Project Funding: 2014 - 2017

NRA: 2013 NASA: The Science of Terra and Aqua   

Funded by NASA

Abstract:
The opportunity now exists to begin to monitor land cover disturbances as they occur, greatly improving the value of the information provided. For instance, in cases of encroachment on protected areas or illegal logging, the value of information declines precipitously with time. Building on recent innovations in time series analysis of Landsat data, we propose to develop new methodologies that combine Landsat and MODIS for near real-time monitoring. To monitor land cover disturbance through time, it is necessary to be able to compare observations of the same places at different times. This is a challenging task using MODIS data as the observations from different days have varying view angles and pixel sizes, and cover slightly different areas. Our proposed research combines MODIS and Landsat data in a way that allows for near real-time monitoring. The method uses a time- series of Landsat images to predict the next MODIS image (including the precise location of each observations for any given day). The predicted MODIS observations are compared with new MODIS acquisitions to find change. The predicted MODIS images represent what the surface should look like assuming no change, and the differences in the spectral signatures between predicted and observed MODIS images become the 'signal' used for detecting land cover change. Initial tests show that the method can detect subpixel forest change with producer's and user's accuracy between 80% and 90%, and that patches of forest change as small as 5 to 7 ha in size were detected on a daily basis. These encouraging results indicate that the proposed fusion method holds promise for improving monitoring of disturbance in near real-time. Our objective is to develop and refine the fusion methodology to produce a land cover disturbance product capable of flagging areas on near-real time basis over large areas. We propose to test and validate the method in a variety of locations around the world.

Publications:

Tang, X., Bullock, E. L., Olofsson, P., Estel, S., Woodcock, C. E. 2019. Near real-time monitoring of tropical forest disturbance: New algorithms and assessment framework. Remote Sensing of Environment. 224, 202-218. DOI: 10.1016/j.rse.2019.02.003

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]