Integrating Google Earth Engine for biotope type classification

Vegetation mapping is an essential component in the domain of nature and environmental protection. Traditional approaches, aligned with current guidelines, necessitate high research specifications typically fulfilled through terrain mapping efforts. Despite its efficacy, this method encounters limitations in terms of seasonality and the timely processing of extensive areas. In contrast, remote sensing-based models offer noteworthy advantages under their season-independence and rapid large-scale processing capabilities. The present initiative seeks to leverage new technologies, such as cloud computing, to augment conventional supervised remote sensing classifications by merging ecological expertise with the sophisticated capabilities of cloud computing technology. For a few years now, the “Google Earth Engine” (GEE) platform has made it possible to carry out geospatial processing data and analyze them based on a huge time satellite imagery series at large study area in combination with multivariate statistical methods. It also enables the integration of location and laser scan data as well as geospatial information systems data. Challenges and Research Requirements in the project: Integrating the processing chain into a cloud platform poses considerable challenges and necessitates extensive research for a coherent, smooth, and consistent adaptation. From the initial model selection to the subsequent post-processing phase, passing through exhaustive feature selection analysis and model evaluation, which are crucial phases of adaptation, require exhaustive research to ensure the accomplishment of expected outcomes in the project. Recognizing the complexity of this task, the Egger Natural Space Planning Company requires the expertise of a remote sensing scientist specializing in cloud computing platforms. The scientist, their expertise in the field, and their scientific input are vital for providing a perspective and conducting the needed research to identify optimal approaches that align with the project's expected outputs.