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Lookup NU author(s): Dr Nick CutlerORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Monitoring soil erosion in the Arctic tundra is complicated by the highly fragmentated nature of the landscape and the limited spatial resolution of even high-resolution satellite data. The expansion of shrubs across the Arctic has led to substantial changes in vegetation composition that alter the spectral reflectance and directly affect vegetation indices such as the normalized difference vegetation index (NDVI), which is widely applied for environmental monitoring. This change can mask soil erosion if datasets with too coarse spatial resolutions are used, as increases in NDVI driven by shrub expansion can obscure concurrent increases in barren land cover. Here we created land cover maps from a multispectral uncrewed aerial vehicle (UAV) and land cover survey and assessed satellite imagery from PlanetScope, Sentinel-2 and Landsat-8 for several areas in north-eastern Iceland. Additionally, we used a novel application of the Shannon evenness index (SHEI) to evaluate levels of pixel mixing. Our results show that shrub expansion can lead to spectral confusion, which can obscure soil erosion processes and emphasize the importance of considering spatial resolution when monitoring highly fragmented landscapes. We demonstrate that remote sensing data with a resolution < 3 m greatly improves the amount of information captured in an Icelandic tundra environment. The spatial resolution of Landsat data (30 m) is inadequate for environmental monitoring in our study area. We found that the best platform for monitoring tundra land cover is Sentinel-2 when used in combination with multispectral UAV acquisitions for validation. Our study has the potential to improve environmental monitoring capabilities by introducing the use of SHEI to assess pixel mixing and determine optimal spatial resolutions. This approach combined with comparing remote sensing imagery of different spatial and time scales significantly advances our comprehension of land cover changes, including greening and soil degradation, in the Arctic tundra.
Author(s): Kodl G, Streeter R, Cutler N, Bolch T
Publication type: Article
Publication status: Published
Journal: Remote Sensing of Environment
Year: 2024
Volume: 301
Print publication date: 01/02/2024
Online publication date: 12/12/2023
Acceptance date: 27/11/2023
Date deposited: 20/02/2024
ISSN (print): 0034-4257
ISSN (electronic): 1879-0704
Publisher: Elservier
URL: https://doi.org/10.1016/j.rse.2023.113935
DOI: 10.1016/j.rse.2023.113935
Data Access Statement: The data and R code that support the findings of this study are openly available on GitHub at https://github.com/georg-kodl/erosion-scale. UAV-derived orthomosaics and land cover maps underpinning this publication can be accessed at https://doi. org/10.17630/f1e25320-7c79-4876-9719-4c1131cd8ed4 (Kodl and Streeter, 2023). Other remote sensing datasets are publicly available including: Landsat-8 (https://earthexplorer.usgs.gov/), Sentinel-2 (https://dataspace.copernicus.eu/). A specific quota of PlanetScope datasets are freely available for researcher purposed at (https://planet. com). Landsat datasets for NDVI time series are freely accessible through (https://earthengine.google.com/).
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