Skip to main content
Harmonizing Global Land-Use Datasets with Remote-Sensing Observations of Forest-Based Land-Use Changes
Project Start Date
05/05/2025
Project End Date
05/25/2028

Team Members:

Person Name Person role on project Affiliation
Louise Chini Principal Investigator University of Marlyand, , US
Abstract

Land-Cover and Land-Use Change (LCLUC) plays a critical role in driving climate change through both biogeophysical and biogeochemical changes. To advance the ability of Earth System Models (ESMs) to account for the effects of LCLUC, and to contribute to building a "Land-Earth System Digital Twin", it is essential that land-use datasets be improved by increasing their spatial resolution and incorporating the latest novel observations from remote sensing data. The current paradigm for representing land system changes in ESMs is to provide reconstructed and/or projected land-use to the models, where they are then converted into associated land-cover changes. However, with development of the next generation of regionally-refined and kilometer-scale climate models underway, and with a wealth of high resolution remote sensing data of land-cover changes available, there is an opportunity to incorporate these land cover changes directly in models simulating higher-resolution climate over recent decades. In addition, by incorporating these observations into existing land-use datasets as well (such as the Land-Use Harmonization dataset), we can explore the impact that conversions between land use and land cover have on the climate response of ESMs. This is especially important for forest-based changes in the Amazon where deforestation, wood harvesting, shifting cultivation, and reforestation/afforestation are often large and are known to have significant impacts on the local climate. Therefore, to investigate the effects of forest change on the near-surface climate of the Amazon and the impact of high-resolution, remote-sensing-based LCLUC data for achieving a more realistic climate simulation in ESMs, we propose to (1) build a high-resolution Land Digital Twin dataset, based on NASA remote-sensing data (a combination Landsat, ICESat-2, and GEDI data) for forest cover, forest canopy height, and forest biomass changes for the Amazon region for the years 2000-2020; (2) use the Land Digital Twin dataset as an input to prescribe the land surface in a high-resolution, regionally-refined version of CESM, both directly via land cover inputs and via an existing land-use dataset; and (3) validate and evaluate the climate response of CESM to these improved land cover changes over the Amazon region for the years 2000-2020.