This project is focused on developing global tree cover change data products based on Landsat satellite imagery, which will be available for display and download on the Global Forest Watch 2.0 (GFW 2.0) web platform. There are three main components of this project: (1) Produce a Landsat-based global, annualized tree cover change product at 30 meter resolution for the years 2000 through 2012 (complete). Once completed, work with WRI and its partners and contractors to incorporate and launch the dataset on the GFW 2.0 website (launched on February 20, 2014) concurrent with publication by Dr. Hansen in a scientific journal (published in Science on November 15, 2013). (2) Produce annual updates of the above dataset for the years 2013 (published), 2014 (published), and 2015 (to be created and published in 2016) and publish on the GFW 2.0 website. (3) Commence work to develop, refine, and test an “as-it-happens” deforestation alarm system based on Landsat 7 and/or 8 imagery. The alert system will process Landsat imagery at 30 meter resolution as it becomes available on a rolling basis, identifying and displaying on a map tree cover change in near-real-time. UMD will begin by creating and piloting the alarm system for a set of countries or forest regions (e.g., remaining intact tropical forests) and will include some formal validation (e.g., RapidEye tasking and characterization) for accuracy. Once the pilot system becomes available in a stable form, UMD will work with WRI and its partners and contractors to make the system and its resulting data displayed on the GFW 2.0 website and explore with WRI potential applications for the system on GFW 2.0.
GFW 2.0. website: www.globalforestwatch.org
Subgrant: Global Restoration Monitoring Application
The Restoration App will monitor the recovery of vegetation (e.g., trees, shrubs) in forest, wetland, and grassland ecosystems at relatively high resolution, high periodicity, and global coverage. To do this, the Restoration App will combine a suite of remote sensing technologies. For instance, it will run automated algorithms against the U.S. Geological Survey Landsat 8 satellite imagery and the European Space Agency’s Sentinel 2A satellite imagery. The time series will allow for accurate detection of regrowth patterns and differentiation between crops, grasslands, plantations, and natural tree/shrub recovery dynamics. The algorithm will also estimate vegetation height from remote-sensing imagery, and differentiate between early regrowth, mature regrowth, crops, and other states. The University of Maryland may use higher resolution imagery to calibrate the algorithms, validate monitoring outputs, and hone in on “hotspots” for further monitoring.
Pickering, J., Stehman, S.V., Tyukavina, A., Potapov, P., Watt, P., Jantz, S.M., Bholanath, P., Hansen, M.C. (2018) Quantifying the trade-off between cost and precision in estimating area of forest loss and degradation using probability sampling in Guyana. Remote Sensing of Environment 221, 122-135.
Zarin, D. J., Harris, N. L., Baccini, A., Aksenov, D., Hansen, M. C., Ramos, C. A., Azevedo, T., Margono, B.A., Alencar, A.C., Gabris, C., Allegretti, A., Potapov, P., Farina, M., Walker W.S., Shevade, V.S., Loboda, T.V., Turubanova S., Tyukavina A. (2015) Can carbon emissions from tropical deforestation drop by 50% in five years? Global change biology