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Cited by 667

The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.

Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems

Open access
Stow, Douglas A, Hope, Allen, McGuire, David, Verbyla, David, Gamon, John, Huemmrich, Fred, Houston, Stan, Racine, Charles, Sturm, Matthew, Tape, Kenneth, Hinzman, Larry, Yoshikawa, Kenji, Tweedie, Craig, Noyle, Brian, Silapaswan, Cherie, Douglas, David, Griffith, Brad, Jia, Gensuo, Epstein, Howard, Walker, Donald, Daeschner, Scott, Petersen, Aaron, Zhou, Liming, Myneni, Ranga·2004·Remote Sensing of Environment

Abstract

The objective of this paper is to review research conducted over the past decade on the application of multi-temporal remote sensing for monitoring changes of Arctic tundra lands. Emphasis is placed on results from the National Science Foundation Land–Air–Ice Interactions (LAII) program and on optical remote sensing techniques. Case studies demonstrate that ground-level sensors on stationary or moving track platforms and wide-swath imaging sensors on polar orbiting satellites are particularly useful for capturing optical remote sensing data at sufficient frequency to study tundra vegetation dynamics and changes for the cloud prone Arctic. Less frequent imaging with high spatial resolution instruments on aircraft and lower orbiting satellites enable more detailed analyses of land cover change and calibration/validation of coarser resolution observations. The strongest signals of ecosystem change detected thus far appear to correspond to expansion of tundra shrubs and changes in the amount and extent of thaw lakes and ponds. Changes in shrub cover and extent have been documented by modern repeat imaging that matches archived historical aerial photography . NOAA Advanced Very High Resolution Radiometer (AVHRR) time series provide a 20-year record for determining changes in greenness that relates to photosynthetic activity, net primary production, and growing season length. The strong contrast between land materials and surface waters enables changes in lake and pond extent to be readily measured and monitored.

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Metadata

Authors
Stow, D. A., Hope, A., McGuire, D., Verbyla, D., Gamon, J., Huemmrich, F., Houston, S., Racine, C., Sturm, M., Tape, K., Hinzman, L., Yoshikawa, K., Tweedie, C., Noyle, B., Silapaswan, C., Douglas, D., Griffith, B., Jia, G., Epstein, H., Walker, D., Daeschner, S., Petersen, A., Zhou, L., & Myneni, R.
Venue
Remote Sensing of Environment
Publisher
Elsevier BV
Published
2004-02
Volume
89
Issue
3
Pages
281-308
ISSN
0034-4257 (ISSN-L)
OpenAlex
W2123807796
Cited by
667
References
63
FWCI
14.28
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems. https://app.cochat.ai/papers/10.1016/j.rse.2003.10.018
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Stow, D. A., Hope, A., McGuire, D., Verbyla, D., Gamon, J., Huemmrich, F., Houston, S., Racine, C., Sturm, M., Tape, K., Hinzman, L., Yoshikawa, K., Tweedie, C., Noyle, B., Silapaswan, C., Douglas, D., Griffith, B., Jia, G., Epstein, H., . . . Myneni, R. (2004). Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2003.10.018

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.