A Data Storage, Analysis, and Project Administration Engine (TMFdw) for Small- to Medium-Size Interdisciplinary Ecological Research Programs with Full Raster Data Capabilities

Paulina Grigusova (), Christian Beilschmidt, Maik Dobbermann, Johannes Drönner, Michael Mattig, Pablo Sanchez, Nina Farwig and Jörg Bendix
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Paulina Grigusova: Laboratory for Climatology and Remote Sensing (LCRS), Department of Geography, University of Marburg, D-35032 Marburg, Germany
Christian Beilschmidt: Geo Engine GmbH, Am Kornacker 68, D-35041 Marburg, Germany
Maik Dobbermann: Laboratory for Climatology and Remote Sensing (LCRS), Department of Geography, University of Marburg, D-35032 Marburg, Germany
Johannes Drönner: Geo Engine GmbH, Am Kornacker 68, D-35041 Marburg, Germany
Michael Mattig: Geo Engine GmbH, Am Kornacker 68, D-35041 Marburg, Germany
Pablo Sanchez: Instituto Nacional de Biodiversidad (INABIO), Pje. Rumipamba N. 341 y Av. de los Shyris (Parque La Carolina), Quito 170102, Ecuador
Nina Farwig: Department of Biology, Conservation Ecology, University of Marburg, D-35032 Marburg, Germany
Jörg Bendix: Laboratory for Climatology and Remote Sensing (LCRS), Department of Geography, University of Marburg, D-35032 Marburg, Germany

Data, 2024, vol. 9, issue 12, 1-21

Abstract: Over almost 20 years, a data storage, analysis, and project administration engine (TMFdw) has been continuously developed in a series of several consecutive interdisciplinary research projects on functional biodiversity of the southern Andes of Ecuador. Starting as a “working database”, the system now includes program management modules and literature databases, which are all accessible via a web interface. Originally designed to manage data in the ecological Research Unit 816 (SE Ecuador), the open software is now being used in several other environmental research programs, demonstrating its broad applicability. While the system was mainly developed for abiotic and biotic tabular data in the beginning, the new research program demands full capabilities to work with area-wide and high-resolution big models and remote sensing raster data. Thus, a raster engine was recently implemented based on the Geo Engine technology. The great variety of pre-implemented desktop GIS-like analysis options for raster point and vector data is an important incentive for researchers to use the system. A second incentive is to implement use cases prioritized by the researchers. As an example, we present machine learning models to generate high-resolution (30 m) microclimate raster layers for the study area in different temporal aggregation levels for the most important variables of air temperature, humidity, precipitation, and solar radiation. The models implemented as use cases outperform similar models developed in other research programs.

Keywords: working database; big raster data; raster engine; use case; area-wide microclimate (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
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