BACI: 2015-2019

TOWARDS A BIOSPHERE ATMOSPHERE CHANGE INDEX

WP2 Consistent EO datasets across space, time and wavelength

Lead: UCL
Main Contact: Mat Disney, WP2 Leader


UCL will lead WP2, which will provide the core requirement of timely and consistent spatial data to be used as input to the BACI analysis framework. These will primarily be EO data, but also additional spatial data such as elevation and slope/aspect. WP2 will provide a generic, scaleable framework for combining data from multiple streams for input into BACI index analysis, effectively a multi-source, surface change detection system. The output of WP2 will be: a system ‘state vector’ representing the state of a point/region on the land surface at a given time as a function of input data (reflectance, Δreflectance i.e. change in reflectance since the last observation, LST, backscatter and multi-temporal backscatter statistics, interferometric coherence, soil moisture, freeze/thaw, snow characteristics, albedo, vegetation state, ancillary), with uncertainty.

The framework will be demonstrated at core project sites at fine scale, as well as at scales of the selected regional focus areas. The framework will generate (within spectral domains) wavelength-consistent surface products, and assess options of how other, higher spatial resolution intermittent data streams can be integrated.

OBJECTIVES

  • To provide a novel framework for optimally combining EO data from a range of sources, at a range of scales and wavelengths.
  • Input will comprise: passive and active optical, emitted and active microwave EO data. The framework will ingest data from current and historical sensors but the framework will specifically allow for the ingestion of forthcoming data, particularly from ESA Sentinels.
  • Output will be a description of surface state with uncertainty that can be ingested directly into the BACI analysis, without requirement for conversion to higher-level model-derived products both regionally, with ‘cut-outs’ and options for near-real time assessments.

