Advanced Computational and Statistical Techniques for Biomonitoring and Aquatic Ecosystem Service Management (DETECT)
Main funder
Funder's project number: 289076
Funds granted by main funder (€)
- 204 793,00
Funding program
Project timetable
Project start date: 01/09/2015
Project end date: 31/08/2018
Summary
The loss of aquatic biodiversity and associated Ecosystem Services is one of the most pressing problems on Earth. Sophisticated biomonitoring programs have therefore been established to understand patterns and identify drivers of biodiversity loss. We develop the use of advanced techniques in signal and image processing, computer vision, data mining, and statistics which can be used to solve issues related to human error and cost efficiency of taxa identification. The combination of individual research efforts in this highly interdisciplinary effort will subsequently be used to renovate a classical and partly outdated process to assess a specimen’s taxonomic identity, and related data acquisition. The novel computational advances made during this project can thus directly be put in action and will facilitate cost-effective biomonitoring and promote reliable aquatic ecosystem service management on a global scale.
Principal Investigator
Other persons related to this project (JYU)
Primary responsible unit
Fields of science
Keywords (YSO)
Related publications and other outputs
- Human experts vs. machines in taxa recognition (2020) Ärje, Johanna; et al.; A1; OA
- The value of perfect and imperfect information in lake monitoring and management (2020) Koski, Vilja; et al.; A1; OA
- Empirical Bayes improves assessments of diversity and similarity when overdispersion prevails in taxonomic counts with no covariates (2019) Divino, Fabio; et al.; A1
- Benchmark Database for Fine-Grained Image Classification of Benthic Macroinvertebrates (2018) Raitoharju, Jenni; et al.; A1; OA
- The effect of automated taxa identification errors on biological indices (2017) Ärje, Johanna; et al.; A1; OA
- Understanding the statistical properties of the percent model affinity index can improve biomonitoring related decision making (2016) Ärje, Johanna; et al.; A1