A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the ‘Osprey’) using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations.

[1]  M. Musiani,et al.  Does climate change and plant phenology research neglect the Arctic tundra? , 2018, Ecosphere.

[2]  K. Cook An evaluation of the effectiveness of low-cost UAVs and structure from motion for geomorphic change detection , 2017 .

[3]  Jean Duchon,et al.  Splines minimizing rotation-invariant semi-norms in Sobolev spaces , 1976, Constructive Theory of Functions of Several Variables.

[4]  Satoshi Tsuyuki,et al.  Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer-Broadleaf Forest: Comparison with Airborne Laser Scanning , 2018, Remote. Sens..

[5]  Steven J. Phillips,et al.  Shifts in Arctic vegetation and associated feedbacks under climate change , 2013 .

[6]  P. Treitz,et al.  Examining spectral reflectance features related to Arctic percent vegetation cover: Implications for hyperspectral remote sensing of Arctic tundra , 2017 .

[7]  D. M. Lawrence,et al.  Climate change and the permafrost carbon feedback , 2014, Nature.

[8]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[9]  Dimitrios Moshou,et al.  Evaluation of UAV imagery for mapping Silybum marianum weed patches , 2017 .

[10]  Howard E. Epstein,et al.  Differentiating among Four Arctic Tundra Plant Communities at Ivotuk, Alaska Using Field Spectroscopy , 2016, Remote. Sens..

[11]  David Axelsson,et al.  Drones in arctic environments , 2017 .

[12]  Wu Xiao,et al.  A review of UAV monitoring in mining areas: current status and future perspectives , 2019, International Journal of Coal Science & Technology.

[13]  G. Schaepman‐Strub,et al.  The response of Arctic vegetation to the summer climate: relation between shrub cover, NDVI, surface albedo and temperature , 2011 .

[14]  F. Stuart Chapin,et al.  Plant functional types as predictors of transient responses of arctic vegetation to global change , 1996 .

[15]  Ranga B. Myneni,et al.  Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems , 2004 .

[16]  Jitendra Kumar,et al.  Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional Neural Networks , 2019, Remote. Sens..

[17]  Xiaoyu Chen,et al.  Unmanned Aerial Vehicle for Remote Sensing Applications - A Review , 2019, Remote. Sens..

[18]  Miska Luoto,et al.  Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data , 2019, Remote Sensing of Environment.

[19]  Jonathon J. Donager,et al.  UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA , 2017 .

[20]  M. E. Schaepman,et al.  Assessing Vegetation Function with Imaging Spectroscopy , 2019, Surveys in Geophysics.

[21]  Nithya Rajan,et al.  Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development , 2018, PloS one.

[22]  R. Clark,et al.  Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data , 2003 .

[23]  Kamaruzaman Jusoff,et al.  Satellite Data Classification Accuracy Assessment Based from Reference Dataset , 2008 .

[24]  C. S. Reddy,et al.  Characterization of Species Diversity and Forest Health using AVIRIS-NG Hyperspectral Remote Sensing Data , 2019, Current Science.

[25]  Dar A. Roberts,et al.  Predicted distribution of visible and near-infrared radiant flux above and below a transmittant leaf , 1990 .

[26]  J. Dash,et al.  The MERIS terrestrial chlorophyll index , 2004 .

[27]  Paul Treitz,et al.  Remote Sensing of Arctic Vegetation: Relations between the NDVI, Spatial Resolution and Vegetation Cover on Boothia Peninsula, Nunavut , 2009 .

[28]  M. Soycan,et al.  DIGITAL ELEVATION MODEL PRODUCTION FROM SCANNED TOPOGRAPHIC CONTOUR MAPS VIA THIN PLATE SPLINE INTERPOLATION , 2009 .

[29]  Jin Chen,et al.  Mapping plastic greenhouse with medium spatial resolution satellite data: Development of a new spectral index , 2017 .

[30]  Arundhati Misra,et al.  Mangrove Species Discrimination and Health Assessment using AVIRIS-NG Hyperspectral Data , 2019, Current Science.

[31]  Chuanfa Chen,et al.  Robust Interpolation of DEMs From Lidar-Derived Elevation Data , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[32]  M. Raynolds,et al.  Recent trends and remaining challenges for optical remote sensing of Arctic tundra vegetation: A review and outlook , 2020 .

[33]  Steven F. Oberbauer,et al.  Plot-scale evidence of tundra vegetation change and links to recent summer warming. , 2012 .

[34]  Paul E. Johnson,et al.  Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 Site , 1986 .

[35]  Jens Klump,et al.  Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development , 2019 .

[36]  James F. Reynolds,et al.  Arctic ecosystems in a changing climate : an ecophysiological perspective , 1993 .

[37]  Ben Somers,et al.  A weighted linear spectral mixture analysis approach to address endmember variability in agricultural production systems , 2009 .

[38]  Douglas C. Morton,et al.  Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar , 2020 .

[39]  D. Walker,et al.  Hierarchical subdivision of Arctic tundra based on vegetation response to climate, parent material and topography , 2000, Global change biology.

[40]  Anne D. Bjorkman,et al.  Complexity revealed in the greening of the Arctic , 2019, Nature Climate Change.

[41]  Marcel Schwieder,et al.  Ground-Based Hyperspectral Characterization of Alaska Tundra Vegetation along Environmental Gradients , 2013, Remote. Sens..

[42]  Donald A. Walker,et al.  The Circumpolar Arctic Vegetation Map: AVHRR-derived base maps, environmental controls, and integrated mapping procedures , 2002 .

[43]  Ian Olthof,et al.  A raster version of the Circumpolar Arctic Vegetation Map (CAVM) , 2019, Remote Sensing of Environment.

