Project Funding: 2011 - 2014

NRA: 2009 NASA: The Science of Terra and Aqua   

Funded by NASA

Abstract:
The synoptic nature of satellite-based earth observation data enables the consistent characterization of forest cover across space and over time. Information on forest extent and change is necessary for carbon accounting efforts as well as for parameterizing global-scale biogeochemical, hydrological, biodiversity and climate models. Due to the vast area that must be examined, earth observation data offer one of the few viable information sources suitable for global-scale monitoring of forest dynamics. Data from NASA’s MODerate Resolution Imaging Spectroradiometer (MODIS) have been used to estimate global forest cover in the form of the Vegetation Continuous Field of percent tree cover, a standard product of the MODIS Land Science Team. The method for creating the VCF tree cover layer was later adapted for estimating forest cover change. Turn-key algorithms have been implemented per forest biome (boreal, temperate, dry and humid tropics) and used to indicate areas of homogeneous forest cover loss for stratified sampling of Landsat data to create per biome and global estimates of gross forest cover loss. However, coarse resolution data such as MODIS lack sufficient spatial detail to provide reliable area estimates of forest extent and change. In order to overcome this shortcoming, MODIS time-series data and derived products have been integrated with Landsat time-series data sets. MODIS generic forest cover mapping efforts have been augmented to create 250m cover maps of mature forest stands that can be taken as consistent dark targets. These targets are used to pre-process and characterize Landsat time-series data sets. The integrated use of MODIS and Landsat enables the mass-processing of the Landsat archive. Prototype results have been made for Indonesia, the Congo Basin, European Russia and Quebec (over 20,000 Landsat images). For each of these examples, an exhaustive mining of the Landsat archive was executed, profiting from the opening of the EROS Landsat archive since December 2008. The method relies on MODIS-driven normalization procedures to automatically generate per-Landsat pixel cloud and shadow assessments and subsequent forest extent and change characterizations. Global-scale mapping of forest extent and change at Landsat scale is feasible using this integrated MODIS and Landsat methodology. Another global source of information available for quantifying forests is data from the Geoscience Laser Altimetry System (GLAS) instrument on board the Ice, Cloud, and Elevation Satellite (ICESat). Unlike the passive optical MODIS and Landsat sensors, GLAS is a waveform sampling lidar sensor originally designed for observation of ice sheets. Lidar metrics have also been used extensively to characterize vegetation structure. Because lidar metrics are partly determined by the amount of lidar energy that reaches the ground surface, they are sensitive to both vegetation vertical structure and horizontal canopy density. As a result, they are related to a range of canopy structure attributes and above-ground biomass. Moreover, lidar canopy structure metrics have been shown to be important predictors of breeding bird habitat selection and species diversity. The objective of this study is to further the current integrated Landsat-MODIS method by incorporating data from GLAS. For this study, metrics derived from GLAS, such as canopy height and height of median energy (HOME), will be tested for deriving time-series estimates of forest structure within the current MODIS-Landsat operational algorithm. The method will extend the forest structure information content of GLAS to the time and space domains of MODIS and Landsat. In summary, MODIS provides consistent time-series inputs and results that enable the radiometric normalization and mass-processing of the Landsat archive, to be calibrated for forest structure characterization using samples of GLAS data.

Publications:

Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., Townshend, J. R. G. 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science. 342(6160), 850-853. DOI: 10.1126/science.1244693

Margono, B. A., Potapov, P. V., Turubanova, S., Stolle, F., Hansen, M. C. 2014. Primary forest cover loss in Indonesia over 2000-2012. Nature Climate Change. 4(8), 730-735. DOI: 10.1038/nclimate2277

Tyukavina, A., Stehman, S. V., Potapov, P. V., Turubanova, S. A., Baccini, A., Goetz, S. J., Laporte, N. T., Houghton, R. A., Hansen, M. C. 2013. National-scale estimation of gross forest aboveground carbon loss: a case study of the Democratic Republic of the Congo. Environmental Research Letters. 8(4), 044039. DOI: 10.1088/1748-9326/8/4/044039

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

• Combining Decision Rules with Spatial Data to Assess Climate Mitigation Options for Indonesia’s Peat Land Carbon Stocks   --   (Earl C Saxon, Stuart M Sheppard)   [abstract]
• Forest cover and aboveground carbon loss in natural and managed tropical forests in 2000-2012   --   (Alexandra Tyukavina, Alessandro Baccini, Matthew Hansen, Peter Potapov, Stephen Stehman, Richard A. Houghton, Alexander Krylov, Svetlana Turubanova, Scott J. Goetz)   [abstract]

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

• NASA Terrestrial Ecology (TE) Project Profile