Use of remote sensing for the early detection of drought stress on endangered forest sites
The project tries to answer questions related to the adaptation of the forest sector to climate change. The processing chain should detect changes in physiological/morphological status of forest trees under drought stress on large areas in a highly automatic way. The investigations are aimed toward the pre-visual detection of forest-tree drought-stress at a reversible stage. The system should identify forest sites with drought stress endangered trees in advance of a drop out, an information required for the preferential start of restructure measures. The approach integrates existing geo-information from the forest sector with spectral, height and anisotropy information from remote sensing systems of the latest generation in a spatial-temporal context (4D approach). Anisotropy is still a fundamental challenge in remote sensing and must be considered for qualitative data evaluations. A new aspect of the ForDroughtDet project is the exploration of anisotropy features as additional, independent information source. The new generation of stereo data capturing satellite systems like the Ziyuan-3 pair allows analyzing these information sources on large areas.
The project takes advantage of existing measurement sites and ongoing measurements on physiology and morphology of forest trees under artificial drought stress at the Kranzberg Roof Experiment (KROOF). The systematic goniometric spectral measurements of the ForDroughtDet project complement these measurements, deepening the knowledge about the manifestation of artificially induced drought stress in close range remotely sensing measurements. In the next step, this knowledge helps to detect stress symptoms on large areas by means of remote sensing methods. The evaluation chain relies on a combination of (hyper-) spectral and multidirectional data sets from research airplanes (HySpex and 3K) and from satellite data of the Copernicus Program of the European Space Agency (ESA)(Sentinel-2, Sentinel-3) and national missions (EnMAP, TanDEM-X). The results deliver empirical support for the "stress-gradient hypothesis" (SGH) and serve to generate second-generation forest risk maps.