Large Systems Dynamics through Remote Sensing

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Remote sensing has become a valuable tool and a proven methodology for ecosystem scientists to monitor and understand major disturbance events and their historical regimes at regional and global scale. Retrospective analysis applied at time series of remote-sensed imageries focuses primarily in quantifying systems' properties both from a spatial and temporal perspective so that the evolutionary trajectory of a system can be defined according to the spatial, temporal, and qualitative nature of disturbance events occurring and evaluated at different scales. Natural or anthropogenic disturbance regimes (e.g., forest fire cycles, land-cover conversion, or crop rotation) can be described in terms of spatial extent (e.g., hectares of burned areas) and distribution (e.g., spatial arrangements and patch shapes), as well as their intensity (i.e., the energy released per unit area and time), or the frequency and seasonality of their occurrence over time.

Systems' properties can be indirectly estimated within the limits of the ecological information contained in remote-sensed data. Systems' vulnerability/fragility can be portrayed in time as objectively recorded by changes or no changes in subsequent images. Systems' resilience can be investigated by operationally quantifying structural or, to a lesser degree, functional variability induced by disturbances or cross-scale interactions (e.g., climatic forcing or extreme events) not undermining the identity of a system. But it is necessary to keep in mind that remote technologies rely on surfaces' properties, measurements of reflected light in different regions of the electromagnetic spectrum collected by satellite, or air-borne optical remote sensors. Remote-sensed information is not a direct measure of ecosystems' processes of community structure, species populations, or species diversity.

Near-global-scale remote-sensing data sets have been available to the scientific community continuously since the early 1980s from a series of meteorological satellite missions managed by the National Oceanic and Atmospheric Administration (NOAA), carrying the advanced very high resolution radiometer (AVHRR) sensor family. Despite their coarse spatial resolution of 1.1 km, AVHRR data are readily accessible and provide the only near-continuous, long-term (27 years) measurements of key ecological parameters, such as habitat extent, heterogeneity, or primary productivity, at regional or global scales. A second satellite mission can provide an ever longer time span, with an increased spatial and radiometric resolution, from 15 to 120 m pixels and from four to seven multispectral and one panchromatic bands. The LANDSAT program, jointly managed by National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS), is operating since the early 1970s but data cannot provide near-real-time ecosystem monitoring across broad areas because of the relatively long site revisit times of the satellite (16-18 days). However, the LANDSAT data record is the longest of any satellite and its improved spatial resolution enables the detection of subtle environmental changes that could be missed by coarser-resolution sensors.

An example of retrospective analysis by remote sensing is the identification of major continental patterns of natural and anthropogenic disturbance regimes and large-scale fragility across North America based on the analysis of a 19-year record over the period 1982-2000 of AVHRR satellite observations of vegetation phenology. The emerging historical picture is of cycles of cold and heat waves linked to periodic droughts, tropical storms and forest fires, large-scale forest logging, and herbivorous insect outbreaks as among the most important causes of ecosystems disturbance. Areas potentially influenced by major disturbance events amount to more than 766 000 km2. The highest proportion of relevant changes (i.e., highest fragility) has been detected in forests, tundra shrublands, and wetland areas in the subcontinental regions of the Pacific Northwest, Alaska, and central Canada. In the Great Lakes region fragility was mainly associated to cropland areas, whereas in the western United States with grassland areas. When analyzed in the temporal domain a high interannual variability emerged in disturbance events with the periods of highest detection frequency of relevant changes in 1987-89, 1995-97, and 1999. Nearly 65% of observed fragility had a duration of between 12 and 13 consecutive months, percentage that increased to 95% considering events up to 20 consecutive months.

Another perspective in remote-sensed time-series analysis is the measure of type, timing, and intensity of the coupling ofdifferent hierarchical levels' dynamics. For example, by comparing recorded information of temperature and precipitation with the evolutionary trajectory of ecosystems, synthesized by remote-sensed indices, it is possible to explore cross-scale relations like higher-hierarchical-level constraints (e.g., remember interaction exercised by climatic variables) or to shed light on lower-level interactions (e.g., fast lower-level dynamics able to reduce the effects of higherlevel bounds). Figure 2 presents monthly values for mean temperature, total precipitation, and mean normalized difference vegetation index (NDVI), calculated from the moderate resolution imaging spectroradiometer of the NASA's Earth Observing System, for three major ecosystems of the Apulia region (southern Italy) from January 2000 to January 2006.

A statistical analysis of the relation of NDVI versus temperature and precipitation can be carried out by calculating cross-correlation coefficients for different time lags. Trends show that forest's NDVI values are positively correlated to temperature (i.e., high temperatures are associated to high NDVI values) while natural grasslands and arable lands are negatively correlated (i.e., high temperatures are associated to low NDVI values) for different time lags. High photosynthetic rates are reached during spring and summer in forests but not in dry Mediterranean grasslands and in arable lands after crop harvest. Water availability is positively correlated to grasslands NDVI up to 4 months lags, underlining its limiting factor role in Mediterranean prairies' productivity. Arable lands show a similar pattern but with no statistically significant cross-correlation at lag 0 and 1 (i.e., comparing mean NDVIs to total precipitation of the same or previous month) and lower coefficients up to 4 months lag. Crop production is concentrated in the first half of spring, depending heavily on groundwater more than precipitations. This is an example of bottom-up cross-scale interaction where agriculture, as a fast anthropogenic variable, interferes with higherlevel climatic constraints. Forests have an opposite pattern showing decreasing negative cross-correlation coefficients up to 4 months lags and indicating a second example of bottom-up cross-scale interaction because this ecosystem type has a buffer capacity to overcome drought periods and water shortages.

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