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Arctic tundra shrubification can obscure increasing levels of soil erosion in NDVI assessments of land cover derived from satellite imagery
被引:8
作者:
Kodl, Georg
[1
,4
]
Streeter, Richard
[1
]
Cutler, Nick
[2
]
Bolch, Tobias
[3
]
机构:
[1] Univ St Andrews, Sch Geog & Sustainable Dev, St Andrews, Scotland
[2] Newcastle Univ, Sch Geog Polit & Sociol, Newcastle upon Tyne, England
[3] Graz Univ Technol, Inst Geodesy, Graz, Austria
[4] Univ St Andrews, Sch Geog & Sustainable Dev, Irvine Bldg North St, St Andrews KY16 9AL, Scotland
关键词:
Soil erosion;
Shrub expansion;
Arctic tundra;
Mixed pixel;
NDVI;
Shannon evenness index (SHEI);
SHRUB EXPANSION;
VEGETATION;
PENINSULA;
SENSORS;
TRENDS;
FOREST;
SHEEP;
D O I:
10.1016/j.rse.2023.113935
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
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.
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页数:17
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