Vegetation plays a key role in the monitoring of land cover, with forest monitoring being a special form. For the response of vegetation the visible spectral range is as important as the near and short-wave infrared. Especially in red and blue light a large part is absorbed by leaf pigments and chlorophyll. In the NIR, on the other hand, healthy plants reflect strongly (see lecture section on vegetation monitoring). The characteristic reflection properties of vegetation can be used for forest monitoring. In order to obtain spatially accurate information on forest areas, terrestrial forest monitoring is extended and supported by remote sensing (Ackermann et al., 2014; Fagan and Defries, 2009; Pause et al., 2016; Romijn et al., 2015). This is particularly true when the most up-to-date information is needed to record the condition of a forest, which is often difficult to record in-situ on such a large scale (Lausch et al., 2016).
About 30% of Germany's land area is covered with forest. The extent of the forests is well documented and can also be derived with high spatial accuracy from freely available data. In addition to health status and growth, satellite or aerial photographs can also be used to estimate variables relevant to forestry, such as forest types and tree species, tree heights, stand densities or number of trees per hectare (n/ha), stand basal areas (m²) and wood volume (m³), carbon stock.
The identification and spatial assessment of forest damage are central elements of forest monitoring. The damages are manifold and are caused by insects (bark beetles), fire (drought & heat) or wind (storm & hurricane). Remote sensing is used to monitor the health of vegetation on a large scale and to gain a quick overview of affected forest areas following storm events or pest infestations. Several studies have already shown the applicability of remote sensing for the detection of windthrow areas (Einzmann et al., 2017; Remelgado et al., 2014; Schwarz et al., 2003; Seitz and Straub, 2017; Steinmeier et al., 2002). The data used often include commercial radar systems and very high-resolution optical sensors. Since these data are limited and not always available, the use of open data is a preferred approach for forest owners and for institutions and organizations, but also for research, education and teaching. Thus, freely available remote sensing data with large spatial coverage are useful and necessary for forest observation, e.g. for the detection of wind throw areas.