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

1Adams, J., Lewis, P., Disney, M. (2018). Decoupling canopy structure and leaf biochemistry: Testing the utility of Directional area scattering factor (DASF). Remote Sensing, 10(12): 1911. doi:10.3390/rs10121911.
2Alexander, M. R., Rollinson, C. R., Babst, F., Trouet, V., Moore, D. J. P. (2018). Relative influences of multiple sources of uncertainty on cumulative and incremental tree-ring-derived aboveground biomass estimates. Trees, 32(1), 265-276. doi:10.1007/s00468-017-1629-0.
Postprint available
3Babst, F., Bodesheim, P., Charney, N., Friend, A. D., Girardin, M. P., Klesse, S., Moore, D. J., Seftigen, K., Björklund, J., Bouriaud, O., Dawson, A., DeRose, R. J., Dietze, M. C., Eckes, A. H., Enquist, B., Frank, D. C., Mahecha, M. D., Poulter, B., Record, S., Trouet, V., Turton, R. H., Zhang, Z., Evans, M. E. (2018). When tree rings go global: Challenges and opportunities for retro- and prospective insight. Quaternary Science Reviews, 197, 1-20. doi:10.1016/j.quascirev.2018.07.009.
4Babst, F., Bouriaud, O., Poulter, B., Trouet, V., Girardin, M. P., Frank, D. C. (2019). Twentieth century redistribution in climatic drivers of global tree growth. Science Advances, 5(1): eaat4313. doi:10.1126/sciadv.aat4313.
5Babst, 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
6Belmecheri, S., Babst, F., Hudson, A. R., Betancourt, J., Trouet, V. (2017). Northern hemisphere jet stream position indices as diagnostic tools for climate and ecosystem dynamics. Earth Interactions, 21(8), 1-23. doi:10.1175/EI-D-16-0023.1.
7Chernetskiy, M., Gobron, N., Gómez-Dans, J., Morgan, O., Lewis, M. D. P., Schmullius, C. (2018). Simulating arbitrary hyperspectral bandsets from multispectral observations via a generic Earth Observation-Land Data Assimilation System (EO-LDAS). Advances in Space Research, 62(7), 1654-1674. doi:10.1016/j.asr.2018.07.015.
8Chernetskiy, M., Gomez-Dans, J., Gobron, N., Morgan, O., Lewis, P., Truckenbrodt, S., Schmullius, C. (2017). Estimation of FAPAR over croplands using MISR data and the Earth Observation Land Data Assimilation System (EO-LDAS). Remote Sensing, 9(7): 656. doi:10.3390/rs9070656.
9Cremer, F., Urbazaev, M., Berger, C., Mahecha, M. D., Schmullius, C., Thiel, C. (2018). An image transform based on temporal decomposition. IEEE Geoscience and Remote Sensing Letters, 15(4), 537-541. doi:10.1109/LGRS.2018.2791658.
10Disney, M. I., Vicari, M. B., Burt, A., Calders, K., Lewis, S. L., Raumonen, P., Wilkes, P. (2018). Weighing trees with lasers: advances, challenges and opportunities. Interface Focus, 8(2): 20170048. doi:10.1098/rsfs.2017.0048.
11Disney, 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.
12Erb, 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
13Evans, M. E. K., Falk, D. A., Arizpe, A., Swetnam, T. L., Babst, F., Holsinger, K. E. (2017). Fusing tree-ring and forest inventory data to infer influences on tree growth. Ecosphere, 8(7): e01889. doi:10.1002/ecs2.1889.
14Flach, 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.
15Hamunyela, 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.
16Joshi, 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.
17Jung, 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
18Klesse, S., Babst, F., Lienert, S., Spahni, R., Joos, F., Bouriaud, O., Carrer, M., Filippo, A. D., Poulter, B., Trotsiuk, V., Wilson, R., Frank, D. C. (2018). A combined tree ring and vegetation model assessment of european forest growth sensitivity to interannual climate variability. Global Biogeochemical Cycles, 32(8), 1226-1240. doi:10.1029/2017GB005856.
19Koirala, 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
20Lu, X., Liang, E., Wang, Y., Babst, F., Leavitt, S. W., Camarero, J. J. (2019). Past the climate optimum: Recruitment is declining at the world’s highest juniper shrublines on the Tibetan Plateau. Ecology, 100(2): e02557. doi:10.1002/ecy.2557.
21Marchand, W., Girardin, M. P., Gauthier, S., Hartmann, H., Bouriaud, O., Babst, F., Bergeron, Y. (2018). Untangling methodological and scale considerations in growth and productivity trend estimates of Canada's forests. Environmental Research Letters, 13: 093001. doi:10.1088/1748-9326/aad82a.
22Montane, F., Fox, A. M., Arellano, A. F., MacBean, N., Alexander, M. R., Dye, A., Bishop, D. A., Trouet, V., Babst, F., Hessl, A. E., Pederson, N., Blanken, P. D., Bohrer, G., Gough, C. M., Litvak, M. E., Novick, K. A., Phillips, R. P., Wood, J. D., Moore, D. J. P. (2017). Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools, and turnover in temperate forests. Geoscientific Model Development, 10(9), 3499-3517. doi:10.5194/gmd-10-3499-2017.
