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Abstract
Grasslands are an important global ecosystem, providing essential ecological and economic ecosystem services. Over the last couple decades, as a result of climate change and human activities, nearly 50% of global grasslands have degraded. Woody plant encroachment (WPE), one of the outcomes of climate change and human-related activities, negatively affects grasslands’ ecology, as well as their ability to produce food for livestock, habitats for wildlife, and economic returns for rangeland managers. Long-term monitoring of grassland status can facilitate grassland restoration. Additionally, the study of factors that influence grassland dynamics (e.g., grazing, fire, land use, or climate) can help in the restoration of grasslands. Remote sensing (RS) has been used to map the spatiotemporal distribution of WPE by using a wide variety of sensors and methods, necessitating a review on the effectiveness of RS data for WPE monitoring. Based on the importance of RS data and the rate at which grassland ecosystems are changing, this paper provides a literature review on a theoretical basis for quantifying WPE using RS and on existing RS approaches for WPE monitoring. Lastly, it identifies the current challenges associated with quantifying spatio-temporal variability in WPE that future research will need to overcome.
RÉSUMÉ
Les prairies représentent un écosystème mondial important, fournissant des services écologiques et économiques essentiels. Au cours des deux dernières décennies, en raison du changement climatique et des activités humaines, près de 50% des prairies mondiales se sont dégradées. L’empiètement des plantes ligneuses (WPE), l’un des résultats du changement climatique et des activités liées à l’homme, affecte négativement l’écologie des prairies, ainsi que leur capacité à produire de la nourriture pour le bétail, des habitats pour la faune et des rendements économiques pour les gestionnaires de pâturages. La surveillance à long terme de l’état des prairies peut faciliter leur restauration. De plus, l’étude des facteurs qui influencent la dynamique des prairies (p. ex., pâturage, incendie, utilisation des terres ou climat) peut aider à leur restauration. La télédétection (RS) a été utilisée pour cartographier la distribution spatio-temporelle de l’empiètement des plantes ligneuses (WPE) en utilisant une grande variété de capteurs et de méthodes, ce qui nécessite un examen de leur efficacité pour cette surveillance. Basé sur l’importance des données RS et la vitesse à laquelle les écosystèmes des prairies changent, cet article examine les bases théoriques pour quantifier WPE à l’aide de RS et des approches RS existantes. Enfin, les défis actuels associés à la quantification de la variabilité spatio-temporelle de WPE que les recherches futures devront surmonter sont présentés.
Acknowledgements
The authors would like to thank Merek Wigness for guiding us to this research topic. We would also like to acknowledge Paul Hackett and his graduate student research team for their fruitful suggestions. Lastly, the authors are very thankful to the two anonymous reviewers who drastically helped to improve this manuscript with their detailed and extensive list of suggestions. This project was completed with funding from NSERC and the University of Saskatchewan.
Disclosure statement
The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Notes
1 Calculated based on the total land cover of shrubland, grassland, and hay/pasture in the United States in 2001. Source: Multi-Resolution Land Characteristics Consortium (Citation2001).
2 Can also be a climatic factor when fire occurs naturally without human disturbance (e.g., without fire initiation or control).
3 These included protein, lignin, ash, moisture at 135ºC, neutral detergent fiber, acid detergent fiber, total digestible, digestible energy, net energy for lactation, net energy for maintenance, and net energy for gain.
4 PRecursore IperSpettrale della Missione Applicativa.
5 Environmental Monitoring and Analysis Program.
6 Spaceborne Hyperspectral Applicative Land and Ocean Mission.
7 Hyperspectral Infrared Imager.
8 Hyperspectral X Imagery.
9 Captured from first return airborne LiDAR pulse data.
10 NDVI takes values between −1 and 1; More information on vegetation indices can be found in Section “Using spectral indices to identify WPE.”
11 Chlorophyll Absorption Index.
12 Normalized Difference Lignin Index.
13 Normalized Difference Nitrogen Index.
14 The higher the negative regression coefficients, the more efficient the performance.
15 Cellulose Absorption Index.
16 Derivative-based Green Vegetation Index.
17 Leaf Water Vegetation Index.
18 The higher the positive regression coefficients, the more efficient the performance.
19 Tasseled Cap.
20 Greenness Ratio.
21 Weighted Difference Vegetation Index.
22 Normalized Difference Moisture Index.
23 The authors selected the ten best vegetation indices to enhance classification accuracies with parsimonious representation; the optimal result was achieved with rapid computation (Damelin and Miller Citation2011).