1. Global Earth Science Community
  2. Community Data System Programs
  3. ACCESS Projects
  4. Enabling Centralized Access to Land Cover Data for Climate Change Integrated Assessment Modeling

Enabling Centralized Access to Land Cover Data for Climate Change Integrated Assessment Modeling

The proposed activity will develop and demonstrate data systems that enable effective and efficient use of NASA satellite remote sensing data to derive global land cover data required by climate change integrated assessment (IA) models. Currently, integrated assessment models rely heavily on land cover data derived from Advanced Very High Resolution Radiometer measurements collected during the 1992 -1993 period in order to characterize crop and pasture cover as well as distributions of naturally occurring vegetation. The project will assemble and make available through centralized access NASA remote sensing data needed to improve land cover characterizations within integrated assessment models and distribute new data as they are developed by the IA modeling community.

Climate change integrated assessment models relate human factors and activities, such as demography, energy use, technology, the economy, agriculture, forestry and land use to greenhouse gas emissions, other perturbations to the climate system, and to the resulting radiative forcing of climate change. Such models are used in diverse studies to investigate potential pathways of future climate change, vulnerability, and options for mitigation or adaptation. Among the most prominent products derived using integrated assessment models are emissions scenarios or other representations of potential future pathways of human activities as they affect the climate system. Such scenarios are the basis for major climate change assessments such as those of the Intergovernmental Panel on Climate Change.

The project will make remote sensing data available to IA modeling groups in global mosaics and in formats that are compatible with other data used with IA models. Efficient access to NASA remote sensing data will enable modeling groups to derive improved land cover data, including continuous fields for crop, pasture, urban and irrigated land, as required by IA models. Remote sensing data in these formats and with global coverage as well as prototype land cover products produced by this project in cooperation with the IA modeling community will be applicable to global modeling and analysis of terrestrial ecosystems for biogeochemistry, climate change impacts, biodiversity, and other similar studies at larger spatial scales.

Consistent use of remote sensing data by different groups collaborating in the development of land cover data products for use with IA models is important. Even in cases where different investigators or groups derive alternative IA model data sets, the data are far more useful if the underlying remote sensing data are consistent, allowing isolation of differences in assumptions and processing that are most relevant to IA models. Recognizing this use of IA models, the project will also develop and demonstrate a data distribution system especially well-suited to the IA modeling community, which requires version control to insure consistency between analyses by different modeling groups worldwide. Integrated assessment modeling groups and other interested users will also be able to acquire data served by a version control server.

Expected products of the proposed project include global remote sensing data for use in developing land cover characterizations within IA models, prototype land cover data sets developed by the project and data sets developed by the IA community, centralized access to land cover data by the IA community and other users, and a system for data distribution with version control and data file synchronization between servers and user computers. Results of the project will be reported at major meetings, described technical reports, and summarized in journal publications.

William Emmanuel - PI, Pacific Northwest National Laboratory

Last Updated: Nov 15, 2017 at 3:58 PM EST