Remote Sensing of Vegetation Dynamics
- LAST MODIFIED: 07 January 2025
- DOI: 10.1093/obo/9780199830060-0256
- LAST MODIFIED: 07 January 2025
- DOI: 10.1093/obo/9780199830060-0256
Introduction
Vegetation dynamics is the term applied to changes that occur in the spatial distribution, seasonality, horizontal extent, vertical structure and composition, and temporal trends and variations associated with growth, senescence, dormancy, death, and regeneration. For this article, it excludes land clearing detection and agricultural rotations. It operates across a wide range of spatial and temporal scales from instantaneous biochemical processes to biome-scale shifts that occur over decades to millennia. Satellite remote sensing has been an indispensable tool in the monitoring of these changes since the late twentieth century. Prior to the advent of satellites, airborne photography, and historical ground-based photographic comparisons were used to identify changes. However, these usually only provided snapshots in time with no record of the process of change observed in the comparison. The launch of polar orbiting satellites carrying the National Oceanic and Atmospheric Administration’s Advanced Very High-Resolution Radiometer (NOAA AVHRR) with daily repeat overpasses provided the means for capturing, for the first time, the behavior of global vegetation across the seasons and years. The spectral bands on AVHRR included a red visible band and a near-infrared (NIR) band. The normalized ratio of reflectance between these bands, the Normalized Difference Vegetation Index (NDVI), provided a robustly sensitive indicator of photosynthetic capacity—a measure of vegetation greenness. The seminal scientific studies using NDVI images from AVHRR over the Sahel and southern Africa published in the early 1980s ushered in the modern era of global monitoring of vegetation dynamics with satellite remote sensing. Since that time, remote sensing of vegetation dynamics and change has become the standard approach to monitoring from plot, paddock, and farm to landscape, region, country, and across the globe. The suite of remote sensing types, sensor configurations, and products has expanded greatly to include radar polarimetry and interferometry, LiDAR (light detection and ranging) detection of three-dimensional vegetation structure, imaging spectroscopy for detection of vegetation biochemistry (measuring absorption and reflectance within hundreds of narrow spectral bands), very high resolution imaging down to centimeter pixels from hundreds of small satellites, thermal remote sensing of vegetation, and highly sophisticated products measuring evapotranspiration, net primary productivity (NPP), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR) etc. at 500–1000 m resolution with more than twenty years of global daily imaging. The spatial and temporal fidelity at global scale has now been enhanced by having multiple multispectral sensors from NASA (National Aeronautics and Space Administration) and ESA (European Space Agency) with 10–30 m pixel resolution and compatible spectral characteristics in orbit at the same time. This topic has seen exponential growth in publications over the past decade with more than twenty-thousand entries found in searches with Scopus or Web of Science. This article provides an introduction to this vast array of publications addressing vegetation dynamics and dynamic change.
General Overviews
Remote sensing of vegetation dynamics is a very broad subject. This section lists some books and articles that provide either a complete overview, or a detailed insight into a major topic. Hobbs and Mooney 2012 provides a very comprehensive treatment of remote sensing of vegetation dynamics. Jones and Vaughan 2010 is a more general remote sensing text but provides specific coverage of vegetation. Pettorelli 2013 describes in detail the Normalized Difference Vegetation Index (NDVI) as the enabling technology for monitoring vegetation dynamics. Berger, et al. 2022 provides an overview of the role of remote sensing in monitoring agricultural crops. Karlson and Ostwald 2015 describes the origins and development of remote sensing of vegetation dynamics through the lens of a review for the Sudano-Sahelian zone from 1975 onward. Kadmon and Harari-Kremer 1999 provides a key perspective on the use of aerial photography for monitoring vegetation dynamics. The remote sensing of vegetation dynamics in grassland is described in Ali, et al. 2016, while Frolking, et al. 2009 examines the role of remote sensing in monitoring forest disturbance and recovery. Szpakowski and Jensen 2019 provides a current review of the role of remote sensing in fire ecology. A wide array of studies targeting specific regions and biomes are covered under Global Studies and Regional Studies.
