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Title: Mapping Arctic plant functional type distributions in the Barrow Environmental Observatory using WorldView-2 and LiDAR datasets

Abstract

Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detecting and ranging (LiDAR)-derived digital elevation models (DEM) to characterize the tundra landscape in and around the Barrow Environmental Observatory (BEO), a 3021-hectare research reserve located at the northern edge of the Alaskan Arctic Coastal Plain. Vegetation surveys were conducted during the growing season (June August) of 2012 from 48 1 m 1 m plots in the study region for estimating the percent cover of PFTs (i.e., sedges, grasses, forbs, shrubs, lichens and mosses). Statistical relationships were developed between spectral and topographic remote sensing characteristics and PFT fractions at the vegetation plots from field surveys. These derived relationships were employed to statistically upscale PFT fractions for our study region of 586 hectares at 0.25-m resolution around the sampling areas within the BEO, which was bounded by the LiDAR footprint. We employed an unsupervised clustering for stratification of this polygonal tundra landscape and used the clusters for segregating the field data for our upscaling algorithm over our study region, which was an inverse distance weighted (IDW)more » interpolation. We describe two versions of PFT distribution maps upscaled by IDW from WorldView-2 imagery and LiDAR: (1) a version computed from a single image in the middle of the growing season; and (2) a version computed from multiple images through the growing season. This approach allowed us to quantify the value of phenology for improving PFT distribution estimates. We also evaluated the representativeness of the field surveys by measuring the Euclidean distance between every pixel. This guided the ground-truthing campaign in late July of 2014 for addressing uncertainty based on representativeness analysis by selecting 24 1 m x 1 m plots that were well and poorly represented. Ground-truthing indicated that including phenology had a better accuracy (R2 = 0.75, RMSE = 9.94) than the single image upscaling (R2 = 0.63, RMSE = 12.05) predicted from IDW.We also updated our upscaling approach to include the 24 ground-truthing plots, and a second ground-truthing campaign in late August of 2014 indicated a better accuracy for the phenology model (R2 = 0.61, RMSE = 13.78) than only using the original 48 plots for the phenology model (R2 = 0.23, RMSE = 17.49). We believe that the cluster-based IDW upscaling approach and the representativeness analysis offer new insights for upscaling high-resolution data in fragmented landscapes. This analysis and approach provides PFT maps needed to inform land surface models in Arctic ecosystems.« less

Authors:
 [1];  [1];  [2];  [1];  [1];  [3];  [1]
  1. Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Univ. of Bristol, Bristol (United Kingdom)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1327746
Alternate Identifier(s):
OSTI ID: 1328281
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Remote Sensing
Additional Journal Information:
Journal Volume: 8; Journal Issue: 9; Journal ID: ISSN 2072-4292
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Arctic; WorldView-2; plant functional type; interpolation; clustering

Citation Formats

Langford, Zachary, Kumar, Jitendra, Hoffman, Forrest, Norby, Richard J., Wullschleger, Stan, Sloan, Victoria, and Iversen, Colleen. Mapping Arctic plant functional type distributions in the Barrow Environmental Observatory using WorldView-2 and LiDAR datasets. United States: N. p., 2016. Web. doi:10.3390/rs8090733.
Langford, Zachary, Kumar, Jitendra, Hoffman, Forrest, Norby, Richard J., Wullschleger, Stan, Sloan, Victoria, & Iversen, Colleen. Mapping Arctic plant functional type distributions in the Barrow Environmental Observatory using WorldView-2 and LiDAR datasets. United States. https://doi.org/10.3390/rs8090733
Langford, Zachary, Kumar, Jitendra, Hoffman, Forrest, Norby, Richard J., Wullschleger, Stan, Sloan, Victoria, and Iversen, Colleen. 2016. "Mapping Arctic plant functional type distributions in the Barrow Environmental Observatory using WorldView-2 and LiDAR datasets". United States. https://doi.org/10.3390/rs8090733. https://www.osti.gov/servlets/purl/1327746.
@article{osti_1327746,
title = {Mapping Arctic plant functional type distributions in the Barrow Environmental Observatory using WorldView-2 and LiDAR datasets},
author = {Langford, Zachary and Kumar, Jitendra and Hoffman, Forrest and Norby, Richard J. and Wullschleger, Stan and Sloan, Victoria and Iversen, Colleen},
abstractNote = {Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detecting and ranging (LiDAR)-derived digital elevation models (DEM) to characterize the tundra landscape in and around the Barrow Environmental Observatory (BEO), a 3021-hectare research reserve located at the northern edge of the Alaskan Arctic Coastal Plain. Vegetation surveys were conducted during the growing season (June August) of 2012 from 48 1 m 1 m plots in the study region for estimating the percent cover of PFTs (i.e., sedges, grasses, forbs, shrubs, lichens and mosses). Statistical relationships were developed between spectral and topographic remote sensing characteristics and PFT fractions at the vegetation plots from field surveys. These derived relationships were employed to statistically upscale PFT fractions for our study region of 586 hectares at 0.25-m resolution around the sampling areas within the BEO, which was bounded by the LiDAR footprint. We employed an unsupervised clustering for stratification of this polygonal tundra landscape and used the clusters for segregating the field data for our upscaling algorithm over our study region, which was an inverse distance weighted (IDW) interpolation. We describe two versions of PFT distribution maps upscaled by IDW from WorldView-2 imagery and LiDAR: (1) a version computed from a single image in the middle of the growing season; and (2) a version computed from multiple images through the growing season. This approach allowed us to quantify the value of phenology for improving PFT distribution estimates. We also evaluated the representativeness of the field surveys by measuring the Euclidean distance between every pixel. This guided the ground-truthing campaign in late July of 2014 for addressing uncertainty based on representativeness analysis by selecting 24 1 m x 1 m plots that were well and poorly represented. Ground-truthing indicated that including phenology had a better accuracy (R2 = 0.75, RMSE = 9.94) than the single image upscaling (R2 = 0.63, RMSE = 12.05) predicted from IDW.We also updated our upscaling approach to include the 24 ground-truthing plots, and a second ground-truthing campaign in late August of 2014 indicated a better accuracy for the phenology model (R2 = 0.61, RMSE = 13.78) than only using the original 48 plots for the phenology model (R2 = 0.23, RMSE = 17.49). We believe that the cluster-based IDW upscaling approach and the representativeness analysis offer new insights for upscaling high-resolution data in fragmented landscapes. This analysis and approach provides PFT maps needed to inform land surface models in Arctic ecosystems.},
doi = {10.3390/rs8090733},
url = {https://www.osti.gov/biblio/1327746}, journal = {Remote Sensing},
issn = {2072-4292},
number = 9,
volume = 8,
place = {United States},
year = {Tue Sep 06 00:00:00 EDT 2016},
month = {Tue Sep 06 00:00:00 EDT 2016}
}

