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. These include conservation of biological diversity and climatic regulation, which are crucial for local livelihoods (FAO, 2010). Forests and forested ecosystems are rapidly degrading under increasing global change pressure (Peng et al., 2011) and the expansion of human population and economies (Hansen et al., 2008). Deforestation and the loss of carbon and biodiversity associated with conversion of forests to agricultural land, legal and illegal timber harvesting, drought stress, biotic stress, and recurrent wildfires are some of the most important processes affecting forested landscapes.

EnMAP data will facilitate the efficient characterization of the spatial distribution of forest ecosystems and support inventories of forest resources. Accordingly, the following main scientific tasks are considered important for forest applications:

  • Mapping of forest species distributions using hyperspectral, fused and multitemporal datasets, exploring the potential of advanced classification algorithms, texture and object information, and linkages to geographic databases, etc.;
  • Estimation of forest biomass, above-ground carbon, and productivity;
  • Detection of biodiversity hotspots and essential biodiversity variables for forest management and protection planning;
  • Assimilation of biochemical and structural forest parameters into process models;
  • Enhancement and development of invertible vegetation canopy reflectance models for the extraction of forest parameters, and forest mensuration, health, and risk assessment;
  • Investigation of the viability of phenological signatures through indicators of canopy pigments and chemistry with regard to eco-physiological processes;
  • Development of improved indicators of forest condition and health;
  • Development of forest monitoring procedures, including multi-temporal and multi-sensor data, for the detection of changes in forest quality and canopy cover; and
  • Creation of advanced expert systems to improve the efficiency of hyperspectral information extraction within the forestry context.
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