Papers that were supported by this WP

1Babst, F., Poulter, B., Bodesheim, P., Mahecha, M. D., Frank, D. C. (2017). Improved tree-ring archives will support earth-system science. Nature Ecology & Evolution, 1: 0008. doi:10.1038/s41559-016-0008.
Postprint available
2Disney, M., Muller, J.-P., Kharbouche, S., Kaminski, T., Voßbeck, M., Lewis, P., Pinty, B. (2016). A new global fAPAR and LAI dataset derived from optimal Albedo estimates: Comparison with MODIS products. Remote Sensing, 8(4): 275. doi:10.3390/rs8040275.
3Erb, K.-H., Fetzel, T., Plutzar, C., Kastner, T., Lauk, C., Mayer, A., Niedertscheider, M., Körner, C., Haberl, H. (2016). Biomass turnover time in terrestrial ecosystems halved by land use. Nature geoscience, 9, 674-678. doi:10.1038/ngeo2782.
Postprint available
4Flach, M., Gans, F., Brenning, A., Denzler, J., Reichstein, M., Rodner, E., Bathiany, S., Bodesheim, P., Guanche, Y., Sippel, S., Mahecha, M. D. (2017). Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques. Earth System Dynamics, 8(3), 677-696. doi:10.5194/esd-8-677-2017.
5Hamunyela, E., Reiche, J., Verbesselt, J., Herold, M. (2017). Using Space-Time Features to Improve Detection of Forest Disturbances from Landsat Time Series. Remote Sensing, 9(6): 515. doi:10.3390/rs9060515.
6Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Rudbeck Jepsen, M., Kuemmerle, T., Meyfroidt, P., Mitchard, E. T. A., Reiche, J., Ryan, C. M., 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(1): 70. doi:10.3390/rs8010070.
7Jung, M., Reichstein, M., Schwalm, C. R., Huntingford, C., Sitch, S., Ahlström, A., Arneth, A., Camps-Valls, G., Ciais, P., Friedlingstein, P., Gans, F., Ichii, K., Jain, A. K., Kato, E., Papale, D., Poulter, B., Raduly, B., Rödenbeck, C., Tramontana, G., Viovy, N., Wang, Y.-P., Weber, U., Zaehle, S., Zeng, N. (2017). Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature, 541(7638), 516-520. doi:10.1038/nature20780.
Postprint available
8Koirala, S., Jung, M., Reichstein, M., de Graaf, I. E. M., Camps-Valls, G., Ichii, K., Papale, D., Raduly, B., Schwalm, C. R., Tramontana, G., Carvalhais, N. (2017). Global distribution of groundwater-vegetation spatial covariation. Geophysical Research Letters, 44(9), 4134-4142. doi:10.1002/2017GL072885.
Posprint available
9Papale, D., Black, T. A., Carvalhais, N., Cescatti, A., Chen, J., Jung, M., Kiely, G., Lasslop, G., Mahecha, M. D., Margolis, H., Merbold, L., Montagnani, L., Moors, E., Olesen, J. E., Reichstein, M., Tramontana, G., van Gorsel, E., Wohlfahrt, G., Ráduly, B. (2015). Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks. Journal of Geophysical Research: Biogeosciences, 120(10), 1941-1957. doi:10.1002/2015JG002997.
Postprint available
10Reiche, J., de Bruin, S., Hoekman, D., Verbesselt, J., Herold, M. (2015). A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection. Remote Sensing, 7(5), 4973-4996. doi:10.3390/rs70504973.
11Reiche, J., Lucas, R., Mitchell, A. L., Verbesselt, J., Hoekman, D. H., Haarpaintner, J., Kellndorfer, J. M., Rosenqvist, A., Lehmann, E. A., Woodcock, C. E., Seifert, F. M., Herold, M. (2016). Combining satellite data for better tropical forest monitoring. Nature Climate Change, 6, 120-122. doi:10.1038/nclimate2919.
Postprint available
12Sippel, S., Lange, H., Mahecha, M. D., Hauhs, M., Bodesheim, P., Kaminski, T., Gans, F., Rosso, O. A. (2016). Diagnosing the dynamics of observed and simulated ecosystem gross primary productivity with time causal information theory quantifiers. PLoS One, 11(10): e0164960. doi:10.1371/journal.pone.0164960.
13Sippel, S., Zscheischler, J., Reichstein, M. (2016). Ecosystem impacts of climate extremes crucially depend on the timing (commentary). Proc.Natl.Acad.Sci.USA, 113(21), 5768-5770. doi:10.1073/pnas.1605667113.
14Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., Papale, D. (2016). Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences, 13(14), 4291-4313. doi:10.5194/bg-13-4291-2016.
15Vaglio Laurin, G., Hawthorne, W. D., Chiti, T., Di Paola, A., Cazzolla Gatti, R., Marconi, S., Noce, S., Grieco, E., Pirotti, F., Valentini, R. (2016). Does degradation from selective logging and illegal activities differently impact forest resources? A case study in Ghana. iForest - Biogeosciences and Forestry, 9, 354-362. doi:10.3832/ifor1779-008.
16Vaglio Laurin, G., Pirotti, F., Callegari, M., Chen, Q., Cuozzo, G., Lingua, E., Notarnicola, C., Papale, D. (2017). Potential of ALOS2 and NDVI to estimate forest above-ground biomass, and comparison with lidar-derived estimates. Remote Sensing, 9: 18. doi:10.3390/rs9010018.
17Vaglio Laurin, G., Puletti, N., Chen, Q., Corona, P., Papale, D., Valentini, R. (2016). Above ground biomass and tree species richness estimation with airborne lidar in tropical Ghana forests. International Journal of Applied Earth Observation and Geoinformation, 52, 371-379. doi:10.1016/j.jag.2016.07.008.
18Vaglio Laurin, G., Puletti, N., Hawthorne, W., Liesenberg, V., Corona, P., Papale, D., Chen, Q., Valentini, R. (2016). Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data. Remote Sensing of Environment, 176, 163-176. doi:10.1016/j.rse.2016.01.017.
Postprint available
19Vittucci, C., Ferrazzoli, P., Kerr, Y., Richaume, P., Guerriero, L., Rahmoune, R., Vaglio Laurin, G. (2016). SMOS retrieval over forests: Exploitation of optical depth and tests of soil moisture estimates. Remote Sensing of Environment, 180, 115-127. doi:10.1016/j.rse.2016.03.004.
20Zscheischler, J., Mahecha, M. D., Avitabile, V., Calle, L., Carvalhais, N., Ciais, P., Gans, F., Gruber, N., Hartmann, J., Herold, M., Ichii, K., Jung, M., Landschützer, P., Laruelle, G. G., Lauerwald, R., Papale, D., Peylin, P., Poulter, B., Ray, D., Regnier, P., Rödenbeck, C., Roman-Cuesta, R. M., Schwalm, C., Tramontana, G., Tyukavina, A. T., Valentini, R., van der Werf, G., West, T. O., Wolf, J. E., Reichstein, M. (2017). Reviews and syntheses: An empirical spatiotemporal description of the global surface–atmosphere carbon fluxes: opportunities and data limitations. Biogeosciences, 14(15), 3685-3703. doi:10.5194/bg-14-3685-2017.