Katelyn Dolan

TL;DR: In this article, a robust, replicable and scalable framework that maps forest aboveground biomass over large areas at fine-resolution by linking airborne lidar and field data with machine learning algorithms is presented.

TL;DR: In this paper, the ICESat Geoscience Laser Altimeter System (GLAS) lidar data were combined with Landsat-based disturbance history maps to assess forest regeneration rates in three regions of the eastern United States (Maine, Virginia, and Mississippi).

TL;DR: In this paper, an approach that relies on field measurements of understory vegetation cover and height on US Department of Agriculture Forest Service, Forest Inventory and Analysis (FIA) subplots was developed to estimate aboveground understory carbon.

Abstract: There are strong relationships between climate and ecosystems. With the prospect of anthropogenic forcing accelerating climate change, there is a need to understand how terrestrial vegetation responds to this change as it influences the carbon balance. Previous studies have primarily addressed this question using empirically based models relating the observed pattern of vegetation and climate, together with scenarios of potential future climate change, to predict how vegetation may redistribute. Unlike previous studies, here we use an advanced mechanistic, individually based, ecosystem model to predict the terrestrial vegetation response from future climate change. The use of such a model opens up opportunities to test with remote sensing data, and the possibility of simulating the transient response to climate change over large domains. The model was first run with a current climatology at half-degree resolution and compared to remote sensing data on dominant plant functional types for northern North America for validation. Future climate data were then used as inputs to predict the equilibrium response of vegetation in terms of dominant plant functional type and carbon redistribution. At the domain scale, total forest cover changed by ~2% and total carbon storage increased by ~8% in response to climate change. These domain level changes were the result of much larger gross changes within the domain. Evergreen forest cover decreased 48% and deciduous forest cover increased 77%. The dominant plant functional type changed on 58% of the sites, while total carbon in deciduous vegetation increased 107% and evergreen vegetation decreased 31%. The percent of terrestrial carbon from deciduous and evergreen plant functional types changed from 27%/73% under current climate conditions, to 54%/46% under future climate conditions. These large predicted changes in vegetation and carbon in response to future climate change are comparable to previous empirically based estimates, and motivate the need for future development with this mechanistic model to estimate the transient response to future climate changes.