Sentinels supporting carbon estimates and REDD+

The focus of SENSE-CARBON was on the development of new methods for the comprehensive monitoring of land cover, land use and their changes, which serve a better characterization of carbon stocks, for example within the framework of REDD+. The regional focus of the project was on the Amazon region of Brazil. The improved characterization of vegetation gradients and the derivation of large-scale and high-resolution REDD-relevant classes by means of a synergetic use of different data sets and dense time series were of crucial importance.
The overall goal of the project was to close methodological gaps in the operational development of REDD-relevant classifications. For this purpose, the SENSE-VCARBON team relied on data archives as well as new acquisitions of ASAR, RADARSET-2, ALOS-2, TerraSAR-X, TandDEM-X, RapdiEye and Landsat images to explore the potential of the Sentinel-1 and Sentinel-2 data.

Principal investigators
Hostert, Patrick Prof. Dr. (Details) (Geomatics)

Participating external organisations

Bundesministerium für Wirtschaft und Technologie

Duration of project
Start date: 05/2013
End date: 12/2016

Research Areas
Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography, Physical Geography

Research Areas
Big Data, Entwaldungsmonitoring, Landnutzung und Landnutzungswandel, Maschinelles Lernen

Griffiths, P., Jakimow, B., & Hostert, P. (2018). Reconstructing long term annual deforestation dynamics in Pará and Mato Grosso using the Landsat archive. Remote Sensing of Environment, 216, 497-513.
Hagensieker, R., Roscher, R., Rosentreter, J., Jakimow, B., & Waske, B. (2017). Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data. International Journal of Applied Earth Observation and Geoinformation, 63, 244-256. 10.1016/j.jag.2017.07.019
Hagensieker, R., & Waske, B. (2017). Synergetic potentials of C-band SAR and multi-spectral imagery for tropical classifications in Northern Mato Grosso (BR). In, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5486-5489). 10.1109/IGARSS.2017.8128246
Hagensieker, R., & Waske, B. (2018). Evaluation of Multi-Frequency SAR Images for Tropical Land Cover Mapping. Remote Sensing, 10. 10.3390/rs10020257
Hostert, P., Griffiths, P., van der Linden, S., & Pflugmacher, D. (2015). Time Series Analyses in a New Era of Optical Satellite Data. In C. Kuenzer, S. Dech, & W. Wagner (Eds.), Remote Sensing Time Series: Revealing Land Surface Dynamics (pp. 25-41). Cham: Springer International Publishing.
Jakimow, B., Griffiths, P., van der Linden, S., & Hostert, P. (2018). Mapping pasture management in the Brazilian Amazon from dense Landsat time series. Remote Sensing of Environment, 205, 453-468.
Jakimow, B., van der Linden, S., Thiel, F., Frantz, D., & Hostert, P. (2020). Visualizing and labeling dense multi-sensor earth observation time series: The EO Time Series Viewer. Environmental Modelling & Software, 125. 10.1016/j.envsoft.2020.104631
Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, R.M., Kuemmerle, T., Meyfroidt, P., Mitchard, T.E., Reiche, J., Ryan, M.C., & Waske, B. (2016). A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring. Remote Sensing, 8.
Müller, H., Griffiths, P., & Hostert, P. (2016). Long-term deforestation dynamics in the Brazilian Amazon—Uncovering historic frontier development along the Cuiabá–Santarém highway. International Journal of Applied Earth Observation and Geoinformation, 44, 61-69.

Last updated on 2022-09-09 at 01:09