16-KP-V13; 16-KP-V14; 16-KP-V15; 16-KP-V16; 16-KP-V17; 16-KP-V18; 16-KP-V19; 16-KP-V20; 16-KP-V21; 16-KP-V22; 16-KP-V23; 16-KP-V24; 16-KP-V25; 16-KP-V26; 16-KP-V27; 16-KP-V28; 16-KP-V29; 16-KP-V30; 16-KP-V31; 16-KP-V32; 16-KP-V33; 16-KP-V34; 16-KP-V35; 16-KP-V36; 16-KP-V37; 16-KP-V38; 16-KP-V39; 16-KP-V40; 16-KP-V41; 16-KP-V42; 16-KP-V43; 16-KP-V44; 16-KP-V45; 16-KP-V46; 16-KP-V47; 16-KP-V48; 16-KP-V49; 16-KP-V50; 16-KP-V51; 16-KP-V52; 16-KP-V53; 16-KP-V54; 16-KP-V55; 16-KP-V56; 16-KP-V57; 16-KP-V58; 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; 18-LD-VP012-Tit-Ary; Area; Area/locality; AWI_Envi; AWI Arctic Land Expedition; B19-T1; B19-T2; Batagay 2019; Campaign; Central Yakutia; Chatanga2011; Chukotka; Chukotka 2018; Comment; DATE/TIME; ELEVATION; EN18061; EN18062; EN18063; EN18064; EN18065; EN18066; EN18067; EN18068; EN18069; EN18070_centre; EN18070_edge; EN18070_end; EN18070_transition; EN18071; EN18072; EN18073; EN18074; EN18075; EN18076; EN18077; EN18078; EN18079; EN18080; EN18081; EN18082; EN18083; EN21201; EN21202; EN21203; EN21204; EN21205; EN21206; EN21207; EN21208; EN21209; EN21210; EN21211; EN21212; EN21213; EN21214; EN21215; EN21216; EN21217; EN21218; EN21219; EN21220; EN21221; EN21222; EN21223; EN21224; EN21225; EN21226; EN21227; EN21228; EN21229; EN21230; EN21231; EN21232; EN21233; EN21234; EN21235; EN21236; EN21237; EN21238; EN21239; EN21240; EN21241; EN21242; EN21243; EN21244; EN21245; EN21246; EN21247; EN21248; EN21249; EN21250; EN21251; EN21252; EN21253; EN21254; EN21255; EN21256; EN21257; EN21258; EN21259; EN21260; EN21261; EN21262; EN21263; EN21264; Event label; Forest; Forest type; FTa; FTc; FTe; Genus; Gini coefficient; HAND; Height, maximum; Height, quantile; Individual Trees; Keperveem_2016; Kolyma, Russia; Kytalyk-Pokhodsk_2012, Kolyma2012; LATITUDE; Lena 2018; LONGITUDE; Mean values; Median values; MULT; Multiple investigations; Number of species; Number of trees; Omoloy2014; Plot; Polar Terrestrial Environmental Systems @ AWI; Principal investigator; Quantile (25th); Quantile (75th); Quantile (90th); Quantile (98th); Reference/source; RU-Land_2011_Khatanga; RU-Land_2012_Kytalyk_Kolyma; RU-Land_2013_Taymyr; RU-Land_2014_Omoloy; RU-Land_2016_Keperveem; RU-Land_2018_Chukotka; RU-Land_2018_Lena; RU-Land_2018_Yakutia; RU-Land_2019_Batagay; RU-Land_2021_Yakutia; Sampling by hand; Shannon Diversity Index; Siberia; Site; Taymyr; Taymyr2013; Tree, basal area, at base; Tree, basal area, at breast height; Tree, volume, conical; Tree, volume, smallian; Tree density; Tree height; Trees, basal area; Trees, volume, conical; Trees, volume, smallian; TY04VI; TY04VII; TY07VI; TY07VII; TY09VI; TY09VII; Vegetation survey; VEGSUR; Yakutia
    Type: Dataset
    Format: text/tab-separated-values, 7323 data points
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    DATA PANGAEA
  • 10
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    Tree data set from forest inventories in north-eastern Siberia - Tree heights (2022)
    PANGAEA
    Details
    Publication Date: 2024-01-10
    Description: The data set presents more than 32,000 of about 40,000 trees, which were surveyed during several Russian-German expeditions by the North-Eastern Federal University Yakutsk and the Alfred-Wegener-Institute Potsdam in the North-East of the Russian Federation between the years 2011 and 2021. The purpose was to gather information on trees and forests in this region, which was then used to understand tree line migration, stand infilling and natural disturbance and succession processes and to initialize and validate a forest model. Trees are located on more than 160 vegetation plots, each of which has a size of several hundred square meters. For every tree, height was estimated, and the species recorded, while a few individuals were subject to more detailed inventory. This table contains every standing tree of at least 40 cm height that was encountered on the vegetation plots described in the Plot Data Base. It partially overlaps with the dataset “Tree data set from forest inventories in north-eastern Siberia - Tree measurements.” (doi: 10.1594/PANGAEA.949861), which additionally contains details on small or lying deadwood.
