Image: Bavarian National Park (Germany), DESIS / ISS RGB (639 nm, 550 nm, 470 nm)
Source: DLR/Teledyne Brown

Worldwide, forests provide timber and non-timber products as well as numerous environmental goods and services, such as conservation of biological diversity and climate control, which are crucial for local livelihoods. However, forests and forested ecosystems are being rapidly depleted and under increasing pressure due to global warming and expanding human populations and economies. Deforestation associated with the conversion of forests to agricultural land, legal and illegal timber harvesting, and recurrent wildfires are some of the most important processes that affect forested landscapes. The pressing need is for sustainable forest management combining economic interests with ecologic concerns.

Future EnMAP satellite data can be used to efficiently characterize the spatial distribution of forest ecosystems and provide an inventory of forest resources. Accordingly, the following main scientific tasks are considered important for forest applications:

  • Map forest species distribution using hyperspectral, fused and multi-temporal data sets; explore the potential of advanced classification algorithms, texture and object information, linkages to geographic databases, etc.
  • Estimate forest biomass and above-ground carbon
  • Assimilate biochemical and structural forest parameters into process models
  • Enhance and develop invertible vegetation canopy reflectance models for the forest environment, extract forest parameters, forest mensuration, health and risk assessment
  • Investigate the viability of phenological signatures through indicators of canopy pigments and chemistry with regard to ecophysiological processes
  • Develop improved optical indices to serve as bio-indicators of forest condition
  • Develop forest monitoring procedures including multi-temporal and multi-sensor data to detect changes in forest quality and canopy cover, and
  • Create advanced expert systems to improve the efficiency of hyperspectral information extraction in the forestry context.
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