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.
Financer
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
Publications
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. https://doi.org/10.1016/j.rse.2018.07.010
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. https://doi.org/10.1007/978-3-319-15967-6_2
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. https://doi.org/10.1016/j.rse.2017.10.009
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. https://doi.org/10.3390/rs8010070
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. https://doi.org/10.1016/j.jag.2015.07.005
