Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Fractional cover of green vegetation (FCover) is a key variable when observing Arctic vegetation under a changing climate. Vegetation changes over large areas are traditionally monitored by linking plot-scale measurements to satellite data. However, integrating field and satellite data is not straightforward. Typically, the satellite data are at a much coarser scale in comparison to field measurements. Here, we studied how Unmanned Aerial Systems (UASs) can be used to bridge this gap. We covered three 250 m x 250 m sites in Fennoscandian tundra with varying productivity ana FCover, ranging from barren vegetation to shrub tundra. The UAS sites were then used to train satellite data-based FCover models.

First, we created a binary vegetation classification (absent, present) by using UAS-derived RGB-orthomosaics and logistic regression. Secondly, we used the classification to calculate FCover to Planet CubeSat (3 m), Sentinel-2A MSI (10 m, 20 m), and Landsat 8 OLI (30 m) grids, and examined how well FCover is explained by various spectral vegetation indices (VI) derived from satellite data.

The overall classification accuracies for the UAS sites were >= 90%. The UAS-FCover were strongly related to the tested VIs (D-2 89% at best). The explained deviance was generally higher for coarser resolution data, indicating that the effect of data resolution should be taken into account when comparing results from different sensors. VIs based on red-edge (at 740 nm, 783 nm), or near-infrared and shortwave infrared (SWIR) had the highest performance. We recommend wider inspection of red-edge and SWIR bands for future Arctic vegetation research.

Our results demonstrate that UASs can be used for observing FCover at multiple scales. Individual UAS sites can serve as focus areas, which provide information at the finest resolution (e.g. individual plants), whereas a sample of several UAS sites can be used to train satellite data and examine vegetation over larger extents.

Original languageEnglish
JournalRemote Sensing of Environment
Volume224
Pages (from-to)119-132
Number of pages14
ISSN0034-4257
DOIs
Publication statusPublished - Apr 2019
MoE publication typeA1 Journal article-refereed

Fields of Science

  • Unmanned aerial vehicles
  • UAV
  • Drones
  • Modifiable Area Unit Problem (MAUP)
  • Upscaling
  • Resolution
  • Arctic
  • High-latitude
  • Monitoring
  • ARCTIC TUNDRA
  • LAND-COVER
  • SPATIAL-RESOLUTION
  • ACCURACY
  • CLIMATE
  • INDEX
  • NDVI
  • AREA
  • PHOTOGRAMMETRY
  • CLASSIFICATION
  • 1172 Environmental sciences

Cite this

@article{430a739bd73444de8bd16cb6c242c5ce,
title = "Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data",
abstract = "Fractional cover of green vegetation (FCover) is a key variable when observing Arctic vegetation under a changing climate. Vegetation changes over large areas are traditionally monitored by linking plot-scale measurements to satellite data. However, integrating field and satellite data is not straightforward. Typically, the satellite data are at a much coarser scale in comparison to field measurements. Here, we studied how Unmanned Aerial Systems (UASs) can be used to bridge this gap. We covered three 250 m x 250 m sites in Fennoscandian tundra with varying productivity ana FCover, ranging from barren vegetation to shrub tundra. The UAS sites were then used to train satellite data-based FCover models.First, we created a binary vegetation classification (absent, present) by using UAS-derived RGB-orthomosaics and logistic regression. Secondly, we used the classification to calculate FCover to Planet CubeSat (3 m), Sentinel-2A MSI (10 m, 20 m), and Landsat 8 OLI (30 m) grids, and examined how well FCover is explained by various spectral vegetation indices (VI) derived from satellite data.The overall classification accuracies for the UAS sites were >= 90{\%}. The UAS-FCover were strongly related to the tested VIs (D-2 89{\%} at best). The explained deviance was generally higher for coarser resolution data, indicating that the effect of data resolution should be taken into account when comparing results from different sensors. VIs based on red-edge (at 740 nm, 783 nm), or near-infrared and shortwave infrared (SWIR) had the highest performance. We recommend wider inspection of red-edge and SWIR bands for future Arctic vegetation research.Our results demonstrate that UASs can be used for observing FCover at multiple scales. Individual UAS sites can serve as focus areas, which provide information at the finest resolution (e.g. individual plants), whereas a sample of several UAS sites can be used to train satellite data and examine vegetation over larger extents.",
keywords = "Unmanned aerial vehicles, UAV, Drones, Modifiable Area Unit Problem (MAUP), Upscaling, Resolution, Arctic, High-latitude, Monitoring, Unmanned aerial vehicles, UAV, Drones, Modifiable Area Unit Problem (MAUP), Upscaling, Resolution, Arctic, High-latitude, Monitoring, ARCTIC TUNDRA, LAND-COVER, SPATIAL-RESOLUTION, ACCURACY, CLIMATE, INDEX, NDVI, AREA, PHOTOGRAMMETRY, CLASSIFICATION, 1172 Environmental sciences",
author = "Henri Riihim{\"a}ki and Miska Luoto and Janne Heiskanen",
year = "2019",
month = "4",
doi = "10.1016/j.rse.2019.01.030",
language = "English",
volume = "224",
pages = "119--132",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "EXCERPTA MEDICA INC-ELSEVIER SCIENCE INC",