    Keywords: 11-CH-02II; 11-CH-02III; 11-CH-06I; 11-CH-06III; 11-CH-12I; 11-CH-12II; 11-CH-17I; 11-CH-17II; 13-TY-02-VI; 13-TY-02-VII; 13-TY-04-VI; 13-TY-04-VII; 13-TY-07-VI; 13-TY-07-VII; 13-TY-09-VI; 13-TY-09-VII; 14-OM-02-V1; 14-OM-02-V2; 14-OM-20-V4; 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-EN18009; 16-KP-01-EN18010; 16-KP-01-EN18012; 16-KP-01-EN18014; 16-KP-01-EN18021; 16-KP-01-EN18024; 16-KP-01-EN18025; 16-KP-01-EN18026; 16-KP-01-EN18027; 16-KP-V01; 16-KP-V02; 16-KP-V03; 16-KP-V04; 16-KP-V05; 16-KP-V06; 16-KP-V08; 16-KP-V10; 16-KP-V11; 16-KP-V12; 16-KP-V13; 16-KP-V14; 16-KP-V15; 16-KP-V16; 16-KP-V17; 16-KP-V18; 16-KP-V19; 16-KP-V20; 16-KP-V21; 16-KP-V22; 16-KP-V26; 16-KP-V27; 16-KP-V28; 16-KP-V29; 16-KP-V30; 16-KP-V31; 16-KP-V32; 16-KP-V34; 16-KP-V35; 16-KP-V36; 16-KP-V37; 16-KP-V38; 16-KP-V39; 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-EN18034; 18-BIL-02-EN18035; AWI_Envi; AWI Arctic Land Expedition; Campaign; Central Yakutia; Chatanga2011; Chukotka; Chukotka 2018; Comment; DATE/TIME; EN18061; EN18062; EN18063; EN18064; EN18065; EN18066; EN18067; EN18068; EN18069; EN18070_centre; EN18070_edge; EN18070_transition; EN18071; EN18072; EN18073; EN18074; EN18075; EN18076; EN18077; EN18078; EN18079; EN18080; EN18081; EN18082; EN18083; EN21202; EN21203; EN21204; EN21205; EN21206; EN21207; EN21209; EN21211; EN21212; EN21213; EN21215; EN21217; EN21219; EN21221; EN21222; EN21223; EN21225; EN21226; EN21227; EN21228; EN21229; EN21230; EN21231; EN21232; EN21233; EN21234; EN21235; EN21236; EN21237; EN21238; EN21239; EN21240; EN21241; EN21242; EN21244; EN21245; EN21246; EN21247; EN21248; EN21249; EN21250; EN21251; EN21252; EN21253; EN21254; EN21255; EN21256; EN21258; EN21259; EN21260; EN21261; Event label; Field observation; Forest; Genus; Growth form; HAND; Individual Trees; Keperveem_2016; LATITUDE; Latitude, center; LONGITUDE; Longitude, center; Maximum; Omoloy2014; Polar Terrestrial Environmental Systems @ AWI; Principal investigator; RU-Land_2011_Khatanga; RU-Land_2013_Taymyr; RU-Land_2014_Omoloy; RU-Land_2016_Keperveem; RU-Land_2018_Chukotka; RU-Land_2018_Yakutia; RU-Land_2021_Yakutia; Sampling by hand; Siberia; Species; Subsample ID; Taymyr; Taymyr2013; Tree, basal area, at base; Tree, basal area, at breast height; Tree, crown base; Tree, diameter, at base; Tree, survey protocol; Tree, volume, conical; Tree, volume, smallian; Tree crown diameter; Tree height; Tree ID; Trees, diameter at breast height; Vegetation survey; VEGSUR; Vitality; Yakutia
    Type: Dataset
    Format: text/tab-separated-values, 578779 data points
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    DATA PANGAEA
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