Environmental Engineering Reference
In-Depth Information
FIGURE 17.3
Technological advances in data measurement. Remote sensing offers new techniques to collect
data at high spatial resolution over large landscape scales. This image was taken over an area of lowland rain for-
est and cleared lands on the island of Hawaii. Different colors highlight the distinct chemical signatures of trees
and other vegetation, canopy heights, and crown sizes, which allow the estimation of relative size and amount of
above ground biomass of different plant species. (Map from
Townsend et al. 2008.)
information about objects or phenomena without being in physical contact with the object
or system under investigation, and includes a suite of techniques and tools that have been
successfully used for decades in areas as diverse as traffic control, meteorology, the mili-
tary, and astronomy. Recently, the development of new and more refined airborne
platforms and technologies, better spatial and spectral resolution, and the gradual reduc-
tion of costs have resulted in remote sensing rapidly becoming one of the most promising
tools for the study of ecosystems. By using satellites, aircrafts, ships, or helicopters, it is
now possible to collect data in inaccessible areas and at spatial scales and speeds once
unthinkable. Technologies such as high-fidelity imagining spectroscopy (HFIS) or light
detection and ranging (LIDAR) allow collection of data about plant canopy structure and
characteristics (e.g., water content, nitrogen, or pigment concentrations), estimates of
species richness, and surveys of coastal dynamics and sediment transport from streams to
oceans (
Figure 17.3
). It is also possible to estimate primary productivity (see Chapter 2)
and biogeochemical processes at multiple scales, from the local to the global, and even
monitor in almost real-time dynamic processes such as atmospheric concentration of
chemicals, deforestation, glacial retreat, or desertification (
Campbell and Wynne 2011
).
Frontiers for ecosystem science are likely to include developing algorithms to link sensor
output to ecosystem processes of interest (e.g., spectral output and nutrient content in
forests), and the ability to remotely sense soil properties, such as nutrient content, mois-
ture, or organic matter content. The resulting data will offer new opportunities to develop
and test ecosystem models and theory across broad spatial and temporal scales. For exam-
ple, key questions such as how species invasion rates and patterns will vary in response to
global change, how albedo and foliar nitrogen can be used as proxies for temporal and
spatial controls on the variation in global carbon fluxes and productivity, or how thermo-
karst lakes (formed by permafrost thaw) change in relation to climate variability will be