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Works referenced in this record:

Soil Organic Carbon Storage and Distribution in Arctic Tundra, Barrow, Alaska
journal, January 1999


Representativeness-based sampling network design for the State of Alaska
journal, June 2013


Mapping plant functional types from MODIS data using multisource evidential reasoning
journal, March 2008


Integration of WorldView-2 and airborne LiDAR data for tree species level carbon stock mapping in Kayar Khola watershed, Nepal
journal, June 2015


The unseen iceberg: plant roots in arctic tundra
journal, September 2014


Remote sensing of tundra gross ecosystem productivity and light use efficiency under varying temperature and moisture conditions
journal, March 2010


Tundra vegetation change near Barrow, Alaska (1972–2010)
journal, January 2012


Shrub expansion in tundra ecosystems: dynamics, impacts and research priorities
journal, October 2011


Plant functional types as predictors of transient responses of arctic vegetation to global change
journal, June 1996


High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm
journal, August 2012


Assessment of fine-scale plant species beta diversity using WorldView-2 satellite spectral dissimilarity
journal, November 2013


Land surface phenology along urban to rural gradients in the U.S. Great Plains
journal, August 2015


Plant functional type mapping for earth system models
journal, January 2011


Nonlinear controls on evapotranspiration in arctic coastal wetlands
journal, January 2011


Anisotropic Scattered Data Interpolation for Pushbroom Image Rectification
journal, May 2014


Remote sensing of plant functional types: Tansley review
journal, May 2010


Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data
journal, December 2015


Trajectory of the Arctic as an integrated system
journal, December 2013


Climate change, phenology, and phenological control of vegetation feedbacks to the climate system
journal, February 2013


Using field observations to inform thermal hydrology models of permafrost dynamics with ATS (v0.83)
journal, January 2015


Tundra Soils Formed over Ice Wedges, Northern Alaska1
journal, January 1967


Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems
journal, February 2004


Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data
journal, December 2012


Abrupt increase in permafrost degradation in Arctic Alaska
journal, January 2006


Arctic Landscapes in Transition: Responses to Thawing Permafrost
journal, June 2010


Differential response of carbon fluxes to climate in three peatland ecosystems that vary in the presence and stability of permafrost: Carbon fluxes and permafrost thaw
journal, August 2014


Arctic Tundra Vegetation Functional Types Based on Photosynthetic Physiology and Optical Properties
journal, April 2013


Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets : Research Article
journal, August 2014


New analysis reveals representativeness of the AmeriFlux network
journal, January 2003


Surface hydrology of an arctic ecosystem: Multiscale analysis of a flooding and draining experiment using spectral reflectance
journal, January 2011


Shrub expansion in tundra ecosystems: dynamics, impacts and research priorities
text, January 2011


Using field observations to inform thermal hydrology models of permafrost dynamics with ATS (v0.83)
journal, January 2015


Modeling the spatiotemporal variability in subsurface thermal regimes across a low-relief polygonal tundra landscape
journal, January 2016


Works referencing / citing this record:

An overview of ABoVE airborne campaign data acquisitions and science opportunities
journal, July 2019


Impacts of temperature and soil characteristics on methane production and oxidation in Arctic tundra
journal, January 2018


Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional Neural Networks
journal, January 2019


Mapping Arctic Tundra Vegetation Communities Using Field Spectroscopy and Multispectral Satellite Data in North Alaska, USA
journal, November 2016


Land Cover Mapping in Northern High Latitude Permafrost Regions with Satellite Data: Achievements and Remaining Challenges
journal, November 2016


20 cm resolution mapping of tundra vegetation communities provides an ecological baseline for important research areas in a changing Arctic environment
journal, October 2019