McIntyre, P. J., J. H. Thorne, C. R. Dolanc, A. L. Flint, L. E. Flint, M. Kelly and D. D. Ackerly. 2015. Twentieth-century shifts in forest structure in California: Denser forests, smaller trees, and increased dominance of oaks. Proceedings of the National Academy of Sciences 112(5): 1458-1463


We document changes in forest structure between historical (1930s) and contemporary (2000s) surveys of California vegetation through comparisons of tree abundance and size across the state and within several ecoregions. Across California, tree density in forested regions increased by 30% between the two time periods, whereas forest biomass in the same regions declined, as indicated by a 19% reduction in basal area.


Kelly, M. and S. Di Tommaso. 2015. Mapping forests with Lidar provides flexible, accurate data with many uses. California Agriculture 69(1): 14-20

The use of remote sensing for forest inventory, fire management, and wildlife habitat conservation planning has a decades-long and productive history, especially in California. The history of forest remote sensing in California follows a transition from aerial photography to digital remote sensing, in which Landsat plays a significant role, and today shows an increasing reliance on Lidar analysis. In California where forests are complex and difficult to accurately map, numerous remote sensing scientists have pioneered development of methodologies for forest mapping with Lidar.  Lidar has been used successfully here in a number of ways: to capture forest structure; to map individual trees in forests and critical wildlife habitat characteristics; to predict forest volume and biomass; to develop inputs for forest fire behavior modeling, and to map forest topography and infrastructure. In commemoration of the centennial of California Forestry, this paper reviews the ways in which Lidar technology – small and large footprint, discrete and waveform data – has been used to map California forests. Journal link.


Lei, S. and M. Kelly. 2015. Evaluating adaptive collaborative management in Sierra Nevada forests by exploring public meeting dialogues using Self-Organizing Maps. Society and Natural Resources (28)8: 873-890

Heat map of discussion in SNAMP meetings: red conveys most consistently discussed topics; blue conveys least consistently discussed

Heat map of discussion in SNAMP meetings: red conveys most consistently discussed topics; blue conveys least consistently discussed

Collaborative adaptive management (CAM) is an appropriate management regime for social-ecological systems because it aims to reduce management uncertainties and fosters collaboration among diverse stakeholders. We evaluate the effectiveness of CAM in fostering collaboration among contentious multiparty environmental stakeholders based the Sierra Nevada Adaptive Management Project (SNAMP). Our evaluation focuses on facilitated public multiparty discussions (2005-2012). Self-organizing maps (SOM), an unsupervised machine-learning method, were used to process, organize, and visualize the public meeting notes. We found that public discussion remained focused on the project content, yet the more contentious and critical issues dominated the discussions through time. Integration across topics could be improved. These results suggest that SNAMP collaborative adaptive management seems to have helped participants focus on the key issues as well as advance their discussions over time. Given the effectiveness of SOMs in analyzing text, we provide suggestions on how natural resource managers might use SOM.