Project Funding: 2011 - 2014

NRA: 2009 NASA: The Science of Terra and Aqua   

Funded by NASA

Abstract:
This proposal responds to ROSES09 A.41 (Science of Terra and Aqua) Program Element 2.2: Science Data Analysis and Program Element 2.4: Algorithms - Existing Product Refinement, focusing on data products and research related to and supporting the MODIS global land cover product (MCD12). This product consists of two independent suites of scientific data sets (SDS): MCD12Q1 (global land cover type) and MCD12Q2 (global land cover dynamics). The MCD12Q1 product provides five land cover classifications and the MCD12Q2 product provides seven SDS layers that characterize the land surface phenology of global ecosystems. Both products are currently produced at 500-m spatial resolution. Here, we propose two sets of activities associated with each product suite. First, we propose to continue production of these products, both of which are generated at Boston University. As part of this effort we propose to implement modest algorithm refinements that address specific known weaknesses, and to collaborate with colleagues at NASA GSFC to migrate MCD12Q2 production software from our data processing facility at Boston University to operational production facilities at GSFC. Second, we propose a set of science data analysis activities that address new science and data analysis challenges. Specifically, we propose a suite of tasks designed to characterize seasonal to decadal scale processes and dynamics in global land cover and land surface phenology. To achieve this goal we will use statistical methods that exploit the 10+ year time series archive of MODIS data. In support of these goals we have identified a set of specific case studies and collaborations, focusing on major patterns of change and disturbance in terrestrial ecosystems during the MODIS era. We also describe a comprehensive set of validation activities, again leveraging ongoing activities and collaborations, designed to provide improved characterization of the error and uncertainty properties of both MCD12 products.

Publications:

Abercrombie, S. P., Friedl, M. A. 2016. Improving the Consistency of Multitemporal Land Cover Maps Using a Hidden Markov Model. IEEE Transactions on Geoscience and Remote Sensing. 54(2), 703-713. DOI: 10.1109/TGRS.2015.2463689

Friedl, M. A., Gray, J. M., Melaas, E. K., Richardson, A. D., Hufkens, K., Keenan, T. F., Bailey, A., O'Keefe, J. 2014. A tale of two springs: using recent climate anomalies to characterize the sensitivity of temperate forest phenology to climate change. Environmental Research Letters. 9(5), 054006. DOI: 10.1088/1748-9326/9/5/054006

Gray, J. M., Frolking, S., Kort, E. A., Ray, D. K., Kucharik, C. J., Ramankutty, N., Friedl, M. A. 2014. Direct human influence on atmospheric CO2 seasonality from increased cropland productivity. Nature. 515(7527), 398-401. DOI: 10.1038/nature13957

Huang, X., Schneider, A., Friedl, M. A. 2016. Mapping sub-pixel urban expansion in China using MODIS and DMSP/OLS nighttime lights. Remote Sensing of Environment. 175, 92-108. DOI: 10.1016/j.rse.2015.12.042

Keenan, T. F., Gray, J., Friedl, M. A., Toomey, M., Bohrer, G., Hollinger, D. Y., Munger, J. W., O'Keefe, J., Schmid, H. P., Wing, I. S., Yang, B., Richardson, A. D. 2014. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nature Climate Change. 4(7), 598-604. DOI: 10.1038/nclimate2253

Li, L., Friedl, M., Xin, Q., Gray, J., Pan, Y., Frolking, S. 2014. Mapping Crop Cycles in China Using MODIS-EVI Time Series. Remote Sensing. 6(3), 2473-2493. DOI: 10.3390/rs6032473

Melaas, E. K., Friedl, M. A., Richardson, A. D. 2016. Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States. Global Change Biology. 22(2), 792-805. DOI: 10.1111/gcb.13122

Salmon, J. M., Friedl, M. A., Frolking, S., Wisser, D., Douglas, E. M. 2015. Global rain-fed, irrigated, and paddy croplands: A new high resolution map derived from remote sensing, crop inventories and climate data. International Journal of Applied Earth Observation and Geoinformation. 38, 321-334. DOI: 10.1016/j.jag.2015.01.014

Sulla-Menashe, D., Kennedy, R. E., Yang, Z., Braaten, J., Krankina, O. N., Friedl, M. A. 2014. Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation. Remote Sensing of Environment. 151, 114-123. DOI: 10.1016/j.rse.2013.07.042

Verma, M., Friedl, M. A., Law, B. E., Bonal, D., Kiely, G., Black, T. A., Wohlfahrt, G., Moors, E. J., Montagnani, L., Marcolla, B., Toscano, P., Varlagin, A., Roupsard, O., Cescatti, A., Arain, M. A., D'Odorico, P. 2015. Improving the performance of remote sensing models for capturing intra- and inter-annual variations in daily GPP: An analysis using global FLUXNET tower data. Agricultural and Forest Meteorology. 214-215, 416-429. DOI: 10.1016/j.agrformet.2015.09.005

2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)

• Deriving Land Cover Fractional Maps with Unmixing Algorithm   --   (Uttam Kumar, Cristina Milesi, S Kumar Raja, Ramakrishna R. Nemani, Sangram Ganguly)   [abstract]

2013 NASA Terrestrial Ecology Science Team Meeting Poster(s)

• Large scale maps of cropping intensity from MODIS   --   (Josh Gray, Mark Friedl, Steve Frolking)   [abstract]
• Linking interannual phenology from MODIS and Landsat   --   (Eli Melaas, Mark Friedl, Chris Holden, Josh Gray)   [abstract]

2011 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)

• Detecting Forest Disturbance in the Pacific Northwest From MODIS Time Series Using Temporal Segmentation   --   (Damien Sulla-Menashe, Zhiqiang Yang, Justin Braaten, Olga Krankina, Robert E Kennedy, Mark Friedl)   [abstract]

More details may be found in the following project profile(s):

• NASA Terrestrial Ecology (TE) Project Profile