Project Funding: 2016 - 2019

NRA: 2015 NASA: Carbon Monitoring System   

Funded by NASA

Abstract:
Many tropical countries are experiencing high rates of forest disturbance with of cycles of degradation, cultivation and recovery, but neither the activities nor the terrestrial carbon dynamics associated with the activities are properly tracked in existing REDD+ related Measurement, Reporting and Verification (MRV) systems. This situation is especially true for post-disturbance landscapes and degraded forests, as the trajectories of the land surface activities and carbon dynamics following disturbance are gradual in nature and inherently difficult to monitor. We propose to improve modeling of the carbon dynamics of areas that have experienced disturbance by combining a time series-based approach for monitoring changes on the land surface with a spatially and temporally explicit carbon bookkeeping approach. We have developed algorithms that track the land surface by analyzing time series of all available observations from the Landsat sensors complemented by data from space-borne radar instruments and other optical sensors. Implementations are currently underway across the United States and the Colombian Amazon. Additionally, we have developed open source software tools and educational materials that provide detailed hands-on instructions in support of capacity building efforts in collaboration with SilvaCarbon. We propose a novel framework for estimation of carbon emissions and removals by including detailed information on the fate of the landscape. We will modify a recently developed bookkeeping model so that it runs at the pixel-level (spatially explicit) by directly integrating the results of time series information on conversion between land categories and forest degradation. The characterization of post-disturbance tropical landscapes is critical for accurate accounting of terrestrial carbon pools and fluxes because of the high productivity and carbon density of forests in this region. Therefore, in addition to the time series analysis of the land surface, the temporal dynamics of vegetation structure and recovery following disturbance will be investigated using existing space-borne lidar data in combination with data from upcoming NASA lidar missions. Following best practices protocols for statistical inference of change in area and carbon emissions, unbiased estimates with the uncertainty quantified in the form of confidence intervals will be constructed. Prototype applications of the proposed methodology will be implemented in Colombia and Cambodia, two tropical countries representing different levels of capacity and different types of forest disturbance. A SilvaCarbon effort is underway to complete a comprehensive time series-based analysis of the conversions between the land categories and post-disturbance landscapes across the Colombian Amazon that will be used together with a set of existing field measurements of biomass in a prototype application of the proposed methodology. The methodology will be implemented in Cambodia, where field-measured data on biomass are scarcer, capacity needs greater and the rate of deforestation and forest degradation higher. Engagement with stakeholders and countries will be enhanced by collaboration with in-country SilvaCarbon activities focused on enhancing and supporting systems of MRV for REDD+ activities (including the provision of input for designing field measurement programs). In addition, a spatially and temporally explicit model for estimating the carbon dynamics related to land surface activities will be added to the open source suite of software to provide a more complete framework for the enhancement of MRV systems in the tropics.

Publications:

Arevalo, P., Bullock, E. L., Woodcock, C. E., Olofsson, P. 2020. A Suite of Tools for Continuous Land Change Monitoring in Google Earth Engine. Frontiers in Climate. 2. DOI: 10.3389/fclim.2020.576740

Arevalo, P., Olofsson, P., Woodcock, C. E. 2020. Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: A test methodology for REDD+ reporting. Remote Sensing of Environment. 238, 111051. DOI: 10.1016/j.rse.2019.01.013

Bullock, E. L., Woodcock, C. E., Souza, C., Olofsson, P. 2020. Satellite-based estimates reveal widespread forest degradation in the Amazon. Global Change Biology. 26(5), 2956-2969. DOI: 10.1111/gcb.15029

Olofsson, P., Arevalo, P., Espejo, A. B., Green, C., Lindquist, E., McRoberts, R. E., Sanz, M. J. 2020. Mitigating the effects of omission errors on area and area change estimates. Remote Sensing of Environment. 236, 111492. DOI: 10.1016/j.rse.2019.111492

Tang, X., Hutyra, L. R., Arevalo, P., Baccini, A., Woodcock, C. E., Olofsson, P. 2020. Spatiotemporal tracking of carbon emissions and uptake using time series analysis of Landsat data: A spatially explicit carbon bookkeeping model. Science of The Total Environment. 720, 137409. DOI: 10.1016/j.scitotenv.2020.137409

Tang, X., Woodcock, C. E., Olofsson, P., Hutyra, L. R. 2021. Spatiotemporal assessment of land use/land cover change and associated carbon emissions and uptake in the Mekong River Basin. Remote Sensing of Environment. 256, 112336. DOI: 10.1016/j.rse.2021.112336

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

• NASA Terrestrial Ecology (TE) Project Profile
• NASA Carbon Monitoring System (CMS) Project Profile