23Papale, 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
24Pappas, C., Mahecha, M. D., Frank, D. C., Babst, F., Koutsoyiannis, D. (2017). Ecosystem functioning is enveloped by hydrometeorological variability. Nature Ecology & Evolution, 1(9), 1263-1270. doi:10.1038/s41559-017-0277-5.
Postprint available
25Reiche, 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.
26Reiche, J., Hamunyela, E., Verbesselt, J., Hoekman, D., Herold, M. (2018). Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2. Remote Sensing of Environment, 204, 147-161. doi:10.1016/j.rse.2017.10.034.
27Reiche, 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
28Santos, M. J., Disney, M., Chave, J. (2018). Detecting human presence and influence on Neotropical forests with remote sensing. Remote Sensing, 10(10): 1593. doi:10.3390/rs10101593.
29Seftigen, K., Frank, D. C., Björklund, J., Babst, F., Poulter, B. (2018). The climatic drivers of normalized difference vegetation index and tree-ring-based estimates of forest productivity are spatially coherent but temporally decoupled in Northern Hemispheric forests. Global Ecology and Biogeography, 27(11), 1352-1365. doi:10.1111/geb.12802.
30Sippel, 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.
31Sippel, 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.
32Tramontana, 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.
33Trouet, V., Babst, F., Meko, M. (2018). Recent enhanced high-summer North Atlantic Jet variability emerges from three-century context. Nature Communications, 9: 180. doi:10.1038/s41467-017-02699-3.
34Urban, M., Berger, C., Mudau, T. E., Heckel, K., Truckenbrodt, J., Odipo, V. O., Smit, I. P. J., Schmullius, C. (2018). Surface moisture and vegetation cover analysis for drought monitoring in the southern Krüger National Park using sentinel-1, sentinel-2, and landsat-8. Remote Sensing, 10: 1482. doi:10.3390/rs10091482.
35Vaglio Laurin, G., Avezzano, R., Bacciu, V., Del Frate, F., Papale, D., Virelli, M. (2018). COSMO-SkyMed potential to detect and monitor Mediterranean maquis fires and regrowth: a pilot study in Capo Figari, Sardinia, Italy. iForest - Biogeosciences and Forestry, 11, 389-395. doi:10.3832/ifor2623-011.
36Vaglio Laurin, G., Belli, C., Bianconi, R., Laranci, P., Papale, D. (2018). Early mapping of industrial tomato in Central and Southern Italy with Sentinel 2, aerial and RapidEye additional data. Journal of Agricultural Science, 156(3), 396-407. doi:10.1017/S0021859618000400.
Postprint available
37Vaglio 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.
38Vaglio 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.
39Vaglio 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.
Postprint available
40Vaglio 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
41Vicari, M. B., Pisek, J., Disney, M. (2019). New estimates of leaf angle distribution from terrestrial LiDAR: Comparison with measured and modelled estimates from nine broadleaf tree species. Agricultural and Forest Meteorology, 264, 322-333. doi:10.1016/j.agrformet.2018.10.021.
42Vittucci, 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.
Postprint available
43Vittucci, C., Ferrazzoli, P., Kerr, Y., Richaume, P., Guerriero, L., Vaglio Laurin, G. (2018). Spatial and temporal properties of SMOS retrieval over tropical forests. In IEEE International Geoscience and Remote Sensing Symposium 2018 (pp. 2153-2156).
44Vittucci, C., Vaglio Laurin, G., Tramontana, G., Ferrazzoli, P., Guerriero, L., Papale, D. (2019). Vegetation optical depth at L-band and above ground biomass in the tropical range: Evaluating their relationships at continental and regional scales. International Journal of Applied Earth Observation and Geoinformation, 77, 151-161. doi:10.1016/j.jag.2019.01.006.
45Wilkes, P., Disney, M., Vicari, M. B., Calders, K., Burt, A. (2018). Estimating urban above ground biomass with multi-scale LiDAR. Carbon Balance and Management, 13(1): 10. doi:10.1186/s13021-018-0098-0.
46Wu, X., Liu, H., Li, X., Ciais, P., Babst, F., Guo, W., Zhang, C., Magliulo, V., Pavelka, M., Liu, S., Huang, Y., Wang, P., Shi, C., Ma, Y. (2018). Differentiating drought legacy effects on vegetation growth over the temperate Northern Hemisphere. Global Change Biology, 24(1), 504-516. doi:10.1111/gcb.13920.
47Zhang, Z., Babst, F., Bellassen, V., Frank, D., Launois, T., Tan, K., Ciais, P., Poulter, B. (2018). Converging climate sensitivities of European forests between observed radial tree growth and vegetation models. Ecosystems, 21(3), 410-425. doi:10.1007/s10021-017-0157-5.
Postprint available
48Zscheischler, 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.