The long-term monitoring need
Natural resource managers are not only concerned with mapping and tracking disturbance events as they unfold in near-real-time. At longer time scales and often broader spatial scales, the need of planners and managers is to have inventories and assessments of slow-acting changes that are usually hard to map, quantify and interpret. While these slow-acting changes are easy to overlook, they can have profound influence on outcomes. This change includes incremental land use conversion, succession associated with altered disturbance or climate regimes, or gradual mortality or decline from invasive species.
Viewing ForWarn’s NDVI history provides a useful way to contextualize the impacts of one or more disturbances on the NDVI of a given pixel in ways that are not always evident or understandable from ForWarn’s near-real-time change maps. Near-real-time change products exploit ForWarn’s historical MODIS NDVI dataset by deriving different baselines for a specified period of the year. In contrast, long-term monitoring and analysis emphasizes inter-seasonal to inter-annual change, multiyear behavior such as attrition or recovery, and provides quantitative insights that can be used for predictive purposes.
Your ability to efficiently track short and long-term impacts is possible because ForWarn uses a continuous monitoring approach for the entire conterminous United States. Vegetation is systematically tracked, regardless of whether it has been disturbed or stressed or not. This provides rich monitoring insights about “normal” behavior, the cumulative effects of multiple disturbances, and the post-disturbance behavior that could be either recovery as expected or a vegetational type conversion.
Measures for long-term monitoring
The long-term condition of vegetation can be tracked by trends in the drivers of change through weather data, fire or insect occurrence databases, and also by the condition of the vegetation directly. The latter approach is also the monitoring approach used by the plot sample-based Forest Inventory and Analysis program (http://www.fia.fs.fed.us). In that ForWarn helps both recognize disturbances and track impacts, it provides a particularly useful toolset for integrative monitoring across programmatic efforts.
ForWarn’s historical database includes 46 NDVI products every year—a richness that results from having satellite overpasses each day and the ability to filter out clouds and poor atmospheric conditions. This temporal richness translates to phenological insights, such as the ability to recognize within-season changes from ephemeral disturbances and climate stress that would be otherwise undetectable or ambiguous, given the complexity of drivers at work in a given landscape. While no single phenological measure may be applicable across all vegetation types or geographic regions, numerous measures have been formulated that capture change that helps identify the causal drivers and the impacts that result. These include basic statistical measures, seasonal parameters, and annual percentiles as described below.
Basic annual measures
Useful monitoring insights can come from basic statistical measures such as each year’s median, maximum or minimum NDVI, and within-year variance or range. Similarly, the area under the annual NDVI curve (i.e., what ForWarn refers to as the large integral) tells us something about the type of vegetation present, annual productivity or the duration of the growing season. It is important to remember that these values are robust statistics, as they are generalized from 46 values for each year rather than derived from a single summer image, as often happens with Landsat, that may not be representative of the growing season or sensitive to change that occurs any time of year. Basic sub-annual (i.e., seasonal) values can also be calculated, such as the maximum value during the low-NDVI winter months—an alternative strategy to overcome false estimates of evergreen vegetation caused by heavy snowpack suppressing NDVI.
|Basic annual measure||Indicator of…|
|Mean||Vegetation type, successional status, snowpack in colder climates|
|Median||Vegetation type, successional status|
|Maximum||Vegetation type, peak productivity|
|Minimum||Evergreenness, snowpack in colder climates|
|Range||Deciduousness, snowpack in colder climates|
|Area under the curve||Vegetation type, productivity, successional status, snowpack|
Interannual flux in these basic measures is caused by disturbance, successional recovery and often climate variation. Disturbance and recovery are manifest locally (Figure 4) while climatic variability that includes drought, snowpack depth or growing season productivity, is more generally landscape to regional in scope. As NDVI is an integrative measure for vegetation, its usefulness for specific measures of interest often needs to be demonstrated. For example, while it is well established that conifers are stressed by drought, their community response is less reliable than what is observed for grass-shrub or hardwood-mixed forest types.
Seasonal parameters and their derivatives
The classic land surface phenology curve for mid-latitude forests forms a hump with a rapid rise and fall that correspond to spring and autumn. While the basic form of this curve is dictated by seasonal factors governed by incoming solar radiation, variation in the year-to-year position of this curve usually reflects temperature or moisture conditions (Figure 5).
