The IIASA/ESM based BGC-MAN model was calibrated and validated with TrEco data for the dominant ecosystems and land use forms of the Congo basin, including virgin forests, managed forests, forest fallows after shifting cultivation and savannahs.
Geo-Wiki provides anyone with the means to engage in monitoring of the earth's surface by classifying satellite, drone or ground-level imagery. Data can be input via desktop or mobile devices, with campaigns and games used to incentivize input. These innovative techniques have been used to successfully integrate citizen-derived data sources with expert and authoritative data to address pressing policy-related questions (e.g. European environmental policy, SDG indicators and more).
Data on the key drivers of tropical forest loss were collected using the Geo-Wiki platform. The data, collected within a geographical area of 30 degrees North and South of the equator, are openly available and are already being used in different studies.
OSCAR is a model of reduced-complexity that describes the interactions between large-scale components of the Earth system that relate to anthropogenic climate change. Its modules are calibrated to emulate the behavior of complex process-based models.
Data on built-up surfaces around the world were collected via a Geo-Wiki campaign, where this data set is intended for use as an independent validation of global maps of built-up surfaces.
SHELscape is a spatially-explicit agent-based model for understanding short-run post-natural disasters non-linear adjustment processes in a multi-market framework.
The GROW Citizen Observatory was developed in a Horizon 2020 funded project to engage citizens (interested in growing food and improving the health of their soils) in sharing their data with a large community of growers across Europe.
Geo-Wiki is an online application for the visualization and crowdsourcing of land cover and land use data, where the first data collection campaigns ran during 2011 and 2012.
Data on agricultural field size has been collected via Geo-Wiki campaigns, where this type of data can provide some indication of agricultural practices, and it helps us to determine what types of satellite data are needed for agricultural monitoring in different parts of the world.
Data collection using mobile technology has facilitated the collection of in situ or ground-based data on land use/land cover including geo-tagged photographs that document the landscape.
Several data collection activities related to forest cover, forest biomass and forest management have been coordinated by IIASA’s NoDES group in the past