Ali, Iftikhar, Fiona Cawkwell, Edward Dwyer, Brian Barrett, and Stuart Green. 2016. Satellite remote sensing of grasslands: From observation to management. Journal of Plant Ecology 9:649–671.
DOI: 10.1093/jpe/rtw005
This review provides a comprehensive coverage of remote sensing of grasslands and includes topics, citations, and discussion of the importance of time series data and the limitations of spatial scale for paddock dynamics.
Berger, Katja, Miriam Machwitz, Marlena Kycko, et al. 2022. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review. Remote Sensing of Environment 280:113198.
DOI: 10.1016/j.rse.2022.113198
Provides an overview of approaches to remote sensing of stress in agricultural crops. Gives an assessment of types of stress, how they manifest in time, and what response occur in plant. Also provides a comprehensive examination of the literature covering many methods, sensors, and crop types.
Frolking, Stephen, Michael W. Palace, David B. Clark, Jeffrey Q. Chambers, Herman H. Shugart, and George C. Hurtt. 2009. Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. Journal of Geophysical Research: Biogeosciences 114.
DOI: 10.1029/2008JG000911
Explores the application for remote sensing to monitoring of forest disturbance and recovery. In part this involves change detection, which is outside the scope here, but in part it also involves regrowth or invasive processes that fall under vegetation dynamics.
Hobbs, Richard J., and Harold A. Mooney, eds. 2012. Remote sensing of biosphere functioning. Ecological Studies 79. New York: Springer-Verlag.
This classic volume from the Springer-Verlag Ecological Studies series is an updated version of the original published in 1990. It provides comprehensive treatment of remote sensing of all aspects of the biosphere. A number of chapters address vegetation dynamics, including ecosystem structure, primary productivity, canopy biochemistry, field experiments, spatial and temporal processes, and landscape processes.
Jones, Hamlyn G., and Robin A. Vaughan. 2010. Remote sensing of vegetation: Principles, techniques, and applications. New York: Oxford Univ. Press.
A comprehensive remote sensing text, but contains relevant chapters on satellite systems and sensors, vegetation indices and spectral analysis, and integrated applications.
Kadmon, Ronen, and Ruthie Harari-Kremer. 1999. Studying long-term vegetation dynamics using digital processing of historical aerial photographs. Remote Sensing of Environment 68:164–176.
DOI: 10.1016/S0034-4257(98)00109-6
This especially useful study addresses the valuable resource of aerial photography for monitoring vegetation dynamics using a Mediterranean data set. It documents approaches and limitations to their use.
Karlson, Martin, and Madelene Ostwald. 2015. Remote sensing of vegetation in the Sudano-Sahelian zone: A literature review from 1975 to 2014. Journal of Arid Environments 124:257–269.
DOI: 10.1016/j.jaridenv.2015.08.022
The Sudano-Sahelian zone of Africa provided the primary target for the seminal legacy studies of vegetation dynamics using the NDVI. This review provides an insightful analysis of remote sensing of vegetation dynamics in this area over a period of forty years.
Pettorelli, Nathalie. 2013. The Normalized Difference Vegetation Index. New York: Oxford Univ. Press.
DOI: 10.1093/acprof:osobl/9780199693160.001.0001
Provides a comprehensive overview of the application of NDVI to monitoring vegetation dynamics. Contains chapters on vegetation indices in general, and applications of NDVI to environmental monitoring, plant ecology, wildlife management, conservation biology, ecosystem services, limitations, and future directions and challenges.
Szpakowski, David M., and Jennifer L. R. Jensen. 2019. A review of the applications of remote sensing in fire ecology. Remote Sensing 11:2638.
DOI: 10.3390/rs11222638
Covers all aspects of remote sensing of fire ecology including the roles of multi-spectral sensors, lidar, and unmanned aerial systems, and the issues of fire risk, fuel mapping, active fire detection, burn area and severity assessment, and postfire recovery assessment.
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