Laboratory for
Climatology and Remote Sensing

Abstract:

Global biodiversity is threatened by climate and land cover change. The research unit "Biodiversity and Sustainable Management of a Megadiverse Mountain Ecosystem in South Ecuador" (FOR 816) funded by the German research council (Deutsche Forschungsgemeinschaft, DFG) is working in one of the hottest hotspots of biodiversity of the world. In this region the pressure from the local population on the environment is severe resulting in a high deforestation rate. Sustainable management systems have to be developed on a regional scale to counteract the loss of livelihood of the local population. Numerical models are capable to investigate the changes of the mentioned future land cover changes and its response to climatic and hydrologic variability. The chance to test numerously land use scenarios without interfering into the real environment offers the possibility to investigate and to evaluate the proposed management strategies. The presented work targets at an analysis of the impact of the predicted land cover changes in respect of the ecosystem services of climate and water regulation. Therefore a state-of-the-art land surface model called "Community Land Model" (CLM) is setup in a regional scale. The parametrization of the vegetation is implemented using plant functional types (PFT). The PFTs are defined a priori with vegetation classes based on ecological field surveys. Three central hypotheses are formulated to support the parametrization of the model. The completed work offers a regionalized model setup to analyze different land cover developments in reference to energy and water fluxes between the soil, the vegetation and the atmosphere under changing climatic conditions. Besides the appraisal of the stated hypotheses other innovative contributions are made. The new values for the pre-installed CLM-PFT of tropical evergreen trees add to the current improvements made to the CLM.

Abstract:

Land degradation is a major environmental and social issue in temperate steppes. It is commonly determined from vegetation cover using remote sensing techniques. Steppes in eastern Mongolia are subject to resource extraction activities, such as mining and oil extraction, which affect land degradation. Recent technological progress in remote sensing has facilitated the acquirement of high-resolution data by, for example, the CubeSat satellite or unmanned aerial vehicles (UAV), providing data for detailed maps of vegetation cover and plant functional groups (PFGs). Traditional methods for monitoring vegetation cover often face typical scale issues, such as the upscaling of vegetation parameters if plot-scale field measurements are integrated to satellite data. Here, we studied the spatial distribution of PFG using machine learning and a combination of field measurements, UAV imagery (spatial resolution: 2 cm), and PlanetScope multi-temporal imagery. We provide two products at two spatial resolutions: one for UAV data, which is restricted to comparatively small areas around field measurements, and one for PlanetScope, which covers large parts of northeastern Mongolia. The results showed that the overall accuracies of UAV classification were 91–95%, whereas those of PlanetScope were 78–95%. In integrating the classified UAV data to the PlaneScope data, our proposed model minimized the scale issue that often impedes classification. Importantly, our findings revealed that the ecological effects of dirt road and railroad extended up to 60–120 m into the adjacent, otherwise less degraded steppe vegetation. A comparison between burned and unburned areas in different years indicates that wildfires affect the composition of PFG in reducing the fractional cover of graminoids and forbs, and that increasing cover of bare ground leads to a distinct and patchy mosaic of different vegetation types.

Abstract:

Fast Fog and Low Stratus Detection (FFLSD) is a processing tool enabling detection of fog and low stratus (FLS) on very large time-series of Meteosat Second Generation data. We combine approaches for FLS detection by day and night into one homogeneous and efficient processing tool. The main unification improvements are: (1) consistent spatial tiling instead of various pixel clustering approaches, (2) uniform generation of sun and viewing angle dependent thresholds for individual tiles, (3) flexible study areas instead of area dependence, (4) parallel raster processing using the Open Computing Language (OpenCL), (5) and efficient algorithms for complex processing steps like histogram generation and analysis. As a result, users are enabled to generate products like long-term FLS climatologies with reasonable resources and short processing times. FFLSD is available as open source and allows reuse and extensions for other tasks.