Project Details
Description
Currently within the EU’s Earth Observation (EO) monitoring framework, there is a need for low-cost methods for acquiring high quality in-situ data to create accurate and well-validated environmental monitoring products. The aim of the LandSense project is to build a far reaching citizen observatory for Land Use and Land Cover (LULC) monitoring that will also function as a technology innovation marketplace. LandSense will deploy advanced tools, services and resources to mobilize and engage citizens to collect in-situ observations (i.e. ground-based data and visual interpretations of EO imagery). Integrating these citizen-driven in-situ data collections with established authoritative and open access data sources will help reduce costs, extend GEOSS and Copernicus capacities, and support comprehensive environmental monitoring systems. New LandSense services (LandSense Campaigner, FarmLand Support, Change Detector and Quality Assurance & Control) will be deployed in three demonstration cases that will address critical LULC issues in the areas of urbanization, agricultural land use and forest/habitat monitoring. Policy-relevant campaigns will be implemented in close collaboration with multiple stakeholders to ensure that citizen observations contribute to EU-wide environmental governance and decision-making. There will be numerous pathways to citizen empowerment via the LandSense Engagement Platform, i.e. tools for discussion, online voting collaborative mapping, as well as events linked to various campaigns involving public consultation. Simultaneously, to improve Europe’s role in the business of in-situ monitoring, LandSense will create sustainable business models to support market uptake and innovation of its novel added-value products and services.
| Acronym | LANDSENSE |
|---|---|
| Status | Finished |
| Effective start/end date | 1/09/16 → 31/12/20 |
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Research output
- 3 Article
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Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series
Masolele, R. N., De Sy, V., Herold, M., Marcos, D., Verbesselt, J., Gieseke, F., Mullissa, A. G. & Martius, C., 1 Oct 2021, In: Remote Sensing of Environment. 264, 112600.Research output: Contribution to journal › Article › Academic › peer-review
Open Access109 Link opens in a new tab Citations (Scopus) -
Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery
Schepaschenko, D., See, L., Lesiv, M., Bastin, J.-F., Mollicone, D., Tsendbazar, N.-E., Bastin, L., McCallum, I., Laso Bayas, J. C., Baklanov, A., Perger, C., Dürauer, M. & Fritz, S., Jul 2019, In: Surveys in Geophysics. 40, 4, p. 839-862Research output: Contribution to journal › Article › Academic › peer-review
Open Access55 Link opens in a new tab Citations (Scopus) -
Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2
Reiche, J., Hamunyela, E., Verbesselt, J., Hoekman, D. & Herold, M., Jan 2018, In: Remote Sensing of Environment. 204, p. 147-161Research output: Contribution to journal › Article › Academic › peer-review
228 Link opens in a new tab Citations (Scopus)