Climate change is altering the species composition, structure, and function of vegetation in natural terrestrial ecosystems. These changes can also impact the essential ecosystem goods and services derived from these ecosystems. Following disturbances, remote-sensing datasets have been used to monitor the disturbance and describe antecedent conditions as a means of understanding vulnerability to change. To a lesser extent, they have also been used to predict when desired ecosystems are vulnerable to degradation or loss. In this paper, we review studies that have applied remote sensing imagery to characterize vegetation vulnerability in both retrospective and prospective modes. We first review vulnerability research in natural terrestrial ecosystems including temperate forests, tropical forests, boreal forests, semi-arid lands, coastal areas, and the arctic. We then evaluate whether remote sensing can evaluate vulnerability sufficiently in advance of future events in order to allow the implementation of mitigation strategies, or whether it can only describe antecedent conditions a posteriori. The majority of existing research has evaluated vulnerability retrospectively, but key studies highlight the considerable potential for the development of early warnings of future vulnerability. We conclude that future research needs to focus on the development of a greater number of remotely sensed metrics to be used in a prospective mode in assessing vulnerability of terrestrial vegetation under change.
Bibliographical noteFunding Information:
Smith was supported by the National Aeronautics and Space Administration (NASA) under award NNX11AO24G. Partial support for Tinkham was received from the National Science Foundation Idaho EPSCoR Grants: EPS-0814387 and EPS-0701898. Partial support for Alessa and Kliskey was obtained from the National Science Foundation for OPP Grants ARC-0327296, ARC-0328686, and ARC-0755966. The authors wish to thank Dr. Richard Waring, and two anonymous reviewers whose honesty and candor considerably improved this manuscript.
© 2014 Elsevier Inc.
- Climate change
- Ecological early warning systems