Seasonal parameters are intended to capture the date and NDVI values of these seasonal transitions and how they vary among years. By themselves, periodic NDVI values don’t tell us when the onset of spring or fall occurs, as values need to be kept in annual context. Severe disturbance can cause disruption in the normal NDVI progression such that decline occurs earlier than in undisturbed neighboring sites, even though the climate season per se has not changed. In general though, parameters mark biologically significant turning points in seasons such as when seasonal transitions occur. Those data provide a powerful measure for describing how climate affects vegetation responses.
ForWarn includes a suite of phenological products that characterize five seasonal transitions (Table below, Figure 6). Each year has two winter minimums, defined as “left minimum” and “right minimum”, and a single growing season “maximum”. Both the NDVI values and the day of year (or “position”) are determined. From these metrics, the 20th and 80th percent greenup values and dates for spring and the 80th and 20th percent values and dates for fall browndown are determined. With two maps for each of these seasonal parameters—the NDVI value and day of year position—there are 14 maps available for every year.
|Primary seasonal parameters||Indicator of…|
|Left minimum||Early year non-growing season evergreenness|
|20 Left||Onset of Spring|
|80 Left||Start of Summer|
|Maximum||Peak of Summer growing season|
|80 Right||Onset of Fall|
|20 Right||End of Fall|
|Right minimum||Late year non-growing season evergreenness|
Two measures are associated with these seven seasonal parameters for each year–these are the NDVI value and the day of year when the parameter is achieved. This allows us to contrive measures based on changes in NDVI, changes in time or some combination of both, as in seasonal rates of change.
|Secondary seasonal parameters||Based on…|
|Slope of spring (rate of greenup)||Days between 20 Left and 80 Left|
|Slope of fall (rate of browndown)||Days between 20 Right and 80 Right|
|Duration of growing season||Days between 20 Left and 20 Right|
|Duration of summer||Days between 80 Left and 80 Right|
|Growing season amplitude||NDVI difference between 20 Left/Right and Max|
|Growing season skewness||Offset of Max versus mean of 20/80 Left/Right|
|Growing season peakyness||Shape of the curve|
|100th minus the 30th||Growing season deciduousness|
|30th||Non-growing season evergreenness|
|50th (median)||A general measure of productivity and vegetation type|
Phenologically-based vegetation types
The NDVI profile of a grid cell can change considerably from one year to the next, but all change is not biologically important. Subtle differences in weather, ephemeral disturbance from minor storm events and atmospheric noise can distract from the vegetational information that is embedded in ForWarn’s NDVI time series. Moreover, with over 140 million MODIS pixels in the conterminous US, it is hard to make sense of so many different phenological profiles efficiently. Some type of classification or typing can be applied to make these data more useful and accessible.
Statistical clustering of annual NDVI profiles provides a robust approach to classification. When just a few different clustered types are generated, the resultant types reveal only the most basic seasonal and productivity attributes. When a larger number of divisions are generated, subtle differences in productivity, the onset of spring or fall or nuances associated with disturbances can be resolved.
ForWarn uses non-hierarchical K-means clustering of the 46 NDVI values per year for all years in the dataset together, so that a single grid cell can potentially have membership in as many phenological categories as there are years of history. When relatively few clusters are generated, membership in a group is likely to be stable unless there is a severe disturbance. When several thousand types are generated, membership is more likely to shift. Clustering is performed with 50, 100, 200, 500, 1000 and 5000 divisions to provide different levels of division depending on classification needs (figure 7, 8).
These phenologically-based vegetation types are calculated annually so that shifts in membership from one type to the next can be tracked more efficiently than is possible when raw NDVI profiles are used. Membership shifts over time is useful for vegetational transition and successional modeling, prioritization efforts, and assessment of vegetation patterns and representation of biologically important changes over time.
Putting long-term monitoring to work
Any of the annual or seasonal derived products described above could be used for long-term forest or landscape monitoring. The measure selected should be closely aligned with the management question asked and the ability of that measure to function fairly for a particular ecosystem or climate.
Some of the most promising applications of long-term NDVI data are these:
- Efficient mapping of evergreen or deciduous decline
- Efficient mapping of evergreen or deciduous recovery
- Coarse prioritization of areas of concern within a landscape
- Successional dynamics of NDVI types
- Robust characterization of normal behavior for contextualizing change