Though plants are not individually mobile like animals, they still exhibit interesting and complex dynamics. Vegetation at any given location changes through time (seasonal changes for example) and in space (in response to climatic or landscape factors). Vegetation dynamics operate over a range of time periods. Changes occur on a daily (diurnal) basis, for example changes in leaf orientation in response to the sun's movement and water stress. Irregular, episodic alterations of disturbance and successional recovery happen over various time periods. Regional and global spatial changes in plant communities and their distributions typically occur over long periods of time.

4.1 Phenology

Rhythmic seasonal variations such as growth, flowering, senescence and shedding of leaves (for deciduous trees) occur on the order of weeks and months. The timing of these regular seasonal changes over the course of a year is referred to as phenology.

Remote sensing can be used to study seasonal variations in the landscape by acquiring and combining imagery at various times through the year. Multitemporal data reveal the seasonal changes in vegetation reflectance. This information is very useful in the classification of land cover types, as well as characterizing important ecological changes that affect plant productivity.

In general, remote sensing observes canopies rather than individual plants, so some subtle changes in plants (such as bud break) may not be observed directly. Seasonal changes in vegetation that are most observable are changes in leaf area index (LAI) and leaf color. For example, consider the changes a maple tree undergoes throughout a year: in the winter the branches are bare (LAI approximately 0) but after only a few weeks of spring, the derived LAI can be 6 or 7. When autumn arrives photosynthesis stops, the leaves lose their chlorophyll, change color, and finally drop.

Seasonal changes in vegetation and observed LAI are driven by a variety of factors: day length, temperature, precipitation and species phylogenetics. In the temperate and polar regions, temperature is a limiting factor. In deserts and drier regions plants respond quickly to rainfall events. In areas with monsoons, rain occurs during predictable times and the vegetation growth has evolved to be in sync with monsoon season.

Table 2: Some Seasonal Opportunities for Vegetation Typing and Monitoring


Typing Strategies

Monitoring Strategies

Early Spring

In leaf-off conditions, separate deciduous from conifer woods, herbaceous understory visible

Usually herbaceous understory has an early green flush, and is then visible from above

Late Spring

Best overall separation of canopy reflectances, due to diverse leaf emergence calendars

Onset of major production is a vital time to judge performance

Early Summer

Generally poor discrimination due to haze and pervasive lushness

Production maximum is another key attribute, although often there is too much NPP to measure remotely

Late Summer

Excellent separation as drought response shows variability

Complicated by minor components (e.g., riparian) keeping SVIs high due to persistent greenness


Leaf turning, frost kill of undergrowth

Quiet period, length of rest may be important


Snow background helps separate conifer components, especially when combined with warm season maps

If no snow, the base level of production is important

Wet Season

Maximum water for evaluating wetland and flood subsidy types

Production maxima, perturbed by too much water

Dry Season

Herbaceous senescence (high albedo) can overwhelm the sometimes subtle image manifestation of subdominants

Production minima, perturbed by fire smoke and burn scars

Summary: in cases with few choices of seasonal image coverage, it is best to select the rising and falling times of maximum change (late spring, late summer, onset of wet and dry seasons)

Most remote sensing studies of phenology use AVHRR (Advanced Very High Resolution Radiometer) satellite data as it provides the greatest number of observations throughout a year and global coverage. A large number of observations are required because many must be rejected due to clouds or bad viewing angles. A popular tool for studying vegetation dynamics with the AVHRR data is the use of a spectral vegetation index (SVI) such as NDVI (Normalized Difference Vegetation Index). SVIs are discussed in detail in Section 5.

NDVI is derived from AVHRR reflectances at intervals throughout a year. The resulting multitemporal AVHRR NDVI datasets vividly illustrate global vegetation phenology and have been used successfully to assemble global land cover maps. Land cover type is determined by factors such as date of the start of vegetation growth, length of the growing season, and maximum value of NDVI.

Interpretation of multitemporal NDVI data involves careful examination, because factors other than vegetation changes can affect seasonal NDVI patterns and values. Snow cover generally produces negative NDVIs, so the presence in a scene of snow cover that subsequently melts can dramatically raise NDVIs without any change in the vegetation (which might be evergreen). Also, seasonal changes in sun angle affect NDVI values and must be considered. Factors influencing the variability observed in SVIs are discussed in Section 5.3.

4.2 Disturbance

Episodes of disturbance affect vegetation structure and composition. Disturbance can be caused by natural or anthropogenic forces, such as fire, volcanic eruptions, floods, severe storms, erosion, meteor impacts, disease, agriculture, forest clearcutting, biomass burning, grazing, urbanization, habitat fractionation and destruction, introduction of competing species, and air and water pollution. The response of plant communities to disturbance generates successional recovery which affects the compositional and structural characteristics of vegetation. Satellite and airborne sensors can be focused on known areas of disturbance and monitor successional changes occurring in areas that have been disturbed.

4.3 Long-Term Global Change

A single plant is rooted where it grows, yet plant communities or portions of them can move spatially over long periods of time in response to changing climatic, landscape or ecosystem factors. Long-term change may or may not be cyclical. For example, pollen analysis revealed that during interglacial periods of the Pleistocene, plant communities in North America slowly migrated northward in response to favorable warming trends, and conversely migrated southward during glacial periods. Other long-term trends in addition to plant community migration are changes in species and genetic diversity, which have been showing signs of decline, and changes in the durations of growing seasons.

To recognize and assess true long-term changes in vegetation (whether they be compositional, structural, spatial or seasonal), a thorough knowledge of the normal fluctuations in the distribution patterns and cyclical behavior of plant communities must first be established. The extent of spatial expansions and contractions, magnitudes and ranges of the vegetation signal detected by remote sensors, timing and durations of changes and dynamic states all need to be documented and understood.

Remote sensing is the tool that can monitor vegetation relatively rapidly, and repeatedly, on a global basis. Initial efforts using remote sensing to study vegetation began this process of baseline surveying -- first exploring what attributes of vegetation canopies remote sensing can detect, and documenting (with the aid of surveys conducted on the ground) where various plant communities are and how they normally vary.

Only when the normal variability in vegetation phenology and community distribution patterns has been established can features and perturbations occurring beyond those normal ranges be recognized. Each year's global NDVI (normalized difference vegetation index) sequence may show similarities and differences compared to that of preceding years. The cumulative NDVI record and knowledge of its variability help researchers to assess each new year's NDVI patterns in context. Present and future advancements lie in refining how remote sensing can best measure and monitor vegetation variability.