Next Article in Journal
Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry
Next Article in Special Issue
Editorial for Special Issue “Optical Remote Sensing of Boreal Forests”
Previous Article in Journal
Error Source Analysis and Correction of GF-3 Polarimetric Data
Previous Article in Special Issue
Estimation of Gap Fraction and Foliage Clumping in Forest Canopies
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(11), 1686;

Vegetation Indices Do Not Capture Forest Cover Variation in Upland Siberian Larch Forests

Department of Geography, Colgate University, Hamilton, NY 13346, USA
Northeast Science Station, Pacific Institute for Geography, Far East Branch, Russian Academy of Sciences, Cherskiy 678830, Russia
Department of Forestry, Forest and Wildlife Research Center, Mississippi State University, Starkville, MS 39759, USA
Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ 86011, USA
Woods Hole Research Center, Falmouth, MA 02540, USA
Author to whom correspondence should be addressed.
Received: 21 September 2018 / Revised: 22 October 2018 / Accepted: 23 October 2018 / Published: 25 October 2018
(This article belongs to the Special Issue Optical Remote Sensing of Boreal Forests)
Full-Text   |   PDF [4274 KB, uploaded 25 October 2018]   |  


Boreal forests are changing in response to climate, with potentially important feedbacks to regional and global climate through altered carbon cycle and albedo dynamics. These feedback processes will be affected by vegetation changes, and feedback strengths will largely rely on the spatial extent and timing of vegetation change. Satellite remote sensing is widely used to monitor vegetation dynamics, and vegetation indices (VIs) are frequently used to characterize spatial and temporal trends in vegetation productivity. In this study we combine field observations of larch forest cover across a 25 km2 upland landscape in northeastern Siberia with high-resolution satellite observations to determine how the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) are related to forest cover. Across 46 forest stands ranging from 0% to 90% larch canopy cover, we find either no change, or declines in NDVI and EVI derived from PlanetScope CubeSat and Landsat data with increasing forest cover. In conjunction with field observations of NDVI, these results indicate that understory vegetation likely exerts a strong influence on vegetation indices in these ecosystems. This suggests that positive decadal trends in NDVI in Siberian larch forests may correspond primarily to increases in understory productivity, or even to declines in forest cover. Consequently, positive NDVI trends may be associated with declines in terrestrial carbon storage and increases in albedo, rather than increases in carbon storage and decreases in albedo that are commonly assumed. Moreover, it is also likely that important ecological changes such as large changes in forest density or variable forest regrowth after fire are not captured by long-term NDVI trends. View Full-Text
Keywords: boreal forest; NDVI; phenology; greening; Arctic; Siberia; larch; CubeSat boreal forest; NDVI; phenology; greening; Arctic; Siberia; larch; CubeSat

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Loranty, M.M.; Davydov, S.P.; Kropp, H.; Alexander, H.D.; Mack, M.C.; Natali, S.M.; Zimov, N.S. Vegetation Indices Do Not Capture Forest Cover Variation in Upland Siberian Larch Forests. Remote Sens. 2018, 10, 1686.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top