The Living Planet Fellowship - ISLAND2VAP


Quality and quantity of current high-resolution optical Earth observation data is unprecedented and provides an opportunity to advance remote sensing-based land systems analyses. However, cloud coverage and a lack of gridded higher-level products still hampers the widespread usability of such data. This research project addressed these shortcomings by developing toolsets to combine data streams from ESA Sentinel-2 and USGS/NASA Landsat-8. It for the first time allowed for the systematic (i.e. weekly, monthly, seasonal-) generation of composited reflectance and downstream value-added products (e.g. percent cover estimates, annual phenology metrics). These novel integrated time-series were for the first time synergistically exploited to address land-use science questions related to agricultural land use and land use intensity that can only be answered when using dense time-series based on wall-to-wall analyses at 10-30m spatial resolution. While methods were developed to be capable of working with most ecosystems, a specific focus is on improving agricultural mapping and analyses. We specifically focused on crop type mapping and grassland use intensity.


Projektleitung
Hostert, Patrick Prof. Dr. (Details) (Geomatik)

Laufzeit
Projektstart: 03/2015
Projektende: 03/2017

Forschungsbereiche
Geodäsie, Photogrammetrie, Fernerkundung, Geoinformatik, Kartographie

Forschungsfelder
Big Data

Publikationen
Griffiths, P., Nendel, C., & Hostert, P. (2018). National-scale crop- and land-cover map of Germany (2016) based on imagery acquired by Sentinel-2A MSI and Landsat-8 OLI. Retrieved from: https://doi.org/10.1594/PANGAEA.893195
Griffiths, P., Nendel, C., & Hostert, P. (2019). Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping. Remote Sensing of Environment, 220, 135-151. doi:https://doi.org/10.1016/j.rse.2018.10.031
Griffiths, P., Nendel, C., Pickert, J., & Hostert, P. (2019). Towards national-scale characterization of grassland use intensity from integrated Sentinel-2 and Landsat time series. Remote Sensing of Environment. doi:https://doi.org/10.1016/j.rse.2019.03.017

Zuletzt aktualisiert 2021-04-01 um 17:45