}

TY - JOUR

T1 - Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data

AU - Riihimäki, Henri

AU - Luoto, Miska

AU - Heiskanen, Janne

PY - 2019/4

Y1 - 2019/4

N2 - Fractional cover of green vegetation (FCover) is a key variable when observing Arctic vegetation under a changing climate. Vegetation changes over large areas are traditionally monitored by linking plot-scale measurements to satellite data. However, integrating field and satellite data is not straightforward. Typically, the satellite data are at a much coarser scale in comparison to field measurements. Here, we studied how Unmanned Aerial Systems (UASs) can be used to bridge this gap. We covered three 250 m x 250 m sites in Fennoscandian tundra with varying productivity ana FCover, ranging from barren vegetation to shrub tundra. The UAS sites were then used to train satellite data-based FCover models.First, we created a binary vegetation classification (absent, present) by using UAS-derived RGB-orthomosaics and logistic regression. Secondly, we used the classification to calculate FCover to Planet CubeSat (3 m), Sentinel-2A MSI (10 m, 20 m), and Landsat 8 OLI (30 m) grids, and examined how well FCover is explained by various spectral vegetation indices (VI) derived from satellite data.The overall classification accuracies for the UAS sites were >= 90%. The UAS-FCover were strongly related to the tested VIs (D-2 89% at best). The explained deviance was generally higher for coarser resolution data, indicating that the effect of data resolution should be taken into account when comparing results from different sensors. VIs based on red-edge (at 740 nm, 783 nm), or near-infrared and shortwave infrared (SWIR) had the highest performance. We recommend wider inspection of red-edge and SWIR bands for future Arctic vegetation research.Our results demonstrate that UASs can be used for observing FCover at multiple scales. Individual UAS sites can serve as focus areas, which provide information at the finest resolution (e.g. individual plants), whereas a sample of several UAS sites can be used to train satellite data and examine vegetation over larger extents.

AB - Fractional cover of green vegetation (FCover) is a key variable when observing Arctic vegetation under a changing climate. Vegetation changes over large areas are traditionally monitored by linking plot-scale measurements to satellite data. However, integrating field and satellite data is not straightforward. Typically, the satellite data are at a much coarser scale in comparison to field measurements. Here, we studied how Unmanned Aerial Systems (UASs) can be used to bridge this gap. We covered three 250 m x 250 m sites in Fennoscandian tundra with varying productivity ana FCover, ranging from barren vegetation to shrub tundra. The UAS sites were then used to train satellite data-based FCover models.First, we created a binary vegetation classification (absent, present) by using UAS-derived RGB-orthomosaics and logistic regression. Secondly, we used the classification to calculate FCover to Planet CubeSat (3 m), Sentinel-2A MSI (10 m, 20 m), and Landsat 8 OLI (30 m) grids, and examined how well FCover is explained by various spectral vegetation indices (VI) derived from satellite data.The overall classification accuracies for the UAS sites were >= 90%. The UAS-FCover were strongly related to the tested VIs (D-2 89% at best). The explained deviance was generally higher for coarser resolution data, indicating that the effect of data resolution should be taken into account when comparing results from different sensors. VIs based on red-edge (at 740 nm, 783 nm), or near-infrared and shortwave infrared (SWIR) had the highest performance. We recommend wider inspection of red-edge and SWIR bands for future Arctic vegetation research.Our results demonstrate that UASs can be used for observing FCover at multiple scales. Individual UAS sites can serve as focus areas, which provide information at the finest resolution (e.g. individual plants), whereas a sample of several UAS sites can be used to train satellite data and examine vegetation over larger extents.

KW - Unmanned aerial vehicles

KW - UAV

KW - Drones

KW - Modifiable Area Unit Problem (MAUP)

KW - Upscaling

KW - Resolution

KW - Arctic

KW - High-latitude

KW - Monitoring

KW - Unmanned aerial vehicles

KW - UAV

KW - Drones

KW - Modifiable Area Unit Problem (MAUP)

KW - Upscaling

KW - Resolution

KW - Arctic

KW - High-latitude

KW - Monitoring

KW - ARCTIC TUNDRA

KW - LAND-COVER

KW - SPATIAL-RESOLUTION

KW - ACCURACY

KW - CLIMATE

KW - INDEX

KW - NDVI

KW - AREA

KW - PHOTOGRAMMETRY

KW - CLASSIFICATION

KW - 1172 Environmental sciences

U2 - 10.1016/j.rse.2019.01.030

DO - 10.1016/j.rse.2019.01.030

M3 - Article

VL - 224

SP - 119

EP - 132

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

ER -