If global land vegetation stayed unchanged through the decades and centuries, we could have learned about it progressively as needed, and would not have to concern ourselves so much about its changing status. But vegetation does change, and frequently in ways that illustrate opportunities for more wisely managing resources. Vegetation dynamics can be indicators of processes, productivity trends, and ecosystem interactions that can be otherwise difficult to monitor. Vegetation cover around the world can therefore be considered as a sensitive array of monitoring instrumentation; we only need to learn better ways of measuring and interpreting the patterns of its change.
Before evaluating a sequence of monitoring data, it is often necessary to subdivide an area being monitored into distinct groupings (or strata) of vegetation types. Differences in growing environments, in competition both between and within species, and in cycles of seasonal stress affect different vegetation types to varying degrees, so that a separate monitoring time series within each vegetation type or strata offers better information for understanding than just one aggregate time series that lumps all strata. Some substantial changes in time and space may result in fundamental changes in any given vegetation type; this scenario requires periodic reassessments of existing vegetation strata for long-term monitoring programs. If and when certain specific varieties of vegetation warrant special concern, a categorization or classification scheme can be designed to efficiently isolate, monitor and evaluate their condition.
A continuum of variation among physical environments and disturbance pressures around the world have created a diverse assortment of vegetation. To study these differing cover types on a global basis, seasonality contrasts must be considered and adjusted for, and deviances beyond normal variability must be distinguished from normality. One way to screen out short-term instabilities of these special circumstances is to establish a long-term cyclical baseline record that gives a robust picture of normal behavior. From such long-term records, thresholds of variation around the norm can be derived for each locale which represent the expected annual and possibly multiyear oscillations. However, if the baseline observations are made for all vegetation as a whole undivided entity, the process could fail to recognize some substantial changes if a signal gets obscured within the lumped variability of many combined signals. Subdividing vegetation into different type categories addresses this issue by providing the ability to monitor each type's specific variability and unique signature.
Apart from their utility in isolating and assessing variability, vegetation categories serve many purposes in practical applications and research objectives. For example, vegetation types are useful as climate markers, indicators of grazing capacity and wildlife habitat, and for assessing soil-crop suitability, planning data for timber or paper production, disease, and fire history. Additionally, other parameters and information (such as albedo adjustments within climate models) can be derived using vegetation type and amount. Ideally, each application would have its own classification scheme, but this is not practical or efficient for continental or global expanses. Also, use of more standardized classification schemes contributes to easier exchange, dialogue and independent interpretation of results. Most consumers in need of large area vegetation data obtain public domain products, with the expectation that somewhere within the generic information will be some useful specifics relevant to their application.