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Continuous research efforts in the modeling of forest ecosystems subsequently led to the development of process-based and gap models.
These bands are missing on some remote sensing sensors, and their importance for the modeling of forest biomass was already noted when Landsat TM was new [38].
The second part was dedicated to improving the combustion models of degradation gases used in the modeling of forest fire behavior.
Spatial modeling of forest patterns can provide information on the potential impact of various management strategies on large landscapes over long time frames.
Efforts to quantify live and dead forest biomass at the local to regional scale has been hindered by the uncertainty surrounding the measurement and modeling of forest ecosystem processes and fluxes.
Diligent modeling of forest response to rising temperatures and increased moisture stress suggests, in fact, that these two fires were harbingers of worse to come.
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From a spatial monitoring and modelling perspective, models of forest growth are mature and capture well biomass increment, but do not account for forest depletions, which can result in large changes in biomass and impact the ability of forests to exchange gasses with the atmosphere.
Long-term forest observational studies provide the empirical basis for forest policies and for developing models of forest dynamics.
Overly simple conceptual models of forest dynamics easily become transformed into biased mental models of how forests naturally develop and what kind of structures they display.
However, the known models of forest growth are not "one-point" models.
While there are a number of existing physical models of forest interception, few studies have summarized or compared these models.
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