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The Chilean-Peruvian coastal desert stretches between 8 and 28 degrees southern latitude and is one of the ecologically most extreme geo-ecosystems in the world with annual precipitation of less than 1 mm for some parts. Plant growth is limited to so-called fog oases, in which fog is deposited on highly specialized plant’s surfaces such as Tillandsia spp. so that they can mainly cover their entire water requirements by periodically occurring coastal fog. Against this background, our study shows the potential of a multi-scale remote sensing approach for the monitoring and analysis of fog geo-ecosystem distribution patterns (locally also known as Loma-formation). The results of our study can be used as bio-indicators for climate change.
In a first step, a vegetation inventory for the entire study area is established. For this purpose, a convolutional neural network was designed which detects vegetation cover based on WorldView-3 imagery. The results are used to stratify the study area to sites, where detailed analyses are conducted. On this scale, remotely piloted aircraft systems (RPAS) are used on the selected test sites to generate orthophoto-mosaics and digital surface models with a spatial resolution between 3 and 10 cm. From these products, structural characteristics (e.g. degree of coverage, horizontal and periodic patterns) and a detailed recording of Tillandsia vegetation is derived. For this purpose, object-based image analysis modelling techniques are developed. In cooperation with the Heidelberg University Institute for Biodiversity and Plant Systematics, the endemic Tillandsia landbeckii is being examined for vitality and genetic diversity. The relationship between the distribution of Loma-formation and fog properties (like occurrence, frequency and intensity) is analyzed by correlation of the measured field data, which are recorded by a structural network of weather stations.
To achieve a supra-regional extrapolation of the vegetation degree and structural features, the approach is extrapolated to very high-resolution, multi-spectral WorldView-3 images. The products derived from the RPAS missions serve as a reference and are used to calibrate and validate the classification of WorldView-3 data. The described approach thus comprises several scale levels. First, the vegetation cover is recorded via satellite images. From these observations, two test sites are selected for a detailed investigation of the vegetation structure parameters. Finally, these results are upscaled to the entire study area.
The study shows the integrative approach of several remote sensing products with a multi-level hierarchy. Furthermore, in-situ observations are integrated into the procedure to gain better understanding of the interaction between atmosphere and biosphere in the Chilean-Peruvian coastal desert. The results provide a framework for the protection of fragile fog ecosystems and their endemic species.
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