[44]  R. Congalton,et al.  Accuracy assessment: a user's perspective , 1986 .

[45]  M. Sturm,et al.  Climate change: Increasing shrub abundance in the Arctic , 2001, Nature.

[46]  Arko Lucieer,et al.  An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds , 2012, Remote. Sens..

[47]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[48]  Erle C. Ellis,et al.  High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision , 2013 .

[49]  M. Schaepman,et al.  From local to regional: Functional diversity in differently managed alpine grasslands , 2020 .

[50]  Andrew K. Skidmore,et al.  Advances in remote sensing of vegetation function and traits , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[51]  K. Moffett,et al.  Remote Sens , 2015 .

[52]  R. Fraser,et al.  UAV photogrammetry for mapping vegetation in the low-Arctic , 2016 .

[53]  S. Ollinger Sources of variability in canopy reflectance and the convergent properties of plants. , 2011, The New phytologist.

[54]  Anne D. Bjorkman,et al.  Plant traits inform predictions of tundra responses to global change. , 2018, The New phytologist.

[55]  C. Tucker,et al.  Increased plant growth in the northern high latitudes from 1981 to 1991 , 1997, Nature.

[56]  Mark C. Vanderwel,et al.  Allometric equations for integrating remote sensing imagery into forest monitoring programmes , 2016, Global change biology.

[57]  P. Thornton,et al.  Alder Distribution and Expansion Across a Tundra Hillslope: Implications for Local N Cycling , 2019, Front. Plant Sci..

[58]  D. Klein,et al.  Decrease of lichens in Arctic ecosystems: the role of wildfire, caribou, reindeer, competition and climate in north-western Alaska , 2009 .

[59]  Arko Lucieer,et al.  HyperUAS—Imaging Spectroscopy from a Multirotor Unmanned Aircraft System , 2014, J. Field Robotics.

[60]  Maja K. Sundqvist,et al.  Patchy field sampling biases understanding of climate change impacts across the Arctic , 2018, Nature Ecology & Evolution.

[61]  Peter R. Nelson,et al.  Regional Quantitative Cover Mapping of Tundra Plant Functional Types in Arctic Alaska , 2017, Remote. Sens..

[62]  Benjamin Smith,et al.  Vegetation demographics in Earth System Models: A review of progress and priorities , 2018, Global change biology.

[63]  L. D. Hinzman,et al.  Hydrological variations among watersheds with varying degrees of permafrost , 2002 .

[64]  S. Goetz,et al.  Shrub expansion in tundra ecosystems: dynamics, impacts and research priorities , 2011, Environmental Research Letters.

[65]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[66]  J. Welker,et al.  Landscape Heterogeneity of Shrub Expansion in Arctic Alaska , 2012, Ecosystems.

[67]  D. Morton,et al.  Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion , 2018 .

[68]  J. Eitel,et al.  Scaling Thermal Properties from the Leaf to the Canopy in the Alaskan Arctic Tundra , 2016, Arctic, Antarctic, and Alpine Research.

[69]  Clayton C. Kingdon,et al.  Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy , 2015 .

[70]  Robert H. Fraser,et al.  Mapping northern land cover fractions using Landsat ETM , 2007 .

[71]  Michael E. Schaepman,et al.  Drivers of shortwave radiation fluxes in Arctic tundra across scales , 2017 .

[72]  R. Franke Smooth Interpolation of Scattered Data by Local Thin Plate Splines , 1982 .

[73]  Chuanfa Chen,et al.  A robust method of thin plate spline and its application to DEM construction , 2012, Comput. Geosci..

[74]  M. Westoby,et al.  ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .

[75]  Roberta E. Martin,et al.  Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels , 2008 .

[76]  Giles M. Foody,et al.  Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data , 1996 .

[77]  Erle C. Ellis,et al.  Using lightweight unmanned aerial vehicles to monitor tropical forest recovery , 2015 .

[78]  I. Stirling,et al.  Ecological Dynamics Across the Arctic Associated with Recent Climate Change , 2009, Science.

[79]  J. Kattge,et al.  Plant functional types in Earth system models: past experiences and future directions for application of dynamic vegetation models in high-latitude ecosystems. , 2014, Annals of botany.

[80]  M. Mallory,et al.  Financial costs of conducting science in the Arctic: examples from seabird research , 2018, Arctic Science.

[81]  Susan L Ustin,et al.  Remote sensing of plant functional types. , 2010, The New phytologist.

[82]  Donatella Zona,et al.  Numerical Terradynamic Simulation Group 11-2016 Mapping Arctic Tundra Vegetation Communities Using Field Spectroscopy and Multispectral Satellite Data in North Alaska , USA , 2017 .

[83]  J. Masek,et al.  The vegetation greenness trend in Canada and US Alaska from 1984–2012 Landsat data , 2016 .

[84]  D. Walker,et al.  Biotic controls over spectral reflectance of arctic tundra vegetation , 2005 .

[85]  Ryutaro Tateishi,et al.  Production of Global Land Cover Data – GLCNMO2013 , 2017 .

[86]  P. Cox,et al.  Observing terrestrial ecosystems and the carbon cycle from space , 2015, Global change biology.

[87]  G. Schaepman‐Strub,et al.  Arctic shrub effects on NDVI, summer albedo and soil shading , 2014 .

[88]  P. Zarco-Tejada,et al.  Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera , 2012 .

[89]  Norman Kerle,et al.  Accuracy assessment of real-time kinematics (RTK) measurements on unmanned aerial vehicles (UAV) for direct geo-referencing , 2020, Geo spatial Inf. Sci..

[90]  Isla H Myers-Smith,et al.  Vegetation monitoring using multispectral sensors – best practices and lessons learned from high latitudes , 2018, bioRxiv.